removed print statements

R/app_server.R

@@ -8,6 +8,7 @@
 # Below increases the max data upload size from the Shiny default of 5MB per file
 # to 30MB for file
 options(shiny.maxRequestSize = 30 * 1024^2)
+options(warn = 2)
 app_server <- function(input, output, session) {
   # Your application server logic
   tadat <- shiny::reactiveValues() # create a list object that holds reactive values passed between modules

R/mod_data_flagging.R

@@ -90,13 +90,14 @@ mod_data_flagging_server <- function(id, tadat) {
     # Runs whenever selected flags are changed
     shiny::observeEvent(tadat$selected_flags, {
       if (!is.null(tadat$removals)) {
-        tadat$removals = dplyr::select(tadat$removals, -(dplyr::starts_with(flag_prefix)))
+        tadat$removals = dplyr::select(tadat$removals,-(dplyr::starts_with(flag_prefix)))
       }
       # Loop through the flags
       for (flag in tadat$selected_flags) {
         # If not all the values are NA, add the test results to removals
         if (!is.null(tadat$removals)) {
           if (!all(is.na(values$testResults[flag]))) {
+            # Problem here?
             tadat$removals[paste0(flag_prefix, flag)] = values$testResults[flag]
           }
         }
@@ -124,7 +125,6 @@ mod_data_flagging_server <- function(id, tadat) {
 
     shiny::observeEvent(tadat$flags_present, {
       if (tadat$flags_present) {
-
         # A table (raw rows, flags) indicating whether each record passes each test
         values$testResults <- flagCensus(tadat$raw)
 
@@ -193,7 +193,7 @@ mod_data_flagging_server <- function(id, tadat) {
       # Add flagging columns to raw table, make sure line below is
       # not commented out once done with testing
       tadat$raw <- applyFlags(tadat$raw, tadat$orgs)
-
+      
       #write.csv(tadat$raw, "flagged.csv")
       #tadat$raw = utils::read.csv("flagged.csv") # THIS IS TRIPS WORKING FILE FOR TESTING, COMMENT OUT WHEN COMMITTING TO DEVELOP
 
@@ -226,11 +226,11 @@ mod_data_flagging_server <- function(id, tadat) {
           text = "Converting depth units to inches...",
           session = shiny::getDefaultReactiveDomain()
         )
-
+        
         tadat$raw <-
           TADA::TADA_ConvertDepthUnits(tadat$raw, unit = "in")
-
-        }
+        
+      }
       if (input$m2f == "meters") {
         shinybusy::show_modal_spinner(
           spin = "double-bounce",

R/mod_filtering.R

@@ -1,6 +1,16 @@
 mod_filtering_ui <- function(id) {
   ns <- NS(id)
   tagList(
+    shiny::fluidRow(column(
+      6,
+      shiny::radioButtons(
+        ns("field_sel"),
+        label = "",
+        choices = c("key", "most", "all"),
+        selected = "key",
+        inline = TRUE
+      )
+    )),
     htmltools::HTML("<h3>Select field to filter on:</h3>"),
     htmltools::HTML(
       "Key columns are listed in the table below, along with the number of unique values present in that field. These counts do not include unique values from results flagged for removal. Click on a field name and a new table will appear below showing the counts associated with each unique value in the selected field."
@@ -169,6 +179,11 @@ mod_filtering_server <- function(id, tadat) {
       }
     })
 
+
+    shiny::observeEvent(tadat$field_sel, {
+      shiny::updateRadioButtons(session, "field_sel", selected = tadat$field_sel)
+    })
+
     # reset all filters in bottom table
     shiny::observeEvent(input$resetFilters, {
       # empty selected table on open

R/utils_flag_functions.R

@@ -5,9 +5,7 @@ test_table <- utils::read.csv("inst/flag_tests.csv")
 # test_table = utils::read.csv(app_sys("flag_tests.csv"))
 prompt_table <- prompt_table[order(prompt_table$Order),]
 prompts <- prompt_table$Prompt
-# As of 1/10/2024, this isn't being generated by the flagging process
-missing_flags = list("TADA.SingleOrgDup.Flag")
-active_flags = setdiff(unique(merge(prompt_table, test_table)$columnName), missing_flags)
+active_flags = unique(merge(prompt_table, test_table)$columnName)
 levs <- prompt_table$Level
 n_switches <- length(prompts)
 flag_types <- prompt_table$flagType
@@ -79,32 +77,33 @@ getCounts <- function(sites, removed_records) {
 }
 # Settings for each flag function in flag page mock up
 applyFlags <- function(in_table, orgs) {
+  out <- TADA::TADA_IDCensoredData(in_table)
   # Invalid Speciation
-  out <- TADA::TADA_FlagSpeciation(in_table, clean = "none")
-  
+  out <- TADA::TADA_FlagSpeciation(out, clean = "none")
+
   # Invalid fraction
   out <- TADA::TADA_FlagFraction(out, clean = FALSE)
-  
+
   # Invalid result unit
   out <- TADA::TADA_FlagResultUnit(out, clean = "none")
-
+  print(paste0("Step 5: ", nrow(out)))
   # QC rep/blank
   out <- TADA::TADA_FindQCActivities(out, clean = FALSE)
-  
+
   # Result is flagged as suspect by data submitter
   out <- TADA::TADA_FlagMeasureQualifierCode(out, clean = FALSE, define = TRUE)
-  
+
   # Invalid analytical method
   out <- TADA::TADA_FlagMethod(out, clean = FALSE)
 
   # Single org duplicative uploads
   out <- TADA::TADA_FindPotentialDuplicatesSingleOrg(out)
-  
+
   # multiple org duplicative uploads
   ## NOTE: THIS FUNCTION USES A REACTIVE OBJECT AS AN INPUT
   out <-
     TADA::TADA_FindPotentialDuplicatesMultipleOrgs(out, org_hierarchy = orgs)
-  
+
   # QAPP Not Approved - this flag isn't looking for a TADA-created flag column,
   # so do not need to run any flag function here. If switched ON, remove all data
   # with QAPPApproved == N or NA.
@@ -119,10 +118,10 @@ applyFlags <- function(in_table, orgs) {
 
   # Aggregated continuous data
   out <- TADA::TADA_FindContinuousData(out, clean = FALSE)
-  
+
   # Above WQX Upper Threshold
   out <- TADA::TADA_FlagAboveThreshold(out, clean = FALSE)
-  
+
   # Below WQX Lower Threshold
   out <- TADA::TADA_FlagBelowThreshold(out, clean = FALSE)
 
@@ -141,11 +140,13 @@ applyFlags <- function(in_table, orgs) {
 checkFlagColumns <- function(dataset) {
   missing <- setdiff(active_flags, names(dataset))
   found = setdiff(active_flags, missing)
-  print(missing)
-  print(found)
   if (length(missing) > length(found)) {
     return(FALSE)
   } else {
+    if (length(missing) > 0){
+      print("Missing the following fields that are in the csv files:")
+      print(missing)
+    }
     return(TRUE)
   }
 }