diff --git a/.Rhistory b/.Rhistory index 9e6b9604..81512123 100644 --- a/.Rhistory +++ b/.Rhistory @@ -1,13 +1,3 @@ -## Install New Package (locally) -setwd("..") # return to root directory first -devtools::install(myLibrary -, build_vignettes = TRUE -, quick = FALSE -, reload = TRUE) -## Reload library -library(myLibrary,character.only = TRUE) -# change wd back to package -setwd(file.path(dir_base, myLibrary)) library(ContDataQC) # 00. Set up # Parameters @@ -510,3 +500,13 @@ runApp('C:/Users/Erik.Leppo/OneDrive - Tetra Tech, Inc/MyDocs_OneDrive/GitHub/Co shiny::runApp('C:/Users/Erik.Leppo/OneDrive - Tetra Tech, Inc/MyDocs_OneDrive/GitHub/ContDataQC/inst/shiny-examples/ContDataQC') shiny::runApp('inst/shiny-examples/ContDataQC') runApp('inst/shiny-examples/ContDataQC') +getwd() +shiny::runApp('inst/shiny-examples/ContDataQC') +runApp('inst/shiny-examples/ContDataQC') +runApp('inst/shiny-examples/ContDataQC') +runApp('inst/shiny-examples/ContDataQC') +runApp('inst/shiny-examples/ContDataQC') +?contdataqc +?ContDataQC +if(!require(remotes)){install.packages("remotes")} #install if needed +remotes::install_github("USEPA/ContDataQC", force = TRUE, build_vignettes = TRUE) diff --git a/ContDataQC.Rproj b/ContDataQC.Rproj index cba1b6b7..9ee5baec 100644 --- a/ContDataQC.Rproj +++ b/ContDataQC.Rproj @@ -1,4 +1,5 @@ Version: 1.0 +ProjectId: c0be144b-0346-467a-9164-bbe4208c3fbe RestoreWorkspace: No SaveWorkspace: No diff --git a/DESCRIPTION b/DESCRIPTION index 2b4c7e8c..a8a00153 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,12 +1,13 @@ Package: ContDataQC Title: Quality Control (QC) of Continous Monitoring Data -Version: 2.0.7.9005 +Version: 2.1.0 Authors@R: c( person("Erik W", "Leppo", email="Erik.Leppo@tetratech.com",role=c("aut","cre")), person("Ann","Roseberry Lincoln", role="ctb"), person("Jen", "Stamp", role="ctb"), person("David", "Gibbs", role="ctb"), person("Ben", "Block", role="ctb"), + person("Michael", "Pennino", role="ctb"), person("Yadong", "Xu", role="ctb")) Description: Quality control checks on continuous data files from Hobo data loggers. Also aggregates and subsets files by a given date range. And runs basic statistics. Outputs reports to Word (requires knitr and Pandoc). Depends: @@ -14,8 +15,8 @@ Depends: License: MIT + file LICENSE Encoding: UTF-8 LazyData: true -URL: https://github.com/leppott/ContDataQC -BugReports: https://github.com/leppott/ContDataQC/issues +URL: https://github.com/USEPA/ContDataQC +BugReports: https://github.com/USEPA/ContDataQC/issues Imports: dataRetrieval, doBy, diff --git a/NEWS.md b/NEWS.md index baae5f17..31ed2612 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,250 +1,259 @@ NEWS-ContDataQC ================ - + - #> Last Update: 2022-09-22 14:03:40 + #> Last Update: 2024-10-07 10:08:23.189563 -# Version History +# Version History + +# v2.1.0 + +2024-10-07 + +- update: EPA version produced for publication in SoftwareX journal. + - R package code did not change + - Updates to readme, description, and other similar files. + - link to R shiny app now included ## v2.0.7.9005 2022-09-22 -- update: update Shiny app - - Fix numbered tabs 7 and 8 +- update: update Shiny app + - Fix numbered tabs 7 and 8 ## v2.0.7.9004 2022-09-22 -- update: update Shiny app - - Update links - - Remove (comment out) EPA template - - Rearrange Console and Test Data tabs +- update: update Shiny app + - Update links + - Remove (comment out) EPA template + - Rearrange Console and Test Data tabs ## v2.0.7.9003 2022-09-19 -- update: update Shiny app - - Update text +- update: update Shiny app + - Update text ## v2.0.7.9002 2022-09-15 -- update: update Shiny app - - Change names of navbar tabs and order +- update: update Shiny app + - Change names of navbar tabs and order ## v2.0.7.9001 2022-09-15 -- style: Use EPA Template for shiny app +- style: Use EPA Template for shiny app ## v2.0.7 2022-06-15 -- refactor: Bump to whole version number +- refactor: Bump to whole version number ## v2.0.6.9028 2022-06-15 -- refactor: Add dummy files to blank Shiny app folders to avoid being - deleted +- refactor: Add dummy files to blank Shiny app folders to avoid being + deleted ## v2.0.6.9027 2022-06-15 -- refactor: Update readme examples to use remotes instead of devtools - package +- refactor: Update readme examples to use remotes instead of devtools + package ## v2.0.6.9026 2022-06-15 -- feature: Add miniDOT functions to Shiny, Issue \#141 +- feature: Add miniDOT functions to Shiny, Issue \#141 ## v2.0.6.9025 2022-06-07 -- fix: Modify config.R for salinity, Issue \#138 - - Update other copies in package +- fix: Modify config.R for salinity, Issue \#138 + - Update other copies in package ## v2.0.6.9024 2022-05-31 -- fix: Modify flags so can handle all na, Issue \#143 - - fun.QC.file.R - - fun.QC.R - - Report_QC.rmd - - Base package and Shiny +- fix: Modify flags so can handle all na, Issue \#143 + - fun.QC.file.R + - fun.QC.R + - Report_QC.rmd + - Base package and Shiny ## v2.0.6.9023 2022-05-25 -- refactor: Update minidot_cat function, Issue \#141 - - Remove filename parameter and use serial number from input file -- feature: Add format minidot function, Issue \#141 +- refactor: Update minidot_cat function, Issue \#141 + - Remove filename parameter and use serial number from input file +- feature: Add format minidot function, Issue \#141 ## v2.0.6.9022 2022-05-25 -- refactor: Updates to minidot_cat function, Issue \#141 -- refactor: Add missing return value for help script line 937 +- refactor: Updates to minidot_cat function, Issue \#141 +- refactor: Add missing return value for help script line 937 ## v2.0.6.9021 2022-05-25 -- feature: Add miniDOT concatenate function, Issue \#141 - - Modified compile_do_folder.R from mnsentinellakes +- feature: Add miniDOT concatenate function, Issue \#141 + - Modified compile_do_folder.R from mnsentinellakes ## v2.0.6.9020 2022-05-24 -- refactor: Update Report_QC.rmd used in Shiny app, Issue \#117 -- feature: Prototype site data summary report, Issue \#142 -- feature: Add google analytics to Shiny app +- refactor: Update Report_QC.rmd used in Shiny app, Issue \#117 +- feature: Prototype site data summary report, Issue \#142 +- feature: Add google analytics to Shiny app ## v2.0.6.9019 2022-05-17 -- fix: Update Report_QC.rmd table statements with useNA = ifany, Issue - \#117 -- fix: Add new parameters to QC routine - - DO adj, Issue \#117 - - DO pct sat, Issue \#117 - - Salinity, Issue \#138 +- fix: Update Report_QC.rmd table statements with useNA = ifany, Issue + \#117 +- fix: Add new parameters to QC routine + - DO adj, Issue \#117 + - DO pct sat, Issue \#117 + - Salinity, Issue \#138 ## v2.0.6.9018 2022-05-11 -- feature: Add salinity to config file, Issue \#138 -- feature: Add salinity to config.out RMD, Issue \#138 +- feature: Add salinity to config file, Issue \#138 +- feature: Add salinity to config.out RMD, Issue \#138 ## v2.0.6.9017 2022-05-11 -- fix: Modify config file for DO percent saturation +- fix: Modify config file for DO percent saturation ## v2.0.6.9016 2022-04-14 -- refactor: Add keywords internal to two new functions -- fix: Rebuild package so new functions included -- docs: Add Yadong Xu as contributor +- refactor: Add keywords internal to two new functions +- fix: Rebuild package so new functions included +- docs: Add Yadong Xu as contributor ## v2.0.6.9015 2022-04-14 -- feature: Add new functions from Yadong for EMVL project - - Pull request \#137 +- feature: Add new functions from Yadong for EMVL project + - Pull request \#137 ## v2.0.6.9014 2022-01-18 -- refactor: Update Tim’s Threshold Analysis zip file +- refactor: Update Tim’s Threshold Analysis zip file ## v2.0.6.9013 2022-01-18 -- fix: Update broken links in Shiny to Config_Lakes_Metric.R, Issue - \#132 - - Linux case sensitive and wasn’t working online -- refactor: Update text for readability in Shiny for Edit Thresholds +- fix: Update broken links in Shiny to Config_Lakes_Metric.R, Issue + \#132 + - Linux case sensitive and wasn’t working online +- refactor: Update text for readability in Shiny for Edit Thresholds ## v2.0.6.9012 2022-01-17 -- refactor: Update QC Thresholds tab text in Shiny, Issue \#131 +- refactor: Update QC Thresholds tab text in Shiny, Issue \#131 ## v2.0.6.9011 2022-01-17 -- refactor: Update QC Thresholds tab in Shiny, Issue \#131 - - Change order and names of tabs - - Add Evaluate code as a download +- refactor: Update QC Thresholds tab in Shiny, Issue \#131 + - Change order and names of tabs + - Add Evaluate code as a download ## v2.0.6.9010 2021-12-22 -- fix: Updated custom config thresholds to handle quotes, Issue \#130 -- docs: Added Ben Block as contributor for work on custom thresholds +- fix: Updated custom config thresholds to handle quotes, Issue \#130 +- docs: Added Ben Block as contributor for work on custom thresholds ## v2.0.6.9009 2021-12-22 -- fix: Duplicate entry in config file, Issue \#130 - - Updated all versions in package and shiny folders - - Chlorophylla spike thresholds duplicated +- fix: Duplicate entry in config file, Issue \#130 + - Updated all versions in package and shiny folders + - Chlorophylla spike thresholds duplicated ## v2.0.6.9008 2021-12-21 -- feature: Shiny, add custom thresholds for config file, Issue \#130 - - merge pull request +- feature: Shiny, add custom thresholds for config file, Issue \#130 + - merge pull request ## v2.0.6.9007 2021-10-25 -- refactor: Rename “master” branch to “main”. +- refactor: Rename “master” branch to “main”. ## v2.0.6.9006 2021-10-25 -- fix: Update fun.Stats.R and fun.Stats.File.R to use WaterLevel - instead of GageHeight, Issue \#124 +- fix: Update fun.Stats.R and fun.Stats.File.R to use WaterLevel instead + of GageHeight, Issue \#124 ## v2.0.6.9005 2021-06-25 -- refactor: Update config file with changes to units. - - Correct some that were not correct -- refactor: Update config copy script in data-raw -- docs: Add markdown package to DESCRIPTION Suggests - - Needed to to avoid GitHub Action build failure when creating - vignettes +- refactor: Update config file with changes to units. + - Correct some that were not correct +- refactor: Update config copy script in data-raw +- docs: Add markdown package to DESCRIPTION Suggests + - Needed to to avoid GitHub Action build failure when creating + vignettes ## v2.0.6.9004 2021-06-25 -- style: Shiny, update code style in ui, server, and global - - Not all code just code relevant to current refactor item -- refactor: Shiny add all parameters to table on 4MainFunction, - CheckInput tab - - Issue \#127 +- style: Shiny, update code style in ui, server, and global + - Not all code just code relevant to current refactor item +- refactor: Shiny add all parameters to table on 4MainFunction, + CheckInput tab + - Issue \#127 ## v2.0.6.9003 2021-06-23 -- refactor: Updated version number in ui.R +- refactor: Updated version number in ui.R ## v2.0.6.9002 @@ -256,655 +265,648 @@ folder. 2021-06-23 -- refactor: Shiny updates to app description text +- refactor: Shiny updates to app description text ## v2.0.6.9000 2021-06-23 -- style: Removed Documents folder - - Move to new repository +- style: Removed Documents folder + - Move to new repository ## v2.0.6 2021-06-21 -- refactor: Create new version release +- refactor: Create new version release ## v2.0.5.9109 2021-06-21 -- refactor: Consolidate Shiny app updates from pull request, Issue - \#126 +- refactor: Consolidate Shiny app updates from pull request, Issue \#126 ## v2.0.5.9108 2021-06-18 -- refactor: Update Shiny - - Final updates to section 7 +- refactor: Update Shiny + - Final updates to section 7 ## v2.0.5.9107 2021-06-18 -- refactor: Update Shiny - - Beginning updates to section 7 +- refactor: Update Shiny + - Beginning updates to section 7 ## v2.0.5.9106 2021-06-18 -- refactor: Update Shiny - - Updated section 8 text +- refactor: Update Shiny + - Updated section 8 text ## v2.0.5.9105 2021-06-18 -- refactor: Update Shiny - - Updated text in section 9 +- refactor: Update Shiny + - Updated text in section 9 ## v2.0.5.9104 2021-06-18 -- refactor: Update Shiny - - Updates to section 2 and section 3 - added sidebarpanels within - tabsets rather than at bottom of pages +- refactor: Update Shiny + - Updates to section 2 and section 3 - added sidebarpanels within + tabsets rather than at bottom of pages ## v2.0.5.9103 2021-06-18 -- refactor: Update Shiny - - Updates sections 8 & 9. +- refactor: Update Shiny + - Updates sections 8 & 9. ## v2.0.5.9102 2021-06-18 -- refactor: Update Shiny - - Updated section 4a, 4b, and 5. +- refactor: Update Shiny + - Updated section 4a, 4b, and 5. ## v2.0.5.9101 2021-06-18 -- refactor: Update Shiny - - Updated 4a documents link +- refactor: Update Shiny + - Updated 4a documents link ## v2.0.5.9100 2021-06-18 -- refactor: Update Shiny - - Made final updates to section 3a code and began section 4 code - updates +- refactor: Update Shiny + - Made final updates to section 3a code and began section 4 code + updates ## v2.0.5.9099 2021-06-18 -- refactor: Update Shiny - - Updated 3b files +- refactor: Update Shiny + - Updated 3b files ## v2.0.5.9098 2021-06-18 -- refactor: Update Shiny - - Updated 3a section +- refactor: Update Shiny + - Updated 3a section ## v2.0.5.9097 2021-06-18 -- refactor: Update Shiny - - Updated tab 6 information +- refactor: Update Shiny + - Updated tab 6 information ## v2.0.5.9096 2021-06-18 -- refactor: Update Shiny - - Updated 2d HOBOware reformat +- refactor: Update Shiny + - Updated 2d HOBOware reformat ## v2.0.5.9095 2021-06-18 -- refactor: Update Shiny - - Added 2c info to rmd and documents +- refactor: Update Shiny + - Added 2c info to rmd and documents ## v2.0.5.9094 2021-06-18 -- refactor: Update Shiny - - Updates to 2a and 2b text and data sources. Added files to - documents folder. +- refactor: Update Shiny + - Updates to 2a and 2b text and data sources. Added files to documents + folder. ## v2.0.5.9093 2021-06-18 -- refactor: Update Shiny - - Updated 2a text and created Documents folder +- refactor: Update Shiny + - Updated 2a text and created Documents folder ## v2.0.5.9092 2021-06-18 -- refactor: Update Shiny - - moved tab_Reformat.R under tab_2DataPrep subtab “Formatting - files” +- refactor: Update Shiny + - moved tab_Reformat.R under tab_2DataPrep subtab “Formatting files” ## v2.0.5.9091 2021-06-18 -- refactor: Update Shiny - - Update to ui.R subfiles - added all necessary new tab - structures. - - Added content to tab_1Overview using .rmd file. +- refactor: Update Shiny + - Update to ui.R subfiles - added all necessary new tab structures. + - Added content to tab_1Overview using .rmd file. ## v2.0.5.9090 2021-06-18 -- refactor: Update Shiny - - Beginning updates in ui.R and subsequent code to accommodate - application changes. +- refactor: Update Shiny + - Beginning updates in ui.R and subsequent code to accommodate + application changes. ## v2.0.5.9089 2021-04-19 -- refactor: Remove extra columns from QC routine, Issue \#123 - - fun.QC.R - - fun.QC.File.R +- refactor: Remove extra columns from QC routine, Issue \#123 + - fun.QC.R + - fun.QC.File.R ## v2.0.5.9088 2021-04-19 -- refactor: Shiny, changes to tabs +- refactor: Shiny, changes to tabs ## v2.0.5.9087 2021-04-19 -- refactor: Shiny - - Rearrange tabs - - Split out Advanced tab into Console, Config, and Hobo +- refactor: Shiny + - Rearrange tabs + - Split out Advanced tab into Console, Config, and Hobo ## v2.0.5.9086 2021-04-02 -- style: Fix spelling, devtools::spell_check() - - README - - NEWS - - fun.parseID.R - - fun.Report.File.R - - Vignette_ContDataQC.Rmd - - Vignette_Config.RMD - - fun.GageData.R - - Vignette_Lakes.Rmd -- refactor: Reknit files - - README - - Vignettes -- style: Fix items from goodpractice::gp() - - Trim lines to 80 characters - - CompSiteCDF.R - - test_export_rLakeAnalyzer.R - - RBIcalc.R - - format.Hobo.R - - fun.Helper.R - - Export.IHA.R - - Export.StreamThermal.R - - fun.CustomConfig.R - - fun.PeriodStats.R - - fun.Stats.R (partial) - - fun.DateFormat.R (partial) - - Replace ‘=’ with ‘\<-’ - - fun.Helper.R - - Change T or F to TRUE or FALSE - - CompSiteCDF.R +- style: Fix spelling, devtools::spell_check() + - README + - NEWS + - fun.parseID.R + - fun.Report.File.R + - Vignette_ContDataQC.Rmd + - Vignette_Config.RMD + - fun.GageData.R + - Vignette_Lakes.Rmd +- refactor: Reknit files + - README + - Vignettes +- style: Fix items from goodpractice::gp() + - Trim lines to 80 characters + - CompSiteCDF.R + - test_export_rLakeAnalyzer.R + - RBIcalc.R + - format.Hobo.R + - fun.Helper.R + - Export.IHA.R + - Export.StreamThermal.R + - fun.CustomConfig.R + - fun.PeriodStats.R + - fun.Stats.R (partial) + - fun.DateFormat.R (partial) + - Replace ‘=’ with ‘\<-’ + - fun.Helper.R + - Change T or F to TRUE or FALSE + - CompSiteCDF.R ## v2.0.5.9085 2021-03-31 -- feature: Add custom config with metric units for Lakes, Issue \#119 - - extdata.Lakes.metric.R +- feature: Add custom config with metric units for Lakes, Issue \#119 + - extdata.Lakes.metric.R ## v2.0.5.9084 2021-02-24 -- feature: Shiny formatHobo add inputselect for date format, Issue - \#117 -- refactor: Shiny server.R remove full.names from zip routine - preventing proper construction of download zip file, Issue \#109 +- feature: Shiny formatHobo add inputselect for date format, Issue \#117 +- refactor: Shiny server.R remove full.names from zip routine preventing + proper construction of download zip file, Issue \#109 ## v2.0.5.9083 2021-02-23 -- refactor: Fix date in example reformatHobo for DO file, Issue \#117 -- refactor: Update reformatHobo date time conversion to account for no - seconds and 2 vs 4 digit years, Issue \#117 +- refactor: Fix date in example reformatHobo for DO file, Issue \#117 +- refactor: Update reformatHobo date time conversion to account for no + seconds and 2 vs 4 digit years, Issue \#117 ## v2.0.5.9082 2021-02-23 -- refactor: Shiny server, zip files add -j to strip folder names, - Issue \#109 -- feature: Add additional DO parameters to config, Issue \#117 -- refactor: Update other versions of config, Issue \#117 -- docs: Update README install directions -- docs: README, trim lines to 80 characters -- docs: Move planned updates from NEWS to README -- refactor: Update Report_Config.rmd for new DO parameters, Issue - \#117 -- refactor: Update Report_Config.rmd so sensor depth and water level - are next to each other -- refactor: Copy updated Report_Config.rmd to Shiny app, Issue \#117 -- feature: Add additional DO parameters (n=3) to reformatHobo function - , Issue \#117 +- refactor: Shiny server, zip files add -j to strip folder names, Issue + \#109 +- feature: Add additional DO parameters to config, Issue \#117 +- refactor: Update other versions of config, Issue \#117 +- docs: Update README install directions +- docs: README, trim lines to 80 characters +- docs: Move planned updates from NEWS to README +- refactor: Update Report_Config.rmd for new DO parameters, Issue \#117 +- refactor: Update Report_Config.rmd so sensor depth and water level are + next to each other +- refactor: Copy updated Report_Config.rmd to Shiny app, Issue \#117 +- feature: Add additional DO parameters (n=3) to reformatHobo function , + Issue \#117 ## v2.0.5.9081 2021-02-08 -- refactor: Rework logger deployment start/end to use first non-NA - date time instead of first and last row; Issue \#107 - - fun.QC.R - - fun.QC.File.R +- refactor: Rework logger deployment start/end to use first non-NA date + time instead of first and last row; Issue \#107 + - fun.QC.R + - fun.QC.File.R ## v2.0.5.9080 2021-02-05 -- style: Trim lines and fix spelling, fun.DateTimeFormat +- style: Trim lines and fix spelling, fun.DateTimeFormat ## v2.0.5.9079 2021-02-04 -- refactor: Update config spike flags for water level, Issue \#116 - - Update other versions of config +- refactor: Update config spike flags for water level, Issue \#116 + - Update other versions of config ## v2.0.5.9078 2021-02-04 -- refactor: Update Config_Template.zip in Shiny, Issue \#116 -- docs: Retain NEWS.md for pkgdown to build changelog +- refactor: Update Config_Template.zip in Shiny, Issue \#116 +- docs: Retain NEWS.md for pkgdown to build changelog ## v2.0.5.9077 2021-02-04 -- refactor: Update thresholds in config.R for sensor depth, Issue - \#116 +- refactor: Update thresholds in config.R for sensor depth, Issue \#116 ## v2.0.5.9076 2021-02-04 -- refactor: Modify “gross” check for water level, sensor depth, and - discharge , Issue \#113 - - Was only checking for negative values - - Set to evaluate like all other measured values - - fun.Helper.R, fun.CalcQCStats() -- refactor: Update config.R comments for gross check as above, Issue - \#113 - - Update other copies of config -- refactor: Add missing spike thresholds for water level, config.R -- refactor: Set water level tests to values for sensor depth +- refactor: Modify “gross” check for water level, sensor depth, and + discharge , Issue \#113 + - Was only checking for negative values + - Set to evaluate like all other measured values + - fun.Helper.R, fun.CalcQCStats() +- refactor: Update config.R comments for gross check as above, Issue + \#113 + - Update other copies of config +- refactor: Add missing spike thresholds for water level, config.R +- refactor: Set water level tests to values for sensor depth ## v2.0.5.9075 2021-01-27 -- feature: Begin updates to add comparison of deployment periods, - Issue \#108 - - Add helper functions for rquery.t.test() -- feature: Add MonthDay to data during QC operation, Issue \#111 -- style: Trim some lines to 80 characters - - fun.Stats.R +- feature: Begin updates to add comparison of deployment periods, Issue + \#108 + - Add helper functions for rquery.t.test() +- feature: Add MonthDay to data during QC operation, Issue \#111 +- style: Trim some lines to 80 characters + - fun.Stats.R ## v2.0.5.9074 2021-01-25 -- docs: Update example in ContDataQC for aggregating lake/depth files, - Issue \#85 +- docs: Update example in ContDataQC for aggregating lake/depth files, + Issue \#85 ## v2.0.5.9073 2021-01-22 -- refactor: Change ContDataQC() parameter fun.AddDeployCol from - default FALSE to TRUE - - Shiny app will now auto add the deployment field +- refactor: Change ContDataQC() parameter fun.AddDeployCol from default + FALSE to TRUE + - Shiny app will now auto add the deployment field ## v2.0.5.9072 2021-01-21 -- refactor: Report_QC.rmd, add Water Level vs. Water Temp plots, Issue - \#110 -- style: Report_QC.rmd, trim lines to 80 characters -- refactor: Copy Report_QC.rmd to shiny +- refactor: Report_QC.rmd, add Water Level vs. Water Temp plots, Issue + \#110 +- style: Report_QC.rmd, trim lines to 80 characters +- refactor: Copy Report_QC.rmd to shiny ## v2.0.5.9071 2021-01-21 -- refactor: Revise Vignette_Config so it doesn’t fail. +- refactor: Revise Vignette_Config so it doesn’t fail. ## v2.0.5.9070 2021-01-21 -- refactor: Replace getwd() with tempdir() in examples - - export_rLakeAnalyzer - - PeriodStats - - Export.IHA of getwd() to save file -- feature: Create logger deployment column during QC routine, Issue - \#107 - - ContDataQC - - fun.QC - - fun.QC.File -- feature: Add logger deployment to config.R, Issue \#107 - - myName.LoggerDeployment - - myName.LoggerDeployment.start - - myName.LoggerDeployment.end - - Added to myNames.Order (add after DateTime) -- style: Trim lines to 80 characters - - Export.rLakeAnalyzer - - Vignette_Config - - ContDataQC_Vignette -- refactor: Rename ContDataQC_Vignette to Vignette_ContDataQC -- refactor: Update config.R -- style: Updates to config.R, make order of Air and Water Pressure - consistent -- refactor: Create data-raw file to propagate changes in config.R to - shiny -- refactor: Update Vignette_Config with changes to config.R -- refactor: Update Config.R and its dependent versions - - inst.ORIG.R - - inst-examples\_Template.R - - inst-examples\_Template.zip -- refactor: Replace getwd() with tempdir() - - Vignette_Lakes - - Vignette_ContDataQC -- docs: Update examples in Vignette_ContDataQC -- docs: Update example in Export.IHA with text from ContDataQC +- refactor: Replace getwd() with tempdir() in examples + - export_rLakeAnalyzer + - PeriodStats + - Export.IHA of getwd() to save file +- feature: Create logger deployment column during QC routine, Issue + \#107 + - ContDataQC + - fun.QC + - fun.QC.File +- feature: Add logger deployment to config.R, Issue \#107 + - myName.LoggerDeployment + - myName.LoggerDeployment.start + - myName.LoggerDeployment.end + - Added to myNames.Order (add after DateTime) +- style: Trim lines to 80 characters + - Export.rLakeAnalyzer + - Vignette_Config + - ContDataQC_Vignette +- refactor: Rename ContDataQC_Vignette to Vignette_ContDataQC +- refactor: Update config.R +- style: Updates to config.R, make order of Air and Water Pressure + consistent +- refactor: Create data-raw file to propagate changes in config.R to + shiny +- refactor: Update Vignette_Config with changes to config.R +- refactor: Update Config.R and its dependent versions + - inst.ORIG.R + - inst-examples\_Template.R + - inst-examples\_Template.zip +- refactor: Replace getwd() with tempdir() + - Vignette_Lakes + - Vignette_ContDataQC +- docs: Update examples in Vignette_ContDataQC +- docs: Update example in Export.IHA with text from ContDataQC ## v2.0.5.9069 2021-01-20 -- tests: Add test for export_rLakeAnalyzer +- tests: Add test for export_rLakeAnalyzer ## v2.0.5.9068 2021-01-18 -- refactor: Add “RAW” columns for flags in QC step Issue \#106 +- refactor: Add “RAW” columns for flags in QC step Issue \#106 ## v2.0.5.9067 2021-01-18 -- refactor: Shiny server, turn off renameAggOutput, Issue \#104 +- refactor: Shiny server, turn off renameAggOutput, Issue \#104 ## v2.0.5.9066 2021-01-15 -- refactor: Add ability to Shiny to handle DOCX files to - renameAggOutput in global.R -- refactor: Redefine file name output for AggregateData.File - - Use input file names to get date range -- docs: Remove docs folder for pkgdown site - - Using GitHub Actions to build pkgdown in a different branch -- style: Trim to 80 character lines - - zfun.AggregateData - - zfun.AggregateData.File +- refactor: Add ability to Shiny to handle DOCX files to renameAggOutput + in global.R +- refactor: Redefine file name output for AggregateData.File + - Use input file names to get date range +- docs: Remove docs folder for pkgdown site + - Using GitHub Actions to build pkgdown in a different branch +- style: Trim to 80 character lines + - zfun.AggregateData + - zfun.AggregateData.File ## v2.0.5.9065 2021-01-14 -- refactor: Update name of Config.zip to Config_Template.zip in shiny - ui.R +- refactor: Update name of Config.zip to Config_Template.zip in shiny + ui.R ## v2.0.5.9064 2021-01-14 -- refactor: Rename custom config and add instructions, Issue \#104 - - Won’t work if use newenv() within custom file -- docs: Update text in config.out function about newenv() +- refactor: Rename custom config and add instructions, Issue \#104 + - Won’t work if use newenv() within custom file +- docs: Update text in config.out function about newenv() ## v2.0.5.9063 2021-01-13 -- fix: Shiny app custom config not loading +- fix: Shiny app custom config not loading ## v2.0.5.9063 2021-01-13 -- fix: Correct download of www/Config.zip -- fix: Replace unneeded concatenation without arguments - - Aquarius_Data_Acquisition_PADEP -- style: Trim lines to 80 characters - - fun.Config.Out -- fix: Example output to tempdir() rather than getwd() - - fun.Config.Out +- fix: Correct download of www/Config.zip +- fix: Replace unneeded concatenation without arguments + - Aquarius_Data_Acquisition_PADEP +- style: Trim lines to 80 characters + - fun.Config.Out +- fix: Example output to tempdir() rather than getwd() + - fun.Config.Out ## v2.0.5.9062 2021-01-08 -- refactor: Update version number display in Shiny app. +- refactor: Update version number display in Shiny app. ## v2.0.5.9061 2021-01-08 -- feature: Update formatHobo to output water level, Issue \#100 +- feature: Update formatHobo to output water level, Issue \#100 ## v2.0.5.9060 2021-01-08 -- refactor: Update CompSite.CDF 2nd example +- refactor: Update CompSite.CDF 2nd example ## v2.0.5.9059 2021-01-08 -- fix: Update CompSite.CDF so does not save to working directory - - use tempdir in example and update code to use dir.output -- style: Trim to 80 character lines - - fun.Helper.R +- fix: Update CompSite.CDF so does not save to working directory + - use tempdir in example and update code to use dir.output +- style: Trim to 80 character lines + - fun.Helper.R ## v2.0.5.9058 2021-01-08 -- refactor: Modify LakeID\~Depth siteID to LakeID–Depth - - File names with \~ are non-portable. -- style: Trim to 80 characters. - - Vignette_Lakes - - fun.ParseID -- refactor: Update help text in fun.ParseID for change in Lake ID - delimiter +- refactor: Modify LakeID~Depth siteID to LakeID–Depth + - File names with ~ are non-portable. +- style: Trim to 80 characters. + - Vignette_Lakes + - fun.ParseID +- refactor: Update help text in fun.ParseID for change in Lake ID + delimiter ## v2.0.5.9057 2021-01-07 -- feature: Modify formatHobo to use water level, issue \#100 +- feature: Modify formatHobo to use water level, issue \#100 ## v2.0.5.9056 2021-01-07 -- feature: Add water level as a parameter for QC checks, Issue \#100 -- refactor: Remove gage height as a parameter for QC checks, Issue - \#100 -- feature: Modify USGS download to change gage height to water level, - Issue \#100 - - Match HOBOware terminology (most of user base) -- style: Trim lines to 80 characters - - config.R - - fun.GageData.R - - fun.Report.R - - fun.Report.File.R -- fix: Update copies and version of config.R with edits, Issue \#100 - - Package - - data.R - - inst.COOL - - inst.ExcludeFailsFalse - - inst.ORIG - - inst.RMDdir - - inst.WARM - - Shiny - - www.R - - www.zip -- feature: Update rmd files from GageHeight to WaterLevel - - Report_Config - - Report_QC -- refactor: Copy RMD files from package directory to shiny -- refactor: Remove unneeded concatenation of a constant - - server.R - - Export.rlakeAnalyzer.R -- feature: Change Gage Height to Water Level, fun.QC -- feature: Add Water Level, fun.QC.File -- refactor: Change getwd() to tempdir(), ContDataQC example -- refactor: Update ContDataQC example - - getwd() to tempdir() - - paste0() to file.path() -- fix: Fix typo in fun.Stats.File.R - - seq_len(row… to seq_len(nrow… -- refactor: Remove use of installr package as is windows only - - Move example text to details of ContDataQC function +- feature: Add water level as a parameter for QC checks, Issue \#100 +- refactor: Remove gage height as a parameter for QC checks, Issue \#100 +- feature: Modify USGS download to change gage height to water level, + Issue \#100 + - Match HOBOware terminology (most of user base) +- style: Trim lines to 80 characters + - config.R + - fun.GageData.R + - fun.Report.R + - fun.Report.File.R +- fix: Update copies and version of config.R with edits, Issue \#100 + - Package + - data.R + - inst.COOL + - inst.ExcludeFailsFalse + - inst.ORIG + - inst.RMDdir + - inst.WARM + - Shiny + - www.R + - www.zip +- feature: Update rmd files from GageHeight to WaterLevel + - Report_Config + - Report_QC +- refactor: Copy RMD files from package directory to shiny +- refactor: Remove unneeded concatenation of a constant + - server.R + - Export.rlakeAnalyzer.R +- feature: Change Gage Height to Water Level, fun.QC +- feature: Add Water Level, fun.QC.File +- refactor: Change getwd() to tempdir(), ContDataQC example +- refactor: Update ContDataQC example + - getwd() to tempdir() + - paste0() to file.path() +- fix: Fix typo in fun.Stats.File.R + - seq_len(row… to seq_len(nrow… +- refactor: Remove use of installr package as is windows only + - Move example text to details of ContDataQC function ## v2.0.5.9055 2021-01-04 -- refactor: Add config_R.zip to shiny app in www folder -- refactor: server.R update fun.myReport.format to “” from “html” +- refactor: Add config_R.zip to shiny app in www folder +- refactor: server.R update fun.myReport.format to “” from “html” ## v2.0.5.9054 2021-01-04 -- chore: Recreate pkgdown GitHub Action +- chore: Recreate pkgdown GitHub Action ## v2.0.5.9053 2021-01-04 -- chore: Rebuild pkgdown by removing all +- chore: Rebuild pkgdown by removing all ## v2.0.5.9052 2021-01-04 -- chore: Pkgdown site -- chore: Config change default report format from HTML to DOCX, Issue - \#102 - - Update data/config.R - - Copy to inst/shiny-examples/ContDataQC/www - - Update inst/shiny-examples/ContDataQC/config.RMDdir.R - - Copy to inst/extdata/config.ORIG.R - - Update inst/extdata/config.RMDdir.R -- chore: Add to .Rbuildignore - - data-raw folder - - shiny rsconnect -- fix: Add missing global variable bindings - - ContDataQC - - fun.AggregateData - - fun.Report - - fun.Report.File - - fun.Stats -- fix: README, replace failing license badge +- chore: Pkgdown site +- chore: Config change default report format from HTML to DOCX, Issue + \#102 + - Update data/config.R + - Copy to inst/shiny-examples/ContDataQC/www + - Update inst/shiny-examples/ContDataQC/config.RMDdir.R + - Copy to inst/extdata/config.ORIG.R + - Update inst/extdata/config.RMDdir.R +- chore: Add to .Rbuildignore + - data-raw folder + - shiny rsconnect +- fix: Add missing global variable bindings + - ContDataQC + - fun.AggregateData + - fun.Report + - fun.Report.File + - fun.Stats +- fix: README, replace failing license badge ## v2.0.5.9051 2020-12-29 -- chore: Add docs folder for pkgdown +- chore: Add docs folder for pkgdown ## v2.0.5.9050 2020-12-29 -- style: Replace ‘=’ with ‘\<-’ -- style: Avoid long code lines over 80 characters - - partial -- style: Remove trailing blank lines - - partial -- docs: DESCRIPTION, add reshape2 to Suggests - - Only used in Export.rLakeAnalyzer -- docs: Update license information -- fix: Export.IHA, Add missing parameter to function and update code -- fix: Add foo:: to function calls where missing - - fun.Helper -- docs: CompSiteCDF, add missing argument to documentation. -- docs: fun.ParseID, rename arguments so function and documentation - match -- docs: data.R, Add “data_raw_ECO66G12_AW_20160128_20160418” -- fix: Export.IHA, fix argument name, myFile to fun.myFile -- chore: CompSiteCDF, fix agument name, myFile to file.input +- style: Replace ‘=’ with ‘\<-’ +- style: Avoid long code lines over 80 characters + - partial +- style: Remove trailing blank lines + - partial +- docs: DESCRIPTION, add reshape2 to Suggests + - Only used in Export.rLakeAnalyzer +- docs: Update license information +- fix: Export.IHA, Add missing parameter to function and update code +- fix: Add foo:: to function calls where missing + - fun.Helper +- docs: CompSiteCDF, add missing argument to documentation. +- docs: fun.ParseID, rename arguments so function and documentation + match +- docs: data.R, Add “data_raw_ECO66G12_AW_20160128_20160418” +- fix: Export.IHA, fix argument name, myFile to fun.myFile +- chore: CompSiteCDF, fix agument name, myFile to file.input ## v2.0.5.9049 2020-12-28 -- fix: Add foo:: to function calls where missing - - fun.QC.File - - fun.PeriodStats - - fun.QC.File - - fun.Helper - - ProcessData_CompSiteCDF - - ProcessData_PeriodSummary - - ProcessData_test2 - - ProcessData_test4 -- fix: ContDataQC example, comment out installr section. +- fix: Add foo:: to function calls where missing + - fun.QC.File + - fun.PeriodStats + - fun.QC.File + - fun.Helper + - ProcessData_CompSiteCDF + - ProcessData_PeriodSummary + - ProcessData_test2 + - ProcessData_test4 +- fix: ContDataQC example, comment out installr section. ## v2.0.5.9048 2020-12-28 -- docs: Reapply GitHub Action pkgdown. +- docs: Reapply GitHub Action pkgdown. ## v2.0.5.9047 2020-12-28 -- docs: DESCRIPTION, installr to Remotes +- docs: DESCRIPTION, installr to Remotes ## v2.0.5.9046 2020-12-28 -- docs: Remove old pkgdown yaml from root directory. - - GitHub Action for pkgdown was not building. +- docs: Remove old pkgdown yaml from root directory. + - GitHub Action for pkgdown was not building. ## v2.0.5.9045 @@ -921,1284 +923,1275 @@ zFun.AggregateData + config docs: DESCRIPTION, Add Remotes for non-CRAN 2020-12-25 -- ci: Add GitHub Action test-coverage -- docs: ReadMe, Add codecov badge -- docs: ReadMe, Add codefactor badge +- ci: Add GitHub Action test-coverage +- docs: ReadMe, Add codecov badge +- docs: ReadMe, Add codefactor badge ## v2.0.5.9043 2020-12-25 -- docs: Add GitHub Action pkgdown (adds gh-pages branch) -- docs: Remove docs folder for pkgdown website +- docs: Add GitHub Action pkgdown (adds gh-pages branch) +- docs: Remove docs folder for pkgdown website ## v2.0.5.9042 2020-12-25 -- ci: Add GitHub Actions CI -- ci: Remove Travis CI yaml -- docs: ReadMe, Add GitHub Actions CI CMD check badge -- docs: ReadMe, Add lifecycle badge +- ci: Add GitHub Actions CI +- ci: Remove Travis CI yaml +- docs: ReadMe, Add GitHub Actions CI CMD check badge +- docs: ReadMe, Add lifecycle badge ## v2.0.5.9041 2020-12-22 -- Shiny, USGS gage data - - Add placeholder text for start and end dates (YYYY-MM-DD). - - Add console message output for QA testing. - - Modify zip file name for download, Issue \#98 +- Shiny, USGS gage data + - Add placeholder text for start and end dates (YYYY-MM-DD). + - Add console message output for QA testing. + - Modify zip file name for download, Issue \#98 ## v2.0.5.9040 2020-12-21 -- Shiny add helper function for QC routine to keep only first entry - when group by Date.Time. - - Also add ‘unique’ after QC checks to avoid extra lines being - created. +- Shiny add helper function for QC routine to keep only first entry when + group by Date.Time. + - Also add ‘unique’ after QC checks to avoid extra lines being + created. ## v2.0.5.9039 2020-12-18 -- Shiny, minor tweak. - - v2.0.5.9038 had an edit to server.R that broke the formatHobo - function when run on ShinyApps.io. - - Undid the edit. - - Added message output for the log file to track the error’s - location. +- Shiny, minor tweak. + - v2.0.5.9038 had an edit to server.R that broke the formatHobo + function when run on ShinyApps.io. + - Undid the edit. + - Added message output for the log file to track the error’s location. ## v2.0.5.9038 2020-12-16 -- formatHobo, update help text, Issue \#97 +- formatHobo, update help text, Issue \#97 ## v2.0.5.9037 2020-12-16 -- Shiny app, add ContDataQC::formatHobo(), Issue \#96. - - ui.R - - server.R +- Shiny app, add ContDataQC::formatHobo(), Issue \#96. + - ui.R + - server.R ## v2.0.5.9036 2020-12-11 -- global.R - - Change timeFormater function to show time without seperators. - - 20201211_16_32_00 to 20201211_163200 +- global.R + - Change timeFormater function to show time without seperators. + - 20201211_16_32_00 to 20201211_163200 ## v2.0.5.9035 2020-12-11 -- zfun.ContDataQC.R - - Update example for file version of Aggregate. - - myFile “Aw” to “AW”. - - Update example for file version of SummaryStats. - - Add comment characters for second myFile. -- fun.Stats.File.R - - Remove as.numeric() from doBy::summaryBy() statements. -- server.R - - Remove “.” from file path for zip for stats procedure. +- zfun.ContDataQC.R + - Update example for file version of Aggregate. + - myFile “Aw” to “AW”. + - Update example for file version of SummaryStats. + - Add comment characters for second myFile. +- fun.Stats.File.R + - Remove as.numeric() from doBy::summaryBy() statements. +- server.R + - Remove “.” from file path for zip for stats procedure. ## v2.0.5.9034 2020-12-11 -- global.R and server.R - - Fix Aggregate not working (file error), Issue \#92 - - Fix file delete issues, Issue \#93 +- global.R and server.R + - Fix Aggregate not working (file error), Issue \#92 + - Fix file delete issues, Issue \#93 ## v2.0.5.9033 2020-12-10 -- server.R - - Add outline headers. - - Change Config file download. -- ui.R - - Update version number. -- Aggregate Report in Shiny app. +- server.R + - Add outline headers. + - Change Config file download. +- ui.R + - Update version number. +- Aggregate Report in Shiny app. ## v2.0.5.9032 2020-12-03 -- data - - Test2 files and data to ‘test2’. -- Shiny app update RMD files with current ones. +- data + - Test2 files and data to ‘test2’. +- Shiny app update RMD files with current ones. ## v2.0.5.9031 2020-10-16 -- CRAN checks. - - Example lines longer than 100 characters. - - ContDataQC - - RBIcalc - - Add package names for all non base functions. - - +- CRAN checks. + - Example lines longer than 100 characters. + - ContDataQC + - RBIcalc + - Add package names for all non base functions. + - ## v2.0.5.9030 2020-10-07 -- Update to config.load() to ensure custom config files load into the - proper environment. -- zfun.ContDataQC.R - - Load config.ORIG.R from package as first step before custom - config. +- Update to config.load() to ensure custom config files load into the + proper environment. +- zfun.ContDataQC.R + - Load config.ORIG.R from package as first step before custom config. ## v2.0.5.9029 2020-01-27 -- Rebuild pkgdown website. +- Rebuild pkgdown website. ## v2.0.5.9028 2020-01-27 -- Rebuild pkgdown website. +- Rebuild pkgdown website. ## v2.0.5.9027 2020-01-27 -- README - - Add more info about sometimes package dependencies not loading - and things not working. +- README + - Add more info about sometimes package dependencies not loading and + things not working. ## v2.0.5.9026 2019-06-11 -- Add pkgdown website. +- Add pkgdown website. ## v2.0.5.9025 2019-06-11 -- Update config vignette to be more specific on modifying variables. +- Update config vignette to be more specific on modifying variables. ## v2.0.5.9024 2019-05-17 -- Fix for R v3.6.0 staged install, Issue \#88. - - DESCRIPTION - - Remove StagedInstall: no - - README - - Installation instructions, added extra line for devtools to - work properly. -- README - - Add badges. +- Fix for R v3.6.0 staged install, Issue \#88. + - DESCRIPTION + - Remove StagedInstall: no + - README + - Installation instructions, added extra line for devtools to work + properly. +- README + - Add badges. ## v2.0.5.9023 2019-05-02 -- DESCRIPTION - - StagedInstall: no for R v3.6.0. Issue \#88. +- DESCRIPTION + - StagedInstall: no for R v3.6.0. Issue \#88. ## v2.0.5.9022 2019-05-02 -- DESCRIPTION - - StagedInstall: false for R v3.6.0. Issue \#88. +- DESCRIPTION + - StagedInstall: false for R v3.6.0. Issue \#88. ## v2.0.5.9021 2019-04-17 -- Add formatting directions for Hoboware files. - - extdata powerpoint - - Meta PDF +- Add formatting directions for Hoboware files. + - extdata powerpoint + - Meta PDF ## v2.0.5.9020 2019-04-02 -- Update version number. - - Repeated 9018. +- Update version number. + - Repeated 9018. ## v2.0.5.9019 2019-03-26 -- Report_PeriodStats.rmd, Issue \#76 - - Points to lines. - - Add min/max shaded +- Report_PeriodStats.rmd, Issue \#76 + - Points to lines. + - Add min/max shaded ## v2.0.5.9018 2019-03-07 -- ReadMe - - Add link for config file modifications. Issue \#86 +- ReadMe + - Add link for config file modifications. Issue \#86 ## v2.0.5.9017 2019-03-05 -- Vignette for editing config file. Issue \#86 +- Vignette for editing config file. Issue \#86 ## v2.0.5.9016 2019-02-25 -- Ensure the package can handle “LakeID\~Depth” as “SiteID” - - Add Lake delimiter to Config files. - - ContData.env\$myDelim_LakeID \<- “\~” -- Reporte_Aggregate.rmd - - Turn on ggplot for dual parameter plots. -- ContDataQC - - Add example (and data) for File Aggregate with Lake data. Issue - \#85. -- Add a “Lakes” vignette. +- Ensure the package can handle “LakeID~Depth” as “SiteID” + - Add Lake delimiter to Config files. + - ContData.env\$myDelim_LakeID \<- “~” +- Reporte_Aggregate.rmd + - Turn on ggplot for dual parameter plots. +- ContDataQC + - Add example (and data) for File Aggregate with Lake data. Issue + \#85. +- Add a “Lakes” vignette. ## v2.0.5.9015 2019-02-22 -- Config.Out - - Add functionality to save a copy of the config file. +- Config.Out + - Add functionality to save a copy of the config file. ## v2.0.5.9014 2019-02-22 -- Update code to allow for plots in PeriodStats Report to use ggplot. - Issues \#83 and 84. - - Incomplete. Still using base graphics. +- Update code to allow for plots in PeriodStats Report to use ggplot. + Issues \#83 and 84. + - Incomplete. Still using base graphics. ## v2.0.5.9013 2019-02-21 -- Update plots in Aggregate Report to ggplot. Issues \#83 and 84. +- Update plots in Aggregate Report to ggplot. Issues \#83 and 84. ## v2.0.5.9012 2019-02-21 -- Update plots in QC Report to ggplot. Issues \#83 and 84. -- Import files fix, na.strings= - - Use c(““,”NA”) not just ““. +- Update plots in QC Report to ggplot. Issues \#83 and 84. +- Import files fix, na.strings= + - Use c(““,”NA”) not just ““. ## v2.0.5.9011 2019-02-19 -- Export.rLakeAnalyzer, Issue \#82 - - Examples for rLakeAnalyzer functions: - - schmidt.plot - - ts.schmidt.stability +- Export.rLakeAnalyzer, Issue \#82 + - Examples for rLakeAnalyzer functions: + - schmidt.plot + - ts.schmidt.stability ## v2.0.5.9010 2019-02-13 -- Export.rLakeAnalyzer - - Add rLakeAnalyzer package DESCRIPTION under suggests. - - Generate output function. Issue \#82. - - Include example for rLakeAnalyzer::wtr.heat.map - - Added example plot of multiple depths using ggplot2. -- Added ggplot2 to DESCRIPTION suggests. +- Export.rLakeAnalyzer + - Add rLakeAnalyzer package DESCRIPTION under suggests. + - Generate output function. Issue \#82. + - Include example for rLakeAnalyzer::wtr.heat.map + - Added example plot of multiple depths using ggplot2. +- Added ggplot2 to DESCRIPTION suggests. ## v2.0.5.9009 2019-01-21 -- formatHobo - - Update to account for Lake SiteIDs to include depth. +- formatHobo + - Update to account for Lake SiteIDs to include depth. ## v2.0.5.9008 2019-01-17 -- Add shiny app to package, Issue \#81 -- DESCRIPTION - - Add David Gibbs - - Add packages for Shiny and sort. +- Add shiny app to package, Issue \#81 +- DESCRIPTION + - Add David Gibbs + - Add packages for Shiny and sort. ## v2.0.5.9007 2019-01-17 -- Make report output optional in ContDataQC, Issue \#80. - - ContDataQC +- Make report output optional in ContDataQC, Issue \#80. + - ContDataQC ## v2.0.5.9006 2019-01-09 -- Example script to get data from Aquarius. - - Vignette (at end) - - extdata/Aquarius_Data_Aquisition_PADEP.r +- Example script to get data from Aquarius. + - Vignette (at end) + - extdata/Aquarius_Data_Aquisition_PADEP.r ## v2.0.5.9005 2019-01-09 -- configuration files. - - Add Data0_Original folder to each configuration file. +- configuration files. + - Add Data0_Original folder to each configuration file. ## v2.0.5.9004 2019-01-09 -- formatHobo, deal with 2 digit years in Date Time field, Issue \#75 +- formatHobo, deal with 2 digit years in Date Time field, Issue \#75 ## v2.0.5.9003 2019-01-09 -- Configuration thresholds not displaying correctly, Issue \#74. - - Suspect gross hi value was reported incorrectly for all - parameters. - - Used the gross fail low value. - - Updated RMD files. - - Report_Config.rmd - - Report_QC.rmd - - Updated Vignette to use extdata/config.ORIG.R rather than a - manual copy and paste of R/config.R. - - Ensured R/config.R and extdata/config.ORIG.R are identical (used - compare in Notepad++). Differences noted below. - - Section header for Report Format - - myReport.Dir - - different number of blank lines at end of file. +- Configuration thresholds not displaying correctly, Issue \#74. + - Suspect gross hi value was reported incorrectly for all parameters. + - Used the gross fail low value. + - Updated RMD files. + - Report_Config.rmd + - Report_QC.rmd + - Updated Vignette to use extdata/config.ORIG.R rather than a manual + copy and paste of R/config.R. + - Ensured R/config.R and extdata/config.ORIG.R are identical (used + compare in Notepad++). Differences noted below. + - Section header for Report Format + - myReport.Dir + - different number of blank lines at end of file. ## v2.0.5.9002 2019-01-07 -- folder “Data0_Original”, Issue \#73. - - Function ContDataQC - - So is the same as the formatHobo function. - - Update Vignette. +- folder “Data0_Original”, Issue \#73. + - Function ContDataQC + - So is the same as the formatHobo function. + - Update Vignette. ## v2.0.5.9001 2018-12-21 -- Config. - - Add Warm. - - Rename Cold to Cool. +- Config. + - Add Warm. + - Rename Cold to Cool. ## v2.0.5 2018-12-13 -- Revised release including vignette. +- Revised release including vignette. ## v2.0.4.9002 2018-12-13 -- gitignore contained inst/doc. Removed so will load correctly to - GitHub. +- gitignore contained inst/doc. Removed so will load correctly to + GitHub. ## v2.0.4.9001 2018-12-13 -- Rebuild Vignette. Not visible in v2.0.4. +- Rebuild Vignette. Not visible in v2.0.4. ## v2.0.4 2018-12-05 -- New release version. +- New release version. ## v2.0.3.9001 2018-12-05 -- Fix error introduced in v2.0.2.9013, Issue \#68. -- Moved fun.CalcQCStats from fun.QC.R to fun.Helper.R. +- Fix error introduced in v2.0.2.9013, Issue \#68. +- Moved fun.CalcQCStats from fun.QC.R to fun.Helper.R. ## v2.0.3 2018-12-04 -- New release version. +- New release version. ## v2.0.2.9014 2018-12-04 -- Add formatHobo to Vignette. Issue \#69 +- Add formatHobo to Vignette. Issue \#69 ## v2.0.2.9013 2018-11-27 -- Fix file version of functions error. Issue \#68 - - zfun.ContDataQC.R +- Fix file version of functions error. Issue \#68 + - zfun.ContDataQC.R ## v2.0.2.9012 2018-11-27 -- Add function for reformatting HoboWare files. +- Add function for reformatting HoboWare files. ## v2.0.2.9010 2018-11-16 -- DESCRIPTION - - Remove Jon van Sickles as contributor since removed rarify - function. +- DESCRIPTION + - Remove Jon van Sickles as contributor since removed rarify function. ## v2.0.2.9009 2018-11-16 -- rarify, move to another package, Issue \#67 - - Moved to separate package. - \_ - - BioMonTools is for bioassessment and biomonitoring. - - ContDataQC should stay focused on continuous dat. - - DESCRIPTION - - Remote: leppott/BioMonTools - - Vignette - - rarify section updated. - - R/data.R - - Remove reference to data_bio2rarify - - Remove - - R/rarify.R - - data-raw/ProcessData_rarify.R - - data-raw/data_bio2rarify.txt - - dat/data_bio2rarify.rda +- rarify, move to another package, Issue \#67 + - Moved to separate package. + \_ + - BioMonTools is for bioassessment and biomonitoring. + - ContDataQC should stay focused on continuous dat. + - DESCRIPTION + - Remote: leppott/BioMonTools + - Vignette + - rarify section updated. + - R/data.R + - Remove reference to data_bio2rarify + - Remove + - R/rarify.R + - data-raw/ProcessData_rarify.R + - data-raw/data_bio2rarify.txt + - dat/data_bio2rarify.rda ## v2.0.2.9008 2018-11-15 -- PeriodStats - - Modify for input of 1 or 2 parameters. - - If have 2 parameters get: - - 2 sets of output stat files. - - And get plots with 2 y-axes (i.e., both are plotted). - - fun.PeriodStats.R - - Report_PeriodStats.rmd +- PeriodStats + - Modify for input of 1 or 2 parameters. + - If have 2 parameters get: + - 2 sets of output stat files. + - And get plots with 2 y-axes (i.e., both are plotted). + - fun.PeriodStats.R + - Report_PeriodStats.rmd ## v2.0.2.9007 2018-11-14 -- fun.Stats.R - - Fix tapply arguments must have same length error. Issue \# 60. +- fun.Stats.R + - Fix tapply arguments must have same length error. Issue \# 60. ## v2.0.2.9006 2018-11-14 -- Add links to issues in commit. Issues \#57 and \#59. +- Add links to issues in commit. Issues \#57 and \#59. ## v2.0.2.9005 2018-11-14 -- Reports, Issue \#57 and Issue \#59. - - Modify function to use a single report rather than one for each - output format (docx and html). - - fun.Report.R - - fun.Report.File.R - - fun.Config.Out.R - - fun.PeriodStats.R - - fun.QC.R - - fun.QC.File.R - - zfun.AggregateData.R - - zfun.AggregateData.File.R - - fun.Stats.R - - fun.Stats.File.R - - RMD Report files. - - Update YAML header for multiple formats (word and html). - - Update YAML header for table of contents and other items. - - Add “setup” chunk with knitr options so don’t need to - specify in each chunk. - - Make render statement consistent by specifying output - format. - - Removed docx and html versions of RMD files. Only 1 RMD file - per report. - - Report_Aggregate.RMD - - Report_PeriodStats.RMD - - Report_QC.RMD - - Report_Stats.RMD - - Report_Config.RMD +- Reports, Issue \#57 and Issue \#59. + - Modify function to use a single report rather than one for each + output format (docx and html). + - fun.Report.R + - fun.Report.File.R + - fun.Config.Out.R + - fun.PeriodStats.R + - fun.QC.R + - fun.QC.File.R + - zfun.AggregateData.R + - zfun.AggregateData.File.R + - fun.Stats.R + - fun.Stats.File.R + - RMD Report files. + - Update YAML header for multiple formats (word and html). + - Update YAML header for table of contents and other items. + - Add “setup” chunk with knitr options so don’t need to specify in + each chunk. + - Make render statement consistent by specifying output format. + - Removed docx and html versions of RMD files. Only 1 RMD file per + report. + - Report_Aggregate.RMD + - Report_PeriodStats.RMD + - Report_QC.RMD + - Report_Stats.RMD + - Report_Config.RMD ## v2.0.2.9004 2018-11-09 -- Thresholds in QC Report, Issue \#15 - - Report_QC.rmd - - Report_QC_docx.rmd - - Report_QC_html.rmd +- Thresholds in QC Report, Issue \#15 + - Report_QC.rmd + - Report_QC_docx.rmd + - Report_QC_html.rmd ## v2.0.2.9003 2018-11-08 -- Configuration Report, Issue \#15 - - Report_Config.rmd - - New report in RMD folder - - fun.ConfigOut.R - - New function to output new report +- Configuration Report, Issue \#15 + - Report_Config.rmd + - New report in RMD folder + - fun.ConfigOut.R + - New function to output new report ## v2.0.2.9002 2018-09-25 -- Update help file for rarify. Make the same as BCGcalc::rarify. Issue - \#65. -- Still need to modify vignette. +- Update help file for rarify. Make the same as BCGcalc::rarify. Issue + \#65. +- Still need to modify vignette. ## v2.0.2.9001 2018-02-12 -- Added parameter for RMD (report template) directory. - - For Shiny (Issue \#39) and custom reports. Includes error - checking for no RMD file found. -- Added config.RMDdir.R to “extdata” as a test. +- Added parameter for RMD (report template) directory. + - For Shiny (Issue \#39) and custom reports. Includes error checking + for no RMD file found. +- Added config.RMDdir.R to “extdata” as a test. ## v2.0.2 2018-01-31 -- Set release on GitHub. +- Set release on GitHub. ## v2.0.1.9063 2018-01-31 -- Updated ReadMe. - - Provide links to “Guides”; training videos on YouTube with - powerpoint slides and R Notebooks (with video files) on GitHub. +- Updated ReadMe. + - Provide links to “Guides”; training videos on YouTube with + powerpoint slides and R Notebooks (with video files) on GitHub. ## v2.0.1.9062 2018-01-29 -- Add an unmodified version of config.R to external data as - config.ORIG.R. Need for guide videos examples. +- Add an unmodified version of config.R to external data as + config.ORIG.R. Need for guide videos examples. ## v2.0.1.9061 2018-01-18 -- Reports. Missed updating some headers. Searched all files - thoroughly. Issue \#56. -- PeriodStats. Minor update to description in help file. +- Reports. Missed updating some headers. Searched all files thoroughly. + Issue \#56. +- PeriodStats. Minor update to description in help file. ## v2.0.1.9060 2018-01-18 -- PeriodStats - - Make example easier to follow. Issue \#55. - - Allow for use of config file for inclusion/exclusion of - parameter flag “fails”. Issue \#54. - - Report to user in console number of fails removed. - - Added example with config file with exclude.fails = FALSE. -- Update some section headers in report RMD files. Add space between - pound sign and header text. Issue \#56. -- Added more information to the header section of the PeriodStats - report. - - Minimum date in file. - - Difference between minimum date in file and benchmark date. +- PeriodStats + - Make example easier to follow. Issue \#55. + - Allow for use of config file for inclusion/exclusion of parameter + flag “fails”. Issue \#54. + - Report to user in console number of fails removed. + - Added example with config file with exclude.fails = FALSE. +- Update some section headers in report RMD files. Add space between + pound sign and header text. Issue \#56. +- Added more information to the header section of the PeriodStats + report. + - Minimum date in file. + - Difference between minimum date in file and benchmark date. ## v2.0.1.9059 2018-01-17 -- No reproducible error with vignette not being present. Added code to - ReadMe that forces the vignette to build on users computer when the - package is installed. Issue \#49. +- No reproducible error with vignette not being present. Added code to + ReadMe that forces the vignette to build on users computer when the + package is installed. Issue \#49. ## v2.0.1.9058 2018-01-17 -- Updated functions with keywords ‘internal’ so they won’t show up in - help file index. These are functions that shouldn’t be accessed - individually. +- Updated functions with keywords ‘internal’ so they won’t show up in + help file index. These are functions that shouldn’t be accessed + individually. ## v2.0.1.9057 2018-01-17 -- OneDrive created multiple versions of files on computer. Clean up - and resubmit. Some minor work done on package. +- OneDrive created multiple versions of files on computer. Clean up and + resubmit. Some minor work done on package. ## v2.0.1.9056 2017-11-30 -- PeriodStats +- PeriodStats - - Added data input requirements. 20171130. + - Added data input requirements. 20171130. - - Updated example for default report from DOCX to HTML. 20171130. + - Updated example for default report from DOCX to HTML. 20171130. - - Updated Vignette. 20171130. + - Updated Vignette. 20171130. ## v2.0.1.9055 2017-11-30 -- Export.IHA +- Export.IHA - - Updated example to use system username. 20171130. + - Updated example to use system username. 20171130. - - Repeated SiteID line in example. 20171130. + - Repeated SiteID line in example. 20171130. - - Updated Vignette. 20171130. + - Updated Vignette. 20171130. ## v2.0.1.9054 2017-11-30 -- Export.StreamThermal +- Export.StreamThermal - - Update Excel file with category definitions from journal - article. 20171130. + - Update Excel file with category definitions from journal article. + 20171130. - - Add metric definition Excel file to extdata so can use when - create data export. 20171130. + - Add metric definition Excel file to extdata so can use when create + data export. 20171130. - - Update examples in vignette. 20171130. + - Update examples in vignette. 20171130. ## v2.0.1.9053 2017-11-29 -- Readme, fix typo in github install function. 20171129. +- Readme, fix typo in github install function. 20171129. -- Update example in Export.StreamThermal so is self contained. - Vignette did not need to be updated. Included example code for - export to Excel (same as in Export.IHA example). 20171129. +- Update example in Export.StreamThermal so is self contained. Vignette + did not need to be updated. Included example code for export to Excel + (same as in Export.IHA example). 20171129. ## v2.0.1.9052 2017-11-29 -- Update “=” to “\<-” in Export.IHA example in function and vignette. - 20171129. +- Update “=” to “\<-” in Export.IHA example in function and vignette. + 20171129. -- SummaryStats (File) example for non-ContDataQC file. Add minimum - necessary fields to a USGS download. 20171129. +- SummaryStats (File) example for non-ContDataQC file. Add minimum + necessary fields to a USGS download. 20171129. -- Update vignette: +- Update vignette: - - Revised config.R thresholds. 20171129. + - Revised config.R thresholds. 20171129. - - SummaryStats (File) for non-ContDataQC file. 20171129. + - SummaryStats (File) for non-ContDataQC file. 20171129. ## v2.0.1.9051 2017-11-29 -- Reports, typo in “Generated by:”. 20171129. +- Reports, typo in “Generated by:”. 20171129. ## v2.0.1.9050 2017-11-27 -- Updated some QC thresholds in config.R. 20171127. +- Updated some QC thresholds in config.R. 20171127. ## v2.0.1.9049 2017-11-16 -- Fix error in myReport.format and then examples. 20171115. +- Fix error in myReport.format and then examples. 20171115. -- Renamed report RMD files and altered reference in PeriodStats(). - 20171115. +- Renamed report RMD files and altered reference in PeriodStats(). + 20171115. -- Added myReport.Format to config.R and set the default there rather - than in code. 20171115. +- Added myReport.Format to config.R and set the default there rather + than in code. 20171115. -- Add “username” to reports. 20171116. +- Add “username” to reports. 20171116. -- Update vignette. 20171116. +- Update vignette. 20171116. ## v2.0.1.9048 2017-11-15 -- Add variable for report format (docx or html). Default will be - “docx”. Needed for Shiny app since won’t have Pandoc. Pandoc now - ships with RStudio but still don’t have when run package via Shiny. +- Add variable for report format (docx or html). Default will be “docx”. + Needed for Shiny app since won’t have Pandoc. Pandoc now ships with + RStudio but still don’t have when run package via Shiny. -- Test version. Not complete. Vignette needs to be rebuilt. +- Test version. Not complete. Vignette needs to be rebuilt. ## v2.0.1.9047 2017-09-29 -- Aggregate.File. Example file mispelled (AW vs. Aw). 20170928. +- Aggregate.File. Example file mispelled (AW vs. Aw). 20170928. -- File versions of QCRaw, Aggregate, and Stats; change input and - output directory name. 20170929. +- File versions of QCRaw, Aggregate, and Stats; change input and output + directory name. 20170929. -- File QC. Turn off report. Mixed case issue in file names. 20170929. +- File QC. Turn off report. Mixed case issue in file names. 20170929. ## v2.0.1.9046 2017-09-28 -- Add packages used in vignette to “Suggests” in DESCRIPTION. +- Add packages used in vignette to “Suggests” in DESCRIPTION. -- Fixed function example lines wider than 100 characters. +- Fixed function example lines wider than 100 characters. -- Moved necessary packages from “Suggests” to “Imports” in - DESCRIPTION. +- Moved necessary packages from “Suggests” to “Imports” in DESCRIPTION. -- DESCRIPTION, License, out of sync with GitHub. Fixed. +- DESCRIPTION, License, out of sync with GitHub. Fixed. ## v2.0.1.9045 2017-09-28 -- Add knitr:: to all kable commands in RMD files. Had error after - moving knitr from Depends to Suggests. 20170928. +- Add knitr:: to all kable commands in RMD files. Had error after moving + knitr from Depends to Suggests. 20170928. -- Renamed RMD files with ’ - ’ to ’\_’. Recommended in check(). - 20170928. +- Renamed RMD files with ’ - ’ to ’\_’. Recommended in check(). + 20170928. ## v2.0.1.9044 2017-09-28 -- CompSiteCDF funtion complete. 20170928. +- CompSiteCDF funtion complete. 20170928. -- Vignette updated. 20170928. +- Vignette updated. 20170928. ## v2.0.1.9043 2017-09-27 -- DESCRIPTION. As suggested by check(), move packages from Depends to - Suggests. 20170927. +- DESCRIPTION. As suggested by check(), move packages from Depends to + Suggests. 20170927. -- Update ReadMe. 20170927. +- Update ReadMe. 20170927. ## v2.0.1.9042 2017-09-21 -- CompSiteCDF() to compare multiple sites on a site CDF. 20170921. +- CompSiteCDF() to compare multiple sites on a site CDF. 20170921. ## v2.0.1.9041 2017-09-20 -- Export.StreamThermal. +- Export.StreamThermal. -- Rename from Export.ST() to Export.StreamThermal() .20170920. +- Rename from Export.ST() to Export.StreamThermal() .20170920. -- Complete function and documentation. 20170920. +- Complete function and documentation. 20170920. -- Update Vignette. 20170920. +- Update Vignette. 20170920. -- Export.IHA. Tweak opening text. 20170920. +- Export.IHA. Tweak opening text. 20170920. ## v2.0.1.9040 2017-09-20 -- Vignette. - - Tweaked language. 20170919. - - Added section about requiring dataRetrieval v2.7.3 that isn’t on - CRAN yet. 20170919. +- Vignette. + - Tweaked language. 20170919. + - Added section about requiring dataRetrieval v2.7.3 that isn’t on + CRAN yet. 20170919. ## v2.0.1.9039 2017-09-19 -- Vignette added. 20170919. +- Vignette added. 20170919. -- Added daily mean visualization Excel file to . 20170919. +- Added daily mean visualization Excel file to . 20170919. ## v2.0.1.9038 2017-09-12 -- Rename fun.Export.IHA() to Export.IHA(). 20170912. +- Rename fun.Export.IHA() to Export.IHA(). 20170912. ## v2.0.1.9037 2017-09-12 -- Added function, rarify(), from RIVPACS code by John van Sickles - (USEPA). Modified to be a stand alone function and with a seed - parameter for reproducible results. Included example dataset with 2 - samples. 20170912. +- Added function, rarify(), from RIVPACS code by John van Sickles + (USEPA). Modified to be a stand alone function and with a seed + parameter for reproducible results. Included example dataset with 2 + samples. 20170912. -- Added “Bug Reports” to DESCRIPTION file. 20170912. +- Added “Bug Reports” to DESCRIPTION file. 20170912. -- Modified fun.Export.IHA() argument fun.myCol.Discharge to - fun.myCol.Parameter. 20170912. +- Modified fun.Export.IHA() argument fun.myCol.Discharge to + fun.myCol.Parameter. 20170912. ## v2.0.1.9036 2017-09-11 -- Added function, fun.Export.IHA(), to export data in format ready to - use in IHA package. 20170911. +- Added function, fun.Export.IHA(), to export data in format ready to + use in IHA package. 20170911. ## v2.0.1.9035 2017-09-11 -- Updated fun.GageData.R. USGS updated their webservices to use https - instead of http. dataRetrieval package updated to v2.7.3 on GitHub. - Some functions modified within dataRetrieval as had to modify some - code in ContDataQC package. Version number of dataRetrieval *not* - added to DESCRIPTION (not available on CRAN yet). 20170911. +- Updated fun.GageData.R. USGS updated their webservices to use https + instead of http. dataRetrieval package updated to v2.7.3 on GitHub. + Some functions modified within dataRetrieval as had to modify some + code in ContDataQC package. Version number of dataRetrieval *not* + added to DESCRIPTION (not available on CRAN yet). 20170911. ## v2.0.1.9034 2017-09-06 -- Added PeriodStats() to calculate daily stats (mean, min, max, etc) - for a period (N) before a given date. Outputs a CSV of data and DOCX - report. 20170905. +- Added PeriodStats() to calculate daily stats (mean, min, max, etc) for + a period (N) before a given date. Outputs a CSV of data and DOCX + report. 20170905. ## v2.0.1.9033 2017-08-24 -- Changed reports back to DOCX. Will add a user parameter to change in - the future. 20170824. +- Changed reports back to DOCX. Will add a user parameter to change in + the future. 20170824. ## v2.0.1.9032 2017-08-23 -- Added rmarkdown to dependencies in Description. It should install - with Pandoc but some users had to install separately. 20170823 +- Added rmarkdown to dependencies in Description. It should install with + Pandoc but some users had to install separately. 20170823 -- Added install.packages(“installr”) to example in ContDataQC() for - installing Pandoc. 20170823. +- Added install.packages(“installr”) to example in ContDataQC() for + installing Pandoc. 20170823. ## v2.0.1.9031 2017-08-23 -- Reload on GitHub. Shiny can’t download. 20170823. +- Reload on GitHub. Shiny can’t download. 20170823. ## v2.0.1.9030 2017-08-23 -- Another DOCX reference fix. 20170823. +- Another DOCX reference fix. 20170823. ## v2.0.1.9029 2017-08-23 -- Change reference to DOCX variable to .ext to easier to change in the - future. 20170823. +- Change reference to DOCX variable to .ext to easier to change in the + future. 20170823. ## v2.0.1.9028 2017-08-23 -- Convert Reports from DOCX to HTML. Needed for use with Shiny. - 20170823. +- Convert Reports from DOCX to HTML. Needed for use with Shiny. + 20170823. ## v2.0.1.9027 2017-08-23 -- Rebuild before update to GitHub. 20170823. +- Rebuild before update to GitHub. 20170823. ## v2.0.1.9026 2017-08-23 -- Ensure use file.path when read and save files. 20170823. +- Ensure use file.path when read and save files. 20170823. ## v2.0.1.9025 2017-07-31 -- Thresholds. Tweaked config.R. Issue \#36. 20170731. +- Thresholds. Tweaked config.R. Issue \#36. 20170731. ## v2.0.1.9024 2017-07-27 -- Thresholds. Updated config.R with more informed thresholds after - working with more data. Issue \#36. 20170728. +- Thresholds. Updated config.R with more informed thresholds after + working with more data. Issue \#36. 20170728. -- DESCRIPTION. Added Jen Stamp as contributor and added my middle - initial. 20170728. +- DESCRIPTION. Added Jen Stamp as contributor and added my middle + initial. 20170728. ## v2.0.1.9023 2017-06-22 -- QC report (and aggregate report), fixed water temp vs. sensor depth - legend. Colors were swapped. Issue \# 35. 20170622. +- QC report (and aggregate report), fixed water temp vs. sensor depth + legend. Colors were swapped. Issue \# 35. 20170622. ## v2.0.1.9022 2017-06-21 -- QC report header for water temp and sensor depth says water level. - Fixed. Issue \#34. 20170621. +- QC report header for water temp and sensor depth says water level. + Fixed. Issue \#34. 20170621. ## v2.0.1.9021 2017-06-21 -- Dec 31 not being assigned a season in summary stats. Fixed. Issue - \#33. 20170621. +- Dec 31 not being assigned a season in summary stats. Fixed. Issue + \#33. 20170621. ## v2.0.1.9020 2017-06-09 -- QC of gage data not using all parameters present. Fixed. Issue \#32. - 20170609. +- QC of gage data not using all parameters present. Fixed. Issue \#32. + 20170609. -- Added air and water pressure in psi to gage download. - fun.GageData.R. 20170609. +- Added air and water pressure in psi to gage download. fun.GageData.R. + 20170609. -- ContData.env\$myNames.Order missing new fields. Was dropping fields - not included (e.g., gage height). config.R. 20170609. +- ContData.env\$myNames.Order missing new fields. Was dropping fields + not included (e.g., gage height). config.R. 20170609. -- fun.QC.R missing QC routine for gage height. 20170609. +- fun.QC.R missing QC routine for gage height. 20170609. ## v2.0.1.9019 2017-06-07 -- Updated data flow for “aggregate” step so the QC report is generated - “after” the compiled file is created. Issue \#31. 20170607. +- Updated data flow for “aggregate” step so the QC report is generated + “after” the compiled file is created. Issue \#31. 20170607. ## v2.0.1.9018 2017-06-07 -- Updated main function to allow for a single file or a vector of - files. New “file” versions of most scripts. Some updates to the - Reports (rmd files). Issue \#24. 20170607. +- Updated main function to allow for a single file or a vector of files. + New “file” versions of most scripts. Some updates to the Reports (rmd + files). Issue \#24. 20170607. ## v2.0.1.9017 2017-06-06 -- Report_QC.rmd. Correct typo in “Flags” descriptions. Issue \#28. - 20170606. +- Report_QC.rmd. Correct typo in “Flags” descriptions. Issue \#28. + 20170606. ## v2.0.1.9016 2017-05-31 -- File delimiter for strsplit(). Also a regex issue with “.” (dot). - Created special check. 20170531. +- File delimiter for strsplit(). Also a regex issue with “.” (dot). + Created special check. 20170531. ## v2.0.1.9015 2017-05-31 -- File delimiter. Configuration file allows for user defined values. - However, the QC check for StationIDs containing this value uses - regular expressions. Added a special condition for the dot, “.”, - delimiter as regex treats this character as a match for (almost) any - character. 20170531. +- File delimiter. Configuration file allows for user defined values. + However, the QC check for StationIDs containing this value uses + regular expressions. Added a special condition for the dot, “.”, + delimiter as regex treats this character as a match for (almost) any + character. 20170531. ## v2.0.1.9014 2017-05-26 -- Time interval. Fixed calculation of time interval. Certain date time - formats would stop processing or be reported as zero. fun.QC.R, - Report_Aggregate.rmd, Report_QC.rmd, and Report_Stats.rmd. 20170526. +- Time interval. Fixed calculation of time interval. Certain date time + formats would stop processing or be reported as zero. fun.QC.R, + Report_Aggregate.rmd, Report_QC.rmd, and Report_Stats.rmd. 20170526. ## v2.0.1.9013 2017-05-24 -- fun.Stats.R. Had previously commented out stats and plots due to - errors. Single typo fixed in year season stats and reworked daily - summaryBy stats section. 20170524. +- fun.Stats.R. Had previously commented out stats and plots due to + errors. Single typo fixed in year season stats and reworked daily + summaryBy stats section. 20170524. ## v2.0.1.9012 2017-05-24 -- fun.Gage.R. Changed variable “myTZ” to “fun.myTZ” for consistency. - 20170524. +- fun.Gage.R. Changed variable “myTZ” to “fun.myTZ” for consistency. + 20170524. ## v2.0.1.9011 2017-05-23 -- Remove unused argument “fun.myFile.Prefix” from help file for - ContDataQC(). 20170523. +- Remove unused argument “fun.myFile.Prefix” from help file for + ContDataQC(). 20170523. ## v2.0.1.9010 2017-05-22 -- Modify input and export directories so can be any folder. The - default directory is now the working directory. Modified input - parameters for ContDataQC() by removing “fun.myDir.BASE”. Modified - examples and all other necessary scripts. +- Modify input and export directories so can be any folder. The default + directory is now the working directory. Modified input parameters for + ContDataQC() by removing “fun.myDir.BASE”. Modified examples and all + other necessary scripts. ## v2.0.1.9009 2017-05-19 -- Report_Aggregate.rmd. Fix sampling interval (seconds to minutes,line - 23). Left plots as dots instead of lines. 20170519. +- Report_Aggregate.rmd. Fix sampling interval (seconds to minutes,line + 23). Left plots as dots instead of lines. 20170519. -- ContDataQC(). Update examples to with date range. After adding in - “test4” the dates don’t match for “test2”. 20170519. +- ContDataQC(). Update examples to with date range. After adding in + “test4” the dates don’t match for “test2”. 20170519. -- Update “test4” dataset. Change “Water Level ft”” to “Sensor Depth - ft”. 20170519. +- Update “test4” dataset. Change “Water Level ft”” to “Sensor Depth ft”. + 20170519. -- Summary Stats quit with error. Fixed in summaryBy statements. - 20170519. +- Summary Stats quit with error. Fixed in summaryBy statements. + 20170519. ## v2.0.1.9008 2017-05-18 -- Add example for offset data for QC in main function (ContDataQC()). - 20170518. +- Add example for offset data for QC in main function (ContDataQC()). + 20170518. -- Fix plots (legend) in Report_QC.R (remove box as it is only - partially displaying). 20170518. +- Fix plots (legend) in Report_QC.R (remove box as it is only partially + displaying). 20170518. -- Config file. Update Sensor Depth and Water Pressure display names. - 20170518. +- Config file. Update Sensor Depth and Water Pressure display names. + 20170518. -- Label code chunks in RMD files. 20170518. +- Label code chunks in RMD files. 20170518. -- Fix plots (blank) in Report_QC.R for “offset” data. With point type - of “line” the NAs in the data caused nothing to be plotted. Fixed - with na.omit(as.numeric(x)) when subsetted data for plotting. Then - had to fix date range on x-axis. 20170518. +- Fix plots (blank) in Report_QC.R for “offset” data. With point type of + “line” the NAs in the data caused nothing to be plotted. Fixed with + na.omit(as.numeric(x)) when subsetted data for plotting. Then had to + fix date range on x-axis. 20170518. ## v2.0.1.9007 2017-05-17 -- Add additional config file. Modified config.R to include - OlsonNames() in timezone section. Added /extdata/config.TZ.central.R - with only time zone change. Modified examples in zfun.ContaDataQC.R - to include a USGS gage in the central time zone. Modified - ContDataQC() with optional parameter to use helper functions to - include different config files. Will only need paramters that are - different. User provides path to the new R file for new variables to - include in the environment ContData.env. +- Add additional config file. Modified config.R to include OlsonNames() + in timezone section. Added /extdata/config.TZ.central.R with only time + zone change. Modified examples in zfun.ContaDataQC.R to include a USGS + gage in the central time zone. Modified ContDataQC() with optional + parameter to use helper functions to include different config files. + Will only need paramters that are different. User provides path to the + new R file for new variables to include in the environment + ContData.env. ## v2.0.1.9006 2017-05-17 -- zfun.ContDataQC.R. Add in “test4” data (offset times) to examples. - 20170515. +- zfun.ContDataQC.R. Add in “test4” data (offset times) to examples. + 20170515. -- config.R. Fix typo in ContData.env\$myName.GageHeight; missing “.” - between name and unites. 20170517. +- config.R. Fix typo in ContData.env\$myName.GageHeight; missing “.” + between name and unites. 20170517. -- fun.QC.R. Line 1034 bad reference for difftime; renamed - myTimeDiff.all to myT.diff.all. +- fun.QC.R. Line 1034 bad reference for difftime; renamed myTimeDiff.all + to myT.diff.all. -- Confirmed that knitr is silent so the user does not get code - scrolling across screen. 20170517. +- Confirmed that knitr is silent so the user does not get code scrolling + across screen. 20170517. -- Confirmed that steps/tasks in the process are independent of each - other. That is, someone can use some or all routines as each is a - separate function. 20170517. +- Confirmed that steps/tasks in the process are independent of each + other. That is, someone can use some or all routines as each is a + separate function. 20170517. ## v2.0.1.9005 2017-05-15 -- Add data.R to desribe data inlcuded in library. 20170508. +- Add data.R to desribe data inlcuded in library. 20170508. -- fun.QC.R. Fix typo in myT.diff.all operation (line 965). 20170508. +- fun.QC.R. Fix typo in myT.diff.all operation (line 965). 20170508. -- Create data process script in “data-raw” for three example “test1” - files. 20170509. +- Create data process script in “data-raw” for three example “test1” + files. 20170509. -- Revised fun.OffsetCollectionCheck() in fun.Helper.R. Return value is - not boolean. 20170509. +- Revised fun.OffsetCollectionCheck() in fun.Helper.R. Return value is + not boolean. 20170509. -- Revised fun.CalcQCStats() in fun.QC.R. Added ability to handle - offset timing for Air/Water data files. 20170509. +- Revised fun.CalcQCStats() in fun.QC.R. Added ability to handle offset + timing for Air/Water data files. 20170509. -- Fixed typo in finding time interval in markdown files; - Report_Aggregate.RMD, Report_Stats.RMD, and Report_QC.RMD. 20170510. +- Fixed typo in finding time interval in markdown files; + Report_Aggregate.RMD, Report_Stats.RMD, and Report_QC.RMD. 20170510. -- Offset time collections (e.g., Air and Water data in same file but - not starting at the same time). Fixed fun.QC.R(fun.QC() and - fun.CalcQCStats()) to account for this disparity. Previous iteration - flagged every other row. Rows without data are no longer flagged - (unless the data at the regular time interval was missing). - 20170512. +- Offset time collections (e.g., Air and Water data in same file but not + starting at the same time). Fixed fun.QC.R(fun.QC() and + fun.CalcQCStats()) to account for this disparity. Previous iteration + flagged every other row. Rows without data are no longer flagged + (unless the data at the regular time interval was missing). 20170512. -- Applied fix from fun.QC.R for difftime to report markdown files; - Report_Aggregate.RMD, Report_Stats.RMD, and Report_QC.RMD.. - 20170512. +- Applied fix from fun.QC.R for difftime to report markdown files; + Report_Aggregate.RMD, Report_Stats.RMD, and Report_QC.RMD.. 20170512. -- Modified example files in /data-raw/ to use “Sensor Depth” instead - of “Water Level”. Reran ProcessData scripts to recreate data files. - 20170512. +- Modified example files in /data-raw/ to use “Sensor Depth” instead of + “Water Level”. Reran ProcessData scripts to recreate data files. + 20170512. -- fun.QC(); moved “Working” message inside of IF statement to only - reports to user if that data type is present in the data. Avoids - printing to the console all 11 parameters being checked. 20170512. +- fun.QC(); moved “Working” message inside of IF statement to only + reports to user if that data type is present in the data. Avoids + printing to the console all 11 parameters being checked. 20170512. -- config.R; - ContData.env$myName.Turbidity modified value, ContData.env$myName.Chlorophylla - modified typo in name (and changed value), - ContData.env$myLab.Chlorophylla modified value typo, ContData.env$myThresh.RoC.SD.number.Chlorophylla - name fix, - ContData.env$myName.Flag.Chlorophylla name fix, ContData.env$myUnits.Chlorophylla - name fix. 20170512. +- config.R; + ContData.env$myName.Turbidity modified value, ContData.env$myName.Chlorophylla + modified typo in name (and changed value), + ContData.env$myLab.Chlorophylla modified value typo, ContData.env$myThresh.RoC.SD.number.Chlorophylla + name fix, + ContData.env$myName.Flag.Chlorophylla name fix, ContData.env$myUnits.Chlorophylla + name fix. 20170512. -- Offset data files. Fix overall data flags for each parameters. - Currently giving “NA” if any “NA”. And have “NA” due to offset data. - fun.QC.R. 20170512. +- Offset data files. Fix overall data flags for each parameters. + Currently giving “NA” if any “NA”. And have “NA” due to offset data. + fun.QC.R. 20170512. ## v2.0.1.9004 2017-05-05 -- Reformat file comments for proper outlining in RStudio. 20170505. +- Reformat file comments for proper outlining in RStudio. 20170505. -- Fix development version from 2.0.1.0004 to 2.0.1.9004. 20170505. +- Fix development version from 2.0.1.0004 to 2.0.1.9004. 20170505. -- Update Description with version number (last edit was 2.0.1.0000). - 20170505. +- Update Description with version number (last edit was 2.0.1.0000). + 20170505. ## v2.0.1.0003 2017-05-05 -- Change WaterLevel to SensorDepth; config.R, config_COLD.R, - Report_Aggregate.RMD, Report_QC.RMD, fun.QC.R, fun.Stats.R, - zfunAggregateData.R. 20170505. +- Change WaterLevel to SensorDepth; config.R, config_COLD.R, + Report_Aggregate.RMD, Report_QC.RMD, fun.QC.R, fun.Stats.R, + zfunAggregateData.R. 20170505. -- Changed WaterLevel to GageHeight in fun.GageData.R and added new - parameter to config.R. Added to config.R with thresholds. 20170505. +- Changed WaterLevel to GageHeight in fun.GageData.R and added new + parameter to config.R. Added to config.R with thresholds. 20170505. -- Corrected flag names for Turbidity and Chlorophyll a; config.R. - 20170505. +- Corrected flag names for Turbidity and Chlorophyll a; config.R. + 20170505. ## v2.0.1.0002 2017-05-04 -- Corrected error in Julian Day (0:364 instead of 1:365). Fixed in - fun.Stats.R (line 131) by adding “+1”. 20170320. +- Corrected error in Julian Day (0:364 instead of 1:365). Fixed in + fun.Stats.R (line 131) by adding “+1”. 20170320. -- Added 5 parameters (Conductivity, Dissolved Oxygen, pH, Turbidity, - and Chlorophyl a) to those that can be evaluated. Changes made to - env.UserDefinedValues.R and fun.QC.R. 20170323. +- Added 5 parameters (Conductivity, Dissolved Oxygen, pH, Turbidity, and + Chlorophyl a) to those that can be evaluated. Changes made to + env.UserDefinedValues.R and fun.QC.R. 20170323. -- Renamed env.UserDefinedValues.R to config.R. 20170421. +- Renamed env.UserDefinedValues.R to config.R. 20170421. -- Added “config” functions to load user configuration (e.g., - thresholds specific to coldwater streams) \[fun.CustomConfig.R\] - 20170421. +- Added “config” functions to load user configuration (e.g., thresholds + specific to coldwater streams) \[fun.CustomConfig.R\] 20170421. -- Tweak Reports (Report_Aggregate.RMD, Report_QC.RMD, and - Report_Stats.RMD) and fun.QC.R for determining time frequency for - sampling. Was using interval for 10th and 11th samples (or 4th and - 5th in fun.QC.R). Now using median of all time differences. Will - always work as long as have at least 1 sample. Previously could fail - with less than 5 samples (or 11). +- Tweak Reports (Report_Aggregate.RMD, Report_QC.RMD, and + Report_Stats.RMD) and fun.QC.R for determining time frequency for + sampling. Was using interval for 10th and 11th samples (or 4th and 5th + in fun.QC.R). Now using median of all time differences. Will always + work as long as have at least 1 sample. Previously could fail with + less than 5 samples (or 11). ## v2.0.1.0001 2017-03-08 -- Bug fix for missed references to new environment for variables - introduced in v2.0.1. Multiple files affected. Gage data, QC, and - Aggregate working but not Stats. One table in the QC and Aggregate - reports has been commented out. (To be fixed later). +- Bug fix for missed references to new environment for variables + introduced in v2.0.1. Multiple files affected. Gage data, QC, and + Aggregate working but not Stats. One table in the QC and Aggregate + reports has been commented out. (To be fixed later). ## v2.0.1 2017-03-06 -- Bug fix for variable names not showing up. +- Bug fix for variable names not showing up. -- Created environment ContData.env and assigned all variables in - fun.UserDefinedValues.R file to it. +- Created environment ContData.env and assigned all variables in + fun.UserDefinedValues.R file to it. -- Renamed fun.UserDefinedValues.R to env.UserDefinedValues.R. +- Renamed fun.UserDefinedValues.R to env.UserDefinedValues.R. -- Converted all RMD and R files in package to use ContData.env\$. +- Converted all RMD and R files in package to use ContData.env\$. -- Added NEWS.md +- Added NEWS.md -- Edited earlier fix to fun.QC.R for adding day, month, year to data. - Did not use variable names in previous edit. +- Edited earlier fix to fun.QC.R for adding day, month, year to data. + Did not use variable names in previous edit. -- Create RMD files for ReadMe and NEWS. Added RMD to ignore list. +- Create RMD files for ReadMe and NEWS. Added RMD to ignore list. ## v2.0.0 2017-02-28 -- Released on GitHub as a fully documented package. +- Released on GitHub as a fully documented package. -- Update for TN, Modified QC report (RMD) to summarize number of - samples differently. +- Update for TN, Modified QC report (RMD) to summarize number of samples + differently. -- Included tables in Aggregate report (RMD). +- Included tables in Aggregate report (RMD). -- Reworked scripts to be run as a library. +- Reworked scripts to be run as a library. -- Uploaded to GitHub. +- Uploaded to GitHub. ## v1.2.1 2017-01-16 -- Update for NJ, fixed date/time issue when resaving files in Excel. +- Update for NJ, fixed date/time issue when resaving files in Excel. -- Added date/time QC process to be run again at the aggregate step. +- Added date/time QC process to be run again at the aggregate step. ## v1.2.0 2016-05-04 -- NWQMC 2016, Tampa, FL +- NWQMC 2016, Tampa, FL ## v1.1.0 2016-03-31 -- AMAAB 2016, Cacapon, WV +- AMAAB 2016, Cacapon, WV -- Minor +- Minor ## v1.0.0 2015-10-26 -- SWPBA 2015, Myrtle Beach, SC +- SWPBA 2015, Myrtle Beach, SC -- Initial public release. +- Initial public release. diff --git a/NEWS.rmd b/NEWS.rmd index 4a5b9396..ae615df3 100644 --- a/NEWS.rmd +++ b/NEWS.rmd @@ -19,7 +19,15 @@ knitr::opts_chunk$set( cat(paste0("Last Update: ",Sys.time())) ``` -# Version History +# Version History + +# v2.1.0 +2024-10-07 + +* update: EPA version produced for publication in SoftwareX journal. + + R package code did not change + + Updates to readme, description, and other similar files. + + link to R shiny app now included ## v2.0.7.9005 2022-09-22 diff --git a/README.Rmd b/README.Rmd index 0348387b..aa69a583 100644 --- a/README.Rmd +++ b/README.Rmd @@ -21,30 +21,33 @@ cat(paste0("Last Update: ",Sys.time())) Quality control checks on continuous data. Example data is from a HOBO data logger with 30 minute intervals. +# R Shiny application (web-version): +https://shiny.epa.gov/ContDataQC + # Badges -[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/leppott/ContDataQC/graphs/commit-activity) +[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/USEPA/ContDataQC/graphs/commit-activity) [![lifecycle](https://img.shields.io/badge/lifecycle-stable-green.svg)](https://www.tidyverse.org/lifecycle/#stable) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://cran.r-project.org/web/licenses/MIT) [![CodeFactor](https://www.codefactor.io/repository/github/leppott/ContDataQC/badge)](https://www.codefactor.io/repository/github/leppott/ContDataQC) -[![codecov](https://codecov.io/gh/leppott/ContDataQC/branch/master/graph/badge.svg)](https://codecov.io/gh/leppott/ContDataQC) +[![codecov](https://codecov.io/gh/USEPA/ContDataQC/branch/master/graph/badge.svg)](https://codecov.io/gh/USEPA/ContDataQC) [![R-CMD-check](https://github.com/leppott/ContDataQC/workflows/R-CMD-check/badge.svg)](https://github.com/leppott/ContDataQC/actions) -[![GitHub issues](https://img.shields.io/github/issues/leppott/ContDataQC.svg)](https://GitHub.com/leppott/ContDataQC/issues/) +[![GitHub issues](https://img.shields.io/github/issues/USEPA/ContDataQC.svg)](https://GitHub.com/USEPA/ContDataQC/issues/) -[![GitHub release](https://img.shields.io/github/release/leppott/ContDataQC.svg)](https://GitHub.com/leppott/ContDataQC/releases/) -[![Github all releases](https://img.shields.io/github/downloads/leppott/ContDataQC/total.svg)](https://GitHub.com/leppott/ContDataQC/releases/) +[![GitHub release](https://img.shields.io/github/release/USEPA/ContDataQC.svg)](https://GitHub.com/USEPA/ContDataQC/releases/) +[![Github all releases](https://img.shields.io/github/downloads/USEPA/ContDataQC/total.svg)](https://GitHub.com/USEPA/ContDataQC/releases/) # Installation To install the current version of the code from GitHub use the example below. ```{r Install_Basic, eval=FALSE} if(!require(remotes)){install.packages("remotes")} #install if needed -remotes::install_github("leppott/ContDataQC") +remotes::install_github("USEPA/ContDataQC") ``` The vignette (big help file) isn't created when installing from GitHub with the -above command. If you want the vignette download the compressed file from +above command. If you want the vignette, download the compressed file from GitHub and install from that file or install with the command below. The "force = TRUE" command is used to ensure the package will install over and existing install of the same version (e.g., the same version without the @@ -52,7 +55,7 @@ vignettes). ```{r Install_Vignette, eval=FALSE} if(!require(remotes)){install.packages("remotes")} #install if needed -remotes::install_github("leppott/ContDataQC", force = TRUE, build_vignettes = TRUE) +remotes::install_github("USEPA/ContDataQC", force = TRUE, build_vignettes = TRUE) ``` If dependent libraries do not install you can install them separately. This @@ -152,28 +155,6 @@ myDir.BASE <- getwd() setwd(myDir.BASE) ``` -# Planned Updates - -* ~~Spell out "AW"" and other abbreviations (e.g., AirWater). 20170308. On hold.~~ - -* ~~Gaps in data not always evident in the plots. 20170308.~~ - -* ~~Use futile.logger to better log output for user. Issue #29. 20170606.~~ - -* Debug Aggregate operation. 20170919. - -* Create CDFs. Similar to code already used in previous analyses by Lei. 20170919. - -* PeriodStats(), add number and/or percent of observations above given threshold. 20170919. - -* Fix mixed case issue with filenames in "file" versions of QCRaw, Aggregate, and Stats. 20170929. - - + More check data stuff. - + Update vignette - + Threshold number or pct on plot - + Excel file update (see 9/28/2017 email) - -* PeriodStats, standardize range of y-axis for each time period. # Help Every function has a help file with a working example. There is also a vignette @@ -195,7 +176,7 @@ help(package="ContDataQC") The vignette file is located in the “doc” directory of the library in the R install folder. Below is the path to the file on my PC. But it is much easier -to use the code below to call the vignette by name. There is also be a link to +to use the code below to call the vignette by name. There is also a link to the vignette at the top of the help index for the package. "C:\\Programs\\R\\R-3.4.2\\library\\ContDataQC\\doc\\ContDataQC_Vignette.html" @@ -211,9 +192,18 @@ for installing packages. ```{r Install_Vignette2, eval=FALSE} library(remotes) -install_github("leppott/ContDataQC", force=TRUE, build_vignettes=TRUE) +install_github("USEPA/ContDataQC", force=TRUE, build_vignettes=TRUE) ``` +If the vignettes still do not show on your computer, there are pre-loaded html copies +of the vignettes located in the following GitHub folder: + +https://github.com/USEPA/ContDataQC/tree/main/inst/_vignettes + +Or, in the following general path after downloading the package: + +"~ContDataQC\\inst\\_vignettes" + ## Guide Videos Guide videos were created for the ContDataQC package and posted on YouTube. The Powerpoint slides (pptx), R code notebooks (html), and videos are hosted on diff --git a/README.md b/README.md index fd3979e0..ee58d3ee 100644 --- a/README.md +++ b/README.md @@ -3,31 +3,35 @@ README-ContDataQC - #> Last Update: 2022-06-15 17:05:50 + #> Last Update: 2024-12-16 16:29:26.375795 # ContDataQC Quality control checks on continuous data. Example data is from a HOBO data logger with 30 minute intervals. +# R Shiny application (web-version): + + + # Badges -[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/leppott/ContDataQC/graphs/commit-activity) +[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/USEPA/ContDataQC/graphs/commit-activity) [![lifecycle](https://img.shields.io/badge/lifecycle-stable-green.svg)](https://www.tidyverse.org/lifecycle/#stable) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://cran.r-project.org/web/licenses/MIT) [![CodeFactor](https://www.codefactor.io/repository/github/leppott/ContDataQC/badge)](https://www.codefactor.io/repository/github/leppott/ContDataQC) -[![codecov](https://codecov.io/gh/leppott/ContDataQC/branch/master/graph/badge.svg)](https://codecov.io/gh/leppott/ContDataQC) +[![codecov](https://codecov.io/gh/USEPA/ContDataQC/branch/master/graph/badge.svg)](https://codecov.io/gh/USEPA/ContDataQC) [![R-CMD-check](https://github.com/leppott/ContDataQC/workflows/R-CMD-check/badge.svg)](https://github.com/leppott/ContDataQC/actions) [![GitHub -issues](https://img.shields.io/github/issues/leppott/ContDataQC.svg)](https://GitHub.com/leppott/ContDataQC/issues/) +issues](https://img.shields.io/github/issues/USEPA/ContDataQC.svg)](https://GitHub.com/USEPA/ContDataQC/issues/) [![GitHub -release](https://img.shields.io/github/release/leppott/ContDataQC.svg)](https://GitHub.com/leppott/ContDataQC/releases/) +release](https://img.shields.io/github/release/USEPA/ContDataQC.svg)](https://GitHub.com/USEPA/ContDataQC/releases/) [![Github all -releases](https://img.shields.io/github/downloads/leppott/ContDataQC/total.svg)](https://GitHub.com/leppott/ContDataQC/releases/) +releases](https://img.shields.io/github/downloads/USEPA/ContDataQC/total.svg)](https://GitHub.com/USEPA/ContDataQC/releases/) # Installation @@ -36,19 +40,19 @@ below. ``` r if(!require(remotes)){install.packages("remotes")} #install if needed -remotes::install_github("leppott/ContDataQC") +remotes::install_github("USEPA/ContDataQC") ``` The vignette (big help file) isn’t created when installing from GitHub -with the above command. If you want the vignette download the compressed -file from GitHub and install from that file or install with the command -below. The “force = TRUE” command is used to ensure the package will -install over and existing install of the same version (e.g., the same -version without the vignettes). +with the above command. If you want the vignette, download the +compressed file from GitHub and install from that file or install with +the command below. The “force = TRUE” command is used to ensure the +package will install over and existing install of the same version +(e.g., the same version without the vignettes). ``` r if(!require(remotes)){install.packages("remotes")} #install if needed -remotes::install_github("leppott/ContDataQC", force = TRUE, build_vignettes = TRUE) +remotes::install_github("USEPA/ContDataQC", force = TRUE, build_vignettes = TRUE) ``` If dependent libraries do not install you can install them separately. @@ -119,13 +123,13 @@ summary stats. The code was presented at the following workshops. And further developed under contract to USEPA. -- Oct 2015, SWPBA (Region 4 regional biologist meeting, Myrtle Beach, - SC). +- Oct 2015, SWPBA (Region 4 regional biologist meeting, Myrtle Beach, + SC). -- Mar 2016, AMAAB (Region 3 regional biologist meeting, Cacapon, WV). +- Mar 2016, AMAAB (Region 3 regional biologist meeting, Cacapon, WV). -- Apr 2016, NWQMC (National Water Monitoring Council Conference, - Tampa, FL). +- Apr 2016, NWQMC (National Water Monitoring Council Conference, Tampa, + FL). Functions were developed to help data generators handle data from continuous data sensors (e.g., HOBO data loggers). @@ -156,34 +160,6 @@ myDir.BASE <- getwd() setwd(myDir.BASE) ``` -# Planned Updates - -- ~~Spell out “AW”” and other abbreviations (e.g., - AirWater). 20170308. On hold.~~ - -- ~~Gaps in data not always evident in the plots. 20170308.~~ - -- ~~Use futile.logger to better log output for user. Issue - #29. 20170606.~~ - -- Debug Aggregate operation. 20170919. - -- Create CDFs. Similar to code already used in previous analyses by - Lei. 20170919. - -- PeriodStats(), add number and/or percent of observations above given - threshold. 20170919. - -- Fix mixed case issue with filenames in “file” versions of QCRaw, - Aggregate, and Stats. 20170929. - - - More check data stuff. - - Update vignette - - Threshold number or pct on plot - - Excel file update (see 9/28/2017 email) - -- PeriodStats, standardize range of y-axis for each time period. - # Help Every function has a help file with a working example. There is also a @@ -207,10 +183,10 @@ help(package="ContDataQC") The vignette file is located in the “doc” directory of the library in the R install folder. Below is the path to the file on my PC. But it is much easier to use the code below to call the vignette by name. There is -also be a link to the vignette at the top of the help index for the +also a link to the vignette at the top of the help index for the package. -“C:\\Programs\\R\\R-3.4.2\\library\\ContDataQC\\doc\\ContDataQC_Vignette.html” +“C:\Programs\R\R-3.4.2\library\ContDataQC\doc\ContDataQC_Vignette.html” ``` r vignette("ContDataQC_Vignette", package="ContDataQC") @@ -223,9 +199,18 @@ build. See above for installing packages. ``` r library(remotes) -install_github("leppott/ContDataQC", force=TRUE, build_vignettes=TRUE) +install_github("USEPA/ContDataQC", force=TRUE, build_vignettes=TRUE) ``` +If the vignettes still do not show on your computer, there are pre-loaded html copies +of the vignettes located in the following GitHub folder: + +https://github.com/USEPA/ContDataQC/tree/main/inst/_vignettes + +Or, in the following general path after downloading the package: + +"~ContDataQC\inst\\_vignettes" + ## Guide Videos Guide videos were created for the ContDataQC package and posted on @@ -237,13 +222,13 @@ hosted on a companion GitHub site. YouTube video links below. -- Introduction - - -- Config - - -- Basic Functions - - -- Gage Data - - -- Config File Modifications - - +- Introduction + - +- Config + - +- Basic Functions + - +- Gage Data + - +- Config File Modifications + - diff --git a/inst/_vignettes/ContDataQC_Vignette.html b/inst/_vignettes/ContDataQC_Vignette.html new file mode 100644 index 00000000..c322ca6b --- /dev/null +++ b/inst/_vignettes/ContDataQC_Vignette.html @@ -0,0 +1,2429 @@ + + + + + + + + + + + + + + + +ContDataQC Vignette + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Introduction

+

ContDataQC was created by Tetra Tech, Inc. for USEPA in 2017 to meet the needs of the Regional Monitoring Networks to apply a minimum level of quality control to the continuous monitoring data they were collecting.

+

It is envisioned that this library can be a living package and will add additional functions and routines in the future.

+

Many of the examples in this vignette are included in the examples in the corresponding functions. Each function in the ContDataQC library includes example data.

+

The typical user of this package is envisioned as a user of Hobo or other continuous data loggers. Measured parameters are water temperature, sensor depth, air temperature, water pressure, and air pressure. The typical user will retrieve data from their data logger using Hoboware. Once the data is extracted from the logger and save some minimal cleanup to the column heades is performed before using the ‘ContDataQC’ R library.

+

There is a configuration file (config.R) that allows for customization.

+
+
+

Installation

+

The R library is saved on GitHub for ease of updating and distribution. Issues can be tracked, fixed, and code made available for download. Current users can update the library using the same code used to install the library (see below). Devtools is required for the installation.

+
install.packages("devtools")
+library(devtools)
+install_github("leppott/ContDataQC")
+

To contact the authors directly email Erik.Leppo@tetratech.com or Jen.Stamp@tetratech.com.

+

Occassionaly R does not import all of the necessary dependant libraries. Below is code to install all of the necessary libraries for using the ContDataQC library.

+
# install all dependant libraries manually
+# libraries to be installed
+data.packages = c(
+                  "dataRetrieval"   # loads USGS data into R
+                  "zoo",
+                  "knitr",
+                  "survival",
+                  "doBy",
+                  "rmarkdown"
+                  )
+
+# install packages via a 'for' loop
+for (i in 1:length(data.packages)) {
+  #install.packages("doBy",dependencies=TRUE)
+  install.packages(data.packages[i])
+} # end loop
+
+print("Task complete.  Check statements above for errors.")
+

An additional dependancy that needs to be installed separately is Pandoc. This package is used to create the Reports. See R code below to install Pandoc. The package ‘installr’ allows for installation in a single step. If this doesn’t work you may need help from your IT department using the web links listed below. For users of RStudio Pandoc installs with RStudio so no extra install is needed.

+

In version 2.0.1.9048 (2017-11-16) of the ContDataQC package report format was added as a variable. The default is “html”. This can be changed in config.R. If you do not have Pandoc installed the package will still work but the reports in docx format will not be created (will show an error) unless you change the format to “html”.

+
# Install Pandoc (for docx Reports)
+# (if using recent version of RStudio do not have to install separately)
+install.packages("installr") # not needed if already have this package.
+require(installr)
+install.pandoc()
+# The above won't work if don't have admin rights on your computer.
+# Alternative = Download the file below and have your IT dept install for you.
+# https://github.com/jgm/pandoc/releases/download/1.16.0.2/pandoc-1.16.0.2-windows.msi
+# For help for installing via command window:
+# http://www.intowindows.com/how-to-run-msi-file-as-administrator-from-command-prompt-in-windows/
+

During the summer of 2017 USGS changed their webservices from http to https. This necessitated a change in the R library dataRetrieval. As of the date of this Vignette the 2.7.2 version of dataRetrieval on CRAN is not the most current and fails when retrieving data inside of ContDataQC. The error statement is “Error in x[[1]] : subscript out of bounds”. If using the GetGageData operation in the ContDataQC() function it is necessary to get the most current version (>=2.7.3) of dataRetrieval (see code below). The ContDataQC library was tweaked on 11 September 2017 (version v2.0.1.9035) to work with the revised version of dataRetrieval (v2.7.3).

+
# inter-CRAN release hosted on USGS website
+install.packages("dataRetrieval",repos="https://owi.usgs.gov/R")
+# from GitHub
+# install.packages("devtools")
+# library(devtools)
+# install_github("USGS-R/dataRetrieval")
+
+
+

Contents

+
    +

  1. Preparing Raw Data

    2. +

  2. ContDataQC Operations

    3. +

  3. Other Features

    4. +
+
+
+

Preparing Raw Data

+

The ContDataQC library makes some assumptions on file names and structure.

+
+

File Names

+

Should be of the format StationID_DataType_StartDate_EndDate.csv. The delimiter between sections of the filename can be changed in config.R but not the order or the file type (csv). Date formats in the files can be set with config.R but the date format in the filename must be in the format YYYYMMDD (no separators).

+

Data types are Air, Water, Gage, and single letter combinations (i.e., AW, AG, and WG).

+
+
+

File Structure

+

The first line are the field (column) headers. These can be set in config.R.

+
+
+
+

The ContDataQC Function

+

The ContDataQC() function is the main focus of this library. It is intended to allow the user to perform multiple functions from a single command with different inputs.

+
+

Operations

+

The operations of the ContDataQC() function are listed below but will be explained in more detail in their own section. GetGageData QCRaw Aggregate SummaryStats

+

The library assumes that there are the following folders in the working directory: Data0_Original Data1_RAW Data2_QC Data3_Aggregated *Data4_Stats

+

The folder “Data0_Original” is for the files exported from Hoboware (or other data logger software). These are the unedited files.

+

The folder “Data1_RAW” is for the files exported from Hoboware (or other data logger software). This folder contains the files as input for the QC portion of the library. These files may have been modified manually or with the formatHobo function.

+

The folder “Data2_QC” is for the output of the QCRaw operation of the library and the input for the Aggregate operation.

+

The folder “Data3_Aggregated” is the output of the Aggregate operation and the input for the Stats operation.

+

The folder “Data4_Stats” is the output of the SummaryStats operation.

+

For each operation associated with the ContDataQC() function a report is generated as an MS Word DOCX file.

+

The code below should be run before using the examples of each operation. The code below sets up the directories and some parameters for use in the later code.

+
# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original", "Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- getwd()
+
+# Create data directories
+myDir.create <- paste0("./",Selection.SUB[1])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+myDir.create <- paste0("./",Selection.SUB[2])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+myDir.create <- paste0("./",Selection.SUB[3])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+myDir.create <- paste0("./",Selection.SUB[4])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+myDir.create <- paste0("./",Selection.SUB[5])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+
+# Save example data (assumes directory ./Data1_RAW/ exists)
+myData <- data_raw_test2_AW_20130426_20130725
+  write.csv(myData,paste0("./",Selection.SUB[2],"/test2_AW_20130426_20130725.csv"))
+myData <- data_raw_test2_AW_20130725_20131015
+  write.csv(myData,paste0("./",Selection.SUB[2],"/test2_AW_20130725_20131015.csv"))
+myData <- data_raw_test2_AW_20140901_20140930
+  write.csv(myData,paste0("./",Selection.SUB[2],"/test2_AW_20140901_20140930.csv"))
+myData <- data_raw_test4_AW_20160418_20160726
+  write.csv(myData,paste0("./",Selection.SUB[2],"/test4_AW_20160418_20160726.csv"))
+myFile <- "config.TZ.Central.R"
+  file.copy(file.path(path.package("ContDataQC"),"extdata",myFile)
+            ,file.path(getwd(),Selection.SUB[2],myFile))
+
+

GetGageData

+

The GetGageData operation of the ContDataQC() function retrieves USGS gage data based on user input. Example code and console output is below.

+
library(ContDataQC)
+# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original","Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- getwd()
+# Get Gage Data
+myData.Operation    <- "GetGageData" #Selection.Operation[1]
+myData.SiteID       <- "01187300" # Hubbard River near West Hartland, CT
+myData.Type         <- "Gage" #Selection.Type[4] 
+myData.DateRange.Start  <- "2013-01-01"
+myData.DateRange.End    <- "2014-12-31"
+myDir.import <- ""
+myDir.export <- file.path(myDir.BASE,Selection.SUB[2])
+ContDataQC(myData.Operation, myData.SiteID, myData.Type, myData.DateRange.Start
+           , myData.DateRange.End, myDir.import, myDir.export)
+
## [1] "Total items to process = 1"
+## 
+## [1] "Getting available data; 01187300."
+## 
+##     agency_cd  site_no                           station_nm site_tp_cd
+## 530      USGS 01187300 HUBBARD RIVER NEAR WEST HARTLAND, CT         ST
+## 531      USGS 01187300 HUBBARD RIVER NEAR WEST HARTLAND, CT         ST
+## 532      USGS 01187300 HUBBARD RIVER NEAR WEST HARTLAND, CT         ST
+## 533      USGS 01187300 HUBBARD RIVER NEAR WEST HARTLAND, CT         ST
+##     dec_lat_va dec_long_va coord_acy_cd dec_coord_datum_cd  alt_va
+## 530    42.0375   -72.93933            H              NAD83  594.57
+## 531    42.0375   -72.93933            H              NAD83  594.57
+## 532    42.0375   -72.93933            H              NAD83  594.57
+## 533    42.0375   -72.93933            H              NAD83  594.57
+##     alt_acy_va alt_datum_cd   huc_cd data_type_cd parm_cd stat_cd ts_id
+## 530       0.01       NGVD29 01080207           uv   00010    <NA> 66869
+## 531       0.01       NGVD29 01080207           uv   00020    <NA> 66870
+## 532       0.01       NGVD29 01080207           uv   00060    <NA> 66866
+## 533       0.01       NGVD29 01080207           uv   00065    <NA> 66867
+##     loc_web_ds medium_grp_cd parm_grp_cd   srs_id access_cd begin_date
+## 530         NA           wat        <NA>  1645597         0 2015-11-04
+## 531         NA           wat        <NA>  1645720         0 2019-01-17
+## 532         NA           wat        <NA>  1645423         0 1990-10-01
+## 533         NA           wat        <NA> 17164583         0 2007-10-01
+##       end_date count_nu
+## 530 2019-05-17     1290
+## 531 2019-05-17      120
+## 532 2019-05-17    10455
+## 533 2019-05-17     4246
+## 
+## 
+## [1] "Processing item 1 of 1, COMPLETE, 01187300."
+## 
+## [1] "Task COMPLETE; 0.53 min."
+

Example of getting gage data in a different time zone specified in user modified configuration file.

+
# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original","Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- getwd()
+# Get Gage Data (central time zone)
+myData.Operation    <- "GetGageData" #Selection.Operation[1]
+myData.SiteID       <- "07032000" # Mississippi River at Memphis, TN
+myData.Type         <- Selection.Type[4] #"Gage"
+myData.DateRange.Start  <- "2013-01-01"
+myData.DateRange.End    <- "2014-12-31"
+myDir.import <- ""
+myDir.export <- file.path(myDir.BASE,Selection.SUB[2])
+myConfig            <- file.path(getwd(),Selection.SUB[2],"config.TZ.central.R") # include path if not in working directory
+ContDataQC(myData.Operation, myData.SiteID, myData.Type, myData.DateRange.Start, myData.DateRange.End, myDir.import, myDir.export, myConfig)
+
+
+

QCRaw

+

This is operation is the main focus of the ContDataQC library. The raw continuous data files are examined for multiple QC tests and the results saved in a new data file (CSV) and a summary report (DOCX file) are generated.

+
# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original","Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- getwd()
+# QC Raw Data
+myData.Operation <- "QCRaw" #Selection.Operation[2]
+myData.SiteID    <- "test2"
+myData.Type      <- Selection.Type[3] #"AW"
+myData.DateRange.Start  <- "2013-01-01"
+myData.DateRange.End    <- "2014-12-31"
+myDir.import <- file.path(myDir.BASE,Selection.SUB[2]) #"Data1_RAW"
+myDir.export <- file.path(myDir.BASE,Selection.SUB[3]) #"Data2_QC"
+myReport.format <- "docx"
+ContDataQC(myData.Operation, myData.SiteID, myData.Type, myData.DateRange.Start
+           , myData.DateRange.End, myDir.import, myDir.export, myReport.format)
+

In some cases two sensors are used (one for air and one for water) and the timing of the sampling is off between the two. The library handles this fine. Example data below. For this example the report output was changed to “html”.

+
# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original","Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- getwd()
+# QC Raw Data (offset collection times for air and water sensors)
+myData.Operation <- "QCRaw" #Selection.Operation[2]
+myData.SiteID    <- "test4"
+myData.Type      <- Selection.Type[3] #"AW"
+myData.DateRange.Start  <- "2016-04-28"
+myData.DateRange.End    <- "2016-07-26"
+myDir.import <- file.path(myDir.BASE,Selection.SUB[2]) #"Data1_RAW"
+myDir.export <- file.path(myDir.BASE,Selection.SUB[3]) #"Data2_QC"
+myReport.format <- "html"
+ContDataQC(myData.Operation, myData.SiteID, myData.Type, myData.DateRange.Start
+           , myData.DateRange.End, myDir.import, myDir.export, myReport.format)
+
+
+

Aggregate

+

After QC the files are ready for analysis. The Aggregate operation allows the user to combine (or split) files across time periods. No report format was specified so the default “docx” format will be generated.

+
# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original","Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- getwd()
+# Aggregate Data
+myData.Operation <- "Aggregate" #Selection.Operation[3]
+myData.SiteID    <- "test2"
+myData.Type      <- Selection.Type[3] #"AW"
+myData.DateRange.Start  <- "2013-01-01"
+myData.DateRange.End    <- "2014-12-31"
+myDir.import <- file.path(myDir.BASE,Selection.SUB[3]) #"Data2_QC"
+myDir.export <- file.path(myDir.BASE,Selection.SUB[4]) #"Data3_Aggregated"
+#Leave off myReport.format and get default (docx).
+ContDataQC(myData.Operation, myData.SiteID, myData.Type, myData.DateRange.Start
+           , myData.DateRange.End, myDir.import, myDir.export)
+
+
+

SummaryStats

+

The SummaryStats operation calculates a number of statistics on the data for each sampling day. No report format was specified so the default “docx” format will be generated.

+
# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original","Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- getwd()
+# Summary Stats
+myData.Operation <- "SummaryStats" #Selection.Operation[4]
+myData.SiteID    <- "test2"
+myData.Type      <- Selection.Type[3] #"AW"
+myData.DateRange.Start  <- "2013-01-01"
+myData.DateRange.End    <- "2014-12-31"
+myDir.import <- file.path(myDir.BASE,Selection.SUB[4]) #"Data3_Aggregated"
+myDir.export <- file.path(myDir.BASE,Selection.SUB[5]) #"Data4_Stats"
+#Leave off myReport.format and get default (docx).
+ContDataQC(myData.Operation, myData.SiteID, myData.Type, myData.DateRange.Start
+           , myData.DateRange.End, myDir.import, myDir.export)
+
+
+
+

Single File Versions of ContDataQC Operatations

+

The base version of the ContDataQC() function searches the specified directories for files that match the data type and date range. This set up does not work for everyone. Included in the ContDataQC package are “file” versions of the same QCRaw, Aggregate, and SummaryStat functions. These work on a vector of file names (so multiple files can be processed this way).Different report options are used in each example in the code below.

+
#~~~~~~~~~~~~~~
+# File Versions
+#~~~~~~~~~~~~~~
+
+# QC Data
+myData.Operation <- "QCRaw" #Selection.Operation[2]
+#myFile <- "test2_AW_20130426_20130725.csv"
+myFile <- c("test2_AW_20130426_20130725.csv", "test2_AW_20130725_20131015.csv", "test2_AW_20140901_20140930.csv")
+myDir.import <- file.path(".","Data1_RAW")
+myDir.export <- file.path(".","Data2_QC")
+myReport.format <- "docx"
+ContDataQC(myData.Operation, fun.myDir.import=myDir.import, fun.myDir.export=myDir.export, fun.myFile=myFile, myReport.format)
+
+# Aggregate Data
+myData.Operation <- "Aggregate" #Selection.Operation[3]
+myFile <- c("QC_test2_AW_20130426_20130725.csv", "QC_test2_AW_20130725_20131015.csv", "QC_test2_AW_20140901_20140930.csv")
+myDir.import <- file.path(".","Data2_QC")
+myDir.export <- file.path(".","Data3_Aggregated")
+myReport.format <- "html"
+ContDataQC(myData.Operation, fun.myDir.import=myDir.import, fun.myDir.export=myDir.export, fun.myFile=myFile, myReport.format)
+
+# Summary Stats
+myData.Operation <- "SummaryStats" #Selection.Operation[4]
+myFile <- "QC_test2_AW_20130426_20130725.csv"
+#myFile <- c("QC_test2_AW_20130426_20130725.csv", "QC_test2_AW_20130725_20131015.csv", "QC_test2_AW_20140901_20140930.csv")
+myDir.import <- file.path(".","Data2_QC")
+myDir.export <- file.path(".","Data4_Stats")
+#Leave off myReport.format and get default (docx).
+ContDataQC(myData.Operation, fun.myDir.import=myDir.import, fun.myDir.export=myDir.export, fun.myFile=myFile)
+
+
+

Other Features

+

There are other features built into the ContDataQC library. These are outlined below.

+
+
+

Configuration File

+

Through the use of a configuration file the user can change many of the variables in the library (e.g., the names of fields, thresholds, and date formats).

+

The ContDataQC() function allows the user to input their own configuration file. Only variables that are different from the default need to be added. That is, the default configuration is loaded in the library and then the user supplied configuration. This allows the user configuration to overwrite the defaults for that session.

+

The Config.out will output the contents of the configuration file as used by the package and stored in the environment ContData.env.

+

The contents of the configuration file (extdata/config.ORIG.R) are listed below.

+
# User Defined Values
+#
+# User defined values for variables used across multiple functions in this library.
+# The user has the ability to modify the values for names, units, QC thresholds, etc.
+#
+# Saved in a separate environment.
+#
+# https://www.r-bloggers.com/package-wide-variablescache-in-r-packages/
+#
+# Continuous data helper script
+# Default Values
+# Erik.Leppo@tetratech.com (EWL)
+# 20150928
+# 20170323, add 3 parameters (Cond, DO, pH)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User defined variable names for input data
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# It is assumed that this R script is stored in a directory with the data files as subdirectories
+# This script is intended to be "source"d from the main script.
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# @keywords continuous data
+# @examples
+# #Not intended to be accessed indepedant of function ContDataQC().
+# #Data values only.  No functions.  Add to environment so only visible inside library.
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# USER may make modifications in this section but not mandatory
+# this section could be sourced so can use between scripts
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#UserDefinedValues <- NA # default value so shows up in help files
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Environment Name ####
+# Environment for use only by ContDataQC library
+ContData.env <- new.env(parent = emptyenv())
+# assign variables to new environment requires editing of all lines.
+# For example, myDelim <- "_" BECOMES ContData.env$myDelim, "_"
+###
+# list all elements in environment
+# ls(ContData.env)  # all elements in environment
+# as.list(ContData.env)  # all elements in environment with assigned values
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Delimiter in File Names (e.g., test2_AW_201301_20131231.csv)
+ContData.env$myDelim <- "_"
+ContData.env$myDelim_LakeID <- "~"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Acceptable column names for the data ####
+## Basic
+ContData.env$myName.SiteID        <- "SiteID"
+ContData.env$myName.Date          <- "Date"
+ContData.env$myName.Time          <- "Time"
+ContData.env$myName.DateTime      <- "Date.Time"
+#(special characters (e.g., %, space, or /) are converted to "." by R, "deg" converted to "A")
+### IF CHANGE UNITS WILL NEED TO MODIFY THRESHOLDS ###
+ContData.env$myUnits.AirTemp    <- "C" # C or F
+ContData.env$myUnits.WaterTemp  <- ContData.env$myUnits.AirTemp
+ContData.env$myUnits.AirBP      <- "psi"
+ContData.env$myUnits.WaterP     <- ContData.env$myUnits.AirBP
+ContData.env$myUnits.SensorDepth <- "ft"
+ContData.env$myUnits.Discharge  <- "ft3.s"
+ContData.env$myUnits.Cond       <- "uS.cm"
+ContData.env$myUnits.DO         <- "mg.L"
+ContData.env$myUnits.pH         <- "SU"
+ContData.env$myUnits.Turbidity  <- "NTU"
+ContData.env$myUnits.Chlorophylla <- "g.cm3"
+ContData.env$myUnits.GageHeight <- "ft"
+## Logger Fields
+ContData.env$myName.RowID.Water   <- "Water.RowID"
+ContData.env$myName.LoggerID.Water<- "Water.LoggerID"
+ContData.env$myName.RowID.Air     <- "Air.RowID"
+ContData.env$myName.LoggerID.Air  <- "Air.LoggerID"
+## Parameters as appear in logger files
+ContData.env$myName.WaterTemp     <- paste0("Water.Temp.",ContData.env$myUnits.WaterTemp)  # "deg" from HoboWare files sometimes adds "A " in front.  Replace with "." in R.
+ContData.env$myName.AirTemp       <- paste0("Air.Temp.",ContData.env$myUnits.AirTemp)   # "deg" from HoboWare files sometimes adds "A " in front.  Replace with "." in R.
+ContData.env$myName.WaterP        <- paste0("Water.P.",ContData.env$myUnits.WaterP)
+ContData.env$myName.AirBP         <- paste0("Air.BP.",ContData.env$myUnits.WaterP)
+ContData.env$myName.SensorDepth   <- paste0("Sensor.Depth.",ContData.env$myUnits.SensorDepth)
+ContData.env$myName.Discharge     <- paste0("Discharge.",ContData.env$myUnits.Discharge)
+ContData.env$myName.Cond          <- paste0("Conductivity.",ContData.env$myUnits.Cond)
+ContData.env$myName.DO            <- paste0("DO.",ContData.env$myUnits.DO)
+ContData.env$myName.pH            <- paste0("pH.",ContData.env$myUnits.pH)
+ContData.env$myName.Turbidity     <- paste0("Turbidity.",ContData.env$myUnits.DO)
+ContData.env$myName.Chlorophylla   <- paste0("Chlorophylla.",ContData.env$myUnits.pH)
+ContData.env$myName.GageHeight    <- paste0("GageHeight.",ContData.env$myUnits.GageHeight)
+## Plot Labels
+ContData.env$myLab.WaterTemp      <- paste0("Temperature, Water (deg ",ContData.env$myUnits.WaterTemp,")")
+ContData.env$myLab.AirTemp        <- paste0("Temperature, Air (deg ",ContData.env$myUnits.AirTemp,")")
+ContData.env$myLab.Date           <- "Date"
+ContData.env$myLab.DateTime       <- "Date"
+ContData.env$myLab.WaterP         <- paste0("Pressure, Water (",ContData.env$myUnits.AirBP,")")
+ContData.env$myLab.AirBP          <- paste0("Barometric Pressure, Air (",ContData.env$myUnits.WaterP,")")
+ContData.env$myLab.SensorDepth    <- paste0("Sensor Depth (",ContData.env$myUnits.SensorDepth,")",sep="")
+ContData.env$myLab.Temp.BOTH      <- paste0("Temperature (deg ",ContData.env$myUnits.WaterTemp,")")
+ContData.env$myLab.Discharge      <- paste0("Discharge (",sub("\\.","/",ContData.env$myUnits.Discharge),")")  #replace "." with "/"
+ContData.env$myLab.Cond           <- paste0("Conductivity (",sub("\\.","/",ContData.env$myUnits.Cond),")")    #replace "." with "/"
+ContData.env$myLab.DO             <- paste0("Dissolved Oxygen (",sub("\\.","/",ContData.env$myUnits.DO),")")  #replace "." with "/"
+ContData.env$myLab.pH             <- paste0("pH (",ContData.env$myUnits.pH,")")
+ContData.env$myLab.Turbidity      <- paste0("Turbidity (",ContData.env$myUnits.Turbidity,")")
+ContData.env$myLab.Chlorophylla   <- paste0("Chlorophyll a (",sub("\\.","/",ContData.env$myUnits.Chlorophylla),")")    #replace "." with "/"
+ContData.env$myLab.GageHeight     <- paste0("Gage Height (",ContData.env$myUnits.GageHeight,")")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Discrete Measurements ####
+ContData.env$myPrefix.Discrete          <- "Discrete"
+# Discrete, Names
+ContData.env$myName.Discrete.WaterTemp  <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.WaterTemp,sep=".")
+ContData.env$myName.Discrete.AirTemp    <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.AirTemp,sep=".")
+ContData.env$myName.Discrete.WaterP     <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.WaterP,sep=".")
+ContData.env$myName.Discrete.AirBP      <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.AirBP,sep=".")
+ContData.env$myName.Discrete.SensorDepth <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.SensorDepth,sep=".")
+ContData.env$myName.Discrete.Discharge  <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.Discharge,sep=".")
+ContData.env$myName.Discrete.Cond       <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.Cond,sep=".")
+ContData.env$myName.Discrete.DO         <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.DO,sep=".")
+ContData.env$myName.Discrete.pH         <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.pH,sep=".")
+ContData.env$myName.Discrete.Turbidity  <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.Turbidity,sep=".")
+ContData.env$myName.Discrete.Chlorophylla <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.Chlorophylla,sep=".")
+ContData.env$myName.Discrete.GageHeight <- paste(ContData.env$myPrefix.Discrete,ContData.env$myName.GageHeight,sep=".")
+# Discrete, Labels
+ContData.env$myLab.Discrete.WaterTemp   <- paste(ContData.env$myLab.WaterTemp,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.AirTemp     <- paste(ContData.env$myLab.AirTemp,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.WaterP      <- paste(ContData.env$myLab.WaterP,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.AirBP       <- paste(ContData.env$myLab.AirBP,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.SensorDepth <- paste(ContData.env$myLab.SensorDepth,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.Discharge   <- paste(ContData.env$myLab.Discharge,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.Cond        <- paste(ContData.env$myLab.Cond,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.DO          <- paste(ContData.env$myLab.DO,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.pH          <- paste(ContData.env$myLab.pH,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.Turbidity   <- paste(ContData.env$myLab.Turbidity,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.Chlorophylla<- paste(ContData.env$myLab.Chlorophylla,"(Discrete)",sep=" ")
+ContData.env$myLab.Discrete.GageHeight  <- paste(ContData.env$myLab.GageHeight,"(Discrete)",sep=" ")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Automated QC stuff ####
+## data type/stages
+ContData.env$myDataQuality.Raw        <- "RAW"
+ContData.env$myDataQuality.QCauto     <- "QCauto"
+ContData.env$myDataQuality.QCmanual   <- "QCmanual"
+ContData.env$myDataQuality.Final      <- "Final"
+ContData.env$myDataQuality.Aggregated <- "Aggregated"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Directory Names ####
+ContData.env$myName.Dir.0Original  <- "Data0_Original"
+ContData.env$myName.Dir.1Raw       <- "Data1_Raw"
+ContData.env$myName.Dir.2QC        <- "Data2_QC"
+ContData.env$myName.Dir.3Agg       <- "Data3_Aggregated"
+ContData.env$myName.Dir.4Stats     <- "Data4_Stats"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Data Fields ####
+ContData.env$myNames.DataFields <- c(ContData.env$myName.WaterTemp
+                                     , ContData.env$myName.AirTemp
+                                     , ContData.env$myName.WaterP
+                                     , ContData.env$myName.AirBP
+                                     , ContData.env$myName.SensorDepth
+                                     , ContData.env$myName.Discharge
+                                     , ContData.env$myName.Cond
+                                     , ContData.env$myName.DO
+                                     , ContData.env$myName.pH
+                                     , ContData.env$myName.Turbidity
+                                     , ContData.env$myName.Chlorophylla
+                                     , ContData.env$myName.GageHeight
+                                     , ContData.env$myName.Discrete.WaterTemp
+                                     , ContData.env$myName.Discrete.AirTemp
+                                     , ContData.env$myName.Discrete.WaterP
+                                     , ContData.env$myName.Discrete.AirBP
+                                     , ContData.env$myName.Discrete.SensorDepth
+                                     , ContData.env$myName.Discrete.Discharge
+                                     , ContData.env$myName.Discrete.Cond
+                                     , ContData.env$myName.Discrete.DO
+                                     , ContData.env$myName.Discrete.pH
+                                     , ContData.env$myName.Discrete.Turbidity
+                                     , ContData.env$myName.Discrete.Chlorophylla
+                                     , ContData.env$myName.Discrete.GageHeight
+                                     )
+ContData.env$myNames.DataFields.Lab <- c(ContData.env$myLab.WaterTemp
+                                         , ContData.env$myLab.AirTemp
+                                         , ContData.env$myLab.WaterP
+                                         , ContData.env$myLab.AirBP
+                                         , ContData.env$myLab.SensorDepth
+                                         , ContData.env$myLab.Discharge
+                                         , ContData.env$myLab.Cond
+                                         , ContData.env$myLab.DO
+                                         , ContData.env$myLab.pH
+                                         , ContData.env$myLab.Turbidity
+                                         , ContData.env$myLab.Chlorophylla
+                                         , ContData.env$myLab.GageHeight
+                                         , ContData.env$myLab.Discrete.WaterTemp
+                                         , ContData.env$myLab.Discrete.AirTemp
+                                         , ContData.env$myLab.Discrete.WaterP
+                                         , ContData.env$myLab.Discrete.AirBP
+                                         , ContData.env$myLab.Discrete.SensorDepth
+                                         , ContData.env$myLab.Discrete.Discharge
+                                         , ContData.env$myLab.Discrete.Cond
+                                         , ContData.env$myLab.Discrete.DO
+                                         , ContData.env$myLab.Discrete.pH
+                                         , ContData.env$myLab.Discrete.Turbidity
+                                         , ContData.env$myLab.Discrete.Chlorophylla
+                                         , ContData.env$myLab.Discrete.GageHeight
+                                         )
+ContData.env$myNames.DataFields.Col <- c("blue","green","gray","gray","black","brown"
+                                         ,"purple","orange","salmon","rosybrown","aquamarine1")
+#
+## Name Order (change order below to change order in output file)
+ContData.env$myNames.Order <- c(ContData.env$myName.SiteID
+                                , ContData.env$myName.Date
+                                , ContData.env$myName.Time
+                                , ContData.env$myName.DateTime
+                                , ContData.env$myName.WaterTemp
+                                , ContData.env$myName.LoggerID.Air
+                                , ContData.env$myName.RowID.Air
+                                , ContData.env$myName.AirTemp
+                                , ContData.env$myName.WaterP
+                                , ContData.env$myName.AirBP
+                                , ContData.env$myName.SensorDepth
+                                , ContData.env$myName.Discharge
+                                , ContData.env$myName.Cond
+                                , ContData.env$myName.DO
+                                , ContData.env$myName.pH
+                                , ContData.env$myName.Turbidity
+                                , ContData.env$myName.Chlorophylla
+                                , ContData.env$myName.GageHeight
+                                , ContData.env$myName.LoggerID.Water
+                                , ContData.env$myName.RowID.Water
+                                , ContData.env$myName.Discrete.WaterTemp
+                                , ContData.env$myName.Discrete.AirTemp
+                                , ContData.env$myName.Discrete.WaterP
+                                , ContData.env$myName.Discrete.AirBP
+                                , ContData.env$myName.Discrete.SensorDepth
+                                , ContData.env$myName.Discrete.Discharge
+                                , ContData.env$myName.Discrete.Cond
+                                , ContData.env$myName.Discrete.DO
+                                , ContData.env$myName.Discrete.pH
+                                , ContData.env$myName.Discrete.Turbidity
+                                , ContData.env$myName.Discrete.Chlorophylla
+                                , ContData.env$myName.Discrete.GageHeight
+                                )
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Data Quality Flag Values ####
+ContData.env$myFlagVal.Pass    <- "P"
+ContData.env$myFlagVal.NotEval <- "NA"
+ContData.env$myFlagVal.Suspect <- "S"
+ContData.env$myFlagVal.Fail    <- "F"
+ContData.env$myFlagVal.NoData  <- "X"
+ContData.env$myFlagVal.Order   <- c(ContData.env$myFlagVal.Pass
+                                    , ContData.env$myFlagVal.Suspect
+                                    , ContData.env$myFlagVal.Fail
+                                    , ContData.env$myFlagVal.NoData)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# QC Tests and Calculations ####
+#http://stackoverflow.com/questions/16143700/pasting-two-vectors-with-combinations-of-all-vectors-elements
+#myNames.QCTests.Calcs.combo <- as.vector(t(outer(myNames.QCTests,myNames.QCTests.Calcs,paste,sep=".")))
+# combine so can check for and remove later.
+#myNames.DataFields.QCTests.Calcs.combo <- as.vector(t(outer(myNames.DataFields,myNames.QCTests.Calcs.combo,paste,sep=".")))
+# Data Quality Flag Thresholds
+## Gross Min/Max, Fail (equipment)
+### Examines values as outliers versus threholds
+### if value >= Hi or <= Lo then flagged as "Fail"
+ContData.env$myThresh.Gross.Fail.Hi.WaterTemp  <- 30
+ContData.env$myThresh.Gross.Fail.Lo.WaterTemp  <- -2
+ContData.env$myThresh.Gross.Fail.Hi.AirTemp    <- 38
+ContData.env$myThresh.Gross.Fail.Lo.AirTemp    <- -25
+ContData.env$myThresh.Gross.Fail.Hi.WaterP     <- 17
+ContData.env$myThresh.Gross.Fail.Lo.WaterP     <- 13
+ContData.env$myThresh.Gross.Fail.Hi.AirBP      <- 15
+ContData.env$myThresh.Gross.Fail.Lo.AirBP      <- 13
+ContData.env$myThresh.Gross.Fail.Hi.SensorDepth <- 6   # no longer used (only check for negative values for SensorDepth)
+ContData.env$myThresh.Gross.Fail.Lo.SensorDepth <- -1  # no longer used (only check for negative values for SensorDepth)
+ContData.env$myThresh.Gross.Fail.Hi.Discharge  <-  10^5 #dependant upon stream size (only checkf or negative values)
+ContData.env$myThresh.Gross.Fail.Lo.Discharge  <-  -1  #dependant upon stream size
+ContData.env$myThresh.Gross.Fail.Hi.Cond       <- 1500 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Cond       <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.DO         <- 20   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.DO         <- 1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.pH         <- 12   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.pH         <- 3    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.Turbidity         <- 10^5     # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Turbidity         <- -1       # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.Chlorophylla         <- 10^5  # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Chlorophylla          <- -1   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.GageHeight         <- 10^5
+ContData.env$myThresh.Gross.Fail.Lo.GageHeight          <- -1
+## Gross Min/Max, Suspect (extreme)
+### Examines values as outliers versus threholds
+### if value >= Hi or <= Lo then flagged as "Suspect"
+ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp  <- 25
+ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp  <- -0.1
+ContData.env$myThresh.Gross.Suspect.Hi.AirTemp    <- 35
+ContData.env$myThresh.Gross.Suspect.Lo.AirTemp    <- -23
+ContData.env$myThresh.Gross.Suspect.Hi.WaterP     <- 16.8
+ContData.env$myThresh.Gross.Suspect.Lo.WaterP     <- 13.5
+ContData.env$myThresh.Gross.Suspect.Hi.AirBP      <- 14.8
+ContData.env$myThresh.Gross.Suspect.Lo.AirBP      <- 13.0
+ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth <- 5    # no longer used (only check for negative values for SensorDepth)
+ContData.env$myThresh.Gross.Suspect.Lo.SensorDepth <- 0    # no longer used (only check for negative values for SensorDepth)
+ContData.env$myThresh.Gross.Suspect.Hi.Discharge  <-  10^3 #dependant upon stream size (only checkf or negative values
+ContData.env$myThresh.Gross.Suspect.Lo.Discharge  <-  -1   #dependant upon stream size
+ContData.env$myThresh.Gross.Suspect.Hi.Cond       <- 1200  # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Cond       <- 20    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.DO         <- 18    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.DO         <- 2     # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.pH         <- 11    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.pH         <- 4     # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.Turbidity         <- 10^3     # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Turbidity          <- -1      # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.Chlorophylla         <- 10^3  # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Chlorophylla         <- 1     # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.GageHeight         <- 10^3
+ContData.env$myThresh.Gross.Suspect.Lo.GageHeight        <- 1
+## Spike thresholds (absolute change)
+### Examines difference between consecutive measurements
+### if delta >= Hi then flagged as "Fail"
+### if delta >= Lo then flagged as "Suspect"
+ContData.env$myThresh.Spike.Hi.WaterTemp  <- 1.5
+ContData.env$myThresh.Spike.Lo.WaterTemp  <- 1
+ContData.env$myThresh.Spike.Hi.AirTemp    <- 10
+ContData.env$myThresh.Spike.Lo.AirTemp    <- 8
+ContData.env$myThresh.Spike.Hi.WaterP     <- 0.7
+ContData.env$myThresh.Spike.Lo.WaterP     <- 0.5
+ContData.env$myThresh.Spike.Hi.AirBP      <- 0.25
+ContData.env$myThresh.Spike.Lo.AirBP      <- 0.15
+ContData.env$myThresh.Spike.Hi.SensorDepth <- 5
+ContData.env$myThresh.Spike.Lo.SensorDepth <- 3
+ContData.env$myThresh.Spike.Hi.Discharge  <- 10^4 # dependant upon stream size
+ContData.env$myThresh.Spike.Lo.Discharge  <- 10^3 # dependant upon stream size
+ContData.env$myThresh.Spike.Hi.Cond       <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Cond       <- 5    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.DO         <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.DO         <- 5    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.pH         <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.pH         <- 5    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.Turbidity         <- 10^4         # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Turbidity         <- 10^3         # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.Chlorophylla         <- 10^4      # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Chlorophylla         <- 10^3      # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.Chlorophylla         <- 10^4      # this threshold has not been closely evaluated        # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Chlorophylla         <- 10^3      # this threshold has not been closely evaluated
+## Rate of Change (relative change)
+### Examines SD over "period" and difference in consecutive values
+### If delta >= SD.number * SD then flagged as "Suspect"
+ContData.env$myDefault.RoC.SD.number  <- 3
+ContData.env$myDefault.RoC.SD.period  <- 25 #hours
+ContData.env$myThresh.RoC.SD.number.WaterTemp  <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.WaterTemp  <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.AirTemp    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.AirTemp    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.WaterP     <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.WaterP     <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.AirBP      <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.AirBP      <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.SensorDepth <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.SensorDepth <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Discharge  <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Discharge  <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Cond       <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Cond       <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.DO         <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.DO         <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.pH         <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.pH         <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Turbidity         <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Turbidity         <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Chlorophylla         <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Chlorophylla        <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.GageHeight         <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.GageHeight        <- ContData.env$myDefault.RoC.SD.period
+## No Change (flat-line)
+### Examines consecutive values within "Tolerance" of each other
+### If number of consecutive values >= Hi then flagged as "Fail"
+### If number of consecutive values >= Lo (and < Hi) then flagged as "Suspect"
+ContData.env$myDefault.Flat.Hi        <- 30  # maximum is myThresh.Flat.MaxComp
+ContData.env$myDefault.Flat.Lo        <- 15
+ContData.env$myDefault.Flat.Tolerance <- 0.01 # set to one sigdig less than measurements.  Check with fivenum(x)
+ContData.env$myThresh.Flat.Hi.WaterTemp         <- 30
+ContData.env$myThresh.Flat.Lo.WaterTemp         <- 20
+ContData.env$myThresh.Flat.Tolerance.WaterTemp  <- 0.01
+ContData.env$myThresh.Flat.Hi.AirTemp           <- 20
+ContData.env$myThresh.Flat.Lo.AirTemp           <- 15
+ContData.env$myThresh.Flat.Tolerance.AirTemp    <- 0.01
+ContData.env$myThresh.Flat.Hi.WaterP            <- 15
+ContData.env$myThresh.Flat.Lo.WaterP            <- 10
+ContData.env$myThresh.Flat.Tolerance.WaterP     <- 0.001
+ContData.env$myThresh.Flat.Hi.AirBP             <- 15
+ContData.env$myThresh.Flat.Lo.AirBP             <- 10
+ContData.env$myThresh.Flat.Tolerance.AirBP      <- 0.001
+ContData.env$myThresh.Flat.Hi.SensorDepth        <- 60
+ContData.env$myThresh.Flat.Lo.SensorDepth        <- 20
+ContData.env$myThresh.Flat.Tolerance.SensorDepth <- 0.0
+ContData.env$myThresh.Flat.Hi.Discharge         <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Discharge         <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Discharge  <- 0.01
+ContData.env$myThresh.Flat.Hi.Cond              <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Cond              <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Cond       <- 0.01
+ContData.env$myThresh.Flat.Hi.DO                <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.DO                <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.DO         <- 0.01
+ContData.env$myThresh.Flat.Hi.pH                <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.pH                <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.pH         <- 0.01
+ContData.env$myThresh.Flat.Hi.Turbidity               <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Turbidity               <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Turbidity        <- 0.01
+ContData.env$myThresh.Flat.Hi.Chlorophylla                <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Chlorophylla                <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Chlorophylla         <- 0.01
+ContData.env$myThresh.Flat.Hi.GageHeight               <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.GageHeight               <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.GageHeight        <- 0.01
+#
+ContData.env$myThresh.Flat.MaxComp    <- max(ContData.env$myThresh.Flat.Hi.WaterTemp
+                                             , ContData.env$myThresh.Flat.Hi.AirTemp
+                                             , ContData.env$myThresh.Flat.Hi.WaterP
+                                             , ContData.env$myThresh.Flat.Hi.AirBP
+                                             , ContData.env$myThresh.Flat.Hi.Discharge
+                                             , ContData.env$myThresh.Flat.Hi.Cond
+                                             , ContData.env$myThresh.Flat.Hi.DO
+                                             , ContData.env$myThresh.Flat.Hi.pH
+                                             , ContData.env$myThresh.Flat.Hi.Turbidity
+                                             , ContData.env$myThresh.Flat.Hi.Chlorophylla
+                                             , ContData.env$myThresh.Flat.Hi.GageHeight
+                                             )
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Data Fields with Flags ####
+ContData.env$myName.Flag        <- "Flag" # flag prefix
+ContData.env$myNames.Cols4Flags <- c(ContData.env$myName.DateTime,ContData.env$myNames.DataFields)
+ContData.env$myNames.Flags      <- paste(ContData.env$myName.Flag,ContData.env$myNames.Cols4Flags,sep=".")  # define ones using in the calling script
+## flag labels
+ContData.env$myName.Flag.DateTime   <- paste(ContData.env$myName.Flag,ContData.env$myName.DateTime,sep=".")
+ContData.env$myName.Flag.WaterTemp  <- paste(ContData.env$myName.Flag,ContData.env$myName.WaterTemp,sep=".")
+ContData.env$myName.Flag.AirTemp    <- paste(ContData.env$myName.Flag,ContData.env$myName.AirTemp,sep=".")
+ContData.env$myName.Flag.WaterP     <- paste(ContData.env$myName.Flag,ContData.env$myName.WaterP,sep=".")
+ContData.env$myName.Flag.AirBP      <- paste(ContData.env$myName.Flag,ContData.env$myName.AirBP,sep=".")
+ContData.env$myName.Flag.SensorDepth <- paste(ContData.env$myName.Flag,ContData.env$myName.SensorDepth,sep=".")
+ContData.env$myName.Flag.Discharge  <- paste(ContData.env$myName.Flag,ContData.env$myName.Discharge,sep=".")
+ContData.env$myName.Flag.Cond       <- paste(ContData.env$myName.Flag,ContData.env$myName.Cond,sep=".")
+ContData.env$myName.Flag.DO         <- paste(ContData.env$myName.Flag,ContData.env$myName.DO,sep=".")
+ContData.env$myName.Flag.pH         <- paste(ContData.env$myName.Flag,ContData.env$myName.pH,sep=".")
+ContData.env$myName.Flag.Turbidity         <- paste(ContData.env$myName.Flag,ContData.env$myName.Turbidity,sep=".")
+ContData.env$myName.Flag.Chlorophylla        <- paste(ContData.env$myName.Flag,ContData.env$myName.Chlorophylla,sep=".")
+ContData.env$myName.Flag.GageHeight        <- paste(ContData.env$myName.Flag,ContData.env$myName.GageHeight,sep=".")
+# Data Quality Test Names
+ContData.env$myNames.QCTests <- c("Gross","Spike","RoC","Flat")
+ContData.env$myNames.QCCalcs <- c("SD.Time","SD","SDxN",paste("n",1:ContData.env$myThresh.Flat.MaxComp,sep="."),"flat.Lo","flat.Hi")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Exceedance values for stats (default to Gross-Suspect-Hi value) ####
+ContData.env$myExceed.WaterTemp  <- ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp
+ContData.env$myExceed.AirTemp    <- ContData.env$myThresh.Gross.Suspect.Hi.AirTemp
+ContData.env$myExceed.SensorDepth <- ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Date and Time Formats ####
+ContData.env$myFormat.Date           <- "%Y-%m-%d"
+ContData.env$myFormat.Time           <- "%H:%M:%S"
+ContData.env$myFormat.DateTime       <- "%Y-%m-%d %H:%M:%S"
+ContData.env$DateRange.Start.Default <- format(as.Date("1900-01-01"),ContData.env$myFormat.Date) #YYYY-MM-DD
+ContData.env$DateRange.End.Default   <- format(Sys.Date(),ContData.env$myFormat.Date)
+# Time Zone, used in Gage script in dataRetrieval, OlsonNames()
+ContData.env$myTZ <- Sys.timezone() #"America/New_York" (local time zone)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Time Frames (MM-DD) ####
+ContData.env$myTimeFrame.Annual.Start        <- "0101"
+ContData.env$myTimeFrame.Annual.End          <- "1231"
+ContData.env$myTimeFrame.WaterYear.Start     <- "1001"
+#ContData.env$myTimeFrame.WaterYear.End      <- "0930"
+ContData.env$myTimeFrame.Season.Spring.Start <- "0301"
+#ContData.env$myTimeFrame.Season.Spring.End  <- "0531"
+ContData.env$myTimeFrame.Season.Summer.Start <- "0601"
+#ContData.env$myTimeFrame.Season.Summer.End  <- "0831"
+ContData.env$myTimeFrame.Season.Fall.Start   <- "0901"
+#ContData.env$myTimeFrame.Season.Fall.End    <- "1130"
+ContData.env$myTimeFrame.Season.Winter.Start <- "1201"
+#ContData.env$myTimeFrame.Season.Winter.End  <- "0228" #but 0229 in leap year, use start dates only
+# Time Frame Names
+ContData.env$myName.Yr       <- "Year"
+ContData.env$myName.YrMo     <- "YearMonth"
+ContData.env$myName.Mo       <- "Month"
+ContData.env$myName.MoDa     <- "MonthDay"
+ContData.env$myName.JuDa     <- "JulianDay"
+ContData.env$myName.Day      <- "Day"
+ContData.env$myName.Season   <- "Season"
+ContData.env$myName.YrSeason <- "YearSeason"
+# for summary stats
+ContData.env$myNames.Fields.TimePeriods <- c(ContData.env$myName.Yr
+                                            ,ContData.env$myName.YrMo
+                                            ,ContData.env$myName.Mo
+                                            ,ContData.env$myName.MoDa
+                                            ,ContData.env$myName.JuDa
+                                            ,ContData.env$myName.Season
+                                            ,ContData.env$myName.YrSeason)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Exclude Trigger ####
+# Trigger for Stats to exclude (TRUE) or include (FALSE) where flag = "fail"
+ContData.env$myStats.Fails.Exclude <- TRUE  #FALSE #TRUE
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Report Format ####
+ContData.env$myReport.Format <- "html"  # "html" or "docx" # DOCX requires Pandoc.
+ContData.env$myReport.Dir    <- file.path(system.file(package="ContDataQC"), "rmd")
+
+
+

Period Stats

+

The function PeriodStats() generates daily stats (N, mean, min, max, range, std deviation) for the specified time period before a given date. Output is a multiple column CSV (Date and Parameter Name by statistic) and a report (HTML or DOCX) with plots.

+

The input is output of the QC function in ContDataQC. That is, a file with SiteID, Date, Time, Date.Time, and parameters.

+

To get different periods (30, 60, or 90 days) change function input “fun.myPeriod.N”. It is possible to provide a vector for Period.N and Period.Units.

+

The function returns a csv with daily means and a PDF summary with plots into the specified export directory for the specified time period before the given date.

+
# Save example file
+df.x <- DATA_period_test2_Aw_20130101_20141231
+write.csv(df.x,"DATA_period_test2_Aw_20130101_20141231.csv")
+
+# function inputs
+myDate <- "2013-09-30"
+myDate.Format <- "%Y-%m-%d"
+myPeriod.N <- c(30, 60, 90, 120, 1)
+myPeriod.Units <- c("d", "d", "d", "d", "y")
+myFile <- "DATA_period_test2_Aw_20130101_20141231.csv"
+myDir.import <- getwd()
+myDir.export <- getwd()
+myParam.Name <- "Water.Temp.C"
+myDateTime.Name <- "Date.Time"
+myDateTime.Format <- "%Y-%m-%d %H:%M:%S"
+myThreshold <- 20
+myConfig <- ""
+myReport.format <- "docx"
+
+# Run Function
+## default report format (html)
+PeriodStats(myDate
+          , myDate.Format
+          , myPeriod.N
+          , myPeriod.Units
+          , myFile
+          , myDir.import
+          , myDir.export
+          , myParam.Name
+          , myDateTime.Name
+          , myDateTime.Format
+          , myThreshold
+          , myConfig)
+
+
+

Export for IHA

+

The function Export.IHA() allows for the formatting of data for use with the Nature Conservancy’s IHA program as converted to R.

+

https://www.conservationgateway.org/ConservationPractices/Freshwater/EnvironmentalFlows/MethodsandTools/IndicatorsofHydrologicAlteration/Pages/indicators-hydrologic-alt.aspx

+

https://github.com/jasonelaw/iha

+
# 1.  Get Gage Data
+#
+# 1.A. Use ContDataQC and Save (~1min for download)
+myData.Operation    <- "GetGageData" #Selection.Operation[1]
+myData.SiteID       <- "01187300" # Hubbard River near West Hartland, CT
+myData.Type         <- "Gage"
+myData.DateRange.Start  <- "2015-01-01"
+myData.DateRange.End    <- "2016-12-31"
+myDir.import <- getwd()
+myDir.export <- getwd()
+ContDataQC(myData.Operation, myData.SiteID, myData.Type
+           , myData.DateRange.Start, myData.DateRange.End
+           , myDir.import, myDir.export)
+#
+# 1.B. Use saved data
+myFile <- "01187300_Gage_20150101_20161231.csv"
+myCol.DateTime <- "Date.Time"
+myCol.Discharge <- "Discharge.ft3.s"
+#
+# 2. Prep Data
+myData.IHA <- Export.IHA(myFile
+                         , fun.myCol.DateTime = myCol.DateTime
+                         , fun.myCol.Parameter = myCol.Discharge
+                         )
+

The example code below runs the data (generated above) through IHA package. This example makes use of the XLConnect R library to save the IHA output into a single Excel file. Before running the IHA code the IHA and XLConnect packages need to be installed. The devtools package allows for easy installation from GitHub (where the IHA package resides).

+
# Install Libraries (if needed)
+install.packages("devtools")
+library(devtools)
+install_github("jasonelaw/IHA")
+install.packages("XLConnect")
+

IHA example code below that uses the IHA.Export file and produces an Excel file of the IHA output.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# 3. Run IHA
+# Example using returned DF with IHA
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User info
+SiteID <- myData.SiteID
+Notes.User <- Sys.getenv("USERNAME")
+#~~~~~
+# Library (load)
+require(IHA)
+require(XLConnect)
+#~~~~~
+# IHA
+myYr <- "calendar" # "water" or "calendar"
+# IHA Metrics
+## IHA parameters group 1; Magnitude of monthly water conditions
+Analysis.Group.1 <- group1(myData.IHA, year=myYr)
+## IHA parameters group 2: Magnitude of monthly water condition and include 12 parameters
+Analysis.Group.2 <- group2(myData.IHA, year=myYr)
+Analysis.Group.3 <- group3(myData.IHA, year=myYr)
+## IHA parameters group 4; Frequency and duration of high and low pulses
+# defaults to 25th and 75th percentiles
+Analysis.Group.4 <- group4(myData.IHA, year=myYr)
+## IHA parameters group 5; Rate and frequency of water condition changes
+Analysis.Group.5 <- group5(myData.IHA, year=myYr)
+#~~~~~
+# Save Results to Excel (each group on its own worksheet)
+Group.Desc <- c("Magnitude of monthly water conditions"
+                ,"Magnitude of monthly water condition and include 12 parameters"
+                ,"Timing of annual extreme water conditions"
+                ,"Frequency and duration of high and low pulses"
+                ,"Rate and frequency of water condition changes")
+df.Groups <- as.data.frame(cbind(paste0("Group",1:5),Group.Desc))
+#
+myDate <- format(Sys.Date(),"%Y%m%d")
+myTime <- format(Sys.time(),"%H%M%S")
+# Notes section (add min/max dates)
+Notes.Names <- c("Dataset (SiteID)","IHA.Year","Analysis.Date (YYYYMMDD)","Analysis.Time (HHMMSS)","Analysis.User")
+Notes.Data <- c(SiteID, myYr, myDate, myTime, Notes.User)
+df.Notes <- as.data.frame(cbind(Notes.Names,Notes.Data))
+Notes.Summary <- summary(myData.IHA)
+# Open/Create file
+myFile.XLSX <- paste("IHA", SiteID, myYr, myDate, myTime, "xlsx", sep=".")
+wb <- loadWorkbook(myFile.XLSX, create = TRUE) # load workbook, create if not existing
+# create sheets
+createSheet(wb, name = "NOTES")
+createSheet(wb, name = "Group1")
+createSheet(wb, name = "Group2")
+createSheet(wb, name = "Group3")
+createSheet(wb, name = "Group4")
+createSheet(wb, name = "Group5")
+# write to worksheet
+writeWorksheet(wb, df.Notes, sheet = "NOTES", startRow=1)
+writeWorksheet(wb, Notes.Summary, sheet = "NOTES", startRow=10)
+writeWorksheet(wb, df.Groups, sheet="NOTES", startRow=25)
+writeWorksheet(wb, Analysis.Group.1, sheet = "Group1")
+writeWorksheet(wb, Analysis.Group.2, sheet = "Group2")
+writeWorksheet(wb, Analysis.Group.3, sheet = "Group3")
+writeWorksheet(wb, Analysis.Group.4, sheet = "Group4")
+writeWorksheet(wb, Analysis.Group.5, sheet = "Group5")
+# save workbook
+saveWorkbook(wb, myFile.XLSX)
+
+
+

Rarify Taxa Data

+

At times benthic samples include more individuals than the target number or when combining data from multiple programs it is necessary to subsample all samples down to a common number. The rarify funtion is part of the RIVPACS set of R scripts written by USEPA Corvallis. The rarify function is in the package BioMonTools. The example below is from the help file from that package. The BioMonTools package should install with the ContDataQC package.

+

https://github.com/leppott/BioMonTools

+

The function takes a three column data frame (SampleID, TaxonID, Count) and returns a similar dataframe with revised Counts.

+
# library(BioMonTools)
+
+# Subsample to 500 organisms (from over 500 organisms) for 12 samples.
+
+# load bio data
+df_biodata <- data_bio2rarify
+dim(df_biodata)
+View(df_biodata)
+
+# subsample
+mySize <- 500
+Seed_OR <- 18590214
+Seed_WA <- 18891111
+Seed_US <- 17760704
+bugs_mysize <- BioMonTools::rarify(inbug=df_biodata, sample.ID="SampleID"
+                     ,abund="N_Taxa",subsiz=mySize, mySeed=Seed_US)
+
+# view results
+dim(bugs_mysize)
+View(bugs_mysize)
+
+# Compare pre- and post- subsample counts
+df_compare <- merge(df_biodata, bugs_mysize, by=c("SampleID", "TaxaID")
+                    , suffixes = c("_Orig","_500"))
+df_compare <- df_compare[,c("SampleID", "TaxaID", "N_Taxa_Orig", "N_Taxa_500")]
+View(df_compare)
+
+# compare totals
+tbl_totals <- aggregate(cbind(N_Taxa_Orig, N_Taxa_500) ~ SampleID, df_compare, sum)
+View(tbl_totals)
+
+## Not run: 
+# save the data
+write.table(bugs_mysize, paste("bugs",mySize,"txt",sep="."),sep="\t")
+## End(Not run)
+
+
+

Visualization of Daily Means

+

An Excel file is included in the folder of the library that allows for the plotting of up to five years of data for water temperature, sensor depth, and air temperature.

+

The daily values from the SummaryStats operation of the ContDataQC() function can be copied into the Excel file and the plots are auto-updated.

+

These plots could be created in R but the Excel file was included to make the plots available to a wider audience.

+
# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original","Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- getwd()
+# Summary Stats
+myData.Operation <- "SummaryStats" #Selection.Operation[4]
+myData.SiteID    <- "test2"
+myData.Type      <- Selection.Type[3] #"AW"
+myData.DateRange.Start  <- "2013-01-01"
+myData.DateRange.End    <- "2014-12-31"
+myDir.import <- file.path(myDir.BASE,Selection.SUB[4]) #"Data3_Aggregated"
+myDir.export <- file.path(myDir.BASE,Selection.SUB[5]) #"Data4_Stats"
+ContDataQC(myData.Operation, myData.SiteID, myData.Type, myData.DateRange.Start, myData.DateRange.End, myDir.import, myDir.export)
+
+
+

Output for StreamThermal

+

The Export.StreamThermal() function exports data for use with the the StreamThermal library.
+The data input for StreamThermal is a data frame with five columns (SiteID, Date, TMax, TMin, and TMean).

+

The StreamThermal library is only available on GitHub.

+

https://github.com/tsangyp/StreamThermal

+

Install instructions for the StreamThermal library are below.

+
# Install Libraries (if needed)
+install.packages("devtools")
+library(devtools)
+install_github("tsangyp/StreamThermal")
+

The StreamThermal package is set up to work with USGS gage data and this can be downloaded via the dataRetrieval library (as shown in the examples in the StreamThermal library).

+
# 1.1. Get USGS data
+# code from StreamThermal T_frequency example
+ExUSGSStreamTemp<-dataRetrieval::readNWISdv("01382310","00010","2011-01-01","2011-12-31"
+                                            ,c("00001","00002","00003"))
+sitedata<-subset(ExUSGSStreamTemp, select=c("site_no","Date","X_00010_00001","X_00010_00002","X_00010_00003"))
+names(sitedata)<-c("siteID","Date","MaxT","MinT","MeanT")
+knitr::kable(head(sitedata))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
siteIDDateMaxTMinTMeanT
013823102011-01-013.72.02.6
013823102011-01-023.32.12.8
013823102011-01-032.31.01.7
013823102011-01-042.71.21.8
013823102011-01-052.81.52.0
013823102011-01-062.61.21.8
+

It is possible to use data that has been summarized with SummaryStats.

+
# 1.2. Use Unsummarrized data
+myFile <- "DATA_test2_Aw_20130101_20141231.csv"
+myData <- read.csv(file.path(path.package("ContDataQC"),"extdata",myFile), stringsAsFactors=FALSE)
+# Subset
+Col.Keep <- c("SiteID", "Date", "Water.Temp.C")
+sitedata <- myData[,Col.Keep]
+sitedata <- Export.StreamThermal(myData)
+# Show data table
+knitr::kable(head(sitedata))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SiteIDDateMaxTMinTMeanT
Test22013-04-2613.84610.06312.88004
Test22013-04-2713.17312.20712.52940
Test22013-04-2812.88312.59412.74052
Test22013-04-2914.42112.49713.31387
Test22013-04-3015.09111.72213.41990
Test22013-05-0115.56913.84614.63417
+

It is also possible to use data that has been calculated by Period Stats.

+
# 1.3. Use user data that has been QCed
+myData <- DATA_period_test2_Aw_20130101_20141231
+sitedata <- Export.StreamThermal(myData)
+knitr::kable(head(sitedata))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SiteIDDateMaxTMinTMeanT
Test22013-04-2613.84610.06312.88004
Test22013-04-2713.17312.20712.52940
Test22013-04-2812.88312.59412.74052
Test22013-04-2914.42112.49713.31387
Test22013-04-3015.09111.72213.41990
Test22013-05-0115.56913.84614.63417
+

Run StreamThermal functions with ‘sitedata’. The example below saves the output to a variable.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# 3. Run StreamThermal
+# Example using returned DF with StreamThermal
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Library (load)
+require(StreamThermal)
+#~~~~~
+# StreamThermal
+ST.freq <- T_frequency(sitedata)
+ST.mag  <- T_magnitude(sitedata)
+ST.roc  <- T_rateofchange(sitedata)
+ST.tim  <- T_timing(sitedata)
+ST.var  <- T_variability(sitedata)
+

The output of StreamThermal is 5 separate objects. It may be convenient to export that data to Excel with each object on a separate tab. Example code is below using the XLConnect library. This code is similar that provided in the Export.IHA example.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Save Results to Excel (each group on its own worksheet)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+library(XLConnect)
+# Descriptions
+#
+Desc.freq <- "Frequency metrics indicate numbers of days in months or seasons
+that key events exceed user-defined temperatures. "
+#
+Desc.mag <- "Magnitude metrics characterize monthly and seasonal averages and
+the maximum and minimum from daily temperatures as well as 3-, 7-, 14-, 21-,
+and 30-day moving averages for mean and maximum daily temperatures."
+#
+Desc.roc <- "Rate of change metrics include monthly and seasonal rate of
+change, which indicates the difference in magnitude of maximum and minimum
+temperatures divided by number of days between these events."
+#
+Desc.tim <- "Timing metrics indicate Julian days of key events including
+mean, maximum, and minimum temperatures; they also indicate Julian days of
+mean, maximum, and minimum values over moving windows of specified size."
+#
+Desc.var <- "Variability metrics summarize monthly and seasonal range in
+daily mean temperatures as well as monthly coefficient of variation of daily
+mean, maximum, and minimum temperatures. Variability metrics also include
+moving averages for daily ranges and moving variability in extreme
+temperatures, calculated from differences in average high and low
+temperatures over various time periods"
+#
+Group.Desc <- c(Desc.freq, Desc.mag, Desc.roc, Desc.tim, Desc.var)
+df.Groups <- as.data.frame(cbind(c("freq","mag","roc","tim","var"),Group.Desc))
+#
+SiteID <- sitedata[1,1]
+myDate <- format(Sys.Date(),"%Y%m%d")
+myTime <- format(Sys.time(),"%H%M%S")
+Notes.User <- Sys.getenv("USERNAME")
+# Notes section (add min/max dates)
+Notes.Names <- c("Dataset (SiteID)", "Analysis.Date (YYYYMMDD)"
+                 , "Analysis.Time (HHMMSS)", "Analysis.User")
+Notes.Data <- c(SiteID, myDate, myTime, Notes.User)
+df.Notes <- as.data.frame(cbind(Notes.Names, Notes.Data))
+Notes.Summary <- summary(sitedata)
+# Open/Create file
+## New File Name
+myFile.XLSX <- paste("StreamThermal", SiteID, myDate, myTime, "xlsx", sep=".")
+## Copy over template with Metric Definitions
+file.copy(file.path(path.package("ContDataQC"),"extdata","StreamThermal_MetricList.xlsx")
+          , myFile.XLSX)
+## load workbook, create if not existing
+wb <- loadWorkbook(myFile.XLSX, create = TRUE)
+# create sheets
+createSheet(wb, name = "NOTES")
+createSheet(wb, name = "freq")
+createSheet(wb, name = "mag")
+createSheet(wb, name = "roc")
+createSheet(wb, name = "tim")
+createSheet(wb, name = "var")
+# write to worksheet
+writeWorksheet(wb, df.Notes, sheet = "NOTES", startRow=1)
+writeWorksheet(wb, df.Groups, sheet="NOTES", startRow=10)
+writeWorksheet(wb, Notes.Summary, sheet = "NOTES", startRow=20)
+writeWorksheet(wb, ST.freq, sheet = "freq")
+writeWorksheet(wb, ST.mag, sheet = "mag")
+writeWorksheet(wb, ST.roc, sheet = "roc")
+writeWorksheet(wb, ST.tim, sheet = "tim")
+writeWorksheet(wb, ST.var, sheet = "var")
+# save workbook
+saveWorkbook(wb, myFile.XLSX)
+
+
+

Comparison CDFs

+

The function CompSite() allows the user to plot data from multiple sites on a single CDF.

+

The function takes as input a data frame with date and up to 8 columns of parameter data. Column names are Date and SiteIDs and values are daily means for some measurement.

+

More than 8 columns can be used but colors are recycled after 8 and the plot lines will be difficult to distinguish.

+

CDFs are generate for all data, year, season, and year/season and saved to a PDF. Winter + Year is such that December is included with the following year (e.g., Dec 2013, Jan 2014, Feb 2014 are 2014Winter).

+

Two plots per page are generated. The first plot is the proportion of values at a certain value.
+This plot is similar to a histogram.
+The second plot is a CDF of values. The line represents the proportion of values less than or equal to parameter values on the x-axis.

+

An example table to be used as input for the function is shown below. This table has data for five sites.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DateBrownsHubbardParkersColdWBrSwift
2014-01-010.51202080.06908330.2669167-0.00400000.0094167
2014-01-020.32158330.07841670.1561458-0.0040000-0.0040000
2014-01-030.02400000.07900000.1029167-0.00108330.0158333
2014-01-040.02400000.07900000.1385000-0.0016667-0.0005000
2014-01-050.28062500.07900000.2072083-0.0016667-0.0034167
2014-01-060.51977080.07900000.2161458-0.0092500-0.0040000
+

An example plot is shown below. The column names from the input table will appear in the legend of the plot.

+
+
+

Richards-Baker (Flashiness) Index

+

Calculates the RBI from a vector of flow measurements. Inputs are are vector of flow measurements over some time period (e.g., water year). Output is a single value that represents the RBI.

+

The RBI is the sum of absolute differences between days divided by the total flow for the given time period.

+

Baker, D.B., R.P. Richards, T.T. Loftus, and J.W. Kramer. 2004. A New Flashiness Index: Characteristics and Applications to Midwestern Rivers and Streams. April 2004. Journal of the American Water Resources Association (JAWRA). Pages 503:522.

+
# Get Gage Data via the dataRetrieval package from USGS 01187300 2013
+data.gage <- dataRetrieval::readNWISdv("03238500", "00060", "1974-10-01", "1975-09-30")
+head(data.gage)
+
##   agency_cd  site_no       Date X_00060_00003 X_00060_00003_cd
+## 1      USGS 03238500 1974-10-01            38                A
+## 2      USGS 03238500 1974-10-02            32                A
+## 3      USGS 03238500 1974-10-03            26                A
+## 4      USGS 03238500 1974-10-04            20                A
+## 5      USGS 03238500 1974-10-05            23                A
+## 6      USGS 03238500 1974-10-06            23                A
+
# flow data
+data.Q <- data.gage[,4]
+# remove zeros
+data.Q[data.Q==0] <- NA
+RBIcalc(data.Q)
+
## [1] 0.9833356
+

The calculated value from this function was QCed against data in the journal article.

+

Whiteoak Creek near Georgetown, Ohio (03238500) for the water year 1975 was download via the dataRetrieval package and used to generate the RBI. The value calculated by RBIcalc() matches the value in Figure 8 in the journal (0.98). The value was also verified using Excel.

+
+
+

Summary Stats Only

+

The user sometimes will have data that is already QCed or that just needs summary stats. It is possible to run just the summary stats from ContDataQC but need to require certain fields.

+
    +
  • SiteID
    • +
  • Date
    • +
  • Time
    • +
  • Date.Time
    • +
  • Parameter names in ContDataQC format (config.R). Examples below: +
      +
    • Water.Temp.C
      • +
    • Discharge.ft3.s
      • +
    • +
  • QC Flag fields for each parameter in the data (“Flag.” + Parameter Name). Examples below: +
      +
    • Flag.Water.Temp.C
      • +
    • Flag.Discharge.ft3.s
      • +
    • +
+

The example code below will download 2 years of discharge data from a the USGS gage 0118730, add the necessary fields, save the file, and run SummaryStats.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Summary Stats from Other Data
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Get Data, e.g., USGS gage data
+# Get Gage Data via the dataRetrieval package from USGS 01187300 2013 (~4 seconds)
+data.gage <- dataRetrieval::readNWISuv("01187300", "00060", "2013-01-01", "2014-12-31")
+head(data.gage)
+# Rename fields
+myNames <- c("Agency", "SiteID", "Date.Time", "Discharge.ft3.s", "Code", "TZ")
+names(data.gage) <- myNames
+# Add Date and Time
+data.gage[,"Date"] <- as.Date(data.gage[,"Date.Time"])
+data.gage[,"Time"] <-  strftime(data.gage[,"Date.Time"], format="%H:%M:%S")
+# Add "flag" fields that are added by QC function.
+Names.Flags <- paste0("Flag.",c("Date.Time", "Discharge.ft3.s"))
+data.gage[,Names.Flags] <- "P"
+# Save File
+myFile <- "01187300_Gage_20130101_20141231.csv"
+write.csv(data.gage, myFile, row.names=FALSE)
+# Run Stats (File)
+myData.Operation <- "SummaryStats"
+myDir.import <- getwd()
+myDir.export <- getwd()
+ContDataQC(myData.Operation
+           , fun.myDir.import=myDir.import
+           , fun.myDir.export=myDir.export
+           , fun.myFile=myFile)
+
+
+

Format of HOBOware Output

+

Following the suggested RMN guidelines for creating HOBOware (PDF files included in help section of the package) will produce files with a known structure. HOBOware will not create files for use with R. The function formatHobo was created to bridge this gap.

+

Imports a HoboWare output (with minimal tweaks) from a folder, reformats it using defaults from the ContDataQC config file, and exports a CSV to another folder for use with ContDataQC.

+

Below are the default data directories assumed to exist in the working directory. These can be created with code as in the example. Using this function as an example, files will be imported from Data0_Original and exported to Data1_RAW.

+
    +

  • ./Data0_Original/ = Unmodified data logger files.

    • +

  • ./Data1_RAW/ = Data logger files modified for use with library. Modifications for extra rows and file and column names.

    • +

  • ./Data2_QC/ = Repository for library output for QCed files.

    • +

  • ./Data3_Aggregated/ = Repository for library output for aggregated (or split) files.

    • +

  • ./Data4_Stats/ = Repository for library output for statistical summary files.

    • +
+

File format should be “SiteID_SensorType_StartDate_EndData.csv”.

+
    +

  • SiteID = no spaces or underscores

    • +

  • SensorType = Air, Water, or AW (Air + Water in the same file)

    • +

  • Dates = YYYYMMDD (no delimiter)

    • +

  • Delimiter = underscore (as specified in the config file)

    • +
+

No data frames are returned. A CSV file ready for use with the ContDataQC QC function will be generated in the specified output directory.

+
# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB       <- c("Data0_Original", "Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE          <- getwd()
+
+# Create data directories
+myDir.create <- paste0("./",Selection.SUB[1])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+myDir.create <- paste0("./",Selection.SUB[2])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+myDir.create <- paste0("./",Selection.SUB[3])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+myDir.create <- paste0("./",Selection.SUB[4])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+myDir.create <- paste0("./",Selection.SUB[5])
+  ifelse(dir.exists(myDir.create)==FALSE,dir.create(myDir.create),"Directory already exists")
+
+# Save example data (assumes directory ./Data0_Original/ exists)
+fn_1 <- "Charlies_Air_20170726_20170926.csv"
+fn_2 <- "Charlies_AW_20170726_20170926.csv"
+fn_3 <- "Charlies_Water_20170726_20170926.csv"
+fn_4 <- "ECO66G12_AW_20160128_20160418.csv"
+lapply(c(fn_1, fn_2, fn_3, fn_4), function(x)
+       file.copy(system.file("extdata", x, package="ContDataQC")
+       , file.path(myDir.BASE, Selection.SUB[1], x)))
+
+# Function Inputs
+myFiles <- c("Charlies_Air_20170726_20170926.csv"
+             , "Charlies_AW_20170726_20170926.csv"
+             , "Charlies_Water_20170726_20170926.csv")
+myDir.import <- file.path(getwd(), "Data0_Original")
+myDir.export <- file.path(getwd(), "Data1_RAW")
+
+# Run Function (with default config)
+formatHobo(myFiles, myDir.import, myDir.export)
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# QC the generated file with ContDataQC
+myData.Operation <- "QCRaw" #Selection.Operation[2]
+myFile <- myFiles
+myDir.import <- file.path(".","Data1_RAW")
+myDir.export <- file.path(".","Data2_QC")
+myReport.format <- "html"
+ContDataQC(myData.Operation, fun.myDir.import=myDir.import
+          , fun.myDir.export=myDir.export, fun.myFile=myFile
+          , fun.myReport.format=myReport.format)
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Example with unmodified dates
+myFiles <- "ECO66G12_AW_20160128_20160418.csv"
+myDir.import <- file.path(getwd(), "Data0_Original")
+myDir.export <- file.path(getwd(), "Data1_RAW")
+HoboDateFormat <- "MDY"
+
+# Run Function (with default config)
+formatHobo(myFiles, myDir.import, myDir.export, HoboDateFormat)
+
+
+

Export for rLakeAnalyzer

+

Continuous data from Lakes is similar to that used in streams.
+The only difference is the addition of depth to the data.
+There are R packages designed for the analysis of lake monitoring data. One such example is rLakeAnalyzer.

+

To aid users in QCing their continuous lake data an export function to rLakeAnalyzer was created in the ContDataQC package.

+
# Convert Data for use with rLakeAnalyzer
+
+# Data
+fn_CDQC <- "TestLake_Water_20180702_20181012.csv"
+df_CDQC <- read.csv(file.path(system.file(package = "ContDataQC"), "extdata", fn_CDQC))
+
+# Convert Date.Time from factor to POSIXct (make it a date and time field in R)
+df_CDQC[, "Date.Time"] <- as.POSIXct(df_CDQC[, "Date.Time"])
+
+# Columns, date listed first
+col_depth <- "Depth"
+col_CDQC <- c("Date.Time", "temp_F", "DO_conc")
+col_rLA  <- c("datetime", "wtr", "doobs")
+
+# Output Options
+dir_export <- getwd()
+fn_export <- paste0("rLA_", fn_CDQC)
+
+# Run function
+df_rLA <- Export.rLakeAnalyzer(df_CDQC, col_depth, col_CDQC, col_rLA
+                               , dir_export, fn_export)
+
+# Visualize Input and Output
+knitr::kable(head(df_CDQC), caption = "Example ContDataQC to rLakeAnalyze format function input.")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Example ContDataQC to rLakeAnalyze format function input.
SiteIDDate.TimeDO_conctemp_FDepth
TestLake2018-07-02 16:00:002.2449.689
TestLake2018-07-02 17:00:002.5450.769
TestLake2018-07-02 18:00:002.6350.659
TestLake2018-07-02 19:00:002.2649.899
TestLake2018-07-02 20:00:002.4850.079
TestLake2018-07-02 21:00:002.6350.549
+
knitr::kable(head(df_rLA), caption = "Example ContDataQC to rLakeAnalyze format function output.")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Example ContDataQC to rLakeAnalyze format function output.
datetimewtr_3wtr_6wtr_9doobs_3doobs_6doobs_9
2018-07-02 16:00:0071.1065.0549.688.215.442.24
2018-07-02 17:00:0071.2063.1050.768.315.352.54
2018-07-02 18:00:0071.4958.0350.658.374.172.63
2018-07-02 19:00:0071.6757.6049.898.404.352.26
2018-07-02 20:00:0071.5655.6950.078.263.942.48
2018-07-02 21:00:0071.3857.2750.548.404.352.63
+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Plot original data in ggplot
+library(ggplot2)
+
## Registered S3 methods overwritten by 'ggplot2':
+##   method         from 
+##   [.quosures     rlang
+##   c.quosures     rlang
+##   print.quosures rlang
+
# Plot, Create
+p <- ggplot(df_CDQC, aes(x=Date.Time, y=temp_F)) +
+       geom_point(aes(color=Depth)) +
+       scale_color_continuous(trans="reverse") +
+       scale_x_datetime(date_labels = "%Y-%m")
+
+# Plot, Show
+print(p)
+
## Warning: Removed 3 rows containing missing values (geom_point).
+

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# use rLakeAnalyzer
+library(rLakeAnalyzer)
+#library(knitr)
+
+# Filter Data for only temperature
+col_wtr <- colnames(df_rLA)[grepl("wtr_", colnames(df_rLA))]
+df_rLA_wtr <- df_rLA[, c("datetime", col_wtr)]
+
+# Create bathymetry data frame
+df_rLA_bth <- data.frame(depths=c(3,6,9), areas=c(300,200,100))
+
+# Visualize Input Data
+knitr::kable(head(df_rLA_wtr), caption = "rLakeAnalyzer; Example water temperature data")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
rLakeAnalyzer; Example water temperature data
datetimewtr_3wtr_6wtr_9
2018-07-02 16:00:0071.1065.0549.68
2018-07-02 17:00:0071.2063.1050.76
2018-07-02 18:00:0071.4958.0350.65
2018-07-02 19:00:0071.6757.6049.89
2018-07-02 20:00:0071.5655.6950.07
2018-07-02 21:00:0071.3857.2750.54
+
knitr::kable(head(df_rLA_bth), caption = "rLakeAnalyzer; Example depth and area data")
+ + + + + + + + + + + + + + + + + + + + + + +
rLakeAnalyzer; Example depth and area data
depthsareas
3300
6200
9100
+
# Generate Heat Map
+wtr.heat.map(df_rLA_wtr)
+

+
# Generate Schmidt Plot
+schmidt.plot(df_rLA_wtr, df_rLA_bth)
+

+
# Generate Schmidt Stability Values
+df_rLA_Schmidt <- ts.schmidt.stability(df_rLA_wtr, df_rLA_bth)
+
+# Visualize Output Data
+knitr::kable(head(df_rLA_Schmidt), caption = "rLakeAnalyzer; Example Schmidt Stability output.")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
rLakeAnalyzer; Example Schmidt Stability output.
datetimeschmidt.stability
2018-07-02 16:00:00197.8109
2018-07-02 17:00:00196.6225
2018-07-02 18:00:00214.8701
2018-07-02 19:00:00223.8640
2018-07-02 20:00:00226.1239
2018-07-02 21:00:00216.3078
+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Plot original data in ggplot
+library(ggplot2)
+
+# Plot, Create
+p <- ggplot(df_CDQC, aes(x=Date.Time, y=temp_F)) +
+       geom_point(aes(color=Depth)) +
+       scale_color_continuous(trans="reverse") +
+       scale_x_datetime(date_labels = "%Y-%m")
+
+# Plot, Show
+p
+
## Warning: Removed 3 rows containing missing values (geom_point).
+

+
+
+
+

Data from Other Sources

+

It is possible to use data from other sources. The focus of the package has been user’s with no data system. Some users already have a system such as Aquarius. Below is an example script for obtaining data from such a system. The script was contributed by Mark Hoger of PA DEP. This script is saved in the extdata folder as Aquarius_Data_Aquisition_PADEP.r.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# This script accesses data using AQUARIUS Time-Series 3.x's Publish API.
+# 
+# 
+# It depends on the RCurl library; to download and install
+# RCurl into your R environment, use this command:
+#
+#      install.packages('RCurl')
+#
+#
+# Initial command examples were provided by Aquatic Informatics 
+# technical staff. These examples were then adapted by Mark Hoger at 
+# Pennsylvania Department of Environmental Protection.
+#
+# Mark Hoger 
+# mhoger@pa.gov
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Initialize Connection to Database
+#
+library(RCurl)
+
+co<-c()
+# Enter server and AQUARIUS username and password between the ''
+server<-''
+username<-''
+pw<-''
+service<-paste('http://', server, '/aquarius/Publish/AquariusPublishRestService.svc', sep='')
+getauthtoken<-paste(service, '/GetAuthToken?user=', username, '&encPwd=', pw, sep='')
+token<-getURL(.opts=co,getauthtoken)
+co<-curlOptions(.opts=co,httpheader=c(AQAuthToken=token))
+#
+# The above commands should result in a token that will be used to access
+# data stored in the database.
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Data Acquisition
+#
+# Locations and datasets are selected prior to extracting data. This
+# approach reduces the time required to extract data since often only a 
+# subset of the data is needed. 
+#
+# Steps
+# 1. Get a list of locations from database.
+# 2. Choose locations.
+# 3. Get a list of datasets at the chosen locations.
+# 4. Choose datasets (e.g. pH and DO datasets).
+# 5. Extract data from datasets chosen. Can limit extraction by date/time
+#    during this process.
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 1. Get a list of locations. ####
+#
+getlocations<-paste(service, '/GetLocations',sep='')
+locs.all=read.csv(textConnection(getURL(.opts=co, getlocations)))
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 2. Choose locations. ####
+#
+# These steps will depend on the structure of how locations are stored in
+# the database you are pulling from. In PADEP's setup, the folders are
+# structured by Internal/External data, basin, HUC, then stream name. This
+# folder structure is contained in the LOCATIONPATH field and serves as an
+# excellent way of grabbing sites from within a watershed. 
+#
+# All subsetting from the original list of locations, locs.all, is stored 
+# in a dataframe called locs.subset. This dataframe name is used in the 
+# following steps so it is recommended that you rewrite over, locs. subset
+# if multiple steps are done instead of renaming each subset. 
+# 
+# Example subsetting of locations. grepl looks for character strings.
+#
+# To grab all internal data we can use LOCATIONPATH
+locs.subset<-locs.all[grepl('Internal Data',locs.all$LOCATIONPATH),]
+#
+# To search multiple key strings use | as the "or" function: 
+# 'Swatara|Goose' will return locations with either 'Swatara' or 'Goose' 
+# in LOCATIONNAME
+locs.subset<-locs.subset[grepl('Swatara|Goose',locs.subset$LOCATIONNAME),]
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 3. Get a list of datasets. ####
+#
+# A loop function is used to pull a list of all datasets at each location
+# in locs.subset.
+n.sites<-length(locs.subset$IDENTIFIER) # Count locations for the loop
+datalist = list() # Create temporary holder of data
+for (i in 1:n.sites){
+  getdatasets<-paste(service,'/GetDataSetsList?locId=',locs.subset$IDENTIFIER[i],sep='')
+  datalist[[i]]=read.csv(textConnection(getURL(.opts=co,getdatasets)))
+}
+datasets = do.call(rbind, datalist) # Combine data gathered in loop into 
+#                                     a dataframe
+#
+# Make sure no errors were thrown during the loop. If a problem occurred
+# it could be with the naming or formatting in AQUARIUS. For example, the
+# parameter name (Label field of a dataset in Location Manager) cannot 
+# include any spaces. Also, time series that have been created but do not
+# contain any data will cause problems. A good way to find the issue is
+# to see how many elements were created in the loop.
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 4. Choose datasets. ####
+#
+# Like with the locations, you now need to select only that datasets you
+# want. Again, it is recommended to maintain the dataframe name 'datasets'
+# to avoid the need to adjust code in future steps.
+#
+# To remove Field Visit series from the list of datasets use !grepl to 
+# select all datasets except those with the given character string. 
+datasets <- datasets[!grepl("Field Visits", datasets$DataId),]
+#
+# To select datasets based on parameter type, use parameter identifiers.
+# The most common identifiers used by PADEP are below. Parameter  
+# identifiers can be found in AQUARIUS manager under the parameter tab, 
+# Parameter ID column.
+#       TW = Water temperature
+#       SpCond = Specific Conductance
+#       PH = pH
+#       WO = Dissolved oxygen - concentration
+#       WX = Dissolved oxygen - % sat
+#       WT = Turbidity
+#       HG = Stage
+#       QR = Discharge
+#       PA = Atmospheric pressure
+#       44500 = Calculated-derived series
+#
+# I typically don't use grepl to subset parameters just in case we ever  
+# have parameters that contain the same character strings. Instead use 
+# the following subsetting command.
+#
+# To grab only water temperature datasets.
+datasets <- datasets[datasets$Parameter=='TW',]
+# To grab multiple parameter types, use | as the "or" function. For both 
+# pH and DO concentration datasets: 
+datasets <- datasets[datasets$Parameter=='PH' | datasets$Parameter=='WO',]
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 5. Extract data. ####
+#
+# The datasets dataframe does not contain much location information. To 
+# better tie location data to the data about to be extracted, I use merge 
+# to add fields from locs.subset to the datasets dataframe.
+datasets<-merge(datasets,locs.subset,all=FALSE,by.x='LocationId',by.y='IDENTIFIER')
+#
+# Now we finally grab the actual data from the list of datasets in the 
+# dataframe 'datasets'. Again, a loop function is used.
+n.datasets <- length(datasets$DataId) # Count datasets for the loop
+datalist2 = list() # Create temporary holder of data
+for (i in 1:n.datasets){
+         # Date range can be selected here. Blank will return all data
+         # from the datasets. Format is '2016-10-17T00:00:00.000-05:00'
+  start_dt=''
+  end_dt=''
+  gettimeseriesdata<-paste(service, '/GetTimeSeriesData', '?dataId=', datasets$DataId[i], '&queryFrom=', start_dt, '&queryTo=', end_dt, sep='')
+  datalist2[[i]]=read.csv(textConnection(getURL(.opts=co, gettimeseriesdata)), skip=4)
+  datalist2[[i]]$Time=strptime(substr(datalist2[[i]]$Time,0,19),"%FT%T")        
+         # Add some additional identifying columns. Merge with locs.subset
+         # must have been done.
+  datalist2[[i]]$SiteID<-rep(datasets$LocationId[i],dim(datalist2[[i]])[1])
+  datalist2[[i]]$Site<-rep(datasets$LOCATIONNAME[i],dim(datalist2[[i]])[1])
+  datalist2[[i]]$Parameter<-rep(datasets$Parameter[i],dim(datalist2[[i]])[1])
+  datalist2[[i]]$Units<-rep(datasets$Unit[i],dim(datalist2[[i]])[1])
+  datalist2[[i]]$ParameterName<-rep(datasets$DataId[i],dim(datalist2[[i]])[1])
+}
+dat = do.call(rbind, datalist2) # Combine data gathered in loop into 
+#                                 a dataframe
+#
+# See comments at end of Step 3 if you are getting errors during loop.
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ + + + +
+ + + + + + + + diff --git a/inst/_vignettes/Vignette_Config.html b/inst/_vignettes/Vignette_Config.html new file mode 100644 index 00000000..c2003935 --- /dev/null +++ b/inst/_vignettes/Vignette_Config.html @@ -0,0 +1,1365 @@ + + + + + + + + + + + + + + + +ContDataQC, Custom Config Files + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Introduction

+

ContDataQC uses a configuration file (config.R) that +allows for customization.
+Through the use of a configuration file the user can change many of the +variables in the library used in the processing of data (e.g., the names +of fields, thresholds, and date formats).

+

The ContDataQC() function allows the user to input their own +configuration file. Only variables that are different from the default +need to be added. That is, the default configuration is loaded in the +library and then the user supplied configuration. This allows the user +configuration to overwrite the defaults for that session.

+

The Config.out will output the contents of the configuration file as +used by the package and stored in the environment ContData.env.

+
+
+

Viewing Config Setting

+

The contents of the default configuration file +(extdata/config.ORIG.R) can be output with the Config.Out +function. An HTML (default) or DOCX report is generated along with a +text file (.r) of the configuration file.

+
library(ContDataQC)
+dn_export <- tempdir()
+Config.Out(fun.myDir.export = dn_export)
+
+
+

Editing Parameters

+

The config file that is generated by the Config.Out +function can be modified to contain the custom values. Only modified +values need to be included in the custom configuration file. That is, if +you only want to modify a single variable you only need to include this +single variable in your custom configuration file.

+

However, there are some variables that reference the numeric and +name/labels that also need to be modified. That is, if the user modifies +a named variable then every time that named variable is referenced later +in the config file it also has to be uncommented.

+

This process works because the default configuration is always loaded +with the package at start up. Then any parameters specified in a custom +configuration are loaded and overwrite the defaults.

+

The file can be edited in a text editor or in R-Studio. It is not +recommended to use Word or other word processing programs.

+
+

Example Edit

+

If the user wanted to change the change the following three +parameters and related thresholds then the lines referenced in the table +below would also need to be modified.

+
    +

  • “Air.BP.kpa”

    • +

  • “Water.BP.kpa”

    • +

  • “Water.Level.m”

    • +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ItemAirBPWaterPSensorDepthOther
Units616263
Column Name848688
Labels113116119
Discrete Name161164167
Discrete Label198201204
Data Field, Names244:249
Data Field, Labels271:274
Output Name Order307:310
Flags538541544
Thresholds, Gross, Fail358:363
Thresholds, Gross, Suspect385:390
Thresholds, Spike413:418
Thresholds, Rate of Change442:447
Thresholds, Flat475:482
Thresholds, Flat, combo508:417
Flag Labels539:545
Test Names575
+
+
+

R Conventions

+

R code has a few conventions that the user should be aware of when +making edits to the configuration file.

+
    +

  • Any text after a “#” is treated as comments and not +evaluated.

    • +

  • The arrow, “<-”, is treated as a equal sign when assigning +values to a parameter.

    • +

  • All parameters in the configuration file will be a part of the +environment “ContData.env”.

    +
      +
    • Thus, all parameters start with “ContData.env$”
      • +
    • +
+
+
+

Saving Modified File

+

A custom configuration file should follow the format of the default +configuration file. And should be saved as a “.r” file (or “.R”).

+
+
+
+

Config Contents

+

The contents of the default configuration file +(extdata/config.ORIG.R) are listed below. Sections are created with +similar variables. Please note that the window below is narrow and some +lines wrap to the next line.

+
# User Defined Values
+#
+# User defined values for variables used across multiple functions in this
+# library. The user has the ability to modify the values for names, units, QC
+# thresholds, etc.
+#
+# Saved in a separate environment.
+#
+# https://www.r-bloggers.com/package-wide-variablescache-in-r-packages/
+#
+# Continuous data helper script
+# Default Values
+# Erik.Leppo@tetratech.com (EWL)
+# 20150928
+# 20170323, add 3 parameters (Cond, DO, pH)
+# 20210106, replace gageheight with waterlevel
+# 20230828, updated were QC flag test thresholds, Jen
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User defined variable names for input data
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# It is assumed that this R script is stored in a directory with the data files
+# as subdirectories. This script is intended to be "source"d from the main
+# script.
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# @keywords continuous data
+# @examples
+# #Not intended to be accessed indepedant of function ContDataQC().
+# #Data values only.  No functions.  Add to environment so only visible inside
+# #library.
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# USER may make modifications in this section but not mandatory
+# this section could be sourced so can use between scripts
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#UserDefinedValues <- NA # default value so shows up in help files
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Environment Name ####
+# Environment for use only by ContDataQC library
+ContData.env <- new.env(parent = emptyenv())
+# The above line is not used in custom configurations.
+# assign variables to new environment requires editing of all lines.
+# For example, myDelim <- "_" BECOMES ContData.env$myDelim, "_"
+###
+# list all elements in environment
+# ls(ContData.env)  # all elements in environment
+# as.list(ContData.env)  # all elements in environment with assigned values
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Delimiter in File Names (e.g., test2_AW_201301_20131231.csv)
+ContData.env$myDelim <- "_"
+ContData.env$myDelim_LakeID <- "--"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Acceptable column names for the data ####
+## Basic
+ContData.env$myName.SiteID        <- "SiteID"
+ContData.env$myName.Date          <- "Date"
+ContData.env$myName.Time          <- "Time"
+ContData.env$myName.DateTime      <- "Date.Time"
+#(special characters (e.g., %, space, or /) are converted to "." by R
+# , "deg" converted to "A")
+### IF CHANGE UNITS WILL NEED TO MODIFY THRESHOLDS ###
+ContData.env$myUnits.AirTemp    <- "C" # C or F
+ContData.env$myUnits.WaterTemp  <- ContData.env$myUnits.AirTemp
+ContData.env$myUnits.AirBP      <- "psi"
+ContData.env$myUnits.WaterP     <- ContData.env$myUnits.AirBP
+ContData.env$myUnits.SensorDepth <- "ft"
+ContData.env$myUnits.Discharge  <- "ft3.s"
+ContData.env$myUnits.Cond       <- "uS.cm"
+ContData.env$myUnits.DO         <- "mg.L"
+ContData.env$myUnits.DO.adj     <- "mg.L"
+ContData.env$myUnits.DO.pctsat  <- "percent"
+ContData.env$myUnits.pH         <- "SU"
+ContData.env$myUnits.Turbidity  <- "NTU"
+ContData.env$myUnits.Chlorophylla <- "g.cm3"
+ContData.env$myUnits.WaterLevel <- "ft"
+ContData.env$myUnits.Salinity   <- "ppt"
+## Logger Fields ----
+ContData.env$myName.RowID.Water   <- "Water.RowID"
+ContData.env$myName.LoggerID.Water<- "Water.LoggerID"
+ContData.env$myName.RowID.Air     <- "Air.RowID"
+ContData.env$myName.LoggerID.Air  <- "Air.LoggerID"
+ContData.env$myName.LoggerDeployment  <- "Logger.Deployment"
+ContData.env$myName.LoggerDeployment.start <- "start"
+ContData.env$myName.LoggerDeployment.end   <- "end"
+## Parameters as appear in logger files
+ContData.env$myName.WaterTemp     <- paste0("Water.Temp."
+                                            ,ContData.env$myUnits.WaterTemp)
+# "deg" from HoboWare files sometimes adds "A " in front.  Replace with "." in R
+ContData.env$myName.AirTemp       <- paste0("Air.Temp."
+                                            ,ContData.env$myUnits.AirTemp)
+# "deg" from HoboWare files sometimes adds "A " in front.  Replace with "." in R
+ContData.env$myName.AirBP         <- paste0("Air.BP."
+                                            , ContData.env$myUnits.AirBP)
+ContData.env$myName.WaterP        <- paste0("Water.P."
+                                            , ContData.env$myUnits.WaterP)
+ContData.env$myName.SensorDepth   <- paste0("Sensor.Depth."
+                                            , ContData.env$myUnits.SensorDepth)
+ContData.env$myName.Discharge     <- paste0("Discharge."
+                                            , ContData.env$myUnits.Discharge)
+ContData.env$myName.Cond          <- paste0("Conductivity."
+                                            , ContData.env$myUnits.Cond)
+ContData.env$myName.DO            <- paste0("DO.", ContData.env$myUnits.DO)
+ContData.env$myName.DO.adj    <- paste0("DO.adj.", ContData.env$myUnits.DO.adj)
+ContData.env$myName.DO.pctsat     <- paste0("DO.pctsat.", ContData.env$myUnits.DO.pctsat)
+ContData.env$myName.pH            <- paste0("pH."
+                                            , ContData.env$myUnits.pH)
+ContData.env$myName.Turbidity     <- paste0("Turbidity."
+                                            , ContData.env$myUnits.Turbidity)
+ContData.env$myName.Chlorophylla   <- paste0("Chlorophylla."
+                                            , ContData.env$myUnits.Chlorophylla)
+ContData.env$myName.WaterLevel    <- paste0("Water.Level."
+                                            , ContData.env$myUnits.WaterLevel)
+ContData.env$myName.Salinity      <- paste0("Salinity."
+                                            , ContData.env$myUnits.Salinity)
+## Plot Labels
+ContData.env$myLab.Date           <- "Date"
+ContData.env$myLab.DateTime       <- "Date"
+ContData.env$myLab.WaterTemp      <- paste0("Temperature, Water (deg "
+                                            ,ContData.env$myUnits.WaterTemp
+                                            ,")")
+ContData.env$myLab.AirTemp        <- paste0("Temperature, Air (deg "
+                                            ,ContData.env$myUnits.AirTemp
+                                            ,")")
+ContData.env$myLab.AirBP          <- paste0("Barometric Pressure, Air ("
+                                            ,ContData.env$myUnits.WaterP
+                                            ,")")
+ContData.env$myLab.WaterP         <- paste0("Pressure, Water ("
+                                            ,ContData.env$myUnits.AirBP
+                                            ,")")
+ContData.env$myLab.SensorDepth    <- paste0("Sensor Depth ("
+                                            ,ContData.env$myUnits.SensorDepth
+                                            ,")"
+                                            ,sep="")
+ContData.env$myLab.Temp.BOTH      <- paste0("Temperature (deg "
+                                            ,ContData.env$myUnits.WaterTemp
+                                            ,")")
+ContData.env$myLab.Discharge      <- paste0("Discharge ("
+                                            ,sub("\\.","/"
+                                                ,ContData.env$myUnits.Discharge)
+                                            ,")")  #replace "." with "/"
+ContData.env$myLab.Cond           <- paste0("Conductivity ("
+                                            ,sub("\\.","/"
+                                                 ,ContData.env$myUnits.Cond)
+                                            ,")")    #replace "." with "/"
+ContData.env$myLab.DO             <- paste0("Dissolved Oxygen ("
+                                            ,sub("\\.","/"
+                                                 ,ContData.env$myUnits.DO)
+                                            ,")")  #replace "." with "/"
+ContData.env$myLab.DO.adj         <- paste0("Dissolved Oxygen, adjusted ("
+                                            ,sub("\\.","/"
+                                                 ,ContData.env$myUnits.DO.adj)
+                                            ,")")  #replace "." with "/"
+ContData.env$myLab.DO.pctsat <- paste0("Dissolved Oxygen, percent saturation ("
+                                            ,sub("\\.","/"
+                                            ,ContData.env$myUnits.DO.pctsat)
+                                            ,")")  #replace "." with "/"
+ContData.env$myLab.pH             <- paste0("pH ("
+                                            ,ContData.env$myUnits.pH
+                                            ,")")
+ContData.env$myLab.Turbidity      <- paste0("Turbidity ("
+                                            ,ContData.env$myUnits.Turbidity
+                                            ,")")
+ContData.env$myLab.Chlorophylla   <- paste0("Chlorophyll a ("
+                                            ,sub("\\.","/"
+                                             ,ContData.env$myUnits.Chlorophylla)
+                                            ,")")    #replace "." with "/"
+ContData.env$myLab.WaterLevel     <- paste0("Water Level ("
+                                            ,ContData.env$myUnits.WaterLevel
+                                            ,")")
+ContData.env$myLab.Salinity       <- paste0("Salinity ("
+                                            ,ContData.env$myUnits.Salinity
+                                            ,")")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Discrete Measurements ####
+ContData.env$myPrefix.Discrete          <- "Discrete"
+# Discrete, Names
+ContData.env$myName.Discrete.WaterTemp  <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.WaterTemp
+                                                 ,sep=".")
+ContData.env$myName.Discrete.AirTemp    <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.AirTemp
+                                                 ,sep=".")
+ContData.env$myName.Discrete.AirBP      <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.AirBP
+                                                 ,sep=".")
+ContData.env$myName.Discrete.WaterP     <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.WaterP
+                                                 ,sep=".")
+ContData.env$myName.Discrete.SensorDepth <- paste(ContData.env$myPrefix.Discrete
+                                                ,ContData.env$myName.SensorDepth
+                                                  ,sep=".")
+ContData.env$myName.Discrete.Discharge  <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Discharge
+                                                 ,sep=".")
+ContData.env$myName.Discrete.Cond       <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Cond
+                                                 ,sep=".")
+ContData.env$myName.Discrete.DO         <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.DO
+                                                 ,sep=".")
+ContData.env$myName.Discrete.DO.adj     <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.DO.adj
+                                                 ,sep=".")
+ContData.env$myName.Discrete.DO.pctsat  <- paste(ContData.env$myPrefix.Discrete
+                                                 , ContData.env$myName.DO.pctsat
+                                                 , sep=".")
+ContData.env$myName.Discrete.pH         <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.pH
+                                                 ,sep=".")
+ContData.env$myName.Discrete.Turbidity  <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Turbidity
+                                                 ,sep=".")
+ContData.env$myName.Discrete.Chlorophylla <-paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Chlorophylla
+                                                 ,sep=".")
+ContData.env$myName.Discrete.WaterLevel <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.WaterLevel
+                                                 ,sep=".")
+ContData.env$myName.Discrete.Salinity <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Salinity
+                                                 ,sep=".")
+# Discrete, Labels
+ContData.env$myLab.Discrete.WaterTemp   <- paste(ContData.env$myLab.WaterTemp
+                                                 ,"(Discrete)"
+                                                                         ,sep=" ")
+ContData.env$myLab.Discrete.AirTemp     <- paste(ContData.env$myLab.AirTemp
+                                                 ,"(Discrete)"
+                                                                         ,sep=" ")
+ContData.env$myLab.Discrete.AirBP       <- paste(ContData.env$myLab.AirBP
+                                                 ,"(Discrete)"
+                                                                         ,sep=" ")
+ContData.env$myLab.Discrete.WaterP      <- paste(ContData.env$myLab.WaterP
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.SensorDepth <- paste(ContData.env$myLab.SensorDepth
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Discharge   <- paste(ContData.env$myLab.Discharge
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Cond        <- paste(ContData.env$myLab.Cond
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.DO          <- paste(ContData.env$myLab.DO
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.DO.adj      <- paste(ContData.env$myLab.DO.adj
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.DO.pctsat   <- paste(ContData.env$myLab.DO.pctsat
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.pH          <- paste(ContData.env$myLab.pH
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Turbidity   <- paste(ContData.env$myLab.Turbidity
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Chlorophylla<- paste(ContData.env$myLab.Chlorophylla
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.WaterLevel  <- paste(ContData.env$myLab.WaterLevel
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Salinity  <- paste(ContData.env$myLab.Salinity
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Automated QC stuff ####
+## data type/stages
+ContData.env$myDataQuality.Raw        <- "RAW"
+ContData.env$myDataQuality.QCauto     <- "QCauto"
+ContData.env$myDataQuality.QCmanual   <- "QCmanual"
+ContData.env$myDataQuality.Final      <- "Final"
+ContData.env$myDataQuality.Aggregated <- "Aggregated"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Directory Names ####
+ContData.env$myName.Dir.0Original  <- "Data0_Original"
+ContData.env$myName.Dir.1Raw       <- "Data1_Raw"
+ContData.env$myName.Dir.2QC        <- "Data2_QC"
+ContData.env$myName.Dir.3Agg       <- "Data3_Aggregated"
+ContData.env$myName.Dir.4Stats     <- "Data4_Stats"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Data Fields ####
+ContData.env$myNames.DataFields <- c(ContData.env$myName.WaterTemp
+                                     , ContData.env$myName.AirTemp
+                                                       , ContData.env$myName.AirBP
+                                     , ContData.env$myName.WaterP
+                                     , ContData.env$myName.SensorDepth
+                                     , ContData.env$myName.Discharge
+                                     , ContData.env$myName.Cond
+                                     , ContData.env$myName.DO
+                                                       , ContData.env$myName.DO.adj
+                                                       , ContData.env$myName.DO.pctsat
+                                     , ContData.env$myName.pH
+                                     , ContData.env$myName.Turbidity
+                                     , ContData.env$myName.Chlorophylla
+                                     , ContData.env$myName.WaterLevel
+                                                       , ContData.env$myName.Salinity
+                                     , ContData.env$myName.Discrete.WaterTemp
+                                     , ContData.env$myName.Discrete.AirTemp
+                                     , ContData.env$myName.Discrete.WaterP
+                                     , ContData.env$myName.Discrete.AirBP
+                                     , ContData.env$myName.Discrete.SensorDepth
+                                     , ContData.env$myName.Discrete.Discharge
+                                     , ContData.env$myName.Discrete.Cond
+                                     , ContData.env$myName.Discrete.DO
+                                                       , ContData.env$myName.Discrete.DO.adj
+                                                       , ContData.env$myName.Discrete.DO.pctsat
+                                     , ContData.env$myName.Discrete.pH
+                                     , ContData.env$myName.Discrete.Turbidity
+                                     , ContData.env$myName.Discrete.Chlorophylla
+                                     , ContData.env$myName.Discrete.WaterLevel
+                                                       , ContData.env$myName.Discrete.Salinity
+                                     )
+ContData.env$myNames.DataFields.Lab <- c(ContData.env$myLab.WaterTemp
+                                         , ContData.env$myLab.AirTemp
+                                         , ContData.env$myLab.AirBP
+                                         , ContData.env$myLab.WaterP
+                                         , ContData.env$myLab.SensorDepth
+                                         , ContData.env$myLab.Discharge
+                                         , ContData.env$myLab.Cond
+                                         , ContData.env$myLab.DO
+                                         , ContData.env$myLab.DO.adj
+                                         , ContData.env$myLab.DO.pctsat
+                                         , ContData.env$myLab.pH
+                                         , ContData.env$myLab.Turbidity
+                                         , ContData.env$myLab.Chlorophylla
+                                         , ContData.env$myLab.WaterLevel
+                                         , ContData.env$myLab.Salinity
+                                         , ContData.env$myLab.Discrete.WaterTemp
+                                         , ContData.env$myLab.Discrete.AirTemp
+                                         , ContData.env$myLab.Discrete.WaterP
+                                         , ContData.env$myLab.Discrete.AirBP
+                                      , ContData.env$myLab.Discrete.SensorDepth
+                                         , ContData.env$myLab.Discrete.Discharge
+                                         , ContData.env$myLab.Discrete.Cond
+                                         , ContData.env$myLab.Discrete.DO
+                                      , ContData.env$myLab.Discrete.DO.adj
+                                      , ContData.env$myLab.Discrete.DO.pctsat
+                                         , ContData.env$myLab.Discrete.pH
+                                         , ContData.env$myLab.Discrete.Turbidity
+                                      , ContData.env$myLab.Discrete.Chlorophylla
+                                        , ContData.env$myLab.Discrete.WaterLevel
+                                      , ContData.env$myLab.Discrete.Salinity
+                                         )
+ContData.env$myNames.DataFields.Col <- c("blue","green","gray","gray","black"
+                                         ,"brown","purple","orange","salmon"
+                                         ,"rosybrown","aquamarine1")
+#
+## Name Order (change order below to change order in output file)
+ContData.env$myNames.Order <- c(ContData.env$myName.SiteID
+                                , ContData.env$myName.Date
+                                , ContData.env$myName.Time
+                                , ContData.env$myName.DateTime
+                                , ContData.env$myName.LoggerDeployment
+                                , ContData.env$myName.WaterTemp
+                                , ContData.env$myName.LoggerID.Air
+                                , ContData.env$myName.RowID.Air
+                                , ContData.env$myName.AirTemp
+                                , ContData.env$myName.WaterP
+                                , ContData.env$myName.AirBP
+                                , ContData.env$myName.SensorDepth
+                                , ContData.env$myName.Discharge
+                                , ContData.env$myName.Cond
+                                , ContData.env$myName.DO
+                                , ContData.env$myName.DO.adj
+                                , ContData.env$myName.DO.pctsat
+                                , ContData.env$myName.pH
+                                , ContData.env$myName.Turbidity
+                                , ContData.env$myName.Chlorophylla
+                                , ContData.env$myName.WaterLevel
+                                , ContData.env$myName.Salinity
+                                , ContData.env$myName.LoggerID.Water
+                                , ContData.env$myName.RowID.Water
+                                , ContData.env$myName.Discrete.WaterTemp
+                                , ContData.env$myName.Discrete.AirTemp
+                                , ContData.env$myName.Discrete.WaterP
+                                , ContData.env$myName.Discrete.AirBP
+                                , ContData.env$myName.Discrete.SensorDepth
+                                , ContData.env$myName.Discrete.Discharge
+                                , ContData.env$myName.Discrete.Cond
+                                , ContData.env$myName.Discrete.DO
+                                , ContData.env$myName.Discrete.DO.adj
+                                , ContData.env$myName.Discrete.DO.pctsat
+                                , ContData.env$myName.Discrete.pH
+                                , ContData.env$myName.Discrete.Turbidity
+                                , ContData.env$myName.Discrete.Chlorophylla
+                                , ContData.env$myName.Discrete.WaterLevel
+                                , ContData.env$myName.Discrete.Salinity
+                                )
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Data Quality Flag Values ####
+ContData.env$myFlagVal.Pass    <- "P"
+ContData.env$myFlagVal.NotEval <- "NA"
+ContData.env$myFlagVal.Suspect <- "S"
+ContData.env$myFlagVal.Fail    <- "F"
+ContData.env$myFlagVal.NoData  <- "X"
+ContData.env$myFlagVal.Order   <- c(ContData.env$myFlagVal.Pass
+                                    , ContData.env$myFlagVal.Suspect
+                                    , ContData.env$myFlagVal.Fail
+                                    , ContData.env$myFlagVal.NoData)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# QC Tests and Calculations ####
+#http://stackoverflow.com/questions/16143700/pasting-two-vectors-with-combinations-of-all-vectors-elements
+#myNames.QCTests.Calcs.combo <- as.vector(t(outer(myNames.QCTests,myNames.QCTests.Calcs,paste,sep=".")))
+# combine so can check for and remove later.
+#myNames.DataFields.QCTests.Calcs.combo <- as.vector(t(outer(myNames.DataFields,myNames.QCTests.Calcs.combo,paste,sep=".")))
+# Data Quality Flag Thresholds
+# _QC, Gross----
+## Gross Min/Max, Fail (equipment)
+### Examines values as outliers versus threholds
+### if value >= Hi or <= Lo then flagged as "Fail"
+ContData.env$myThresh.Gross.Fail.Hi.WaterTemp    <- 30
+ContData.env$myThresh.Gross.Fail.Lo.WaterTemp    <- -1
+ContData.env$myThresh.Gross.Fail.Hi.AirTemp      <- 38
+ContData.env$myThresh.Gross.Fail.Lo.AirTemp      <- -25
+ContData.env$myThresh.Gross.Fail.Hi.AirBP        <- 15
+ContData.env$myThresh.Gross.Fail.Lo.AirBP        <- 13
+ContData.env$myThresh.Gross.Fail.Hi.WaterP       <- 17
+ContData.env$myThresh.Gross.Fail.Lo.WaterP       <- 13
+ContData.env$myThresh.Gross.Fail.Hi.SensorDepth  <- 10^5 # dependent upon stream size
+ContData.env$myThresh.Gross.Fail.Lo.SensorDepth  <- -1   # dependent upon stream size
+ContData.env$myThresh.Gross.Fail.Hi.Discharge    <- 10^5 # dependent upon stream size
+ContData.env$myThresh.Gross.Fail.Lo.Discharge    <- -1   # dependent upon stream size
+ContData.env$myThresh.Gross.Fail.Hi.Cond         <- 1500 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Cond         <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.DO           <- 18   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.DO           <- 1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.DO.adj       <- 18   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.DO.adj       <- 1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.DO.pctsat    <- 120   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.DO.pctsat    <- -1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.pH           <- 12   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.pH           <- 3    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.Turbidity    <- 10^5 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Turbidity    <- -1   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.Chlorophylla <- 10^5 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Chlorophylla <- -1   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.WaterLevel   <- ContData.env$myThresh.Gross.Fail.Hi.SensorDepth
+ContData.env$myThresh.Gross.Fail.Lo.WaterLevel   <- ContData.env$myThresh.Gross.Fail.Lo.SensorDepth
+ContData.env$myThresh.Gross.Fail.Hi.Salinity      <- 41
+ContData.env$myThresh.Gross.Fail.Lo.Salinity     <- 2
+## Gross Min/Max, Suspect (extreme)
+### Examines values as outliers versus threholds
+### if value >= Hi or <= Lo then flagged as "Suspect"
+ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp    <- 25
+ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp    <- -0.5
+ContData.env$myThresh.Gross.Suspect.Hi.AirTemp      <- 35
+ContData.env$myThresh.Gross.Suspect.Lo.AirTemp      <- -23
+ContData.env$myThresh.Gross.Suspect.Hi.AirBP        <- 14.8
+ContData.env$myThresh.Gross.Suspect.Lo.AirBP        <- 13.0
+ContData.env$myThresh.Gross.Suspect.Hi.WaterP       <- 16.8
+ContData.env$myThresh.Gross.Suspect.Lo.WaterP       <- 13.5
+ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth  <- 10^3 # dependent upon stream size
+ContData.env$myThresh.Gross.Suspect.Lo.SensorDepth  <- 0    # dependent upon stream size
+ContData.env$myThresh.Gross.Suspect.Hi.Discharge    <- 10^3 # dependent upon stream size
+ContData.env$myThresh.Gross.Suspect.Lo.Discharge    <- -1   # dependent upon stream size
+ContData.env$myThresh.Gross.Suspect.Hi.Cond         <- 1200 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Cond         <- 20   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.DO           <- 16   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.DO           <- 2    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.DO.adj       <- 18   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.DO.adj       <- 2   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.DO.pctsat    <- 100   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.DO.pctsat    <- 0    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.pH           <- 11   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.pH           <- 4    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.Turbidity    <- 10^3 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Turbidity    <- -1   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.Chlorophylla <- 10^3 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Chlorophylla <- 1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.WaterLevel   <- ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth
+ContData.env$myThresh.Gross.Suspect.Lo.WaterLevel   <- ContData.env$myThresh.Gross.Suspect.Lo.SensorDept
+ContData.env$myThresh.Gross.Suspect.Hi.Salinity    <- 37
+ContData.env$myThresh.Gross.Suspect.Lo.Salinity    <- 3
+# _QC, Spike ----
+## Spike thresholds (absolute change)
+### Examines difference between consecutive measurements
+### if delta >= Hi then flagged as "Fail"
+### if delta >= Lo then flagged as "Suspect"
+ContData.env$myThresh.Spike.Hi.WaterTemp    <- 2
+ContData.env$myThresh.Spike.Lo.WaterTemp    <- 1.5
+ContData.env$myThresh.Spike.Hi.AirTemp      <- 10
+ContData.env$myThresh.Spike.Lo.AirTemp      <- 8
+ContData.env$myThresh.Spike.Hi.AirBP        <- 0.25
+ContData.env$myThresh.Spike.Lo.AirBP        <- 0.15
+ContData.env$myThresh.Spike.Hi.WaterP       <- 0.7
+ContData.env$myThresh.Spike.Lo.WaterP       <- 0.5
+ContData.env$myThresh.Spike.Hi.SensorDepth  <- 10^4 # dependent upon stream size
+ContData.env$myThresh.Spike.Lo.SensorDepth  <- 10^3 # dependent upon stream size
+ContData.env$myThresh.Spike.Hi.Discharge    <- 10^4 # dependent upon stream size
+ContData.env$myThresh.Spike.Lo.Discharge    <- 10^3 # dependent upon stream size
+ContData.env$myThresh.Spike.Hi.Cond         <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Cond         <- 5    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.DO           <- 4   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.DO           <- 2    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.DO.adj       <- 4   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.DO.adj       <- 2    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.DO.pctsat    <- 25   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.DO.pctsat    <- 10    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.pH           <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.pH           <- 5    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.Turbidity    <- 10^4 # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Turbidity    <- 10^3 # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.Chlorophylla <- 10^4 # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Chlorophylla <- 10^3 # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.WaterLevel  <- ContData.env$myThresh.Spike.Hi.SensorDepth
+ContData.env$myThresh.Spike.Lo.WaterLevel  <- ContData.env$myThresh.Spike.Lo.SensorDepth
+ContData.env$myThresh.Spike.Hi.Salinity    <- 5
+ContData.env$myThresh.Spike.Lo.Salinity    <- 3
+# _QC, ROC----
+## Rate of Change (relative change)
+### Examines SD over "period" and difference in consecutive values
+### If delta >= SD.number * SD then flagged as "Suspect"
+ContData.env$myDefault.RoC.SD.number  <- 6
+ContData.env$myDefault.RoC.SD.period  <- 25 #hours
+ContData.env$myThresh.RoC.SD.number.WaterTemp    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.WaterTemp    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.AirTemp      <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.AirTemp      <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.AirBP        <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.AirBP        <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.WaterP       <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.WaterP       <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.SensorDepth  <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.SensorDepth  <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Discharge    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Discharge    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Cond         <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Cond         <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.DO           <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.DO           <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.DO.adj       <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.DO.adj       <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.DO.pctsat    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.DO.pctsat    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.pH           <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.pH           <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Turbidity    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Turbidity    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Chlorophylla <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Chlorophylla <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.WaterLevel   <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.WaterLevel   <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Salinity     <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Salinity     <- ContData.env$myDefault.RoC.SD.period
+# QC, Flat Line----
+## No Change (flat-line)
+### Examines consecutive values within "Tolerance" of each other
+### If number of consecutive values >= Hi then flagged as "Fail"
+### If number of consecutive values >= Lo (and < Hi) then flagged as "Suspect"
+ContData.env$myDefault.Flat.Hi        <- 30  # maximum is myThresh.Flat.MaxComp
+ContData.env$myDefault.Flat.Lo        <- 15
+ContData.env$myDefault.Flat.Tolerance <- 0.01 # set to one sigdig less than measurements.  Check with fivenum(x)
+ContData.env$myThresh.Flat.Hi.WaterTemp           <- 100
+ContData.env$myThresh.Flat.Lo.WaterTemp           <- 60
+ContData.env$myThresh.Flat.Tolerance.WaterTemp    <- 0.01
+ContData.env$myThresh.Flat.Hi.AirTemp             <- 60
+ContData.env$myThresh.Flat.Lo.AirTemp             <- 30
+ContData.env$myThresh.Flat.Tolerance.AirTemp      <- 0.01
+ContData.env$myThresh.Flat.Hi.AirBP               <- 15
+ContData.env$myThresh.Flat.Lo.AirBP               <- 10
+ContData.env$myThresh.Flat.Tolerance.AirBP        <- 0.001
+ContData.env$myThresh.Flat.Hi.WaterP              <- 15
+ContData.env$myThresh.Flat.Lo.WaterP              <- 10
+ContData.env$myThresh.Flat.Tolerance.WaterP       <- 0.001
+ContData.env$myThresh.Flat.Hi.SensorDepth         <- 60
+ContData.env$myThresh.Flat.Lo.SensorDepth         <- 30
+ContData.env$myThresh.Flat.Tolerance.SensorDepth  <- 0.01
+ContData.env$myThresh.Flat.Hi.Discharge           <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Discharge           <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Discharge    <- 0.01
+ContData.env$myThresh.Flat.Hi.Cond                <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Cond                <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Cond         <- 0.01
+ContData.env$myThresh.Flat.Hi.DO                  <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.DO                  <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.DO           <- 0.01
+ContData.env$myThresh.Flat.Hi.DO.adj              <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.DO.adj              <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.DO.adj       <- 0.01
+ContData.env$myThresh.Flat.Hi.DO.pctsat           <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.DO.pctsat           <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.DO.pctsat    <- 0.01
+ContData.env$myThresh.Flat.Hi.pH                  <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.pH                  <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.pH           <- 0.01
+ContData.env$myThresh.Flat.Hi.Turbidity           <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Turbidity           <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Turbidity    <- 0.01
+ContData.env$myThresh.Flat.Hi.Chlorophylla        <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Chlorophylla        <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Chlorophylla <- 0.01
+ContData.env$myThresh.Flat.Hi.WaterLevel          <- ContData.env$myThresh.Flat.Hi.SensorDepth
+ContData.env$myThresh.Flat.Lo.WaterLevel          <- ContData.env$myThresh.Flat.Lo.SensorDepth
+ContData.env$myThresh.Flat.Tolerance.WaterLevel   <- ContData.env$myThresh.Flat.Tolerance.SensorDepth
+ContData.env$myThresh.Flat.Hi.Salinity            <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Salinity            <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Salinity     <- 0.01
+#
+ContData.env$myThresh.Flat.MaxComp    <- max(ContData.env$myThresh.Flat.Hi.WaterTemp
+                                             , ContData.env$myThresh.Flat.Hi.AirTemp
+                                             , ContData.env$myThresh.Flat.Hi.AirBP
+                                             , ContData.env$myThresh.Flat.Hi.WaterP
+                                             , ContData.env$myThresh.Flat.Hi.SensorDepth
+                                             , ContData.env$myThresh.Flat.Hi.Discharge
+                                             , ContData.env$myThresh.Flat.Hi.Cond
+                                             , ContData.env$myThresh.Flat.Hi.DO
+                                             , ContData.env$myThresh.Flat.Hi.DO.adj
+                                             , ContData.env$myThresh.Flat.Hi.DO.pctsat
+                                             , ContData.env$myThresh.Flat.Hi.pH
+                                             , ContData.env$myThresh.Flat.Hi.Turbidity
+                                             , ContData.env$myThresh.Flat.Hi.Chlorophylla
+                                             , ContData.env$myThresh.Flat.Hi.WaterLevel
+                                             , ContData.env$myThresh.Flat.Hi.Salinity
+                                             )
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Data Fields with Flags ####
+ContData.env$myName.Flag        <- "Flag" # flag prefix
+ContData.env$myNames.Cols4Flags <- c(ContData.env$myName.DateTime
+                                     , ContData.env$myNames.DataFields)
+ContData.env$myNames.Flags      <- paste(ContData.env$myName.Flag
+                                         , ContData.env$myNames.Cols4Flags
+                                         , sep=".")
+# define ones using in the calling script
+
+## flag labels
+ContData.env$myName.Flag.DateTime     <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.DateTime
+                                               , sep=".")
+ContData.env$myName.Flag.WaterTemp    <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.WaterTemp
+                                               , sep=".")
+ContData.env$myName.Flag.AirTemp      <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.AirTemp
+                                               , sep=".")
+ContData.env$myName.Flag.AirBP        <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.AirBP
+                                               , sep=".")
+ContData.env$myName.Flag.WaterP       <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.WaterP
+                                               , sep=".")
+ContData.env$myName.Flag.SensorDepth  <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.SensorDepth
+                                               , sep=".")
+ContData.env$myName.Flag.Discharge    <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.Discharge
+                                               , sep=".")
+ContData.env$myName.Flag.Cond         <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.Cond
+                                               , sep=".")
+ContData.env$myName.Flag.DO           <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.DO
+                                               , sep=".")
+ContData.env$myName.Flag.DO.adj       <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.DO.adj
+                                               , sep=".")
+ContData.env$myName.Flag.DO.pctsat    <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.DO.pctsat
+                                               , sep=".")
+ContData.env$myName.Flag.pH           <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.pH
+                                               , sep=".")
+ContData.env$myName.Flag.Turbidity    <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.Turbidity
+                                               , sep=".")
+ContData.env$myName.Flag.Chlorophylla <- paste(ContData.env$myName.Flag
+                                             , ContData.env$myName.Chlorophylla
+                                               , sep=".")
+ContData.env$myName.Flag.WaterLevel   <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.WaterLevel
+                                               , sep=".")
+ContData.env$myName.Flag.Salinity     <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.Salinity
+                                               , sep=".")
+# Data Quality Test Names
+ContData.env$myNames.QCTests <- c("Gross","Spike","RoC","Flat")
+ContData.env$myNames.QCCalcs <- c("SD.Time"
+                                  , "SD"
+                                  , "SDxN"
+                                  , paste("n"
+                                          , seq_len(
+                                            ContData.env$myThresh.Flat.MaxComp)
+                                          , sep=".")
+                                  , "flat.Lo"
+                                  , "flat.Hi")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Exceedance values for stats (default to Gross-Suspect-Hi value) ####
+ContData.env$myExceed.WaterTemp   <-
+  ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp
+ContData.env$myExceed.AirTemp     <-
+  ContData.env$myThresh.Gross.Suspect.Hi.AirTemp
+ContData.env$myExceed.SensorDepth <-
+  ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Date and Time Formats ####
+ContData.env$myFormat.Date           <- "%Y-%m-%d"
+ContData.env$myFormat.Time           <- "%H:%M:%S"
+ContData.env$myFormat.DateTime       <- "%Y-%m-%d %H:%M:%S"
+ContData.env$DateRange.Start.Default <- format(as.Date("1900-01-01")
+                                               , ContData.env$myFormat.Date)
+                                               #YYYY-MM-DD
+ContData.env$DateRange.End.Default   <- format(Sys.Date()
+                                               , ContData.env$myFormat.Date)
+# Time Zone, used in Gage script in dataRetrieval, OlsonNames()
+ContData.env$myTZ <- Sys.timezone() #"America/New_York" (local time zone)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Time Frames (MM-DD) ####
+ContData.env$myTimeFrame.Annual.Start        <- "0101"
+ContData.env$myTimeFrame.Annual.End          <- "1231"
+ContData.env$myTimeFrame.WaterYear.Start     <- "1001"
+#ContData.env$myTimeFrame.WaterYear.End      <- "0930"
+ContData.env$myTimeFrame.Season.Spring.Start <- "0301"
+#ContData.env$myTimeFrame.Season.Spring.End  <- "0531"
+ContData.env$myTimeFrame.Season.Summer.Start <- "0601"
+#ContData.env$myTimeFrame.Season.Summer.End  <- "0831"
+ContData.env$myTimeFrame.Season.Fall.Start   <- "0901"
+#ContData.env$myTimeFrame.Season.Fall.End    <- "1130"
+ContData.env$myTimeFrame.Season.Winter.Start <- "1201"
+#ContData.env$myTimeFrame.Season.Winter.End  <- "0228" #but 0229 in leap year, use start dates only
+# Time Frame Names
+ContData.env$myName.Yr       <- "Year"
+ContData.env$myName.YrMo     <- "YearMonth"
+ContData.env$myName.Mo       <- "Month"
+ContData.env$myName.MoDa     <- "MonthDay"
+ContData.env$myName.JuDa     <- "JulianDay"
+ContData.env$myName.Day      <- "Day"
+ContData.env$myName.Season   <- "Season"
+ContData.env$myName.YrSeason <- "YearSeason"
+# for summary stats
+ContData.env$myNames.Fields.TimePeriods <- c(ContData.env$myName.Yr
+                                                                ,ContData.env$myName.YrMo
+                                                                ,ContData.env$myName.Mo
+                                                                ,ContData.env$myName.MoDa
+                                                                ,ContData.env$myName.JuDa
+                                                                ,ContData.env$myName.Season
+                                                                ,ContData.env$myName.YrSeason)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Exclude Trigger ####
+# Trigger for Stats to exclude (TRUE) or include (FALSE) where flag = "fail"
+ContData.env$myStats.Fails.Exclude <- TRUE  #FALSE #TRUE
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Report Format ####
+ContData.env$myReport.Format <- "docx" # "html" or "docx"
+# DOCX requires Pandoc.
+ContData.env$myReport.Dir <- file.path(system.file(package="ContDataQC"), "rmd")
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/inst/_vignettes/Vignette_ContDataQC.html b/inst/_vignettes/Vignette_ContDataQC.html new file mode 100644 index 00000000..e5f2e8de --- /dev/null +++ b/inst/_vignettes/Vignette_ContDataQC.html @@ -0,0 +1,3074 @@ + + + + + + + + + + + + + + + +ContDataQC Vignette + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Introduction

+

ContDataQC was created by Tetra Tech, Inc. for USEPA in +2017 to meet the needs of the Regional Monitoring Networks to apply a +minimum level of quality control to the continuous monitoring data they +were collecting.

+

It is envisioned that this library can be a living package and will +add additional functions and routines in the future.

+

Many of the examples in this vignette are included in the examples in +the corresponding functions. Each function in the +ContDataQC library includes example data.

+

The typical user of this package is envisioned as a user of Hobo or +other continuous data loggers. Measured parameters are water +temperature, sensor depth, air temperature, water pressure, and air +pressure. The typical user will retrieve data from their data logger +using Hoboware. Once the data is extracted from the logger and save some +minimal cleanup to the column heades is performed before using the +‘ContDataQC’ R library.

+

There is a configuration file (config.R) that allows for +customization.

+
+
+

Installation

+

The R library is saved on GitHub for ease of updating and +distribution. Issues can be tracked, fixed, and code made available for +download. Current users can update the library using the same code used +to install the library (see below). Devtools is required for the +installation.

+
install.packages("devtools")
+library(devtools)
+install_github("leppott/ContDataQC")
+

To contact the authors directly email or .

+

Occasionaly R does not import all of the necessary dependent +libraries. Below is code to install all of the necessary libraries for +using the ContDataQC library.

+
# install all dependant libraries manually
+# libraries to be installed
+data.packages = c(
+                  "dataRetrieval"   # loads USGS data into R
+                  "zoo",
+                  "knitr",
+                  "survival",
+                  "doBy",
+                  "rmarkdown"
+                  )
+
+# install packages via a 'for' loop
+for (i in 1:length(data.packages)) {
+  #install.packages("doBy",dependencies=TRUE)
+  install.packages(data.packages[i])
+} # end loop
+
+print("Task complete.  Check statements above for errors.")
+

An additional dependency that needs to be installed separately is +Pandoc. This package is used to create the Reports. See R code below to +install Pandoc. The package ‘installr’ allows for installation in a +single step. If this doesn’t work you may need help from your IT +department using the web links listed below. For users of RStudio Pandoc +installs with RStudio so no extra install is needed.

+

In version 2.0.1.9048 (2017-11-16) of the ContDataQC +package report format was added as a variable. The default is “html”. +This can be changed in config.R. If you do not have Pandoc installed the +package will still work but the reports in docx format will not be +created (will show an error) unless you change the format to “html”.

+
# Install Pandoc (for docx Reports)
+# (if using recent version of RStudio do not have to install separately)
+install.packages("installr") # not needed if already have this package.
+require(installr)
+install.pandoc()
+# The above won't work if don't have admin rights on your computer.
+# Alternative = Download the file below and have your IT dept install for you.
+# https://github.com/jgm/pandoc/releases/download/1.16.0.2/pandoc-1.16.0.2-windows.msi
+# For help for installing via command window:
+# http://www.intowindows.com/how-to-run-msi-file-as-administrator-from-command-prompt-in-windows/
+

During the summer of 2017 USGS changed their webservices from http to +https.
+This necessitated a change in the R library dataRetrieval. +As of the date of this Vignette the 2.7.2 version of +dataRetrieval on CRAN is not the most current and fails +when retrieving data inside of ContDataQC. The error +statement is “Error in x[[1]] : subscript out of bounds”. If using the +GetGageData operation in the ContDataQC() function it is necessary to +get the most current version (>=2.7.3) of dataRetrieval +(see code below). The ContDataQC library was tweaked on 11 +September 2017 (version v2.0.1.9035) to work with the revised version of +dataRetrieval (v2.7.3).

+
# inter-CRAN release hosted on USGS website
+install.packages("dataRetrieval", repos="https://owi.usgs.gov/R")
+# from GitHub
+# install.packages("devtools")
+# library(devtools)
+# install_github("USGS-R/dataRetrieval")
+
+
+

Contents

+
    +

  1. Preparing Raw Data

    2. +

  2. ContDataQC Operations

    3. +

  3. Other Features

    4. +
+
+
+

Preparing Raw Data

+

The ContDataQC library makes some assumptions on file +names and structure.

+
+

File Names

+

Should be of the format StationID_DataType_StartDate_EndDate.csv. The +delimiter between sections of the filename can be changed in config.R +but not the order or the file type (csv). Date formats in the files can +be set with config.R but the date format in the filename must be in the +format YYYYMMDD (no separators).

+

Data types are Air, Water, Gage, and single letter combinations +(i.e., AW, AG, and WG).

+
+
+

File Structure

+

The first line are the field (column) headers. These can be set in +config.R.

+
+
+
+

The ContDataQC Function

+

The ContDataQC() function is the main focus of this library. It is +intended to allow the user to perform multiple functions from a single +command with different inputs.

+
+

Operations

+

The operations of the ContDataQC() function are listed below but will +be explained in more detail in their own section. GetGageData +QCRaw Aggregate SummaryStats

+

The library assumes that there are the following folders in the +working directory: Data0_Original Data1_RAW Data2_QC +Data3_Aggregated *Data4_Stats

+

The folder “Data0_Original” is for the files exported from Hoboware +(or other data logger software). These are the unedited files.

+

The folder “Data1_RAW” is for the files exported from Hoboware (or +other data logger software). This folder contains the files as input for +the QC portion of the library. These files may have been modified +manually or with the formatHobo function.

+

The folder “Data2_QC” is for the output of the QCRaw operation of the +library and the input for the Aggregate operation.

+

The folder “Data3_Aggregated” is the output of the Aggregate +operation and the input for the Stats operation.

+

The folder “Data4_Stats” is the output of the SummaryStats +operation.

+

For each operation associated with the ContDataQC() function a report +is generated as an MS Word DOCX file.

+

The code below should be run before using the examples of each +operation. The code below sets up the directories and some parameters +for use in the later code.

+
# Parameters
+Selection.Operation <- c("GetGageData"
+                         , "QCRaw"
+                         , "Aggregate"
+                         , "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   , "Data1_RAW"
+                   , "Data2_QC"
+                   , "Data3_Aggregated"
+                   , "Data4_Stats")
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+
+# Create data directories
+myDir.create <- file.path(myDir.BASE, Selection.SUB[1])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+myDir.create <- file.path(myDir.BASE, Selection.SUB[2])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+myDir.create <- file.path(myDir.BASE, Selection.SUB[3])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+myDir.create <- file.path(myDir.BASE, Selection.SUB[4])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+myDir.create <- file.path(myDir.BASE, Selection.SUB[5])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+
+# Save example data (assumes myDir.BASE directory exists)
+myData <- data_raw_test2_AW_20130426_20130725
+  write.csv(myData, file.path(myDir.BASE
+                              , Selection.SUB[2]
+                              , "test2_AW_20130426_20130725.csv"))
+myData <- data_raw_test2_AW_20130725_20131015
+  write.csv(myData, file.path(myDir.BASE
+                              , Selection.SUB[2]
+                              , "test2_AW_20130725_20131015.csv"))
+myData <- data_raw_test2_AW_20140901_20140930
+  write.csv(myData, file.path(myDir.BASE
+                              , Selection.SUB[2]
+                              , "test2_AW_20140901_20140930.csv"))
+myData <- data_raw_test4_AW_20160418_20160726
+  write.csv(myData, file.path(myDir.BASE
+                              , Selection.SUB[2]
+                              , "test4_AW_20160418_20160726.csv"))
+myFile <- "config.TZ.Central.R"
+  file.copy(file.path(path.package("ContDataQC"), "extdata", myFile)
+            , file.path(myDir.BASE, Selection.SUB[2], myFile))
+
+

GetGageData

+

The GetGageData operation of the ContDataQC() function retrieves USGS +gage data based on user input. Example code and console output is +below.

+
library(ContDataQC)
+# Parameters
+Selection.Operation <- c("GetGageData"
+                         , "QCRaw"
+                         , "Aggregate"
+                         , "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   , "Data1_RAW"
+                   , "Data2_QC"
+                   , "Data3_Aggregated"
+                   , "Data4_Stats")
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+
## [1] "C:\\Users\\ERIK~1.LEP\\AppData\\Local\\Temp\\RtmpiIu3CZ"
+
# Create data directories
+myDir.create <- file.path(myDir.BASE, Selection.SUB[1])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+
## [1] TRUE
+
myDir.create <- file.path(myDir.BASE, Selection.SUB[2])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+
## [1] TRUE
+
myDir.create <- file.path(myDir.BASE, Selection.SUB[3])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+
## [1] TRUE
+
myDir.create <- file.path(myDir.BASE, Selection.SUB[4])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+
## [1] TRUE
+
myDir.create <- file.path(myDir.BASE, Selection.SUB[5])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+
## [1] TRUE
+
# Get Gage Data
+myData.Operation       <- "GetGageData" #Selection.Operation[1]
+myData.SiteID          <- "01187300" # Hubbard River near West Hartland, CT
+myData.Type            <- "Gage" # Selection.Type[4] 
+myData.DateRange.Start <- "2013-01-01"
+myData.DateRange.End   <- "2014-12-31"
+myDir.import           <- ""
+myDir.export           <- file.path(myDir.BASE, "Data1_RAW")
+ContDataQC(myData.Operation
+           , myData.SiteID
+           , myData.Type
+           , myData.DateRange.Start
+           , myData.DateRange.End
+           , myDir.import
+           , myDir.export)
+
## [1] "Total items to process = 1"
+
## GET: https://waterservices.usgs.gov/nwis/site/?seriesCatalogOutput=true&sites=01187300&format=rdb
+
## 
+## [1] "Getting available data; 01187300."
+## 
+##     agency_cd  site_no                           station_nm site_tp_cd
+## 530      USGS 01187300 HUBBARD RIVER NEAR WEST HARTLAND, CT         ST
+## 531      USGS 01187300 HUBBARD RIVER NEAR WEST HARTLAND, CT         ST
+## 532      USGS 01187300 HUBBARD RIVER NEAR WEST HARTLAND, CT         ST
+##     dec_lat_va dec_long_va coord_acy_cd dec_coord_datum_cd alt_va alt_acy_va
+## 530    42.0375   -72.93933            H              NAD83 594.57       0.01
+## 531    42.0375   -72.93933            H              NAD83 594.57       0.01
+## 532    42.0375   -72.93933            H              NAD83 594.57       0.01
+##     alt_datum_cd   huc_cd data_type_cd parm_cd stat_cd ts_id loc_web_ds
+## 530       NGVD29 01080207           uv   00010    <NA> 66869         NA
+## 531       NGVD29 01080207           uv   00060    <NA> 66866         NA
+## 532       NGVD29 01080207           uv   00065    <NA> 66867         NA
+##     medium_grp_cd parm_grp_cd   srs_id access_cd begin_date   end_date count_nu
+## 530           wat        <NA>  1645597         0 2015-11-04 2018-08-08     1008
+## 531           wat        <NA>  1645423         0 1990-10-01 2024-12-17    12496
+## 532           wat        <NA> 17164583         0 2007-10-01 2024-12-17     6287
+
## GET: https://nwis.waterservices.usgs.gov/nwis/iv/?site=01187300&format=waterml,1.1&ParameterCd=00010,00060,00065&startDT=2013-01-01&endDT=2014-12-31
+
## 
+## [1] "Processing item 1 of 1, COMPLETE, 01187300."
+## 
+## [1] "Task COMPLETE; 0.21 min."
+

Example of getting gage data in a different time zone specified in +user modified configuration file.

+
library(ContDataQC)
+# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   ,"Data1_RAW"
+                   ,"Data2_QC"
+                   ,"Data3_Aggregated"
+                   ,"Data4_Stats")
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+# Get Gage Data (central time zone)
+myData.Operation       <- "GetGageData" #Selection.Operation[1]
+myData.SiteID          <- "07032000" # Mississippi River at Memphis, TN
+myData.Type            <- Selection.Type[4] #"Gage"
+myData.DateRange.Start <- "2013-01-01"
+myData.DateRange.End   <- "2014-12-31"
+myDir.import           <- ""
+myDir.export           <- file.path(myDir.BASE, Selection.SUB[2])
+# include path if not in working directory
+myConfig               <- file.path(myDir.BASE, Selection.SUB[2]
+                                    , "config.TZ.central.R")
+ContDataQC(myData.Operation
+           , myData.SiteID
+           , myData.Type
+           , myData.DateRange.Start
+           , myData.DateRange.End
+           , myDir.import
+           , myDir.export
+           , myConfig)
+
+
+

QCRaw

+

This is operation is the main focus of the ContDataQC +library. The raw continuous data files are examined for multiple QC +tests and the results saved in a new data file (CSV) and a summary +report (DOCX file) are generated.

+
library(ContDataQC)
+# Parameters
+Selection.Operation <- c("GetGageData"
+                         , "QCRaw"
+                         , "Aggregate"
+                         , "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   , "Data1_RAW"
+                   , "Data2_QC"
+                   , "Data3_Aggregated"
+                   , "Data4_Stats")
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+# QC Raw Data
+myData.Operation       <- "QCRaw" #Selection.Operation[2]
+myData.SiteID          <- "test2"
+myData.Type            <- Selection.Type[3] #"AW"
+myData.DateRange.Start <- "2013-01-01"
+myData.DateRange.End   <- "2014-12-31"
+myDir.import           <- file.path(myDir.BASE, Selection.SUB[2]) #"Data1_RAW"
+myDir.export           <- file.path(myDir.BASE, Selection.SUB[3]) #"Data2_QC"
+myReport.format        <- "docx"
+ContDataQC(myData.Operation
+           , myData.SiteID
+           , myData.Type
+           , myData.DateRange.Start
+           , myData.DateRange.End
+           , myDir.import
+           , myDir.export
+           , fun.myReport.format = myReport.format)
+

In some cases two sensors are used (one for air and one for water) +and the timing of the sampling is off between the two. The library +handles this fine.
+Example data below. For this example the report output was changed to +“html”.

+
library(ContDataQC)
+# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   ,"Data1_RAW"
+                   ,"Data2_QC"
+                   ,"Data3_Aggregated"
+                   ,"Data4_Stats")
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+# QC Raw Data (offset collection times for air and water sensors)
+myData.Operation       <- "QCRaw" #Selection.Operation[2]
+myData.SiteID          <- "test4"
+myData.Type            <- Selection.Type[3] #"AW"
+myData.DateRange.Start <- "2016-04-28"
+myData.DateRange.End   <- "2016-07-26"
+myDir.import           <- file.path(myDir.BASE, Selection.SUB[2]) #"Data1_RAW"
+myDir.export           <- file.path(myDir.BASE, Selection.SUB[3]) #"Data2_QC"
+myReport.format        <- "html"
+ContDataQC(myData.Operation
+           , myData.SiteID
+           , myData.Type
+           , myData.DateRange.Start
+           , myData.DateRange.End
+           , myDir.import
+           , myDir.export
+           , fun.myReport.format = myReport.format)
+
+
+

Aggregate

+

After QC the files are ready for analysis. The Aggregate operation +allows the user to combine (or split) files across time periods. No +report format was specified so the default “docx” format will be +generated.

+
library(contDataQC)
+# Parameters
+Selection.Operation <- c("GetGageData"
+                         , "QCRaw"
+                         , "Aggregate"
+                         , "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   , "Data1_RAW"
+                   , "Data2_QC"
+                   , "Data3_Aggregated"
+                   , "Data4_Stats")
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+# Aggregate Data
+myData.Operation       <- "Aggregate" #Selection.Operation[3]
+myData.SiteID          <- "test2"
+myData.Type            <- Selection.Type[3] #"AW"
+myData.DateRange.Start <- "2013-01-01"
+myData.DateRange.End   <- "2014-12-31"
+myDir.import           <- file.path(myDir.BASE, Selection.SUB[3]) #"Data2_QC"
+myDir.export           <- file.path(myDir.BASE, Selection.SUB[4]) 
+#"Data3_Aggregated"
+#Leave off myReport.format and get default (docx).
+ContDataQC(myData.Operation
+           , myData.SiteID
+           , myData.Type
+           , myData.DateRange.Start
+           , myData.DateRange.End
+           , myDir.import
+           , myDir.export)
+
+
+

SummaryStats

+

The SummaryStats operation calculates a number of statistics on the +data for each sampling day. No report format was specified so the +default “docx” format will be generated.

+
library(ContDataQC)
+# Parameters
+Selection.Operation <- c("GetGageData"
+                         , "QCRaw"
+                         , "Aggregate"
+                         , "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   , "Data1_RAW"
+                   , "Data2_QC"
+                   , "Data3_Aggregated"
+                   , "Data4_Stats")
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+# Summary Stats
+myData.Operation       <- "SummaryStats" #Selection.Operation[4]
+myData.SiteID          <- "test2"
+myData.Type            <- Selection.Type[3] #"AW"
+myData.DateRange.Start <- "2013-01-01"
+myData.DateRange.End   <- "2014-12-31"
+myDir.import           <- file.path(myDir.BASE, Selection.SUB[4]) 
+#"Data3_Aggregated"
+myDir.export           <- file.path(myDir.BASE, Selection.SUB[5]) #"Data4_Stats"
+#Leave off myReport.format and get default (docx).
+ContDataQC(myData.Operation
+           , myData.SiteID
+           , myData.Type
+           , myData.DateRange.Start
+           , myData.DateRange.End
+           , myDir.import
+           , myDir.export)
+
+
+
+

Single File Versions of ContDataQC Operatations

+

The base version of the ContDataQC() function searches the specified +directories for files that match the data type and date range. This set +up does not work for everyone. Included in the ContDataQC +package are “file” versions of the same QCRaw, Aggregate, and +SummaryStat functions. These work on a vector of file names (so multiple +files can be processed this way).Different report options are used in +each example in the code below.

+
library(ContDataQC)
+#~~~~~~~~~~~~~~
+# File Versions
+#~~~~~~~~~~~~~~
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+
+# QC Data
+myData.Operation <- "QCRaw" #Selection.Operation[2]
+#myFile <- "test2_AW_20130426_20130725.csv"
+myFile <- c("test2_AW_20130426_20130725.csv"
+           , "test2_AW_20130725_20131015.csv"
+           , "test2_AW_20140901_20140930.csv")
+myDir.import <- file.path(myDir.BASE, "Data1_RAW")
+myDir.export <- file.path(myDir.BASE, "Data2_QC")
+myReport.format <- "docx"
+ContDataQC(myData.Operation
+           , fun.myDir.import = myDir.import
+           , fun.myDir.export = myDir.export
+           , fun.myFile = myFile
+           , fun.myReport.format = myReport.format)
+
+# Aggregate Data
+myData.Operation <- "Aggregate" #Selection.Operation[3]
+myFile <- c("QC_test2_Aw_20130426_20130725.csv"
+           , "QC_test2_Aw_20130725_20131015.csv"
+           , "QC_test2_Aw_20140901_20140930.csv")
+myDir.import <- file.path(myDir.BASE, "Data2_QC")
+myDir.export <- file.path(myDir.BASE, "Data3_Aggregated")
+myReport.format <- "html"
+ContDataQC(myData.Operation
+           , fun.myDir.import = myDir.import
+           , fun.myDir.export = myDir.export
+           , fun.myFile = myFile
+           , fun.myReport.format = myReport.format)
+
+# Summary Stats
+myData.Operation <- "SummaryStats" #Selection.Operation[4]
+myFile <- "QC_test2_AW_20130426_20130725.csv"
+#myFile <- c("QC_test2_AW_20130426_20130725.csv"
+#            , "QC_test2_AW_20130725_20131015.csv"
+#            , "QC_test2_AW_20140901_20140930.csv")
+myDir.import <- file.path(myDir.BASE, "Data2_QC")
+myDir.export <- file.path(myDir.BASE, "Data4_Stats")
+#Leave off myReport.format and get default (docx).
+ContDataQC(myData.Operation
+           , fun.myDir.import = myDir.import
+           , fun.myDir.export = myDir.export
+           , fun.myFile = myFile)
+
+
+

Other Features

+

There are other features built into the ContDataQC +library. These are outlined below.

+
+
+

Configuration File

+

Through the use of a configuration file the user can change many of +the variables in the library (e.g., the names of fields, thresholds, and +date formats).

+

The ContDataQC() function allows the user to input their own +configuration file. Only variables that are different from the default +need to be added. That is, the default configuration is loaded in the +library and then the user supplied configuration. This allows the user +configuration to overwrite the defaults for that session.

+

The Config.out will output the contents of the configuration file as +used by the package and stored in the environment ContData.env.

+

The contents of the configuration file (extdata/config.ORIG.R) are +listed below.

+
# User Defined Values
+#
+# User defined values for variables used across multiple functions in this
+# library. The user has the ability to modify the values for names, units, QC
+# thresholds, etc.
+#
+# Saved in a separate environment.
+#
+# https://www.r-bloggers.com/package-wide-variablescache-in-r-packages/
+#
+# Continuous data helper script
+# Default Values
+# Erik.Leppo@tetratech.com (EWL)
+# 20150928
+# 20170323, add 3 parameters (Cond, DO, pH)
+# 20210106, replace gageheight with waterlevel
+# 20230828, updated were QC flag test thresholds, Jen
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User defined variable names for input data
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# It is assumed that this R script is stored in a directory with the data files
+# as subdirectories. This script is intended to be "source"d from the main
+# script.
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# @keywords continuous data
+# @examples
+# #Not intended to be accessed indepedant of function ContDataQC().
+# #Data values only.  No functions.  Add to environment so only visible inside
+# #library.
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# USER may make modifications in this section but not mandatory
+# this section could be sourced so can use between scripts
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#UserDefinedValues <- NA # default value so shows up in help files
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Environment Name ####
+# Environment for use only by ContDataQC library
+ContData.env <- new.env(parent = emptyenv())
+# The above line is not used in custom configurations.
+# assign variables to new environment requires editing of all lines.
+# For example, myDelim <- "_" BECOMES ContData.env$myDelim, "_"
+###
+# list all elements in environment
+# ls(ContData.env)  # all elements in environment
+# as.list(ContData.env)  # all elements in environment with assigned values
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Delimiter in File Names (e.g., test2_AW_201301_20131231.csv)
+ContData.env$myDelim <- "_"
+ContData.env$myDelim_LakeID <- "--"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Acceptable column names for the data ####
+## Basic
+ContData.env$myName.SiteID        <- "SiteID"
+ContData.env$myName.Date          <- "Date"
+ContData.env$myName.Time          <- "Time"
+ContData.env$myName.DateTime      <- "Date.Time"
+#(special characters (e.g., %, space, or /) are converted to "." by R
+# , "deg" converted to "A")
+### IF CHANGE UNITS WILL NEED TO MODIFY THRESHOLDS ###
+ContData.env$myUnits.AirTemp    <- "C" # C or F
+ContData.env$myUnits.WaterTemp  <- ContData.env$myUnits.AirTemp
+ContData.env$myUnits.AirBP      <- "psi"
+ContData.env$myUnits.WaterP     <- ContData.env$myUnits.AirBP
+ContData.env$myUnits.SensorDepth <- "ft"
+ContData.env$myUnits.Discharge  <- "ft3.s"
+ContData.env$myUnits.Cond       <- "uS.cm"
+ContData.env$myUnits.DO         <- "mg.L"
+ContData.env$myUnits.DO.adj     <- "mg.L"
+ContData.env$myUnits.DO.pctsat  <- "percent"
+ContData.env$myUnits.pH         <- "SU"
+ContData.env$myUnits.Turbidity  <- "NTU"
+ContData.env$myUnits.Chlorophylla <- "g.cm3"
+ContData.env$myUnits.WaterLevel <- "ft"
+ContData.env$myUnits.Salinity   <- "ppt"
+## Logger Fields ----
+ContData.env$myName.RowID.Water   <- "Water.RowID"
+ContData.env$myName.LoggerID.Water<- "Water.LoggerID"
+ContData.env$myName.RowID.Air     <- "Air.RowID"
+ContData.env$myName.LoggerID.Air  <- "Air.LoggerID"
+ContData.env$myName.LoggerDeployment  <- "Logger.Deployment"
+ContData.env$myName.LoggerDeployment.start <- "start"
+ContData.env$myName.LoggerDeployment.end   <- "end"
+## Parameters as appear in logger files
+ContData.env$myName.WaterTemp     <- paste0("Water.Temp."
+                                            ,ContData.env$myUnits.WaterTemp)
+# "deg" from HoboWare files sometimes adds "A " in front.  Replace with "." in R
+ContData.env$myName.AirTemp       <- paste0("Air.Temp."
+                                            ,ContData.env$myUnits.AirTemp)
+# "deg" from HoboWare files sometimes adds "A " in front.  Replace with "." in R
+ContData.env$myName.AirBP         <- paste0("Air.BP."
+                                            , ContData.env$myUnits.AirBP)
+ContData.env$myName.WaterP        <- paste0("Water.P."
+                                            , ContData.env$myUnits.WaterP)
+ContData.env$myName.SensorDepth   <- paste0("Sensor.Depth."
+                                            , ContData.env$myUnits.SensorDepth)
+ContData.env$myName.Discharge     <- paste0("Discharge."
+                                            , ContData.env$myUnits.Discharge)
+ContData.env$myName.Cond          <- paste0("Conductivity."
+                                            , ContData.env$myUnits.Cond)
+ContData.env$myName.DO            <- paste0("DO.", ContData.env$myUnits.DO)
+ContData.env$myName.DO.adj    <- paste0("DO.adj.", ContData.env$myUnits.DO.adj)
+ContData.env$myName.DO.pctsat     <- paste0("DO.pctsat.", ContData.env$myUnits.DO.pctsat)
+ContData.env$myName.pH            <- paste0("pH."
+                                            , ContData.env$myUnits.pH)
+ContData.env$myName.Turbidity     <- paste0("Turbidity."
+                                            , ContData.env$myUnits.Turbidity)
+ContData.env$myName.Chlorophylla   <- paste0("Chlorophylla."
+                                            , ContData.env$myUnits.Chlorophylla)
+ContData.env$myName.WaterLevel    <- paste0("Water.Level."
+                                            , ContData.env$myUnits.WaterLevel)
+ContData.env$myName.Salinity      <- paste0("Salinity."
+                                            , ContData.env$myUnits.Salinity)
+## Plot Labels
+ContData.env$myLab.Date           <- "Date"
+ContData.env$myLab.DateTime       <- "Date"
+ContData.env$myLab.WaterTemp      <- paste0("Temperature, Water (deg "
+                                            ,ContData.env$myUnits.WaterTemp
+                                            ,")")
+ContData.env$myLab.AirTemp        <- paste0("Temperature, Air (deg "
+                                            ,ContData.env$myUnits.AirTemp
+                                            ,")")
+ContData.env$myLab.AirBP          <- paste0("Barometric Pressure, Air ("
+                                            ,ContData.env$myUnits.WaterP
+                                            ,")")
+ContData.env$myLab.WaterP         <- paste0("Pressure, Water ("
+                                            ,ContData.env$myUnits.AirBP
+                                            ,")")
+ContData.env$myLab.SensorDepth    <- paste0("Sensor Depth ("
+                                            ,ContData.env$myUnits.SensorDepth
+                                            ,")"
+                                            ,sep="")
+ContData.env$myLab.Temp.BOTH      <- paste0("Temperature (deg "
+                                            ,ContData.env$myUnits.WaterTemp
+                                            ,")")
+ContData.env$myLab.Discharge      <- paste0("Discharge ("
+                                            ,sub("\\.","/"
+                                                ,ContData.env$myUnits.Discharge)
+                                            ,")")  #replace "." with "/"
+ContData.env$myLab.Cond           <- paste0("Conductivity ("
+                                            ,sub("\\.","/"
+                                                 ,ContData.env$myUnits.Cond)
+                                            ,")")    #replace "." with "/"
+ContData.env$myLab.DO             <- paste0("Dissolved Oxygen ("
+                                            ,sub("\\.","/"
+                                                 ,ContData.env$myUnits.DO)
+                                            ,")")  #replace "." with "/"
+ContData.env$myLab.DO.adj         <- paste0("Dissolved Oxygen, adjusted ("
+                                            ,sub("\\.","/"
+                                                 ,ContData.env$myUnits.DO.adj)
+                                            ,")")  #replace "." with "/"
+ContData.env$myLab.DO.pctsat <- paste0("Dissolved Oxygen, percent saturation ("
+                                            ,sub("\\.","/"
+                                            ,ContData.env$myUnits.DO.pctsat)
+                                            ,")")  #replace "." with "/"
+ContData.env$myLab.pH             <- paste0("pH ("
+                                            ,ContData.env$myUnits.pH
+                                            ,")")
+ContData.env$myLab.Turbidity      <- paste0("Turbidity ("
+                                            ,ContData.env$myUnits.Turbidity
+                                            ,")")
+ContData.env$myLab.Chlorophylla   <- paste0("Chlorophyll a ("
+                                            ,sub("\\.","/"
+                                             ,ContData.env$myUnits.Chlorophylla)
+                                            ,")")    #replace "." with "/"
+ContData.env$myLab.WaterLevel     <- paste0("Water Level ("
+                                            ,ContData.env$myUnits.WaterLevel
+                                            ,")")
+ContData.env$myLab.Salinity       <- paste0("Salinity ("
+                                            ,ContData.env$myUnits.Salinity
+                                            ,")")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Discrete Measurements ####
+ContData.env$myPrefix.Discrete          <- "Discrete"
+# Discrete, Names
+ContData.env$myName.Discrete.WaterTemp  <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.WaterTemp
+                                                 ,sep=".")
+ContData.env$myName.Discrete.AirTemp    <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.AirTemp
+                                                 ,sep=".")
+ContData.env$myName.Discrete.AirBP      <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.AirBP
+                                                 ,sep=".")
+ContData.env$myName.Discrete.WaterP     <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.WaterP
+                                                 ,sep=".")
+ContData.env$myName.Discrete.SensorDepth <- paste(ContData.env$myPrefix.Discrete
+                                                ,ContData.env$myName.SensorDepth
+                                                  ,sep=".")
+ContData.env$myName.Discrete.Discharge  <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Discharge
+                                                 ,sep=".")
+ContData.env$myName.Discrete.Cond       <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Cond
+                                                 ,sep=".")
+ContData.env$myName.Discrete.DO         <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.DO
+                                                 ,sep=".")
+ContData.env$myName.Discrete.DO.adj     <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.DO.adj
+                                                 ,sep=".")
+ContData.env$myName.Discrete.DO.pctsat  <- paste(ContData.env$myPrefix.Discrete
+                                                 , ContData.env$myName.DO.pctsat
+                                                 , sep=".")
+ContData.env$myName.Discrete.pH         <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.pH
+                                                 ,sep=".")
+ContData.env$myName.Discrete.Turbidity  <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Turbidity
+                                                 ,sep=".")
+ContData.env$myName.Discrete.Chlorophylla <-paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Chlorophylla
+                                                 ,sep=".")
+ContData.env$myName.Discrete.WaterLevel <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.WaterLevel
+                                                 ,sep=".")
+ContData.env$myName.Discrete.Salinity <- paste(ContData.env$myPrefix.Discrete
+                                                 ,ContData.env$myName.Salinity
+                                                 ,sep=".")
+# Discrete, Labels
+ContData.env$myLab.Discrete.WaterTemp   <- paste(ContData.env$myLab.WaterTemp
+                                                 ,"(Discrete)"
+                                                                         ,sep=" ")
+ContData.env$myLab.Discrete.AirTemp     <- paste(ContData.env$myLab.AirTemp
+                                                 ,"(Discrete)"
+                                                                         ,sep=" ")
+ContData.env$myLab.Discrete.AirBP       <- paste(ContData.env$myLab.AirBP
+                                                 ,"(Discrete)"
+                                                                         ,sep=" ")
+ContData.env$myLab.Discrete.WaterP      <- paste(ContData.env$myLab.WaterP
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.SensorDepth <- paste(ContData.env$myLab.SensorDepth
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Discharge   <- paste(ContData.env$myLab.Discharge
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Cond        <- paste(ContData.env$myLab.Cond
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.DO          <- paste(ContData.env$myLab.DO
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.DO.adj      <- paste(ContData.env$myLab.DO.adj
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.DO.pctsat   <- paste(ContData.env$myLab.DO.pctsat
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.pH          <- paste(ContData.env$myLab.pH
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Turbidity   <- paste(ContData.env$myLab.Turbidity
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Chlorophylla<- paste(ContData.env$myLab.Chlorophylla
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.WaterLevel  <- paste(ContData.env$myLab.WaterLevel
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+ContData.env$myLab.Discrete.Salinity  <- paste(ContData.env$myLab.Salinity
+                                                 ,"(Discrete)"
+                                                 ,sep=" ")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Automated QC stuff ####
+## data type/stages
+ContData.env$myDataQuality.Raw        <- "RAW"
+ContData.env$myDataQuality.QCauto     <- "QCauto"
+ContData.env$myDataQuality.QCmanual   <- "QCmanual"
+ContData.env$myDataQuality.Final      <- "Final"
+ContData.env$myDataQuality.Aggregated <- "Aggregated"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Directory Names ####
+ContData.env$myName.Dir.0Original  <- "Data0_Original"
+ContData.env$myName.Dir.1Raw       <- "Data1_Raw"
+ContData.env$myName.Dir.2QC        <- "Data2_QC"
+ContData.env$myName.Dir.3Agg       <- "Data3_Aggregated"
+ContData.env$myName.Dir.4Stats     <- "Data4_Stats"
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Data Fields ####
+ContData.env$myNames.DataFields <- c(ContData.env$myName.WaterTemp
+                                     , ContData.env$myName.AirTemp
+                                                       , ContData.env$myName.AirBP
+                                     , ContData.env$myName.WaterP
+                                     , ContData.env$myName.SensorDepth
+                                     , ContData.env$myName.Discharge
+                                     , ContData.env$myName.Cond
+                                     , ContData.env$myName.DO
+                                                       , ContData.env$myName.DO.adj
+                                                       , ContData.env$myName.DO.pctsat
+                                     , ContData.env$myName.pH
+                                     , ContData.env$myName.Turbidity
+                                     , ContData.env$myName.Chlorophylla
+                                     , ContData.env$myName.WaterLevel
+                                                       , ContData.env$myName.Salinity
+                                     , ContData.env$myName.Discrete.WaterTemp
+                                     , ContData.env$myName.Discrete.AirTemp
+                                     , ContData.env$myName.Discrete.WaterP
+                                     , ContData.env$myName.Discrete.AirBP
+                                     , ContData.env$myName.Discrete.SensorDepth
+                                     , ContData.env$myName.Discrete.Discharge
+                                     , ContData.env$myName.Discrete.Cond
+                                     , ContData.env$myName.Discrete.DO
+                                                       , ContData.env$myName.Discrete.DO.adj
+                                                       , ContData.env$myName.Discrete.DO.pctsat
+                                     , ContData.env$myName.Discrete.pH
+                                     , ContData.env$myName.Discrete.Turbidity
+                                     , ContData.env$myName.Discrete.Chlorophylla
+                                     , ContData.env$myName.Discrete.WaterLevel
+                                                       , ContData.env$myName.Discrete.Salinity
+                                     )
+ContData.env$myNames.DataFields.Lab <- c(ContData.env$myLab.WaterTemp
+                                         , ContData.env$myLab.AirTemp
+                                         , ContData.env$myLab.AirBP
+                                         , ContData.env$myLab.WaterP
+                                         , ContData.env$myLab.SensorDepth
+                                         , ContData.env$myLab.Discharge
+                                         , ContData.env$myLab.Cond
+                                         , ContData.env$myLab.DO
+                                         , ContData.env$myLab.DO.adj
+                                         , ContData.env$myLab.DO.pctsat
+                                         , ContData.env$myLab.pH
+                                         , ContData.env$myLab.Turbidity
+                                         , ContData.env$myLab.Chlorophylla
+                                         , ContData.env$myLab.WaterLevel
+                                         , ContData.env$myLab.Salinity
+                                         , ContData.env$myLab.Discrete.WaterTemp
+                                         , ContData.env$myLab.Discrete.AirTemp
+                                         , ContData.env$myLab.Discrete.WaterP
+                                         , ContData.env$myLab.Discrete.AirBP
+                                      , ContData.env$myLab.Discrete.SensorDepth
+                                         , ContData.env$myLab.Discrete.Discharge
+                                         , ContData.env$myLab.Discrete.Cond
+                                         , ContData.env$myLab.Discrete.DO
+                                      , ContData.env$myLab.Discrete.DO.adj
+                                      , ContData.env$myLab.Discrete.DO.pctsat
+                                         , ContData.env$myLab.Discrete.pH
+                                         , ContData.env$myLab.Discrete.Turbidity
+                                      , ContData.env$myLab.Discrete.Chlorophylla
+                                        , ContData.env$myLab.Discrete.WaterLevel
+                                      , ContData.env$myLab.Discrete.Salinity
+                                         )
+ContData.env$myNames.DataFields.Col <- c("blue","green","gray","gray","black"
+                                         ,"brown","purple","orange","salmon"
+                                         ,"rosybrown","aquamarine1")
+#
+## Name Order (change order below to change order in output file)
+ContData.env$myNames.Order <- c(ContData.env$myName.SiteID
+                                , ContData.env$myName.Date
+                                , ContData.env$myName.Time
+                                , ContData.env$myName.DateTime
+                                , ContData.env$myName.LoggerDeployment
+                                , ContData.env$myName.WaterTemp
+                                , ContData.env$myName.LoggerID.Air
+                                , ContData.env$myName.RowID.Air
+                                , ContData.env$myName.AirTemp
+                                , ContData.env$myName.WaterP
+                                , ContData.env$myName.AirBP
+                                , ContData.env$myName.SensorDepth
+                                , ContData.env$myName.Discharge
+                                , ContData.env$myName.Cond
+                                , ContData.env$myName.DO
+                                , ContData.env$myName.DO.adj
+                                , ContData.env$myName.DO.pctsat
+                                , ContData.env$myName.pH
+                                , ContData.env$myName.Turbidity
+                                , ContData.env$myName.Chlorophylla
+                                , ContData.env$myName.WaterLevel
+                                , ContData.env$myName.Salinity
+                                , ContData.env$myName.LoggerID.Water
+                                , ContData.env$myName.RowID.Water
+                                , ContData.env$myName.Discrete.WaterTemp
+                                , ContData.env$myName.Discrete.AirTemp
+                                , ContData.env$myName.Discrete.WaterP
+                                , ContData.env$myName.Discrete.AirBP
+                                , ContData.env$myName.Discrete.SensorDepth
+                                , ContData.env$myName.Discrete.Discharge
+                                , ContData.env$myName.Discrete.Cond
+                                , ContData.env$myName.Discrete.DO
+                                , ContData.env$myName.Discrete.DO.adj
+                                , ContData.env$myName.Discrete.DO.pctsat
+                                , ContData.env$myName.Discrete.pH
+                                , ContData.env$myName.Discrete.Turbidity
+                                , ContData.env$myName.Discrete.Chlorophylla
+                                , ContData.env$myName.Discrete.WaterLevel
+                                , ContData.env$myName.Discrete.Salinity
+                                )
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+## Data Quality Flag Values ####
+ContData.env$myFlagVal.Pass    <- "P"
+ContData.env$myFlagVal.NotEval <- "NA"
+ContData.env$myFlagVal.Suspect <- "S"
+ContData.env$myFlagVal.Fail    <- "F"
+ContData.env$myFlagVal.NoData  <- "X"
+ContData.env$myFlagVal.Order   <- c(ContData.env$myFlagVal.Pass
+                                    , ContData.env$myFlagVal.Suspect
+                                    , ContData.env$myFlagVal.Fail
+                                    , ContData.env$myFlagVal.NoData)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# QC Tests and Calculations ####
+#http://stackoverflow.com/questions/16143700/pasting-two-vectors-with-combinations-of-all-vectors-elements
+#myNames.QCTests.Calcs.combo <- as.vector(t(outer(myNames.QCTests,myNames.QCTests.Calcs,paste,sep=".")))
+# combine so can check for and remove later.
+#myNames.DataFields.QCTests.Calcs.combo <- as.vector(t(outer(myNames.DataFields,myNames.QCTests.Calcs.combo,paste,sep=".")))
+# Data Quality Flag Thresholds
+# _QC, Gross----
+## Gross Min/Max, Fail (equipment)
+### Examines values as outliers versus threholds
+### if value >= Hi or <= Lo then flagged as "Fail"
+ContData.env$myThresh.Gross.Fail.Hi.WaterTemp    <- 30
+ContData.env$myThresh.Gross.Fail.Lo.WaterTemp    <- -1
+ContData.env$myThresh.Gross.Fail.Hi.AirTemp      <- 38
+ContData.env$myThresh.Gross.Fail.Lo.AirTemp      <- -25
+ContData.env$myThresh.Gross.Fail.Hi.AirBP        <- 15
+ContData.env$myThresh.Gross.Fail.Lo.AirBP        <- 13
+ContData.env$myThresh.Gross.Fail.Hi.WaterP       <- 17
+ContData.env$myThresh.Gross.Fail.Lo.WaterP       <- 13
+ContData.env$myThresh.Gross.Fail.Hi.SensorDepth  <- 10^5 # dependent upon stream size
+ContData.env$myThresh.Gross.Fail.Lo.SensorDepth  <- -1   # dependent upon stream size
+ContData.env$myThresh.Gross.Fail.Hi.Discharge    <- 10^5 # dependent upon stream size
+ContData.env$myThresh.Gross.Fail.Lo.Discharge    <- -1   # dependent upon stream size
+ContData.env$myThresh.Gross.Fail.Hi.Cond         <- 1500 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Cond         <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.DO           <- 18   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.DO           <- 1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.DO.adj       <- 18   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.DO.adj       <- 1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.DO.pctsat    <- 120   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.DO.pctsat    <- -1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.pH           <- 12   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.pH           <- 3    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.Turbidity    <- 10^5 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Turbidity    <- -1   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.Chlorophylla <- 10^5 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Lo.Chlorophylla <- -1   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Fail.Hi.WaterLevel   <- ContData.env$myThresh.Gross.Fail.Hi.SensorDepth
+ContData.env$myThresh.Gross.Fail.Lo.WaterLevel   <- ContData.env$myThresh.Gross.Fail.Lo.SensorDepth
+ContData.env$myThresh.Gross.Fail.Hi.Salinity      <- 41
+ContData.env$myThresh.Gross.Fail.Lo.Salinity     <- 2
+## Gross Min/Max, Suspect (extreme)
+### Examines values as outliers versus threholds
+### if value >= Hi or <= Lo then flagged as "Suspect"
+ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp    <- 25
+ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp    <- -0.5
+ContData.env$myThresh.Gross.Suspect.Hi.AirTemp      <- 35
+ContData.env$myThresh.Gross.Suspect.Lo.AirTemp      <- -23
+ContData.env$myThresh.Gross.Suspect.Hi.AirBP        <- 14.8
+ContData.env$myThresh.Gross.Suspect.Lo.AirBP        <- 13.0
+ContData.env$myThresh.Gross.Suspect.Hi.WaterP       <- 16.8
+ContData.env$myThresh.Gross.Suspect.Lo.WaterP       <- 13.5
+ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth  <- 10^3 # dependent upon stream size
+ContData.env$myThresh.Gross.Suspect.Lo.SensorDepth  <- 0    # dependent upon stream size
+ContData.env$myThresh.Gross.Suspect.Hi.Discharge    <- 10^3 # dependent upon stream size
+ContData.env$myThresh.Gross.Suspect.Lo.Discharge    <- -1   # dependent upon stream size
+ContData.env$myThresh.Gross.Suspect.Hi.Cond         <- 1200 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Cond         <- 20   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.DO           <- 16   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.DO           <- 2    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.DO.adj       <- 18   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.DO.adj       <- 2   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.DO.pctsat    <- 100   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.DO.pctsat    <- 0    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.pH           <- 11   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.pH           <- 4    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.Turbidity    <- 10^3 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Turbidity    <- -1   # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.Chlorophylla <- 10^3 # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Lo.Chlorophylla <- 1    # this threshold has not been closely evaluated
+ContData.env$myThresh.Gross.Suspect.Hi.WaterLevel   <- ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth
+ContData.env$myThresh.Gross.Suspect.Lo.WaterLevel   <- ContData.env$myThresh.Gross.Suspect.Lo.SensorDept
+ContData.env$myThresh.Gross.Suspect.Hi.Salinity    <- 37
+ContData.env$myThresh.Gross.Suspect.Lo.Salinity    <- 3
+# _QC, Spike ----
+## Spike thresholds (absolute change)
+### Examines difference between consecutive measurements
+### if delta >= Hi then flagged as "Fail"
+### if delta >= Lo then flagged as "Suspect"
+ContData.env$myThresh.Spike.Hi.WaterTemp    <- 2
+ContData.env$myThresh.Spike.Lo.WaterTemp    <- 1.5
+ContData.env$myThresh.Spike.Hi.AirTemp      <- 10
+ContData.env$myThresh.Spike.Lo.AirTemp      <- 8
+ContData.env$myThresh.Spike.Hi.AirBP        <- 0.25
+ContData.env$myThresh.Spike.Lo.AirBP        <- 0.15
+ContData.env$myThresh.Spike.Hi.WaterP       <- 0.7
+ContData.env$myThresh.Spike.Lo.WaterP       <- 0.5
+ContData.env$myThresh.Spike.Hi.SensorDepth  <- 10^4 # dependent upon stream size
+ContData.env$myThresh.Spike.Lo.SensorDepth  <- 10^3 # dependent upon stream size
+ContData.env$myThresh.Spike.Hi.Discharge    <- 10^4 # dependent upon stream size
+ContData.env$myThresh.Spike.Lo.Discharge    <- 10^3 # dependent upon stream size
+ContData.env$myThresh.Spike.Hi.Cond         <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Cond         <- 5    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.DO           <- 4   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.DO           <- 2    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.DO.adj       <- 4   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.DO.adj       <- 2    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.DO.pctsat    <- 25   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.DO.pctsat    <- 10    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.pH           <- 10   # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.pH           <- 5    # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.Turbidity    <- 10^4 # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Turbidity    <- 10^3 # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.Chlorophylla <- 10^4 # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Lo.Chlorophylla <- 10^3 # this threshold has not been closely evaluated
+ContData.env$myThresh.Spike.Hi.WaterLevel  <- ContData.env$myThresh.Spike.Hi.SensorDepth
+ContData.env$myThresh.Spike.Lo.WaterLevel  <- ContData.env$myThresh.Spike.Lo.SensorDepth
+ContData.env$myThresh.Spike.Hi.Salinity    <- 5
+ContData.env$myThresh.Spike.Lo.Salinity    <- 3
+# _QC, ROC----
+## Rate of Change (relative change)
+### Examines SD over "period" and difference in consecutive values
+### If delta >= SD.number * SD then flagged as "Suspect"
+ContData.env$myDefault.RoC.SD.number  <- 6
+ContData.env$myDefault.RoC.SD.period  <- 25 #hours
+ContData.env$myThresh.RoC.SD.number.WaterTemp    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.WaterTemp    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.AirTemp      <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.AirTemp      <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.AirBP        <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.AirBP        <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.WaterP       <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.WaterP       <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.SensorDepth  <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.SensorDepth  <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Discharge    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Discharge    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Cond         <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Cond         <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.DO           <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.DO           <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.DO.adj       <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.DO.adj       <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.DO.pctsat    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.DO.pctsat    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.pH           <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.pH           <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Turbidity    <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Turbidity    <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Chlorophylla <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Chlorophylla <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.WaterLevel   <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.WaterLevel   <- ContData.env$myDefault.RoC.SD.period
+ContData.env$myThresh.RoC.SD.number.Salinity     <- ContData.env$myDefault.RoC.SD.number
+ContData.env$myThresh.RoC.SD.period.Salinity     <- ContData.env$myDefault.RoC.SD.period
+# QC, Flat Line----
+## No Change (flat-line)
+### Examines consecutive values within "Tolerance" of each other
+### If number of consecutive values >= Hi then flagged as "Fail"
+### If number of consecutive values >= Lo (and < Hi) then flagged as "Suspect"
+ContData.env$myDefault.Flat.Hi        <- 30  # maximum is myThresh.Flat.MaxComp
+ContData.env$myDefault.Flat.Lo        <- 15
+ContData.env$myDefault.Flat.Tolerance <- 0.01 # set to one sigdig less than measurements.  Check with fivenum(x)
+ContData.env$myThresh.Flat.Hi.WaterTemp           <- 100
+ContData.env$myThresh.Flat.Lo.WaterTemp           <- 60
+ContData.env$myThresh.Flat.Tolerance.WaterTemp    <- 0.01
+ContData.env$myThresh.Flat.Hi.AirTemp             <- 60
+ContData.env$myThresh.Flat.Lo.AirTemp             <- 30
+ContData.env$myThresh.Flat.Tolerance.AirTemp      <- 0.01
+ContData.env$myThresh.Flat.Hi.AirBP               <- 15
+ContData.env$myThresh.Flat.Lo.AirBP               <- 10
+ContData.env$myThresh.Flat.Tolerance.AirBP        <- 0.001
+ContData.env$myThresh.Flat.Hi.WaterP              <- 15
+ContData.env$myThresh.Flat.Lo.WaterP              <- 10
+ContData.env$myThresh.Flat.Tolerance.WaterP       <- 0.001
+ContData.env$myThresh.Flat.Hi.SensorDepth         <- 60
+ContData.env$myThresh.Flat.Lo.SensorDepth         <- 30
+ContData.env$myThresh.Flat.Tolerance.SensorDepth  <- 0.01
+ContData.env$myThresh.Flat.Hi.Discharge           <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Discharge           <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Discharge    <- 0.01
+ContData.env$myThresh.Flat.Hi.Cond                <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Cond                <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Cond         <- 0.01
+ContData.env$myThresh.Flat.Hi.DO                  <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.DO                  <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.DO           <- 0.01
+ContData.env$myThresh.Flat.Hi.DO.adj              <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.DO.adj              <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.DO.adj       <- 0.01
+ContData.env$myThresh.Flat.Hi.DO.pctsat           <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.DO.pctsat           <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.DO.pctsat    <- 0.01
+ContData.env$myThresh.Flat.Hi.pH                  <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.pH                  <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.pH           <- 0.01
+ContData.env$myThresh.Flat.Hi.Turbidity           <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Turbidity           <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Turbidity    <- 0.01
+ContData.env$myThresh.Flat.Hi.Chlorophylla        <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Chlorophylla        <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Chlorophylla <- 0.01
+ContData.env$myThresh.Flat.Hi.WaterLevel          <- ContData.env$myThresh.Flat.Hi.SensorDepth
+ContData.env$myThresh.Flat.Lo.WaterLevel          <- ContData.env$myThresh.Flat.Lo.SensorDepth
+ContData.env$myThresh.Flat.Tolerance.WaterLevel   <- ContData.env$myThresh.Flat.Tolerance.SensorDepth
+ContData.env$myThresh.Flat.Hi.Salinity            <- ContData.env$myDefault.Flat.Hi * 2
+ContData.env$myThresh.Flat.Lo.Salinity            <- ContData.env$myDefault.Flat.Lo * 2
+ContData.env$myThresh.Flat.Tolerance.Salinity     <- 0.01
+#
+ContData.env$myThresh.Flat.MaxComp    <- max(ContData.env$myThresh.Flat.Hi.WaterTemp
+                                             , ContData.env$myThresh.Flat.Hi.AirTemp
+                                             , ContData.env$myThresh.Flat.Hi.AirBP
+                                             , ContData.env$myThresh.Flat.Hi.WaterP
+                                             , ContData.env$myThresh.Flat.Hi.SensorDepth
+                                             , ContData.env$myThresh.Flat.Hi.Discharge
+                                             , ContData.env$myThresh.Flat.Hi.Cond
+                                             , ContData.env$myThresh.Flat.Hi.DO
+                                             , ContData.env$myThresh.Flat.Hi.DO.adj
+                                             , ContData.env$myThresh.Flat.Hi.DO.pctsat
+                                             , ContData.env$myThresh.Flat.Hi.pH
+                                             , ContData.env$myThresh.Flat.Hi.Turbidity
+                                             , ContData.env$myThresh.Flat.Hi.Chlorophylla
+                                             , ContData.env$myThresh.Flat.Hi.WaterLevel
+                                             , ContData.env$myThresh.Flat.Hi.Salinity
+                                             )
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Data Fields with Flags ####
+ContData.env$myName.Flag        <- "Flag" # flag prefix
+ContData.env$myNames.Cols4Flags <- c(ContData.env$myName.DateTime
+                                     , ContData.env$myNames.DataFields)
+ContData.env$myNames.Flags      <- paste(ContData.env$myName.Flag
+                                         , ContData.env$myNames.Cols4Flags
+                                         , sep=".")
+# define ones using in the calling script
+
+## flag labels
+ContData.env$myName.Flag.DateTime     <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.DateTime
+                                               , sep=".")
+ContData.env$myName.Flag.WaterTemp    <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.WaterTemp
+                                               , sep=".")
+ContData.env$myName.Flag.AirTemp      <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.AirTemp
+                                               , sep=".")
+ContData.env$myName.Flag.AirBP        <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.AirBP
+                                               , sep=".")
+ContData.env$myName.Flag.WaterP       <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.WaterP
+                                               , sep=".")
+ContData.env$myName.Flag.SensorDepth  <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.SensorDepth
+                                               , sep=".")
+ContData.env$myName.Flag.Discharge    <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.Discharge
+                                               , sep=".")
+ContData.env$myName.Flag.Cond         <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.Cond
+                                               , sep=".")
+ContData.env$myName.Flag.DO           <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.DO
+                                               , sep=".")
+ContData.env$myName.Flag.DO.adj       <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.DO.adj
+                                               , sep=".")
+ContData.env$myName.Flag.DO.pctsat    <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.DO.pctsat
+                                               , sep=".")
+ContData.env$myName.Flag.pH           <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.pH
+                                               , sep=".")
+ContData.env$myName.Flag.Turbidity    <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.Turbidity
+                                               , sep=".")
+ContData.env$myName.Flag.Chlorophylla <- paste(ContData.env$myName.Flag
+                                             , ContData.env$myName.Chlorophylla
+                                               , sep=".")
+ContData.env$myName.Flag.WaterLevel   <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.WaterLevel
+                                               , sep=".")
+ContData.env$myName.Flag.Salinity     <- paste(ContData.env$myName.Flag
+                                               , ContData.env$myName.Salinity
+                                               , sep=".")
+# Data Quality Test Names
+ContData.env$myNames.QCTests <- c("Gross","Spike","RoC","Flat")
+ContData.env$myNames.QCCalcs <- c("SD.Time"
+                                  , "SD"
+                                  , "SDxN"
+                                  , paste("n"
+                                          , seq_len(
+                                            ContData.env$myThresh.Flat.MaxComp)
+                                          , sep=".")
+                                  , "flat.Lo"
+                                  , "flat.Hi")
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Exceedance values for stats (default to Gross-Suspect-Hi value) ####
+ContData.env$myExceed.WaterTemp   <-
+  ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp
+ContData.env$myExceed.AirTemp     <-
+  ContData.env$myThresh.Gross.Suspect.Hi.AirTemp
+ContData.env$myExceed.SensorDepth <-
+  ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Date and Time Formats ####
+ContData.env$myFormat.Date           <- "%Y-%m-%d"
+ContData.env$myFormat.Time           <- "%H:%M:%S"
+ContData.env$myFormat.DateTime       <- "%Y-%m-%d %H:%M:%S"
+ContData.env$DateRange.Start.Default <- format(as.Date("1900-01-01")
+                                               , ContData.env$myFormat.Date)
+                                               #YYYY-MM-DD
+ContData.env$DateRange.End.Default   <- format(Sys.Date()
+                                               , ContData.env$myFormat.Date)
+# Time Zone, used in Gage script in dataRetrieval, OlsonNames()
+ContData.env$myTZ <- Sys.timezone() #"America/New_York" (local time zone)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Time Frames (MM-DD) ####
+ContData.env$myTimeFrame.Annual.Start        <- "0101"
+ContData.env$myTimeFrame.Annual.End          <- "1231"
+ContData.env$myTimeFrame.WaterYear.Start     <- "1001"
+#ContData.env$myTimeFrame.WaterYear.End      <- "0930"
+ContData.env$myTimeFrame.Season.Spring.Start <- "0301"
+#ContData.env$myTimeFrame.Season.Spring.End  <- "0531"
+ContData.env$myTimeFrame.Season.Summer.Start <- "0601"
+#ContData.env$myTimeFrame.Season.Summer.End  <- "0831"
+ContData.env$myTimeFrame.Season.Fall.Start   <- "0901"
+#ContData.env$myTimeFrame.Season.Fall.End    <- "1130"
+ContData.env$myTimeFrame.Season.Winter.Start <- "1201"
+#ContData.env$myTimeFrame.Season.Winter.End  <- "0228" #but 0229 in leap year, use start dates only
+# Time Frame Names
+ContData.env$myName.Yr       <- "Year"
+ContData.env$myName.YrMo     <- "YearMonth"
+ContData.env$myName.Mo       <- "Month"
+ContData.env$myName.MoDa     <- "MonthDay"
+ContData.env$myName.JuDa     <- "JulianDay"
+ContData.env$myName.Day      <- "Day"
+ContData.env$myName.Season   <- "Season"
+ContData.env$myName.YrSeason <- "YearSeason"
+# for summary stats
+ContData.env$myNames.Fields.TimePeriods <- c(ContData.env$myName.Yr
+                                                                ,ContData.env$myName.YrMo
+                                                                ,ContData.env$myName.Mo
+                                                                ,ContData.env$myName.MoDa
+                                                                ,ContData.env$myName.JuDa
+                                                                ,ContData.env$myName.Season
+                                                                ,ContData.env$myName.YrSeason)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Exclude Trigger ####
+# Trigger for Stats to exclude (TRUE) or include (FALSE) where flag = "fail"
+ContData.env$myStats.Fails.Exclude <- TRUE  #FALSE #TRUE
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Report Format ####
+ContData.env$myReport.Format <- "docx" # "html" or "docx"
+# DOCX requires Pandoc.
+ContData.env$myReport.Dir <- file.path(system.file(package="ContDataQC"), "rmd")
+
+
+

Period Stats

+

The function PeriodStats() generates daily stats (N, mean, min, max, +range, std deviation) for the specified time period before a given date. +Output is a multiple column CSV (Date and Parameter Name by statistic) +and a report (HTML or DOCX) with plots.

+

The input is output of the QC function in ContDataQC. That is, a file +with SiteID, Date, Time, Date.Time, and parameters.

+

To get different periods (30, 60, or 90 days) change function input +“fun.myPeriod.N”. It is possible to provide a vector for Period.N and +Period.Units.

+

The function returns a csv with daily means and a PDF summary with +plots into the specified export directory for the specified time period +before the given date.

+
library(ContDataQC)
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+# Save example file
+df.x <- DATA_period_test2_Aw_20130101_20141231
+write.csv(df.x
+          , file.path(myDir.BASE, "DATA_period_test2_Aw_20130101_20141231.csv"))
+
+# function inputs
+myDate <- "2013-09-30"
+myDate.Format <- "%Y-%m-%d"
+myPeriod.N <- c(30, 60, 90, 120, 1)
+myPeriod.Units <- c("d", "d", "d", "d", "y")
+myFile <- "DATA_period_test2_Aw_20130101_20141231.csv"
+myDir.import <- myDir.BASE
+myDir.export <- myDir.BASE
+myParam.Name <- "Water.Temp.C"
+myDateTime.Name <- "Date.Time"
+myDateTime.Format <- "%Y-%m-%d %H:%M:%S"
+myThreshold <- 20
+myConfig <- ""
+myReport.format <- "docx"
+
+# Run Function
+## default report format (html)
+PeriodStats(myDate
+          , myDate.Format
+          , myPeriod.N
+          , myPeriod.Units
+          , myFile
+          , myDir.import
+          , myDir.export
+          , myParam.Name
+          , myDateTime.Name
+          , myDateTime.Format
+          , myThreshold
+          , myConfig)
+
+
+

Export for IHA

+

The function Export.IHA() allows for the formatting of data for use +with the Nature Conservancy’s IHA program as converted to R.

+

https://www.conservationgateway.org/ConservationPractices/Freshwater/EnvironmentalFlows/MethodsandTools/IndicatorsofHydrologicAlteration/Pages/indicators-hydrologic-alt.aspx

+

https://github.com/jasonelaw/iha

+
library(ContDataQC)
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+
+# 1.  Get Gage Data
+#
+# 1.A. Use ContDataQC and Save (~1min for download)
+myData.Operation    <- "GetGageData" #Selection.Operation[1]
+myData.SiteID       <- "01187300" # Hubbard River near West Hartland, CT
+myData.Type         <- "Gage"
+myData.DateRange.Start  <- "2015-01-01"
+myData.DateRange.End    <- "2016-12-31"
+myDir.import <- myDir.BASE
+myDir.export <- myDir.BASE
+ContDataQC(myData.Operation
+           , myData.SiteID
+           , myData.Type
+           , myData.DateRange.Start
+           , myData.DateRange.End
+           , myDir.import
+           , myDir.export)
+#
+# 1.B. Use saved data
+#myData.SiteID   <- "01187300"
+myFile <- "01187300_Gage_20150101_20161231.csv"
+myCol.DateTime <- "Date.Time"
+myCol.Discharge <- "Discharge.ft3.s"
+#
+# 2. Prep Data
+myData.IHA <- Export.IHA(fun.myFile = myFile
+                         , fun.myDir.import = myDir.BASE
+                         , fun.myDir.export = myDir.BASE
+                         , fun.myCol.DateTime = myCol.DateTime
+                         , fun.myCol.Parameter = myCol.Discharge
+                         )
+

The example code below runs the data (generated above) through IHA +package.
+This example makes use of the XLConnect R library to save +the IHA output into a single Excel file. Before running the IHA code the +IHA and XLConnect packages need to be +installed. The devtools package allows for easy +installation from GitHub (where the IHA package resides).

+
# Install Libraries (if needed)
+install.packages("devtools")
+library(devtools)
+install_github("jasonelaw/IHA")
+install.packages("XLConnect")
+

IHA example code below that uses the IHA.Export file and produces an +Excel file of the IHA output.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# 3. Run IHA
+# Example using returned DF with IHA
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# User info
+SiteID <- myData.SiteID
+Notes.User <- Sys.getenv("USERNAME")
+#~~~~~
+# Library (load)
+library(IHA)
+library(XLConnect)
+#~~~~~
+# IHA
+myYr <- "calendar" # "water" or "calendar"
+# IHA Metrics
+## IHA parameters group 1; Magnitude of monthly water conditions
+Analysis.Group.1 <- group1(myData.IHA, year=myYr)
+## IHA parameters group 2: Magnitude of monthly water condition and include 12 parameters
+Analysis.Group.2 <- group2(myData.IHA, year=myYr)
+Analysis.Group.3 <- group3(myData.IHA, year=myYr)
+## IHA parameters group 4; Frequency and duration of high and low pulses
+# defaults to 25th and 75th percentiles
+Analysis.Group.4 <- group4(myData.IHA, year=myYr)
+## IHA parameters group 5; Rate and frequency of water condition changes
+Analysis.Group.5 <- group5(myData.IHA, year=myYr)
+#~~~~~
+# Save Results to Excel (each group on its own worksheet)
+Group.Desc <- c("Magnitude of monthly water conditions"
+              ,"Magnitude of monthly water condition and include 12 parameters"
+                ,"Timing of annual extreme water conditions"
+                ,"Frequency and duration of high and low pulses"
+                ,"Rate and frequency of water condition changes")
+df.Groups <- as.data.frame(cbind(paste0("Group",1:5),Group.Desc))
+#
+myDate <- format(Sys.Date(),"%Y%m%d")
+myTime <- format(Sys.time(),"%H%M%S")
+# Notes section (add min/max dates)
+Notes.Names <- c("Dataset (SiteID)","IHA.Year","Analysis.Date (YYYYMMDD)","Analysis.Time (HHMMSS)","Analysis.User")
+Notes.Data <- c(SiteID, myYr, myDate, myTime, Notes.User)
+df.Notes <- as.data.frame(cbind(Notes.Names,Notes.Data))
+Notes.Summary <- summary(myData.IHA)
+# Open/Create file
+myFile.XLSX <- paste("IHA", SiteID, myYr, myDate, myTime, "xlsx", sep=".")
+wb <- loadWorkbook(myFile.XLSX, create = TRUE) 
+# load workbook, create if not existing
+# create sheets
+createSheet(wb, name = "NOTES")
+createSheet(wb, name = "Group1")
+createSheet(wb, name = "Group2")
+createSheet(wb, name = "Group3")
+createSheet(wb, name = "Group4")
+createSheet(wb, name = "Group5")
+# write to worksheet
+writeWorksheet(wb, df.Notes, sheet = "NOTES", startRow=1)
+writeWorksheet(wb, Notes.Summary, sheet = "NOTES", startRow=10)
+writeWorksheet(wb, df.Groups, sheet="NOTES", startRow=25)
+writeWorksheet(wb, Analysis.Group.1, sheet = "Group1")
+writeWorksheet(wb, Analysis.Group.2, sheet = "Group2")
+writeWorksheet(wb, Analysis.Group.3, sheet = "Group3")
+writeWorksheet(wb, Analysis.Group.4, sheet = "Group4")
+writeWorksheet(wb, Analysis.Group.5, sheet = "Group5")
+# save workbook
+saveWorkbook(wb, myFile.XLSX)
+
+
+

Rarify Taxa Data

+

At times benthic samples include more individuals than the target +number or when combining data from multiple programs it is necessary to +subsample all samples down to a common number. The rarify funtion is +part of the RIVPACS set of R scripts written by USEPA Corvallis. The +rarify function is in the package BioMonTools. The example below is from +the help file from that package. The BioMonTools package should install +with the ContDataQC package.

+

https://github.com/leppott/BioMonTools

+

The function takes a three column data frame (SampleID, TaxonID, +Count) and returns a similar dataframe with revised Counts.

+
library(BioMonTools)
+
+# Subsample to 500 organisms (from over 500 organisms) for 12 samples.
+
+# load bio data
+df_biodata <- data_bio2rarify
+dim(df_biodata)
+View(df_biodata)
+
+# subsample
+mySize <- 500
+Seed_OR <- 18590214
+Seed_WA <- 18891111
+Seed_US <- 17760704
+bugs_mysize <- BioMonTools::rarify(inbug=df_biodata, sample.ID="SampleID"
+                     ,abund="N_Taxa",subsiz=mySize, mySeed=Seed_US)
+
+# Compare pre- and post- subsample counts
+df_compare <- merge(df_biodata, bugs_mysize, by=c("SampleID", "TaxaID")
+                    , suffixes = c("_Orig","_500"))
+df_compare <- df_compare[,c("SampleID", "TaxaID", "N_Taxa_Orig", "N_Taxa_500")]
+
+# compare totals
+tbl_totals <- aggregate(cbind(N_Taxa_Orig, N_Taxa_500) ~ SampleID
+                        , df_compare, sum)
+knitr::kable(tbl_totals
+             , caption = "Sample totals before and after subsampling.")
+
+
+

Visualization of Daily Means

+

An Excel file is included in the folder of the library that allows +for the plotting of up to five years of data for water temperature, +sensor depth, and air temperature.

+

The daily values from the SummaryStats operation of the ContDataQC() +function can be copied into the Excel file and the plots are +auto-updated.

+

These plots could be created in R but the Excel file was included to +make the plots available to a wider audience.

+
(myDir.BASE <- tempdir()) # create and print temp directory for example data
+# Parameters
+Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   ,"Data1_RAW"
+                   ,"Data2_QC"
+                   ,"Data3_Aggregated"
+                   ,"Data4_Stats")
+myDir.BASE <- getwd()
+# Summary Stats
+myData.Operation <- "SummaryStats" #Selection.Operation[4]
+myData.SiteID    <- "test2"
+myData.Type      <- Selection.Type[3] #"AW"
+myData.DateRange.Start  <- "2013-01-01"
+myData.DateRange.End    <- "2014-12-31"
+myDir.import <- file.path(myDir.BASE,Selection.SUB[4]) #"Data3_Aggregated"
+myDir.export <- file.path(myDir.BASE,Selection.SUB[5]) #"Data4_Stats"
+ContDataQC(myData.Operation
+           , myData.SiteID
+           , myData.Type
+           , myData.DateRange.Start
+           , myData.DateRange.End
+           , myDir.import
+           , myDir.export)
+
+
+

Output for StreamThermal

+

The Export.StreamThermal() function exports data for use with the the +StreamThermal library.
+The data input for StreamThermal is a data frame with five columns +(SiteID, Date, TMax, TMin, and TMean).

+

The StreamThermal library is only available on +GitHub.

+

https://github.com/tsangyp/StreamThermal

+

Install instructions for the StreamThermal library are +below.

+
# Install Libraries (if needed)
+install.packages("devtools")
+library(devtools)
+install_github("tsangyp/StreamThermal")
+

The StreamThermal package is set up to work with USGS +gage data and this can be downloaded via the dataRetrieval +library (as shown in the examples in the StreamThermal +library).

+
# 1.1. Get USGS data
+# code from StreamThermal T_frequency example
+ExUSGSStreamTemp <- dataRetrieval::readNWISdv("01382310"
+                                              ,"00010"
+                                              ,"2011-01-01"
+                                              ,"2011-12-31"
+                                              ,c("00001","00002","00003"))
+
## GET: https://waterservices.usgs.gov/nwis/dv/?site=01382310&format=waterml,1.1&ParameterCd=00010&StatCd=00001,00002,00003&startDT=2011-01-01&endDT=2011-12-31
+
sitedata <- subset(ExUSGSStreamTemp
+                   , select = c("site_no"
+                                ,"Date"
+                                ,"X_00010_00001"
+                                ,"X_00010_00002"
+                                ,"X_00010_00003"))
+names(sitedata) <- c("siteID","Date","MaxT","MinT","MeanT")
+knitr::kable(head(sitedata))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
siteIDDateMaxTMinTMeanT
013823102011-01-013.72.02.6
013823102011-01-023.32.12.8
013823102011-01-032.31.01.7
013823102011-01-042.71.21.8
013823102011-01-052.81.52.0
013823102011-01-062.61.21.8
+

It is possible to use data that has been summarized with +SummaryStats.

+
library(ContDataQC)
+# 1.2. Use Unsummarrized data
+myFile <- "DATA_test2_Aw_20130101_20141231.csv"
+myData <- read.csv(file.path(path.package("ContDataQC")
+                             ,"extdata"
+                             ,myFile)
+                   , stringsAsFactors=FALSE)
+# Subset
+Col.Keep <- c("SiteID", "Date", "Water.Temp.C")
+sitedata <- myData[,Col.Keep]
+sitedata <- Export.StreamThermal(myData)
+# Show data table
+knitr::kable(head(sitedata))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SiteIDDateMaxTMinTMeanT
Test22013-04-2613.84610.06312.88004
Test22013-04-2713.17312.20712.52940
Test22013-04-2812.88312.59412.74052
Test22013-04-2914.42112.49713.31387
Test22013-04-3015.09111.72213.41990
Test22013-05-0115.56913.84614.63417
+

It is also possible to use data that has been calculated by Period +Stats.

+
library(ContDataQC)
+# 1.3. Use user data that has been QCed
+myData   <- DATA_period_test2_Aw_20130101_20141231
+sitedata <- Export.StreamThermal(myData)
+knitr::kable(head(sitedata))
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
SiteIDDateMaxTMinTMeanT
Test22013-04-2613.84610.06312.88004
Test22013-04-2713.17312.20712.52940
Test22013-04-2812.88312.59412.74052
Test22013-04-2914.42112.49713.31387
Test22013-04-3015.09111.72213.41990
Test22013-05-0115.56913.84614.63417
+

Run StreamThermal functions with ‘sitedata’. The example below saves +the output to a variable.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# 3. Run StreamThermal
+# Example using returned DF with StreamThermal
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Library (load)
+require(StreamThermal)
+#~~~~~
+# StreamThermal
+ST.freq <- T_frequency(sitedata)
+ST.mag  <- T_magnitude(sitedata)
+ST.roc  <- T_rateofchange(sitedata)
+ST.tim  <- T_timing(sitedata)
+ST.var  <- T_variability(sitedata)
+

The output of StreamThermal is 5 separate objects. It may be +convenient to export that data to Excel with each object on a separate +tab. Example code is below using the XLConnect library. +This code is similar that provided in the Export.IHA example.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Save Results to Excel (each group on its own worksheet)
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+library(XLConnect)
+# Descriptions
+#
+Desc.freq <- "Frequency metrics indicate numbers of days in months or seasons
+that key events exceed user-defined temperatures. "
+#
+Desc.mag <- "Magnitude metrics characterize monthly and seasonal averages and
+the maximum and minimum from daily temperatures as well as 3-, 7-, 14-, 21-,
+and 30-day moving averages for mean and maximum daily temperatures."
+#
+Desc.roc <- "Rate of change metrics include monthly and seasonal rate of
+change, which indicates the difference in magnitude of maximum and minimum
+temperatures divided by number of days between these events."
+#
+Desc.tim <- "Timing metrics indicate Julian days of key events including
+mean, maximum, and minimum temperatures; they also indicate Julian days of
+mean, maximum, and minimum values over moving windows of specified size."
+#
+Desc.var <- "Variability metrics summarize monthly and seasonal range in
+daily mean temperatures as well as monthly coefficient of variation of daily
+mean, maximum, and minimum temperatures. Variability metrics also include
+moving averages for daily ranges and moving variability in extreme
+temperatures, calculated from differences in average high and low
+temperatures over various time periods"
+#
+Group.Desc <- c(Desc.freq, Desc.mag, Desc.roc, Desc.tim, Desc.var)
+df.Groups <- as.data.frame(cbind(c("freq","mag","roc","tim","var"),Group.Desc))
+#
+SiteID <- sitedata[1,1]
+myDate <- format(Sys.Date(),"%Y%m%d")
+myTime <- format(Sys.time(),"%H%M%S")
+Notes.User <- Sys.getenv("USERNAME")
+# Notes section (add min/max dates)
+Notes.Names <- c("Dataset (SiteID)", "Analysis.Date (YYYYMMDD)"
+                 , "Analysis.Time (HHMMSS)", "Analysis.User")
+Notes.Data <- c(SiteID, myDate, myTime, Notes.User)
+df.Notes <- as.data.frame(cbind(Notes.Names, Notes.Data))
+Notes.Summary <- summary(sitedata)
+# Open/Create file
+## New File Name
+myFile.XLSX <- paste("StreamThermal", SiteID, myDate, myTime, "xlsx", sep=".")
+## Copy over template with Metric Definitions
+file.copy(file.path(path.package("ContDataQC")
+                    ,"extdata"
+                    ,"StreamThermal_MetricList.xlsx")
+          , myFile.XLSX)
+## load workbook, create if not existing
+wb <- loadWorkbook(myFile.XLSX, create = TRUE)
+# create sheets
+createSheet(wb, name = "NOTES")
+createSheet(wb, name = "freq")
+createSheet(wb, name = "mag")
+createSheet(wb, name = "roc")
+createSheet(wb, name = "tim")
+createSheet(wb, name = "var")
+# write to worksheet
+writeWorksheet(wb, df.Notes, sheet = "NOTES", startRow=1)
+writeWorksheet(wb, df.Groups, sheet="NOTES", startRow=10)
+writeWorksheet(wb, Notes.Summary, sheet = "NOTES", startRow=20)
+writeWorksheet(wb, ST.freq, sheet = "freq")
+writeWorksheet(wb, ST.mag, sheet = "mag")
+writeWorksheet(wb, ST.roc, sheet = "roc")
+writeWorksheet(wb, ST.tim, sheet = "tim")
+writeWorksheet(wb, ST.var, sheet = "var")
+# save workbook
+saveWorkbook(wb, myFile.XLSX)
+
+
+

Comparison CDFs

+

The function CompSite() allows the user to plot data from multiple +sites on a single CDF.

+

The function takes as input a data frame with date and up to 8 +columns of parameter data. Column names are Date and SiteIDs and values +are daily means for some measurement.

+

More than 8 columns can be used but colors are recycled after 8 and +the plot lines will be difficult to distinguish.

+

CDFs are generate for all data, year, season, and year/season and +saved to a PDF. Winter + Year is such that December is included with the +following year (e.g., Dec 2013, Jan 2014, Feb 2014 are 2014Winter).

+

Two plots per page are generated. The first plot is the proportion of +values at a certain value.
+This plot is similar to a histogram.
+The second plot is a CDF of values. The line represents the proportion +of values less than or equal to parameter values on the x-axis.

+

An example table to be used as input for the function is shown below. +This table has data for five sites.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DateBrownsHubbardParkersColdWBrSwift
2014-01-010.51202080.06908330.2669167-0.00400000.0094167
2014-01-020.32158330.07841670.1561458-0.0040000-0.0040000
2014-01-030.02400000.07900000.1029167-0.00108330.0158333
2014-01-040.02400000.07900000.1385000-0.0016667-0.0005000
2014-01-050.28062500.07900000.2072083-0.0016667-0.0034167
2014-01-060.51977080.07900000.2161458-0.0092500-0.0040000
+

An example plot is shown below. The column names from the input table +will appear in the legend of the plot. +

+
+
+

Richards-Baker (Flashiness) Index

+

Calculates the RBI from a vector of flow measurements. Inputs are are +vector of flow measurements over some time period (e.g., water year). +Output is a single value that represents the RBI.

+

The RBI is the sum of absolute differences between days divided by +the total flow for the given time period.

+

Baker, D.B., R.P. Richards, T.T. Loftus, and J.W. Kramer. 2004. A New +Flashiness Index: Characteristics and Applications to Midwestern Rivers +and Streams. April 2004. Journal of the American Water Resources +Association (JAWRA). Pages 503:522.

+
# Get Gage Data via the dataRetrieval package from USGS 01187300 2013
+data.gage <- dataRetrieval::readNWISdv("03238500"
+                                       , "00060"
+                                       , "1974-10-01"
+                                       , "1975-09-30")
+
## GET: https://waterservices.usgs.gov/nwis/dv/?site=03238500&format=waterml,1.1&ParameterCd=00060&StatCd=00003&startDT=1974-10-01&endDT=1975-09-30
+
head(data.gage)
+
##   agency_cd  site_no       Date X_00060_00003 X_00060_00003_cd
+## 1      USGS 03238500 1974-10-01            38                A
+## 2      USGS 03238500 1974-10-02            32                A
+## 3      USGS 03238500 1974-10-03            26                A
+## 4      USGS 03238500 1974-10-04            20                A
+## 5      USGS 03238500 1974-10-05            23                A
+## 6      USGS 03238500 1974-10-06            23                A
+
# flow data
+data.Q <- data.gage[,4]
+# remove zeros
+data.Q[data.Q==0] <- NA
+RBIcalc(data.Q)
+
## [1] 0.9833356
+

The calculated value from this function was QCed against data in the +journal article.

+

Whiteoak Creek near Georgetown, Ohio (03238500) for the water year +1975 was download via the dataRetrieval package and used to +generate the RBI. The value calculated by RBIcalc() matches the value in +Figure 8 in the journal (0.98). The value was also verified using +Excel.

+
+
+

Summary Stats Only

+

The user sometimes will have data that is already QCed or that just +needs summary stats. It is possible to run just the summary stats from +ContDataQC but need to require certain fields.

+
    +
  • SiteID
    • +
  • Date
    • +
  • Time
    • +
  • Date.Time
    • +
  • Parameter names in ContDataQC format (config.R). Examples below: +
      +
    • Water.Temp.C
      • +
    • Discharge.ft3.s
      • +
    • +
  • QC Flag fields for each parameter in the data (“Flag.” + Parameter +Name).
    +Examples below: +
      +
    • Flag.Water.Temp.C
      • +
    • Flag.Discharge.ft3.s
      • +
    • +
+

The example code below will download 2 years of discharge data from a +USGS gage 0118730, add the necessary fields, save the file, and run +SummaryStats.

+
(myDir.BASE <- tempdir()) # create and print temp directory for example data
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Summary Stats from Other Data
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Get Data, e.g., USGS gage data
+# Get Gage Data via the dataRetrieval package from USGS 01187300 2013 
+#    (~4 seconds)
+data.gage <- dataRetrieval::readNWISuv("01187300"
+                                       , "00060"
+                                       , "2013-01-01"
+                                       , "2014-12-31")
+head(data.gage)
+# Rename fields
+myNames <- c("Agency", "SiteID", "Date.Time", "Discharge.ft3.s", "Code", "TZ")
+names(data.gage) <- myNames
+# Add Date and Time
+data.gage[,"Date"] <- as.Date(data.gage[,"Date.Time"])
+data.gage[,"Time"] <-  strftime(data.gage[,"Date.Time"], format="%H:%M:%S")
+# Add "flag" fields that are added by QC function.
+Names.Flags <- paste0("Flag.",c("Date.Time", "Discharge.ft3.s"))
+data.gage[,Names.Flags] <- "P"
+# Save File
+myFile <- "01187300_Gage_20130101_20141231.csv"
+write.csv(data.gage, myFile, row.names=FALSE)
+# Run Stats (File)
+myData.Operation <- "SummaryStats"
+myDir.import <- myDir.BASE
+myDir.export <- myDir.BASE
+ContDataQC(myData.Operation
+           , fun.myDir.import=myDir.import
+           , fun.myDir.export=myDir.export
+           , fun.myFile=myFile)
+
+
+

Format of HOBOware Output

+

Following the suggested RMN guidelines for creating HOBOware (PDF +files included in help section of the package) will produce files with a +known structure.
+HOBOware will not create files for use with R. The function formatHobo +was created to bridge this gap.

+

Imports a HoboWare output (with minimal tweaks) from a folder, +reformats it using defaults from the ContDataQC config file, and exports +a CSV to another folder for use with ContDataQC.

+

Below are the default data directories assumed to exist in the +working directory. These can be created with code as in the example. +Using this function as an example, files will be imported from +Data0_Original and exported to Data1_RAW.

+
    +

  • ./Data0_Original/ = Unmodified data logger files.

    • +

  • ./Data1_RAW/ = Data logger files modified for use with library. +Modifications for extra rows and file and column names.

    • +

  • ./Data2_QC/ = Repository for library output for QCed +files.

    • +

  • ./Data3_Aggregated/ = Repository for library output for +aggregated (or split) files.

    • +

  • ./Data4_Stats/ = Repository for library output for statistical +summary files.

    • +
+

File format should be “SiteID_SensorType_StartDate_EndData.csv”.

+
    +

  • SiteID = no spaces or underscores

    • +

  • SensorType = Air, Water, or AW (Air + Water in the same +file)

    • +

  • Dates = YYYYMMDD (no delimiter)

    • +

  • Delimiter = underscore (as specified in the config file)

    • +
+

No data frames are returned. A CSV file ready for use with the +ContDataQC QC function will be generated in the specified output +directory.

+
# Parameters
+Selection.Operation <- c("GetGageData"
+                         , "QCRaw"
+                         , "Aggregate"
+                         , "SummaryStats")
+Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   , "Data1_RAW"
+                   , "Data2_QC"
+                   , "Data3_Aggregated"
+                   , "Data4_Stats")
+(myDir.BASE <- tempdir()) # create and print temp directory for example data
+
+# Create data directories
+myDir.create <- file.path(myDir.BASE, Selection.SUB[1])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+myDir.create <- file.path(myDir.BASE, Selection.SUB[2])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+myDir.create <- file.path(myDir.BASE, Selection.SUB[3])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+myDir.create <- file.path(myDir.BASE, Selection.SUB[4])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+myDir.create <- file.path(myDir.BASE, Selection.SUB[5])
+  ifelse(dir.exists(myDir.create) == FALSE
+         , dir.create(myDir.create)
+         , "Directory already exists")
+
+# Save example data (assumes directory ./Data0_Original/ exists)
+fn_1 <- "Charlies_Air_20170726_20170926.csv"
+fn_2 <- "Charlies_AW_20170726_20170926.csv"
+fn_3 <- "Charlies_Water_20170726_20170926.csv"
+fn_4 <- "ECO66G12_AW_20160128_20160418.csv"
+lapply(c(fn_1, fn_2, fn_3, fn_4), function(x)
+       file.copy(system.file("extdata", x, package="ContDataQC")
+       , file.path(myDir.BASE, Selection.SUB[1], x)))
+
+# Function Inputs
+myFiles <- c("Charlies_Air_20170726_20170926.csv"
+             , "Charlies_AW_20170726_20170926.csv"
+             , "Charlies_Water_20170726_20170926.csv")
+myDir.import <- file.path(myDir.BASE, "Data0_Original")
+myDir.export <- file.path(myDir.BASE, "Data1_RAW")
+
+# Run Function (with default config)
+formatHobo(myFiles, myDir.import, myDir.export)
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# QC the generated file with ContDataQC
+myData.Operation <- "QCRaw" #Selection.Operation[2]
+myFile <- myFiles
+myDir.import <- file.path(".","Data1_RAW")
+myDir.export <- file.path(".","Data2_QC")
+myReport.format <- "html"
+ContDataQC(myData.Operation
+           , fun.myDir.import = myDir.import
+          , fun.myDir.export = myDir.export
+          , fun.myFile = myFile
+          , fun.myReport.format = myReport.format)
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Example with unmodified dates
+myFiles <- "ECO66G12_AW_20160128_20160418.csv"
+myDir.import <- file.path(myDir.BASE, "Data0_Original")
+myDir.export <- file.path(myDir.BASE, "Data1_RAW")
+HoboDateFormat <- "MDY"
+
+# Run Function (with default config)
+formatHobo(myFiles, myDir.import, myDir.export, HoboDateFormat)
+
+
+

Export for rLakeAnalyzer

+

Continuous data from Lakes is similar to that used in streams.
+The only difference is the addition of depth to the data.
+There are R packages designed for the analysis of lake monitoring data. +One such example is rLakeAnalyzer.

+

To aid users in QCing their continuous lake data an export function +to rLakeAnalyzer was created in the ContDataQC +package.

+
(myDir.BASE <- tempdir()) # create and print temp directory for example data
+
## [1] "C:\\Users\\ERIK~1.LEP\\AppData\\Local\\Temp\\RtmpiIu3CZ"
+
# Convert Data for use with rLakeAnalyzer
+
+# Data
+fn_CDQC <- "TestLake_Water_20180702_20181012.csv"
+df_CDQC <- read.csv(file.path(system.file(package = "ContDataQC")
+                              , "extdata"
+                              , fn_CDQC))
+
+# Convert Date.Time from factor to POSIXct (make it a date and time field in R)
+df_CDQC[, "Date.Time"] <- as.POSIXct(df_CDQC[, "Date.Time"])
+
+# Columns, date listed first
+col_depth <- "Depth"
+col_CDQC <- c("Date.Time", "temp_F", "DO_conc")
+col_rLA  <- c("datetime", "wtr", "doobs")
+
+# Output Options
+dir_export <- myDir.BASE
+fn_export  <- paste0("rLA_", fn_CDQC)
+
+# Run function
+df_rLA <- Export.rLakeAnalyzer(df_CDQC
+                               , col_depth
+                               , col_CDQC
+                               , col_rLA
+                               , dir_export
+                               , fn_export)
+
+# Visualize Input and Output
+knitr::kable(head(df_CDQC)
+        , caption = "Example ContDataQC to rLakeAnalyze format function input.")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Example ContDataQC to rLakeAnalyze format function +input.
SiteIDDate.TimeDO_conctemp_FDepth
TestLake2018-07-02 16:00:002.2449.689
TestLake2018-07-02 17:00:002.5450.769
TestLake2018-07-02 18:00:002.6350.659
TestLake2018-07-02 19:00:002.2649.899
TestLake2018-07-02 20:00:002.4850.079
TestLake2018-07-02 21:00:002.6350.549
+
knitr::kable(head(df_rLA)
+       , caption = "Example ContDataQC to rLakeAnalyze format function output.")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Example ContDataQC to rLakeAnalyze format function +output.
datetimewtr_3wtr_6wtr_9doobs_3doobs_6doobs_9
2018-07-02 16:00:0071.1065.0549.688.215.442.24
2018-07-02 17:00:0071.2063.1050.768.315.352.54
2018-07-02 18:00:0071.4958.0350.658.374.172.63
2018-07-02 19:00:0071.6757.6049.898.404.352.26
2018-07-02 20:00:0071.5655.6950.078.263.942.48
2018-07-02 21:00:0071.3857.2750.548.404.352.63
+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Plot original data in ggplot
+library(ggplot2)
+
+# Plot, Create
+p <- ggplot(df_CDQC, aes(x=Date.Time, y=temp_F)) +
+       geom_point(aes(color=Depth), na.rm = TRUE) +
+       scale_color_continuous(trans="reverse") +
+       scale_x_datetime(date_labels = "%Y-%m")
+
+# Plot, Show
+print(p)
+

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# use rLakeAnalyzer
+library(rLakeAnalyzer)
+#library(knitr)
+
+# Filter Data for only temperature
+col_wtr <- colnames(df_rLA)[grepl("wtr_", colnames(df_rLA))]
+df_rLA_wtr <- df_rLA[, c("datetime", col_wtr)]
+
+# Create bathymetry data frame
+df_rLA_bth <- data.frame(depths=c(3,6,9), areas=c(300,200,100))
+
+# Visualize Input Data
+knitr::kable(head(df_rLA_wtr)
+             , caption = "rLakeAnalyzer; Example water temperature data")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
rLakeAnalyzer; Example water temperature data
datetimewtr_3wtr_6wtr_9
2018-07-02 16:00:0071.1065.0549.68
2018-07-02 17:00:0071.2063.1050.76
2018-07-02 18:00:0071.4958.0350.65
2018-07-02 19:00:0071.6757.6049.89
2018-07-02 20:00:0071.5655.6950.07
2018-07-02 21:00:0071.3857.2750.54
+
knitr::kable(head(df_rLA_bth)
+             , caption = "rLakeAnalyzer; Example depth and area data")
+ + + + + + + + + + + + + + + + + + + + + + +
rLakeAnalyzer; Example depth and area data
depthsareas
3300
6200
9100
+
# Generate Heat Map
+wtr.heat.map(df_rLA_wtr)
+

+
# Generate Schmidt Plot
+schmidt.plot(df_rLA_wtr, df_rLA_bth)
+

+
# Generate Schmidt Stability Values
+df_rLA_Schmidt <- ts.schmidt.stability(df_rLA_wtr, df_rLA_bth)
+
+# Visualize Output Data
+knitr::kable(head(df_rLA_Schmidt)
+             , caption = "rLakeAnalyzer; Example Schmidt Stability output.")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
rLakeAnalyzer; Example Schmidt Stability output.
datetimeschmidt.stability
2018-07-02 16:00:00197.8109
2018-07-02 17:00:00196.6225
2018-07-02 18:00:00214.8701
2018-07-02 19:00:00223.8640
2018-07-02 20:00:00226.1239
2018-07-02 21:00:00216.3078
+
+
+
+

Data from Other Sources

+

It is possible to use data from other sources. The focus of the +package has been user’s with no data system. Some users already have a +system such as Aquarius. Below is an example script for obtaining data +from such a system.
+The script was contributed by Mark Hoger of PA DEP. This script is saved +in the extdata folder as Aquarius_Data_Aquisition_PADEP.r.

+
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# This script accesses data using AQUARIUS Time-Series 3.x's Publish API.
+# 
+# 
+# It depends on the RCurl library; to download and install
+# RCurl into your R environment, use this command:
+#
+#      install.packages('RCurl')
+#
+#
+# Initial command examples were provided by Aquatic Informatics 
+# technical staff. These examples were then adapted by Mark Hoger at 
+# Pennsylvania Department of Environmental Protection.
+#
+# Mark Hoger 
+# mhoger@pa.gov
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Initialize Connection to Database
+#
+library(RCurl)
+
+co<-c()
+# Enter server and AQUARIUS username and password between the ''
+server<-''
+username<-''
+pw<-''
+service<-paste('http://'
+               , server
+               , '/aquarius/Publish/AquariusPublishRestService.svc'
+               , sep='')
+getauthtoken<-paste(service
+                    , '/GetAuthToken?user='
+                    , username, '&encPwd='
+                    , pw
+                    , sep='')
+token<-getURL(.opts=co,getauthtoken)
+co<-curlOptions(.opts=co,httpheader=c(AQAuthToken=token))
+#
+# The above commands should result in a token that will be used to access
+# data stored in the database.
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+#
+# Data Acquisition
+#
+# Locations and datasets are selected prior to extracting data. This
+# approach reduces the time required to extract data since often only a 
+# subset of the data is needed. 
+#
+# Steps
+# 1. Get a list of locations from database.
+# 2. Choose locations.
+# 3. Get a list of datasets at the chosen locations.
+# 4. Choose datasets (e.g. pH and DO datasets).
+# 5. Extract data from datasets chosen. Can limit extraction by date/time
+#    during this process.
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 1. Get a list of locations. ####
+#
+getlocations<-paste(service, '/GetLocations',sep='')
+locs.all=read.csv(textConnection(getURL(.opts=co, getlocations)))
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 2. Choose locations. ####
+#
+# These steps will depend on the structure of how locations are stored in
+# the database you are pulling from. In PADEP's setup, the folders are
+# structured by Internal/External data, basin, HUC, then stream name. This
+# folder structure is contained in the LOCATIONPATH field and serves as an
+# excellent way of grabbing sites from within a watershed. 
+#
+# All subsetting from the original list of locations, locs.all, is stored 
+# in a dataframe called locs.subset. This dataframe name is used in the 
+# following steps so it is recommended that you rewrite over, locs. subset
+# if multiple steps are done instead of renaming each subset. 
+# 
+# Example subsetting of locations. grepl looks for character strings.
+#
+# To grab all internal data we can use LOCATIONPATH
+locs.subset<-locs.all[grepl('Internal Data',locs.all$LOCATIONPATH),]
+#
+# To search multiple key strings use | as the "or" function: 
+# 'Swatara|Goose' will return locations with either 'Swatara' or 'Goose' 
+# in LOCATIONNAME
+locs.subset<-locs.subset[grepl('Swatara|Goose',locs.subset$LOCATIONNAME),]
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 3. Get a list of datasets. ####
+#
+# A loop function is used to pull a list of all datasets at each location
+# in locs.subset.
+n.sites<-length(locs.subset$IDENTIFIER) # Count locations for the loop
+datalist = list() # Create temporary holder of data
+for (i in 1:n.sites){
+  getdatasets<-paste(service
+                     ,'/GetDataSetsList?locId='
+                     ,locs.subset$IDENTIFIER[i]
+                     ,sep='')
+  datalist[[i]]=read.csv(textConnection(getURL(.opts=co,getdatasets)))
+}
+datasets = do.call(rbind, datalist) # Combine data gathered in loop into 
+#                                     a dataframe
+#
+# Make sure no errors were thrown during the loop. If a problem occurred
+# it could be with the naming or formatting in AQUARIUS. For example, the
+# parameter name (Label field of a dataset in Location Manager) cannot 
+# include any spaces. Also, time series that have been created but do not
+# contain any data will cause problems. A good way to find the issue is
+# to see how many elements were created in the loop.
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 4. Choose datasets. ####
+#
+# Like with the locations, you now need to select only that datasets you
+# want. Again, it is recommended to maintain the dataframe name 'datasets'
+# to avoid the need to adjust code in future steps.
+#
+# To remove Field Visit series from the list of datasets use !grepl to 
+# select all datasets except those with the given character string. 
+datasets <- datasets[!grepl("Field Visits", datasets$DataId),]
+#
+# To select datasets based on parameter type, use parameter identifiers.
+# The most common identifiers used by PADEP are below. Parameter  
+# identifiers can be found in AQUARIUS manager under the parameter tab, 
+# Parameter ID column.
+#       TW = Water temperature
+#       SpCond = Specific Conductance
+#       PH = pH
+#       WO = Dissolved oxygen - concentration
+#       WX = Dissolved oxygen - % sat
+#       WT = Turbidity
+#       HG = Stage
+#       QR = Discharge
+#       PA = Atmospheric pressure
+#       44500 = Calculated-derived series
+#
+# I typically don't use grepl to subset parameters just in case we ever  
+# have parameters that contain the same character strings. Instead use 
+# the following subsetting command.
+#
+# To grab only water temperature datasets.
+datasets <- datasets[datasets$Parameter=='TW',]
+# To grab multiple parameter types, use | as the "or" function. For both 
+# pH and DO concentration datasets: 
+datasets <- datasets[datasets$Parameter=='PH' | datasets$Parameter=='WO',]
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+# Step 5. Extract data. ####
+#
+# The datasets dataframe does not contain much location information. To 
+# better tie location data to the data about to be extracted, I use merge 
+# to add fields from locs.subset to the datasets dataframe.
+datasets<-merge(datasets
+                ,locs.subset
+                ,all=FALSE
+                ,by.x='LocationId'
+                ,by.y='IDENTIFIER')
+#
+# Now we finally grab the actual data from the list of datasets in the 
+# dataframe 'datasets'. Again, a loop function is used.
+n.datasets <- length(datasets$DataId) # Count datasets for the loop
+datalist2 = list() # Create temporary holder of data
+for (i in 1:n.datasets){
+         # Date range can be selected here. Blank will return all data
+         # from the datasets. Format is '2016-10-17T00:00:00.000-05:00'
+  start_dt=''
+  end_dt=''
+  gettimeseriesdata<-paste(service, '/GetTimeSeriesData', '?dataId=', datasets$DataId[i], '&queryFrom=', start_dt, '&queryTo=', end_dt, sep='')
+  datalist2[[i]]=read.csv(textConnection(getURL(.opts=co, gettimeseriesdata))
+                          , skip=4)
+  datalist2[[i]]$Time=strptime(substr(datalist2[[i]]$Time,0,19),"%FT%T")        
+         # Add some additional identifying columns. Merge with locs.subset
+         # must have been done.
+  datalist2[[i]]$SiteID<-rep(datasets$LocationId[i],dim(datalist2[[i]])[1])
+  datalist2[[i]]$Site<-rep(datasets$LOCATIONNAME[i],dim(datalist2[[i]])[1])
+  datalist2[[i]]$Parameter<-rep(datasets$Parameter[i],dim(datalist2[[i]])[1])
+  datalist2[[i]]$Units<-rep(datasets$Unit[i],dim(datalist2[[i]])[1])
+  datalist2[[i]]$ParameterName<-rep(datasets$DataId[i],dim(datalist2[[i]])[1])
+}
+dat = do.call(rbind, datalist2) # Combine data gathered in loop into 
+#                                 a dataframe
+#
+# See comments at end of Step 3 if you are getting errors during loop.
+#
+#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/inst/_vignettes/Vignette_Lakes.html b/inst/_vignettes/Vignette_Lakes.html new file mode 100644 index 00000000..94e7f980 --- /dev/null +++ b/inst/_vignettes/Vignette_Lakes.html @@ -0,0 +1,582 @@ + + + + + + + + + + + + + + + +ContDataQC Lake ID String Vignette + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Introduction

+

ContDataQC was created by Tetra Tech, Inc. for USEPA in +2017 to meet the needs of the Regional Monitoring Networks to apply a +minimum level of quality control to the continuous monitoring data they +were collecting.

+

It is envisioned that this library can be a living package and will +add additional functions and routines in the future.

+

Many of the examples in this vignette are included in the examples in +the corresponding functions. Each function in the +ContDataQC library includes example data.

+

The typical user of this package is envisioned as a user of Hobo or +other continuous data loggers. Measured parameters are water +temperature, sensor depth, air temperature, water pressure, and air +pressure. The typical user will retrieve data from their data logger +using Hoboware. Once the data is extracted from the logger and save some +minimal cleanup to the column heades is performed before using the +‘ContDataQC’ R library.

+

There is a second type of user for this package and that is of the +user with data loggers in lakes (or other waterbodies) with sensors at +different depths.
+Many of the package features work equally well with these data types +with a few modifications.

+
+
+

Workflow

+

It is envisioned that a user of data at different depths at the same +location can use the QC function by working with each depth file +separately. The “formatHobo” and “QC” steps will accommodate the lake +profile data (assuming the SiteID is specified as below in the next +section).

+
+

Site ID

+

The ContDataQC package is set up to handle a single +“SiteID” field. This is not appropriate for measurements at different +depths for a single location. To work around this the package uses a +delimiter, “–”, between the SiteID and depth (units optional). For +example “Ellis–1.0m” would be entered into the “SiteID” field.

+
+
+
+

FormatHobo

+

The Format function works the same on depth profile data files.

+
# Packages
+library(ContDataQC)
+
+# Parameters
+# Selection.Operation <- c("GetGageData","QCRaw", "Aggregate", "SummaryStats")
+# Selection.Type      <- c("Air","Water","AW","Gage","AWG","AG","WG")
+# Selection.SUB <- c("Data0_Original"
+#                   , "Data1_RAW","Data2_QC","Data3_Aggregated","Data4_Stats")
+myDir.BASE <- tempdir()
+
+# Function Inputs
+fn1 <- "Ellis--1.0m_Water_20180524_20180918.csv"
+fn2 <- "Ellis--3.0m_Water_20180524_20180918.csv"
+
+myFiles <- c(fn1, fn2)
+myDir.import <- file.path(myDir.BASE, "Data0_Original")
+myDir.export <- file.path(myDir.BASE, "Data1_RAW")
+
+myFormat = "YMD"
+
+# Run Function (with default config)
+formatHobo(myFiles, myDir.import, myDir.export, fun.HoboDateFormat=myFormat)
+
+

QC

+

The QC function works the same on depth profile data files.

+
# Packages
+library(ContDataQC)
+
+# Parameters
+Selection.Operation <- c("GetGageData"
+                         ,"QCRaw"
+                         , "Aggregate"
+                         , "SummaryStats")
+Selection.Type <- c("Air","Water","AW","Gage","AWG","AG","WG")
+Selection.SUB <- c("Data0_Original"
+                   , "Data1_RAW"
+                   , "Data2_QC"
+                   , "Data3_Aggregated"
+                   , "Data4_Stats")
+myDir.BASE <- 
+
+# Files
+fn1 <- "Ellis--1.0m_Water_20180524_20180918.csv"
+fn2 <- "Ellis--3.0m_Water_20180524_20180918.csv"
+
+# QC Data
+myData.Operation <- "QCRaw" #Selection.Operation[2]
+myFile <- c(fn1, fn2)
+myDir.import <- file.path(myDir.BASE, "Data1_RAW")
+myDir.export <- file.path(myDir.BASE, "Data2_QC")
+myReport.format <- "html"
+ContDataQC(myData.Operation
+           , fun.myDir.import = myDir.import
+           , fun.myDir.export = myDir.export
+           , fun.myFile = myFile
+           , fun.myReport.format = myReport.format)
+
+
+

Aggregate

+

The file version of the aggregate function will merge all file +provided in the fun.myFile parameter passed to ContDatQC(). The +resulting file will have the name of the first file with “Append_X”. +Where “X” will be the number of files that were aggregated.

+
# Packages
+library(ContDataQC)
+
+myDir.BASE <- tempdir()
+
+# Files
+myFile <- c("QC_Ellis--1.0m_Water_20180524_20180918.csv"
+            , "QC_Ellis--3.0m_Water_20180524_20180918.csv")
+
+# Aggregate Data
+myData.Operation <- "Aggregate" #Selection.Operation[3]
+myFile <- c("QC_Ellis--1.0m_Water_20180524_20180918.csv"
+            , "QC_Ellis--3.0m_Water_20180524_20180918.csv")
+myDir.import <- file.path(myDir.BASE, "Data2_QC")
+myDir.export <- file.path(myDir.BASE, "Data3_Aggregated")
+myReport.format <- "html"
+ContDataQC(myData.Operation
+           , fun.myDir.import = myDir.import
+           , fun.myDir.export = myDir.export
+           , fun.myFile = myFile
+           , fun.myReport.format = myReport.format)
+
+
+

Parse ID

+

An additional function, “ParseID”, is provided to parse the combined +ID string with SiteID, depth, and units into 3 separate fields. The +original SiteID is copied to SiteID_original and then the SiteID, depth, +and depth_units are then populated. The configuration file contains the +desired delimiter (defaulted as “–”).

+
myDir.BASE <- tempdir()
+
+# Parse ID string and save
+dir_files <- file.path(myDir.BASE, "Data3_Aggregated")
+setwd(dir_files)
+
+fun.myFileName.In <- "DATA_QC_Ellis--1.0m_Water_20180524_20180918_Append_2.csv"
+fun.myFileName.Out <- "Ellis_FixedID.csv"
+fun.ColName.ID <- "SiteID"
+fun.Delim <- "--"
+
+fun.ParseID(fun.myFileName.In
+            , fun.myFileName.Out
+            , fun.ColName.ID
+            , fun.Delim)
+
+
+
+

Example Analysis of Aggregated Data

+

Once the depth profiles have been individually QCed and then +Aggregated the user can analyse the data from a single file. Example +plot is below.

+
# Example code to Plot data
+library(ggplot2)
+
+# Read file
+df_Ellis <- read.csv(file.path(system.file("extdata", package="ContDataQC")
+                               , "Ellis.csv"))
+df_Ellis[, "Date.Time"] <- as.POSIXct(df_Ellis[, "Date.Time"])
+
+# Plot, Create
+p <- ggplot(df_Ellis, aes(x = Date.Time, y = Water.Temp.C, group = ID_Depth)) +
+  geom_point(aes(color = ID_Depth)) +
+  scale_color_continuous(trans = "reverse") +
+  scale_x_datetime(date_labels = "%Y-%m")
+
+# Plot, Show
+print(p)
+

+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/inst/shiny-examples/ContDataQC/data/remove.txt b/inst/shiny-examples/ContDataQC/data/remove.txt new file mode 100644 index 00000000..e69de29b diff --git a/inst/shiny-examples/ContDataQC/external/remove.txt b/inst/shiny-examples/ContDataQC/external/remove.txt new file mode 100644 index 00000000..e69de29b diff --git a/inst/shiny-examples/ContDataQC/external/tab_1_About.R b/inst/shiny-examples/ContDataQC/external/tab_1_About.R new file mode 100644 index 00000000..5bf06d5e --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_1_About.R @@ -0,0 +1,38 @@ +# About + +function() { + + tabPanel("About" + , mainPanel( + tabsetPanel(type = "tabs" + + , tabPanel("Overview" + , p(paste0("Version ", version, ".")) + , includeHTML("www/RMD_HTML/App_1a_Overview.html") + )## tabPanel ~ END + + , tabPanel("Test Files" + , includeHTML("www/RMD_HTML/App_1b_TestData.html") + )## tabPanel ~ END + + , tabPanel("Basic Information" + , includeHTML("www/RMD_HTML/App_1c_FAQ.html") + )## tabPanel~ END + + , tabPanel("Tips" + ,includeHTML("www/RMD_HTML/App_1d_Tips.html") + )# tabPanel ~ QC Tips ~ END + + , tabPanel("Advanced" + ,includeHTML("www/RMD_HTML/App_1e_Advanced.html") + )# tabPanel ~ Advanced ~ END + + , tabPanel("Related Apps" + , includeHTML("www/RMD_HTML/App_1f_RelatedApps.html") + )## tabPanel ~ END + + + )## tabsetPanel ~ END + )## mainPanel ~ END + ) ## tabPanel ~ Config ~ END +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_1a_Overview.R b/inst/shiny-examples/ContDataQC/external/tab_1a_Overview.R new file mode 100644 index 00000000..0c1bd8c6 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_1a_Overview.R @@ -0,0 +1,11 @@ +# Panel, Overview + +function(){ + + tabPanel("About" + , mainPanel( + includeHTML("www/RMD_HTML/App_1Overview.html") + , tags$head(includeHTML(("google-analytics.html"))) + )## mainPanel ~ END + ) #tabPanel ~END +}##FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_1b_TestData.R b/inst/shiny-examples/ContDataQC/external/tab_1b_TestData.R new file mode 100644 index 00000000..81ae9d1b --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_1b_TestData.R @@ -0,0 +1,10 @@ +# Panel, Test Data + +function() { + + tabPanel("Test Data" + , mainPanel( + includeHTML("www/RMD_HTML/App_7TestData.html") + )## mainPanel ~ END + ) ## tabPanel ~ Config ~ END +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_1c_FAQ.R b/inst/shiny-examples/ContDataQC/external/tab_1c_FAQ.R new file mode 100644 index 00000000..9912e508 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_1c_FAQ.R @@ -0,0 +1,11 @@ +# Panel, FAQ + +function() { + + tabPanel("Basis Info" + , mainPanel( + includeHTML("www/RMD_HTML/App_2FAQ.html") + + )## mainPanel ~ END + ) #tabPanel ~ END +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_2_DataPrep.R b/inst/shiny-examples/ContDataQC/external/tab_2_DataPrep.R new file mode 100644 index 00000000..dc6bdd16 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_2_DataPrep.R @@ -0,0 +1,137 @@ +# Panel, Data Prep + +function() { + + tabPanel("Data Preparation" + , mainPanel( + tabsetPanel(type = "tabs" + + , tabPanel("Formatting Files" + , includeHTML("www/RMD_HTML/App_2a_FormattingFiles.html") + )## tabPanel ~ END + + , tabPanel("Automated Reformatting" + ,tabsetPanel(type = "tabs" + , tabPanel("About" + , includeHTML("www/RMD_HTML/App_2az_About.html") + )## tabPanel ~ END + # Hoboware, reformat ---- + , tabPanel("HOBOware Reformat" + ,sidebarLayout( + sidebarPanel( + tags$div(title="Select one HOBOware csv files to upload here" + , fileInput("selectedFiles_HOBO" + ,label="Choose files" + , multiple = TRUE + , accept = ".csv" + , width = "600px") # wider for long file names + )# tags$div ~ END + ,selectInput("HOBO_DateFormat" + ,label = "Choose date format from the DateTime field in the input file" + ,choices = c("MDY" + , "YMD" + , "DMY" + , NULL) + ,selected = "MDY") + #Only shows the "Run operation" button after data are uploaded + ,tags$div(title="Click to run selected operation (HOBO reformat)" + ,uiOutput('ui.runProcess_HOBO') + )# tags$div ~ END + , br() + #Only shows the "Download" button after the process has run + ,tags$div(title="Click to download your data (HOBO reformat)" + ,uiOutput('ui.downloadData_HOBO') + )# tags$div ~ END + )# sidebarPanel~ END + , mainPanel( + includeHTML("www/RMD_HTML/App_2a1_HOBO.html") + # , img(src = "Fig_HOBOreformat_v1_20210617.png" + # , height = 600, width = 1000) + ) ## mainPanel ~ END + )# sidebarLayout~ END + )## tabPanel ~ HOBO ~ END + + # miniDOT, cat ---- + , tabPanel("miniDOT Concatenate" + , sidebarLayout( + sidebarPanel( + tags$div(title = "Select one or more miniDOT txt files to upload here" + , fileInput("selectedFiles_miniDOT_cat" + , label = "Choose files" + , multiple = TRUE + , accept = ".txt" + , width = "600px") # same as HOBO + )##tags$div ~ END + , br() + #Only shows the "Run operation" button after data are uploaded + ,tags$div(title = "Click to run selected operation (miniDOT concatenate)" + ,uiOutput('ui.runProcess_miniDOT_cat') + )# tags$div ~ END + , br() + #Only shows the "Download" button after the process has run + ,tags$div(title = "Click to download your data (miniDOT_cat)" + ,uiOutput('ui.downloadData_miniDOT_cat') + )# tags$div ~ END + )# sidebarPanel~ END + , mainPanel( + includeHTML("www/RMD_HTML/App_2a2_miniDOT1.html") + + ) ## mainPanel ~ END + )# sidebarLayout~ END + )## tabPanel ~ miniDOT cat ~ END + + # miniDOT, reformat ---- + , tabPanel("miniDOT Reformat" + , sidebarLayout( + sidebarPanel( + tags$div(title = "Select one miniDOT csv files to upload here" + , fileInput("selectedFiles_miniDOT_reformat" + , label = "Choose files" + , multiple = FALSE + , accept = ".csv" + , width = "600px") # same as HOBO + )##tags$div ~ END + , br() + #Only shows the "Run operation" button after data are uploaded + ,tags$div(title = "Click to run selected operation (miniDOT reformat)" + ,uiOutput('ui.runProcess_miniDOT_reformat') + )# tags$div ~ END + , br() + #Only shows the "Download" button after the process has run + , tags$div(title = "Click to download your data (miniDOT_reformat)" + ,uiOutput('ui.downloadData_miniDOT_reformat') + )# tags$div ~ END + )# sidebarPanel~ END + , mainPanel( + includeHTML("www/RMD_HTML/App_2a3_miniDOT2.html") + + ) ## mainPanel ~ END + )# sidebarLayout~ END + )## tabPanel ~ miniDOT reformat ~ END + + + )## tabsetPanel + )## tabPanel ~ END + + , tabPanel("Discrete Data" + , includeHTML("www/RMD_HTML/App_2cz_DiscreteData.html") + )## tabPanel ~ END + + , tabPanel("Organizing Files" + , includeHTML("www/RMD_HTML/App_2b_OrganizingFiles.html") + )## tabPanel ~ END + + , tabPanel("Naming Files" + , includeHTML("www/RMD_HTML/App_2c_NamingFiles.html") + # included pic in RMD to HTML + # , img(src = "Fig_NamingFiles_v1_20210617.png" + # , height = 600, width = 1000) + )## tabPanel~ END + + + + + )## tabsetPanel ~ END + )## mainPanel ~ END + ) ## tabPanel ~ Config ~ END +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_2a_FormatSensors.R b/inst/shiny-examples/ContDataQC/external/tab_2a_FormatSensors.R new file mode 100644 index 00000000..c39e2cb6 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_2a_FormatSensors.R @@ -0,0 +1,26 @@ +# Data Prep, About/Formatting + +function() { + + tabPanel("Format Sensors" + , mainPanel( + tabsetPanel(type = "tabs" + + , tabPanel("Hobo" + , includeHTML("www/RMD_HTML/App_2a1_HOBO.html") + )## tabPanel ~ END + + , tabPanel("Hobo" + , includeHTML("www/RMD_HTML/App_2a1_HOBO.html") + )## tabPanel~ END + + , tabPanel("miniDOT" + , includeHTML("www/RMD_HTML/App_2a2_miniDOT.html") + )## tabPanel ~ END + + + + )## tabsetPanel ~ END + )## mainPanel ~ END + ) ## tabPanel ~ Config ~ END +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_3_MainFunc.R b/inst/shiny-examples/ContDataQC/external/tab_3_MainFunc.R new file mode 100644 index 00000000..a26e2fb1 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_3_MainFunc.R @@ -0,0 +1,121 @@ +# Panel, Calc +# main function and instructions + + +function(){ + + tabPanel("Main Functions" + + , mainPanel( + + tabsetPanel(type = "tabs" + + , tabPanel("Run Functions" + , sidebarLayout( + sidebarPanel( + p("1. Upload customized QC test thresholds ('Main Functions >> QC Thresholds >> Upload Custom Thresholds' tab) if desired; otherwise, the default thresholds ('Main Functions >> QC Thresholds >> Default' tab) will be used.") + , p("2. Confirm whether the following configuration file listed is the correct one:") + , p(textOutput("fn_input_display_config_main")) + , p("3. Upload input files.") + #The selected input file + #Tool tip code from https://stackoverflow.com/questions/16449252/tooltip-on-shiny-r + , tags$div(title = "Select one or more csv files to upload here" + + #Only allows csv files to be imported + , fileInput("selectedFiles" + , label = "Choose files" + , multiple = TRUE + , accept = ".csv") + )# tags$div ~ END + + , p("4. Choose operation to perform.") + #Operation to be performed on the selected data + ,selectInput("Operation" + #,label = "Choose operation to perform" + ,label = "Select from drop-down menu:" # mjp update + ,choices = c("", + "QC raw data", + "Aggregate QC'd data", + "Run summary statistics") + ,selected = "") + + #Only shows the "Run operation" button after data are uploaded + ,tags$div(title="Click to run selected operation" + ,uiOutput('ui.runProcess') + )# tags$div ~ END + ,br() + ,br() + + #Only shows the "Download" button after the process has run + ,tags$div(title="Click to download your data" + ,uiOutput('ui.downloadData') + )# tags$div ~ END + + )# sidebarPanel + , mainPanel( + includeHTML("www/RMD_HTML/App_3b_CheckInput.html") + + #Shows an empty table until files are input + ,tableOutput("nullTable1") + ,tableOutput("nullTable2") + ,tableOutput("nullTable3") + + #Outputs the table with properties of the input spreadsheets, + #and a testing table of the beginning of the spreadsheets + ,tableOutput("summaryTable1") + ,tableOutput("summaryTable2") + ,tableOutput("summaryTable3") + + ,br() + + #Shows a note if the user uploads non-QCed data and selects + #the Aggregate or Summarize processes + ,h4(textOutput("aggUnQCedData")) + ,h4(textOutput("summUnQCedData")) + + #Shows a note if spreadsheets with multiple sites are selected + #for the Aggregate process + ,h4(textOutput("moreThanOneSite")) + + )# main panel + + + )# sidebarLayout + + + )##tabPanel ~ Check + + , tab_3c_QCThresh() + + , tabPanel("Instructions" + , tabsetPanel(type = "tabs" + , tabsetPanel(type="tabs" + ,tabPanel("Workflow" + , includeHTML("www/RMD_HTML/App_3a1_About.html") + ) # tabPanel ~ Run Functions ~ END + , tabPanel("QC Reports" + , includeHTML("www/RMD_HTML/App_3a2_QCReports.html")) + , tabPanel("Aggregate" + , includeHTML("www/RMD_HTML/App_3a3_Aggregate.html")) + , tabPanel("Summary Stats" + , includeHTML("www/RMD_HTML/App_3a4_SummaryStats.html")) + + ) # tabsetPanel ~ END + + )## tabsetPanel + )##tabPanel ~ About + + # , tabPanel("Advanced?" + # )##tabPanel ~ Adv + + + )##tabsetPanel ~ END + + + )##mainPanel ~ END + )#tabPanel ~ END + + + + +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_3c_QCThresh.R b/inst/shiny-examples/ContDataQC/external/tab_3c_QCThresh.R new file mode 100644 index 00000000..e6c05480 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_3c_QCThresh.R @@ -0,0 +1,186 @@ +# Panel, QC Thresholds +# Ben Block, 2021-06-16 +# Erik Leppo, 2022-12-19, add Gross boxes +# 2023-09-12, change boxes to width 3 (from 2) +# and remove sidebar and main panel width specifications +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +function() { + + tabPanel("QC Thresholds" + , mainPanel( + tabsetPanel(type = "tabs" + # 3c1. Default---- + , tabPanel("Default" + , includeHTML("www/RMD_HTML/App_3c1_QCThresh_Defaults.html") + )## tabPanel ~ CURRENT ~ END + + # 3c2. Upload Custom ---- + , tabPanel("Upload Custom Thresholds" + ,sidebarLayout( + sidebarPanel( + tags$div(title="Select R configuration file to upload here" + #Only allows R files to be imported + ,fileInput("configFile" + , label = "Choose configuration file" + , multiple = FALSE + , accept = ".R") + ) # tag$div ~ END + , br() + , p("The following lists the Configuration file currently being used:") + , p(textOutput("fn_input_display_config_config")) + , br() + + #Only shows the "Default configuration" button after a user-selected file has been used + ,tags$div(title = "Click to use default configuration file" + ,uiOutput('ui.defaultConfig')) + )# sidebarPanel~ END + , mainPanel( + includeHTML("www/RMD_HTML/App_3c2_QCThresh_Upload.html") + ) ## mainPanel ~ END + )# sidebarLayout~ END + )## tabPanel ~ Custom Thresh ~ END + + # 3c3. Eval---- + , tabPanel("Evaluate Thresholds" + , includeHTML("www/RMD_HTML/App_3c3_QCThresh_Eval.html") + # , br() + # , downloadButton("but_thresh_code") + )## tabPanel ~ Eval Thresh + + # 3c4. Edit ---- + , tabPanel("Edit Thresholds" + ,shinyjs::useShinyjs() + # ,h3("Edit your thresholds here by parameter:") + # ,h4("1. Select a parameter for edit using drop-down") + # ,h4("2. Edit thresholds using boxes on right") + # ,h4("3. Save changes (for each parameter)") + # ,h4("4. Download custom thresholds file") + ,sidebarLayout( + sidebarPanel(#width = 5 + # , style = "position:fixed"#;width:22%" + h4("1. Select parameter") + ,selectInput(inputId = "QC_Param_Input" + , label = NULL + , choices = c("Air Temperature" = "AirTemp" + ,"Barometric Pressure, air" = "AirBP" + ,"Chlorophyll a" = "Chla" + ,"Conductivity" = "Cond" + ,"Discharge" = "Discharge" + ,"Dissolved Oxygen" = "DO" + ,"Dissolved Oxygen, adjusted" = "DOadj" + ,"Dissolved Oxygen, percent saturation" = "DOpctsat" + ,"pH" = "pH" + ,"Sensor Depth" = "SensDepth" + ,"Turbidity" = "Turbid" + ,"Water Level" = "WtrLvl" + ,"Water Pressure" = "WaterP" + ,"Water Temperature" = "WaterTemp" + )) # selectInput + ,textOutput("QC_Param_Unit") + ,h4("2. Edit thresholds") + ,p("see boxes under each flag test type to the right at bottom of this page") + ,h4("3. Save your changes") + ,p(paste("Note: Save changes for one parameter at a time")) + ,actionButton(inputId = "QC_SaveBttn" + , label = "Save changes") + ,h4("4. Download your custom thresholds file") + ,p(paste("Note: The downloaded file must be reuploaded in the next step")) + ,downloadButton(outputId = "QC_Thresh_Download" + #, label = "Download custom thresholds file") + , label = "Download") + , h4("5. Upload custom threshold file") + , p("Go to the 'Upload Custom Thresholds' tab, browse to the custom thresholds file you downloaded above, upload that file, and then, under 'Main Functions', run the ‘QC raw data’ function to generate QC reports. If you skip this step, the default QC thresholds will be used.") + + ) # sidebarPanel~ END + , mainPanel(#width = 12 + includeHTML("www/RMD_HTML/App_3c4_QCThresh_Edit.html") + , tags$hr() + , h4("Gross Thresholds") + , p(paste("Test if data point exceeds a user-defined" + , "threshold.")) + , fluidRow( + column(width = sizeThreshEdit, + numericInput(inputId = "GR_Fail_Max" + , label = "Fail (Hi)" + , value = "1")) + ,column(width = sizeThreshEdit, + numericInput(inputId = "GR_Fail_Min" + , label = "Fail (Lo)" + , value = "1")) + , column(width = sizeThreshEdit, + numericInput(inputId = "GR_Sus_Max" + , label = "Suspect (Hi)" + , value = "1")) + , column(width = sizeThreshEdit, + numericInput(inputId = "GR_Sus_Min" + , label = "Suspect (Lo)" + , value = "1")) + ) # fluidRow + , tags$hr() + , h4("Spike Thresholds") + , p(paste("Test if data point exceeds a user-defined" + ,"threshold relative to the previous data point.")) + , fluidRow( + column(width = sizeThreshEdit, + numericInput(inputId = "Spike_Fail" + , label = "Fail" + , value = "1")) + ,column(width = sizeThreshEdit, + numericInput(inputId = "Spike_Sus" + , label = "Suspect" + , value = "1")) + ) # fluidRow + , tags$hr() + , h4("Rate of Change Limits") + , p(paste("Test if data point exceeds a number of" + ,"standard deviations from the previous" + ,"data points over a user-defined time period.")) + , fluidRow( + column(width = sizeThreshEdit, + numericInput(inputId = "RoC_SDs" + , label = "SDs" + , value = "1")) + ,column(width = sizeThreshEdit, + numericInput(inputId = "RoC_Hrs" + , label = "Hours" + , value = "1")) + ) # fluidRow + , tags$hr() + , h4("Flat Line Test") + , p(paste("Test if data point is within a user-defined" + ,"threshold from previous data points over" + ,"a user defined range.")) + , fluidRow( + column(width = sizeThreshEdit, + numericInput(inputId = "Flat_Fail" + , label = "Fail" + , value = "1")) + ,column(width = sizeThreshEdit, + numericInput(inputId = "Flat_Sus" + , label = "Suspect" + , value = "1")) + ,column(width = sizeThreshEdit, + numericInput(inputId = "Flat_Toler" + , label = "Tolerance" + , value = "1")) + ) # fluidRow + # , tags$hr() + # , h4("3. Save your changes") + # , p(paste("Note: Save changes for one parameter at a time")) + # , actionButton(inputId = "QC_SaveBttn" + # , label = "Save changes") + # , tags$hr() + # , h4("4. Download your custom threshold file") + # , p(paste("Note: The downloaded file must be reuploaded")) + # , downloadButton(outputId = "QC_Thresh_Download" + # , label = "Download custom thresholds file") + )## mainPanel ~ END + )# sidebarLayout~ END + ) ## tabPanel ~ Edit Thresh + + + )## tabsetPanel ~ END + )## mainPanel ~ END + ) ## tabPanel ~ Config ~ END +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_4_USGSgage.R b/inst/shiny-examples/ContDataQC/external/tab_4_USGSgage.R new file mode 100644 index 00000000..e8c6f470 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_4_USGSgage.R @@ -0,0 +1,34 @@ +# Panel, USGS gage data + +function(){ + + tabPanel("Download USGS Gage Data" + ,fluidRow( + column(5, + includeHTML("www/RMD_HTML/App_4_USGSgage.html") + )# column ~ END + + ,column(5, offset = 1 + ,h3("Download USGS gage data:") + ,br() + ,textInput("USGSsite" + , label = "USGS site ID(s) (separated by commas and spaces)") + ,textInput("startDate" + , label = "Starting date (YYYY-MM-DD)" + , placeholder = "YYYY-MM-DD") + ,textInput("endDate" + , label = "Ending date (YYYY-MM-DD)" + , placeholder = "YYYY-MM-DD") + ,br() + ,actionButton("getUSGSData", "Retrieve USGS data") + ,br() + ,br() + + #Only shows the "Download" button after the process has run + ,tags$div(title="Click to download your USGS gage data" + ,uiOutput('ui.downloadUSGSData')) + )# column ~ END + )# fluidRow ~ END + )## tabPanel ~ END + +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_5_Troubleshooting.R b/inst/shiny-examples/ContDataQC/external/tab_5_Troubleshooting.R new file mode 100644 index 00000000..94626b61 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_5_Troubleshooting.R @@ -0,0 +1,26 @@ +# Panel, Console + +function() { + tabPanel("Troubleshooting" + , mainPanel( + tabsetPanel(type = "tabs" + # , tabPanel("Troubleshooting" + # ,includeHTML("www/RMD_HTML/App_5a_Troubleshooting.html") + # # ,tableOutput("logText") + # # ,tableOutput("logTextUSGS") + # # ,tags$b(textOutput("logTextMessage") + # ) + + , tabPanel("Console" + ,includeHTML("www/RMD_HTML/App_5b_Console.html") + ,tableOutput("logText") + ,tableOutput("logTextUSGS") + ,tags$b(textOutput("logTextMessage")) + )##tabPanel + # , tabPanel("Contact Info" + # ,includeHTML("www/RMD_HTML/App_5c_Contact.html") + # )##tabPanel + )##tabsetPanel + )##mainPanel + )##tabPanel +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_5b_Console.R b/inst/shiny-examples/ContDataQC/external/tab_5b_Console.R new file mode 100644 index 00000000..265252b8 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_5b_Console.R @@ -0,0 +1,12 @@ +# Panel, Console + +function() { + + tabPanel("Console" + ,includeHTML("www/RMD_HTML/App_8Console.html") + ,tableOutput("logText") + ,tableOutput("logTextUSGS") + ,tags$b(textOutput("logTextMessage")) + + ) ## tabPanel ~ Adv Feat ~ END +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/external/tab_9_Status.R b/inst/shiny-examples/ContDataQC/external/tab_9_Status.R new file mode 100644 index 00000000..e84c1781 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/external/tab_9_Status.R @@ -0,0 +1,11 @@ +# Panel, Status + +function() { + + tabPanel("Status" + , mainPanel( + includeHTML("www/RMD_HTML/App_9Status.html") + + )## mainPanel ~ END + )# tabPanel ~ END +}## FUNCTION ~ END diff --git a/inst/shiny-examples/ContDataQC/global.R b/inst/shiny-examples/ContDataQC/global.R index b1aa01ba..3b9c22c4 100644 --- a/inst/shiny-examples/ContDataQC/global.R +++ b/inst/shiny-examples/ContDataQC/global.R @@ -14,13 +14,16 @@ library(shinyjs) ###For downloading new versions of ContDataQC and USGS' DataRetrieval package #library(devtools) -#install_github("leppott/ContDataQC") +#install_github("USEPA/ContDataQC") #install_github("USGS-R/dataRetrieval") # #Seems necessary for making R able to zip files when run locally. Allows R to # #access Window's zipping abilities # Sys.setenv(PATH = paste(Sys.getenv("PATH"), "C:\\Rtools\\bin", sep = ";")) +# Version Number +version <- "2.1.0" + #Maximum individual file size that can be uploaded is 70 MB options(shiny.maxRequestSize = 70 * 1024^2) @@ -28,7 +31,20 @@ options(shiny.maxRequestSize = 70 * 1024^2) dataTemplate <- read.csv(file = "continuous_data_template_2017_11_15.csv" , header = TRUE) -#Extracts properties of the input spreadsheets +# Environment ---- +# Load default config if not present +# 2020-10-02 (ContDataQC.R), added to global 2022-12-22 +if (exists("ContData.env", mode = "environment") == FALSE) { + source(system.file(package = "ContDataQC", "extdata", "config.ORIG.R") + , local = TRUE) +}## exists ~ END + +# Other ---- +sizeThreshEdit <- 4 + +# Functions ---- + +##Extracts properties of the input spreadsheets fileParse <- function(inputFile) { #Extracts site ID, start and end dates, and record count. @@ -38,27 +54,27 @@ fileParse <- function(inputFile) { #Dates are formatted differently for data input to QCRaw and data input #to Aggregate and Summarize (output from QCRaw) - if("Flag" %in% substr(colnames(inputFile),1,4)) { - startDate <-min(as.Date(inputFile$Date.Time, format = "%Y-%m-%d")) + if ("Flag" %in% substr(colnames(inputFile),1,4)) { + startDate <- min(as.Date(inputFile$Date.Time, format = "%Y-%m-%d")) endDate <- max(as.Date(inputFile$Date.Time, format = "%Y-%m-%d")) } #Recognizes dates if they are in a Date field as opposed to a Date.Time field - else if("Date" %in% colnames(inputFile)){ - startDate <-min(as.Date(inputFile$Date, format = "%m/%d/%Y")) + else if ("Date" %in% colnames(inputFile)) { + startDate <- min(as.Date(inputFile$Date, format = "%m/%d/%Y")) endDate <- max(as.Date(inputFile$Date, format = "%m/%d/%Y")) } #Recognizes dates if they are in a "Date Time" field # as opposed to a Date.Time field - else if("Date Time" %in% colnames(inputFile)){ - startDate <-min(as.Date(inputFile$`Date Time`, format = "%m/%d/%Y")) + else if ("Date Time" %in% colnames(inputFile)) { + startDate <- min(as.Date(inputFile$`Date Time`, format = "%m/%d/%Y")) endDate <- max(as.Date(inputFile$`Date Time`, format = "%m/%d/%Y")) } #Recognizes dates if they are in a Date.Time field else { - startDate <-min(as.Date(inputFile$Date.Time, format = "%m/%d/%Y")) + startDate <- min(as.Date(inputFile$Date.Time, format = "%m/%d/%Y")) endDate <- max(as.Date(inputFile$Date.Time, format = "%m/%d/%Y")) } @@ -143,7 +159,7 @@ timeFormatter <- function(time) { #Converts a string of USGS site IDs (comma delimited) #into an array of site IDs for gage data retrieval USGSsiteParser <- function(siteIDs) { - USGSsiteVector <- unlist(strsplit(siteIDs, split=", ")) + USGSsiteVector <- unlist(strsplit(siteIDs, split = ", ")) return(USGSsiteVector) } @@ -203,7 +219,7 @@ renameAggOutput <- function(directory, fileAttribsTable) { #Populates vectors for the input and output files if the input files are #output from the Aggregate process - if ("DATA_DATA" %in% substr(allFiles,1,9)){ + if ("DATA_DATA" %in% substr(allFiles,1,9)) { csvInputs <- list.files(directory, pattern = "^DATA_QC.*csv") csvOutput <- list.files(directory, pattern = "DATA_DATA_QC.*csv") htmlOutput <- list.files(directory, pattern = "DATA_DATA_QC.*html") @@ -227,7 +243,7 @@ renameAggOutput <- function(directory, fileAttribsTable) { data.type.inputs <- vector() #Extracts the data type from all input files - for (i in seq_len(length(csvInputs))){ + for (i in seq_len(length(csvInputs))) { csvInputs.parts <- strsplit(csvInputs[i], myDelim) data.type.inputs.number <- which(csvInputs.parts[[1]] == fileAttribsTable[1, 2]) + 1 @@ -243,9 +259,9 @@ renameAggOutput <- function(directory, fileAttribsTable) { #Changes the output file names if the input files have different data types #and have the same start and end dates - if(data.type.inputs[1] != data.type.inputs[2] + if (data.type.inputs[1] != data.type.inputs[2] && minStartDate == maxStartDate - && minEndDate == maxEndDate){ + && minEndDate == maxEndDate) { #Creates a data.frame for converting the input data types to the # output data type @@ -259,8 +275,8 @@ renameAggOutput <- function(directory, fileAttribsTable) { , combined.data.type) #Finds the rows in the conversion data.frame which have that date type - type1 <- which(data.type.conversion[,1]==data.type.inputs[1]) - type2 <- which(data.type.conversion[,2]==data.type.inputs[2]) + type1 <- which(data.type.conversion[,1] == data.type.inputs[1]) + type2 <- which(data.type.conversion[,2] == data.type.inputs[2]) #Finds the one row on the conversion data.frame that corresponds to # those data types @@ -268,20 +284,20 @@ renameAggOutput <- function(directory, fileAttribsTable) { , 3]) #Changes the data type in the output csv to the correct datatype - csvOutput.data.type <- gsub(paste("_", data.type.inputs[1], "_", sep=""), - paste("_", output.type, "_", sep=""), + csvOutput.data.type <- gsub(paste("_", data.type.inputs[1], "_", sep = ""), + paste("_", output.type, "_", sep = ""), csvOutput) #Sometimes the Aggregate process uses the second data type in its output # name. #This captures that eventuality. - csvOutput.data.type <- gsub(paste("_", data.type.inputs[2], "_", sep=""), - paste("_", output.type, "_", sep=""), + csvOutput.data.type <- gsub(paste("_", data.type.inputs[2], "_", sep = ""), + paste("_", output.type, "_", sep = ""), csvOutput.data.type) #Removes the "_append_x" from the output csv name - csvOutput.data.type <- gsub(paste("_Append_", length(csvInputs), sep=""), - paste("", sep=""), + csvOutput.data.type <- gsub(paste("_Append_", length(csvInputs), sep = ""), + paste("", sep = ""), csvOutput.data.type) #Renames the csv @@ -290,25 +306,25 @@ renameAggOutput <- function(directory, fileAttribsTable) { #Changes the data type in the output html to the correct datatype - htmlOutput.data.type <- gsub(paste("_", data.type.inputs[1], "_", sep=""), - paste("_", output.type, "_", sep=""), + htmlOutput.data.type <- gsub(paste("_", data.type.inputs[1], "_", sep = ""), + paste("_", output.type, "_", sep = ""), htmlOutput) #Sometimes the Aggregate process uses the second data type in its output # name. #This captures that eventuality. - htmlOutput.data.type <- gsub(paste("_", data.type.inputs[2], "_", sep=""), - paste("_", output.type, "_", sep=""), + htmlOutput.data.type <- gsub(paste("_", data.type.inputs[2], "_", sep = ""), + paste("_", output.type, "_", sep = ""), htmlOutput.data.type) #Removes the "_append_x" from the output html name - htmlOutput.data.type <- gsub(paste("_Append_", length(csvInputs), sep=""), - paste("", sep=""), + htmlOutput.data.type <- gsub(paste("_Append_", length(csvInputs), sep = ""), + paste("", sep = ""), htmlOutput.data.type) #Removes the "_Report_Aggregate" from the output html name - htmlOutput.data.type <- gsub(paste("_Report_Aggregate", sep=""), - paste("", sep=""), + htmlOutput.data.type <- gsub(paste("_Report_Aggregate", sep = ""), + paste("", sep = ""), htmlOutput.data.type) #Renames the html output @@ -334,27 +350,27 @@ renameAggOutput <- function(directory, fileAttribsTable) { #Determines how many characters should be removed from end of csv output # name (through the (inaccurate) second date in the name, which is not the # latest date of measurement) - csvCharsToRemove <- 8+1+8+1+6+1+nchar(numFiles)+4 + csvCharsToRemove <- 8 + 1 + 8 + 1 + 6 + 1 + nchar(numFiles) + 4 # date min (8) + sep (1) + date max (8) + sep (1) + append (6) + # NumFiles + .ext (4) #Removes the specified number of characters - csvNewName <- substr(csvOutput, 0, nchar(csvOutput)-csvCharsToRemove) + csvNewName <- substr(csvOutput, 0, nchar(csvOutput) - csvCharsToRemove) #Adds the last recorded date to the truncated file name - csvNewName <- paste(csvNewName, minDate, "_", maxDate, ".csv", sep="") + csvNewName <- paste(csvNewName, minDate, "_", maxDate, ".csv", sep = "") #Replaces the old file name with the new one file.rename(file.path(".", "data", csvOutput) , file.path(".", "data", csvNewName)) #Same as above but for the HTML reports - htmlCharsToRemove <- 8+1+8+1+6+1+nchar(numFiles)+1+6+1+8+6 + htmlCharsToRemove <- 8 + 1 + 8 + 1 + 6 + 1 + nchar(numFiles) + 1 + 6 + 1 + 8 + 6 # date min (8) + sep (1) + date max (8) + sep (1) + append (6) + NumFiles + # sep (1) + Report (6) + sep (1) + Aggregate (8) + .ext (6?) - htmlNewName <- substr(htmlOutput, 0, nchar(htmlOutput)-htmlCharsToRemove) - htmlNewName <- paste(htmlNewName, minDate, "_", maxDate, ".html", sep="") + htmlNewName <- substr(htmlOutput, 0, nchar(htmlOutput) - htmlCharsToRemove) + htmlNewName <- paste(htmlNewName, minDate, "_", maxDate, ".html", sep = "") file.rename(file.path(".", "data", htmlOutput) , file.path(".", "data", htmlNewName)) @@ -386,7 +402,7 @@ nameParse <- function(strFile, process) { ,tolower(substring(strFile.DataType ,2 ,nchar(strFile.DataType))) - ,sep="") + ,sep = "") strFile.Date.Start <- as.Date(strFile.parts[[1]][4],"%Y%m%d") strFile.Date.End <- as.Date(strFile.parts[[1]][5],"%Y%m%d") } @@ -401,7 +417,7 @@ nameParse <- function(strFile, process) { ,tolower(substring(strFile.DataType ,2 ,nchar(strFile.DataType))) - ,sep="") + ,sep = "") strFile.Date.Start <- as.Date(strFile.parts[[1]][5],"%Y%m%d") strFile.Date.End <- as.Date(strFile.parts[[1]][6],"%Y%m%d") } @@ -415,7 +431,7 @@ nameParse <- function(strFile, process) { ,tolower(substring(strFile.DataType ,2 ,nchar(strFile.DataType))) - ,sep="") + ,sep = "") strFile.Date.Start <- as.Date(strFile.parts[[1]][3],"%Y%m%d") strFile.Date.End <- as.Date(strFile.parts[[1]][4],"%Y%m%d") } diff --git a/inst/shiny-examples/ContDataQC/miniDOT_reformat/7392-354869.csv b/inst/shiny-examples/ContDataQC/miniDOT_reformat/7392-354869.csv new file mode 100644 index 00000000..97e3b7ac --- /dev/null +++ b/inst/shiny-examples/ContDataQC/miniDOT_reformat/7392-354869.csv @@ -0,0 +1,15913 @@ +"Date.Time","Battery","Water.Temp.C","DO.mg.L","DO.pctsat.percent","Q","SiteID" +"2019-12-04 21:54:00","3.44","17.667","8.741",NA,"1.005","7392-354869" +"2019-12-04 22:24:00","3.44","17.878","8.706",NA,"1.006","7392-354869" +"2019-12-04 22:54:00","3.44","18.156","8.617",NA,"1.005","7392-354869" +"2019-12-04 23:24:00","3.44","18.442","8.575",NA,"1.006","7392-354869" +"2019-12-04 23:54:00","3.45","18.644","8.556",NA,"1.006","7392-354869" +"2019-12-05 00:24:00","3.45","18.854","8.539",NA,"1.007","7392-354869" +"2019-12-05 00:54:00","3.45","19.047","8.515",NA,"1.006","7392-354869" +"2019-12-05 01:24:00","3.45","19.223","8.468",NA,"1.006","7392-354869" +"2019-12-05 01:54:00","3.45","19.391","8.442",NA,"1.005","7392-354869" +"2019-12-05 02:24:00","3.45","19.499","8.46",NA,"1.007","7392-354869" +"2019-12-05 02:54:00","3.45","19.617","8.415",NA,"1.005","7392-354869" +"2019-12-05 03:24:00","3.45","19.726","8.415",NA,"1.006","7392-354869" +"2019-12-05 03:54:00","3.45","19.843","8.423",NA,"1.006","7392-354869" +"2019-12-05 04:24:00","3.45","19.968","8.412",NA,"1.006","7392-354869" 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+"2020-11-07 23:17:00","3.08","13.103","6.792",NA,"1.114","7392-354869" +"2020-11-07 23:47:00","3.08","13.286","6.78",NA,"1.113","7392-354869" +"2020-11-08 00:17:00","3.08","13.496","6.711",NA,"1.114","7392-354869" +"2020-11-08 00:47:00","3.08","13.487","6.725",NA,"1.113","7392-354869" +"2020-11-08 01:17:00","3.07","13.269","6.709",NA,"1.113","7392-354869" +"2020-11-08 01:47:00","3.07","13.374","6.814",NA,"1.113","7392-354869" +"2020-11-08 02:17:00","3.08","13.243","6.705",NA,"1.113","7392-354869" +"2020-11-08 02:47:00","3.08","13.155","6.751",NA,"1.113","7392-354869" +"2020-11-08 03:17:00","3.07","13.111","6.727",NA,"1.113","7392-354869" +"2020-11-08 03:47:00","3.07","12.997","6.74",NA,"1.113","7392-354869" +"2020-11-08 04:17:00","3.08","12.83","6.813",NA,"1.114","7392-354869" +"2020-11-08 04:47:00","3.07","12.645","6.915",NA,"1.113","7392-354869" +"2020-11-08 05:17:00","3.07","12.46","6.977",NA,"1.114","7392-354869" +"2020-11-08 05:47:00","3.08","12.319","6.985",NA,"1.113","7392-354869" +"2020-11-08 06:17:00","3.07","12.186","6.993",NA,"1.113","7392-354869" +"2020-11-08 06:47:00","3.07","12.053","6.973",NA,"1.113","7392-354869" +"2020-11-08 07:17:00","3.08","11.92","6.958",NA,"1.114","7392-354869" +"2020-11-08 07:47:00","3.07","11.786","6.932",NA,"1.114","7392-354869" +"2020-11-08 08:17:00","3.07","11.67","6.87",NA,"1.114","7392-354869" +"2020-11-08 08:47:00","3.07","11.554","6.792",NA,"1.113","7392-354869" diff --git a/inst/shiny-examples/ContDataQC/rmd/App_1a_Overview.rmd b/inst/shiny-examples/ContDataQC/rmd/App_1a_Overview.rmd new file mode 100644 index 00000000..47c9af6e --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_1a_Overview.rmd @@ -0,0 +1,74 @@ +--- +title: "Overview" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE, fig.align="center") +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# About ContDataQC + +The ContDataQC Shiny app is a free R-based tool that was developed to help water quality monitoring programs quality control (QC), merge and summarize continuous sensor data files in a standardized, more efficient way. It also has a function that allows users to download U.S. Geological Survey gage data at sites and over periods of their choosing. This Shiny app is linked to the ContDataQC R package, which was developed for EPA by Tetra Tech in support of the EPA and is +available on GitHub + + Exit EPA Website + + . + +The ContDataQC R package and Shiny app were developed with the following objectives in mind: ensure that a certain (minimum) level of QC is being performed on continuous data files; standardize and speed up data QC and reduce missed errors; and standardize data for further analysis and sharing. ContDataQC is currently configured for temperature, water level, discharge, conductivity, dissolved oxygen (DO), pH, turbidity, chlorophyll-a and salinity data. Users with advanced R skills can add any other parameters that they desire by making edits to the configuration file, which can be viewed in R or in Notepad. + +# ContDataQC Shiny App Workflow + +The table below lays out the general workflow for using the ContDataQC app. + +```{r} +library(readxl) +library(knitr) +library(kableExtra) +# state directories +table.dir <- "tables" +table.file <- "App_Information_Tables.xlsx" +table.sheet <- "About_Overview" + +table <- read_excel(file.path(table.dir, table.file), sheet = table.sheet + , na = c("NA", ""), trim_ws = TRUE, skip = 0 + , col_names = TRUE) + +options(knitr.kable.NA = '') +# kable(table1) +table %>% + kbl() %>% + kable_styling(full_width = F, position = "left") +``` + +# Funding + +Development of the ContDataQC R Shiny app has been funded by the United States +Environmental Protection Agency (EPA) Office of Research and Development (ORD) as part of a larger project to establish Regional Monitoring Networks (RMNs) for freshwater streams and inland lakes. The RMNs are a volunteer, collaborative effort in which entities collect long-term continuous temperature and hydrologic data at targeted sites to detect changes +over time. For more information on the RMNs, please refer to the +Regional Monitoring Networks (RMN) to Detect Changing Baselines in Freshwater Wadeable Streams (Final Report). + +```{r} + +# Removed this from the end of directly above because it is not an external link +# +# +# Exit EPA Website +# +# . +``` + +# Disclaimer + +[NOTICE] The ContDataQC application has not yet received final approval by EPA, a formal peer review is pending. Be advised that any text, figures, or data provided here do not necessarily reflect the views or policies of the EPA. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_1b_TestData.rmd b/inst/shiny-examples/ContDataQC/rmd/App_1b_TestData.rmd new file mode 100644 index 00000000..52a387f7 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_1b_TestData.rmd @@ -0,0 +1,47 @@ +--- +title: "Test Date" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +editor_options: + markdown: + wrap: 72 +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +## Test Files +Below are test files that you can download and run through the Shiny app. They are from Onset HOBO U20 water level loggers (non-vented pressure transducers) and miniDOT DO sensors. Please contact us if you have example files from other sensors that you would like us to add to this website. + +* [Onset HOBO U20](TestData_HOBOU20_20220822.zip) [ZIP] +* [miniDOT DO](TestData_miniDOT.zip) [ZIP] + +## Onset HOBO U20 +The Onset HOBO U20 test data consist of two comma-separated values (CSV) files. The files are from the same site but cover two different time periods. You'll see two folders: + +* **Test** + + + The two HOBO U20 test files are in the Data0_Original folder. Go to the **Data Preparation >> Automated Reformatting >> HOBOware Reformat** tab and run both files through the reformatting function. Multiple files can be run through the function at the same time (i.e., in ‘batches’). The function reformats the files so they are ready to run through the QC report function. Next, go to the **Main Functions >> Run Functions** tab, import the reformatted files, run them through the ‘QC raw data’ function (in drop-down menu; multiple files can be run through the function at the same time), check the flagged data (and edit if needed), aggregate the two QC’d CSV files with the ‘Aggregate QC’d data’ function (in drop-down menu), and run the ‘Summary Statistics’ function (in drop-down menu) to summarize the data. + +* **Completed** + + + This folder has the complete set of outputs for both files so that you can see examples of the QC reports and other outputs without having to run the test data through the functions yourself. + +## miniDOT DO + +The miniDOT DO test files are from one site and one deployment period. +You’ll see two sets of folders. Within each folder are example input and output files. + +* **miniDOT_concatenate** - When data are initially downloaded from miniDOT sensors, there are separate .txt files for each day (in this example, 325 individual files). Go to the **Data Preparation >> Automated Reformatting >> miniDOT Concatenate** tab and run the **Concatenate** function to combine them all into one file. + +* **miniDOT_reformat** – Go to the **Data Preparation >> Automated Reformatting >> miniDOT Reformat** tab and run the combined file through the Reformat function. The function reformats the file so that it is ready to run through the QC report function. Next, go to the **Main Functions >> Run Functions** tab, import the reformatted file, run it through the ‘QC raw data’ function, and check the flagged data (edit if needed). diff --git a/inst/shiny-examples/ContDataQC/rmd/App_1c_FAQ.rmd b/inst/shiny-examples/ContDataQC/rmd/App_1c_FAQ.rmd new file mode 100644 index 00000000..fe24de51 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_1c_FAQ.rmd @@ -0,0 +1,59 @@ +--- +title: "Basic Information" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Basic Information + +**Requirements:** You do not need R software installed on your computer to run this Shiny app. You only need an internet connection. + +**Failure to run:** When the Shiny app fails to run, the screen will gray out and the message 'disconnected from the server' will appear. The problem typically stems from a formatting issue with the input file, in particular the Date/Time field. Check your input file, refresh the application and try again. If you still have problems, click the 'Contact Us' link at the bottom of this page and request assistance. Be prepared to share a copy of your input file(s) and the error message. + +**Speed:** If you have a slow internet connection, you can run ContDataQC as a Shiny app on your local computer and it will likely be faster. Running it locally requires that you have R software and the ContDataQC R package installed on your computer, which can be downloaded from GitHub + + Exit EPA Website + + . + +**Internet browsers:** This Shiny app has been tested and run successfully with Google Chrome, Firefox and Microsoft Edge. It may be compatible with other browsers but they have not been tested. Google Chrome is most frequently used. Some browsers (like Chrome and Firefox) allow users to specify what folder to download files to, which can save time. + +**Limits on input file(s):** The input file size limit is currently set to 70 MB. This can be increased upon request (see Contact Us). There is no limit on the number of files you can upload but the more files you run at once, the longer the processing time. It's often faster and easier to run smaller file batches (e.g., 4 smaller batches of 1 to 5 files versus 20 files all at once). + +**Security:** Other people cannot see or download your files from the application. Each time the application is run it is independent from all other versions. That is, each user has their own copy of the app. Once the browser is closed, that version is removed from the server along with all of the uploaded data. + +**Simultaneous use:** Because this Shiny app is on a Shiny server, multiple people can use it at the same time. + +**Customizing QC test thresholds:** Instructions on how to evaluate and customize thresholds can be found on the **Main Functions >> QC Thresholds page.** + +**Usability on phones:** Mobile use of the ContDataQC Shiny app is possible, although the screen size of a phone may make it impractical. + +**Data summary and visualization:** The ContDataSumViz Shiny app, which was designed for data visualization and reporting for continuous monitoring data, has more summary and visualization options. A beta version of this Shiny app is available at: ContDataSumViz + + Exit EPA Website + + + +R code is available at: GitHub + + Exit EPA Website + + + +**Downloading USGS gage data:**You can simultaneously download data from different USGS gages but only for the same time period. + +**Aggregating files with overlapping dates:** If you run the aggregate function on input files with overlapping date ranges, all records (even duplicates) are included in the output. It is up to the user to identify when these situations are occurring and remove the duplicate rows before running the files through the various functions. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_1d_Tips.rmd b/inst/shiny-examples/ContDataQC/rmd/App_1d_Tips.rmd new file mode 100644 index 00000000..8d6f3614 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_1d_Tips.rmd @@ -0,0 +1,102 @@ +--- +title: "Main Functions 5c" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Tips + +Below are tips and links to resources from the Regional Monitoring Network (RMN) project. To date, RMN partners have worked primarily with thermal and hydrologic data, and more recently, with dissolved oxygen data from lakes. The data are typically downloaded 2-4 times a year (i.e., the data used are not ‘real-time’ data). + +## Site visits + +It helps speed up the Quality Control (QC) process if you follow a checklist during site visits and document anything that might affect the quality of the data (e.g., sensor out of water or buried in sediment, beaver activity, low battery). + +* **Resource:** [Site visit checklist](SiteVisitChecklist.zip) [ZIP] + +## Sensor configuration + +Some people have had problems with air and water sensors being out of sync ( +e.g., one records at 11:00 and the other records at 11:07). If you are deploying +air and water sensors at a site, make sure you configure them so that they are +recording at the same time. This will make data processing faster and easier. + +* **Resource:** [Onset HOBO configuration tips](HOBO_ConfigLaunch_20170803.pdf) [PDF] + +Another issue that sometimes occurs is data overlap (where more than one file has measurements covering part of the same time periods). Make sure you clear the sensor's memory when you download data and relaunch sensors to avoid overlapping data. + +* **Resource:** [Onset HOBO download and relaunch tips](HOBO_DataDownload_20170823.pdf) [PDF] + +## QC workflow + +There are generally two QC workflow scenarios: 1) users QC their data after each download +and work with one file per site at a time; or 2) users have a backlog of data +that cover multiple deployment periods that they aggregate into one file before +performing QC. + +* **Resources:** Suggested workflows for each scenario: + + [Single file](Workflow_QC_report_20220824.pdf) [PDF] + + [Multiple files](Workflow_DataPileup_20220824.pdf) [PDF] + +## Data edits + +When reviewing the QC reports, it is important to be consistent in how you +handle flagged data. Here is an example approach: + +* If you are certain a data point is erroneous, delete the measurement and flag +as ‘F’. If you are not sure, flag the data point as ‘S’ and do not delete. Let the +people using the data decide whether to remove questionable measurements from +their analysis. + +* Document that you checked each point flagged as ‘F’ and ‘S’ by adding a note +or data qualifier to the Comment column. + +* Leave cells with missing data as they are (i.e., do not delete them). + +* **Resource:** [List of example data qualifiers](DataQualifiers_20220210.xlsx) [XLSX] + + +## Accuracy checks + +Accuracy checks are comparisons of discrete or in situ measurements taken in the +lab and/or in the field with sensor measurements from the closest date/time. The +difference between the sensor and discrete measurements should be within the +accuracy quoted by the manufacturer (e.g., ±0.2°C if you are using the Onset +HOBO proV2 sensor). + +* **Resource:** [Example accuracy check worksheet](EXAMPLE_AccuracyCheckWkst.xlsx) [XLSX] + +## Visual checks of time series plots + +Visual checks of time series plots are an important part of the QC process. Some issues, such as dewatering, sediment burial, ice cover, and beaver activity, show fairly common patterns and we've been compiling examples of these patterns to help people recognize potential QC issues with their data. + +* **Resource:** [Visual checks](PlotQC_WatchList_20220824.pdf) [PDF] + +## Checking sensor data against other data sources + +Some partners have been downloading data from nearby weather stations and USGS +gages, as well as modeled air temperature and precipitation data from sources +such as Daymet (Daymet + + Exit EPA Website + + ), and comparing those data to sensor measurements as part of their QC process. + +* **Resource:** [Daymet or USGS gage check](Daymet_Wx_Gage.zip) [ZIP] + +## Troubleshooting + +This tab in the ContDataQC shows output from the R console when running the QC, aggregating, summarizing, and USGS data retrieval processes. This information could be used to help understand and troubleshot why there might be an error or issue with the application. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_1e_Advanced.rmd b/inst/shiny-examples/ContDataQC/rmd/App_1e_Advanced.rmd new file mode 100644 index 00000000..85e383b3 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_1e_Advanced.rmd @@ -0,0 +1,28 @@ +--- +title: "Advanced" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` +# Advanced + +Users with intermediate to advanced-level computer skills can customize the settings of the ContDataQC app (e.g., by adding new parameters, changing units, changing file naming and formatting requirements, changing the format of one of the QC reports from Word to html or PDF) by editing a plain text [configuration file](Config_default.zip) [ZIP]. You can do this in R or Notepad. + +Some users may prefer to use the ContDataQC +R code + + Exit EPA Website + + instead of the Shiny app. While using the R code allows for some additional layers of customization, when it comes to file naming schemes and setting directories, users have more flexibility using the Shiny app because they can browse to the desired input file(s). diff --git a/inst/shiny-examples/ContDataQC/rmd/App_1f_RelatedApps.rmd b/inst/shiny-examples/ContDataQC/rmd/App_1f_RelatedApps.rmd new file mode 100644 index 00000000..103650fc --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_1f_RelatedApps.rmd @@ -0,0 +1,131 @@ +--- +title: "Related Apps" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results = 'asis', echo = FALSE, warning = FALSE, message = FALSE, fig.align = "center") +# needed for trouble shooting +boo_DEBUG <- FALSE +if (boo_DEBUG == TRUE) { + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Related Apps + +Below are links to some additional Shiny apps and/or R code that may also be useful for working with continuous sensor data. Some are stored on the NALMS Shiny server + + Exit EPA Website + + . + +### ContDataSumViz, for summarizing and visualizing QC’d continuous sensor data + +Shiny app (beta version): ContDataSumViz + + Exit EPA Website + + + +R code: GitHub: ContDataSumViz + + Exit EPA Website + + + +### Logger Processing App, for processing, organizing, and vetting continuous environmental logger data + +This app has the same QC flag tests as ContDataQC plus more, including an interactive plot that allows you to flag or unflag data points. + +Shiny app: Aquatic Ecological Logger Processing App + + Exit EPA Website + + + +GitHub: GitHub: logger-processing + + Exit EPA Website + + + +### LakeMonitoR, for summarizing and visualizing QC’d continuous vertical profile data for lakes + +Several of its outputs are based on the rLakeAnalyzer R package (see below). + +Shiny app: LakeMonitoR + + Exit EPA Website + + + +GitHub: GitHub: LakeMonitoR + + Exit EPA Website + + + +### rLakeAnalyzer, an R package for summarizing and visualizing continuous vertical profile data for lakes + +Available online: rLakeAnalyzer + + Exit EPA Website + + + +Read, J.S., Hamilton, D.P., Jones, I.D., Muraoka, K., Winslow, L.A., Kroiss, R., Wu, C.H., Gaiser, E., 2011. Derivation of lake mixing and stratification indices from high-resolution lake buoy data. Environmental Modelling & Software 26: 1325e1336. + +Winslow, L, J. Read, R. Woolway, J. Brentrup, T. Leach, J. Zwart, S. Albers, and D. Collinge. 2019. rLakeAnalyzer: Lake Physics Tools. R Package version 1.11.4.1. + +### Regional Monitoring Network (RMN) protocol documents for deploying continuous temperature, water level, or DO sensors and time lapse cameras in streams or lakes + +Shiny app: RMN Documents/ + + Exit EPA Website + + + +### Water Resources for Shiny, a resource for both those who are interested in learning how to create Shiny apps and those who are already experienced with making apps but want to see what other people are doing and what tools and packages are available for the community to use + +Available online: Shiny_for_Water_Resources/ + + Exit EPA Website + + + +### driftR, an R package that corrects drift in water quality data + +This package implements either one- or two-point variable data corrections based on the number of standards used to calibrate the sensor of interest, then linearly interpolates the correction over the period of interest. + +Available online: GitHub: driftR + + Exit EPA Website + + + +Shaughnessy, A.R., Prener, C.G. and E.A. Hasenmueller. 2019. An R package for correcting continuous water quality monitoring data for drift. Environmental Monitoring and Assessment 191, 445. +https://doi.org/10.1007/s10661-019-7586-x + + Exit EPA Website + + + +### R package for detecting outliers + +Available online: https://cran.r-project.org/web/packages/outliers/outliers.pdf + + Exit EPA Website + + [PDF] + +Campulova, M., Campula, R. and J. Holesovsky. 2022. An R package for identification of outliers in environmental time series data. Environmental Modelling & Software, Volume 155. https://www.sciencedirect.com/science/article/abs/pii/S1364815222001414 + + Exit EPA Website + + diff --git a/inst/shiny-examples/ContDataQC/rmd/App_2a1_HOBO.rmd b/inst/shiny-examples/ContDataQC/rmd/App_2a1_HOBO.rmd new file mode 100644 index 00000000..dec2f82a --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_2a1_HOBO.rmd @@ -0,0 +1,92 @@ +--- +title: "3d HOBOware reformat" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG == TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# HOBOware Reformat + +The HOBOware reformat function is for data files from Onset HOBO sensors. It converts them to a format that is ready to run through the QC report function (**Main Functions >> Run Functions >> QC raw data (from drop-down menu)**). This saves you time and eliminates errors that sometimes occur when manually reformatting files. + +HOBO sensor files must first be opened with HOBOware software, then processed and exported as CSV files. The instructions in the files below describe how to generate CSV files, allowing you to run them through the reformat function without manipulation: + +* [HOBOware default settings](HOBOware_DefaultSettings_20220824.pdf) [PDF] + +* [Temperature (proV2)](HOBOware_Temperature_20220825.pdf) [PDF] + +* [Water level (U20 series)](HOBOware_WaterLevel_U20_20220824.pdf) [PDF] + +* [Dissolved oxygen (U26)](HOBOware_DO_20220824.pdf) [PDF] + +### Instructions + +* Export CSV files from HOBOware (following the instructions above for whichever sensor type(s) you have) and place them into the Data0_Original folder. + +* Choose your file by clicking the ‘Browse’ button and selecting the input file(s). You can run multiple files at once. The status bar will tell you when the upload is complete. + +* Select the Date format in your input file (options: YMD, MDY, DMY) + +* After you select your input file click the ‘Reformat HOBOware file(s)’ button that appears; a status bar will appear in the lower right corner. + +* After the function is done running, a ‘Download’ button will appear. Click this button. + +* You will be prompted to save a zipped file named ‘format_HOBO_date_time’. Save the zipped file in the Data1_RAW folder. + +* Unzip/extract the file(s) or the files won’t be seen as an option to upload to the QC report function (**Main Functions >> Run Functions >> QC raw data (from drop-down menu)**). After file is unzipped, a new folder called ‘HOBO’ will appear. The original zipped file can be deleted if desired. + +Time-saver tip: some internet browsers, like Google Chrome and Mozilla Firefox, allow users to change settings so that users can select which folder to download files to (versus files going to the default Download folder). + +* Mozilla Firefox: go to Settings > General > Files and Applications > Check box for “always ask where to save files.” +* [Google Chrome file saving tip](GoogleChrome_TimesaverTips.pdf) [PDF] + +Your files are now ready to be run through the QC function! +Below are examples of what HOBO U20 CSV files look like before and after they are run through the reformat function. The reformat function does the following: + +* Changes column headings, as needed. + +* Creates a new ‘SiteID’ column and populates it with the SiteID that it takes from the first part of the input file name (the SiteID should precede the first underscore in the file name). + +* Creates RowID columns for air and water sensors. + +* Creates LoggerID fields for air and water sensors based on the LGR S/N entries in the column headings. + +* Removes the first row (Plot Title). + +If there is a problem with the input file, the screen will turn gray and it will say ‘disconnected from the server’ in the lower left. + + +## Example +Example input to and output from running the HOBOware reformat function on a HOBO U20 water level logger file: + +**Before** + +```{r, fig.alt="HOBO U-20 file before running the reformat function"} +knitr::include_graphics("RMD_Images/HOBOreformat_BEFORE.jpg") +``` + +**After** + +```{r, fig.alt="HOBO U-20 file after running the reformat function."} +knitr::include_graphics("RMD_Images/HOBOreformat_AFTER.jpg") +``` + +## Additional Notes + +* If you enter the wrong date format, the function will still run but your date field will be blank and you will not receive an error message. We recommend checking your output files to make sure the date field came through, as well as the other desired parameters (if those are not named correctly, they will be missing from the output file and you will not receive an error message). + +* Unwanted ‘logged’ text entries in the bottom row of the CSV: these occur if you forget to uncheck the ‘select internal logger events to plot’ boxes during Plot SetUp in HOBOware. + +* Two serial numbers in the column heading (you should only have one): make sure under Preferences-Export settings, you only have the ‘include logger serial number’ box checked, NOT the ‘include sensor serial number or label if available’ box). diff --git a/inst/shiny-examples/ContDataQC/rmd/App_2a2_miniDOT1.Rmd b/inst/shiny-examples/ContDataQC/rmd/App_2a2_miniDOT1.Rmd new file mode 100644 index 00000000..5b9e6f65 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_2a2_miniDOT1.Rmd @@ -0,0 +1,42 @@ +--- +title: "DataPrep_miniDOT_Cat, 3e" +output: html_fragment +--- +```{r setup, include=FALSE} +# --- +# title: "DataPrep_miniDOT_Cat" +# date: '2022-06-01' +# --- +knitr::opts_chunk$set(echo = FALSE) +``` + +# miniDOT DO Concatenate + +When data are initially downloaded from miniDOT sensors, there are separate +.txt files for each day (in this example, 325 individual files). Users have two options for combining them into one CSV file: 1) the **ContDataQC Concatenate** function; or 2) the **PME java app** that comes with the logger. + +## Instructions + +* Click the ‘Browse’ button and select all the individual files that you want to combine. The status bar will tell you when the upload is complete (be patient – you may not see anything happen for the first 30 seconds or so, depending on how many files you are uploading). + +* After you select your input file(s), click the ‘Concatenate’ button that appears; a status bar will appear in the lower right corner. + +* After the function is done running, a ‘Download’ button will appear. Click this button. + +* You will be prompted to save a zipped file named ‘miniDOT_cat_date_time’. Save the zipped file in the Data0_Original folder. + +* Unzip/extract the file(s) or the files won’t be seen as an option to upload to the Reformat function. After file is unzipped, a new folder called ‘miniDOT_cat’ will appear. The original zipped file can be deleted if desired. + +## Test files + +[miniDOT test data](TestData_miniDOT.zip) [ZIP] + +Below is an example of what the output file looks like after running the **Concatenate** function. + +```{r, fig.alt="miniDOT DO file after running the Concatenate function."} +knitr::include_graphics("RMD_Images/miniDOT_AFTER_Concatenate.jpg") +``` + +## Additional Notes + +If you're wondering why your DO saturation values are all NA, this occurs when no inputs are provided to calculate DO percent saturation. For more information, please see the miniDOT manual or contact the miniDOT support team. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_2a3_miniDOT2.Rmd b/inst/shiny-examples/ContDataQC/rmd/App_2a3_miniDOT2.Rmd new file mode 100644 index 00000000..7748b326 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_2a3_miniDOT2.Rmd @@ -0,0 +1,67 @@ +--- +title: "DataPrep_miniDOT_Reformat, 3f" +output: html_fragment +editor_options: + markdown: + wrap: 72 +--- + +```{r setup, include=FALSE} +# --- +# title: "DataPrep_miniDOT_Reformat" +# date: '2022-06-01' +# --- +knitr::opts_chunk$set(echo = FALSE) +``` + +# miniDOT DO Reformat + +After the daily files are concatenated, run the combined file through +the miniDOT DO Reformat function. The function reformats the file so that it is +ready to run through the QC report function. + +## Instructions + +- Click the 'Browse' button and select the input file(s). You can run + multiple files at once. The status bar will tell you when the upload + is complete. + +- After you select your input file(s), click the 'Reformat miniDOT file(s)' button that appears; a status bar will appear in the lower right corner. + +- After the function is done running, a 'Download' button will appear. + Click this button. + +- You will be prompted to save a zipped file named + 'miniDOT_reformat_date_time'. Save the zipped file in the Data1_RAW + folder. + +- Unzip/extract the file(s) or the files won't be seen as an + option to upload to the QC report function (Main Functions - QC raw + data). After file is unzipped, a new folder called + 'miniDOT_reformat' will appear. The original zipped file can be + deleted if desired. + +- See below for examples of files before and after reformatting. + +## Test files + +[miniDOT test data](TestData_miniDOT.zip) [ZIP] + +Below is an example of what files look like before and after running the **miniDOT DO Reformat** function. + +**Before** + + +```{r, fig.alt="miniDOT DO file before running the reformat function."} +knitr::include_graphics("RMD_Images/miniDOT_Before_Reformat.jpg") +``` + +**After** + +```{r, fig.alt="miniDOT DO file after running the reformat function."} +knitr::include_graphics("RMD_Images/miniDOT_After_Reformat.jpg") +``` + +## Additional Notes + +If you're wondering why your DO saturation values are all NA, this occurs when no inputs are provided to calculate DO percent saturation. For more information, please see the miniDOT manual or contact the miniDOT support team. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_2a_FormattingFiles.rmd b/inst/shiny-examples/ContDataQC/rmd/App_2a_FormattingFiles.rmd new file mode 100644 index 00000000..b0ccb096 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_2a_FormattingFiles.rmd @@ -0,0 +1,63 @@ +--- +title: "Formatting Files" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END + +# 20230911, file missing so recreated from Jen's "updates" +``` + +# Formatting Files + +Input files need to be formatted a specific way for the ContDataQC Shiny app and R package to work: + +* Input files must be comma-delimited (CSV) (but not CSV UTF-8 files). + +* There are two required fields: SiteID; and “Date Time” (with Date and Time combined into one field). + +* If using the default [configuration file](Config_default.zip) [ZIP], column headings must match the capitalization, spelling and symbology shown in the table below (for example, R will not recognize ‘sensor depth ft’; it needs to be ‘Sensor Depth ft’). + +* Either periods or spaces in the column headings are acceptable. For example, in the table below, air temperature is shown as ‘’Air Temp C’ but it can also be entered as ‘Air.Temp.C’. + +* For units, slashes, spaces or periods (e.g., DO mg/L, DO.mg.L or DO mg L) are acceptable. Do NOT use parentheses. + +* The columns do not have to be in any specific order, but column headings must match those shown in the table below. + +* Advanced users can add new parameters to the configuration file (beyond the ones show in the table below). + +* Optional fields include rowID, loggerID (serial number) and discrete measurements. Discrete (in situ) measurements can be entered for any parameter (just add ‘Discrete’ to the beginning of the column heading; for example, ‘Discrete Air Temp C’). The discrete data point will be included in the time series plots that are generated when the user runs the QC function. If using Onset HOBO sensors, you can save time by using the automated reformat function ('Data Preparation >> Automated Formatting >> HOBOware Reformat' tab). + + +## Data Formatting Information + +```{r table_1} +library(readxl) +library(knitr) +library(kableExtra) +# state directories +table.dir <- "tables" +table.file <- "App_Information_Tables.xlsx" +tab2a.dir <- "DataPrep_Table2c" + +table2c <- read_excel(file.path(table.dir, table.file), sheet = tab2a.dir + , na = c("NA", ""), trim_ws = TRUE, skip = 0 + , col_names = TRUE) + +options(knitr.kable.NA = '') +# kable(table1) +table2c %>% + kbl() %>% + kable_styling(full_width = F, position = "left") +``` diff --git a/inst/shiny-examples/ContDataQC/rmd/App_2az_About.rmd b/inst/shiny-examples/ContDataQC/rmd/App_2az_About.rmd new file mode 100644 index 00000000..238704b6 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_2az_About.rmd @@ -0,0 +1,32 @@ +--- +title: "Automated Reformating, About" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Automated Reformatting + +These functions convert files coming directly from sensors or proprietary sensor software like HOBOware into a format that is ready to run through the QC report function (**Main Functions >> Run Functions >> QC raw data (from drop-down menu)**). This saves you time and eliminates errors that sometimes occur when manually reformatting files. + +Currently, reformatting functions are available for two types of sensors: + +* Onset HOBO (currently water level (U20), temperature (pro V2 and Pendant), and DO (U26)) + +* miniDOT DO + +Reformatting functions for other types of sensors can be added in the future, if requested. + + diff --git a/inst/shiny-examples/ContDataQC/rmd/App_2b_OrganizingFiles.rmd b/inst/shiny-examples/ContDataQC/rmd/App_2b_OrganizingFiles.rmd new file mode 100644 index 00000000..e48dc0ae --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_2b_OrganizingFiles.rmd @@ -0,0 +1,46 @@ +--- +title: "3a Organizing Data" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results = 'asis', echo = FALSE, warning = FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if (boo_DEBUG == TRUE) { + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Organizing Files + +If you do not already have a good system for organizing continuous data files, consider setting up a folder for each site, and within each site folder, set up subfolders as shown in the table below. A quick way to get started is to download this [ready-made set of (empty) folders](SiteX_20230828.zip) [ZIP]. Put this file in the desired directory, unzip it and then replace ‘SiteX’ with the appropriate site name. + +You have more flexibility with file organization when using the ContDataQC Shiny app vs the R package. With the Shiny app, it is not necessary to name the folders exactly as shown below because you can browse to the desired input files and browse to save the output files to the desired location. However, if you decide to use the R package at some point, keeping the five ‘Data’ folders (Data0_Original, Data1_RAW, Data2_QC, Data3_Aggregated and Data4_Stats) will be important since the R code searches in specific folders for input files and outputs files to specific directories. Advanced users can modify the default directories by editing the configuration file. + + +### Folder descriptions +```{r table_1} +library(readxl) +library(knitr) +library(kableExtra) +# state directories +table.dir <- "tables" +table.file <- "App_Information_Tables.xlsx" +tab2a.dir <- "DataPrep_Table2a" + +table2a <- read_excel(file.path(table.dir, table.file), sheet = tab2a.dir + , na = c("NA", ""), trim_ws = TRUE, skip = 0 + , col_names = TRUE) + +options(knitr.kable.NA = '') +# kable(table1) +table2a %>% + kbl() %>% + kable_styling(full_width = F, position = "left") +``` diff --git a/inst/shiny-examples/ContDataQC/rmd/App_2c_NamingFiles.rmd b/inst/shiny-examples/ContDataQC/rmd/App_2c_NamingFiles.rmd new file mode 100644 index 00000000..4a11502c --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_2c_NamingFiles.rmd @@ -0,0 +1,51 @@ +--- +title: "3b Naming Files" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Naming Files + +You have more flexibility with naming files when using the ContDataQC Shiny app vs the R +package because you can browse to the desired input file(s), whereas the R +package searches through files in a specified directory and selects data files based on the file name. Even if you are using the Shiny app, consider using the file naming scheme below since you may decide to use the R package at some point in the future. Another reason to use the recommended file naming scheme is because it keeps files well-organized and sorts them in chronological order. This is particularly helpful if you are using HOBO U20 water level loggers, as it makes matching air and water sensor files easier during the processing step and helps you identify gaps in the data. + +If you are using the R package, there are four components to the file name for single sensor deployments, which are separated by underscores (“_”). + +**SiteID_DataType_StartDate_EndDate** + +Example: Hunting_Water_20130426_20130725 + +* SiteID (no spaces or underscores) = Hunting + +* Data Type options: Air, Water, AW (air and water sensor data are combined into the same file), Gage, DO + +* StartDate (YYYYMMDD) = 20130426 + +* EndDate (YYYYMMDD) = 20130725 + +For multi-sensor deployments (e.g., temperature sensors deployed at multiple depths at a lake), add depth and units or some other differentiating characteristic, such as sensor type, to the SiteID, separated by +double-dashes: for example, + +RussWood--02M_Water_20130426_20130725 + +Depth can include decimals (for example, 0.1M) and does not need to +include leading zeros (either 01M or 1M are acceptable). Units can be upper or lower case (M or m), and abbreviated (M) or spelled out (meters). + +You can change the double-dash separator if desired by customizing the +[configuration file](Config_default.zip) [ZIP] (for example, you could change it to a tilde ~). + +If you are using the ContDataQC Shiny app instead of the R package, you are not restricted to using the Data Types shown above (Air, Water, AW, etc.). If you have more than one type of sensor deployed at the same depth (e.g., both a DO and temperature sensor at 2M), consider using sensor type in the second part of the file name: for example, RussWood--02M_TEMP_20130426_20130725 or RussWood--02M_DO_20130426_20130725. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_2cz_DiscreteData.rmd b/inst/shiny-examples/ContDataQC/rmd/App_2cz_DiscreteData.rmd new file mode 100644 index 00000000..ee785683 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_2cz_DiscreteData.rmd @@ -0,0 +1,36 @@ +--- +title: "Discrete Data" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END + +# 20230911, new tab from Jen's "updates" +``` + +# Discrete (In Situ) Data + +Discrete measurements are important for validating your data, and in some cases, can be used to correct for sensor drift. Accuracy checks are comparisons of discrete or in situ measurements taken in the lab or in the field with sensor measurements from the closest date/time. The difference between the sensor and discrete measurements should be within the accuracy quoted by the manufacturer (e.g., ±0.2°C if you are using the Onset HOBO proV2 sensor). + +Discrete (in situ) measurements will be included in the time series plots in the QC reports (see example QC plot below) if the user manually enters them into the input file. This should be done after the file has been formatted (for example, after a HOBO file has been run through the Reformat function). + +To enter the discrete data: + +* Add a new column to the CSV, named 'Discrete' followed by the parameter name and units (for example, ‘Discrete Air Temp C’). + +* Enter the data into the row that most closely matches the date/time of the sensor measurement. + +* **Resource:** [Example accuracy check worksheet](EXAMPLE_AccuracyCheckWkst.xlsx) [XLSX] + +![Example QC plot with discrete data](RMD_Images/2.3_Discrete_Fig1_20230828.jpg){fig-alt="Example QC plot with discrete data plotted with black triangles. Scatterplot of Water Temperature (y-axis) against Date (x-axis). Some values are 'discrete' measurements."} diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3a1_About.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3a1_About.rmd new file mode 100644 index 00000000..a9cb3495 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3a1_About.rmd @@ -0,0 +1,43 @@ +--- +title: "Main Functions 5a" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Workflow + +1. **Select QC flag test thresholds.** Use the default thresholds (**Main Functions >> QC Thresholds >> Default** tab) or upload a customized configuration file (**Main Functions >> QC Thresholds >> Upload Custom Thresholds** tab). If you have one or more years of continuous data for a site, we encourage you to evaluate the performance of the QC test thresholds for each parameter at that site and customize the configuration file if needed. Click **Main Functions >> QC Thresholds >> Evaluate Thresholds** tab for tips on evaluating thresholds. To learn how to edit thresholds, click **Main Functions >> QC Thresholds >> Edit Thresholds** tab. + +2. **Check the configuration file name.** Make sure the correct configuration file has been uploaded prior to running the QC reports. + +3. **Upload the desired input file(s).** + +4. **Run the ‘QC raw data’** function to generate QC reports. + +5. **After the data file(s) are QC’d, users can run them through the Aggregate function** to merge them with other files. The input files need to be from the same site and share common parameters. + +6. **The user can run the aggregated file through the Summary Statistics function** to generate CSV files with an assortment of summary statistics (e.g., min, median, max, quartiles) and a PDF with time series plots for each parameter. + +After running each function, users must unzip/extract the files or the files won’t be seen as an option to upload for the next step. After files are unzipped, new folders will automatically appear and the original zipped files can be deleted if desired. + +Time-saver tip: some internet browsers, like Google Chrome and Mozilla Firefox, allow users to change settings so that users can select which folder to download files to (versus files going to the default Download folder). + +* Mozilla Firefox: go to Settings > General > Files and Applications > Check box for “always ask where to save files.” + +* [Google Chrome file saving tip](GoogleChrome_TimesaverTips.pdf) [PDF] + +Some users may prefer to use the R code instead of the Shiny app. +For more information, see the ‘About - Advanced’ tab diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3a2_QCReports.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3a2_QCReports.rmd new file mode 100644 index 00000000..08a867c0 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3a2_QCReports.rmd @@ -0,0 +1,61 @@ +--- +title: "Main Functions 5a" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +### QC reports + +**Example** [QC reports](Example_QCreports.zip) [ZIP] + +Instructions for generating QC reports using the ContDataQC Shiny app: + +1. Click the ‘Browse’ button and select the input file(s) from the Data1_RAW +folder. You can run multiple files at once. The status bar will tell you when +the upload is complete. + +2. Select ‘QC raw data’ from the ‘Choose operation to perform’ drop-down menu + +3. Click the ‘Run operation’ button will appears. A status bar will +appear in the lower right corner. + +4. After the function is done running, click the ‘Download’ button that appears. Click this button. + +5. You will be prompted to save a zipped file named ‘QCRaw.date_time.’ Save it in the Data2_QC folder. + +6. Unzip/extract the file(s). After file is unzipped, a new folder called ‘data’ will appear, which contains QC reports that have ‘QC_’ as the prefix in the file name. The original zipped file can be deleted if desired. + +7. Open the QC reports. There are two types of outputs: a Word document Data Quality Control Report and a comma-delimited (.csv) spreadsheet. If users prefer a Hypertext Markup Language (HTML) report instead of Word, they can change the format in the configuration file (this requires some familiarity with R code). The reports include results from the ‘flag’ tests (gross, spike, rate of change and flat line), missing data checks, and time series plots. + +8. Review each set of files, check flagged data points, do visual checks on the time series plots, and decide if and how to make corrections. For tips on the QC process, see the **About >> Tips** subtab. + +The Data Quality Control Report is organized into four main sections (starting with an overall summary followed by summaries for each parameter): + +1. Data file information (siteID, date range, parameters, recording interval, QC test thresholds reference table). + +2. ‘Count’ tables (# measurements/day) so that you can find inconsistencies +(too few measurements, indicating missing data, or too many measurements) for each parameter. + +3. Results from the four ‘flag’ tests (gross, spike, rate of change and flat line), which show the # of entries for each parameter marked as pass (P), suspect (S), fail (F), missing data (X) or not available (NA). + +4. Time series plots for each individual parameter and two sets of combined +parameters (water and air temperature; water temperature and water level). If the input file includes discrete/in situ values that were taken during site visits for accuracy checks, the discrete values are plotted with the sensor data. + +The CSV file contains the time series data from the sensor plus additional data columns with flags for each test and parameter (e.g., Flag.Spike.Water.Temp.C, Flag.Flat.Water.Temp.C), comment fields for each parameter (e.g., Comment.MOD.Water.Temp.C) so that the user can document rationale for any changes, and a duplicate set of the original ‘raw’ data values (e.g., RAW.Water.Temp.C). The ‘raw’ values are included so that the user can maintain documentation of changes that are made and if needed go back to change how they handled a correction. + +Currently there are no automated corrections programmed into the QC function. +The user must make the changes manually. For tips on the QC process, see the +‘About - Tips’ subtab. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3a3_Aggregate.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3a3_Aggregate.rmd new file mode 100644 index 00000000..0949cac3 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3a3_Aggregate.rmd @@ -0,0 +1,36 @@ +--- +title: "Main Functions 5a" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +### Aggregate Output + +**Example file** [Aggregate output](Example_Aggregate.zip) [ZIP] + +Instructions for generating aggregated reports using the ContDataQC Shiny app: + +1. Click the ‘Browse’ button and select the QC’d input files that you’d like to combine. The input files need to be from the same site and share common parameters. The status bar will tell you when the upload is complete. + +2. Select ‘Aggregate QC’d data’ from the ‘Choose operation to perform’ drop-down menu. + +3. Click the ‘Run operation’ button that appears. A status bar will appear in the lower right corner. + +4. After the function is done running, click the ‘Download’ button that appears. You will be prompted to save a zipped file named ‘Aggregate.date_time.’ Save it in the Data3_Aggregated folder. + +5. Unzip/extract the file(s). After file is unzipped, a new folder called ‘data’ will appear. If a ‘data’ folder already exists, outputs will go into that folder. The aggregate reports have ‘DATA_’ as the prefix in the file name. The original zipped file can be deleted if desired. + +6. Open and review the aggregated reports. As with the QC output, there are two types of outputs: a Word document Data Quality Control Report and a comma-delimited (.csv) spreadsheet. If users prefer a Hypertext Markup Language (HTML) report instead of Word, they can change the format in the configuration file (this requires some familiarity with R code). diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3a4_SummaryStats.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3a4_SummaryStats.rmd new file mode 100644 index 00000000..082ca2e0 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3a4_SummaryStats.rmd @@ -0,0 +1,38 @@ +--- +title: "Main Functions 5a" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +### Summary Statistics + +**Example file** [Summary Stats output](Example_SumStats.zip) [ZIP] + +Instructions for generating summary statistics reports using the ContDataQC Shiny app: + +1. Click the 'Browse' button and select the desired input file from the +Data3_Aggregated folder. The status bar will tell you when the upload is +complete. + +2. Select 'Summary statistics' from the 'Choose operation to perform' drop-down menu. + +3. Click the 'Run operation' button that appears. A status bar will appear in the lower right corner. + +4. After the function is done running, click the ‘Download’ button that appears. You will be prompted to save a zipped file named ‘SummaryStats.date_time.’ Save it in the Data4_Stats folder. + +5. Unzip/extract the file(s). After file is unzipped, a new folder called ‘data’ will appear. If a ‘data’ folder already exists, outputs will go into that folder. The summary stats reports have ‘STATS_’ as the prefix in the file name. The original zipped file can be deleted if desired. + +6. Open and review the summary statistics reports. There should be two sets of CSV files and a PDF with time series plots for each parameter. One of the CSVs has daily mean values; the other has a wider range of summary statistics (e.g., min, median, max, quartiles). diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3b_CheckInput.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3b_CheckInput.rmd new file mode 100644 index 00000000..b96c5f6a --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3b_CheckInput.rmd @@ -0,0 +1,29 @@ +--- +title: "Main Functions 5b" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Check input file before running the QC function + +After uploading the input file(s), check the summary table to see what +parameters are in the uploaded files. Ensure that all the desired parameters are +present before running the QC function. + +Note: sometimes people have a mix of correct and incorrect column headings in +the input file. When this happens, R does not give you an error message; it only +carries forward the parameters that have the correct column headings and leaves +the others blank. Thus, checking this summary table is important. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3c1_QCThresh_Defaults.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3c1_QCThresh_Defaults.rmd new file mode 100644 index 00000000..28bf4ea2 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3c1_QCThresh_Defaults.rmd @@ -0,0 +1,105 @@ +--- +title: "QC Thresholds 3a" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Default QC flag test thresholds + +If a customized configuration file is not uploaded prior to running the QC function, the default thresholds and units listed below will be used for the four QC flag tests. + +The QC function runs each data point in the input file through the following four 'flag' tests (see Default QC test thresholds table, below, for specific threshold values used in each flag test for each parameter). + +### Flag test descriptions + +```{r table_1} +library(readxl) +library(knitr) +library(kableExtra) +# state directories +table.dir <- "tables" +table.file <- "App_Information_Tables.xlsx" +tab3a1.dir <- "QCThresh_Table3a1" + +table3a1 <- read_excel(file.path(table.dir, table.file), sheet = tab3a1.dir + , na = c("NA", ""), trim_ws = TRUE, skip = 0 + , col_names = TRUE) + +options(knitr.kable.NA = '') +# kable(table1) +table3a1 %>% + kbl() %>% + kable_styling(full_width = F, position = "left") +``` + +The 'flag' tests were selected based on the following references: + +- U.S. Integrated Ocean Observing System, 2017. Manual for the Use of Real-Time Oceanographic Data Quality Control Flags, Version 1.1. 43 pp. +https://cdn.ioos.noaa.gov/media/2017/12/QARTOD-Data-Flags-Manual_Final_version1.1.pdf + + Exit EPA Website + + [PDF] + +- Wagner, R.J., Boulger, R.W., Oblinger, C.J., Smith, B.A., 2006. Guidelines and Standard Procedures for Continuous Water-quality Monitors: Station Operation, Record Computation, and Data Reporting. U.S. Geological Survey Techniques and Methods 1-D3, 51 pp. þ 8 attachments. +http://pubs.water.usgs.gov/tm1d3 + + Exit EPA Website + + . + +Each data point is flagged as one of the following codes by ContDataQC: + +- P = Pass + +- S = Suspect + +- F = Fail + +- X = No Data or Not Applicable (NA) + +The default thresholds for temperature are based on data from cool, freshwater, wadeable, medium to high gradient streams in the eastern US. Default thresholds for the hydrologic parameters, turbidity, and chlorophyll-a are set very high since values vary widely depending on site-specific factors such as stream size. Once you have one or more years of continuous data at a site, we strongly encourage you to evaluate the performance of the QC test thresholds for each parameter and customize the thresholds as needed by editing the configuration file. For more information, see the 'Upload Custom Thresholds' tab. + +Units are also an important consideration. The default configuration puts hydrologic parameters (e.g., water level) in feet, which is generally preferred for streams among RMN partners. However, with lakes, more people prefer meters. If units are changed from feet to meters but there is not a corresponding change in thresholds, the QC tests lose relevance. + +Configuration files can be downloaded here: + +- [Default](Config_default.zip) [ZIP], with hydrologic parameters in feet + +- [Customized](Config_Lakes_meters.zip) [ZIP], with hydrologic parameters in meters + +The table below shows the Fail and Suspect default QC test thresholds for each parameter as of 2023-08-28. + +### Default QC test thresholds + +```{r table_2} +library(readxl) +library(knitr) +library(kableExtra) +# state directories +table.dir <- "tables" +table.file <- "App_Information_Tables.xlsx" +tab3a2.dir <- "QCThresh_Table3a2" + +table3a2 <- read_excel(file.path(table.dir, table.file), sheet = tab3a2.dir + , na = c("NA", ""), trim_ws = TRUE, skip = 0 + , col_names = TRUE) + +# kable(table2) +table3a2 %>% + kbl() %>% + kable_styling(full_width = F, position = "left") +``` diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3c2_QCThresh_Upload.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3c2_QCThresh_Upload.rmd new file mode 100644 index 00000000..30139e06 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3c2_QCThresh_Upload.rmd @@ -0,0 +1,32 @@ +--- +title: "QC Thresholds 4d" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +## Upload Custom Thresholds + +Click the 'Browse' button (to the left) and select the desired customized +configuration file. The status bar will tell you when the file has been +uploaded. A new button will appear that gives you the option of returning to the default configuration file. Do not click this unless you change your mind and no longer want to use the customized file. + +Go to the ‘Run Functions’ tab; verify that the correct configuration file has been uploaded (by checking the file name under “2. Confirm correct configuration file is being used”); select the desired input file(s); and run them through the QC function. Save the output to the desired location. Open the Word QC report and scroll to the ‘Thresholds, Quick Reference’ table (in the middle section of the report) and verify that the correct thresholds were used. + +Be aware that each time you close and reopen the Shiny app, it will +automatically revert back to the default configuration file. Also – remember the sequencing! If you forget to upload the customized configuration file prior to running the QC function, it will use the default thresholds. + +## Test file +[Example customized configuration file](Custom_QC_Config_ECO66G20_test1.R) [R] diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3c3_QCThresh_Eval.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3c3_QCThresh_Eval.rmd new file mode 100644 index 00000000..a21be2e8 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3c3_QCThresh_Eval.rmd @@ -0,0 +1,30 @@ +--- +title: "QC Thresholds 4b" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +## Evaluate Thresholds + +If you have one or more years of continuous data for a site, we encourage you to evaluate the performance of the QC test thresholds for each parameter at that site and customize the configuration file if needed. Make sure you consider what units you are using, since units have a large effect on thresholds. + +**Resources for evaluating thresholds:** + +* [Pivot tables and charts](EvaluateThresholds.zip) [ZIP] for evaluating the Unrealistic values ('Gross range') and Spike tests in Excel. + +* [R code statistics](TimMartin_R_ThresholdEval.zip) [ZIP] that can help inform thresholds for all four QC tests. + +* [Threshold evaluation worksheet](ThresholdsCheckWorksheet_20220826.xlsx) [XLSX] that lists the default thresholds for each parameter and has columns where you enter customized thresholds for one or multiple sites. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_3c4_QCThresh_Edit.rmd b/inst/shiny-examples/ContDataQC/rmd/App_3c4_QCThresh_Edit.rmd new file mode 100644 index 00000000..429961be --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_3c4_QCThresh_Edit.rmd @@ -0,0 +1,101 @@ +--- +title: "QC Thresholds 4c" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +## Edit Thresholds + +There are two options for editing thresholds: + +* Download the default configuration file, open it in R or Notepad++, make edits, and save the revised file. + +* Use the interactive functions at the bottom of this page. + +For either option, include the StationID and other relevant information in the file name (e.g., perhaps it's customized for a particular season) and save it in a folder that is easy to find since you'll want to reuse the file each time you QC data for that site in the future. + +After you save the file, go to the 'Upload Custom Thresholds' tab, browse to the custom thresholds file you saved, upload that file, and then, under 'Run Functions', generate the QC reports. If you forget the upload step, the default QC thresholds will be used. + +### Option 1: download the default configuration file, open the file in R or Notepad++, make edits, and save. + +Configuration files can be downloaded here: + +* [Default](Config_default.zip) [ZIP], with hydrologic parameters in feet. + +* [Hydrologic parameters in meters](Config_Lakes_meters.zip) [ZIP]. + +Navigate to the ‘QC tests and calculations’ section of the configuration file, which is about halfway through the file. Edit thresholds as desired. Save the file. + +When you download the configuration file(s), you will see two types of files: an R file (.R) and a text (.txt) file for users who do not have R installed on their computer. You can download R from this website: +cran.r-project.org + + Exit EPA Website + + . +You may also want to download RStudio + + Exit EPA Website + + , which is a separate program but is a useful code editor and interface for R. If you cannot install R on your computer, we recommend using Notepad or +Notepad++ + + Exit EPA Website + + +(some people prefer Notepad++ because it has color-coding). + + +### Option 2: use the interactive functions on this page. + +* Select a parameter using the drop-down menu in 1. Select parameter (upper left of this page) + +* Edit thresholds for each fail test type using the boxes below. + +* After you make changes for each parameter, click ‘Save changes’ as you go (upper left of this page). Otherwise, when you switch to a different parameter, it won’t save the changes you made to the previous parameter. + +* Download the custom thresholds file. + +* Note that if you do not want to use the default units (see table below), you will need to use Option 1. + +```{r} +Parameter <- c("Temperature" + , "Sensor depth and water level" + , "Discharge" + , "Dissolved oxygen" + , "DO pctsat" + , "Conductivity" + , "Pressure" + , "pH" + , "Turbidity" + , "Salinity" + , "Chlorophylla") +Units <- c("Celsius" + , "feet" + , "ft3/s" + , "mg/L" + , "%" + , "uS/cm" + , "psi" + , "SU" + , "NTU" + , "ppt" + , "g/cm3") +df_param_units <- data.frame(Parameter, Units) +cap_param_units <- "Default Units" +knitr::kable(df_param_units, caption = cap_param_units) +``` + +[For more information, this link provides instructions for editing thresholds](EditingQCtestThresholds_20220117.pdf) [PDF] diff --git a/inst/shiny-examples/ContDataQC/rmd/App_4_USGSgage.rmd b/inst/shiny-examples/ContDataQC/rmd/App_4_USGSgage.rmd new file mode 100644 index 00000000..8c21570e --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_4_USGSgage.rmd @@ -0,0 +1,37 @@ +--- +title: "Download USGS gage data" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Instructions + +You can find USGS gage locations and IDs using the +National Water Information System Mapper + + Exit EPA Website + + . + +To download data (at the right): + +1. Enter as many USGS station IDs as desired, separated by commas and spaces (e.g., 01187300, 01493000, 01639500). + +2. Enter a starting and ending date for which data will be retrieved; the same date range will be used for every station. + +3. Click the 'Retrieve USGS data' button. A progress bar will appear in the bottom-right of the tab. It will advance as each file is completed (i.e., if you select three stations, it will wait at 0%, jump to 33%, jump to 66%, and then jump to 100%). + +4. After data retrieval completes, a download button will appear. Click the button to download a zip file of the records. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_5b_Console.rmd b/inst/shiny-examples/ContDataQC/rmd/App_5b_Console.rmd new file mode 100644 index 00000000..2419fed0 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_5b_Console.rmd @@ -0,0 +1,23 @@ +--- +title: "Console" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# R console output + +This panel shows messages output by the QC, aggregating, summarizing, and USGS +data retrieval processes. diff --git a/inst/shiny-examples/ContDataQC/rmd/App_9_Status.rmd b/inst/shiny-examples/ContDataQC/rmd/App_9_Status.rmd new file mode 100644 index 00000000..b80e5cad --- /dev/null +++ b/inst/shiny-examples/ContDataQC/rmd/App_9_Status.rmd @@ -0,0 +1,111 @@ +--- +title: "Status" +date: "`r format(Sys.time(), '%Y-%m-%d %H:%M:%S')`" +always_allow_html: true +output: + html_fragment: + toc: yes +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(results='asis', echo=FALSE, warning=FALSE, message = FALSE) +# needed for trouble shooting +boo_DEBUG <- FALSE +if(boo_DEBUG==TRUE){ + # myConfig <- file.path(system.file(package="ContDataQC"), "extdata", "config.ORIG.R") + # source(myConfig) +}## IF ~ boo_DEBUG ~ END +``` + +# Status + +### Who to contact if you have problems or comments +This is an open-source, collaborative effort and we welcome your feedback. If you encounter problems with the Shiny app and/or have suggestions on ways to further improve it, please contact Erik W. Leppo from Tetra Tech via email (Erik.Leppo@tetratech.com) or via a post on the ContDataQC GitHub page (https://github.com/leppott/ContDataQC/discussions + + Exit EPA Website + + ). + +### Experience to date +We have been working on and off on the ContDataQC R package and Shiny app since 2017. Development of the ContDataQC R tools has been funded primarily by the United States Environmental Protection Agency (EPA) Office of Research and Development (ORD) as part of a larger project to establish Regional Monitoring Networks (RMNs) for freshwater streams and inland lakes. The lead EPA scientist on the RMN work is Britta Bierwagen (bierwagen.britta@epa.gov). Shane Bowe from the Red Lake Band of Chippewa Indians has also funded parts of its development. + +So far ContDataQC has only been used on continuous data that is being manually downloaded periodically (generally one to three times a year). It has not been used on ‘real-time’ continuous data. It was originally developed for sensors deployed in streams at single depths but is now being used on lake sensor array data as well, where sensors are being deployed at multiple, fixed depths at the same site. To date, we have worked primarily with thermal and hydrologic data (the primary variables being collected at RMN sites), but users have expressed interest in using it on other parameters as well. The Shiny app is currently set up to run dissolved oxygen, conductivity, pH, turbidity and chlorophyll-a data but the default QC test thresholds for those parameters will need to be carefully checked and refined. + +### Plans for 2021-2022 +In fall of 2020, a major advancement occurred when we moved the Shiny app to the Tetra Tech Shiny server. This made the app much more accessible to RMN partners and to other outside users as well. EPA is planning to host the ContDataQC Shiny app on an EPA Shiny server as well. We expect this to happen over the coming year. + +We aren’t planning to make any major changes to the main functions in ContDataQC but will continue to work on the items below as resources permit. We’ll also be shifting our focus more to data summary and visualization, for which we are developing separate R packages and Shiny apps (for more information, see bottom of page). + +**Here are the ContDataQC items we’re currently working on -** + +* Improving the instructional materials + +* Refining the QC test thresholds, not just for temperature and hydrology (the primary variables being collected at RMN sites), but also for dissolved oxygen, conductivity, pH and turbidity. + +* Gaining a better understanding of the most common QC issues being encountered and how to best detect and address (or potentially prevent) those issues. + +* Improving guidance on addressing errors and flagged data points and documenting corrections and metadata. + +**ContDataQC ‘wish list’ items (that we lack funding to address but will tackle if resources permit) -** + +* Automated accuracy check + + + Users would upload in-situ files and the R code would automatically match the in-situ measurements with the closest sensor measurement, calculate the difference between the two measurements and report whether the sensor is meeting accuracy specs. + +* Weather station check + + + Users would upload weather station data and the R code would match the measurements with the sensor data, generate time series plots in which the measurements are overlaid on one another, and generate some basic statistical measures to compare the two sets of data. + +* Daily range check (max-min) + + + This QC check is used by the USFS NorWeST temperature sensor network. It would be added in as a new function. + +* ‘Transition’ check + + + We’re finding most errors concentrated around the times when the sensors are being taken out of position for data download. If you’re only QCing individual files after each download, you’re not evaluating the transition between data from the previous file and the new file. This check would bring in data from 1-2 days prior to the start time of the file being evaluated and alert users to data points that need to be trimmed. + +* Automatic adjustments to gross limit QC test thresholds based on season and depth + + + Right now the gross limit QC test is pretty limited because it doesn’t capture seasonal differences without the user creating multiple customized configuration files (and then the user would need to break up the input files for each time period). We’d like to figure out a way to have the R code automatically adjust the upper and lower gross limit thresholds based on seasonal considerations, and for lakes, for it to also make adjustments to gross limit thresholds based on depth. + +* Drift correction + + + We’d add a feature that automates drift corrections. + +**Other R packages and Shiny apps that may be of interest** + +Tim Martin from Minnesota DNR (tim.martin@state.mn.us) has been a great collaborator and has developed a QC app that includes the ContData QC flag tests as well as some other features (including an interactive plot in which data points are color-coded based on flags and the user can add or remove flags). The app is currently being hosted on the Tetra Tech Shiny server - +https://tetratech-wtr-wne.shinyapps.io/logger_processing/ + + Exit EPA Website + + +If you use the app, Tim welcomes your feedback! + +For summary and visualization of continuous lake data, we’re actively working on LakeMonitoR - +R package - https://leppott.github.io/LakeMonitoR/ + + Exit EPA Website + + +Shiny app - https://nalms.shinyapps.io/LakeMonitoR/ + + Exit EPA Website + + + +This work is being funded by Shane Bowe from the Red Lake Band of Chippewa Indians (via a BIA grant) and EPA ORD. + +Over the coming year, we’ll also be working on new tools for summarizing and visualizing the stream RMN data (thermal, hydrologic and biological data). + +There are other existing software programs for working with continuous data. One example is the open source Python-based tool by Horsburgh et al. (2015), which provides a data quality control work flow, allows for flagging of data based on visual inspection, can apply drift corrections and interpolate missing values (if desired) and saves both original and edited data for documentation. Other existing software options include a sensorQC R package by USGS (https://github.com/USGS-R/sensorQC + + Exit EPA Website + + ) and rLakeAnalyzer (Read et al. 2011, Winslow et al. 2018) (https://CRAN.R-project.org/package=rLakeAnalyzer + + Exit EPA Website + + ), which have statistical outlier detection tools. + + diff --git a/inst/shiny-examples/ContDataQC/rmd/RMD_Images/1.1_Fig1_About_20230828.jpg b/inst/shiny-examples/ContDataQC/rmd/RMD_Images/1.1_Fig1_About_20230828.jpg new file mode 100644 index 00000000..b9358350 Binary files /dev/null and b/inst/shiny-examples/ContDataQC/rmd/RMD_Images/1.1_Fig1_About_20230828.jpg differ diff --git a/inst/shiny-examples/ContDataQC/rmd/RMD_Images/1.6_Fig1_Advanced_20230828.jpg b/inst/shiny-examples/ContDataQC/rmd/RMD_Images/1.6_Fig1_Advanced_20230828.jpg new file mode 100644 index 00000000..95a9eb6a Binary files /dev/null and b/inst/shiny-examples/ContDataQC/rmd/RMD_Images/1.6_Fig1_Advanced_20230828.jpg differ diff --git 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a/inst/shiny-examples/ContDataQC/rmd/tables/miniDOTreformat_ AFTER_20240226.xlsx b/inst/shiny-examples/ContDataQC/rmd/tables/miniDOTreformat_ AFTER_20240226.xlsx new file mode 100644 index 00000000..2c40ca6c Binary files /dev/null and b/inst/shiny-examples/ContDataQC/rmd/tables/miniDOTreformat_ AFTER_20240226.xlsx differ diff --git a/inst/shiny-examples/ContDataQC/rmd/tables/miniDOTreformat_ BEFORE_20240226.xlsx b/inst/shiny-examples/ContDataQC/rmd/tables/miniDOTreformat_ BEFORE_20240226.xlsx new file mode 100644 index 00000000..eeeea399 Binary files /dev/null and b/inst/shiny-examples/ContDataQC/rmd/tables/miniDOTreformat_ BEFORE_20240226.xlsx differ diff --git a/inst/shiny-examples/ContDataQC/server.R b/inst/shiny-examples/ContDataQC/server.R index c2e1129a..6d6d82c3 100644 --- a/inst/shiny-examples/ContDataQC/server.R +++ b/inst/shiny-examples/ContDataQC/server.R @@ -100,6 +100,56 @@ shinyServer(function(input, output, session) { return(allFiles_miniDOT_reformat()$datapath) }) + # Display Import FileNames---- + + output$fn_input_display_config_main <- renderText({ + #**SAME CODE AS CONFIG** + + #Allows users to use their own configuration/threshold files for QC. + #Copies the status of the config file to this event. + config_type <- config$x + + if (config_type == "uploaded") { + #If a configuration file has been uploaded, the app uses it + + config <- file.path("data", input$configFile$name) + + } else { + #If no configuration file has been uploaded, the default is used + + config <- system.file("extdata", "Config.ORIG.R", package="ContDataQC") + + }## IF ~ config_type ~ END + + return(paste0(config_type, "; ", basename(config))) + + + })## fn_input_display_config + + output$fn_input_display_config_config <- renderText({ + #**SAME CODE AS MAIN** + + #Allows users to use their own configuration/threshold files for QC. + #Copies the status of the config file to this event. + config_type <- config$x + + if (config_type == "uploaded") { + #If a configuration file has been uploaded, the app uses it + + config <- file.path("data", input$configFile$name) + + } else { + #If no configuration file has been uploaded, the default is used + + config <- system.file("extdata", "Config.ORIG.R", package="ContDataQC") + + }## IF ~ config_type ~ END + + return(paste0(config_type, "; ", basename(config))) + + + })## fn_input_display_config + # Reactive, Main ---- #Creates a reactive object that stores whether a configuration file has been uploaded config <- reactiveValues( @@ -241,12 +291,13 @@ shinyServer(function(input, output, session) { #Creates a summary data.frame as a reactive object. #This table includes file name, station ID, start date, end date, and record count. table1 <- reactive({ + # 20230908, drop dates, cols 3 and 4 #Shows the table headings before files are input if (is.null(allFiles())) { #Subsets the columns of the pre-upload data for display - nullTable1 <- fileAttribsNull()[c(1:5)] + nullTable1 <- fileAttribsNull()[c(1, 2, 5)] #Sends the empty table to be displayed return(nullTable1) @@ -254,8 +305,8 @@ shinyServer(function(input, output, session) { #Subsets the file attribute table with just #file name, site ID, start date, end date, and number of records - summaryTable1 <- fileAttribsFull()[, c(1:5)] - colnames(summaryTable1) <- colnames(fileAttribsFull()[c(1:5)]) + summaryTable1 <- fileAttribsFull()[, c(1, 2, 5)] + colnames(summaryTable1) <- colnames(fileAttribsFull()[c(1, 2, 5)]) return(summaryTable1) }) @@ -876,7 +927,6 @@ shinyServer(function(input, output, session) { #The QCRaw and Summarize functions can be fed individual input files #in order to have the progress bar increment after each one is processed else { - #Iterates through all the selected files in the data.frame #to perform the QC script on them individually for (i in seq_len(nrow(allFiles()))) { @@ -889,7 +939,6 @@ shinyServer(function(input, output, session) { #Saves the R console output of ContDataQC() consoleRow <- capture.output( - #Runs ContDataQC() on an individual file ContDataQC(operation, fun.myDir.import = file.path(".", "data"), @@ -1243,7 +1292,7 @@ shinyServer(function(input, output, session) { if (is.null(console$disp) && is.null(consoleUSGS$disp)){ - beforeRun <- paste("Check here for script messages after running process...") + beforeRun <- paste("Check here for script messages after running each process.") return(beforeRun) } }) @@ -1275,7 +1324,8 @@ shinyServer(function(input, output, session) { #Shows the "Default" button after a user-selected config file is uploaded output$ui.defaultConfig <- renderUI({ if (is.null(input$configFile)) return() - actionButton("defaultConfig", "Return to default configuration file") + actionButton("defaultConfig", "Default Config" + , width = "100%") }) #Changes the config object status to a file being uploaded @@ -1318,7 +1368,7 @@ shinyServer(function(input, output, session) { # source("www/Config.R") # Read in dataset - df_Config <- read.table("www/Config.R", sep = "|", quote = "") + df_Config <- read.table("www/Config_Template.R", sep = "|", quote = "") # df_Config <- read.table("R/Config.R", sep = "|", quote = "") @@ -1587,12 +1637,12 @@ shinyServer(function(input, output, session) { ### Chla #### } else if (input$QC_Param_Input == "Chla"){ - updateNumericInput(session, "GR_Fail_Max", value = df_Config_react$df[Loc.Gross.Fail.Hi.Chlorophylla,3]) + updateNumericInput(session, "GR_Fail_Max", value = ContData.env$myThresh.Gross.Fail.Hi.Chlorophylla) #df_Config_react$df[Loc.Gross.Fail.Hi.Chlorophylla,3]) updateNumericInput(session, "GR_Fail_Min", value = df_Config_react$df[Loc.Gross.Fail.Lo.Chlorophylla,3]) - updateNumericInput(session, "GR_Sus_Max", value = df_Config_react$df[Loc.Gross.Suspect.Hi.Chlorophylla,3]) + updateNumericInput(session, "GR_Sus_Max", value = ContData.env$myThresh.Gross.Suspect.Hi.Chlorophylla) #df_Config_react$df[Loc.Gross.Suspect.Hi.Chlorophylla,3]) updateNumericInput(session, "GR_Sus_Min", value = df_Config_react$df[Loc.Gross.Suspect.Lo.Chlorophylla,3]) - updateNumericInput(session, "Spike_Fail", value = df_Config_react$df[Loc.Spike.Hi.Chlorophylla,3]) - updateNumericInput(session, "Spike_Sus", value = df_Config_react$df[Loc.Spike.Lo.Chlorophylla,3]) + updateNumericInput(session, "Spike_Fail", value = ContData.env$myThresh.Spike.Hi.Chlorophylla) #df_Config_react$df[Loc.Spike.Hi.Chlorophylla,3]) + updateNumericInput(session, "Spike_Sus", value = ContData.env$myThresh.Spike.Lo.Chlorophylla) #df_Config_react$df[Loc.Spike.Lo.Chlorophylla,3]) updateNumericInput(session, "RoC_SDs", value = df_Config_react$df[Loc.RoC.SD.number.Chlorophylla,3]) updateNumericInput(session, "RoC_Hrs", value = df_Config_react$df[Loc.RoC.SD.period.Chlorophylla,3]) updateNumericInput(session, "Flat_Fail", value = (df_Config_react$df[Loc.myDefault.Flat.Hi,3])*2) @@ -1615,12 +1665,12 @@ shinyServer(function(input, output, session) { ### Discharge #### } else if (input$QC_Param_Input == "Discharge"){ - updateNumericInput(session, "GR_Fail_Max", value = df_Config_react$df[Loc.Gross.Fail.Hi.Discharge,3]) + updateNumericInput(session, "GR_Fail_Max", value = ContData.env$myThresh.Gross.Fail.Hi.Discharge) #df_Config_react$df[Loc.Gross.Fail.Hi.Discharge,3]) updateNumericInput(session, "GR_Fail_Min", value = df_Config_react$df[Loc.Gross.Fail.Lo.Discharge,3]) - updateNumericInput(session, "GR_Sus_Max", value = df_Config_react$df[Loc.Gross.Suspect.Hi.Discharge,3]) + updateNumericInput(session, "GR_Sus_Max", value = ContData.env$myThresh.Gross.Suspect.Hi.Discharge) #df_Config_react$df[Loc.Gross.Suspect.Hi.Discharge,3]) updateNumericInput(session, "GR_Sus_Min", value = df_Config_react$df[Loc.Gross.Suspect.Lo.Discharge,3]) - updateNumericInput(session, "Spike_Fail", value = df_Config_react$df[Loc.Spike.Hi.Discharge,3]) - updateNumericInput(session, "Spike_Sus", value = df_Config_react$df[Loc.Spike.Lo.Discharge,3]) + updateNumericInput(session, "Spike_Fail", value = ContData.env$myThresh.Spike.Hi.Discharge)# df_Config_react$df[Loc.Spike.Hi.Discharge,3]) + updateNumericInput(session, "Spike_Sus", value = ContData.env$myThresh.Spike.Lo.Discharge) #df_Config_react$df[Loc.Spike.Lo.Discharge,3]) updateNumericInput(session, "RoC_SDs", value = df_Config_react$df[Loc.RoC.SD.number.Discharge,3]) updateNumericInput(session, "RoC_Hrs", value = df_Config_react$df[Loc.RoC.SD.period.Discharge,3]) updateNumericInput(session, "Flat_Fail", value = (df_Config_react$df[Loc.myDefault.Flat.Hi,3])*2) @@ -1685,12 +1735,12 @@ shinyServer(function(input, output, session) { ### SensDepth #### } else if (input$QC_Param_Input == "SensDepth"){ - updateNumericInput(session, "GR_Fail_Max", value = df_Config_react$df[Loc.Gross.Fail.Hi.SensorDepth,3]) + updateNumericInput(session, "GR_Fail_Max", value = ContData.env$myThresh.Gross.Fail.Hi.SensorDepth) #df_Config_react$df[Loc.Gross.Fail.Hi.SensorDepth,3]) updateNumericInput(session, "GR_Fail_Min", value = df_Config_react$df[Loc.Gross.Fail.Lo.SensorDepth,3]) - updateNumericInput(session, "GR_Sus_Max", value = df_Config_react$df[Loc.Gross.Suspect.Hi.SensorDepth,3]) + updateNumericInput(session, "GR_Sus_Max", value = ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth) #df_Config_react$df[Loc.Gross.Suspect.Hi.SensorDepth,3]) updateNumericInput(session, "GR_Sus_Min", value = df_Config_react$df[Loc.Gross.Suspect.Lo.SensorDepth,3]) - updateNumericInput(session, "Spike_Fail", value = df_Config_react$df[Loc.Spike.Hi.SensorDepth,3]) - updateNumericInput(session, "Spike_Sus", value = df_Config_react$df[Loc.Spike.Lo.SensorDepth,3]) + updateNumericInput(session, "Spike_Fail", value = ContData.env$myThresh.Spike.Hi.SensorDepth) #df_Config_react$df[Loc.Spike.Hi.SensorDepth,3]) + updateNumericInput(session, "Spike_Sus", value = ContData.env$myThresh.Spike.Lo.SensorDepth) #df_Config_react$df[Loc.Spike.Lo.SensorDepth,3]) updateNumericInput(session, "RoC_SDs", value = df_Config_react$df[Loc.RoC.SD.number.SensorDepth,3]) updateNumericInput(session, "RoC_Hrs", value = df_Config_react$df[Loc.RoC.SD.period.SensorDepth,3]) updateNumericInput(session, "Flat_Fail", value = df_Config_react$df[Loc.Flat.Hi.SensorDepth,3]) @@ -1699,12 +1749,12 @@ shinyServer(function(input, output, session) { ### Turbid #### } else if (input$QC_Param_Input == "Turbid"){ - updateNumericInput(session, "GR_Fail_Max", value = df_Config_react$df[Loc.Gross.Fail.Hi.Turbidity,3]) + updateNumericInput(session, "GR_Fail_Max", value = ContData.env$myThresh.Gross.Fail.Hi.Turbidity) #df_Config_react$df[Loc.Gross.Fail.Hi.Turbidity,3]) updateNumericInput(session, "GR_Fail_Min", value = df_Config_react$df[Loc.Gross.Fail.Lo.Turbidity,3]) - updateNumericInput(session, "GR_Sus_Max", value = df_Config_react$df[Loc.Gross.Suspect.Hi.Turbidity,3]) + updateNumericInput(session, "GR_Sus_Max", value = ContData.env$myThresh.Gross.Suspect.Hi.Turbidity) #df_Config_react$df[Loc.Gross.Suspect.Hi.Turbidity,3]) updateNumericInput(session, "GR_Sus_Min", value = df_Config_react$df[Loc.Gross.Suspect.Lo.Turbidity,3]) - updateNumericInput(session, "Spike_Fail", value = df_Config_react$df[Loc.Spike.Hi.Turbidity,3]) - updateNumericInput(session, "Spike_Sus", value = df_Config_react$df[Loc.Spike.Lo.Turbidity,3]) + updateNumericInput(session, "Spike_Fail", value = ContData.env$myThresh.Spike.Hi.Turbidity) #df_Config_react$df[Loc.Spike.Hi.Turbidity,3]) + updateNumericInput(session, "Spike_Sus", value = ContData.env$myThresh.Spike.Lo.Turbidity) #df_Config_react$df[Loc.Spike.Lo.Turbidity,3]) updateNumericInput(session, "RoC_SDs", value = df_Config_react$df[Loc.RoC.SD.number.Turbidity,3]) updateNumericInput(session, "RoC_Hrs", value = df_Config_react$df[Loc.RoC.SD.period.Turbidity,3]) updateNumericInput(session, "Flat_Fail", value = (df_Config_react$df[Loc.myDefault.Flat.Hi,3])*2) @@ -1713,12 +1763,12 @@ shinyServer(function(input, output, session) { ### WtrLvl #### } else if (input$QC_Param_Input == "WtrLvl"){ - updateNumericInput(session, "GR_Fail_Max", value = df_Config_react$df[Loc.Gross.Fail.Hi.SensorDepth,3]) - updateNumericInput(session, "GR_Fail_Min", value = df_Config_react$df[Loc.Gross.Fail.Lo.SensorDepth,3]) - updateNumericInput(session, "GR_Sus_Max", value = df_Config_react$df[Loc.Gross.Suspect.Hi.SensorDepth,3]) - updateNumericInput(session, "GR_Sus_Min", value = df_Config_react$df[Loc.Gross.Suspect.Lo.SensorDepth,3]) - updateNumericInput(session, "Spike_Fail", value = df_Config_react$df[Loc.Spike.Hi.SensorDepth,3]) - updateNumericInput(session, "Spike_Sus", value = df_Config_react$df[Loc.Spike.Lo.SensorDepth,3]) + updateNumericInput(session, "GR_Fail_Max", value = ContData.env$myThresh.Gross.Fail.Hi.WaterLevel) #df_Config_react$df[Loc.Gross.Fail.Hi.SensorDepth,3]) + updateNumericInput(session, "GR_Fail_Min", value = ContData.env$myThresh.Gross.Fail.Lo.WaterLevel) #df_Config_react$df[Loc.Gross.Fail.Lo.SensorDepth,3]) + updateNumericInput(session, "GR_Sus_Max", value = ContData.env$myThresh.Gross.Suspect.Hi.WaterLevel) #df_Config_react$df[Loc.Gross.Suspect.Hi.SensorDepth,3]) + updateNumericInput(session, "GR_Sus_Min", value = ContData.env$myThresh.Gross.Suspect.Lo.WaterLevel) #df_Config_react$df[Loc.Gross.Suspect.Lo.SensorDepth,3]) + updateNumericInput(session, "Spike_Fail", value = ContData.env$myThresh.Spike.Hi.WaterLevel) #df_Config_react$df[Loc.Spike.Hi.SensorDepth,3]) + updateNumericInput(session, "Spike_Sus", value = ContData.env$myThresh.Spike.Lo.WaterLevel) #df_Config_react$df[Loc.Spike.Lo.SensorDepth,3]) updateNumericInput(session, "RoC_SDs", value = df_Config_react$df[Loc.RoC.SD.number.WaterLevel,3]) updateNumericInput(session, "RoC_Hrs", value = df_Config_react$df[Loc.RoC.SD.period.WaterLevel,3]) updateNumericInput(session, "Flat_Fail", value = df_Config_react$df[Loc.Flat.Hi.SensorDepth,3]) @@ -2179,7 +2229,7 @@ shinyServer(function(input, output, session) { , sep = "\t", row.names = FALSE, col.names = FALSE, quote = FALSE) ### WaterTemp #### - } else if(input$QC_Param_Input == "WaterTemp"){ + } else if (input$QC_Param_Input == "WaterTemp") { df_Config_react$df[Loc.Gross.Fail.Hi.WaterTemp, 1] <- paste0("ContData.env$myThresh.Gross.Fail.Hi.WaterTemp <- ", input$GR_Fail_Max) df_Config_react$df[Loc.Gross.Fail.Lo.WaterTemp, 1] <- paste0("ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- ", input$GR_Fail_Min) df_Config_react$df[Loc.Gross.Suspect.Hi.WaterTemp, 1] <- paste0("ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp <- ", input$GR_Sus_Max) @@ -2225,7 +2275,7 @@ shinyServer(function(input, output, session) { # Enable download button once save button is clicked at least once observe({ - if(input$QC_SaveBttn > 0){ + if (input$QC_SaveBttn > 0) { Sys.sleep(1) # enable the download button diff --git a/inst/shiny-examples/ContDataQC/ui.R b/inst/shiny-examples/ContDataQC/ui.R index 7c9aca0e..356e5c64 100644 --- a/inst/shiny-examples/ContDataQC/ui.R +++ b/inst/shiny-examples/ContDataQC/ui.R @@ -5,545 +5,534 @@ # Updated 2020 (Erik) # 2021-04-19, Erik, modular format # 2021-06-15, Ben Block, updated formatting +# 2022-08-11, Erik, rework tab layout # Source ---- -# tab_Intro <- source("external/tab_Intro.R", local = TRUE)$value -# tab_Calc <- source("external/tab_Calc.R", local = TRUE)$value -# tab_Console <- source("external/tab_Console.R", local = TRUE)$value -# tab_Config <- source("external/tab_Config.R", local = TRUE)$value -# tab_Reformat <- source("external/tab_Reformat.R", local = TRUE)$value -# tab_USGSgage <- source("external/tab_USGSgage.R", local = TRUE)$value -# tab_FAQ <- source("external/tab_FAQ.R", local = TRUE)$value +tab_1_About <- source("external/tab_1_About.R", local = TRUE)$value +tab_1a_Overview <- source("external/tab_1a_Overview.R", local = TRUE)$value +tab_1b_TestData <- source("external/tab_1b_TestData.R", local = TRUE)$value +tab_1c_FAQ <- source("external/tab_1c_FAQ.R", local = TRUE)$value +tab_2_DataPrep <- source("external/tab_2_DataPrep.R", local = TRUE)$value +tab_3_MainFunc <- source("external/tab_3_MainFunc.R", local = TRUE)$value +tab_3c_QCThresh <- source("external/tab_3c_QCThresh.R", local = TRUE)$value +tab_4_USGSgage <- source("external/tab_4_USGSgage.R", local = TRUE)$value +tab_5_Troubleshooting <- source("external/tab_5_Troubleshooting.R" + , local = TRUE)$value +tab_5b_Console <- source("external/tab_5b_Console.R", local = TRUE)$value +#tab_9_Status <- source("external/tab_9_Status.R", local = TRUE)$value -tab_1Overview <- source("external/tab_1Overview.R", local = TRUE)$value -tab_2FAQ <- source("external/tab_2FAQ.R", local = TRUE)$value -tab_3DataPrep <- source("external/tab_3DataPrep.R", local = TRUE)$value -tab_4QCThresh <- source("external/tab_4QCThresh.R", local = TRUE)$value -tab_5MainFunc <- source("external/tab_5MainFunc.R", local = TRUE)$value -tab_6USGSgage <- source("external/tab_6USGSgage.R", local = TRUE)$value -tab_7TestData <- source("external/tab_7TestData.R", local = TRUE)$value -tab_8Console <- source("external/tab_8Console.R", local = TRUE)$value -#tab_9Status <- source("external/tab_9Status.R", local = TRUE)$value - - -shinyUI( - # VERSION, 1, current [non-EPA] ---- - navbarPage("Continuous data QC, summary, and statistics - v2.0.7", - theme= shinytheme("spacelab") - ,tab_1Overview() - ,tab_2FAQ() - ,tab_3DataPrep() - ,tab_4QCThresh() - ,tab_5MainFunc() - ,tab_6USGSgage() - ,tab_7TestData() - ,tab_8Console() - #,tab_9Status() - )## navbarPage ~ END -)## shinyUI ~ END # shinyUI( -# # VERSION, 0, old ---- -# navbarPage("Continuous data QC, summary, and statistics - v2.0.6", -# theme= shinytheme("spacelab"), -# #also liked "cerulean" at https://rstudio.github.io/shinythemes/ -# # tabPan, Site Intro -# tab_Intro(), -# # tabPan, Calc -# tab_Calc(), -# # tabPan, Adv Feat -# tab_Console(), -# tab_Config(), -# tab_Reformat(), -# # tabPan, USGS -# tab_USGSgage() , -# # tabPan, FAQ -# tab_FAQ() -# +# # VERSION, 1, current [non-EPA] ---- +# navbarPage("Continuous Data QC, Summary, and Statistics" +# , theme = shinytheme("spacelab") +# , tab_1_About() +# #, tab_1a_Overview() +# #, tab_1c_FAQ() +# , tab_2_DataPrep() +# , tab_3_MainFunc() +# #,tab_3c_QCThresh() +# , tab_4_USGSgage() +# , tab_5_Troubleshooting() +# #, tab_5b_Console() +# #, tab_x_TestData() +# #, tab_9_Status() # )## navbarPage ~ END # )## shinyUI ~ END #~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # VERSION, EPA Template ---- -# 2022-09-15 +# OneEPA_template_for_ShinyApps_(update_title_on_line_28___UI_code_on_215___dateUpdated_on_441___server_code_on_457)_.R +# 2023-12-15 +#~~~~~~~~~~~ +# Using separate UI and Server files so commented out server code at bottom +# commented out require(shiny) +# Changed ui <- foo to shinyUI(foo) then added closing parenthesis at end +# 3 places to edit: title, UI code, date (~500) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# shinyUI( -# fluidPage( -# tags$html(class = "no-js", lang="en"), -# tags$head( -# HTML( -# " -# -# -# " -# ), -# tags$meta(charset="utf-8"), -# tags$meta(property="og:site_name", content="US EPA"), -# #tags$link(rel = "stylesheet", type = "text/css", href = "css/uswds.css"), -# tags$link(rel = "stylesheet", type = "text/css", href = "https://cdnjs.cloudflare.com/ajax/libs/uswds/3.0.0-beta.3/css/uswds.min.css", integrity="sha512-ZKvR1/R8Sgyx96aq5htbFKX84hN+zNXN73sG1dEHQTASpNA8Pc53vTbPsEKTXTZn9J4G7R5Il012VNsDEReqCA==", crossorigin="anonymous", referrerpolicy="no-referrer"), -# tags$meta(property="og:url", content="https://www.epa.gov/themes/epa_theme/pattern-lab/.markup-only.html"), -# tags$link(rel="canonical", href="https://www.epa.gov/themes/epa_theme/pattern-lab/.markup-only.html"), -# tags$link(rel="shortlink", href="https://www.epa.gov/themes/epa_theme/pattern-lab/.markup-only.html"), -# tags$meta(property="og:url", content="https://www.epa.gov/themes/epa_theme/pattern-lab/.markup-only.html"), -# tags$meta(property="og:image", 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tags$title('ContDataQC | US EPA'), -# tags$link(rel="icon", type="image/x-icon", href="https://www.epa.gov/themes/epa_theme/images/favicon.ico"), -# tags$meta(name="msapplication-TileColor", content="#FFFFFF"), -# tags$meta(name="msapplication-TileImage", content="https://www.epa.gov/themes/epa_theme/images/favicon-144.png"), -# tags$meta(name="application-name", content=""), -# tags$meta(name="msapplication-config", content="https://www.epa.gov/themes/epa_theme/images/ieconfig.xml"), -# tags$link(rel="apple-touch-icon-precomposed", sizes="196x196", href="https://www.epa.gov/themes/epa_theme/images/favicon-196.png"), -# tags$link(rel="apple-touch-icon-precomposed", sizes="152x152", href="https://www.epa.gov/themes/epa_theme/images/favicon-152.png"), -# tags$link(rel="apple-touch-icon-precomposed", sizes="144x144", href="https://www.epa.gov/themes/epa_theme/images/favicon-144.png"), -# tags$link(rel="apple-touch-icon-precomposed", sizes="120x120", 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href="https://www.epa.gov/themes/epa_theme/fonts/merriweather/Latin-Merriweather-Bold.woff2", as="font", crossorigin="anonymous"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/ajax-progress.module.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/autocomplete-loading.module.css?r6lsex" ), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/js.module.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/sticky-header.module.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/system-status-counter.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/system-status-report-counters.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/system-status-report-general-info.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/tabledrag.module.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/tablesort.module.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/tree-child.module.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/themes/epa_theme/css/styles.css?r6lsex"), -# tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/themes/epa_theme/css-lib/colorbox.min.css?r6lsex"), -# -# tags$script(src = 'https://cdnjs.cloudflare.com/ajax/libs/uswds/3.0.0-beta.3/js/uswds-init.min.js'), -# #fix container-fluid that boostrap RShiny uses -# tags$style(HTML( -# '.container-fluid { -# padding-right: 0; -# padding-left: 0; -# margin-right: 0; -# margin-left: 0; -# } -# .tab-content { -# margin-right: 30px; -# margin-left: 30px; -# }' -# )) -# ), -# tags$body( -# class="path-themes not-front has-wide-template", id="top", -# tags$script( -# src = 'https://cdnjs.cloudflare.com/ajax/libs/uswds/3.0.0-beta.3/js/uswds.min.js' -# ) -# ), -# -# # Site Header -# HTML( -# ' -# -# -# -# -# -#
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An official website of the United States government

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-# Official websites use .gov -#
A .gov website belongs to an official government organization in the United States. -#

-#
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-# HTTPS -#
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-# Secure .gov websites use HTTPS -#
A lock () or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites. -#

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' -# ), -# -# # Individual Page Header -# HTML( -# '
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' -# ), -# -# # Insert your UI code here ---- -# # ## Begin example UI code -# # titlePanel("Hello Shiny!"), -# # sidebarLayout -# # ( -# # sidebarPanel -# # ( -# # sliderInput -# # ( -# # "obs", -# # "Number of observations:", -# # min = 0, -# # max = 1000, -# # value = 500 -# # ) -# # ), -# # mainPanel -# # ( -# # plotOutput("distPlot") -# # ) -# # ) -# # ## End example UI code -# -# # IMPORTANT! For a navbar page, you will need to place the header and footer inside the navbar section (as shown below) - -# # you will then want to comment out lines 201-213 and lines 254-263 -# navbarPage( -# title = h2("Continuous data QC, summary, and statistics - v2.0.7"), -# #theme= shinytheme("spacelab"), -# header = HTML( -# '
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-#
' -# ), +# # ui <- fluidPage( + +shinyUI(fluidPage( + tags$html(class = "no-js", lang="en"), + tags$head( + HTML( + " + + + " + ), + tags$meta(charset="utf-8"), + tags$meta(property="og:site_name", content="US EPA"), + #tags$link(rel = "stylesheet", type = "text/css", href = "css/uswds.css"), + tags$link(rel = "stylesheet", type = "text/css", href = "https://cdnjs.cloudflare.com/ajax/libs/uswds/3.0.0-beta.3/css/uswds.min.css", integrity="sha512-ZKvR1/R8Sgyx96aq5htbFKX84hN+zNXN73sG1dEHQTASpNA8Pc53vTbPsEKTXTZn9J4G7R5Il012VNsDEReqCA==", crossorigin="anonymous", referrerpolicy="no-referrer"), + tags$meta(property="og:url", content="https://www.epa.gov/themes/epa_theme/pattern-lab/.markup-only.html"), + tags$link(rel="canonical", href="https://www.epa.gov/themes/epa_theme/pattern-lab/.markup-only.html"), + tags$link(rel="shortlink", href="https://www.epa.gov/themes/epa_theme/pattern-lab/.markup-only.html"), + tags$meta(property="og:url", content="https://www.epa.gov/themes/epa_theme/pattern-lab/.markup-only.html"), + tags$meta(property="og:image", content="https://www.epa.gov/sites/all/themes/epa/img/epa-standard-og.jpg"), + tags$meta(property="og:image:width", content="1200"), + tags$meta(property="og:image:height", content="630"), + tags$meta(property="og:image:alt", content="U.S. Environmental Protection Agency"), + tags$meta(name="twitter:card", content="summary_large_image"), + tags$meta(name="twitter:image:alt", content="U.S. Environmental Protection Agency"), + tags$meta(name="twitter:image:height", content="600"), + tags$meta(name="twitter:image:width", content="1200"), + tags$meta(name="twitter:image", content="https://www.epa.gov/sites/all/themes/epa/img/epa-standard-twitter.jpg"), + tags$meta(name="MobileOptimized", content="width"), + tags$meta(name="HandheldFriendly", content="true"), + tags$meta(name="viewport", content="width=device-width, initial-scale=1.0"), + tags$meta(`http-equiv`="x-ua-compatible", content="ie=edge"), + tags$script(src = "js/pattern-lab-head-script.js"), + ## update this line with your title:---- + tags$title('Continuous Data QC, Summary, and Statistics | US EPA'), + tags$link(rel="icon", type="image/x-icon", href="https://www.epa.gov/themes/epa_theme/images/favicon.ico"), + tags$meta(name="msapplication-TileColor", content="#FFFFFF"), + tags$meta(name="msapplication-TileImage", content="https://www.epa.gov/themes/epa_theme/images/favicon-144.png"), + tags$meta(name="application-name", content=""), + tags$meta(name="msapplication-config", content="https://www.epa.gov/themes/epa_theme/images/ieconfig.xml"), + tags$link(rel="apple-touch-icon-precomposed", sizes="196x196", href="https://www.epa.gov/themes/epa_theme/images/favicon-196.png"), + tags$link(rel="apple-touch-icon-precomposed", sizes="152x152", href="https://www.epa.gov/themes/epa_theme/images/favicon-152.png"), + tags$link(rel="apple-touch-icon-precomposed", sizes="144x144", href="https://www.epa.gov/themes/epa_theme/images/favicon-144.png"), + tags$link(rel="apple-touch-icon-precomposed", sizes="120x120", href="https://www.epa.gov/themes/epa_theme/images/favicon-120.png"), + tags$link(rel="apple-touch-icon-precomposed", sizes="114x114", href="https://www.epa.gov/themes/epa_theme/images/favicon-114.png"), + tags$link(rel="apple-touch-icon-precomposed", sizes="72x72", href="https://www.epa.gov/themes/epa_theme/images/favicon-72.png"), + tags$link(rel="apple-touch-icon-precomposed", href="https://www.epa.gov/themes/epa_theme/images/favicon-180.png"), + tags$link(rel="icon", href="https://www.epa.gov/themes/epa_theme/images/favicon-32.png", sizes="32x32"), + tags$link(rel="preload", href="https://www.epa.gov/themes/epa_theme/fonts/source-sans-pro/sourcesanspro-regular-webfont.woff2", as="font", crossorigin="anonymous"), + tags$link(rel="preload", href="https://www.epa.gov/themes/epa_theme/fonts/source-sans-pro/sourcesanspro-bold-webfont.woff2", as="font", crossorigin="anonymous"), + tags$link(rel="preload", href="https://www.epa.gov/themes/epa_theme/fonts/merriweather/Latin-Merriweather-Bold.woff2", as="font", crossorigin="anonymous"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/ajax-progress.module.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/autocomplete-loading.module.css?r6lsex" ), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/js.module.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/sticky-header.module.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/system-status-counter.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/system-status-report-counters.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/system-status-report-general-info.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/tabledrag.module.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/tablesort.module.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/core/themes/stable/css/system/components/tree-child.module.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/themes/epa_theme/css/styles.css?r6lsex"), + tags$link(rel="stylesheet", media="all", href="https://www.epa.gov/themes/epa_theme/css-lib/colorbox.min.css?r6lsex"), + + tags$script(src = 'https://cdnjs.cloudflare.com/ajax/libs/uswds/3.0.0-beta.3/js/uswds-init.min.js'), + #fix container-fluid that boostrap RShiny uses + tags$style(HTML( + '.container-fluid { + padding-right: 0; + padding-left: 0; + margin-right: 0; + margin-left: 0; + } + .tab-content { + margin-right: 30px; + margin-left: 30px; + }' + )) + ), + tags$body( + class="path-themes not-front has-wide-template", id="top", + tags$script( + src = 'https://cdnjs.cloudflare.com/ajax/libs/uswds/3.0.0-beta.3/js/uswds.min.js' + ) + ), + + # Site Header + HTML( + ' + + + + + +
+
+
+
+
+
+ U.S. flag +
+
+

An official website of the United States government

+ +
+ +
+
+
+
+
+ Dot gov +
+

+ Official websites use .gov +
A .gov website belongs to an official government organization in the United States. +

+
+
+
+ HTTPS +
+

+ Secure .gov websites use HTTPS +
A lock () or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites. +

+
+
+
+
+
+
+
+
+ +
+
+
+
+
+
+
+ + +
+
+
+
+ + + + + + + + + + +
+
+
+
+
+
+ +
+
+
' + ), + + # Individual Page Header + HTML( + '
+
+
+
+
+
+
+ Contact Us +
+
+
' + ), + + ## Insert your UI code here---- + ## Begin example UI code + + navbarPage("Continuous Data QC, Summary, and Statistics" + , theme = shinytheme("spacelab") + , tab_1_About() + #, tab_1a_Overview() + #, tab_1c_FAQ() + , tab_2_DataPrep() + , tab_3_MainFunc() + #,tab_3c_QCThresh() + , tab_4_USGSgage() + , tab_5_Troubleshooting() + #, tab_5b_Console() + #, tab_x_TestData() + #, tab_9_Status() + )## navbarPage ~ END + + ## End example UI code + + # IMPORTANT! For a navbar page, you will need to place the header and footer inside the navbar section (as shown below) - + # you will then want to comment out lines 201-213 and lines 254-263 + # navbarPage( + # title = h2("Sample App"), + # header = HTML( + # '
+ #
+ #
+ #
+ #
+ #
+ #
+ # Contact Us + #
+ #
+ #
' + # ), + # footer = HTML( + # '
+ #
+ #
+ #
+ #

Contact Us to ask a question, provide feedback, or report a problem.

+ #
+ #
+ #
' + # ), + # tabPanel("Sample Tab 1"), + # tabPanel("Sample Tab 2"), + # ), + + # Individual Page Footer + ,HTML( + '
+
+
+
+

Contact Us to ask a question, provide feedback, or report a problem.

+
+
+
' + ), + + # Site Footer + HTML( + '
+ + + + + + + + ' + ) +) # END fluidPage +) # END shinyUI + +# # Insert your server code here +# ## Begin example server code +# server <- function(input, output) +# { +# output$distPlot <- renderPlot({ +# hist(rnorm(input$obs)) +# }) +# } +# ## End example server code # -# # Site Footer -# HTML( -# '
-# -# -# -# -# -# -# -# ' -# ) -# ) # END fluidPage -# )## shinyUI +# shinyApp(ui = ui, server = server) diff --git a/inst/shiny-examples/ContDataQC/www/Config.R b/inst/shiny-examples/ContDataQC/www/Config.R index fa4d4cfe..b7489708 100644 --- a/inst/shiny-examples/ContDataQC/www/Config.R +++ b/inst/shiny-examples/ContDataQC/www/Config.R @@ -14,6 +14,7 @@ # 20150928 # 20170323, add 3 parameters (Cond, DO, pH) # 20210106, replace gageheight with waterlevel +# 20230828, updated were QC flag test thresholds, Jen #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # User defined variable names for input data #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -398,18 +399,18 @@ ContData.env$myFlagVal.Order <- c(ContData.env$myFlagVal.Pass , ContData.env$myFlagVal.Fail , ContData.env$myFlagVal.NoData) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# QC Tests and Calculations #### +# QC Tests and Calculations (Thresholds) #### #http://stackoverflow.com/questions/16143700/pasting-two-vectors-with-combinations-of-all-vectors-elements #myNames.QCTests.Calcs.combo <- as.vector(t(outer(myNames.QCTests,myNames.QCTests.Calcs,paste,sep="."))) # combine so can check for and remove later. #myNames.DataFields.QCTests.Calcs.combo <- as.vector(t(outer(myNames.DataFields,myNames.QCTests.Calcs.combo,paste,sep="."))) -# Data Quality Flag Thresholds +# Data Quality Flag Thresholds #### # _QC, Gross---- ## Gross Min/Max, Fail (equipment) ### Examines values as outliers versus threholds ### if value >= Hi or <= Lo then flagged as "Fail" ContData.env$myThresh.Gross.Fail.Hi.WaterTemp <- 30 -ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -2 +ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -1 ContData.env$myThresh.Gross.Fail.Hi.AirTemp <- 38 ContData.env$myThresh.Gross.Fail.Lo.AirTemp <- -25 ContData.env$myThresh.Gross.Fail.Hi.AirBP <- 15 @@ -422,9 +423,9 @@ ContData.env$myThresh.Gross.Fail.Hi.Discharge <- 10^5 # dependent upon stream ContData.env$myThresh.Gross.Fail.Lo.Discharge <- -1 # dependent upon stream size ContData.env$myThresh.Gross.Fail.Hi.Cond <- 1500 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.Cond <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Fail.Hi.DO <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO <- 18 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO <- 1 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 18 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO.adj <- 1 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Hi.DO.pctsat <- 120 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO.pctsat <- -1 # this threshold has not been closely evaluated @@ -442,7 +443,7 @@ ContData.env$myThresh.Gross.Fail.Lo.Salinity <- 2 ### Examines values as outliers versus threholds ### if value >= Hi or <= Lo then flagged as "Suspect" ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp <- 25 -ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.1 +ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.5 ContData.env$myThresh.Gross.Suspect.Hi.AirTemp <- 35 ContData.env$myThresh.Gross.Suspect.Lo.AirTemp <- -23 ContData.env$myThresh.Gross.Suspect.Hi.AirBP <- 14.8 @@ -455,10 +456,10 @@ ContData.env$myThresh.Gross.Suspect.Hi.Discharge <- 10^3 # dependent upon str ContData.env$myThresh.Gross.Suspect.Lo.Discharge <- -1 # dependent upon stream size ContData.env$myThresh.Gross.Suspect.Hi.Cond <- 1200 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.Cond <- 20 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Suspect.Hi.DO <- 18 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.DO <- 16 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.DO.adj <- 18 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Suspect.Lo.DO.adj <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.DO.adj <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.DO.pctsat <- 100 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO.pctsat <- 0 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.pH <- 11 # this threshold has not been closely evaluated @@ -476,8 +477,8 @@ ContData.env$myThresh.Gross.Suspect.Lo.Salinity <- 3 ### Examines difference between consecutive measurements ### if delta >= Hi then flagged as "Fail" ### if delta >= Lo then flagged as "Suspect" -ContData.env$myThresh.Spike.Hi.WaterTemp <- 1.5 -ContData.env$myThresh.Spike.Lo.WaterTemp <- 1 +ContData.env$myThresh.Spike.Hi.WaterTemp <- 2 +ContData.env$myThresh.Spike.Lo.WaterTemp <- 1.5 ContData.env$myThresh.Spike.Hi.AirTemp <- 10 ContData.env$myThresh.Spike.Lo.AirTemp <- 8 ContData.env$myThresh.Spike.Hi.AirBP <- 0.25 @@ -490,10 +491,10 @@ ContData.env$myThresh.Spike.Hi.Discharge <- 10^4 # dependent upon stream size ContData.env$myThresh.Spike.Lo.Discharge <- 10^3 # dependent upon stream size ContData.env$myThresh.Spike.Hi.Cond <- 10 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Lo.Cond <- 5 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Hi.DO <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Lo.DO <- 5 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Hi.DO.adj <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Lo.DO.adj <- 5 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO.adj <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO.adj <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Hi.DO.pctsat <- 25 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Lo.DO.pctsat <- 10 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Hi.pH <- 10 # this threshold has not been closely evaluated @@ -510,7 +511,7 @@ ContData.env$myThresh.Spike.Lo.Salinity <- 3 ## Rate of Change (relative change) ### Examines SD over "period" and difference in consecutive values ### If delta >= SD.number * SD then flagged as "Suspect" -ContData.env$myDefault.RoC.SD.number <- 3 +ContData.env$myDefault.RoC.SD.number <- 6 ContData.env$myDefault.RoC.SD.period <- 25 #hours ContData.env$myThresh.RoC.SD.number.WaterTemp <- ContData.env$myDefault.RoC.SD.number ContData.env$myThresh.RoC.SD.period.WaterTemp <- ContData.env$myDefault.RoC.SD.period @@ -550,11 +551,11 @@ ContData.env$myThresh.RoC.SD.period.Salinity <- ContData.env$myDefault.RoC.S ContData.env$myDefault.Flat.Hi <- 30 # maximum is myThresh.Flat.MaxComp ContData.env$myDefault.Flat.Lo <- 15 ContData.env$myDefault.Flat.Tolerance <- 0.01 # set to one sigdig less than measurements. Check with fivenum(x) -ContData.env$myThresh.Flat.Hi.WaterTemp <- 30 -ContData.env$myThresh.Flat.Lo.WaterTemp <- 20 +ContData.env$myThresh.Flat.Hi.WaterTemp <- 100 +ContData.env$myThresh.Flat.Lo.WaterTemp <- 60 ContData.env$myThresh.Flat.Tolerance.WaterTemp <- 0.01 -ContData.env$myThresh.Flat.Hi.AirTemp <- 20 -ContData.env$myThresh.Flat.Lo.AirTemp <- 15 +ContData.env$myThresh.Flat.Hi.AirTemp <- 60 +ContData.env$myThresh.Flat.Lo.AirTemp <- 30 ContData.env$myThresh.Flat.Tolerance.AirTemp <- 0.01 ContData.env$myThresh.Flat.Hi.AirBP <- 15 ContData.env$myThresh.Flat.Lo.AirBP <- 10 @@ -563,7 +564,7 @@ ContData.env$myThresh.Flat.Hi.WaterP <- 15 ContData.env$myThresh.Flat.Lo.WaterP <- 10 ContData.env$myThresh.Flat.Tolerance.WaterP <- 0.001 ContData.env$myThresh.Flat.Hi.SensorDepth <- 60 -ContData.env$myThresh.Flat.Lo.SensorDepth <- 20 +ContData.env$myThresh.Flat.Lo.SensorDepth <- 30 ContData.env$myThresh.Flat.Tolerance.SensorDepth <- 0.0 ContData.env$myThresh.Flat.Hi.Discharge <- ContData.env$myDefault.Flat.Hi * 2 ContData.env$myThresh.Flat.Lo.Discharge <- ContData.env$myDefault.Flat.Lo * 2 @@ -592,8 +593,8 @@ ContData.env$myThresh.Flat.Tolerance.Chlorophylla <- 0.01 ContData.env$myThresh.Flat.Hi.WaterLevel <- ContData.env$myThresh.Flat.Hi.SensorDepth ContData.env$myThresh.Flat.Lo.WaterLevel <- ContData.env$myThresh.Flat.Lo.SensorDepth ContData.env$myThresh.Flat.Tolerance.WaterLevel <- ContData.env$myThresh.Flat.Tolerance.SensorDepth -ContData.env$myThresh.Flat.Hi.Salinity <- ContData.env$myThresh.Flat.Hi.SensorDepth * 2 -ContData.env$myThresh.Flat.Lo.Salinity <- ContData.env$myThresh.Flat.Lo.SensorDepth * 2 +ContData.env$myThresh.Flat.Hi.Salinity <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.Salinity <- ContData.env$myDefault.Flat.Lo * 2 ContData.env$myThresh.Flat.Tolerance.Salinity <- 0.01 # ContData.env$myThresh.Flat.MaxComp <- max(ContData.env$myThresh.Flat.Hi.WaterTemp diff --git a/inst/shiny-examples/ContDataQC/www/Config_Default.R b/inst/shiny-examples/ContDataQC/www/Config_Default.R index fa4d4cfe..b7489708 100644 --- a/inst/shiny-examples/ContDataQC/www/Config_Default.R +++ b/inst/shiny-examples/ContDataQC/www/Config_Default.R @@ -14,6 +14,7 @@ # 20150928 # 20170323, add 3 parameters (Cond, DO, pH) # 20210106, replace gageheight with waterlevel +# 20230828, updated were QC flag test thresholds, Jen #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # User defined variable names for input data #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -398,18 +399,18 @@ ContData.env$myFlagVal.Order <- c(ContData.env$myFlagVal.Pass , ContData.env$myFlagVal.Fail , ContData.env$myFlagVal.NoData) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# QC Tests and Calculations #### +# QC Tests and Calculations (Thresholds) #### #http://stackoverflow.com/questions/16143700/pasting-two-vectors-with-combinations-of-all-vectors-elements #myNames.QCTests.Calcs.combo <- as.vector(t(outer(myNames.QCTests,myNames.QCTests.Calcs,paste,sep="."))) # combine so can check for and remove later. #myNames.DataFields.QCTests.Calcs.combo <- as.vector(t(outer(myNames.DataFields,myNames.QCTests.Calcs.combo,paste,sep="."))) -# Data Quality Flag Thresholds +# Data Quality Flag Thresholds #### # _QC, Gross---- ## Gross Min/Max, Fail (equipment) ### Examines values as outliers versus threholds ### if value >= Hi or <= Lo then flagged as "Fail" ContData.env$myThresh.Gross.Fail.Hi.WaterTemp <- 30 -ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -2 +ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -1 ContData.env$myThresh.Gross.Fail.Hi.AirTemp <- 38 ContData.env$myThresh.Gross.Fail.Lo.AirTemp <- -25 ContData.env$myThresh.Gross.Fail.Hi.AirBP <- 15 @@ -422,9 +423,9 @@ ContData.env$myThresh.Gross.Fail.Hi.Discharge <- 10^5 # dependent upon stream ContData.env$myThresh.Gross.Fail.Lo.Discharge <- -1 # dependent upon stream size ContData.env$myThresh.Gross.Fail.Hi.Cond <- 1500 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.Cond <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Fail.Hi.DO <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO <- 18 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO <- 1 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 18 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO.adj <- 1 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Hi.DO.pctsat <- 120 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO.pctsat <- -1 # this threshold has not been closely evaluated @@ -442,7 +443,7 @@ ContData.env$myThresh.Gross.Fail.Lo.Salinity <- 2 ### Examines values as outliers versus threholds ### if value >= Hi or <= Lo then flagged as "Suspect" ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp <- 25 -ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.1 +ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.5 ContData.env$myThresh.Gross.Suspect.Hi.AirTemp <- 35 ContData.env$myThresh.Gross.Suspect.Lo.AirTemp <- -23 ContData.env$myThresh.Gross.Suspect.Hi.AirBP <- 14.8 @@ -455,10 +456,10 @@ ContData.env$myThresh.Gross.Suspect.Hi.Discharge <- 10^3 # dependent upon str ContData.env$myThresh.Gross.Suspect.Lo.Discharge <- -1 # dependent upon stream size ContData.env$myThresh.Gross.Suspect.Hi.Cond <- 1200 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.Cond <- 20 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Suspect.Hi.DO <- 18 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.DO <- 16 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.DO.adj <- 18 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Suspect.Lo.DO.adj <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.DO.adj <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.DO.pctsat <- 100 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO.pctsat <- 0 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.pH <- 11 # this threshold has not been closely evaluated @@ -476,8 +477,8 @@ ContData.env$myThresh.Gross.Suspect.Lo.Salinity <- 3 ### Examines difference between consecutive measurements ### if delta >= Hi then flagged as "Fail" ### if delta >= Lo then flagged as "Suspect" -ContData.env$myThresh.Spike.Hi.WaterTemp <- 1.5 -ContData.env$myThresh.Spike.Lo.WaterTemp <- 1 +ContData.env$myThresh.Spike.Hi.WaterTemp <- 2 +ContData.env$myThresh.Spike.Lo.WaterTemp <- 1.5 ContData.env$myThresh.Spike.Hi.AirTemp <- 10 ContData.env$myThresh.Spike.Lo.AirTemp <- 8 ContData.env$myThresh.Spike.Hi.AirBP <- 0.25 @@ -490,10 +491,10 @@ ContData.env$myThresh.Spike.Hi.Discharge <- 10^4 # dependent upon stream size ContData.env$myThresh.Spike.Lo.Discharge <- 10^3 # dependent upon stream size ContData.env$myThresh.Spike.Hi.Cond <- 10 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Lo.Cond <- 5 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Hi.DO <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Lo.DO <- 5 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Hi.DO.adj <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Lo.DO.adj <- 5 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO.adj <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO.adj <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Hi.DO.pctsat <- 25 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Lo.DO.pctsat <- 10 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Hi.pH <- 10 # this threshold has not been closely evaluated @@ -510,7 +511,7 @@ ContData.env$myThresh.Spike.Lo.Salinity <- 3 ## Rate of Change (relative change) ### Examines SD over "period" and difference in consecutive values ### If delta >= SD.number * SD then flagged as "Suspect" -ContData.env$myDefault.RoC.SD.number <- 3 +ContData.env$myDefault.RoC.SD.number <- 6 ContData.env$myDefault.RoC.SD.period <- 25 #hours ContData.env$myThresh.RoC.SD.number.WaterTemp <- ContData.env$myDefault.RoC.SD.number ContData.env$myThresh.RoC.SD.period.WaterTemp <- ContData.env$myDefault.RoC.SD.period @@ -550,11 +551,11 @@ ContData.env$myThresh.RoC.SD.period.Salinity <- ContData.env$myDefault.RoC.S ContData.env$myDefault.Flat.Hi <- 30 # maximum is myThresh.Flat.MaxComp ContData.env$myDefault.Flat.Lo <- 15 ContData.env$myDefault.Flat.Tolerance <- 0.01 # set to one sigdig less than measurements. Check with fivenum(x) -ContData.env$myThresh.Flat.Hi.WaterTemp <- 30 -ContData.env$myThresh.Flat.Lo.WaterTemp <- 20 +ContData.env$myThresh.Flat.Hi.WaterTemp <- 100 +ContData.env$myThresh.Flat.Lo.WaterTemp <- 60 ContData.env$myThresh.Flat.Tolerance.WaterTemp <- 0.01 -ContData.env$myThresh.Flat.Hi.AirTemp <- 20 -ContData.env$myThresh.Flat.Lo.AirTemp <- 15 +ContData.env$myThresh.Flat.Hi.AirTemp <- 60 +ContData.env$myThresh.Flat.Lo.AirTemp <- 30 ContData.env$myThresh.Flat.Tolerance.AirTemp <- 0.01 ContData.env$myThresh.Flat.Hi.AirBP <- 15 ContData.env$myThresh.Flat.Lo.AirBP <- 10 @@ -563,7 +564,7 @@ ContData.env$myThresh.Flat.Hi.WaterP <- 15 ContData.env$myThresh.Flat.Lo.WaterP <- 10 ContData.env$myThresh.Flat.Tolerance.WaterP <- 0.001 ContData.env$myThresh.Flat.Hi.SensorDepth <- 60 -ContData.env$myThresh.Flat.Lo.SensorDepth <- 20 +ContData.env$myThresh.Flat.Lo.SensorDepth <- 30 ContData.env$myThresh.Flat.Tolerance.SensorDepth <- 0.0 ContData.env$myThresh.Flat.Hi.Discharge <- ContData.env$myDefault.Flat.Hi * 2 ContData.env$myThresh.Flat.Lo.Discharge <- ContData.env$myDefault.Flat.Lo * 2 @@ -592,8 +593,8 @@ ContData.env$myThresh.Flat.Tolerance.Chlorophylla <- 0.01 ContData.env$myThresh.Flat.Hi.WaterLevel <- ContData.env$myThresh.Flat.Hi.SensorDepth ContData.env$myThresh.Flat.Lo.WaterLevel <- ContData.env$myThresh.Flat.Lo.SensorDepth ContData.env$myThresh.Flat.Tolerance.WaterLevel <- ContData.env$myThresh.Flat.Tolerance.SensorDepth -ContData.env$myThresh.Flat.Hi.Salinity <- ContData.env$myThresh.Flat.Hi.SensorDepth * 2 -ContData.env$myThresh.Flat.Lo.Salinity <- ContData.env$myThresh.Flat.Lo.SensorDepth * 2 +ContData.env$myThresh.Flat.Hi.Salinity <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.Salinity <- ContData.env$myDefault.Flat.Lo * 2 ContData.env$myThresh.Flat.Tolerance.Salinity <- 0.01 # ContData.env$myThresh.Flat.MaxComp <- max(ContData.env$myThresh.Flat.Hi.WaterTemp diff --git a/inst/shiny-examples/ContDataQC/www/Config_Lakes_meters.zip b/inst/shiny-examples/ContDataQC/www/Config_Lakes_meters.zip index 93189a12..a01ef6bc 100644 Binary files a/inst/shiny-examples/ContDataQC/www/Config_Lakes_meters.zip and b/inst/shiny-examples/ContDataQC/www/Config_Lakes_meters.zip differ diff --git a/inst/shiny-examples/ContDataQC/www/Config_Lakes_metric.R b/inst/shiny-examples/ContDataQC/www/Config_Lakes_metric.R index 7baeed0c..1e7450ea 100644 --- a/inst/shiny-examples/ContDataQC/www/Config_Lakes_metric.R +++ b/inst/shiny-examples/ContDataQC/www/Config_Lakes_metric.R @@ -15,6 +15,7 @@ # 20170323, add 3 parameters (Cond, DO, pH) # 20210106, replace gageheight with waterlevel # 20210331, Lakes centric version, units as metric +# 20230828, updated were QC flag test thresholds for Lakes, Jen #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # User defined variable names for input data #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -396,7 +397,7 @@ ContData.env$myFlagVal.Order <- c(ContData.env$myFlagVal.Pass ### Examines values as outliers versus threholds ### if value >= Hi or <= Lo then flagged as "Fail" ContData.env$myThresh.Gross.Fail.Hi.WaterTemp <- 30 -ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -2 +ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -1 ContData.env$myThresh.Gross.Fail.Hi.AirTemp <- 38 ContData.env$myThresh.Gross.Fail.Lo.AirTemp <- -25 ContData.env$myThresh.Gross.Fail.Hi.AirBP <- 15 @@ -409,9 +410,9 @@ ContData.env$myThresh.Gross.Fail.Hi.Discharge <- 10^5 # dependent upon stream ContData.env$myThresh.Gross.Fail.Lo.Discharge <- -1 # dependent upon stream size ContData.env$myThresh.Gross.Fail.Hi.Cond <- 1500 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.Cond <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Fail.Hi.DO <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO <- 18 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO <- 1 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 18 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO.adj <- 1 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Hi.DO.pctsat <- 120 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO.pctsat <- -1 # this threshold has not been closely evaluated @@ -427,7 +428,7 @@ ContData.env$myThresh.Gross.Fail.Lo.WaterLevel <- ContData.env$myThresh.Gross. ### Examines values as outliers versus threholds ### if value >= Hi or <= Lo then flagged as "Suspect" ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp <- 25 -ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.1 +ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.5 ContData.env$myThresh.Gross.Suspect.Hi.AirTemp <- 35 ContData.env$myThresh.Gross.Suspect.Lo.AirTemp <- -23 ContData.env$myThresh.Gross.Suspect.Hi.AirBP <- 14.8 @@ -440,9 +441,9 @@ ContData.env$myThresh.Gross.Suspect.Hi.Discharge <- 10^3 # dependent upon str ContData.env$myThresh.Gross.Suspect.Lo.Discharge <- -1 # dependent upon stream size ContData.env$myThresh.Gross.Suspect.Hi.Cond <- 1200 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.Cond <- 20 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Suspect.Hi.DO <- 18 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.DO <- 16 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO <- 2 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Suspect.Hi.DO.adj <- 18 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.DO.adj <- 16 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO.adj <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.DO.pctsat <- 100 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO.pctsat <- 0 # this threshold has not been closely evaluated @@ -459,8 +460,8 @@ ContData.env$myThresh.Gross.Suspect.Lo.WaterLevel <- ContData.env$myThresh.Gro ### Examines difference between consecutive measurements ### if delta >= Hi then flagged as "Fail" ### if delta >= Lo then flagged as "Suspect" -ContData.env$myThresh.Spike.Hi.WaterTemp <- 1.5 -ContData.env$myThresh.Spike.Lo.WaterTemp <- 1 +ContData.env$myThresh.Spike.Hi.WaterTemp <- 2 +ContData.env$myThresh.Spike.Lo.WaterTemp <- 1.5 ContData.env$myThresh.Spike.Hi.AirTemp <- 10 ContData.env$myThresh.Spike.Lo.AirTemp <- 8 ContData.env$myThresh.Spike.Hi.AirBP <- 0.25 @@ -473,10 +474,10 @@ ContData.env$myThresh.Spike.Hi.Discharge <- 10^4 # dependent upon stream size ContData.env$myThresh.Spike.Lo.Discharge <- 10^3 # dependent upon stream size ContData.env$myThresh.Spike.Hi.Cond <- 10 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Lo.Cond <- 5 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Hi.DO <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Lo.DO <- 5 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Hi.DO.adj <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Lo.DO.adj <- 5 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO.adj <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO.adj <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Hi.DO.pctsat <- 25 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Lo.DO.pctsat <- 10 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Hi.pH <- 10 # this threshold has not been closely evaluated @@ -493,7 +494,7 @@ ContData.env$myThresh.Spike.Lo.WaterLevel <- ContData.env$myThresh.Spike.Lo.Sen ## Rate of Change (relative change) ### Examines SD over "period" and difference in consecutive values ### If delta >= SD.number * SD then flagged as "Suspect" -ContData.env$myDefault.RoC.SD.number <- 3 +ContData.env$myDefault.RoC.SD.number <- 6 ContData.env$myDefault.RoC.SD.period <- 25 #hours ContData.env$myThresh.RoC.SD.number.WaterTemp <- ContData.env$myDefault.RoC.SD.number ContData.env$myThresh.RoC.SD.period.WaterTemp <- ContData.env$myDefault.RoC.SD.period @@ -531,11 +532,11 @@ ContData.env$myThresh.RoC.SD.period.WaterLevel <- ContData.env$myDefault.RoC.S ContData.env$myDefault.Flat.Hi <- 30 # maximum is myThresh.Flat.MaxComp ContData.env$myDefault.Flat.Lo <- 15 ContData.env$myDefault.Flat.Tolerance <- 0.01 # set to one sigdig less than measurements. Check with fivenum(x) -ContData.env$myThresh.Flat.Hi.WaterTemp <- 30 -ContData.env$myThresh.Flat.Lo.WaterTemp <- 20 +ContData.env$myThresh.Flat.Hi.WaterTemp <- 100 +ContData.env$myThresh.Flat.Lo.WaterTemp <- 60 ContData.env$myThresh.Flat.Tolerance.WaterTemp <- 0.01 -ContData.env$myThresh.Flat.Hi.AirTemp <- 20 -ContData.env$myThresh.Flat.Lo.AirTemp <- 15 +ContData.env$myThresh.Flat.Hi.AirTemp <- 60 +ContData.env$myThresh.Flat.Lo.AirTemp <- 30 ContData.env$myThresh.Flat.Tolerance.AirTemp <- 0.01 ContData.env$myThresh.Flat.Hi.AirBP <- 15 ContData.env$myThresh.Flat.Lo.AirBP <- 10 diff --git a/inst/shiny-examples/ContDataQC/www/Config_Template.R b/inst/shiny-examples/ContDataQC/www/Config_Template.R index 6ceca78d..8200c6b2 100644 --- a/inst/shiny-examples/ContDataQC/www/Config_Template.R +++ b/inst/shiny-examples/ContDataQC/www/Config_Template.R @@ -14,6 +14,7 @@ # 20150928 # 20170323, add 3 parameters (Cond, DO, pH) # 20210106, replace gageheight with waterlevel +# 20230828, updated were QC flag test thresholds, Jen #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # User defined variable names for input data #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -34,7 +35,7 @@ #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ # Environment Name #### # Environment for use only by ContDataQC library -#ContData.env <- new.env(parent = emptyenv()) +# ContData.env <- new.env(parent = emptyenv()) # The above line is not used in custom configurations. # assign variables to new environment requires editing of all lines. # For example, myDelim <- "_" BECOMES ContData.env$myDelim, "_" @@ -398,18 +399,18 @@ ContData.env$myFlagVal.Order <- c(ContData.env$myFlagVal.Pass , ContData.env$myFlagVal.Fail , ContData.env$myFlagVal.NoData) #~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -# QC Tests and Calculations #### +# QC Tests and Calculations (Thresholds) #### #http://stackoverflow.com/questions/16143700/pasting-two-vectors-with-combinations-of-all-vectors-elements #myNames.QCTests.Calcs.combo <- as.vector(t(outer(myNames.QCTests,myNames.QCTests.Calcs,paste,sep="."))) # combine so can check for and remove later. #myNames.DataFields.QCTests.Calcs.combo <- as.vector(t(outer(myNames.DataFields,myNames.QCTests.Calcs.combo,paste,sep="."))) -# Data Quality Flag Thresholds +# Data Quality Flag Thresholds #### # _QC, Gross---- ## Gross Min/Max, Fail (equipment) ### Examines values as outliers versus threholds ### if value >= Hi or <= Lo then flagged as "Fail" ContData.env$myThresh.Gross.Fail.Hi.WaterTemp <- 30 -ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -2 +ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -1 ContData.env$myThresh.Gross.Fail.Hi.AirTemp <- 38 ContData.env$myThresh.Gross.Fail.Lo.AirTemp <- -25 ContData.env$myThresh.Gross.Fail.Hi.AirBP <- 15 @@ -422,9 +423,9 @@ ContData.env$myThresh.Gross.Fail.Hi.Discharge <- 10^5 # dependent upon stream ContData.env$myThresh.Gross.Fail.Lo.Discharge <- -1 # dependent upon stream size ContData.env$myThresh.Gross.Fail.Hi.Cond <- 1500 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.Cond <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Fail.Hi.DO <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO <- 18 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO <- 1 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 18 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO.adj <- 1 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Hi.DO.pctsat <- 120 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Fail.Lo.DO.pctsat <- -1 # this threshold has not been closely evaluated @@ -442,7 +443,7 @@ ContData.env$myThresh.Gross.Fail.Lo.Salinity <- 2 ### Examines values as outliers versus threholds ### if value >= Hi or <= Lo then flagged as "Suspect" ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp <- 25 -ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.1 +ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.5 ContData.env$myThresh.Gross.Suspect.Hi.AirTemp <- 35 ContData.env$myThresh.Gross.Suspect.Lo.AirTemp <- -23 ContData.env$myThresh.Gross.Suspect.Hi.AirBP <- 14.8 @@ -455,10 +456,10 @@ ContData.env$myThresh.Gross.Suspect.Hi.Discharge <- 10^3 # dependent upon str ContData.env$myThresh.Gross.Suspect.Lo.Discharge <- -1 # dependent upon stream size ContData.env$myThresh.Gross.Suspect.Hi.Cond <- 1200 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.Cond <- 20 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Suspect.Hi.DO <- 18 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.DO <- 16 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.DO.adj <- 18 # this threshold has not been closely evaluated -ContData.env$myThresh.Gross.Suspect.Lo.DO.adj <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.DO.adj <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.DO.pctsat <- 100 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Lo.DO.pctsat <- 0 # this threshold has not been closely evaluated ContData.env$myThresh.Gross.Suspect.Hi.pH <- 11 # this threshold has not been closely evaluated @@ -476,8 +477,8 @@ ContData.env$myThresh.Gross.Suspect.Lo.Salinity <- 3 ### Examines difference between consecutive measurements ### if delta >= Hi then flagged as "Fail" ### if delta >= Lo then flagged as "Suspect" -ContData.env$myThresh.Spike.Hi.WaterTemp <- 1.5 -ContData.env$myThresh.Spike.Lo.WaterTemp <- 1 +ContData.env$myThresh.Spike.Hi.WaterTemp <- 2 +ContData.env$myThresh.Spike.Lo.WaterTemp <- 1.5 ContData.env$myThresh.Spike.Hi.AirTemp <- 10 ContData.env$myThresh.Spike.Lo.AirTemp <- 8 ContData.env$myThresh.Spike.Hi.AirBP <- 0.25 @@ -490,10 +491,10 @@ ContData.env$myThresh.Spike.Hi.Discharge <- 10^4 # dependent upon stream size ContData.env$myThresh.Spike.Lo.Discharge <- 10^3 # dependent upon stream size ContData.env$myThresh.Spike.Hi.Cond <- 10 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Lo.Cond <- 5 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Hi.DO <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Lo.DO <- 5 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Hi.DO.adj <- 10 # this threshold has not been closely evaluated -ContData.env$myThresh.Spike.Lo.DO.adj <- 5 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO.adj <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO.adj <- 2 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Hi.DO.pctsat <- 25 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Lo.DO.pctsat <- 10 # this threshold has not been closely evaluated ContData.env$myThresh.Spike.Hi.pH <- 10 # this threshold has not been closely evaluated @@ -510,7 +511,7 @@ ContData.env$myThresh.Spike.Lo.Salinity <- 3 ## Rate of Change (relative change) ### Examines SD over "period" and difference in consecutive values ### If delta >= SD.number * SD then flagged as "Suspect" -ContData.env$myDefault.RoC.SD.number <- 3 +ContData.env$myDefault.RoC.SD.number <- 6 ContData.env$myDefault.RoC.SD.period <- 25 #hours ContData.env$myThresh.RoC.SD.number.WaterTemp <- ContData.env$myDefault.RoC.SD.number ContData.env$myThresh.RoC.SD.period.WaterTemp <- ContData.env$myDefault.RoC.SD.period @@ -550,11 +551,11 @@ ContData.env$myThresh.RoC.SD.period.Salinity <- ContData.env$myDefault.RoC.S ContData.env$myDefault.Flat.Hi <- 30 # maximum is myThresh.Flat.MaxComp ContData.env$myDefault.Flat.Lo <- 15 ContData.env$myDefault.Flat.Tolerance <- 0.01 # set to one sigdig less than measurements. Check with fivenum(x) -ContData.env$myThresh.Flat.Hi.WaterTemp <- 30 -ContData.env$myThresh.Flat.Lo.WaterTemp <- 20 +ContData.env$myThresh.Flat.Hi.WaterTemp <- 100 +ContData.env$myThresh.Flat.Lo.WaterTemp <- 60 ContData.env$myThresh.Flat.Tolerance.WaterTemp <- 0.01 -ContData.env$myThresh.Flat.Hi.AirTemp <- 20 -ContData.env$myThresh.Flat.Lo.AirTemp <- 15 +ContData.env$myThresh.Flat.Hi.AirTemp <- 60 +ContData.env$myThresh.Flat.Lo.AirTemp <- 30 ContData.env$myThresh.Flat.Tolerance.AirTemp <- 0.01 ContData.env$myThresh.Flat.Hi.AirBP <- 15 ContData.env$myThresh.Flat.Lo.AirBP <- 10 @@ -563,7 +564,7 @@ ContData.env$myThresh.Flat.Hi.WaterP <- 15 ContData.env$myThresh.Flat.Lo.WaterP <- 10 ContData.env$myThresh.Flat.Tolerance.WaterP <- 0.001 ContData.env$myThresh.Flat.Hi.SensorDepth <- 60 -ContData.env$myThresh.Flat.Lo.SensorDepth <- 20 +ContData.env$myThresh.Flat.Lo.SensorDepth <- 30 ContData.env$myThresh.Flat.Tolerance.SensorDepth <- 0.0 ContData.env$myThresh.Flat.Hi.Discharge <- ContData.env$myDefault.Flat.Hi * 2 ContData.env$myThresh.Flat.Lo.Discharge <- ContData.env$myDefault.Flat.Lo * 2 @@ -592,8 +593,8 @@ ContData.env$myThresh.Flat.Tolerance.Chlorophylla <- 0.01 ContData.env$myThresh.Flat.Hi.WaterLevel <- ContData.env$myThresh.Flat.Hi.SensorDepth ContData.env$myThresh.Flat.Lo.WaterLevel <- ContData.env$myThresh.Flat.Lo.SensorDepth ContData.env$myThresh.Flat.Tolerance.WaterLevel <- ContData.env$myThresh.Flat.Tolerance.SensorDepth -ContData.env$myThresh.Flat.Hi.Salinity <- ContData.env$myThresh.Flat.Hi.SensorDepth * 2 -ContData.env$myThresh.Flat.Lo.Salinity <- ContData.env$myThresh.Flat.Lo.SensorDepth * 2 +ContData.env$myThresh.Flat.Hi.Salinity <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.Salinity <- ContData.env$myDefault.Flat.Lo * 2 ContData.env$myThresh.Flat.Tolerance.Salinity <- 0.01 # ContData.env$myThresh.Flat.MaxComp <- max(ContData.env$myThresh.Flat.Hi.WaterTemp diff --git a/inst/shiny-examples/ContDataQC/www/Config_Template.zip b/inst/shiny-examples/ContDataQC/www/Config_Template.zip index 93c311a4..51193bea 100644 Binary files a/inst/shiny-examples/ContDataQC/www/Config_Template.zip and b/inst/shiny-examples/ContDataQC/www/Config_Template.zip differ diff --git a/inst/shiny-examples/ContDataQC/www/Config_default.zip b/inst/shiny-examples/ContDataQC/www/Config_default.zip index fd24f8ef..c463f7d0 100644 Binary files a/inst/shiny-examples/ContDataQC/www/Config_default.zip and b/inst/shiny-examples/ContDataQC/www/Config_default.zip differ diff --git a/inst/shiny-examples/ContDataQC/www/Custom_QC_Config_ECO66G20_test1.R b/inst/shiny-examples/ContDataQC/www/Custom_QC_Config_ECO66G20_test1.R new file mode 100644 index 00000000..a90cc08c --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/Custom_QC_Config_ECO66G20_test1.R @@ -0,0 +1,717 @@ +# User Defined Values +# +# User defined values for variables used across multiple functions in this +# library. The user has the ability to modify the values for names, units, QC +# thresholds, etc. +# +# Saved in a separate environment. +# +# https://www.r-bloggers.com/package-wide-variablescache-in-r-packages/ +# +# Continuous data helper script +# Default Values +# Erik.Leppo@tetratech.com (EWL) +# 20150928 +# 20170323, add 3 parameters (Cond, DO, pH) +# 20210106, replace gageheight with waterlevel +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# User defined variable names for input data +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# It is assumed that this R script is stored in a directory with the data files +# as subdirectories. This script is intended to be "source"d from the main +# script. +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# @keywords continuous data +# @examples +# #Not intended to be accessed indepedant of function ContDataQC(). +# #Data values only. No functions. Add to environment so only visible inside +# #library. +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# USER may make modifications in this section but not mandatory +# this section could be sourced so can use between scripts +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +#UserDefinedValues <- NA # default value so shows up in help files +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Environment Name #### +# Environment for use only by ContDataQC library +#ContData.env <- new.env(parent = emptyenv()) +# The above line is not used in custom configurations. +# assign variables to new environment requires editing of all lines. +# For example, myDelim <- "_" BECOMES ContData.env$myDelim, "_" +### +# list all elements in environment +# ls(ContData.env) # all elements in environment +# as.list(ContData.env) # all elements in environment with assigned values +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Delimiter in File Names (e.g., test2_AW_201301_20131231.csv) +ContData.env$myDelim <- "_" +ContData.env$myDelim_LakeID <- "--" +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Acceptable column names for the data #### +## Basic +ContData.env$myName.SiteID <- "SiteID" +ContData.env$myName.Date <- "Date" +ContData.env$myName.Time <- "Time" +ContData.env$myName.DateTime <- "Date.Time" +#(special characters (e.g., %, space, or /) are converted to "." by R +# , "deg" converted to "A") +### IF CHANGE UNITS WILL NEED TO MODIFY THRESHOLDS ### +ContData.env$myUnits.AirTemp <- "C" # C or F +ContData.env$myUnits.WaterTemp <- ContData.env$myUnits.AirTemp +ContData.env$myUnits.AirBP <- "psi" +ContData.env$myUnits.WaterP <- ContData.env$myUnits.AirBP +ContData.env$myUnits.SensorDepth <- "ft" +ContData.env$myUnits.Discharge <- "ft3.s" +ContData.env$myUnits.Cond <- "uS.cm" +ContData.env$myUnits.DO <- "mg.L" +ContData.env$myUnits.DO.adj <- "mg.L" +ContData.env$myUnits.DO.pctsat <- "%" +ContData.env$myUnits.pH <- "SU" +ContData.env$myUnits.Turbidity <- "NTU" +ContData.env$myUnits.Chlorophylla <- "g.cm3" +ContData.env$myUnits.WaterLevel <- "ft" +## Logger Fields ---- +ContData.env$myName.RowID.Water <- "Water.RowID" +ContData.env$myName.LoggerID.Water<- "Water.LoggerID" +ContData.env$myName.RowID.Air <- "Air.RowID" +ContData.env$myName.LoggerID.Air <- "Air.LoggerID" +ContData.env$myName.LoggerDeployment <- "Logger.Deployment" +ContData.env$myName.LoggerDeployment.start <- "start" +ContData.env$myName.LoggerDeployment.end <- "end" +## Parameters as appear in logger files +ContData.env$myName.WaterTemp <- paste0("Water.Temp." + ,ContData.env$myUnits.WaterTemp) +# "deg" from HoboWare files sometimes adds "A " in front. Replace with "." in R +ContData.env$myName.AirTemp <- paste0("Air.Temp." + ,ContData.env$myUnits.AirTemp) +# "deg" from HoboWare files sometimes adds "A " in front. Replace with "." in R +ContData.env$myName.AirBP <- paste0("Air.BP." + , ContData.env$myUnits.AirBP) +ContData.env$myName.WaterP <- paste0("Water.P." + , ContData.env$myUnits.WaterP) +ContData.env$myName.SensorDepth <- paste0("Sensor.Depth." + , ContData.env$myUnits.SensorDepth) +ContData.env$myName.Discharge <- paste0("Discharge." + , ContData.env$myUnits.Discharge) +ContData.env$myName.Cond <- paste0("Conductivity." + , ContData.env$myUnits.Cond) +ContData.env$myName.DO <- paste0("DO." + , ContData.env$myUnits.DO) +ContData.env$myName.DO.adj <- paste0("DO.adj." + , ContData.env$myUnits.DO) +ContData.env$myName.DO.pctsat <- paste0("DO.pctsat." + , ContData.env$myUnits.DO) +ContData.env$myName.pH <- paste0("pH." + , ContData.env$myUnits.pH) +ContData.env$myName.Turbidity <- paste0("Turbidity." + , ContData.env$myUnits.DO) +ContData.env$myName.Chlorophylla <- paste0("Chlorophylla." + , ContData.env$myUnits.pH) +ContData.env$myName.WaterLevel <- paste0("Water.Level." + , ContData.env$myUnits.WaterLevel) +## Plot Labels +ContData.env$myLab.Date <- "Date" +ContData.env$myLab.DateTime <- "Date" +ContData.env$myLab.WaterTemp <- paste0("Temperature, Water (deg " + ,ContData.env$myUnits.WaterTemp + ,")") +ContData.env$myLab.AirTemp <- paste0("Temperature, Air (deg " + ,ContData.env$myUnits.AirTemp + ,")") +ContData.env$myLab.AirBP <- paste0("Barometric Pressure, Air (" + ,ContData.env$myUnits.WaterP + ,")") +ContData.env$myLab.WaterP <- paste0("Pressure, Water (" + ,ContData.env$myUnits.AirBP + ,")") +ContData.env$myLab.SensorDepth <- paste0("Sensor Depth (" + ,ContData.env$myUnits.SensorDepth + ,")" + ,sep="") +ContData.env$myLab.Temp.BOTH <- paste0("Temperature (deg " + ,ContData.env$myUnits.WaterTemp + ,")") +ContData.env$myLab.Discharge <- paste0("Discharge (" + ,sub("\\.","/" + ,ContData.env$myUnits.Discharge) + ,")") #replace "." with "/" +ContData.env$myLab.Cond <- paste0("Conductivity (" + ,sub("\\.","/" + ,ContData.env$myUnits.Cond) + ,")") #replace "." with "/" +ContData.env$myLab.DO <- paste0("Dissolved Oxygen (" + ,sub("\\.","/" + ,ContData.env$myUnits.DO) + ,")") #replace "." with "/" +ContData.env$myLab.DO.adj <- paste0("Dissolved Oxygen, adjusted (" + ,sub("\\.","/" + ,ContData.env$myUnits.DO.adj) + ,")") #replace "." with "/" +ContData.env$myLab.DO.pctsat <- paste0("Dissolved Oxygen + , percent saturation (" + ,sub("\\.","/" + ,ContData.env$myUnits.DO.pctsat) + ,")") #replace "." with "/" +ContData.env$myLab.pH <- paste0("pH (" + ,ContData.env$myUnits.pH + ,")") +ContData.env$myLab.Turbidity <- paste0("Turbidity (" + ,ContData.env$myUnits.Turbidity + ,")") +ContData.env$myLab.Chlorophylla <- paste0("Chlorophyll a (" + ,sub("\\.","/" + ,ContData.env$myUnits.Chlorophylla) + ,")") #replace "." with "/" +ContData.env$myLab.WaterLevel <- paste0("Water Level (" + ,ContData.env$myUnits.WaterLevel + ,")") +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Discrete Measurements #### +ContData.env$myPrefix.Discrete <- "Discrete" +# Discrete, Names +ContData.env$myName.Discrete.WaterTemp <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.WaterTemp + ,sep=".") +ContData.env$myName.Discrete.AirTemp <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.AirTemp + ,sep=".") +ContData.env$myName.Discrete.AirBP <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.AirBP + ,sep=".") +ContData.env$myName.Discrete.WaterP <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.WaterP + ,sep=".") +ContData.env$myName.Discrete.SensorDepth <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.SensorDepth + ,sep=".") +ContData.env$myName.Discrete.Discharge <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.Discharge + ,sep=".") +ContData.env$myName.Discrete.Cond <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.Cond + ,sep=".") +ContData.env$myName.Discrete.DO <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.DO + ,sep=".") +ContData.env$myName.Discrete.DO.adj <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.DO.adj + ,sep=".") +ContData.env$myName.Discrete.DO.pctsat <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.DO.pctsat + ,sep=".") +ContData.env$myName.Discrete.pH <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.pH + ,sep=".") +ContData.env$myName.Discrete.Turbidity <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.Turbidity + ,sep=".") +ContData.env$myName.Discrete.Chlorophylla <-paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.Chlorophylla + ,sep=".") +ContData.env$myName.Discrete.WaterLevel <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.WaterLevel + ,sep=".") +# Discrete, Labels +ContData.env$myLab.Discrete.WaterTemp <- paste(ContData.env$myLab.WaterTemp + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.AirTemp <- paste(ContData.env$myLab.AirTemp + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.AirBP <- paste(ContData.env$myLab.AirBP + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.WaterP <- paste(ContData.env$myLab.WaterP + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.SensorDepth <- paste(ContData.env$myLab.SensorDepth + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.Discharge <- paste(ContData.env$myLab.Discharge + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.Cond <- paste(ContData.env$myLab.Cond + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.DO <- paste(ContData.env$myLab.DO + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.DO.adj <- paste(ContData.env$myLab.DO + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.DO.pctsat <- paste(ContData.env$myLab.DO + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.pH <- paste(ContData.env$myLab.pH + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.Turbidity <- paste(ContData.env$myLab.Turbidity + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.Chlorophylla<- paste(ContData.env$myLab.Chlorophylla + ,"(Discrete)" + ,sep=" ") +ContData.env$myLab.Discrete.WaterLevel <- paste(ContData.env$myLab.WaterLevel + ,"(Discrete)" + ,sep=" ") +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Automated QC stuff #### +## data type/stages +ContData.env$myDataQuality.Raw <- "RAW" +ContData.env$myDataQuality.QCauto <- "QCauto" +ContData.env$myDataQuality.QCmanual <- "QCmanual" +ContData.env$myDataQuality.Final <- "Final" +ContData.env$myDataQuality.Aggregated <- "Aggregated" +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Directory Names #### +ContData.env$myName.Dir.0Original <- "Data0_Original" +ContData.env$myName.Dir.1Raw <- "Data1_Raw" +ContData.env$myName.Dir.2QC <- "Data2_QC" +ContData.env$myName.Dir.3Agg <- "Data3_Aggregated" +ContData.env$myName.Dir.4Stats <- "Data4_Stats" +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Data Fields #### +ContData.env$myNames.DataFields <- c(ContData.env$myName.WaterTemp + , ContData.env$myName.AirTemp + , ContData.env$myName.AirBP + , ContData.env$myName.WaterP + , ContData.env$myName.SensorDepth + , ContData.env$myName.Discharge + , ContData.env$myName.Cond + , ContData.env$myName.DO + , ContData.env$myName.DO.adj + , ContData.env$myName.DO.pctsat + , ContData.env$myName.pH + , ContData.env$myName.Turbidity + , ContData.env$myName.Chlorophylla + , ContData.env$myName.WaterLevel + , ContData.env$myName.Discrete.WaterTemp + , ContData.env$myName.Discrete.AirTemp + , ContData.env$myName.Discrete.WaterP + , ContData.env$myName.Discrete.AirBP + , ContData.env$myName.Discrete.SensorDepth + , ContData.env$myName.Discrete.Discharge + , ContData.env$myName.Discrete.Cond + , ContData.env$myName.Discrete.DO + , ContData.env$myName.Discrete.DO.adj + , ContData.env$myName.Discrete.DO.pctsat + , ContData.env$myName.Discrete.pH + , ContData.env$myName.Discrete.Turbidity + , ContData.env$myName.Discrete.Chlorophylla + , ContData.env$myName.Discrete.WaterLevel + ) +ContData.env$myNames.DataFields.Lab <- c(ContData.env$myLab.WaterTemp + , ContData.env$myLab.AirTemp + , ContData.env$myLab.AirBP + , ContData.env$myLab.WaterP + , ContData.env$myLab.SensorDepth + , ContData.env$myLab.Discharge + , ContData.env$myLab.Cond + , ContData.env$myLab.DO + , ContData.env$myLab.DO.adj + , ContData.env$myLab.DO.pctsat + , ContData.env$myLab.pH + , ContData.env$myLab.Turbidity + , ContData.env$myLab.Chlorophylla + , ContData.env$myLab.WaterLevel + , ContData.env$myLab.Discrete.WaterTemp + , ContData.env$myLab.Discrete.AirTemp + , ContData.env$myLab.Discrete.WaterP + , ContData.env$myLab.Discrete.AirBP + , ContData.env$myLab.Discrete.SensorDepth + , ContData.env$myLab.Discrete.Discharge + , ContData.env$myLab.Discrete.Cond + , ContData.env$myLab.Discrete.DO + , ContData.env$myLab.Discrete.DO.adj + , ContData.env$myLab.Discrete.DO.pctsat + , ContData.env$myLab.Discrete.pH + , ContData.env$myLab.Discrete.Turbidity + , ContData.env$myLab.Discrete.Chlorophylla + , ContData.env$myLab.Discrete.WaterLevel + ) +ContData.env$myNames.DataFields.Col <- c("blue","green","gray","gray","black" + ,"brown","purple","orange","salmon" + ,"rosybrown","aquamarine1") +# +## Name Order (change order below to change order in output file) +ContData.env$myNames.Order <- c(ContData.env$myName.SiteID + , ContData.env$myName.Date + , ContData.env$myName.Time + , ContData.env$myName.DateTime + , ContData.env$myName.LoggerDeployment + , ContData.env$myName.WaterTemp + , ContData.env$myName.LoggerID.Air + , ContData.env$myName.RowID.Air + , ContData.env$myName.AirTemp + , ContData.env$myName.WaterP + , ContData.env$myName.AirBP + , ContData.env$myName.SensorDepth + , ContData.env$myName.Discharge + , ContData.env$myName.Cond + , ContData.env$myName.DO + , ContData.env$myName.DO.adj + , ContData.env$myName.DO.pctsat + , ContData.env$myName.pH + , ContData.env$myName.Turbidity + , ContData.env$myName.Chlorophylla + , ContData.env$myName.WaterLevel + , ContData.env$myName.LoggerID.Water + , ContData.env$myName.RowID.Water + , ContData.env$myName.Discrete.WaterTemp + , ContData.env$myName.Discrete.AirTemp + , ContData.env$myName.Discrete.WaterP + , ContData.env$myName.Discrete.AirBP + , ContData.env$myName.Discrete.SensorDepth + , ContData.env$myName.Discrete.Discharge + , ContData.env$myName.Discrete.Cond + , ContData.env$myName.Discrete.DO + , ContData.env$myName.Discrete.DO.adj + , ContData.env$myName.Discrete.DO.pctsat + , ContData.env$myName.Discrete.pH + , ContData.env$myName.Discrete.Turbidity + , ContData.env$myName.Discrete.Chlorophylla + , ContData.env$myName.Discrete.WaterLevel + ) +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +## Data Quality Flag Values #### +ContData.env$myFlagVal.Pass <- "P" +ContData.env$myFlagVal.NotEval <- "NA" +ContData.env$myFlagVal.Suspect <- "S" +ContData.env$myFlagVal.Fail <- "F" +ContData.env$myFlagVal.NoData <- "X" +ContData.env$myFlagVal.Order <- c(ContData.env$myFlagVal.Pass + , ContData.env$myFlagVal.Suspect + , ContData.env$myFlagVal.Fail + , ContData.env$myFlagVal.NoData) +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# QC Tests and Calculations #### +#http://stackoverflow.com/questions/16143700/pasting-two-vectors-with-combinations-of-all-vectors-elements +#myNames.QCTests.Calcs.combo <- as.vector(t(outer(myNames.QCTests,myNames.QCTests.Calcs,paste,sep="."))) +# combine so can check for and remove later. +#myNames.DataFields.QCTests.Calcs.combo <- as.vector(t(outer(myNames.DataFields,myNames.QCTests.Calcs.combo,paste,sep="."))) +# Data Quality Flag Thresholds +# _QC, Gross---- +## Gross Min/Max, Fail (equipment) +### Examines values as outliers versus threholds +### if value >= Hi or <= Lo then flagged as "Fail" +ContData.env$myThresh.Gross.Fail.Hi.WaterTemp <- 28 +ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -2 +ContData.env$myThresh.Gross.Fail.Hi.AirTemp <- 38 +ContData.env$myThresh.Gross.Fail.Lo.AirTemp <- -25 +ContData.env$myThresh.Gross.Fail.Hi.AirBP <- 15 +ContData.env$myThresh.Gross.Fail.Lo.AirBP <- 13 +ContData.env$myThresh.Gross.Fail.Hi.WaterP <- 17 +ContData.env$myThresh.Gross.Fail.Lo.WaterP <- 13 +ContData.env$myThresh.Gross.Fail.Hi.SensorDepth <- 10^5 # dependent upon stream size +ContData.env$myThresh.Gross.Fail.Lo.SensorDepth <- -1 # dependent upon stream size +ContData.env$myThresh.Gross.Fail.Hi.Discharge <- 10^5 # dependent upon stream size +ContData.env$myThresh.Gross.Fail.Lo.Discharge <- -1 # dependent upon stream size +ContData.env$myThresh.Gross.Fail.Hi.Cond <- 1500 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Lo.Cond <- 10 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Lo.DO <- 1 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO.adj <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Lo.DO.adj <- 1 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.DO.pctsat <- 120 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Lo.DO.pctsat <- -1 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.pH <- 12 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Lo.pH <- 3 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.Turbidity <- 10^5 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Lo.Turbidity <- -1 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.Chlorophylla <- 10^5 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Lo.Chlorophylla <- -1 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Fail.Hi.WaterLevel <- ContData.env$myThresh.Gross.Fail.Hi.SensorDepth +ContData.env$myThresh.Gross.Fail.Lo.WaterLevel <- ContData.env$myThresh.Gross.Fail.Lo.SensorDepth +## Gross Min/Max, Suspect (extreme) +### Examines values as outliers versus threholds +### if value >= Hi or <= Lo then flagged as "Suspect" +ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp <- 26 +ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.1 +ContData.env$myThresh.Gross.Suspect.Hi.AirTemp <- 35 +ContData.env$myThresh.Gross.Suspect.Lo.AirTemp <- -23 +ContData.env$myThresh.Gross.Suspect.Hi.AirBP <- 14.8 +ContData.env$myThresh.Gross.Suspect.Lo.AirBP <- 13.0 +ContData.env$myThresh.Gross.Suspect.Hi.WaterP <- 16.8 +ContData.env$myThresh.Gross.Suspect.Lo.WaterP <- 13.5 +ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth <- 10^3 # dependent upon stream size +ContData.env$myThresh.Gross.Suspect.Lo.SensorDepth <- 0 # dependent upon stream size +ContData.env$myThresh.Gross.Suspect.Hi.Discharge <- 10^3 # dependent upon stream size +ContData.env$myThresh.Gross.Suspect.Lo.Discharge <- -1 # dependent upon stream size +ContData.env$myThresh.Gross.Suspect.Hi.Cond <- 1200 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.Cond <- 20 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.DO <- 18 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.DO <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.DO.adj <- 18 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.DO.adj <- 2 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.DO.pctsat <- 100 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.DO.pctsat <- 0 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.pH <- 11 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.pH <- 4 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.Turbidity <- 10^3 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.Turbidity <- -1 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.Chlorophylla <- 10^3 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Lo.Chlorophylla <- 1 # this threshold has not been closely evaluated +ContData.env$myThresh.Gross.Suspect.Hi.WaterLevel <- ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth +ContData.env$myThresh.Gross.Suspect.Lo.WaterLevel <- ContData.env$myThresh.Gross.Suspect.Lo.SensorDept +# _QC, Spike ---- +## Spike thresholds (absolute change) +### Examines difference between consecutive measurements +### if delta >= Hi then flagged as "Fail" +### if delta >= Lo then flagged as "Suspect" +ContData.env$myThresh.Spike.Hi.WaterTemp <- 1 +ContData.env$myThresh.Spike.Lo.WaterTemp <- 0.8 +ContData.env$myThresh.Spike.Hi.AirTemp <- 10 +ContData.env$myThresh.Spike.Lo.AirTemp <- 8 +ContData.env$myThresh.Spike.Hi.AirBP <- 0.25 +ContData.env$myThresh.Spike.Lo.AirBP <- 0.15 +ContData.env$myThresh.Spike.Hi.WaterP <- 0.7 +ContData.env$myThresh.Spike.Lo.WaterP <- 0.5 +ContData.env$myThresh.Spike.Hi.SensorDepth <- 10^4 # dependent upon stream size +ContData.env$myThresh.Spike.Lo.SensorDepth <- 10^3 # dependent upon stream size +ContData.env$myThresh.Spike.Hi.Discharge <- 10^4 # dependent upon stream size +ContData.env$myThresh.Spike.Lo.Discharge <- 10^3 # dependent upon stream size +ContData.env$myThresh.Spike.Hi.Cond <- 10 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.Cond <- 5 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO <- 10 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO <- 5 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO.adj <- 10 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO.adj <- 5 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.DO.pctsat <- 25 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.DO.pctsat <- 10 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.pH <- 10 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.pH <- 5 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.Turbidity <- 10^4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.Turbidity <- 10^3 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.Chlorophylla <- 10^4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.Chlorophylla <- 10^3 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.Chlorophylla <- 10^4 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Lo.Chlorophylla <- 10^3 # this threshold has not been closely evaluated +ContData.env$myThresh.Spike.Hi.WaterLevel <- ContData.env$myThresh.Spike.Hi.SensorDepth +ContData.env$myThresh.Spike.Lo.WaterLevel <- ContData.env$myThresh.Spike.Lo.SensorDepth +# _QC, ROC---- +## Rate of Change (relative change) +### Examines SD over "period" and difference in consecutive values +### If delta >= SD.number * SD then flagged as "Suspect" +ContData.env$myDefault.RoC.SD.number <- 5 +ContData.env$myDefault.RoC.SD.period <- 25 #hours +ContData.env$myThresh.RoC.SD.number.WaterTemp <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.WaterTemp <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.AirTemp <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.AirTemp <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.AirBP <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.AirBP <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.WaterP <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.WaterP <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.SensorDepth <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.SensorDepth <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.Discharge <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.Discharge <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.Cond <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.Cond <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.DO <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.DO <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.DO.adj <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.DO.adj <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.DO.pctsat <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.DO.pctsat <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.pH <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.pH <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.Turbidity <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.Turbidity <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.Chlorophylla <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.Chlorophylla <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.WaterLevel <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.WaterLevel <- ContData.env$myDefault.RoC.SD.period +# QC, Flat Line---- +## No Change (flat-line) +### Examines consecutive values within "Tolerance" of each other +### If number of consecutive values >= Hi then flagged as "Fail" +### If number of consecutive values >= Lo (and < Hi) then flagged as "Suspect" +ContData.env$myDefault.Flat.Hi <- 30 # maximum is myThresh.Flat.MaxComp +ContData.env$myDefault.Flat.Lo <- 15 +ContData.env$myDefault.Flat.Tolerance <- 0.01 # set to one sigdig less than measurements. Check with fivenum(x) +ContData.env$myThresh.Flat.Hi.WaterTemp <- 50 +ContData.env$myThresh.Flat.Lo.WaterTemp <- 30 +ContData.env$myThresh.Flat.Tolerance.WaterTemp <- 0.01 +ContData.env$myThresh.Flat.Hi.AirTemp <- 20 +ContData.env$myThresh.Flat.Lo.AirTemp <- 15 +ContData.env$myThresh.Flat.Tolerance.AirTemp <- 0.01 +ContData.env$myThresh.Flat.Hi.AirBP <- 15 +ContData.env$myThresh.Flat.Lo.AirBP <- 10 +ContData.env$myThresh.Flat.Tolerance.AirBP <- 0.001 +ContData.env$myThresh.Flat.Hi.WaterP <- 15 +ContData.env$myThresh.Flat.Lo.WaterP <- 10 +ContData.env$myThresh.Flat.Tolerance.WaterP <- 0.001 +ContData.env$myThresh.Flat.Hi.SensorDepth <- 60 +ContData.env$myThresh.Flat.Lo.SensorDepth <- 20 +ContData.env$myThresh.Flat.Tolerance.SensorDepth <- 0.0 +ContData.env$myThresh.Flat.Hi.Discharge <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.Discharge <- ContData.env$myDefault.Flat.Lo * 2 +ContData.env$myThresh.Flat.Tolerance.Discharge <- 0.01 +ContData.env$myThresh.Flat.Hi.Cond <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.Cond <- ContData.env$myDefault.Flat.Lo * 2 +ContData.env$myThresh.Flat.Tolerance.Cond <- 0.01 +ContData.env$myThresh.Flat.Hi.DO <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.DO <- ContData.env$myDefault.Flat.Lo * 2 +ContData.env$myThresh.Flat.Tolerance.DO <- 0.01 +ContData.env$myThresh.Flat.Hi.DO.adj <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.DO.adj <- ContData.env$myDefault.Flat.Lo * 2 +ContData.env$myThresh.Flat.Tolerance.DO.adj <- 0.01 +ContData.env$myThresh.Flat.Hi.DO.pctsat <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.DO.pctsat <- ContData.env$myDefault.Flat.Lo * 2 +ContData.env$myThresh.Flat.Tolerance.DO.pctsat <- 0.01 +ContData.env$myThresh.Flat.Hi.pH <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.pH <- ContData.env$myDefault.Flat.Lo * 2 +ContData.env$myThresh.Flat.Tolerance.pH <- 0.01 +ContData.env$myThresh.Flat.Hi.Turbidity <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.Turbidity <- ContData.env$myDefault.Flat.Lo * 2 +ContData.env$myThresh.Flat.Tolerance.Turbidity <- 0.01 +ContData.env$myThresh.Flat.Hi.Chlorophylla <- ContData.env$myDefault.Flat.Hi * 2 +ContData.env$myThresh.Flat.Lo.Chlorophylla <- ContData.env$myDefault.Flat.Lo * 2 +ContData.env$myThresh.Flat.Tolerance.Chlorophylla <- 0.01 +ContData.env$myThresh.Flat.Hi.WaterLevel <- ContData.env$myThresh.Flat.Hi.SensorDepth +ContData.env$myThresh.Flat.Lo.WaterLevel <- ContData.env$myThresh.Flat.Lo.SensorDepth +ContData.env$myThresh.Flat.Tolerance.WaterLevel <- ContData.env$myThresh.Flat.Tolerance.SensorDepth +# +ContData.env$myThresh.Flat.MaxComp <- max(ContData.env$myThresh.Flat.Hi.WaterTemp + , ContData.env$myThresh.Flat.Hi.AirTemp + , ContData.env$myThresh.Flat.Hi.AirBP + , ContData.env$myThresh.Flat.Hi.WaterP + , ContData.env$myThresh.Flat.Hi.SensorDepth + , ContData.env$myThresh.Flat.Hi.Discharge + , ContData.env$myThresh.Flat.Hi.Cond + , ContData.env$myThresh.Flat.Hi.DO + , ContData.env$myThresh.Flat.Hi.DO.adj + , ContData.env$myThresh.Flat.Hi.DO.pctsat + , ContData.env$myThresh.Flat.Hi.pH + , ContData.env$myThresh.Flat.Hi.Turbidity + , ContData.env$myThresh.Flat.Hi.Chlorophylla + , ContData.env$myThresh.Flat.Hi.WaterLevel + ) +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Data Fields with Flags #### +ContData.env$myName.Flag <- "Flag" # flag prefix +ContData.env$myNames.Cols4Flags <- c(ContData.env$myName.DateTime + , ContData.env$myNames.DataFields) +ContData.env$myNames.Flags <- paste(ContData.env$myName.Flag + , ContData.env$myNames.Cols4Flags + , sep=".") +# define ones using in the calling script + +## flag labels +ContData.env$myName.Flag.DateTime <- paste(ContData.env$myName.Flag + , ContData.env$myName.DateTime + , sep=".") +ContData.env$myName.Flag.WaterTemp <- paste(ContData.env$myName.Flag + , ContData.env$myName.WaterTemp + , sep=".") +ContData.env$myName.Flag.AirTemp <- paste(ContData.env$myName.Flag + , ContData.env$myName.AirTemp + , sep=".") +ContData.env$myName.Flag.AirBP <- paste(ContData.env$myName.Flag + , ContData.env$myName.AirBP + , sep=".") +ContData.env$myName.Flag.WaterP <- paste(ContData.env$myName.Flag + , ContData.env$myName.WaterP + , sep=".") +ContData.env$myName.Flag.SensorDepth <- paste(ContData.env$myName.Flag + , ContData.env$myName.SensorDepth + , sep=".") +ContData.env$myName.Flag.Discharge <- paste(ContData.env$myName.Flag + , ContData.env$myName.Discharge + , sep=".") +ContData.env$myName.Flag.Cond <- paste(ContData.env$myName.Flag + , ContData.env$myName.Cond + , sep=".") +ContData.env$myName.Flag.DO <- paste(ContData.env$myName.Flag + , ContData.env$myName.DO + , sep=".") +ContData.env$myName.Flag.DO.adj <- paste(ContData.env$myName.Flag + , ContData.env$myName.DO.adj + , sep=".") +ContData.env$myName.Flag.DO.pctsat <- paste(ContData.env$myName.Flag + , ContData.env$myName.DO.pctsat + , sep=".") +ContData.env$myName.Flag.pH <- paste(ContData.env$myName.Flag + , ContData.env$myName.pH + , sep=".") +ContData.env$myName.Flag.Turbidity <- paste(ContData.env$myName.Flag + , ContData.env$myName.Turbidity + , sep=".") +ContData.env$myName.Flag.Chlorophylla <- paste(ContData.env$myName.Flag + , ContData.env$myName.Chlorophylla + , sep=".") +ContData.env$myName.Flag.WaterLevel <- paste(ContData.env$myName.Flag + , ContData.env$myName.WaterLevel + , sep=".") +# Data Quality Test Names +ContData.env$myNames.QCTests <- c("Gross","Spike","RoC","Flat") +ContData.env$myNames.QCCalcs <- c("SD.Time" + , "SD" + , "SDxN" + , paste("n" + , seq_len( + ContData.env$myThresh.Flat.MaxComp) + , sep=".") + , "flat.Lo" + , "flat.Hi") +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Exceedance values for stats (default to Gross-Suspect-Hi value) #### +ContData.env$myExceed.WaterTemp <- + ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp +ContData.env$myExceed.AirTemp <- + ContData.env$myThresh.Gross.Suspect.Hi.AirTemp +ContData.env$myExceed.SensorDepth <- + ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Date and Time Formats #### +ContData.env$myFormat.Date <- "%Y-%m-%d" +ContData.env$myFormat.Time <- "%H:%M:%S" +ContData.env$myFormat.DateTime <- "%Y-%m-%d %H:%M:%S" +ContData.env$DateRange.Start.Default <- format(as.Date("1900-01-01") + , ContData.env$myFormat.Date) + #YYYY-MM-DD +ContData.env$DateRange.End.Default <- format(Sys.Date() + , ContData.env$myFormat.Date) +# Time Zone, used in Gage script in dataRetrieval, OlsonNames() +ContData.env$myTZ <- Sys.timezone() #"America/New_York" (local time zone) +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Time Frames (MM-DD) #### +ContData.env$myTimeFrame.Annual.Start <- "0101" +ContData.env$myTimeFrame.Annual.End <- "1231" +ContData.env$myTimeFrame.WaterYear.Start <- "1001" +#ContData.env$myTimeFrame.WaterYear.End <- "0930" +ContData.env$myTimeFrame.Season.Spring.Start <- "0301" +#ContData.env$myTimeFrame.Season.Spring.End <- "0531" +ContData.env$myTimeFrame.Season.Summer.Start <- "0601" +#ContData.env$myTimeFrame.Season.Summer.End <- "0831" +ContData.env$myTimeFrame.Season.Fall.Start <- "0901" +#ContData.env$myTimeFrame.Season.Fall.End <- "1130" +ContData.env$myTimeFrame.Season.Winter.Start <- "1201" +#ContData.env$myTimeFrame.Season.Winter.End <- "0228" #but 0229 in leap year, use start dates only +# Time Frame Names +ContData.env$myName.Yr <- "Year" +ContData.env$myName.YrMo <- "YearMonth" +ContData.env$myName.Mo <- "Month" +ContData.env$myName.MoDa <- "MonthDay" +ContData.env$myName.JuDa <- "JulianDay" +ContData.env$myName.Day <- "Day" +ContData.env$myName.Season <- "Season" +ContData.env$myName.YrSeason <- "YearSeason" +# for summary stats +ContData.env$myNames.Fields.TimePeriods <- c(ContData.env$myName.Yr + ,ContData.env$myName.YrMo + ,ContData.env$myName.Mo + ,ContData.env$myName.MoDa + ,ContData.env$myName.JuDa + ,ContData.env$myName.Season + ,ContData.env$myName.YrSeason) +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Exclude Trigger #### +# Trigger for Stats to exclude (TRUE) or include (FALSE) where flag = "fail" +ContData.env$myStats.Fails.Exclude <- TRUE #FALSE #TRUE +#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +# Report Format #### +ContData.env$myReport.Format <- "docx" # "html" or "docx" +# DOCX requires Pandoc. +ContData.env$myReport.Dir <- file.path(system.file(package="ContDataQC"), "rmd") diff --git a/inst/shiny-examples/ContDataQC/www/QC_Custom_Config.tsv b/inst/shiny-examples/ContDataQC/www/QC_Custom_Config.tsv index 042f11e6..73ee8cd7 100644 --- a/inst/shiny-examples/ContDataQC/www/QC_Custom_Config.tsv +++ b/inst/shiny-examples/ContDataQC/www/QC_Custom_Config.tsv @@ -1,4 +1,3 @@ -ContData.env <- new.env(parent = emptyenv()) ContData.env$myDelim <- "_" ContData.env$myDelim_LakeID <- "--" ContData.env$myName.SiteID <- "SiteID" @@ -14,11 +13,12 @@ ContData.env$myUnits.Discharge <- "ft3.s" ContData.env$myUnits.Cond <- "uS.cm" ContData.env$myUnits.DO <- "mg.L" ContData.env$myUnits.DO.adj <- "mg.L" -ContData.env$myUnits.DO.pctsat <- "%" +ContData.env$myUnits.DO.pctsat <- "percent" ContData.env$myUnits.pH <- "SU" ContData.env$myUnits.Turbidity <- "NTU" ContData.env$myUnits.Chlorophylla <- "g.cm3" ContData.env$myUnits.WaterLevel <- "ft" +ContData.env$myUnits.Salinity <- "ppt" ContData.env$myName.RowID.Water <- "Water.RowID" ContData.env$myName.LoggerID.Water<- "Water.LoggerID" ContData.env$myName.RowID.Air <- "Air.RowID" @@ -40,12 +40,9 @@ ContData.env$myName.Discharge <- paste0("Discharge." , ContData.env$myUnits.Discharge) ContData.env$myName.Cond <- paste0("Conductivity." , ContData.env$myUnits.Cond) -ContData.env$myName.DO <- paste0("DO." - , ContData.env$myUnits.DO) -ContData.env$myName.DO.adj <- paste0("DO.adj." - , ContData.env$myUnits.DO.adj) -ContData.env$myName.DO.pctsat <- paste0("DO.pctsat." - , ContData.env$myUnits.DO.pctsat) +ContData.env$myName.DO <- paste0("DO.", ContData.env$myUnits.DO) +ContData.env$myName.DO.adj <- paste0("DO.adj.", ContData.env$myUnits.DO.adj) +ContData.env$myName.DO.pctsat <- paste0("DO.pctsat.", ContData.env$myUnits.DO.pctsat) ContData.env$myName.pH <- paste0("pH." , ContData.env$myUnits.pH) ContData.env$myName.Turbidity <- paste0("Turbidity." @@ -54,6 +51,8 @@ ContData.env$myName.Chlorophylla <- paste0("Chlorophylla." , ContData.env$myUnits.Chlorophylla) ContData.env$myName.WaterLevel <- paste0("Water.Level." , ContData.env$myUnits.WaterLevel) +ContData.env$myName.Salinity <- paste0("Salinity." + , ContData.env$myUnits.Salinity) ContData.env$myLab.Date <- "Date" ContData.env$myLab.DateTime <- "Date" ContData.env$myLab.WaterTemp <- paste0("Temperature, Water (deg " @@ -91,10 +90,9 @@ ContData.env$myLab.DO.adj <- paste0("Dissolved Oxygen, adjusted (" ,sub("\\.","/" ,ContData.env$myUnits.DO.adj) ,")") -ContData.env$myLab.DO.pctsat <- paste0("Dissolved Oxygen - , percent saturation (" +ContData.env$myLab.DO.pctsat <- paste0("Dissolved Oxygen, percent saturation (" ,sub("\\.","/" - ,ContData.env$myUnits.DO.pctsat) + ,ContData.env$myUnits.DO.pctsat) ,")") ContData.env$myLab.pH <- paste0("pH (" ,ContData.env$myUnits.pH @@ -109,6 +107,9 @@ ContData.env$myLab.Chlorophylla <- paste0("Chlorophyll a (" ContData.env$myLab.WaterLevel <- paste0("Water Level (" ,ContData.env$myUnits.WaterLevel ,")") +ContData.env$myLab.Salinity <- paste0("Salinity (" + ,ContData.env$myUnits.Salinity + ,")") ContData.env$myPrefix.Discrete <- "Discrete" ContData.env$myName.Discrete.WaterTemp <- paste(ContData.env$myPrefix.Discrete ,ContData.env$myName.WaterTemp @@ -138,8 +139,8 @@ ContData.env$myName.Discrete.DO.adj <- paste(ContData.env$myPrefix.Discrete ,ContData.env$myName.DO.adj ,sep=".") ContData.env$myName.Discrete.DO.pctsat <- paste(ContData.env$myPrefix.Discrete - ,ContData.env$myName.DO.pctsat - ,sep=".") + , ContData.env$myName.DO.pctsat + , sep=".") ContData.env$myName.Discrete.pH <- paste(ContData.env$myPrefix.Discrete ,ContData.env$myName.pH ,sep=".") @@ -152,6 +153,9 @@ ContData.env$myName.Discrete.Chlorophylla <-paste(ContData.env$myPrefix.Discrete ContData.env$myName.Discrete.WaterLevel <- paste(ContData.env$myPrefix.Discrete ,ContData.env$myName.WaterLevel ,sep=".") +ContData.env$myName.Discrete.Salinity <- paste(ContData.env$myPrefix.Discrete + ,ContData.env$myName.Salinity + ,sep=".") ContData.env$myLab.Discrete.WaterTemp <- paste(ContData.env$myLab.WaterTemp ,"(Discrete)" ,sep=" ") @@ -176,10 +180,10 @@ ContData.env$myLab.Discrete.Cond <- paste(ContData.env$myLab.Cond ContData.env$myLab.Discrete.DO <- paste(ContData.env$myLab.DO ,"(Discrete)" ,sep=" ") -ContData.env$myLab.Discrete.DO.adj <- paste(ContData.env$myLab.DO +ContData.env$myLab.Discrete.DO.adj <- paste(ContData.env$myLab.DO.adj ,"(Discrete)" ,sep=" ") -ContData.env$myLab.Discrete.DO.pctsat <- paste(ContData.env$myLab.DO +ContData.env$myLab.Discrete.DO.pctsat <- paste(ContData.env$myLab.DO.pctsat ,"(Discrete)" ,sep=" ") ContData.env$myLab.Discrete.pH <- paste(ContData.env$myLab.pH @@ -194,6 +198,9 @@ ContData.env$myLab.Discrete.Chlorophylla<- paste(ContData.env$myLab.Chlorophylla ContData.env$myLab.Discrete.WaterLevel <- paste(ContData.env$myLab.WaterLevel ,"(Discrete)" ,sep=" ") +ContData.env$myLab.Discrete.Salinity <- paste(ContData.env$myLab.Salinity + ,"(Discrete)" + ,sep=" ") ContData.env$myDataQuality.Raw <- "RAW" ContData.env$myDataQuality.QCauto <- "QCauto" ContData.env$myDataQuality.QCmanual <- "QCmanual" @@ -218,6 +225,7 @@ ContData.env$myNames.DataFields <- c(ContData.env$myName.WaterTemp , ContData.env$myName.Turbidity , ContData.env$myName.Chlorophylla , ContData.env$myName.WaterLevel + , ContData.env$myName.Salinity , ContData.env$myName.Discrete.WaterTemp , ContData.env$myName.Discrete.AirTemp , ContData.env$myName.Discrete.WaterP @@ -232,6 +240,7 @@ ContData.env$myNames.DataFields <- c(ContData.env$myName.WaterTemp , ContData.env$myName.Discrete.Turbidity , ContData.env$myName.Discrete.Chlorophylla , ContData.env$myName.Discrete.WaterLevel + , ContData.env$myName.Discrete.Salinity ) ContData.env$myNames.DataFields.Lab <- c(ContData.env$myLab.WaterTemp , ContData.env$myLab.AirTemp @@ -247,6 +256,7 @@ ContData.env$myNames.DataFields.Lab <- c(ContData.env$myLab.WaterTemp , ContData.env$myLab.Turbidity , ContData.env$myLab.Chlorophylla , ContData.env$myLab.WaterLevel + , ContData.env$myLab.Salinity , ContData.env$myLab.Discrete.WaterTemp , ContData.env$myLab.Discrete.AirTemp , ContData.env$myLab.Discrete.WaterP @@ -261,6 +271,7 @@ ContData.env$myNames.DataFields.Lab <- c(ContData.env$myLab.WaterTemp , ContData.env$myLab.Discrete.Turbidity , ContData.env$myLab.Discrete.Chlorophylla , ContData.env$myLab.Discrete.WaterLevel + , ContData.env$myLab.Discrete.Salinity ) ContData.env$myNames.DataFields.Col <- c("blue","green","gray","gray","black" ,"brown","purple","orange","salmon" @@ -286,6 +297,7 @@ ContData.env$myNames.Order <- c(ContData.env$myName.SiteID , ContData.env$myName.Turbidity , ContData.env$myName.Chlorophylla , ContData.env$myName.WaterLevel + , ContData.env$myName.Salinity , ContData.env$myName.LoggerID.Water , ContData.env$myName.RowID.Water , ContData.env$myName.Discrete.WaterTemp @@ -302,6 +314,7 @@ ContData.env$myNames.Order <- c(ContData.env$myName.SiteID , ContData.env$myName.Discrete.Turbidity , ContData.env$myName.Discrete.Chlorophylla , ContData.env$myName.Discrete.WaterLevel + , ContData.env$myName.Discrete.Salinity ) ContData.env$myFlagVal.Pass <- "P" ContData.env$myFlagVal.NotEval <- "NA" @@ -312,10 +325,10 @@ ContData.env$myFlagVal.Order <- c(ContData.env$myFlagVal.Pass , ContData.env$myFlagVal.Suspect , ContData.env$myFlagVal.Fail , ContData.env$myFlagVal.NoData) -ContData.env$myThresh.Gross.Fail.Hi.WaterTemp <- 30 -ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -2 -ContData.env$myThresh.Gross.Fail.Hi.AirTemp <- 999 -ContData.env$myThresh.Gross.Fail.Lo.AirTemp <- -999 +ContData.env$myThresh.Gross.Fail.Hi.WaterTemp <- 28 +ContData.env$myThresh.Gross.Fail.Lo.WaterTemp <- -2 +ContData.env$myThresh.Gross.Fail.Hi.AirTemp <- 38 +ContData.env$myThresh.Gross.Fail.Lo.AirTemp <- -25 ContData.env$myThresh.Gross.Fail.Hi.AirBP <- 15 ContData.env$myThresh.Gross.Fail.Lo.AirBP <- 13 ContData.env$myThresh.Gross.Fail.Hi.WaterP <- 17 @@ -340,10 +353,12 @@ ContData.env$myThresh.Gross.Fail.Hi.Chlorophylla <- 10^5 ContData.env$myThresh.Gross.Fail.Lo.Chlorophylla <- -1 ContData.env$myThresh.Gross.Fail.Hi.WaterLevel <- ContData.env$myThresh.Gross.Fail.Hi.SensorDepth ContData.env$myThresh.Gross.Fail.Lo.WaterLevel <- ContData.env$myThresh.Gross.Fail.Lo.SensorDepth -ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp <- 25 -ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.1 -ContData.env$myThresh.Gross.Suspect.Hi.AirTemp <- 999 -ContData.env$myThresh.Gross.Suspect.Lo.AirTemp <- -99 +ContData.env$myThresh.Gross.Fail.Hi.Salinity <- 41 +ContData.env$myThresh.Gross.Fail.Lo.Salinity <- 2 +ContData.env$myThresh.Gross.Suspect.Hi.WaterTemp <- 26 +ContData.env$myThresh.Gross.Suspect.Lo.WaterTemp <- -0.1 +ContData.env$myThresh.Gross.Suspect.Hi.AirTemp <- 35 +ContData.env$myThresh.Gross.Suspect.Lo.AirTemp <- -23 ContData.env$myThresh.Gross.Suspect.Hi.AirBP <- 14.8 ContData.env$myThresh.Gross.Suspect.Lo.AirBP <- 13.0 ContData.env$myThresh.Gross.Suspect.Hi.WaterP <- 16.8 @@ -368,10 +383,12 @@ ContData.env$myThresh.Gross.Suspect.Hi.Chlorophylla <- 10^3 ContData.env$myThresh.Gross.Suspect.Lo.Chlorophylla <- 1 ContData.env$myThresh.Gross.Suspect.Hi.WaterLevel <- ContData.env$myThresh.Gross.Suspect.Hi.SensorDepth ContData.env$myThresh.Gross.Suspect.Lo.WaterLevel <- ContData.env$myThresh.Gross.Suspect.Lo.SensorDept -ContData.env$myThresh.Spike.Hi.WaterTemp <- 1.5 -ContData.env$myThresh.Spike.Lo.WaterTemp <- 1 -ContData.env$myThresh.Spike.Hi.AirTemp <- 10 -ContData.env$myThresh.Spike.Lo.AirTemp <- 8 +ContData.env$myThresh.Gross.Suspect.Hi.Salinity <- 37 +ContData.env$myThresh.Gross.Suspect.Lo.Salinity <- 3 +ContData.env$myThresh.Spike.Hi.WaterTemp <- 1 +ContData.env$myThresh.Spike.Lo.WaterTemp <- 0.8 +ContData.env$myThresh.Spike.Hi.AirTemp <- 10 +ContData.env$myThresh.Spike.Lo.AirTemp <- 8 ContData.env$myThresh.Spike.Hi.AirBP <- 0.25 ContData.env$myThresh.Spike.Lo.AirBP <- 0.15 ContData.env$myThresh.Spike.Hi.WaterP <- 0.7 @@ -396,12 +413,14 @@ ContData.env$myThresh.Spike.Hi.Chlorophylla <- 10^4 ContData.env$myThresh.Spike.Lo.Chlorophylla <- 10^3 ContData.env$myThresh.Spike.Hi.WaterLevel <- ContData.env$myThresh.Spike.Hi.SensorDepth ContData.env$myThresh.Spike.Lo.WaterLevel <- ContData.env$myThresh.Spike.Lo.SensorDepth +ContData.env$myThresh.Spike.Hi.Salinity <- 5 +ContData.env$myThresh.Spike.Lo.Salinity <- 3 ContData.env$myDefault.RoC.SD.number <- 3 ContData.env$myDefault.RoC.SD.period <- 25 -ContData.env$myThresh.RoC.SD.number.WaterTemp <- ContData.env$myDefault.RoC.SD.number -ContData.env$myThresh.RoC.SD.period.WaterTemp <- ContData.env$myDefault.RoC.SD.period -ContData.env$myThresh.RoC.SD.number.AirTemp <- 3 -ContData.env$myThresh.RoC.SD.period.AirTemp <- 25 +ContData.env$myThresh.RoC.SD.number.WaterTemp <- 3 +ContData.env$myThresh.RoC.SD.period.WaterTemp <- 25 +ContData.env$myThresh.RoC.SD.number.AirTemp <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.AirTemp <- ContData.env$myDefault.RoC.SD.period ContData.env$myThresh.RoC.SD.number.AirBP <- ContData.env$myDefault.RoC.SD.number ContData.env$myThresh.RoC.SD.period.AirBP <- ContData.env$myDefault.RoC.SD.period ContData.env$myThresh.RoC.SD.number.WaterP <- ContData.env$myDefault.RoC.SD.number @@ -426,15 +445,17 @@ ContData.env$myThresh.RoC.SD.number.Chlorophylla <- ContData.env$myDefault.RoC.S ContData.env$myThresh.RoC.SD.period.Chlorophylla <- ContData.env$myDefault.RoC.SD.period ContData.env$myThresh.RoC.SD.number.WaterLevel <- ContData.env$myDefault.RoC.SD.number ContData.env$myThresh.RoC.SD.period.WaterLevel <- ContData.env$myDefault.RoC.SD.period +ContData.env$myThresh.RoC.SD.number.Salinity <- ContData.env$myDefault.RoC.SD.number +ContData.env$myThresh.RoC.SD.period.Salinity <- ContData.env$myDefault.RoC.SD.period ContData.env$myDefault.Flat.Hi <- 30 ContData.env$myDefault.Flat.Lo <- 15 ContData.env$myDefault.Flat.Tolerance <- 0.01 -ContData.env$myThresh.Flat.Hi.WaterTemp <- 30 -ContData.env$myThresh.Flat.Lo.WaterTemp <- 20 -ContData.env$myThresh.Flat.Tolerance.WaterTemp <- 0.01 -ContData.env$myThresh.Flat.Hi.AirTemp <- 20 -ContData.env$myThresh.Flat.Lo.AirTemp <- 15 -ContData.env$myThresh.Flat.Tolerance.AirTemp <- 0.01 +ContData.env$myThresh.Flat.Hi.WaterTemp <- 30 +ContData.env$myThresh.Flat.Lo.WaterTemp <- 20 +ContData.env$myThresh.Flat.Tolerance.WaterTemp <- 0.01 +ContData.env$myThresh.Flat.Hi.AirTemp <- 20 +ContData.env$myThresh.Flat.Lo.AirTemp <- 15 +ContData.env$myThresh.Flat.Tolerance.AirTemp <- 0.01 ContData.env$myThresh.Flat.Hi.AirBP <- 15 ContData.env$myThresh.Flat.Lo.AirBP <- 10 ContData.env$myThresh.Flat.Tolerance.AirBP <- 0.001 @@ -471,6 +492,9 @@ ContData.env$myThresh.Flat.Tolerance.Chlorophylla <- 0.01 ContData.env$myThresh.Flat.Hi.WaterLevel <- ContData.env$myThresh.Flat.Hi.SensorDepth ContData.env$myThresh.Flat.Lo.WaterLevel <- ContData.env$myThresh.Flat.Lo.SensorDepth ContData.env$myThresh.Flat.Tolerance.WaterLevel <- ContData.env$myThresh.Flat.Tolerance.SensorDepth +ContData.env$myThresh.Flat.Hi.Salinity <- ContData.env$myThresh.Flat.Hi.SensorDepth * 2 +ContData.env$myThresh.Flat.Lo.Salinity <- ContData.env$myThresh.Flat.Lo.SensorDepth * 2 +ContData.env$myThresh.Flat.Tolerance.Salinity <- 0.01 ContData.env$myThresh.Flat.MaxComp <- max(ContData.env$myThresh.Flat.Hi.WaterTemp , ContData.env$myThresh.Flat.Hi.AirTemp , ContData.env$myThresh.Flat.Hi.AirBP @@ -485,6 +509,7 @@ ContData.env$myThresh.Flat.MaxComp <- max(ContData.env$myThresh.Flat.Hi.Water , ContData.env$myThresh.Flat.Hi.Turbidity , ContData.env$myThresh.Flat.Hi.Chlorophylla , ContData.env$myThresh.Flat.Hi.WaterLevel + , ContData.env$myThresh.Flat.Hi.Salinity ) ContData.env$myName.Flag <- "Flag" ContData.env$myNames.Cols4Flags <- c(ContData.env$myName.DateTime @@ -537,6 +562,9 @@ ContData.env$myName.Flag.Chlorophylla <- paste(ContData.env$myName.Flag ContData.env$myName.Flag.WaterLevel <- paste(ContData.env$myName.Flag , ContData.env$myName.WaterLevel , sep=".") +ContData.env$myName.Flag.Salinity <- paste(ContData.env$myName.Flag + , ContData.env$myName.Salinity + , sep=".") ContData.env$myNames.QCTests <- c("Gross","Spike","RoC","Flat") ContData.env$myNames.QCCalcs <- c("SD.Time" , "SD" diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1a_Overview.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1a_Overview.html new file mode 100644 index 00000000..277753f6 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1a_Overview.html @@ -0,0 +1,170 @@ + + + +
+

About ContDataQC

+

The ContDataQC Shiny app is a free R-based tool that was developed to +help water quality monitoring programs quality control (QC), merge and +summarize continuous sensor data files in a standardized, more efficient +way. It also has a function that allows users to download U.S. +Geological Survey gage data at sites and over periods of their choosing. +This Shiny app is linked to the ContDataQC R package, which was +developed for EPA by Tetra Tech in support of the EPA and is available +on +GitHub Exit EPA +Website .

+

The ContDataQC R package and Shiny app were developed with the +following objectives in mind: ensure that a certain (minimum) level of +QC is being performed on continuous data files; standardize and speed up +data QC and reduce missed errors; and standardize data for further +analysis and sharing. ContDataQC is currently configured for +temperature, water level, discharge, conductivity, dissolved oxygen +(DO), pH, turbidity, chlorophyll-a and salinity data. Users with +advanced R skills can add any other parameters that they desire by +making edits to the configuration file, which can be viewed in R or in +Notepad.

+
+
+

ContDataQC Shiny App Workflow

+

The table below lays out the general workflow for using the +ContDataQC app.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Action + +Main Tab + +Subtab(s) or Dropdown(s) +
+Organize sensor files + +Data Preparation + +Organizing Files +
+Name sensor files + +Data Preparation + +Naming Files +
+Format sensor files + +Data Preparation + +Formatting Files, Automated Reformatting +
+Evaluate QC test thresholds + +Main Functions + +QC Thresholds >> Evaluate Thresholds +
+Customize thresholds if necessary + +Main Functions + +QC Thresholds >> Edit Thresholds +
+Save and upload customized configuration file (if needed) + +Main Functions + +QC Thresholds >> Upload Custom Thresholds +
+Generate QC reports; review results; remove or correct data points as +needed + +Main Functions + +Run Functions >> QC Raw Data +
+Combine QC’d data files from the same site/different time periods into a +single file + +Main Functions + +Run Functions >> Aggregate QC’d data +
+Generate summary and visualization outputs + +Main Functions + +Run Functions >> Summary Statistics +
+
+
+

Funding

+

Development of the ContDataQC R Shiny app has been funded by the +United States Environmental Protection Agency (EPA) Office of Research +and Development (ORD) as part of a larger project to establish Regional +Monitoring Networks (RMNs) for freshwater streams and inland lakes. The +RMNs are a volunteer, collaborative effort in which entities collect +long-term continuous temperature and hydrologic data at targeted sites +to detect changes over time. For more information on the RMNs, please +refer to the +Regional +Monitoring Networks (RMN) to Detect Changing Baselines in Freshwater +Wadeable Streams (Final Report).

+
+
+

Disclaimer

+

[NOTICE] The ContDataQC application has not yet received final +approval by EPA, a formal peer review is pending. Be advised that any +text, figures, or data provided here do not necessarily reflect the +views or policies of the EPA.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1b_TestData.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1b_TestData.html new file mode 100644 index 00000000..71eb3c87 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1b_TestData.html @@ -0,0 +1,68 @@ + + + +
+

Test Files

+

Below are test files that you can download and run through the Shiny +app. They are from Onset HOBO U20 water level loggers (non-vented +pressure transducers) and miniDOT DO sensors. Please contact us if you +have example files from other sensors that you would like us to add to +this website.

+ +
+
+

Onset HOBO U20

+

The Onset HOBO U20 test data consist of two comma-separated values +(CSV) files. The files are from the same site but cover two different +time periods. You’ll see two folders:

+
    +

  • Test

    +
      +
    • The two HOBO U20 test files are in the Data0_Original folder. Go to +the Data Preparation >> Automated Reformatting >> +HOBOware Reformat tab and run both files through the +reformatting function. Multiple files can be run through the function at +the same time (i.e., in ‘batches’). The function reformats the files so +they are ready to run through the QC report function. Next, go to the +Main Functions >> Run Functions tab, import the +reformatted files, run them through the ‘QC raw data’ function (in +drop-down menu; multiple files can be run through the function at the +same time), check the flagged data (and edit if needed), aggregate the +two QC’d CSV files with the ‘Aggregate QC’d data’ function (in drop-down +menu), and run the ‘Summary Statistics’ function (in drop-down menu) to +summarize the data.
      • +
    • +

  • Completed

    +
      +
    • This folder has the complete set of outputs for both files so that +you can see examples of the QC reports and other outputs without having +to run the test data through the functions yourself.
      • +
    • +
+
+
+

miniDOT DO

+

The miniDOT DO test files are from one site and one deployment +period. You’ll see two sets of folders. Within each folder are example +input and output files.

+
    +

  • miniDOT_concatenate - When data are initially +downloaded from miniDOT sensors, there are separate .txt files for each +day (in this example, 325 individual files). Go to the Data +Preparation >> Automated Reformatting >> miniDOT +Concatenate tab and run the Concatenate +function to combine them all into one file.

    • +

  • miniDOT_reformat – Go to the Data +Preparation >> Automated Reformatting >> miniDOT +Reformat tab and run the combined file through the Reformat +function. The function reformats the file so that it is ready to run +through the QC report function. Next, go to the Main Functions +>> Run Functions tab, import the reformatted file, run it +through the ‘QC raw data’ function, and check the flagged data (edit if +needed).

    • +
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1c_FAQ.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1c_FAQ.html new file mode 100644 index 00000000..e10df3dd --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1c_FAQ.html @@ -0,0 +1,66 @@ + + + +
+

Basic Information

+

Requirements: You do not need R software installed +on your computer to run this Shiny app. You only need an internet +connection.

+

Failure to run: When the Shiny app fails to run, the +screen will gray out and the message ‘disconnected from the server’ will +appear. The problem typically stems from a formatting issue with the +input file, in particular the Date/Time field. Check your input file, +refresh the application and try again. If you still have problems, click +the ‘Contact Us’ link at the bottom of this page and request assistance. +Be prepared to share a copy of your input file(s) and the error +message.

+

Speed: If you have a slow internet connection, you +can run ContDataQC as a Shiny app on your local computer and it will +likely be faster. Running it locally requires that you have R software +and the ContDataQC R package installed on your computer, which can be +downloaded from +GitHub Exit EPA +Website .

+

Internet browsers: This Shiny app has been tested +and run successfully with Google Chrome, Firefox and Microsoft Edge. It +may be compatible with other browsers but they have not been tested. +Google Chrome is most frequently used. Some browsers (like Chrome and +Firefox) allow users to specify what folder to download files to, which +can save time.

+

Limits on input file(s): The input file size limit +is currently set to 70 MB. This can be increased upon request (see +Contact Us). There is no limit on the number of files you can upload but +the more files you run at once, the longer the processing time. It’s +often faster and easier to run smaller file batches (e.g., 4 smaller +batches of 1 to 5 files versus 20 files all at once).

+

Security: Other people cannot see or download your +files from the application. Each time the application is run it is +independent from all other versions. That is, each user has their own +copy of the app. Once the browser is closed, that version is removed +from the server along with all of the uploaded data.

+

Simultaneous use: Because this Shiny app is on a +Shiny server, multiple people can use it at the same time.

+

Customizing QC test thresholds: Instructions on how +to evaluate and customize thresholds can be found on the Main +Functions >> QC Thresholds page.

+

Usability on phones: Mobile use of the ContDataQC +Shiny app is possible, although the screen size of a phone may make it +impractical.

+

Data summary and visualization: The ContDataSumViz +Shiny app, which was designed for data visualization and reporting for +continuous monitoring data, has more summary and visualization options. +A beta version of this Shiny app is available at: ContDataSumViz Exit EPA +Website

+

R code is available at: +GitHub Exit EPA +Website

+

Downloading USGS gage data:You can simultaneously +download data from different USGS gages but only for the same time +period.

+

Aggregating files with overlapping dates: If you run +the aggregate function on input files with overlapping date ranges, all +records (even duplicates) are included in the output. It is up to the +user to identify when these situations are occurring and remove the +duplicate rows before running the files through the various +functions.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1d_Tips.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1d_Tips.html new file mode 100644 index 00000000..66fbf60b --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1d_Tips.html @@ -0,0 +1,118 @@ + + + +
+

Tips

+

Below are tips and links to resources from the Regional Monitoring +Network (RMN) project. To date, RMN partners have worked primarily with +thermal and hydrologic data, and more recently, with dissolved oxygen +data from lakes. The data are typically downloaded 2-4 times a year +(i.e., the data used are not ‘real-time’ data).

+
+

Site visits

+

It helps speed up the Quality Control (QC) process if you follow a +checklist during site visits and document anything that might affect the +quality of the data (e.g., sensor out of water or buried in sediment, +beaver activity, low battery).

+ +
+
+

Sensor configuration

+

Some people have had problems with air and water sensors being out of +sync ( e.g., one records at 11:00 and the other records at 11:07). If +you are deploying air and water sensors at a site, make sure you +configure them so that they are recording at the same time. This will +make data processing faster and easier.

+ +

Another issue that sometimes occurs is data overlap (where more than +one file has measurements covering part of the same time periods). Make +sure you clear the sensor’s memory when you download data and relaunch +sensors to avoid overlapping data.

+ +
+
+

QC workflow

+

There are generally two QC workflow scenarios: 1) users QC their data +after each download and work with one file per site at a time; or 2) +users have a backlog of data that cover multiple deployment periods that +they aggregate into one file before performing QC.

+ +
+
+

Data edits

+

When reviewing the QC reports, it is important to be consistent in +how you handle flagged data. Here is an example approach:

+
    +

  • If you are certain a data point is erroneous, delete the +measurement and flag as ‘F’. If you are not sure, flag the data point as +‘S’ and do not delete. Let the people using the data decide whether to +remove questionable measurements from their analysis.

    • +

  • Document that you checked each point flagged as ‘F’ and ‘S’ by +adding a note or data qualifier to the Comment column.

    • +

  • Leave cells with missing data as they are (i.e., do not delete +them).

    • +

  • Resource: List of example data qualifiers +[XLSX]

    • +
+
+
+

Accuracy checks

+

Accuracy checks are comparisons of discrete or in situ measurements +taken in the lab and/or in the field with sensor measurements from the +closest date/time. The difference between the sensor and discrete +measurements should be within the accuracy quoted by the manufacturer +(e.g., ±0.2°C if you are using the Onset HOBO proV2 sensor).

+ +
+
+

Visual checks of time series plots

+

Visual checks of time series plots are an important part of the QC +process. Some issues, such as dewatering, sediment burial, ice cover, +and beaver activity, show fairly common patterns and we’ve been +compiling examples of these patterns to help people recognize potential +QC issues with their data.

+ +
+
+

Checking sensor data against other data sources

+

Some partners have been downloading data from nearby weather stations +and USGS gages, as well as modeled air temperature and precipitation +data from sources such as Daymet +(Daymet Exit EPA +Website ), and comparing those data to sensor +measurements as part of their QC process.

+ +
+
+

Troubleshooting

+

This tab in the ContDataQC shows output from the R console when +running the QC, aggregating, summarizing, and USGS data retrieval +processes. This information could be used to help understand and +troubleshot why there might be an error or issue with the +application.

+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1e_Advanced.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1e_Advanced.html new file mode 100644 index 00000000..ff79d6bd --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1e_Advanced.html @@ -0,0 +1,21 @@ + + + +
+

Advanced

+

Users with intermediate to advanced-level computer skills can +customize the settings of the ContDataQC app (e.g., by adding new +parameters, changing units, changing file naming and formatting +requirements, changing the format of one of the QC reports from Word to +html or PDF) by editing a plain text configuration file [ZIP]. You can do this +in R or Notepad.

+

Some users may prefer to use the ContDataQC +R +code + +Exit EPA Website instead of the Shiny app. While +using the R code allows for some additional layers of customization, +when it comes to file naming schemes and setting directories, users have +more flexibility using the Shiny app because they can browse to the +desired input file(s).

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1f_RelatedApps.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1f_RelatedApps.html new file mode 100644 index 00000000..64443f7c --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_1f_RelatedApps.html @@ -0,0 +1,106 @@ + + + + diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a1_HOBO.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a1_HOBO.html new file mode 100644 index 00000000..eba17145 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a1_HOBO.html @@ -0,0 +1,105 @@ + + + +
+

HOBOware Reformat

+

The HOBOware reformat function is for data files from Onset HOBO +sensors. It converts them to a format that is ready to run through the +QC report function (Main Functions >> Run Functions +>> QC raw data (from drop-down menu)). This saves you +time and eliminates errors that sometimes occur when manually +reformatting files.

+

HOBO sensor files must first be opened with HOBOware software, then +processed and exported as CSV files. The instructions in the files below +describe how to generate CSV files, allowing you to run them through the +reformat function without manipulation:

+ +
+

Instructions

+
    +

  • Export CSV files from HOBOware (following the instructions above +for whichever sensor type(s) you have) and place them into the +Data0_Original folder.

    • +

  • Choose your file by clicking the ‘Browse’ button and selecting +the input file(s). You can run multiple files at once. The status bar +will tell you when the upload is complete.

    • +

  • Select the Date format in your input file (options: YMD, MDY, +DMY)

    • +

  • After you select your input file click the ‘Reformat HOBOware +file(s)’ button that appears; a status bar will appear in the lower +right corner.

    • +

  • After the function is done running, a ‘Download’ button will +appear. Click this button.

    • +

  • You will be prompted to save a zipped file named +‘format_HOBO_date_time’. Save the zipped file in the Data1_RAW +folder.

    • +

  • Unzip/extract the file(s) or the files won’t be seen as an option +to upload to the QC report function (Main Functions >> Run +Functions >> QC raw data (from drop-down menu)). After +file is unzipped, a new folder called ‘HOBO’ will appear. The original +zipped file can be deleted if desired.

    • +
+

Time-saver tip: some internet browsers, like Google Chrome and +Mozilla Firefox, allow users to change settings so that users can select +which folder to download files to (versus files going to the default +Download folder).

+
    +
  • Mozilla Firefox: go to Settings > General > Files and +Applications > Check box for “always ask where to save files.”
    • +
  • Google Chrome file saving +tip [PDF]
    • +
+

Your files are now ready to be run through the QC function! Below are +examples of what HOBO U20 CSV files look like before and after they are +run through the reformat function. The reformat function does the +following:

+
    +

  • Changes column headings, as needed.

    • +

  • Creates a new ‘SiteID’ column and populates it with the SiteID +that it takes from the first part of the input file name (the SiteID +should precede the first underscore in the file name).

    • +

  • Creates RowID columns for air and water sensors.

    • +

  • Creates LoggerID fields for air and water sensors based on the +LGR S/N entries in the column headings.

    • +

  • Removes the first row (Plot Title).

    • +
+

If there is a problem with the input file, the screen will turn gray +and it will say ‘disconnected from the server’ in the lower left.

+
+
+

Example

+

Example input to and output from running the HOBOware reformat +function on a HOBO U20 water level logger file:

+

Before

+

HOBO U-20 file before running the reformat function

+

After

+

HOBO U-20 file after running the reformat function.

+
+
+

Additional Notes

+
    +

  • If you enter the wrong date format, the function will still run +but your date field will be blank and you will not receive an error +message. We recommend checking your output files to make sure the date +field came through, as well as the other desired parameters (if those +are not named correctly, they will be missing from the output file and +you will not receive an error message).

    • +

  • Unwanted ‘logged’ text entries in the bottom row of the CSV: +these occur if you forget to uncheck the ‘select internal logger events +to plot’ boxes during Plot SetUp in HOBOware.

    • +

  • Two serial numbers in the column heading (you should only have +one): make sure under Preferences-Export settings, you only have the +‘include logger serial number’ box checked, NOT the ‘include sensor +serial number or label if available’ box).

    • +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a2_miniDOT1.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a2_miniDOT1.html new file mode 100644 index 00000000..5c975114 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a2_miniDOT1.html @@ -0,0 +1,47 @@ + + + +
+

miniDOT DO Concatenate

+

When data are initially downloaded from miniDOT sensors, there are +separate .txt files for each day (in this example, 325 individual +files). Users have two options for combining them into one CSV file: 1) +the ContDataQC Concatenate function; or 2) the +PME java app that comes with the logger.

+
+

Instructions

+
    +

  • Click the ‘Browse’ button and select all the individual files +that you want to combine. The status bar will tell you when the upload +is complete (be patient – you may not see anything happen for the first +30 seconds or so, depending on how many files you are +uploading).

    • +

  • After you select your input file(s), click the ‘Concatenate’ +button that appears; a status bar will appear in the lower right +corner.

    • +

  • After the function is done running, a ‘Download’ button will +appear. Click this button.

    • +

  • You will be prompted to save a zipped file named +‘miniDOT_cat_date_time’. Save the zipped file in the Data0_Original +folder.

    • +

  • Unzip/extract the file(s) or the files won’t be seen as an option +to upload to the Reformat function. After file is unzipped, a new folder +called ‘miniDOT_cat’ will appear. The original zipped file can be +deleted if desired.

    • +
+
+
+

Test files

+

miniDOT test data [ZIP]

+

Below is an example of what the output file looks like after running +the Concatenate function.

+

miniDOT DO file after running the Concatenate function.

+
+
+

Additional Notes

+

If you’re wondering why your DO saturation values are all NA, this +occurs when no inputs are provided to calculate DO percent saturation. +For more information, please see the miniDOT manual or contact the +miniDOT support team.

+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a3_miniDOT2.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a3_miniDOT2.html new file mode 100644 index 00000000..dda3c496 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a3_miniDOT2.html @@ -0,0 +1,48 @@ + + + +
+

miniDOT DO Reformat

+

After the daily files are concatenated, run the combined file through +the miniDOT DO Reformat function. The function reformats the file so +that it is ready to run through the QC report function.

+
+

Instructions

+
    +

  • Click the ‘Browse’ button and select the input file(s). You can +run multiple files at once. The status bar will tell you when the upload +is complete.

    • +

  • After you select your input file(s), click the ‘Reformat miniDOT +file(s)’ button that appears; a status bar will appear in the lower +right corner.

    • +

  • After the function is done running, a ‘Download’ button will +appear. Click this button.

    • +

  • You will be prompted to save a zipped file named +‘miniDOT_reformat_date_time’. Save the zipped file in the Data1_RAW +folder.

    • +

  • Unzip/extract the file(s) or the files won’t be seen as an option +to upload to the QC report function (Main Functions - QC raw data). +After file is unzipped, a new folder called ‘miniDOT_reformat’ will +appear. The original zipped file can be deleted if desired.

    • +

  • See below for examples of files before and after +reformatting.

    • +
+
+
+

Test files

+

miniDOT test data [ZIP]

+

Below is an example of what files look like before and after running +the miniDOT DO Reformat function.

+

Before

+

miniDOT DO file before running the reformat function.

+

After

+

miniDOT DO file after running the reformat function.

+
+
+

Additional Notes

+

If you’re wondering why your DO saturation values are all NA, this +occurs when no inputs are provided to calculate DO percent saturation. +For more information, please see the miniDOT manual or contact the +miniDOT support team.

+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a_FormattingFiles.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a_FormattingFiles.html new file mode 100644 index 00000000..730421bb --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2a_FormattingFiles.html @@ -0,0 +1,298 @@ + + + +
+

Formatting Files

+

Input files need to be formatted a specific way for the ContDataQC +Shiny app and R package to work:

+
    +

  • Input files must be comma-delimited (CSV) (but not CSV UTF-8 +files).

    • +

  • There are two required fields: SiteID; and “Date Time” (with Date +and Time combined into one field).

    • +

  • If using the default configuration +file [ZIP], column headings must match the capitalization, spelling +and symbology shown in the table below (for example, R will not +recognize ‘sensor depth ft’; it needs to be ‘Sensor Depth ft’).

    • +

  • Either periods or spaces in the column headings are acceptable. +For example, in the table below, air temperature is shown as ‘’Air Temp +C’ but it can also be entered as ‘Air.Temp.C’.

    • +

  • For units, slashes, spaces or periods (e.g., DO mg/L, DO.mg.L or +DO mg L) are acceptable. Do NOT use parentheses.

    • +

  • The columns do not have to be in any specific order, but column +headings must match those shown in the table below.

    • +

  • Advanced users can add new parameters to the configuration file +(beyond the ones show in the table below).

    • +

  • Optional fields include rowID, loggerID (serial number) and +discrete measurements. Discrete (in situ) measurements can be entered +for any parameter (just add ‘Discrete’ to the beginning of the column +heading; for example, ‘Discrete Air Temp C’). The discrete data point +will be included in the time series plots that are generated when the +user runs the QC function. If using Onset HOBO sensors, you can save +time by using the automated reformat function (‘Data Preparation +>> Automated Formatting >> HOBOware Reformat’ tab).

    • +
+
+

Data Formatting Information

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Status + +Column Headings + +Notes +
+Required + +SiteID + +character (numeric or text) +
+ +Date Time + +“Date Time” (one field); prefer 24H time (military); YYYY-MM-DD is +recommended but not required +
+As available + +Air Temp C + +
+ +Water Temp C + +
+ +Air BP psi + +barometric pressure (BP) +
+ +Water P psi + +water pressure (P) +
+ +Sensor Depth ft + +differs from Water Level in that it is not generated with a reference +water level measurement +
+ +Water Level ft + +generated with a reference water level (which relates water level to a +fixed point) +
+ +Discharge ft3/s + +
+ +Conductivity uS/cm + +
+ +DO mg/L + +
+ +DO adj mg/L + +DO adjusted for salinity (HOBOware) +
+ +DO pctsat % + +% saturation +
+ +pH SU + +
+ +Turbidity NTU + +
+ +Chlorophyll-a g/cm3 + +
+ +Salinity ppt + +
+Optional + +Air LoggerID + +unique identifier (e.g., serial number) for sensor deployed on land +
+ +Water LoggerID + +unique identifier (e.g., serial number) for sensor deployed in the water +
+ +Water RowID + +sequential record number (1=first measurement; largest number = last +measurement) for sensor deployed in the water +
+ +Air RowID + +sequential record number (1=first measurement; largest number = last +measurement) for sensor deployed on land +
+ +Discrete Water Temp C + +Discrete (in situ) measurements can be entered into the row of the CSV +file where the date/time of the sensor measurement most closely matches +the date/time of the in situ measurement; the data point(s) will be +overlaid onto the time series plot in the QC report so that the user can +visually check correspondence between the two. Discrete entries can be +made for any of the parameters listed above (not just the Water Temp C +example shown here). +
+ +Discrete Air Temp C + +
+ +Discrete Water Level ft + +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2az_About.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2az_About.html new file mode 100644 index 00000000..0e1bcfde --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2az_About.html @@ -0,0 +1,21 @@ + + + +
+

Automated Reformatting

+

These functions convert files coming directly from sensors or +proprietary sensor software like HOBOware into a format that is ready to +run through the QC report function (Main Functions >> Run +Functions >> QC raw data (from drop-down menu)). This +saves you time and eliminates errors that sometimes occur when manually +reformatting files.

+

Currently, reformatting functions are available for two types of +sensors:

+
    +

  • Onset HOBO (currently water level (U20), temperature (pro V2 and +Pendant), and DO (U26))

    • +

  • miniDOT DO

    • +
+

Reformatting functions for other types of sensors can be added in the +future, if requested.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2b_OrganizingFiles.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2b_OrganizingFiles.html new file mode 100644 index 00000000..9de14e9d --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2b_OrganizingFiles.html @@ -0,0 +1,175 @@ + + + +
+

Organizing Files

+

If you do not already have a good system for organizing continuous +data files, consider setting up a folder for each site, and within each +site folder, set up subfolders as shown in the table below. A quick way +to get started is to download this ready-made set of (empty) folders [ZIP]. +Put this file in the desired directory, unzip it and then replace +‘SiteX’ with the appropriate site name.

+

You have more flexibility with file organization when using the +ContDataQC Shiny app vs the R package. With the Shiny app, it is not +necessary to name the folders exactly as shown below because you can +browse to the desired input files and browse to save the output files to +the desired location. However, if you decide to use the R package at +some point, keeping the five ‘Data’ folders (Data0_Original, Data1_RAW, +Data2_QC, Data3_Aggregated and Data4_Stats) will be important since the +R code searches in specific folders for input files and outputs files to +specific directories. Advanced users can modify the default directories +by editing the configuration file.

+
+

Folder descriptions

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Folder name + +Description +
+Data0_Original + +For .csv files that are exported out of the sensor software (like +HOBOWare) and need to be reformatted before they can be run through the +QC function. +
+Data1_RAW + +For formatted .csv files that are ready to run through the QC function. +
+Data2_QC + +After you run the QC function, put the QC reports into this folder. Make +corrections to the .csv QC files as needed. When you run the Aggregate +function, you will use the QC’d data files from this folder. +
+Data3_Aggregated + +After you run the Aggregate function to combine the QC’d data files, put +the output files into this folder. When you run the SummaryStats +function, you will use the aggregated data files from this folder. +
+Data4_Stats + +After you run the SummaryStats function, put the output files into this +folder. +
+FieldForms + +Observations from site visits are very important for the QC process as +they can provide clues as to why erroneous measurements or changes may +have occurred (e.g., sensor movement, sensor out of water, sensor buried +in sediment, vandalism). +
+in_situ + +An important QC check (what we call an ‘accuracy check’) involves +comparing in situ measurements taken during site visits to the closest +sensor measurement(s). We recommend keeping an accuracy check worksheet +in this folder (click link in the Discrete Data tab to download an +example worksheet). +
+orig + +If you are overrun with raw files that are not named consistently and +are poorly organized (for example, sometimes files are saved multiple +times with different names and in different folders), it may be helpful +to put all the raw files for a site into one folder, inventory them, and +then sort them into the appropriate folders. +
+Photos + +We recommend taking photos from the same locations during each site +visit. Photographs are important for relocating the sensors and +documenting changes to the monitoring location. +
+R_ConfigurationFiles + +Creating customized configuration file(s) for a site allows users to +adjust QC test thresholds, make changes to units and more. The +customized file(s) need to be uploaded before running the QC function, +otherwise the default settings will be used. +
+SensorFile + +We recommend retaining original sensor files in case you need to go back +and regenerate certain data. In some situations, it may be helpful to +add subfolders for each sensor type (for example, with lakes, there may +be many temperature files mixed in with DO and water level files; in +these cases, it may be easier to find files if you create subfolders for +temperature, DO, and water level). +
+USGSgage + +If there is a nearby, representative USGS gage, we recommend comparing +sensor hydrologic data to the USGS gage data as an additional QC check. +
+WxStation + +If there is a nearby, representative weather station, we recommend +comparing your sensor data to the weather station data as an additional +QC check. Modeled daily air temperature and precipitation data are also +available from Daymet and PRISM. +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2c_NamingFiles.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2c_NamingFiles.html new file mode 100644 index 00000000..69e72116 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2c_NamingFiles.html @@ -0,0 +1,48 @@ + + + +
+

Naming Files

+

You have more flexibility with naming files when using the ContDataQC +Shiny app vs the R package because you can browse to the desired input +file(s), whereas the R package searches through files in a specified +directory and selects data files based on the file name. Even if you are +using the Shiny app, consider using the file naming scheme below since +you may decide to use the R package at some point in the future. Another +reason to use the recommended file naming scheme is because it keeps +files well-organized and sorts them in chronological order. This is +particularly helpful if you are using HOBO U20 water level loggers, as +it makes matching air and water sensor files easier during the +processing step and helps you identify gaps in the data.

+

If you are using the R package, there are four components to the file +name for single sensor deployments, which are separated by underscores +(“_”).

+

SiteID_DataType_StartDate_EndDate

+

Example: Hunting_Water_20130426_20130725

+
    +

  • SiteID (no spaces or underscores) = Hunting

    • +

  • Data Type options: Air, Water, AW (air and water sensor data are +combined into the same file), Gage, DO

    • +

  • StartDate (YYYYMMDD) = 20130426

    • +

  • EndDate (YYYYMMDD) = 20130725

    • +
+

For multi-sensor deployments (e.g., temperature sensors deployed at +multiple depths at a lake), add depth and units or some other +differentiating characteristic, such as sensor type, to the SiteID, +separated by double-dashes: for example,

+

RussWood–02M_Water_20130426_20130725

+

Depth can include decimals (for example, 0.1M) and does not need to +include leading zeros (either 01M or 1M are acceptable). Units can be +upper or lower case (M or m), and abbreviated (M) or spelled out +(meters).

+

You can change the double-dash separator if desired by customizing +the configuration file [ZIP] (for +example, you could change it to a tilde ~).

+

If you are using the ContDataQC Shiny app instead of the R package, +you are not restricted to using the Data Types shown above (Air, Water, +AW, etc.). If you have more than one type of sensor deployed at the same +depth (e.g., both a DO and temperature sensor at 2M), consider using +sensor type in the second part of the file name: for example, +RussWood–02M_TEMP_20130426_20130725 or +RussWood–02M_DO_20130426_20130725.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2cz_DiscreteData.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2cz_DiscreteData.html new file mode 100644 index 00000000..c834899f --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2cz_DiscreteData.html @@ -0,0 +1,31 @@ + + + +
+

Discrete (In Situ) Data

+

Discrete measurements are important for validating your data, and in +some cases, can be used to correct for sensor drift. Accuracy checks are +comparisons of discrete or in situ measurements taken in the lab or in +the field with sensor measurements from the closest date/time. The +difference between the sensor and discrete measurements should be within +the accuracy quoted by the manufacturer (e.g., ±0.2°C if you are using +the Onset HOBO proV2 sensor).

+

Discrete (in situ) measurements will be included in the time series +plots in the QC reports (see example QC plot below) if the user manually +enters them into the input file. This should be done after the file has +been formatted (for example, after a HOBO file has been run through the +Reformat function).

+

To enter the discrete data:

+
    +

  • Add a new column to the CSV, named ‘Discrete’ followed by the +parameter name and units (for example, ‘Discrete Air Temp C’).

    • +

  • Enter the data into the row that most closely matches the +date/time of the sensor measurement.

    • +

  • Resource: Example accuracy check +worksheet [XLSX]

    • +
+
+Example QC plot with discrete data +
Example QC plot with discrete data
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2d_FormattingFiles.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2d_FormattingFiles.html new file mode 100644 index 00000000..ba55b569 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_2d_FormattingFiles.html @@ -0,0 +1,299 @@ + + + +
+

Formatting Files

+

Input files need to be formatted a specific way in order for the +Shiny app and R package to work. Guidelines -

+
    +

  • Input files must be comma-delimited (CSV) (but not CSV UTF-8 +files).

    • +

  • The columns do not have to be in any specific order

    • +

  • There are two required fields: SiteID; and “Date Time” (with Date +and Time combined into one field).

    • +

  • If using the default configuration +file, column names must match the capitalization, spelling and +symbology shown in the table below (for example, R will not recognize +‘sensor depth ft’; it needs to be ‘Sensor Depth ft’)

    • +

  • Either periods or spaces in the column headings are acceptable. +For example, in the table below, air temperature is shown as ‘’Air Temp +C’ but it can also be entered as ‘Air.Temp.C’.

    • +

  • For units, slashes, spaces or periods (e.g., DO mg/L, DO.mg.L or +DO mg L) are acceptable. Do NOT use parentheses.

    • +

  • Advanced R users can add new parameters (beyond the ones show in +the table below) to the configuration file.

    • +

  • Optional fields include rowID, logger ID (serial number) and +discrete measurements. Discrete (in situ) measurements can be entered +for any parameter (just add ‘Discrete’ to the beginning of the column +heading; for example, ‘Discrete Air Temp C’). The discrete data point +will be included in the time series plots that are generated when the +user runs the QC function. If using Onset HOBO sensors, you can save +time by using the automated reformat function (see HOBO Reformat +section)

    • +
+
+

Data formatting information

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Status + +Column headings + +Notes +
+Required + +SiteID + +character (numeric or text) +
+ +Date Time + +“Date Time” (one field) OR “Date” AND “Time” (in 2 separate fields) OR +all 3 (in 3 fields); prefer 24H time (military); YYYY-MM-DD is +recommended but not required +
+As available + +Air Temp C + +
+ +Water Temp C + +
+ +Air BP psi + +barometric pressure (BP) +
+ +Water P psi + +water pressure (P) +
+ +Sensor Depth ft + +differs from Water Level in that it is not generated with a reference +water level measurement +
+ +Water Level ft + +generated with a reference water level (which relates water level to a +fixed point) +
+ +Discharge ft3/s + +
+ +Conductivity uS/cm + +
+ +DO mg/L + +
+ +DO adj mg/L + +DO adjusted for salinity (HOBOware) +
+ +DO pctsat % + +% saturation +
+ +pH SU + +
+ +Turbidity NTU + +
+ +Chlorophylla g/cm3 + +
+ +Salinity ppt + +
+Optional + +Air LoggerID + +unique identifier (e.g., serial number) for sensor deployed on land +
+ +Water LoggerID + +unique sensor identifier (e.g., serial number) for sensor deployed in +the water +
+ +Water RowID + +sequential record number (1=first measurement; largest number = last +measurement) for sensor deployed in the water +
+ +Air RowID + +sequential record number (1=first measurement; largest number = last +measurement) for sensor deployed on land +
+ +Discrete Water Temp C + +Discrete (in situ) measurements can be entered into the row of the CSV +file where the date/time of the sensor measurement most closely matches +with the date/time of the in situ measurement; the data point(s) will be +overlaid onto the time series plot in the QC report so that the user can +visually check the correspondence between the two. Discrete entries can +be made for any of the parameters listed above (not just the Water Temp +C example shown here) +
+ +Discrete Air Temp C + +
+ +Discrete Water Level ft + +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a1_About.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a1_About.html new file mode 100644 index 00000000..5ea1a279 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a1_About.html @@ -0,0 +1,51 @@ + + + +
+

Workflow

+
    +

  1. Select QC flag test thresholds. Use the default +thresholds (Main Functions >> QC Thresholds >> +Default tab) or upload a customized configuration file +(Main Functions >> QC Thresholds >> Upload Custom +Thresholds tab). If you have one or more years of continuous +data for a site, we encourage you to evaluate the performance of the QC +test thresholds for each parameter at that site and customize the +configuration file if needed. Click Main Functions >> QC +Thresholds >> Evaluate Thresholds tab for tips on +evaluating thresholds. To learn how to edit thresholds, click +Main Functions >> QC Thresholds >> Edit +Thresholds tab.

    2. +

  2. Check the configuration file name. Make sure the +correct configuration file has been uploaded prior to running the QC +reports.

    3. +

  3. Upload the desired input file(s).

    4. +

  4. Run the ‘QC raw data’ function to generate QC +reports.

    5. +

  5. After the data file(s) are QC’d, users can run them +through the Aggregate function to merge them with other files. +The input files need to be from the same site and share common +parameters.

    6. +

  6. The user can run the aggregated file through the Summary +Statistics function to generate CSV files with an assortment of +summary statistics (e.g., min, median, max, quartiles) and a PDF with +time series plots for each parameter.

    7. +
+

After running each function, users must unzip/extract the files or +the files won’t be seen as an option to upload for the next step. After +files are unzipped, new folders will automatically appear and the +original zipped files can be deleted if desired.

+

Time-saver tip: some internet browsers, like Google Chrome and +Mozilla Firefox, allow users to change settings so that users can select +which folder to download files to (versus files going to the default +Download folder).

+
    +

  • Mozilla Firefox: go to Settings > General > Files and +Applications > Check box for “always ask where to save +files.”

    • +

  • Google Chrome file +saving tip [PDF]

    • +
+

Some users may prefer to use the R code instead of the Shiny app. For +more information, see the ‘About - Advanced’ tab

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a2_QCReports.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a2_QCReports.html new file mode 100644 index 00000000..8f970e87 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a2_QCReports.html @@ -0,0 +1,68 @@ + + + +
+

QC reports

+

Example QC +reports [ZIP]

+

Instructions for generating QC reports using the ContDataQC Shiny +app:

+
    +

  1. Click the ‘Browse’ button and select the input file(s) from the +Data1_RAW folder. You can run multiple files at once. The status bar +will tell you when the upload is complete.

    2. +

  2. Select ‘QC raw data’ from the ‘Choose operation to perform’ +drop-down menu

    3. +

  3. Click the ‘Run operation’ button will appears. A status bar will +appear in the lower right corner.

    4. +

  4. After the function is done running, click the ‘Download’ button +that appears. Click this button.

    5. +

  5. You will be prompted to save a zipped file named +‘QCRaw.date_time.’ Save it in the Data2_QC folder.

    6. +

  6. Unzip/extract the file(s). After file is unzipped, a new folder +called ‘data’ will appear, which contains QC reports that have ‘QC_’ as +the prefix in the file name. The original zipped file can be deleted if +desired.

    7. +

  7. Open the QC reports. There are two types of outputs: a Word +document Data Quality Control Report and a comma-delimited (.csv) +spreadsheet. If users prefer a Hypertext Markup Language (HTML) report +instead of Word, they can change the format in the configuration file +(this requires some familiarity with R code). The reports include +results from the ‘flag’ tests (gross, spike, rate of change and flat +line), missing data checks, and time series plots.

    8. +

  8. Review each set of files, check flagged data points, do visual +checks on the time series plots, and decide if and how to make +corrections. For tips on the QC process, see the About >> +Tips subtab.

    9. +
+

The Data Quality Control Report is organized into four main sections +(starting with an overall summary followed by summaries for each +parameter):

+
    +

  1. Data file information (siteID, date range, parameters, recording +interval, QC test thresholds reference table).

    2. +

  2. ‘Count’ tables (# measurements/day) so that you can find +inconsistencies (too few measurements, indicating missing data, or too +many measurements) for each parameter.

    3. +

  3. Results from the four ‘flag’ tests (gross, spike, rate of change +and flat line), which show the # of entries for each parameter marked as +pass (P), suspect (S), fail (F), missing data (X) or not available +(NA).

    4. +

  4. Time series plots for each individual parameter and two sets of +combined parameters (water and air temperature; water temperature and +water level). If the input file includes discrete/in situ values that +were taken during site visits for accuracy checks, the discrete values +are plotted with the sensor data.

    5. +
+

The CSV file contains the time series data from the sensor plus +additional data columns with flags for each test and parameter (e.g., +Flag.Spike.Water.Temp.C, Flag.Flat.Water.Temp.C), comment fields for +each parameter (e.g., Comment.MOD.Water.Temp.C) so that the user can +document rationale for any changes, and a duplicate set of the original +‘raw’ data values (e.g., RAW.Water.Temp.C). The ‘raw’ values are +included so that the user can maintain documentation of changes that are +made and if needed go back to change how they handled a correction.

+

Currently there are no automated corrections programmed into the QC +function. The user must make the changes manually. For tips on the QC +process, see the ‘About - Tips’ subtab.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a3_Aggregate.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a3_Aggregate.html new file mode 100644 index 00000000..1db37671 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a3_Aggregate.html @@ -0,0 +1,33 @@ + + + +
+

Aggregate Output

+

Example file Aggregate output [ZIP]

+

Instructions for generating aggregated reports using the ContDataQC +Shiny app:

+
    +

  1. Click the ‘Browse’ button and select the QC’d input files that +you’d like to combine. The input files need to be from the same site and +share common parameters. The status bar will tell you when the upload is +complete.

    2. +

  2. Select ‘Aggregate QC’d data’ from the ‘Choose operation to +perform’ drop-down menu.

    3. +

  3. Click the ‘Run operation’ button that appears. A status bar will +appear in the lower right corner.

    4. +

  4. After the function is done running, click the ‘Download’ button +that appears. You will be prompted to save a zipped file named +‘Aggregate.date_time.’ Save it in the Data3_Aggregated folder.

    5. +

  5. Unzip/extract the file(s). After file is unzipped, a new folder +called ‘data’ will appear. If a ‘data’ folder already exists, outputs +will go into that folder. The aggregate reports have ‘DATA_’ as the +prefix in the file name. The original zipped file can be deleted if +desired.

    6. +

  6. Open and review the aggregated reports. As with the QC output, +there are two types of outputs: a Word document Data Quality Control +Report and a comma-delimited (.csv) spreadsheet. If users prefer a +Hypertext Markup Language (HTML) report instead of Word, they can change +the format in the configuration file (this requires some familiarity +with R code).

    7. +
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a4_SummaryStats.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a4_SummaryStats.html new file mode 100644 index 00000000..b7fece36 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a4_SummaryStats.html @@ -0,0 +1,32 @@ + + + +
+

Summary Statistics

+

Example file Summary +Stats output [ZIP]

+

Instructions for generating summary statistics reports using the +ContDataQC Shiny app:

+
    +

  1. Click the ‘Browse’ button and select the desired input file from +the Data3_Aggregated folder. The status bar will tell you when the +upload is complete.

    2. +

  2. Select ‘Summary statistics’ from the ‘Choose operation to +perform’ drop-down menu.

    3. +

  3. Click the ‘Run operation’ button that appears. A status bar will +appear in the lower right corner.

    4. +

  4. After the function is done running, click the ‘Download’ button +that appears. You will be prompted to save a zipped file named +‘SummaryStats.date_time.’ Save it in the Data4_Stats folder.

    5. +

  5. Unzip/extract the file(s). After file is unzipped, a new folder +called ‘data’ will appear. If a ‘data’ folder already exists, outputs +will go into that folder. The summary stats reports have ‘STATS_’ as the +prefix in the file name. The original zipped file can be deleted if +desired.

    6. +

  6. Open and review the summary statistics reports. There should be +two sets of CSV files and a PDF with time series plots for each +parameter. One of the CSVs has daily mean values; the other has a wider +range of summary statistics (e.g., min, median, max, +quartiles).

    7. +
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a_About.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a_About.html new file mode 100644 index 00000000..857f7ecf --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a_About.html @@ -0,0 +1,128 @@ + + + +
+

Run Functions

+

The three main functions are listed in the drop-down menu under +‘Choose Operation to perform’

+
    +

  • ‘QC raw data’ generates QC reports.

    • +

  • ‘Aggregate QC’d data’ merges the QC’d data files +for the desired time periods together. The input files need to be from +the same site and share common parameters.

    • +

  • ‘Summary statistics’ generates two sets of CSV +files and a PDF with time series plots for each parameter. One of the +CSVs has daily means and the other has a wider range of summary +statistics (e.g., min, median, max, quartiles).

    • +
+

ContDataQC works sequentially, meaning the user needs to run the QC +function before running the Aggregate function, and the Aggregate +function before the SummaryStats function. More detailed information on +each function can be found below.

+

Time-saver tip: if you use Google Chrome as your internet browser, +you can change its settings so that it asks what folder to download +files to (versus files going to the default Download folder). Click here for instructions on how +to set this up.

+
+

QC reports

+

Shiny app instructions

+
    +

  1. Click the ‘Browse’ button and select the input file(s) from the +Data1_RAW folder. You can run multiple files at once. The status bar +will tell you when the upload is complete.

    2. +

  2. Select ‘QC Raw Data’ from the ‘Choose Operation to perform’ +drop-down menu

    3. +

  3. A ‘Run operation’ button will appear. Click this button. A status +bar will appear in the lower right corner.

    4. +

  4. After the function is done running, a ‘Download’ button will +appear. Click this button.

    5. +

  5. Save the zipped file in the Data2_QC folder.

    6. +

  6. Open the QC reports. There are two types of outputs: a Data +Quality Control Report that comes as either a Word document or Hypertext +Markup Language (HTML) (the user specifies the desired format); and a +comma-delimited (.csv) spreadsheet. The reports include results from the +‘flag’ tests (gross, spike, rate of change and flat line), missing data +checks and time series plots. Click here to download an example of a QC +output.

    7. +

  7. Review each set of files, check flagged data points, do visual +checks on the time series plots, and decide if and how to make +corrections. For tips on the QC process, see the ‘QC tips’ +subtab.

    8. +
+

The Data Quality Control Report is organized into four main sections, +starting with an overall summary followed by summaries for each +parameter:

+
    +

  1. Data file information (siteID, date range, parameters, recording +interval, QC test thresholds reference table)

    2. +

  2. ‘Count’ tables (# measurements/day) so that you can find +inconsistencies (too few measurements (=missing data) or too many +measurements)

    3. +

  3. Results from the four ‘flag’ tests (gross, spike, rate of change +and flat line) - # of entries marked as pass (P), suspect (S), fail (F), +missing data (X) or not available (NA)

    4. +

  4. Time series plots for each individual parameter and two sets of +combined parameters (water and air temperature; water temperature and +water level). If the input file includes discrete/in situ values , which +are taken during site visits for accuracy checks, the discrete values +are plotted with the sensor data.

    5. +
+

The CSV file contains the time series data from the sensor plus +additional data columns with flags for each test and parameter (e.g., +Flag.Spike.Water.Temp.C, Flag.Flat.Water.Temp.C), comment fields for +each parameter (e.g., Comment.MOD.Water.Temp.C) so that the user can +document rationale for any changes and a duplicate set of the original +‘raw’ data values (e.g., RAW.Water.Temp.C). The ‘raw’ values are +included so that the user can maintain documentation of changes that are +made and go back at a later time if desired to change how they handled a +correction.

+

Currently there are no automated corrections programmed into the QC +function. The user must make the changes manually. For tips on the QC +process, see the ‘QC tips’ subtab.

+
+
+

Aggregate

+

Shiny app instructions

+
    +

  1. Click the ‘Browse’ button and select the input files from the +Data2_QC folder that you’d like to combine together. The status bar will +tell you when the upload is complete.

    2. +

  2. Select ‘Aggregate QC’d data’ from the ‘Choose Operation to +perform’ drop-down menu

    3. +

  3. A ‘Run operation’ button will appear. Click this button. A status +bar will appear in the lower right corner.

    4. +

  4. After the function is done running, a ‘Download’ button will +appear. Click this button. Save the zipped file in the Data3_Aggregated +folder.

    5. +

  5. Save the zipped file in the Data3_Aggregated folder.

    6. +

  6. Open and review the aggregated reports. As with the QC output, +there are two types of outputs: a Data Quality Control Report that comes +as either a Word document or Hypertext Markup Language (HTML) (the user +specifies the desired format); and a comma-delimited (.csv) spreadsheet. +Click here to download an example of +an Aggregate output.

    7. +
+
+
+

Summary statistics

+

Shiny app instructions

+
    +

  1. Click the ‘Browse’ button and select the desired input file from +the Data3_Aggregated folder. The status bar will tell you when the +upload is complete.

    2. +

  2. Select ‘Summary statistics’ from the ‘Choose Operation to +perform’ drop-down menu

    3. +

  3. A ‘Run operation’ button will appear. Click this button. A status +bar will appear in the lower right corner.

    4. +

  4. After the function is done running, a ‘Download’ button will +appear. Click this button.

    5. +

  5. Save the zipped file in the Data4_Stats folder.

    6. +

  6. Open and review the summary statistics reports. There should be +two sets of CSV files and a PDF with time series plots for each +parameter. One of the CSVs has daily means and the other has a wider +range of summary statistics (e.g., min, median, max, quartiles). Click +here to download an example of a +Summary Stats output.

    7. +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a_DataPrep.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a_DataPrep.html new file mode 100644 index 00000000..4d921552 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3a_DataPrep.html @@ -0,0 +1,177 @@ + + + +
+

Organizing Files

+

If you do not already have a good system for organizing continuous +data files, we recommend setting up folders as shown below as a starting +point. Folders and files can be customized as you gain more experience. +The files should be backed up regularly and accessible to other members +of your data management team.

+

Click here to download a +zipped file with a ready-made set of (empty) folders. Put the file in +the desired location, unzip it and then replace ‘SiteX’ with the +appropriate site name.

+

You have more flexibility with file organization when using the Shiny +app vs R package. With the Shiny app, it is not necessary to name the +folders exactly as shown below because you can browse to the desired +input files and browse to save the output files to the desired location. +However, if you decide to use the R package at some point, keeping the +four ‘Data’ folders (Data0_Original, Data1_RAW, Data2_QC, +Data3_Aggregated and Data4_Stats) will be important since the R code +searches in specific folders for input files and outputs files to +specific directories. Advanced R users can modify the default +directories by editing the configuration file.

+

+
+

Folder descriptions

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Folder name + +Description +
+Data0_Original + +For .csv files that are exported out of the sensor software (like +HOBOWare) that need to be reformatted before they can be run through the +QC function. +
+Data1_RAW + +For formatted .csv files that are ready to run through the QC function. +
+Data2_QC + +After you run the QC function, put the QC reports into this folder. Make +corrections to the .csv QC files as needed. When you run the Aggregate +function, you will use the QC’d data files from this folder. +
+Data3_Aggregated + +After you run the Aggregate function to combine the QC’d data files, put +the output files into this folder. When you run the SummaryStats +function, you will use the aggregated data files from this folder. +
+Data4_Stats + +After you run the SummaryStats function, put the output files into this +folder. +
+FieldForms + +Observations from site visits are very important for the QC process as +they can provide clues as to why erroneous measurements or changes may +have occurred (e.g., sensor movement, sensor out of water, sensor buried +in sediment, vandalism). +
+in_situ + +An important QC check (which we call an ‘accuracy check’) involves +comparing in situ measurements taken during site visits to the closest +sensor measurement(s). We recommend keeping an accuracy check worksheet +in this folder (click here to download an example worksheet). +
+orig + +If you are overrun with raw files that are not named consistently and +are poorly organized (for example, sometimes files are saved multiple +times with different names and in different folders), it may be helpful +to put all the raw files for a site into one folder, inventory them, and +then sort them into the appropriate folders. +
+Photos + +We recommend taking photos from the same locations during each site +visit. Photographs are important for relocating the sensors and +documenting changes to the monitoring location. +
+R_ConfigurationFiles + +Creating a customized configuration file (or files) for a site allows +users to adjust QC test thresholds, make changes to units and more. The +customized file needs to be uploaded before running the QC function, +otherwise the default settings will be used. +
+SensorFile + +We recommend retaining original sensor files in case you need to go back +and regenerate certain data. In some situations, it may be helpful to +add subfolders for each sensor type (for example, with lakes, there may +be many temperature files mixed in with DO and water level files; in +these cases, it may be easier to find files if you create subfolders for +temperature, DO and water level). +
+USGSgage + +If there is a nearby, representative USGS gage, we recommend comparing +sensor hydrologic data to the USGS gage data as an additional QC check. +
+WxStation + +If there is a nearby, representative weather station, we recommend +comparing your sensor data to the weather station data as an additional +QC check. +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3b_CheckInput.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3b_CheckInput.html new file mode 100644 index 00000000..54043d14 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3b_CheckInput.html @@ -0,0 +1,14 @@ + + + +
+

Check input file before running the QC function

+

After uploading the input file(s), check the summary table to see +what parameters are in the uploaded files. Ensure that all the desired +parameters are present before running the QC function.

+

Note: sometimes people have a mix of correct and incorrect column +headings in the input file. When this happens, R does not give you an +error message; it only carries forward the parameters that have the +correct column headings and leaves the others blank. Thus, checking this +summary table is important.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3b_DataPrep.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3b_DataPrep.html new file mode 100644 index 00000000..da1a578b --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3b_DataPrep.html @@ -0,0 +1,50 @@ + + + +
+

Naming Files

+

You have more flexibility with naming files when using the Shiny app +vs R package because you can browse to the desired input file(s), +whereas the R package searches through files in a specified directory +and selects data files based on the file name. Even if you are using the +Shiny app, consider using the file naming scheme below since you may +decide to use the R package at some point in the future. Another reason +to use the recommended file naming scheme is because it keeps files well +organized and easy to sort. For example, if using HOBO U20 water level +loggers, it makes matching air and water sensor files easier during the +processing step and helps you identify gaps in the data. The recommended +file naming scheme is:

+

The recommended file naming scheme is:

+

SiteID_DataType_StartDate_EndDate

+

The four components are separated by underscores (“_”).

+

Example: Hunting_Water_20130426_20130725

+
    +

  • Site ID (no spaces or underscores) = Hunting

    • +

  • DataType: Air, Water, AW (air and water sensor data are combined +into the same file), Gage, DO

    • +

  • Date, Start (YYYYMMDD) = 20130426

    • +

  • Date, End (YYYYMMDD) = 20130725

    • +
+

For multi-depth deployments (e.g., temperature sensors deployed at +multiple depths at a lake), depth and units are added to the SiteID, +separated by double-dashes as shown below:

+

Example: RussWood–01M_TEMP_20130426_20130725

+

Depth can include decimals (for example, 0.1M is ok) and does not +need to include leading zeros (either 01M or 1M are acceptable). Units +can be upper or lower case (M or m), and abbreviated (M) or spelled out +(meters). You can change the double-dash separator if desired by +customizing the configuration file (for +example, you could change it to a tilde ~).

+

If you are using the Shiny app and have more than one type of sensor +deployed at the same depth (for example, both a DO and temperature +sensor at 2M), you can put the sensor type in the second part of the +file name (for example, DataType = TEMP, as shown in the example above, +or DataType = DO).

+

Below are examples of files that follow the recommended naming +scheme.

+
+Example files following recommended naming scheme +
Example files following recommended naming +scheme
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c1_QCThresh_Defaults.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c1_QCThresh_Defaults.html new file mode 100644 index 00000000..35ee0e12 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c1_QCThresh_Defaults.html @@ -0,0 +1,617 @@ + + + +
+

Default QC flag test thresholds

+

If a customized configuration file is not uploaded prior to running +the QC function, the default thresholds and units listed below will be +used for the four QC flag tests.

+

The QC function runs each data point in the input file through the +following four ‘flag’ tests (see Default QC test thresholds table, +below, for specific threshold values used in each flag test for each +parameter).

+
+

Flag test descriptions

+ + + + + + + + + + + + + + + + + + + + + + + + + +
+Flag Test + +Description +
+Unrealistic values (‘Gross range’) + +Entries are flagged if values are above or below upper and lower limits +
+Spikes + +Entries are flagged if adjacent points change by more than ‘x’ amount +
+Rate of change (RoC) + +Entries are flagged if the RoC exceeds a given threshold (e.g., ≥ 3 st +dev within 25 hrs) +
+Flat line + +Entries are flagged if a certain number of consecutive measurements are +within a certain amount of each other (e.g., >10 consecutive +temperature measurements are within 0.01 degrees C of one another) +
+

The ‘flag’ tests were selected based on the following references:

+
    +

  • U.S. Integrated Ocean Observing System, 2017. Manual for the Use +of Real-Time Oceanographic Data Quality Control Flags, Version 1.1. 43 +pp. +https://cdn.ioos.noaa.gov/media/2017/12/QARTOD-Data-Flags-Manual_Final_version1.1.pdf Exit EPA +Website [PDF]

    • +

  • Wagner, R.J., Boulger, R.W., Oblinger, C.J., Smith, B.A., 2006. +Guidelines and Standard Procedures for Continuous Water-quality +Monitors: Station Operation, Record Computation, and Data Reporting. +U.S. Geological Survey Techniques and Methods 1-D3, 51 pp. þ 8 +attachments. +http://pubs.water.usgs.gov/tm1d3 Exit EPA +Website .

    • +
+

Each data point is flagged as one of the following codes by +ContDataQC:

+
    +

  • P = Pass

    • +

  • S = Suspect

    • +

  • F = Fail

    • +

  • X = No Data or Not Applicable (NA)

    • +
+

The default thresholds for temperature are based on data from cool, +freshwater, wadeable, medium to high gradient streams in the eastern US. +Default thresholds for the hydrologic parameters, turbidity, and +chlorophyll-a are set very high since values vary widely depending on +site-specific factors such as stream size. Once you have one or more +years of continuous data at a site, we strongly encourage you to +evaluate the performance of the QC test thresholds for each parameter +and customize the thresholds as needed by editing the configuration +file. For more information, see the ‘Upload Custom Thresholds’ tab.

+

Units are also an important consideration. The default configuration +puts hydrologic parameters (e.g., water level) in feet, which is +generally preferred for streams among RMN partners. However, with lakes, +more people prefer meters. If units are changed from feet to meters but +there is not a corresponding change in thresholds, the QC tests lose +relevance.

+

Configuration files can be downloaded here:

+
    +

  • Default [ZIP], with hydrologic +parameters in feet

    • +

  • Customized [ZIP], with +hydrologic parameters in meters

    • +
+

The table below shows the Fail and Suspect default QC test thresholds +for each parameter as of 2023-08-28.

+
+
+

Default QC test thresholds

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Parameter + +Units + +Flag + +Gross + +Spike + +Rate of Change + +Flat Line +
+Temperature, Water + +deg C + +Fail + +>30 or <-1 + +≥2.0 (±) + + +>100 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +>25 or <-0.5 + +≥1.5 (±) + +≥6 stdev within 25 hrs + +>60 consecutive measurements within 0.01 units of one another +
+Temperature, Air + +deg C + +Fail + +>38 or <-25 + +≥10 (±) + + +>60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +>35 or <-23 + +≥8 (±) + +≥6 stdev within 25 hrs + +>30 consecutive measurements within 0.01 units of one another +
+Sensor Depth or Water Level + +feet + +Fail + +> 10^5 or < -1 + +≥5 (±) + + +>60 consecutive measurements within 0 units of one another +
+ + +Suspect + +> 1000 or < 1 + +≥3 (±) + +≥6 stdev within 25 hrs + +>30 consecutive measurements within 0 units of one another +
+Discharge + +ft3/s + +Fail + +> 10^5 or < -1 + +≥10^4 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 1000 or < 1 + +≥10^3 (±) + +≥6 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+Dissolved oxygen + +mg/L + +Fail + +> 18 or < 1 + +>= 10 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 16 or < 2 + +>= 5 (±) + +≥6 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+Conductivity + +uS/cm + +Fail + +> 1500 or < 10 + +>= 10 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +>1200 or < 20 + +>= 5 (±) + +≥6 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+pH + +SU + +Fail + +> 12 or < 3 + +>= 10 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 11 or < 4 + +>= 5 (±) + +≥6 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+Turbidity + +NTU + +Fail + +> 10^5 or < -1 + +≥10^4 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 1000 or < -1 + +≥10^3 (±) + +≥6 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+Pressure, Water + +psi + +Fail + +>17 or <13 + +≥0.7 (±) + + +>15 consecutive measurements within 0.001 units of one another +
+ + +Suspect + +>16.8 or <13.5 + +≥0.5 (±) + +≥6 stdev within 25 hrs + +>10 consecutive measurements within 0.001 units of one another +
+Barometric Pressure, Air + +psi + +Fail + +>15 or <13 + +≥0.25 (±) + + +>15 consecutive measurements within 0.001 units of one another +
+ + +Suspect + +>14.8 or <13 + +≥0.15 (±) + +≥6 stdev within 25 hrs + +>10 consecutive measurements within 0.001 units of one another +
+Chlorophyll-a + +g/cm3 + +Fail + +> 10^5 or < -1 + +≥10^4 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 1000 or < -1 + +≥10^3 (±) + +≥6 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c2_QCThresh_Eval.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c2_QCThresh_Eval.html new file mode 100644 index 00000000..3e957d42 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c2_QCThresh_Eval.html @@ -0,0 +1,24 @@ + + + +
+

Evaluate Thresholds

+

If you have one or more years of continuous data for a site, we +strongly encourage you to evaluate the performance of the QC test +thresholds for each parameter at that site and customize the +configuration file if needed.

+

Click here for instructions on +how to generate pivot tables and plots to evaluate thresholds for the +Unrealistic values (‘Gross range’) and Spike tests.

+

Another option is to use R code written by Tim Martin (Minnesota DNR) +to generate statistical outputs that can help inform thresholds for all +four QC tests. Click here to +download the R scripts and instructions.

+

To aid with documentation of the threshold evaluation process, click +here to download an +Excel worksheet that lists the default thresholds for each parameter and +has a column where you can enter the customized values for a given site +as well as rationale for making the changes. When doing this, make sure +you consider what units you are using (as well as what the defaults +are), since units have a large effect on thresholds.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c2_QCThresh_Upload.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c2_QCThresh_Upload.html new file mode 100644 index 00000000..39f2bb4a --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c2_QCThresh_Upload.html @@ -0,0 +1,29 @@ + + + +
+

Upload Custom Thresholds

+

Click the ‘Browse’ button (to the left) and select the desired +customized configuration file. The status bar will tell you when the +file has been uploaded. A new button will appear that gives you the +option of returning to the default configuration file. Do not click this +unless you change your mind and no longer want to use the customized +file.

+

Go to the ‘Run Functions’ tab; verify that the correct configuration +file has been uploaded (by checking the file name under “2. Confirm +correct configuration file is being used”); select the desired input +file(s); and run them through the QC function. Save the output to the +desired location. Open the Word QC report and scroll to the ‘Thresholds, +Quick Reference’ table (in the middle section of the report) and verify +that the correct thresholds were used.

+

Be aware that each time you close and reopen the Shiny app, it will +automatically revert back to the default configuration file. Also – +remember the sequencing! If you forget to upload the customized +configuration file prior to running the QC function, it will use the +default thresholds.

+
+ diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c3_QCThresh_Edit.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c3_QCThresh_Edit.html new file mode 100644 index 00000000..35ac7ded --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c3_QCThresh_Edit.html @@ -0,0 +1,111 @@ + + + +
+

Edit Thresholds

+

Click here for +instructions on how to edit thresholds and create customized +configuration files. There are two options (see below). For either +option, save the customized configuration file in a folder that is easy +to find so that you can reuse the file each time you QC data for that +site. It is helpful to include the StationID and other relevant +information in the file name (for example, if thresholds are customized +for a particular season).

+

After you save the file, go to the ‘upload custom thresholds’ tab, +browse to the custom thresholds file, upload the file and then, under +‘Main functions’, generate the QC reports. If you forget the upload +step, the default QC thresholds will be used.

+
+

Option 1, download the default configuration file, open the file in +R or Notepad++, make edits and save.

+

Configuration files can be downloaded here -

+ +

When you download the configuration file(s), you will see two types +of files: an R file (.R) and a text (.txt) file for users who do not +have R installed on their computer. You can download R from this +website: +https://cran.r-project.org. +You may also want to download +RStudio, +which is a separate program but is a useful code editor and interface +(IDE) for R. If you cannot install R on your computer, we recommend use +Notepad or +Notepad++ +(some people prefer Notepad++ because it has color-coding).

+
+
+

Option 2, use the interactive functions shown left and below.

+
    +

  • Select a parameter using the drop-down menu (upper left +box)

    • +

  • Edit thresholds using the boxes below

    • +

  • Save changes (for each parameter)

    • +

  • Download custom thresholds file

    • +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Default Units
ParameterUnits
TemperatureCelsius
Sensor depth and water levelfeet
Dischargeft3/s
Dissolved oxygenmg/L
DO pctsat%
ConductivityuS/cm
Pressurepsi
pHSU
TurbidityNTU
Salinityppt
Chlorophyllag/cm3
+

If you do not want to use the default units, you will need to use +Option 1.

+

Acknowledgment: Tim Martin (MN DNR) wrote the R code that we used for +the interactive functions below.

+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c3_QCThresh_Eval.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c3_QCThresh_Eval.html new file mode 100644 index 00000000..1e7a7192 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c3_QCThresh_Eval.html @@ -0,0 +1,23 @@ + + + +
+

Evaluate Thresholds

+

If you have one or more years of continuous data for a site, we +encourage you to evaluate the performance of the QC test thresholds for +each parameter at that site and customize the configuration file if +needed. Make sure you consider what units you are using, since units +have a large effect on thresholds.

+

Resources for evaluating thresholds:

+
    +

  • Pivot tables and charts +[ZIP] for evaluating the Unrealistic values (‘Gross range’) and Spike +tests in Excel.

    • +

  • R code statistics +[ZIP] that can help inform thresholds for all four QC tests.

    • +

  • Threshold +evaluation worksheet [XLSX] that lists the default thresholds for +each parameter and has columns where you enter customized thresholds for +one or multiple sites.

    • +
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c4_QCThresh_Edit.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c4_QCThresh_Edit.html new file mode 100644 index 00000000..d69e5e8c --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c4_QCThresh_Edit.html @@ -0,0 +1,122 @@ + + + +
+

Edit Thresholds

+

There are two options for editing thresholds:

+
    +

  • Download the default configuration file, open it in R or +Notepad++, make edits, and save the revised file.

    • +

  • Use the interactive functions at the bottom of this +page.

    • +
+

For either option, include the StationID and other relevant +information in the file name (e.g., perhaps it’s customized for a +particular season) and save it in a folder that is easy to find since +you’ll want to reuse the file each time you QC data for that site in the +future.

+

After you save the file, go to the ‘Upload Custom Thresholds’ tab, +browse to the custom thresholds file you saved, upload that file, and +then, under ‘Run Functions’, generate the QC reports. If you forget the +upload step, the default QC thresholds will be used.

+
+

Option 1: download the default configuration file, open the file in +R or Notepad++, make edits, and save.

+

Configuration files can be downloaded here:

+ +

Navigate to the ‘QC tests and calculations’ section of the +configuration file, which is about halfway through the file. Edit +thresholds as desired. Save the file.

+

When you download the configuration file(s), you will see two types +of files: an R file (.R) and a text (.txt) file for users who do not +have R installed on their computer. You can download R from this +website: +cran.r-project.org Exit EPA +Website . You may also want to download +RStudio Exit EPA +Website , which is a separate program but is a useful +code editor and interface for R. If you cannot install R on your +computer, we recommend using Notepad or +Notepad++ Exit EPA +Website (some people prefer Notepad++ because it has +color-coding).

+
+
+

Option 2: use the interactive functions on this page.

+
    +

  • Select a parameter using the drop-down menu in 1. Select +parameter (upper left of this page)

    • +

  • Edit thresholds for each fail test type using the boxes +below.

    • +

  • After you make changes for each parameter, click ‘Save changes’ +as you go (upper left of this page). Otherwise, when you switch to a +different parameter, it won’t save the changes you made to the previous +parameter.

    • +

  • Download the custom thresholds file.

    • +

  • Note that if you do not want to use the default units (see table +below), you will need to use Option 1.

    • +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Default Units
ParameterUnits
TemperatureCelsius
Sensor depth and water levelfeet
Dischargeft3/s
Dissolved oxygenmg/L
DO pctsat%
ConductivityuS/cm
Pressurepsi
pHSU
TurbidityNTU
Salinityppt
Chlorophyllag/cm3
+

For more information, +this link provides instructions for editing thresholds [PDF]

+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c4_QCThresh_Upload.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c4_QCThresh_Upload.html new file mode 100644 index 00000000..ca64b324 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c4_QCThresh_Upload.html @@ -0,0 +1,25 @@ + + + +
+

Upload Custom Thresholds

+

Click the ‘Browse’ button (to the left) and select the desired +customized configuration file. The status bar will tell you when the +file has been uploaded. A new button will appear that gives you the +option of returning to the default configuration file. Do not click this +unless you change your mind and n o longer want to use the customized +file.

+

Go to the ‘Main Functions’ tab; verify that the correct configuration +file has been uploaded (by checking the file name); select the desired +input file(s); run them through the QC function. Save the output to the +desired location. Open the Word or html QC report and scroll to the +‘Thresholds, Quick Reference’ table (in the middle section of the +report). Verify that the correct thresholds were used.

+

Be aware that each time you close and reopen the Shiny app, it will +automatically revert back to the default configuration file. Also – +remember the sequencing! If you forget to upload the customized +configuration file prior to running the QC function, it will use the +default thresholds.

+

Want to try this yourself? Click here to download a test +file.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c_DataPrep.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c_DataPrep.html new file mode 100644 index 00000000..ba55b569 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3c_DataPrep.html @@ -0,0 +1,299 @@ + + + +
+

Formatting Files

+

Input files need to be formatted a specific way in order for the +Shiny app and R package to work. Guidelines -

+
    +

  • Input files must be comma-delimited (CSV) (but not CSV UTF-8 +files).

    • +

  • The columns do not have to be in any specific order

    • +

  • There are two required fields: SiteID; and “Date Time” (with Date +and Time combined into one field).

    • +

  • If using the default configuration +file, column names must match the capitalization, spelling and +symbology shown in the table below (for example, R will not recognize +‘sensor depth ft’; it needs to be ‘Sensor Depth ft’)

    • +

  • Either periods or spaces in the column headings are acceptable. +For example, in the table below, air temperature is shown as ‘’Air Temp +C’ but it can also be entered as ‘Air.Temp.C’.

    • +

  • For units, slashes, spaces or periods (e.g., DO mg/L, DO.mg.L or +DO mg L) are acceptable. Do NOT use parentheses.

    • +

  • Advanced R users can add new parameters (beyond the ones show in +the table below) to the configuration file.

    • +

  • Optional fields include rowID, logger ID (serial number) and +discrete measurements. Discrete (in situ) measurements can be entered +for any parameter (just add ‘Discrete’ to the beginning of the column +heading; for example, ‘Discrete Air Temp C’). The discrete data point +will be included in the time series plots that are generated when the +user runs the QC function. If using Onset HOBO sensors, you can save +time by using the automated reformat function (see HOBO Reformat +section)

    • +
+
+

Data formatting information

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Status + +Column headings + +Notes +
+Required + +SiteID + +character (numeric or text) +
+ +Date Time + +“Date Time” (one field) OR “Date” AND “Time” (in 2 separate fields) OR +all 3 (in 3 fields); prefer 24H time (military); YYYY-MM-DD is +recommended but not required +
+As available + +Air Temp C + +
+ +Water Temp C + +
+ +Air BP psi + +barometric pressure (BP) +
+ +Water P psi + +water pressure (P) +
+ +Sensor Depth ft + +differs from Water Level in that it is not generated with a reference +water level measurement +
+ +Water Level ft + +generated with a reference water level (which relates water level to a +fixed point) +
+ +Discharge ft3/s + +
+ +Conductivity uS/cm + +
+ +DO mg/L + +
+ +DO adj mg/L + +DO adjusted for salinity (HOBOware) +
+ +DO pctsat % + +% saturation +
+ +pH SU + +
+ +Turbidity NTU + +
+ +Chlorophylla g/cm3 + +
+ +Salinity ppt + +
+Optional + +Air LoggerID + +unique identifier (e.g., serial number) for sensor deployed on land +
+ +Water LoggerID + +unique sensor identifier (e.g., serial number) for sensor deployed in +the water +
+ +Water RowID + +sequential record number (1=first measurement; largest number = last +measurement) for sensor deployed in the water +
+ +Air RowID + +sequential record number (1=first measurement; largest number = last +measurement) for sensor deployed on land +
+ +Discrete Water Temp C + +Discrete (in situ) measurements can be entered into the row of the CSV +file where the date/time of the sensor measurement most closely matches +with the date/time of the in situ measurement; the data point(s) will be +overlaid onto the time series plot in the QC report so that the user can +visually check the correspondence between the two. Discrete entries can +be made for any of the parameters listed above (not just the Water Temp +C example shown here) +
+ +Discrete Air Temp C + +
+ +Discrete Water Level ft + +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3d_Tips.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3d_Tips.html new file mode 100644 index 00000000..692cd43a --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3d_Tips.html @@ -0,0 +1,149 @@ + + + +
+

QC Tips

+

Below are instructional materials and links to resources we have +compiled as we continue to gain experience QCing continuous data. To +date, we have worked primarily with thermal and hydrologic data, and +more recently, dissolved oxygen data from lakes. The data are typically +downloaded 2-4 times a year. We have not worked with ‘real-time’ +data.

+

If you have any questions, feedback on ways to improve this process +or suggestions on additional materials that you feel would be helpful to +share, please contact Jen Stamp ().

+
+

Data corrections

+

If you are processing data for the RMNs, we are currently +recommending the following approach

+
    +

  • If you are certain a data point is erroneous, delete the +measurement and flag as ‘F’. If you are not sure, flag the data as ‘S’ +and do not delete. Let the people using the data decide whether to +remove questionable measurements from their analysis.

    • +

  • Document that you checked each point flagged as ‘F’ and ‘S’ by +adding a data qualifier to the Comment column. Click here to download the list of +data qualifiers currently being used by RMN partners.

    • +

  • Leave missing data cells blank.

    • +
+
+
+

Workflow

+

Click here for +recommendations on a workflow for reviewing the QC reports.

+

If you have a backlog of data files from multiple deployments and are +wondering how to most efficiently QC the files, here is a recommended +workflow.

+

Click here for a site visit +checklist to help ensure that you do not forget to document information +that is important for the data QC process.

+
+
+

Sensor configuration

+

Some people have had problems with air and water sensors being out of +sync ( e.g., one records at 11:00 and the other records at 11:07). If +you are deploying air and water sensors at a site, make sure they are +recording at the same time. This will make data processing faster and +easier. If you are using Onset HOBO sensors, click here for tips on configuring +them in a way that ensures they will record at the same time.

+

Another issue that sometimes occurs is data overlap (where more than +one file has measurements covering part of the same time periods). If +you are using Onset HOBO sensors, click here for procedures to follow +during data download to clear sensor memory and avoid overlapping +data.

+
+
+

Accuracy checks

+

Accuracy checks are comparisons of discrete or in situ measurements +taken in the lab and/or in the field with sensor measurements from the +closest date/time. The sensor measurement should be within the accuracy +quoted by the manufacturer ( e.g., ±0.2°C if you are using the Onset +HOBO proV2 sensor). Accuracy checks are used to validate your data, and +in some cases, correct for sensor drift. Click here to download an example of +an accuracy check worksheet being kept by an RMN partner.

+
+
+

Visual checks of time series plots

+

Visual checks of time series plots are an important part of the QC +process. Click here to see a +compilation of patterns to watch for when interpreting the plots. They +include ice cover, beaver activity, leaf packs, dewatering, and dead +batteries. We will continue to add slides to this as we receive +materials.

+
+
+

Checking sensor data against other data sources

+

This is optional but encouraged if time permits. Click here for instructional materials from +David Smith (Kentucky Division of Water) on how he has been downloading +data from nearby weather stations and USGS gages, as well as modeled air +temperature and precipitation data from Daymet ( +https://daymet.ornl.gov/getdata +), and comparing those data to sensor measurements as part of his QC +process.

+
+
+

Additional resources

+

Logger Processing App

+

Developed by Tim Martin at the Minnesota Department of Natural +Resources for processing, organizing, and vetting continuous +environmental logger data. It has the same QC flag tests as ContDataQC +plus more, including an interactive plot that allows you to flag or +unflag data points.

+

https://tetratech-wtr-wne.shinyapps.io/logger_processing/

+

driftR, an R package that corrects drift in water quality +data

+

It implements either one- or two-point variable data corrections +based on the number of standards used to calibrate the sensor of +interest, then linearly interpolates the correction over the period of +interest.

+

https://shaughnessyar.github.io/driftR/

+

Shaughnessy, A.R., Prener, C.G. and E.A. Hasenmueller. 2019. An R +package for correcting continuous water quality monitoring data for +drift. Environmental Monitoring and Assessment 191, 445. +https://doi.org/10.1007/s10661-019-7586-x

+

R package for detecting outliers

+

https://cran.r-project.org/web/packages/outliers/outliers.pdf

+

Campulova, M., Campula, R. and J. Holesovsky. 2022. An R package for +identification of outliers in environmental time series data. +Environmental Modelling & Software, Volume 155. +https://www.sciencedirect.com/science/article/abs/pii/S1364815222001414

+
+
+

Wish list items

+

Items that we want to add in the future when resources permit

+
    +

  • ‘Transition’ check

    • +

  • We’re finding most errors concentrated around the times when the +sensors are being taken out of position for data download. If you’re +only QCing individual files after each download, you’re not evaluating +the transition between data from the previous file and the new file. +This check would bring in data from 1-2 days prior to the start time of +the file being evaluated and alert users to data points that need to be +trimmed.

    • +

  • Automatic adjustments to gross limit QC test thresholds based on +season and depth

    • +

  • Right now the gross limit QC test is pretty limited because it +doesn’t capture seasonal differences without the user creating multiple +customized configuration files (and then the user would need to break up +the input files for each time period). We’d like to figure out a way to +have the R code automatically adjust the upper and lower gross limit +thresholds based on seasonal considerations, and for lakes, for it to +also make adjustments to gross limit thresholds based on depth.

    • +

  • Automated accuracy check

    • +

  • Users would upload in-situ files and the R code would +automatically match the in-situ measurements with the closest sensor +measurement, calculate the difference between the two measurements and +report whether the sensor is meeting accuracy specs.

    • +

  • Drift correction

    • +

  • We’d add a feature that automates drift corrections.

    • +

  • Weather station check

    • +

  • Users would upload weather station data and the R code would +match the measurements with the sensor data, generate time series plots +in which the measurements are overlaid on one another, and generate some +basic statistical measures to compare the two sets of data.

    • +

  • Daily range check (max-min)

    • +

  • This QC check is used by the USFS NorWeST temperature sensor +network. It would be added in as a new function.

    • +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3e_DataPrep.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3e_DataPrep.html new file mode 100644 index 00000000..d1ad1981 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3e_DataPrep.html @@ -0,0 +1,10 @@ + + + +
+

miniDOT Concatenate

+

This function merges daily Dissolved Oxygen data files downloaded +from PME miniDot DO loggers into a single multi-column CSV file. Use if +the data were not compiled using the PME java app on the logger. The +output should be downloaded into the Data0_Original folder.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3f_DataPrep.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3f_DataPrep.html new file mode 100644 index 00000000..ddcef120 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_3f_DataPrep.html @@ -0,0 +1,46 @@ + + + +
+

miniDOT Reformat

+

The miniDOT reformat function converts files to a format that is +ready to run through the DataQC function. This saves you time and +eliminates errors that sometimes occur when manually reformatting +files.

+
+

Instructions

+
    +

  • Click the ‘Browse’ button and select the input file(s). You can +run multiple files at once. The status bar will tell you when the upload +is complete.

    • +

  • Click the ‘Reformat miniDOT file(s)’ button; a status bar will +appear in the lower right corner.

    • +

  • After the function is done running, a ‘Download’ button will +appear. Click this button.

    • +

  • Save the zipped file in the Data1_RAW folder

    • +

  • Your files are now ready to be run through the QC +function!

    • +
+
+
+

Common errors -

+
    +
  • DO percent saturation values of all NA. This occurs when no inputs +are provided to calculate DO percent saturation, and results in. no +statistics being generated for DO percent saturation later in the QC +process.
    • +
+
+
+

Examples

+

Example images of before and and after performing reformat.

+
+Concatenated miniDOT file +
Concatenated miniDOT file
+
+
+Reformated miniDOT file +
Reformated miniDOT file
+
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4_USGSgage.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4_USGSgage.html new file mode 100644 index 00000000..f04c3eb8 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4_USGSgage.html @@ -0,0 +1,22 @@ + + + +
+

Instructions

+

You can find USGS gage locations and IDs using the +National +Water Information System Mapper Exit EPA Website .

+

To download data (at the right):

+
    +

  1. Enter as many USGS station IDs as desired, separated by commas +and spaces (e.g., 01187300, 01493000, 01639500).

    2. +

  2. Enter a starting and ending date for which data will be +retrieved; the same date range will be used for every station.

    3. +

  3. Click the ‘Retrieve USGS data’ button. A progress bar will appear +in the bottom-right of the tab. It will advance as each file is +completed (i.e., if you select three stations, it will wait at 0%, jump +to 33%, jump to 66%, and then jump to 100%).

    4. +

  4. After data retrieval completes, a download button will appear. +Click the button to download a zip file of the records.

    5. +
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4a_QCThresh.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4a_QCThresh.html new file mode 100644 index 00000000..a1702854 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4a_QCThresh.html @@ -0,0 +1,613 @@ + + + +
+

Default QC flag test thresholds

+

If a customized configuration file is not uploaded prior to running +the QC function, the default thresholds and units listed below will be +used for the four QC flag tests.

+

The QC function runs each data point in the input file through the +following four ‘flag’ tests.

+
+

Flag test descriptions

+ + + + + + + + + + + + + + + + + + + + + + + + + +
+Flag Test + +Description +
+Unrealistic values (‘Gross range’) + +Entries are flagged if values are above or below upper and lower limits +
+Spikes + +Entries are flagged if adjacent points change by more than ‘x’ amount +
+Rate of change (RoC) + +Entries are flagged if the RoC exceeds a given threshold (e.g., ≥ 3 st +dev within 25 hrs) +
+Flat line + +Entries are flagged if a certain number of consecutive measurements are +within a certain amount of each other (e.g., >10 consecutive +temperature measurements are within 0.01 degrees C of one another) +
+

The ‘flag’ tests were selected based on the following references:

+ +

R assigns the following flags to each data point:

+
    +

  • P = Pass

    • +

  • S = Suspect

    • +

  • F = Fail

    • +

  • X = No Data or Not Applicable (NA)

    • +
+

The default thresholds for temperature are based on data from cool, +freshwater, wadeable, medium to high gradient streams in the eastern US. +Default thresholds for the hydrologic parameters, turbidity and +chlorophyll-a are set very high since values vary widely depending on +site-specific factors such as stream size. Once you have one or more +years of continuous data at a site, we strongly encourage you to +evaluate the performance of the QC test thresholds for each parameter +and customize the thresholds as needed by editing the configuration +file. For more information, see the ‘Customize QC flag test thresholds’ +tab.

+

Units are an important consideration as well. The default +configuration puts hydrologic parameters (like water level) in feet, +which is generally preferred for streams among RMN partners. However, +with lakes, more people prefer meters. If units are changed from feet to +meters but there is not a corresponding change in thresholds, the QC +tests lose relevance.

+

Configuration files can be downloaded here:

+
    +

  • Default, with hydrologic +parameters in feet

    • +

  • Customized, with hydrologic +parameters in meters

    • +
+

This table shows the default QC test thresholds for each parameter as +of 06/17/2021.

+
+
+

Default QC test thresholds

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Parameter + +Units + +Flag + +Gross + +Spike + +Rate of Change + +Flat Line +
+Temperature, Water + +deg C + +Fail + +>30 or <-2 + +≥1.5 (±) + + +>30 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +>25 or <-0.1 + +≥1.0 (±) + +≥3 stdev within 25 hrs + +>20 consecutive measurements within 0.01 units of one another +
+Temperature, Air + +deg C + +Fail + +>38 or <-25 + +≥10 (±) + + +>20 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +>35 or <-23 + +≥8 (±) + +≥3 stdev within 25 hrs + +>15 consecutive measurements within 0.01 units of one another +
+Sensor Depth or Water Level + +feet + +Fail + +> 10^5 or < -1 + +≥5 (±) + + +>60 consecutive measurements within 0 units of one another +
+ + +Suspect + +> 1000 or < 1 + +≥3 (±) + +≥3 stdev within 25 hrs + +>20 consecutive measurements within 0 units of one another +
+Discharge + +ft3/s + +Fail + +> 10^5 or < -1 + +≥10^4 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 1000 or < 1 + +≥10^3 (±) + +≥3 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+Dissolved oxygen + +mg/L + +Fail + +> 20 or < 1 + +>= 10 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 18 or < 2 + +>= 5 (±) + +≥3 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+Conductivity + +uS/cm + +Fail + +> 1500 or < 10 + +>= 10 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +>1200 or < 20 + +>= 5 (±) + +≥3 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+pH + +SU + +Fail + +> 12 or < 3 + +>= 10 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 11 or < 4 + +>= 5 (±) + +≥3 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+Turbidity + +NTU + +Fail + +> 10^5 or < -1 + +≥10^4 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 1000 or < -1 + +≥10^3 (±) + +≥3 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+Pressure, Water + +psi + +Fail + +>17 or <13 + +≥0.7 (±) + + +>15 consecutive measurements within 0.001 units of one another +
+ + +Suspect + +>16.8 or <13.5 + +≥0.5 (±) + +≥3 stdev within 25 hrs + +>10 consecutive measurements within 0.001 units of one another +
+Barometric Pressure, Air + +psi + +Fail + +>15 or <13 + +≥0.25 (±) + + +>15 consecutive measurements within 0.001 units of one another +
+ + +Suspect + +>14.8 or <13 + +≥0.15 (±) + +≥3 stdev within 25 hrs + +>10 consecutive measurements within 0.001 units of one another +
+Chlorophyll a + +g/cm3 + +Fail + +> 10^5 or < -1 + +≥10^4 (±) + + +> 60 consecutive measurements within 0.01 units of one another +
+ + +Suspect + +> 1000 or < -1 + +≥10^3 (±) + +≥3 stdev within 25 hrs + +> 30 consecutive measurements within 0.01 units of one another +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4b_QCThresh.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4b_QCThresh.html new file mode 100644 index 00000000..3e957d42 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4b_QCThresh.html @@ -0,0 +1,24 @@ + + + +
+

Evaluate Thresholds

+

If you have one or more years of continuous data for a site, we +strongly encourage you to evaluate the performance of the QC test +thresholds for each parameter at that site and customize the +configuration file if needed.

+

Click here for instructions on +how to generate pivot tables and plots to evaluate thresholds for the +Unrealistic values (‘Gross range’) and Spike tests.

+

Another option is to use R code written by Tim Martin (Minnesota DNR) +to generate statistical outputs that can help inform thresholds for all +four QC tests. Click here to +download the R scripts and instructions.

+

To aid with documentation of the threshold evaluation process, click +here to download an +Excel worksheet that lists the default thresholds for each parameter and +has a column where you can enter the customized values for a given site +as well as rationale for making the changes. When doing this, make sure +you consider what units you are using (as well as what the defaults +are), since units have a large effect on thresholds.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4c_QCThresh.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4c_QCThresh.html new file mode 100644 index 00000000..105084a5 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4c_QCThresh.html @@ -0,0 +1,55 @@ + + + +
+

Edit Thresholds

+

Click here for +instructions on how to edit thresholds and create customized +configuration files. There are two options (see below). For either +option, save the customized configuration file in a folder that is easy +to find so that you can reuse the file each time you QC data for that +site. It is helpful to include the StationID and other relevant +information in the file name (for example, if thresholds are customized +for a particular season).

+

After you save the file, go to the ‘upload custom thresholds’ tab, +browse to the custom thresholds file, upload the file and then, under +‘Main functions’, generate the QC reports. If you forget the upload +step, the default QC thresholds will be used.

+
+

Option 1, download the default configuration file, open the file in +R or Notepad++, make edits and save.

+

Configuration files can be downloaded here -

+ +

When you download the configuration file(s), you will see two types +of files: an R file (.R) and a text (.txt) file for users who do not +have R installed on their computer. You can download R from this +website: +https://cran.r-project.org. +You may also want to download +RStudio, +which is a separate program but is a useful code editor and interface +(IDE) for R. If you cannot install R on your computer, we recommend use +Notepad or +Notepad++ +(some people prefer Notepad++ because it has color-coding).

+
+
+

Option 2, use the interactive functions shown left and below.

+
    +

  • Select a parameter using the drop-down menu

    • +

  • Edit thresholds using boxes below

    • +

  • Save changes (for each parameter)

    • +

  • Download custom thresholds file

    • +
+

If you do not want to use the default units, you will need to use +Option 2.

+

Acknowledgment: Tim Martin (MN DNR) wrote the R code that we used for +the interactive functions below.

+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4d_QCThresh.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4d_QCThresh.html new file mode 100644 index 00000000..9c97c060 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_4d_QCThresh.html @@ -0,0 +1,22 @@ + + + +
+

Upload Custom Thresholds

+

Click the ‘Browse’ button (to the left) and select the desired +customized configuration file. The status bar will tell you when the +file has been uploaded. A new button will appear that gives you the +option of returning to the default configuration file. Do not click this +unless you change your mind and n o longer want to use the customized +file.

+

Go to the ‘Main Functions’ tab; select the desired input file(s); run +them through the QC function. Save the output to the desired location. +Open the Word or html QC report and scroll to the ‘Thresholds, Quick +Reference’ table (in the middle section of the report). Verify that the +correct thresholds were used.

+

Be aware that each time you close and reopen the Shiny app, it will +automatically revert back to the default configuration file. Also – +remember the sequencing! If you forget to upload the customized +configuration file prior to running the QC function, it will use the +default thresholds.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5_Troubleshooting.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5_Troubleshooting.html new file mode 100644 index 00000000..5e681c2e --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5_Troubleshooting.html @@ -0,0 +1,9 @@ + + + +
+

Troubleshooting

+

If there are any issues with the Shiny app please log an issue on +GitHub +or contact the developers ( and ).

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5a_MainFunc.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5a_MainFunc.html new file mode 100644 index 00000000..857f7ecf --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5a_MainFunc.html @@ -0,0 +1,128 @@ + + + +
+

Run Functions

+

The three main functions are listed in the drop-down menu under +‘Choose Operation to perform’

+
    +

  • ‘QC raw data’ generates QC reports.

    • +

  • ‘Aggregate QC’d data’ merges the QC’d data files +for the desired time periods together. The input files need to be from +the same site and share common parameters.

    • +

  • ‘Summary statistics’ generates two sets of CSV +files and a PDF with time series plots for each parameter. One of the +CSVs has daily means and the other has a wider range of summary +statistics (e.g., min, median, max, quartiles).

    • +
+

ContDataQC works sequentially, meaning the user needs to run the QC +function before running the Aggregate function, and the Aggregate +function before the SummaryStats function. More detailed information on +each function can be found below.

+

Time-saver tip: if you use Google Chrome as your internet browser, +you can change its settings so that it asks what folder to download +files to (versus files going to the default Download folder). Click here for instructions on how +to set this up.

+
+

QC reports

+

Shiny app instructions

+
    +

  1. Click the ‘Browse’ button and select the input file(s) from the +Data1_RAW folder. You can run multiple files at once. The status bar +will tell you when the upload is complete.

    2. +

  2. Select ‘QC Raw Data’ from the ‘Choose Operation to perform’ +drop-down menu

    3. +

  3. A ‘Run operation’ button will appear. Click this button. A status +bar will appear in the lower right corner.

    4. +

  4. After the function is done running, a ‘Download’ button will +appear. Click this button.

    5. +

  5. Save the zipped file in the Data2_QC folder.

    6. +

  6. Open the QC reports. There are two types of outputs: a Data +Quality Control Report that comes as either a Word document or Hypertext +Markup Language (HTML) (the user specifies the desired format); and a +comma-delimited (.csv) spreadsheet. The reports include results from the +‘flag’ tests (gross, spike, rate of change and flat line), missing data +checks and time series plots. Click here to download an example of a QC +output.

    7. +

  7. Review each set of files, check flagged data points, do visual +checks on the time series plots, and decide if and how to make +corrections. For tips on the QC process, see the ‘QC tips’ +subtab.

    8. +
+

The Data Quality Control Report is organized into four main sections, +starting with an overall summary followed by summaries for each +parameter:

+
    +

  1. Data file information (siteID, date range, parameters, recording +interval, QC test thresholds reference table)

    2. +

  2. ‘Count’ tables (# measurements/day) so that you can find +inconsistencies (too few measurements (=missing data) or too many +measurements)

    3. +

  3. Results from the four ‘flag’ tests (gross, spike, rate of change +and flat line) - # of entries marked as pass (P), suspect (S), fail (F), +missing data (X) or not available (NA)

    4. +

  4. Time series plots for each individual parameter and two sets of +combined parameters (water and air temperature; water temperature and +water level). If the input file includes discrete/in situ values , which +are taken during site visits for accuracy checks, the discrete values +are plotted with the sensor data.

    5. +
+

The CSV file contains the time series data from the sensor plus +additional data columns with flags for each test and parameter (e.g., +Flag.Spike.Water.Temp.C, Flag.Flat.Water.Temp.C), comment fields for +each parameter (e.g., Comment.MOD.Water.Temp.C) so that the user can +document rationale for any changes and a duplicate set of the original +‘raw’ data values (e.g., RAW.Water.Temp.C). The ‘raw’ values are +included so that the user can maintain documentation of changes that are +made and go back at a later time if desired to change how they handled a +correction.

+

Currently there are no automated corrections programmed into the QC +function. The user must make the changes manually. For tips on the QC +process, see the ‘QC tips’ subtab.

+
+
+

Aggregate

+

Shiny app instructions

+
    +

  1. Click the ‘Browse’ button and select the input files from the +Data2_QC folder that you’d like to combine together. The status bar will +tell you when the upload is complete.

    2. +

  2. Select ‘Aggregate QC’d data’ from the ‘Choose Operation to +perform’ drop-down menu

    3. +

  3. A ‘Run operation’ button will appear. Click this button. A status +bar will appear in the lower right corner.

    4. +

  4. After the function is done running, a ‘Download’ button will +appear. Click this button. Save the zipped file in the Data3_Aggregated +folder.

    5. +

  5. Save the zipped file in the Data3_Aggregated folder.

    6. +

  6. Open and review the aggregated reports. As with the QC output, +there are two types of outputs: a Data Quality Control Report that comes +as either a Word document or Hypertext Markup Language (HTML) (the user +specifies the desired format); and a comma-delimited (.csv) spreadsheet. +Click here to download an example of +an Aggregate output.

    7. +
+
+
+

Summary statistics

+

Shiny app instructions

+
    +

  1. Click the ‘Browse’ button and select the desired input file from +the Data3_Aggregated folder. The status bar will tell you when the +upload is complete.

    2. +

  2. Select ‘Summary statistics’ from the ‘Choose Operation to +perform’ drop-down menu

    3. +

  3. A ‘Run operation’ button will appear. Click this button. A status +bar will appear in the lower right corner.

    4. +

  4. After the function is done running, a ‘Download’ button will +appear. Click this button.

    5. +

  5. Save the zipped file in the Data4_Stats folder.

    6. +

  6. Open and review the summary statistics reports. There should be +two sets of CSV files and a PDF with time series plots for each +parameter. One of the CSVs has daily means and the other has a wider +range of summary statistics (e.g., min, median, max, quartiles). Click +here to download an example of a +Summary Stats output.

    7. +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5a_Troubleshooting.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5a_Troubleshooting.html new file mode 100644 index 00000000..4d49bc28 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5a_Troubleshooting.html @@ -0,0 +1,14 @@ + + + +
+

Troubleshooting

+If there are any issues with the Shiny app please log an issue on +xxxxxx +== $0 + +Exit EPA’s website + +

+or contact the developers ( and ).

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5b_Console.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5b_Console.html new file mode 100644 index 00000000..c7be95f4 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5b_Console.html @@ -0,0 +1,8 @@ + + + +
+

R console output

+

This panel shows messages output by the QC, aggregating, summarizing, +and USGS data retrieval processes.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5b_MainFunc.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5b_MainFunc.html new file mode 100644 index 00000000..7c511f08 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5b_MainFunc.html @@ -0,0 +1,14 @@ + + + +
+

Check input file before running the QC function

+

After uploading the input file(s), check the summary table to see +what parameters are in the uploaded files. Ensure that all the desired +parameters are present before running the QC function.

+

Note: sometimes people have a mix of correct and incorrect column +headings in the input file. When this happens, R does not give you an +error message. It only carries forward the parameters that have the +correct column headings and leaves the others blank. That is why +checking this summary table is important.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5c_Contact.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5c_Contact.html new file mode 100644 index 00000000..919d2f5d --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5c_Contact.html @@ -0,0 +1,16 @@ + + + +
+

Contact Information

+This is an open-source, collaborative effort. If you encounter problems +with the Shiny app and/or have suggestions on ways to further improve +it, please contact Erik W. Leppo from Tetra Tech via email () or via a post on the +ContDataQC +GitHub Discussions +page == $0 + +Exit EPA’s website + +

.

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5c_MainFunc.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5c_MainFunc.html new file mode 100644 index 00000000..692cd43a --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_5c_MainFunc.html @@ -0,0 +1,149 @@ + + + +
+

QC Tips

+

Below are instructional materials and links to resources we have +compiled as we continue to gain experience QCing continuous data. To +date, we have worked primarily with thermal and hydrologic data, and +more recently, dissolved oxygen data from lakes. The data are typically +downloaded 2-4 times a year. We have not worked with ‘real-time’ +data.

+

If you have any questions, feedback on ways to improve this process +or suggestions on additional materials that you feel would be helpful to +share, please contact Jen Stamp ().

+
+

Data corrections

+

If you are processing data for the RMNs, we are currently +recommending the following approach

+
    +

  • If you are certain a data point is erroneous, delete the +measurement and flag as ‘F’. If you are not sure, flag the data as ‘S’ +and do not delete. Let the people using the data decide whether to +remove questionable measurements from their analysis.

    • +

  • Document that you checked each point flagged as ‘F’ and ‘S’ by +adding a data qualifier to the Comment column. Click here to download the list of +data qualifiers currently being used by RMN partners.

    • +

  • Leave missing data cells blank.

    • +
+
+
+

Workflow

+

Click here for +recommendations on a workflow for reviewing the QC reports.

+

If you have a backlog of data files from multiple deployments and are +wondering how to most efficiently QC the files, here is a recommended +workflow.

+

Click here for a site visit +checklist to help ensure that you do not forget to document information +that is important for the data QC process.

+
+
+

Sensor configuration

+

Some people have had problems with air and water sensors being out of +sync ( e.g., one records at 11:00 and the other records at 11:07). If +you are deploying air and water sensors at a site, make sure they are +recording at the same time. This will make data processing faster and +easier. If you are using Onset HOBO sensors, click here for tips on configuring +them in a way that ensures they will record at the same time.

+

Another issue that sometimes occurs is data overlap (where more than +one file has measurements covering part of the same time periods). If +you are using Onset HOBO sensors, click here for procedures to follow +during data download to clear sensor memory and avoid overlapping +data.

+
+
+

Accuracy checks

+

Accuracy checks are comparisons of discrete or in situ measurements +taken in the lab and/or in the field with sensor measurements from the +closest date/time. The sensor measurement should be within the accuracy +quoted by the manufacturer ( e.g., ±0.2°C if you are using the Onset +HOBO proV2 sensor). Accuracy checks are used to validate your data, and +in some cases, correct for sensor drift. Click here to download an example of +an accuracy check worksheet being kept by an RMN partner.

+
+
+

Visual checks of time series plots

+

Visual checks of time series plots are an important part of the QC +process. Click here to see a +compilation of patterns to watch for when interpreting the plots. They +include ice cover, beaver activity, leaf packs, dewatering, and dead +batteries. We will continue to add slides to this as we receive +materials.

+
+
+

Checking sensor data against other data sources

+

This is optional but encouraged if time permits. Click here for instructional materials from +David Smith (Kentucky Division of Water) on how he has been downloading +data from nearby weather stations and USGS gages, as well as modeled air +temperature and precipitation data from Daymet ( +https://daymet.ornl.gov/getdata +), and comparing those data to sensor measurements as part of his QC +process.

+
+
+

Additional resources

+

Logger Processing App

+

Developed by Tim Martin at the Minnesota Department of Natural +Resources for processing, organizing, and vetting continuous +environmental logger data. It has the same QC flag tests as ContDataQC +plus more, including an interactive plot that allows you to flag or +unflag data points.

+

https://tetratech-wtr-wne.shinyapps.io/logger_processing/

+

driftR, an R package that corrects drift in water quality +data

+

It implements either one- or two-point variable data corrections +based on the number of standards used to calibrate the sensor of +interest, then linearly interpolates the correction over the period of +interest.

+

https://shaughnessyar.github.io/driftR/

+

Shaughnessy, A.R., Prener, C.G. and E.A. Hasenmueller. 2019. An R +package for correcting continuous water quality monitoring data for +drift. Environmental Monitoring and Assessment 191, 445. +https://doi.org/10.1007/s10661-019-7586-x

+

R package for detecting outliers

+

https://cran.r-project.org/web/packages/outliers/outliers.pdf

+

Campulova, M., Campula, R. and J. Holesovsky. 2022. An R package for +identification of outliers in environmental time series data. +Environmental Modelling & Software, Volume 155. +https://www.sciencedirect.com/science/article/abs/pii/S1364815222001414

+
+
+

Wish list items

+

Items that we want to add in the future when resources permit

+
    +

  • ‘Transition’ check

    • +

  • We’re finding most errors concentrated around the times when the +sensors are being taken out of position for data download. If you’re +only QCing individual files after each download, you’re not evaluating +the transition between data from the previous file and the new file. +This check would bring in data from 1-2 days prior to the start time of +the file being evaluated and alert users to data points that need to be +trimmed.

    • +

  • Automatic adjustments to gross limit QC test thresholds based on +season and depth

    • +

  • Right now the gross limit QC test is pretty limited because it +doesn’t capture seasonal differences without the user creating multiple +customized configuration files (and then the user would need to break up +the input files for each time period). We’d like to figure out a way to +have the R code automatically adjust the upper and lower gross limit +thresholds based on seasonal considerations, and for lakes, for it to +also make adjustments to gross limit thresholds based on depth.

    • +

  • Automated accuracy check

    • +

  • Users would upload in-situ files and the R code would +automatically match the in-situ measurements with the closest sensor +measurement, calculate the difference between the two measurements and +report whether the sensor is meeting accuracy specs.

    • +

  • Drift correction

    • +

  • We’d add a feature that automates drift corrections.

    • +

  • Weather station check

    • +

  • Users would upload weather station data and the R code would +match the measurements with the sensor data, generate time series plots +in which the measurements are overlaid on one another, and generate some +basic statistical measures to compare the two sets of data.

    • +

  • Daily range check (max-min)

    • +

  • This QC check is used by the USFS NorWeST temperature sensor +network. It would be added in as a new function.

    • +
+
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_6_USGSgage.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_6_USGSgage.html new file mode 100644 index 00000000..e02deaf4 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_6_USGSgage.html @@ -0,0 +1,23 @@ + + + +
+

Instructions

+

You can download data from USGS gages on this tab.

+

You can find gage locations and IDs using the +NWIS +Mapper.

+
    +

  1. Enter as many USGS station IDs as you like separated by commas +and spaces (e.g., 01187300, 01493000, 01639500).

    2. +

  2. Enter a starting and ending date for which data will be retrieved +for each station; the same date range will be used for every +station.

    3. +

  3. Click the ‘Retrieve USGS data’ button. A progress bar will appear +in the bottom-right of the tab. It will advance as each file is +completed. Thus, if you select three stations, it will wait at 0%, jump +to 33%, jump to 66%, and then jump to 100%.

    4. +

  4. After data retrieval completes, a download button will appear. +Click the button to download a zip file of the records.

    5. +
+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_8_Console.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_8_Console.html new file mode 100644 index 00000000..4530ac62 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_8_Console.html @@ -0,0 +1,10 @@ + + + +
+

R console output

+

This panel shows messages output by the QC, aggregating, summarizing, +and USGS data retrieval processes. If there are any errors when the tool +runs, please copy the messages and send them and your input files to +Erik Leppo ().

+
diff --git a/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_9_Status.html b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_9_Status.html new file mode 100644 index 00000000..5b13e6a7 --- /dev/null +++ b/inst/shiny-examples/ContDataQC/www/RMD_HTML/App_9_Status.html @@ -0,0 +1,149 @@ + + + +
+

Status

+
+

Who to contact if you have problems or comments

+

This is an open-source, collaborative effort and we welcome your +feedback. If you encounter problems with the Shiny app and/or have +suggestions on ways to further improve it, please contact Erik W. Leppo +from Tetra Tech via email () or via a post on the +ContDataQC GitHub page +(https://github.com/leppott/ContDataQC/discussions Exit EPA +Website ).

+
+
+

Experience to date

+

We have been working on and off on the ContDataQC R package and Shiny +app since 2017. Development of the ContDataQC R tools has been funded +primarily by the United States Environmental Protection Agency (EPA) +Office of Research and Development (ORD) as part of a larger project to +establish Regional Monitoring Networks (RMNs) for freshwater streams and +inland lakes. The lead EPA scientist on the RMN work is Britta Bierwagen +(). Shane Bowe from the Red +Lake Band of Chippewa Indians has also funded parts of its +development.

+

So far ContDataQC has only been used on continuous data that is being +manually downloaded periodically (generally one to three times a year). +It has not been used on ‘real-time’ continuous data. It was originally +developed for sensors deployed in streams at single depths but is now +being used on lake sensor array data as well, where sensors are being +deployed at multiple, fixed depths at the same site. To date, we have +worked primarily with thermal and hydrologic data (the primary variables +being collected at RMN sites), but users have expressed interest in +using it on other parameters as well. The Shiny app is currently set up +to run dissolved oxygen, conductivity, pH, turbidity and chlorophyll-a +data but the default QC test thresholds for those parameters will need +to be carefully checked and refined.

+
+
+

Plans for 2021-2022

+

In fall of 2020, a major advancement occurred when we moved the Shiny +app to the Tetra Tech Shiny server. This made the app much more +accessible to RMN partners and to other outside users as well. EPA is +planning to host the ContDataQC Shiny app on an EPA Shiny server as +well. We expect this to happen over the coming year.

+

We aren’t planning to make any major changes to the main functions in +ContDataQC but will continue to work on the items below as resources +permit. We’ll also be shifting our focus more to data summary and +visualization, for which we are developing separate R packages and Shiny +apps (for more information, see bottom of page).

+

Here are the ContDataQC items we’re currently working on +-

+
    +

  • Improving the instructional materials

    • +

  • Refining the QC test thresholds, not just for temperature and +hydrology (the primary variables being collected at RMN sites), but also +for dissolved oxygen, conductivity, pH and turbidity.

    • +

  • Gaining a better understanding of the most common QC issues being +encountered and how to best detect and address (or potentially prevent) +those issues.

    • +

  • Improving guidance on addressing errors and flagged data points +and documenting corrections and metadata.

    • +
+

ContDataQC ‘wish list’ items (that we lack funding to address +but will tackle if resources permit) -

+
    +

  • Automated accuracy check

    +
      +
    • Users would upload in-situ files and the R code would automatically +match the in-situ measurements with the closest sensor measurement, +calculate the difference between the two measurements and report whether +the sensor is meeting accuracy specs.
      • +
    • +

  • Weather station check

    +
      +
    • Users would upload weather station data and the R code would match +the measurements with the sensor data, generate time series plots in +which the measurements are overlaid on one another, and generate some +basic statistical measures to compare the two sets of data.
      • +
    • +

  • Daily range check (max-min)

    +
      +
    • This QC check is used by the USFS NorWeST temperature sensor +network. It would be added in as a new function.
      • +
    • +

  • ‘Transition’ check

    +
      +
    • We’re finding most errors concentrated around the times when the +sensors are being taken out of position for data download. If you’re +only QCing individual files after each download, you’re not evaluating +the transition between data from the previous file and the new file. +This check would bring in data from 1-2 days prior to the start time of +the file being evaluated and alert users to data points that need to be +trimmed.
      • +
    • +

  • Automatic adjustments to gross limit QC test thresholds based on +season and depth

    +
      +
    • Right now the gross limit QC test is pretty limited because it +doesn’t capture seasonal differences without the user creating multiple +customized configuration files (and then the user would need to break up +the input files for each time period). We’d like to figure out a way to +have the R code automatically adjust the upper and lower gross limit +thresholds based on seasonal considerations, and for lakes, for it to +also make adjustments to gross limit thresholds based on depth.
      • +
    • +

  • Drift correction

    +
      +
    • We’d add a feature that automates drift corrections.
      • +
    • +
+

Other R packages and Shiny apps that may be of +interest

+

Tim Martin from Minnesota DNR () has been a great collaborator +and has developed a QC app that includes the ContData QC flag tests as +well as some other features (including an interactive plot in which data +points are color-coded based on flags and the user can add or remove +flags). The app is currently being hosted on the Tetra Tech Shiny server +- +https://tetratech-wtr-wne.shinyapps.io/logger_processing/ Exit EPA +Website If you use the app, Tim welcomes your +feedback!

+

For summary and visualization of continuous lake data, we’re actively +working on LakeMonitoR - R package - +https://leppott.github.io/LakeMonitoR/ Exit EPA +Website Shiny app - +https://nalms.shinyapps.io/LakeMonitoR/ Exit EPA +Website

+

This work is being funded by Shane Bowe from the Red Lake Band of +Chippewa Indians (via a BIA grant) and EPA ORD.

+

Over the coming year, we’ll also be working on new tools for +summarizing and visualizing the stream RMN data (thermal, hydrologic and +biological data).

+

There are other existing software programs for working with +continuous data. One example is the open source Python-based tool by +Horsburgh et al. (2015), which provides a data quality control work +flow, allows for flagging of data based on visual inspection, can apply +drift corrections and interpolate missing values (if desired) and saves +both original and edited data for documentation. Other existing software +options include a sensorQC R package by USGS +(https://github.com/USGS-R/sensorQC Exit EPA +Website ) and rLakeAnalyzer (Read et al. 2011, +Winslow et al. 2018) +(https://CRAN.R-project.org/package=rLakeAnalyzer Exit EPA +Website ), which have statistical outlier detection +tools.

+
+
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