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thesis_data_traveltime.R
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thesis_data_traveltime.R
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# Helsinki Region Travel Time comparison application
# Helsinki Region Travel Time Matrix 2018 <--> My thesis survey results
# 15.10.2020
# Sampo Vesanen
# Notes:
# - A late addition to the application is making sure all values have two
# decimal places. This is all good for the tooltip values and the values
# printed on the map proper. However, the legend scale is a bit tricky.
# Essential cut() function used in CreateEqualColumn() reduces decimal places
# and I don't know how to prevent this. As a consequence, I have to add the
# decimal places back to the legend with regex, resulting in situations where
# the largest value shown on map can be larger than the top value in the
# color scale. I don't think there are other caveats.
# - .loadingdiv is bound by .col-sm-9, the mainPanel() content. For this
# reason, on first load the loading appears now on top of the screen. As
# the map is loaded, loading text moves to th center of the screen. I could
# not find a solution to this for the time being. This only appeared after
# update to R 4.0.0 and updating several libraries. Before, girafeOutput()
# did not need width and height, and the loading div was placed in the center
# from the start.
# - Of minor importance: select#postal_label_choice does not scale with window
# size
# - Of minor importance: The JavaScript additions to dropdown menus are quite
# shoddy. User can see that they are added each time menu opens. Also, fill
# icons in "color scheme" menu do not appear on the first opening. In fact,
# they only appear after first opening "visualise data" menu once.
# - There is a rare issue where the application complains about non-unique
# breaks. This occurs inside CreateEqualColumn(), where a column presumably
# does not have enough unique values to create the amount of symbology classes
# the app requests. To counter this, reactive object checkSliderInput() is
# created. This handles the error, but not before the map view disappears
# and the error message is shown in top left corner. For now, I do not intend
# to fix this outcome which lasts on the user's screen maybe 15 seconds. The
# only erroneous combination I've discovered so far is 02860 Siikajärvi,
# ttm18_all_pct and 9-11 symbology classes.
#### 1 Initialise --------------------------------------------------------------
rm(list = ls())
gc()
library(shiny)
library(shinythemes)
library(ggplot2)
library(dplyr)
library(htmltools)
library(rgdal)
library(shinyjs)
library(ggiraph)
library(data.table)
library(rgeos)
library(sf)
library(shinyWidgets)
library(ggsn)
library(fst)
library(ggnewscale)
library(ggspatial)
# App version
app_v <- "0071.postal (15.10.2020)"
# Working directory
wd <- "C:/Sampon/Maantiede/Master of the Universe/python"
# Data directories
munspath <- file.path(wd, "thesis_data_r/hcr_muns.shp")
othermunspath <- file.path(wd, "thesis_data_r/other_muns.shp")
subdivpath <- file.path(wd, "thesis_data_r/PKS_suuralue.kml")
waterpath <- file.path(wd, "thesis_data_r/ua2012_water.shp")
roadpath <- file.path(wd, "thesis_data_r/mainroads.shp")
# Thesis' processed data
recordspath <- file.path(wd, "thesis_data_r/records_for_r.csv")
postal_path <- file.path(wd, "thesis_data_r/postal_for_r.csv")
gridzipcodes <- file.path(wd, "thesis_data_r/grid_for_r.csv")
fst_postal_fp <- file.path(wd, "TTM18_postal")
# Comparison app additional functionality
csspath <- file.path(wd, "thesis_data_traveltime_style.css")
jspath <- file.path(wd, "thesis_data_traveltime_script.js")
tooltip_path <- file.path(wd, "thesis_data_traveltime_tooltip.html")
info_path <- file.path(wd, "thesis_data_traveltime_info.html")
# Source functions and postal code variables
source(file.path(wd, "thesis_data_traveltime_funcs.R"))
# Convert Helsinki Region Travel Time Matrix 2018 into fst and then to dataset
# aggregated to postal code area resolution. The user of this script is not
# needed to run this script as the end-product of this script is provided in
# the GitHub repository.
#source(file.path(wd, "thesis_data_traveltime_conv.R"))
#### 2 Import data layers ------------------------------------------------------
#### 2.1 Walking center polygon ------------------------------------------------
# use this CRS information throughout the app
app_crs <- sp::CRS("+init=epsg:3067")
# TTM18 Helsinki walking center polygon. Use fortified version of the walking
# center for visualisation
walkingHki <-
data.frame(
long = c(387678.024778, 387891.53396, 383453.380944, 383239.871737, 387678.024778),
lat = c(6675360.99039, 6670403.35286, 6670212.21613, 6675169.85373, 6675360.99039)) %>%
sf::st_as_sf(coords = c("long", "lat"), crs = app_crs) %>%
dplyr::summarise(geometry = sf::st_combine(geometry)) %>%
sf::st_cast("POLYGON")
# Fortify
walk_f <-
sf::st_coordinates(walkingHki)[, 1:2] %>%
sp::Polygon(.) %>%
ggplot2::fortify(.) %>%
dplyr::mutate(label = "walk")
#### 2.2 Municipality borders --------------------------------------------------
# Get municipality borders. Fortify SP DataFrame for ggplot2. Remove unnecessary
# columns to save memory.
# Shapefile data is Regional population density 2012, Statistics Finland.
# http://urn.fi/urn:nbn:fi:csc-kata00001000000000000226.
muns_f <-
rgdal::readOGR(munspath, stringsAsFactors = TRUE) %>%
sp::spTransform(., app_crs) %>%
{dplyr::left_join(ggplot2::fortify(.),
as.data.frame(.) %>%
dplyr::mutate(id = as.character(dplyr::row_number() - 1)))}
# Bordering municipalities. Does not require any of the shapefile attribute data.
othermuns_f <-
rgdal::readOGR(othermunspath, stringsAsFactors = TRUE) %>%
sp::spTransform(., app_crs) %>%
ggplot2::fortify(.)
#### 2.3 Postal code areas -----------------------------------------------------
postal <-
read.csv(file = postal_path,
header = TRUE,
sep = ",",
colClasses = c(zipcode = "factor", kunta = "factor",
geometry = "character"),
stringsAsFactors = TRUE) %>%
dplyr::select(c(2, 3, 6, 108))
# "postal" geometries are in well-known text format. Some processing is needed
# to utilise these polygons in R. readWKT() uses rgeos.
geometries <- lapply(postal[, "geometry"], "readWKT", p4s = app_crs)
sp_tmp_ID <- mapply(sp::spChFIDs, geometries, as.character(postal[, 1]))
row.names(postal) <- postal[, 1]
# Preserve SpatialPolygons version of "postal" for the spatial join
postal <- sp::SpatialPolygonsDataFrame(
sp::SpatialPolygons(unlist(lapply(sp_tmp_ID, function(x) x@polygons)),
proj4string = app_crs), data = postal)
# Fortify and preserve Polygon attribute data
postal_f <-
postal %>%
{dplyr::left_join(ggplot2::fortify(.),
as.data.frame(.) %>%
dplyr::mutate(id = as.character(zipcode)),
by = "id")} %>%
dplyr::select(-geometry)
# This dataframe, produced in Python, helps find the zipcodes for each YKR_ID.
# 99999 is outside of research area.
ykrid_zipcodes <-
read.csv(file = gridzipcodes,
header = TRUE,
sep = ",",
colClasses = c(YKR_ID = "integer", zipcode = "character"),
stringsAsFactors = TRUE) %>%
dplyr::select(-X)
### 2.4 Subdivisions -----------------------------------------------------------
subdiv_f <-
rgdal::readOGR(subdivpath,
use_iconv = TRUE,
encoding = "UTF-8",
stringsAsFactors = TRUE) %>%
sp::spTransform(., app_crs) %>%
{dplyr::left_join(ggplot2::fortify(.),
as.data.frame(.) %>%
dplyr::mutate(id = as.character(dplyr::row_number() - 1)))} %>%
dplyr::select(-Description) %>%
dplyr::mutate(Name = factor(Name, labels =
c("Vantaa Aviapolis", "Helsinki Southern",
"Vantaa Hakunila", "Helsinki Eastern",
"Helsinki Southeastern", "Kauniainen",
"Helsinki Central", "Vantaa Kivistö",
"Helsinki Northeastern", "Vantaa Koivukylä",
"Vantaa Korso", "Helsinki Western",
"Vantaa Myyrmäki", "Helsinki Northern",
"Espoo Pohjois-Espoo", "Espoo Suur-Espoonlahti",
"Espoo Suur-Kauklahti", "Espoo Suur-Leppävaara",
"Espoo Suur-Matinkylä", "Espoo Suur-Tapiola",
"Vantaa Tikkurila", "Espoo Vanha-Espoo",
"Helsinki Östersundom"))) %>%
dplyr::mutate(Name = factor(Name, levels = sort(levels(Name))))
# Reorder dataframe by subdivision
subdiv_f <- subdiv_f[order(subdiv_f$Name), ]
# 2.5 Thesis survey data -------------------------------------------------------
# Import Python processed thesis survey data. This data is later (chapter 4.2)
# joined with the currently fetched YKR ID Travel Time Matrix 2018 data.
thesisdata <-
read.csv(file = recordspath,
header = TRUE,
sep = ",",
colClasses = c(zipcode = "factor"),
stringsAsFactors = TRUE) %>%
dplyr::select(zipcode, parktime, walktime, timeofday) %>%
dplyr::filter(parktime <= 59,
walktime <= 59) %>%
dplyr::group_by(zipcode) %>%
# Select timings for thesis_ columns
dplyr::mutate(r_sfp = case_when(timeofday == 1 ~ parktime, TRUE ~ NA_integer_),
m_sfp = case_when(timeofday == 2 ~ parktime, TRUE ~ NA_integer_),
all_sfp = parktime,
r_wtd = case_when(timeofday == 1 ~ walktime, TRUE ~ NA_integer_),
m_wtd = case_when(timeofday == 2 ~ walktime, TRUE ~ NA_integer_),
all_wtd = walktime) %>%
# Calculate summaries by zipcode, then round to two decimals
dplyr::summarise(thesis_r_sfp = mean(r_sfp, na.rm = TRUE),
thesis_m_sfp = mean(m_sfp, na.rm = TRUE),
thesis_all_sfp = mean(all_sfp, na.rm = TRUE),
thesis_r_wtd = mean(r_wtd, na.rm = TRUE),
thesis_m_wtd = mean(m_wtd, na.rm = TRUE),
thesis_all_wtd = mean(all_wtd, na.rm = TRUE),
vals_in_zip = length(zipcode)) %>%
dplyr::mutate_if(is.numeric, round, 2)
# NaNs are introduced in calculation of mean. Change to NA. Do not apply changes
# to column zipcode
thesisdata[, -1] <- data.frame(
sapply(thesisdata[, -1], function(x) ifelse(is.nan(x), NA, x)))
#### 2.6 Water and roads -------------------------------------------------------
# Main roads
roads_f <-
rgdal::readOGR(roadpath, stringsAsFactors = TRUE) %>%
sp::spTransform(., app_crs) %>%
ggplot2::fortify(.)
# UA2012 inland water. Use ggspatial to draw this, no fortify needed. ggplot2
# would draw polygon holes (1 island in Espoo) wrong.
water <- rgdal::readOGR(waterpath, stringsAsFactors = TRUE)
#### 2.7 Label comparison plot features ----------------------------------------
# Create labels for zipcodes
zipcode_lbl <- GetCentroids(postal_f, "zipcode", "zipcode")
muns_lbl <- GetCentroids(muns_f, "nimi", "nimi")
subdiv_lbl <- GetCentroids(subdiv_f, "Name", "Name")
# Finetune locations for certain labels in zipcode_lbl. GetCentroids saves the
# second parameter as rownames. We can use that to reliably find correct rows
# to finetune.
zipcode_lbl["00250", 1] %+=% 500 # Taka-Töölö
zipcode_lbl["00980", 1] %-=% 850 # Etelä-Vuosaari
zipcode_lbl["01640", 2] %-=% 300 # Hämevaara
zipcode_lbl["01730", 2] %-=% 500 # Vantaanpuisto
zipcode_lbl["02380", 1] %+=% 2400 # Suvisaaristo
zipcode_lbl["02820", 1] %+=% 1000 # Nupuri-Nuuksio
# Remove municipality names from subdivision annotations
subdiv_lbl$label <- gsub(".* ", "", unique(subdiv_f$Name))
rownames(subdiv_lbl) <- gsub(".* ", "", rownames(subdiv_lbl))
# Manually move labels several subdivision labels to better positions.
subdiv_lbl["Suur-Espoonlahti", "lat"] <- subdiv_lbl["Southern", "lat"]
subdiv_lbl["Suur-Espoonlahti", "long"] <- subdiv_lbl["Pohjois-Espoo", "long"]
subdiv_lbl["Southeastern", "lat"] <- subdiv_lbl["Southern", "lat"] + 1500
subdiv_lbl["Southeastern", "long"] <- subdiv_lbl["Korso", "long"]
subdiv_lbl["Southern", "lat"] %+=% 3000
subdiv_lbl["Östersundom", "lat"] %-=% 500
subdiv_lbl["Östersundom", "long"] %-=% 400
subdiv_lbl["Suur-Matinkylä", "lat"] %-=% 500
# In this named vector the first part is the name of the new, classified
# column. Second part is the original column name where the classification
# was calculated from.
vis_cols <- c("ttm18_r_avg" = "ttm_r_avg",
"ttm18_m_avg" = "ttm_m_avg",
"ttm18_all_avg" = "ttm_all_avg",
"ttm18_r_drivetime" = "ttm_r_drivetime",
"ttm18_m_drivetime" = "ttm_m_drivetime",
"ttm18_all_drivetime" = "ttm_all_drivetime",
"ttm18_r_pct" = "ttm_r_pct",
"ttm18_m_pct" = "ttm_m_pct",
"ttm18_all_pct" = "ttm_all_pct",
"msc_r_sfp" = "thesis_r_sfp",
"msc_m_sfp" = "thesis_m_sfp",
"msc_all_sfp" = "thesis_all_sfp",
"msc_r_wtd" = "thesis_r_wtd",
"msc_m_wtd" = "thesis_m_wtd",
"msc_all_wtd" = "thesis_all_wtd",
"msc_r_drivetime" = "thesis_r_drivetime",
"msc_m_drivetime" = "thesis_m_drivetime",
"msc_all_drivetime" = "thesis_all_drivetime",
"msc_r_pct" = "thesis_r_pct",
"msc_m_pct" = "thesis_m_pct",
"msc_all_pct" = "thesis_all_pct",
"compare_r_sfp" = "comp_r_sfp",
"compare_m_sfp" = "comp_m_sfp",
"compare_all_sfp" = "comp_all_sfp",
"compare_r_wtd" = "comp_r_wtd",
"compare_m_wtd" = "comp_m_wtd",
"compare_all_wtd" = "comp_all_wtd",
"compare_r_drivetime" = "comp_r_drivetime",
"compare_m_drivetime" = "comp_m_drivetime",
"compare_all_drivetime" = "comp_all_drivetime",
"compare_r_pct" = "comp_r_pct",
"compare_m_pct" = "comp_m_pct",
"compare_all_pct" = "comp_all_pct")
#### 3 fst operations ----------------------------------------------------------
#### 3.1 Prepare reactive fetch of TTM18 data ----------------------------------
# NB! The execution of this code will fail at this point if TTM18 data is not
# converted to fst. Please make sure you have a local copy of the fst format
# dataset of TTM18 aggregated to postal code areas.
# Get all of the fst-format TTM18 aggregated to postal code area level
all_postal_fst <- list.files(path = fst_postal_fp,
pattern = ".fst$",
recursive = TRUE,
full.names = TRUE)
### 3.2 Prepare values for locked up class breaks ------------------------------
# In this section prepare all values for the case where map symbology needs to
# remain the same with different settings.
# Get all possible datasets from fst files, then group by destination postal
# code area, and get only the first row for each zipcode
for (i in 1:length(all_postal_fst)) {
if (i == 1) {
# As these fst files are full of multiples, select the first row of each fst
# destination postal code areas
bigdf <-
Reactive_TTM_fetch(all_postal_fst[i], thesisdata, postal_f) %>%
dplyr::group_by(id) %>%
dplyr::filter(row_number() == 1)
} else {
thisRound <-
Reactive_TTM_fetch(all_postal_fst[i], thesisdata, postal_f) %>%
dplyr::group_by(id) %>%
dplyr::filter(row_number() == 1)
bigdf <- rbind(bigdf, thisRound)
}
}
# Get value ranges of the column groups
ttm_avg_range <-
range(c(bigdf$ttm_r_avg, bigdf$ttm_m_avg, bigdf$ttm_all_avg),
na.rm = TRUE)
ttm_drivetime_range <-
range(c(bigdf$ttm_r_drivetime, bigdf$ttm_m_drivetime, bigdf$ttm_all_drivetime),
na.rm = TRUE)
ttm_pct_range <-
range(c(bigdf$ttm_r_pct, bigdf$ttm_m_pct, bigdf$ttm_all_pct),
na.rm = TRUE)
thesis_sfp_range <-
range(c(bigdf$thesis_r_sfp, bigdf$thesis_m_sfp, bigdf$thesis_all_sfp),
na.rm = TRUE)
thesis_wtd_range <-
range(c(bigdf$thesis_r_wtd, bigdf$thesis_m_wtd, bigdf$thesis_all_wtd),
na.rm = TRUE)
thesis_drivetime_range <-
range(c(bigdf$thesis_r_drivetime, bigdf$thesis_m_drivetime, bigdf$thesis_all_drivetime),
na.rm = TRUE)
thesis_pct_range <-
range(c(bigdf$thesis_r_pct, bigdf$thesis_m_pct, bigdf$thesis_all_pct),
na.rm = TRUE)
comp_sfp_range <-
range(c(bigdf$comp_r_sfp, bigdf$comp_m_sfp, bigdf$comp_all_sfp),
na.rm = TRUE)
comp_wtd_range <-
range(c(bigdf$comp_r_wtd, bigdf$comp_m_wtd, bigdf$comp_all_wtd),
na.rm = TRUE)
comp_drivetime_range <-
range(c(bigdf$comp_r_drivetime, bigdf$comp_m_drivetime, bigdf$comp_all_drivetime),
na.rm = TRUE)
comp_pct_range <-
range(c(bigdf$comp_r_pct, bigdf$comp_m_pct, bigdf$comp_all_pct),
na.rm = TRUE)
# Create sequences from column ranges. These will be used to create the equal
# interval classes.
ttm_avg_vals <-
seq(from = ttm_avg_range[1],
to = ttm_avg_range[2],
length.out = nrow(postal_f))
ttm_drivetime_vals <-
seq(from = ttm_drivetime_range[1],
to = ttm_drivetime_range[2],
length.out = nrow(postal_f))
ttm_pct_vals <-
seq(from = ttm_pct_range[1],
to = ttm_pct_range[2],
length.out = nrow(postal_f))
thesis_sfp_vals <-
seq(from = thesis_sfp_range[1],
to = thesis_sfp_range[2],
length.out = nrow(postal_f))
thesis_wtd_vals <-
seq(from = thesis_wtd_range[1],
to = thesis_wtd_range[2],
length.out = nrow(postal_f))
thesis_drivetime_vals <-
seq(from = thesis_drivetime_range[1],
to = thesis_drivetime_range[2],
length.out = nrow(postal_f))
thesis_pct_vals <-
seq(from = thesis_pct_range[1],
to = thesis_pct_range[2],
length.out = nrow(postal_f))
comp_sfp_vals <-
seq(from = comp_sfp_range[1],
to = comp_sfp_range[2],
length.out = nrow(postal_f))
comp_wtd_vals <-
seq(from = comp_wtd_range[1],
to = comp_wtd_range[2],
length.out = nrow(postal_f))
comp_drivetime_vals <-
seq(from = comp_drivetime_range[1],
to = comp_drivetime_range[2],
length.out = nrow(postal_f))
comp_pct_vals <-
seq(from = comp_pct_range[1],
to = comp_pct_range[2],
length.out = nrow(postal_f))
#### 4 Travel Time Comparison ShinyApp -----------------------------------------
server <- function(input, output, session) {
#### 4.1 Reactive elements ---------------------------------------------------
# Launch tooltip legend jQuery UI dialog
shiny::observeEvent(input$info_dialog_btn, {
# The div id='abbr-info' is loaded in "6.4 ShinyApp header", the div itself
# is the separate html file indicated in variable "info_path". Dialog
# window properties are located in .js.
# NB! This contains a brutish solution to the dialog content jumping straight
# to bottom on open. Wait 310 ms (tested to be approx. lowest duration for
# this to work) after opening dialog, then jQuery scrollTop()
shinyjs::runjs("$('#abbr-info').dialog('open');
setTimeout(function() {$('#abbr-info').scrollTop(0);}, 310);")
})
# This reactive object is created to enable listening of multiple inputs user
# can trigger. Triggering an input may require lowering the classes breaks
# inside the function CreateEqualColumn(), otherwise we will get an error
# ""
checkSliderInput <- reactive({
list(input$classIntervals_n, input$calcZip, input$fill_column)
})
shiny::observeEvent(checkSliderInput(), {
# Test classInt::classIntervals() and change classIntervals_n SliderInput
# value if statement holds. Suppress warnings in the test.
inputdf <- thisTTM()
classes_test <- suppressWarnings(
classInt::classIntervals(inputdf[, vis_cols[[input$fill_column]]],
n = input$classIntervals_n,
style = "equal"))
# Object returned from classIntervals() has an attribute "nobs" which I
# use to detect cases where too large input$classIntervals_n is inputted to
# CreateEqualColumn() function.
# The minus one should prevent the breaks error from showing.
if(attributes(classes_test)$nobs - 1 < input$classIntervals_n) {
updateSliderInput(session,
"classIntervals_n",
value = attributes(classes_test)$nobs - 1)
}
})
# Validate ykr-id in the numeric field "input$calcZip"
validate_zipcode <- shiny::eventReactive(input$calcZip, {
# %then% allows only one error message at a time
shiny::validate(
shiny::need(!is.na(as.numeric(input$zipcode)), "Can't contain letters") %then%
shiny::need(nchar(input$zipcode) == 5, "Five digits pls") %then%
shiny::need(input$zipcode %in% unique(ykrid_zipcodes$zipcode),
"Not a valid postal code")
)
input$zipcode
})
# Locked class intervals, option: parameters of the same type (r-m-all trios)
# and all postal code areas.
# Get correct locked classes value range out of this reactive object
locked_class_breaks_all <- shiny::reactive({
if(input$fill_column %in% c("ttm18_r_avg", "ttm18_m_avg", "ttm18_all_avg")) {
ttm_avg_vals
} else if (input$fill_column %in% c("msc_r_sfp", "msc_m_sfp", "msc_all_sfp")) {
thesis_sfp_vals
} else if (input$fill_column %in% c("msc_r_wtd", "msc_m_wtd", "msc_all_wtd")) {
thesis_wtd_vals
} else if (input$fill_column %in% c("ttm18_r_drivetime", "ttm18_m_drivetime",
"ttm18_all_drivetime")) {
ttm_drivetime_vals
} else if (input$fill_column %in% c("msc_r_drivetime", "msc_m_drivetime",
"msc_all_drivetime")) {
thesis_drivetime_vals
} else if (input$fill_column %in% c("ttm18_r_pct", "ttm18_m_pct",
"ttm18_all_pct")) {
ttm_pct_vals
} else if (input$fill_column %in% c("msc_r_pct", "msc_m_pct",
"msc_all_pct")) {
thesis_pct_vals
} else if (input$fill_column %in% c("compare_r_sfp", "compare_m_sfp",
"compare_all_sfp")) {
comp_sfp_vals
} else if (input$fill_column %in% c("compare_r_wtd", "compare_m_wtd",
"compare_all_wtd")) {
comp_wtd_vals
} else if (input$fill_column %in% c("compare_r_drivetime", "compare_m_drivetime",
"compare_all_drivetime")) {
comp_drivetime_vals
} else if (input$fill_column %in% c("compare_r_pct", "compare_m_pct",
"compare_all_pct")) {
comp_pct_vals
}
})
# Locked class intervals, option: parameters of the same type (r-m-all trios)
locked_class_breaks_params <- shiny::reactive({
# Get only the three columns which belong to the current trio of rush hour,
# midday, and all values. use the result of columnFinder() to get correct
# column names out of named vector "vis_cols"
inputdata <- thisTTM()
currentColumns <- columnFinder(input$fill_column, names(vis_cols))
thisDf <- inputdata[, vis_cols[currentColumns]]
# Calculate the value range from smallest value to largest and use
# "theseVals" in CreateEqualColumn() function
thisRange <- range(c(thisDf[, 1], thisDf[, 2], thisDf[, 3]), na.rm = TRUE)
theseVals <- seq(from = thisRange[1],
to = thisRange[2],
length.out = nrow(postal_f))
theseVals
})
# helper_output_zip() and helper_output_symbology(): Print helpful text for
# the user
helper_output_zip <- shiny::reactive({
inputdata <- thisTTM()
thisVal <- inputdata[inputdata$zipcode == validate_zipcode(), ][1, ]
help_output <- paste(
"<p class='helper-div'>",
"<b>Current origin postal code area:</b><br>",
"<i class='icon hourglass-start'></i>",
thisVal[["zipcode"]], " ", thisVal[["nimi"]],
"</p>", sep = "")
help_output
})
helper_output_symbology <- shiny::reactive({
thisVal <- GetSymbologyHelp(input$fill_column)
help_output <- paste(
"<div class='helper-div'>",
"<b>Current symbology selection key:</b><br>",
thisVal,
"</div>", sep = "")
help_output
})
#### Reactive fetch of aggregated TTM18 data
# This is the reactively built Helsinki Region Travel Time Matrix 2018 for the
# origin id inserted by the user. User's chosen YKR_ID value is
# validate_zipcode().
thisTTM <- shiny::reactive({
# Use validate_zipcode() to find the filepath for the needed aggregated
# TTM18 fst file. Reactive_TTM_fetch() then lifts the heavy load.
postal_loc <- grepl(validate_zipcode(), all_postal_fst, fixed = TRUE)
this_fp <- all_postal_fst[postal_loc]
result <- Reactive_TTM_fetch(this_fp, thesisdata, postal_f)
result
})
# equalIntervalsColumn() calculates new class intervals when an input change is
# detected on input$fill_column or amount of classes is changed in
# input$classIntervals_n.
# Also check the state of the locked_breaks dropdown menu, as different behaviour
# is required from CreateEqualColumn() based on the state.
equalIntervalsColumn <- reactive({
inputdata <- thisTTM()
# Check for locked classes dropdown menu state
if(input$locked_breaks == "off") {
# Normal behaviour
res <- CreateEqualColumn(inputdata,
vis_cols[[input$fill_column]],
input$fill_column,
input$classIntervals_n)
} else if (input$locked_breaks == "params") {
# User has selected locked classes: parameters
res <- CreateEqualColumn(inputdata,
vis_cols[[input$fill_column]],
input$fill_column,
input$classIntervals_n,
locked_class_breaks_params())
} else if (input$locked_breaks == "all") {
# User has selected locked classes: parameters and all postal code areas
res <- CreateEqualColumn(inputdata,
vis_cols[[input$fill_column]],
input$fill_column,
input$classIntervals_n,
locked_class_breaks_all())
}
res
})
#### 4.2 ShinyApp outputs ----------------------------------------------------
#### 4.2.1 Da plot -----------------------------------------------------------
output$researcharea <- renderGirafe({
# Reactive value: Insert equal intervals column for ggplot mapping.
inputdata <- equalIntervalsColumn()
# Get the origin zipcode for mapping
originzip <- postal_f[postal_f["zipcode"] == validate_zipcode(), ]
originname <- as.character(originzip$nimi[1])
# Format legend labels (Equal breaks classes). Remove [, ], (, and ). Also
# add list dash. Create named vector for the origin zipcode legend entry.
# \U2012 is endash.
l_labels <-
gsub("(])|(\\()|(\\[)", "", levels(inputdata[, input$fill_column])) %>%
gsub(",", " \U2012 ", .)
legendname <- GetLegendName(input$fill_column, originzip)
# The sum of individual factor levels. Check for locked classes menu state.
# If classes breaks are locked, additional data is needed to calculate
# frequency of class occurrence.
if(input$locked_breaks == "off") {
# Normal behaviour
l_labels <- AddLevelCounts(inputdata,
vis_cols[[input$fill_column]],
input$fill_column,
input$classIntervals_n,
l_labels)
} else if (input$locked_breaks == "params") {
# Classes are locked, option: parameters
l_labels <- AddLevelCounts(inputdata,
vis_cols[[input$fill_column]],
input$fill_column,
input$classIntervals_n,
l_labels,
locked_class_breaks_params())
} else if (input$locked_breaks == "all") {
# Classes are locked, option: parameters and all postal code areas
l_labels <- AddLevelCounts(inputdata,
vis_cols[[input$fill_column]],
input$fill_column,
input$classIntervals_n,
l_labels,
locked_class_breaks_all())
}
# Add two decimal places to all label values. Do this by disassembling
# "l_labels" and determining which values need decimals added.
l_labels <- str_extract_all(l_labels, "\\(?[-0-9,.]+\\)?", simplify = TRUE) %>%
as.data.frame()
l_labels <- apply(l_labels, 1, function(x) {
paste(sprintf("%0.2f", as.numeric(x[1])), " \U2012 ",
sprintf("%0.2f", as.numeric(x[2])), " [", x[3], "]",
sep = "")
})
# Origin id legend label
o_label <- setNames("purple",
originzip[, "nimi"] %>%
unique() %>%
as.character())
# current_subdiv_lbl is created so that any amount of values can be removed
# when necessary from the dataframe. By using a duplicate of subdiv_lbl,
# labels can also be returned into view.
current_subdiv_lbl <- data.frame(subdiv_lbl)
# Get the tooltip from a separate HTML file. Get rid of indentation and
# HTML comments in the function ReadAndClean().
tooltip_content <- ReadAndClean(tooltip_path)
#### 4.2.1.1 Define ggplot obligatory elements ----
g <- ggplot(data = inputdata) +
geom_polygon_interactive(
color = alpha("black", input$postal_vis),
size = 0.3,
aes_string("long", "lat",
group = "group",
fill = input$fill_column,
tooltip = substitute(
sprintf(tooltip_content,
from_zip, originname,
zipcode, nimi,
ttm_sfp, ttm_sfp, ttm_sfp,
ttm_wtd, ttm_wtd, ttm_wtd,
ttm_r_avg, ttm_m_avg, ttm_all_avg,
ttm_r_drivetime, ttm_m_drivetime, ttm_all_drivetime,
ttm_r_pct, ttm_m_pct, ttm_all_pct,
zipcode, vals_in_zip,
thesis_r_sfp, thesis_m_sfp, thesis_all_sfp,
thesis_r_wtd, thesis_m_wtd, thesis_all_wtd,
thesis_r_drivetime, thesis_m_drivetime, thesis_all_drivetime,
thesis_r_pct, thesis_m_pct, thesis_all_pct,
comp_r_sfp, comp_m_sfp, comp_all_sfp,
comp_r_wtd, comp_m_wtd, comp_all_wtd,
comp_r_drivetime, comp_m_drivetime, comp_all_drivetime,
comp_r_pct, comp_m_pct, comp_all_pct))
)) +
# Equal interval classes colouring and labels. drop = FALSE is very
# important in most of the cases of input$fill_column. Levels may end up
# appearing zero times in the data, and that would get them erased from
# the legend and mix everything up. Get actual colouring from user in
# input$brewerpal
scale_fill_brewer(palette = input$brewerpal,
name = paste0(legendname, collapse = ""),
direction = -1,
labels = l_labels,
na.value = "darkgrey",
drop = FALSE) +
#### Origin postal code area element and legend entry
# Plot origin YKR_ID, the starting position for TTM18
geom_polygon(data = originzip,
aes(long, lat, group = group, color = nimi),
fill = NA,
size = 1.2) +
# Get a legend entry for origin ykr id
scale_color_manual(name = "Origin postal\ncode area",
values = o_label,
labels = names(o_label)) +
# Define map extent manually
coord_fixed(xlim = c(min(inputdata$lon) + 1500, max(inputdata$lon) - 1500),
ylim = c(min(inputdata$lat) + 1300, max(inputdata$lat) - 1300)) +
# Scale bar and north arrow. Because we defined visible map extent above,
# manually set scalebar location.
ggsn::scalebar(data = inputdata,
dist_unit = "km",
dist = 2,
st.dist = 0.01,
st.size = 4.75,
height = 0.01,
transform = FALSE,
anchor = c(
x = max(inputdata$lon) - 1000,
y = min(inputdata$lat) + 400)) +
ggsn::north(inputdata,
location = "topright",
scale = 0.04,
symbol = 10) +
# Legend settings. Remove axis titles, text, and ticks.
theme(legend.title = element_text(size = 15),
legend.text = element_text(size = 14),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank())
#### 4.2.1.2 If statements for on-off switches ----
# Roads and water in case we want them mapped
if(input$show_water == TRUE) {
g <- g + ggspatial::geom_spatial_polygon(
data = water,
crs = 3067,
aes(long, lat, group = group),
color = alpha("blue", 0.9),
fill = "lightblue",
size = 0.4)
}
if(input$show_roads == TRUE) {
g <- g + geom_path(
data = roads_f,
aes(long, lat, group = group),
color = "#757575",
size = 0.9)
}
# Plot municipality boundaries on the map
if(input$show_muns == TRUE) {
# Add boundaries of neighboring municipalities for visual reference
g <- g + geom_polygon(data = othermuns_f,
aes(long, lat, group = group),
color = alpha("black", 0.3),
fill = "NA",
size = 0.8) +
# Study area municipality boundaries
geom_polygon(data = muns_f,
aes(long, lat, group = group),
color = alpha("black", 0.9),
fill = "NA",
size = 1.0)
}
# Plot municipality subdivision boundaries on the map
if(input$show_subdiv == TRUE) {
# Municipality boundaries
g <- g + geom_polygon(data = subdiv_f,
aes(long, lat, group = group),
color = alpha("black", 0.6),
fill = "NA",
size = 0.6)
}
# Plot walking center boundaries
if(input$show_walk == TRUE) {
# ggnewscale makes it possible to map additional legends with same
# properties, in this case a new scale_color (origin postal code area
# legend entry is already occupying the slot).
# New legend entry for walking center of Helsinki
g <- g + ggnewscale::new_scale_color() +
geom_polygon(data = walk_f,
aes(long, lat, color = label),
fill = NA,
linetype = "longdash",
key_glyph = "polygon",
size = 0.9) +
scale_color_manual(name = NULL,
values = setNames("#6b01ab", "walk"),
labels = "Helsinki walking\ncenter (TTM18)") +
# Modify legend symbol: rectangle to square by making the symbol larger
guides(colour = guide_legend(override.aes = list(size = 12)))
}
# Plot postal code area labels
if(input$postal_label_choice != "Off") {
if(input$postal_label_choice == "Current symbology") {
# Fetch current symbology values. For the join there has to be a column
# of same name in both dataframes, therefore mutate() one into thisTTM.
this_zipcode_lbl <-
dplyr::left_join(zipcode_lbl,
thisTTM() %>%
dplyr::mutate(label = as.character(zipcode)) %>%
dplyr::select(label, zipcode,
!!rlang::sym(vis_cols[[input$fill_column]])),
by = "label") %>%
dplyr::mutate(label = !!rlang::sym(vis_cols[[input$fill_column]])) %>%
dplyr::distinct(zipcode, .keep_all = TRUE) %>%
dplyr::select(c(long, lat, label))
# Remove rows that have NAs in "label", otherwise they are printed on
# the map
this_zipcode_lbl <- this_zipcode_lbl[!is.na(this_zipcode_lbl$label), ]
# We need to generate this annotation separately for "Current symbology"
# and "postal code areas" because we can't force two decimal places for
# postal codes.
g <- g + with(this_zipcode_lbl,
annotate(geom = "label",
x = long,
y = lat,
label = sprintf("%0.2f", label),
label.size = NA,
fill = alpha("white", 0.5),
size = 4))
} else {
# if "Postal codes", use the object "zipcode_lbl" produced in 2.7.
this_zipcode_lbl <- zipcode_lbl
# Add zipcode labels
g <- g + with(this_zipcode_lbl,
annotate(geom = "label",
x = long,
y = lat,
label = label,
label.size = NA,
fill = alpha("white", 0.5),
size = 4))
}
}
# Plot postal code area labels
if(input$show_muns_labels == TRUE) {
# Disable Kauniainen label on subdiv when muns labels visible
if(input$show_subdiv_labels == TRUE) {
current_subdiv_lbl["Kauniainen", "label"] <- NA
}
# Add zipcode labels
g <- g + with(muns_lbl,