Fixed remaining Notes in PEcAn.MA

CHANGELOG.md

@@ -37,6 +37,8 @@ We are slowly change the license from NCSA opensource to BSD-3 to help with publ
 - rstudio was not working behind traefik.
 - plots now work in docker containers
 - when specifying diferent rstudio user, dev setup would mount pecan folder in wrong path.
+- Code cleanup in PEcAn.MA to protect against global namespace pollution (#2965, #2973; @nanu1605) 
+- Fixed vignettes and cleaned up internal warnings in PEcAn.DB (#2966, #2971; @nanu1605).
 
 ### Changed
 

modules/meta.analysis/DESCRIPTION

@@ -27,7 +27,6 @@ Description: The Predictive Ecosystem Carbon Analyzer (PEcAn) is a scientific
     the functions used in the Bayesian meta-analysis of trait data.
 Imports:
     coda (>= 0.18),
-    XML,
     lattice,
     PEcAn.utils,
     PEcAn.DB,

modules/meta.analysis/R/jagify.R

@@ -14,12 +14,12 @@
 ##' @title Prepare trait data for JAGS meta-analysis
 ##' @param result input trait data
 ##' @param use_ghs (Logical) If `FALSE`, exclude all greenhouse data. If `TRUE`, use all data, including greenhouse data.
-##' @export
 ##' @return result transformed to meet requirements of PEcAn meta-analysis model
+##' @export
 ##' @author David LeBauer
 jagify <- function(result, use_ghs = TRUE) {
 
-
+  
   ## Create new column "trt_id" from column 'name'.  Remove NAs. Assign treatments.
   ## Finally, summarize the results by calculating summary statistics from experimental replicates
   r <- result[!is.na(result$mean), ]
@@ -33,18 +33,16 @@ jagify <- function(result, use_ghs = TRUE) {
 
   r <- PEcAn.DB::assign.treatments(r)
   r <- PEcAn.utils::summarize.result(r)
-  r <- subset(transform(r, 
-                        stat = as.numeric(stat), 
-                        n = as.numeric(n), 
-                        site_id = as.integer(factor(site_id, unique(site_id))), 
-                        greenhouse = as.integer(factor(greenhouse, unique(greenhouse))),
-                        mean = mean, 
-                        citation_id = citation_id,
-                        ghs = greenhouse,
-                        site = site_id,
-                        trt_name = name), 
-              select = c("stat", "n", "site_id", "trt_id", "mean", "citation_id", "greenhouse", 
-                         "ghs", "treatment_id", "site", "trt_name")) # original versions of greenhouse, treatment_id, site_id, and name
+  r$stat <- as.numeric(r$stat)
+  r$n <- as.numeric(r$n)
+  r$site_id <- as.integer(factor(r$site_id, unique(r$site_id)))
+  r$greenhouse <- as.integer(factor(r$greenhouse, unique(r$greenhouse)))
+  r$ghs <- r$greenhouse
+  r$site <- r$site_id
+  r$trt_name <- r$name
+
+  r <- r[, c("stat", "n", "site_id", "trt_id", "mean", "citation_id", "greenhouse", 
+             "ghs", "treatment_id", "site", "trt_name")]
 
   #order by site_id and trt_id, but make sure "control" is the first trt of each site
   uniq <- setdiff(unique(r$trt_id), "control")