USEPA · cristinamullin · Nov 8, 2024 · Oct 7, 2024 · Oct 7, 2024 · Oct 7, 2024
diff --git a/R/Figures.R b/R/Figures.R
@@ -588,8 +588,8 @@ TADA_OverviewMap <- function(.data) {
 #'
 TADA_FlaggedSitesMap <- function(.data) {
   invalid <- TADA_FlagCoordinates(.data, flaggedonly = TRUE)
-  lowres <- invalid[invalid$TADA.InvalidCoordinates.Flag == "Imprecise_lessthan3decimaldigits", ]
-  outsideusa <- invalid[invalid$TADA.InvalidCoordinates.Flag %in% c("LAT_OutsideUSA", "LONG_OutsideUSA"), ]
+  lowres <- invalid[invalid$TADA.SuspectCoordinates.Flag == "Imprecise_lessthan3decimaldigits", ]
+  outsideusa <- invalid[invalid$TADA.SuspectCoordinates.Flag %in% c("LAT_OutsideUSA", "LONG_OutsideUSA"), ]
   nearby <- TADA_FindNearbySites(.data)
   print(colnames(nearby))
   nearby <- TADA_GetUniqueNearbySites(nearby)

diff --git a/R/RequiredCols.R b/R/RequiredCols.R
@@ -177,8 +177,7 @@ require.cols <- c(
   "LongitudeMeasure",
   "TADA.LongitudeMeasure", # generated
   "HorizontalCoordinateReferenceSystemDatumName",
-  "geometry",
-  "TADA.InvalidCoordinates.Flag", # generated
+  "TADA.SuspectCoordinates.Flag", # generated
   "HUCEightDigitCode",
   "MonitoringLocationIdentifier", # required
   "TADA.MonitoringLocationIdentifier",
@@ -200,7 +199,7 @@ extra.cols <- c(
   "ActivityEndDate",
   "ActivityEndTime.Time",
   "ActivityEndTime.TimeZoneCode",
-  "ActivityEndDateTime", # generated by USGS DR
+  "ActivityEndDateTime", # originally generated by USGS DR, # no longer in default dataRetrieval profile? 11/7/24
   "ActivityConductingOrganizationText",
   "SampleAquifer",
   "ActivityLocation.LatitudeMeasure",
@@ -215,8 +214,9 @@ extra.cols <- c(
   "AnalysisStartDate",
   "ResultDetectionQuantitationLimitUrl",
   "LabSamplePreparationUrl",
-  "timeZoneStart",
-  "timeZoneEnd",
+  "timeZoneStart", # no longer in default dataRetrieval profile? 11/7/24
+  "timeZoneEnd", # no longer in default dataRetrieval profile? 11/7/24
+  "ActivityStartTime.TimeZoneCode_offset", # new column from default dataRetrieval profile? 11/7/24
   "SourceMapScaleNumeric",
   "HorizontalAccuracyMeasure.MeasureValue",
   "HorizontalAccuracyMeasure.MeasureUnitCode",
@@ -232,7 +232,9 @@ extra.cols <- c(
   "DrainageAreaMeasure.MeasureValue",
   "DrainageAreaMeasure.MeasureUnitCode",
   "ContributingDrainageAreaMeasure.MeasureValue",
-  "ContributingDrainageAreaMeasure.MeasureUnitCode"
+  "ContributingDrainageAreaMeasure.MeasureUnitCode",
+  "ProviderName",
+  "LastUpdated"
 )
 
 attains.cols <- c(
@@ -256,11 +258,10 @@ attains.cols <- c(
 
 # Only used in TADA Shiny or should be at the end
 last.cols <- c(
-  "ProviderName",
-  "LastUpdated",
   "TADA.Remove",
   "TADA.RemovalReason",
-  "TADAShiny.tab"
+  "TADAShiny.tab",
+  "geometry"
 )
 
 
@@ -329,7 +330,7 @@ TADA_OrderCols <- function(.data) {
 #'
 TADA_GetTemplate <- function() {
   # remove names with TADA. string from require.cols
-  template_cols <- c(require.cols, last.cols)
+  template_cols <- require.cols
   template_cols <- Filter(function(x) !any(grepl("TADA.", x)), template_cols)
   templatedata <- data.frame()
   templatedata <- data.frame(matrix(nrow = 0, ncol = length(template_cols)))
@@ -526,7 +527,7 @@ TADA_RetainRequired <- function(.data) {
   print("TADA_RetainRequired: removing columns not required for TADA workflow including original columns that have been replaced with TADA prefix duplicates.")
 
   # Create list of all columns to be retained
-  keep.cols <- c(require.cols, last.cols)
+  keep.cols <- c(require.cols, attains.cols, last.cols)
 
   # create list of all columns in original data set
   original.cols <- .data %>% names()

diff --git a/R/ResultFlagsDependent.R b/R/ResultFlagsDependent.R
diff --git a/R/ResultFlagsIndependent.R b/R/ResultFlagsIndependent.R
diff --git a/R/TADARefTables.R b/R/TADARefTables.R
@@ -27,7 +27,7 @@ TADA_GetNutrientSummationRef <- function() {
 #' input data relates to target synonyms for TADA.CharacteristicName, TADA.ResultSampleFractionText,
 #' and TADA.MethodSpeciationName. Once the synonym table is created, users may optionally edit the 
 #' target columns in the reference table to meet their needs. Additionally, the function assumes
-#' the user has already removed any data containing invalid characteristic-unit-fraction-speciation 
+#' the user has already removed any data containing suspect characteristic-unit-fraction-speciation 
 #' combinations (i.e. user has already run TADA_FlagFraction, TADA_FlagSpeciation, TADA_FlagResultUnit, 
 #' etc.).
 #'
@@ -44,8 +44,8 @@ TADA_GetNutrientSummationRef <- function() {
 #' # Create a synonym reference table for flagged, cleaned dataframe:
 #' Data_6Tribes_5yClean <- subset(Data_6Tribes_5y, !is.na(Data_6Tribes_5y$TADA.ResultMeasureValue))
 #' Data_6Tribes_5yClean <- TADA_FlagFraction(Data_6Tribes_5yClean, clean = TRUE)
-#' Data_6Tribes_5yClean <- TADA_FlagResultUnit(Data_6Tribes_5yClean, clean = "invalid_only")
-#' Data_6Tribes_5yClean <- TADA_FlagSpeciation(Data_6Tribes_5yClean, clean = "invalid_only")
+#' Data_6Tribes_5yClean <- TADA_FlagResultUnit(Data_6Tribes_5yClean, clean = "suspect_only")
+#' Data_6Tribes_5yClean <- TADA_FlagSpeciation(Data_6Tribes_5yClean, clean = "suspect_only")
 #' Data_6Tribes_5yClean <- TADA_FlagMethod(Data_6Tribes_5yClean, clean = TRUE)
 #' CreateRefTable <- TADA_GetSynonymRef(Data_6Tribes_5yClean)
 #'
@@ -69,20 +69,20 @@ TADA_GetSynonymRef <- function(.data) {
   TADA_CheckColumns(.data, expected_cols)
 
   if (!any(c("TADA.MethodSpeciation.Flag", "TADA.SampleFraction.Flag", "TADA.ResultUnit.Flag") %in% names(.data))) {
-    print("Warning: This dataframe is missing TADA QC flagging columns, indicating that you have not yet run the TADA_FlagResultUnit, TADA_FlagFraction, or TADA_FlagSpeciation functions. It is highly recommended you run these flagging functions and remove Invalid combinations before proceeding to this step.")
+    print("Warning: This dataframe is missing TADA QC flagging columns, indicating that you have not yet run the TADA_FlagResultUnit, TADA_FlagFraction, or TADA_FlagSpeciation functions. It is highly recommended you run these flagging functions and remove Suspect combinations before proceeding to this step.")
   }
 
-  # check to see if any invalid data flags exist
+  # check to see if any suspect data flags exist
   check_inv <- .data[, names(.data) %in% c("TADA.MethodSpeciation.Flag", "TADA.SampleFraction.Flag", "TADA.ResultUnit.Flag")]
   check_inv <- check_inv %>%
     tidyr::pivot_longer(cols = names(check_inv), names_to = "Flag_Column") %>%
-    dplyr::filter(value == "Invalid")
+    dplyr::filter(value == "Suspect")
 
   if (dim(check_inv)[1] > 0) {
     check_inv <- check_inv %>%
       dplyr::group_by(Flag_Column) %>%
       dplyr::summarise("Result Count" = length(value))
-    print("Warning: Your dataframe contains invalid metadata combinations in the following flag columns:")
+    print("Warning: Your dataframe contains suspect metadata combinations in the following flag columns:")
     print(as.data.frame(check_inv))
   }
 

diff --git a/R/Transformations.R b/R/Transformations.R
@@ -41,8 +41,8 @@
 #' # Create a synonym reference table for flagged, cleaned dataframe:
 #' Data_6Tribes_5yClean <- subset(Data_6Tribes_5y, !is.na(Data_6Tribes_5y$TADA.ResultMeasureValue))
 #' Data_6Tribes_5yClean <- TADA_FlagFraction(Data_6Tribes_5yClean, clean = TRUE)
-#' Data_6Tribes_5yClean <- TADA_FlagResultUnit(Data_6Tribes_5yClean, clean = "invalid_only")
-#' Data_6Tribes_5yClean <- TADA_FlagSpeciation(Data_6Tribes_5yClean, clean = "invalid_only")
+#' Data_6Tribes_5yClean <- TADA_FlagResultUnit(Data_6Tribes_5yClean, clean = "suspect_only")
+#' Data_6Tribes_5yClean <- TADA_FlagSpeciation(Data_6Tribes_5yClean, clean = "suspect_only")
 #' Data_6Tribes_5yClean <- TADA_FlagMethod(Data_6Tribes_5yClean, clean = TRUE)
 #' CreateRefTable <- TADA_GetSynonymRef(Data_6Tribes_5yClean)
 #'

diff --git a/R/Utilities.R b/R/Utilities.R
@@ -36,7 +36,7 @@ utils::globalVariables(c(
   "QAPPApprovalAgencyName", "QAPPApprovedIndicator",
   "ResultDetectionConditionText", "ResultMeasureValue",
   "SamplingDesignTypeCode", "Source", "Status", "TADA.ContinuousData.Flag",
-  "TADA.InvalidCoordinates.Flag", "TADA.PotentialDupRowIDs.Flag", "TADA.QAPPDocAvailable",
+  "TADA.SuspectCoordinates.Flag", "TADA.PotentialDupRowIDs.Flag", "TADA.QAPPDocAvailable",
   "Target.Unit", "Type", "Value.Unit", "TADA.AnalyticalMethod.Flag",
   "TADA.MethodSpeciation.Flag", "TADA.ResultUnit.Flag",
   "TADA.SampleFraction.Flag", "YearSummarized", "where", "TADA.CharacteristicName",
@@ -490,7 +490,7 @@ TADA_CheckColumns <- function(.data, expected_cols) {
 #'
 TADA_ConvertSpecialChars <- function(.data, col, percent.ave = TRUE) {
   if (!col %in% names(.data)) {
-    stop("Invalid column name specified for input dataset.")
+    stop("Suspect column name specified for input dataset.")
   }
 
   # Define new column names
@@ -621,7 +621,7 @@ TADA_ConvertSpecialChars <- function(.data, col, percent.ave = TRUE) {
 #' Substitute Preferred Characteristic Name for Deprecated Names
 #'
 #' This function uses the WQX Characteristic domain table to substitute
-#' deprecated (i.e. retired and/or invalid) Characteristic Names with the new
+#' deprecated (i.e. retired and/or suspect) Characteristic Names with the new
 #' name in the TADA.CharacteristicName column. TADA_SubstituteDeprecatedChars is
 #' run within TADA_AutoClean, which runs within TADA_DataRetreival and (if autoclean = TRUE)
 #' in TADA_BigDataRetrieval. Therefore, deprecated characteristic names are
@@ -756,13 +756,24 @@ TADA_FormatDelimitedString <- function(delimited_string, delimiter = ",") {
 #' group.
 #'
 #' @param .data TADA dataframe OR TADA sites dataframe
-#' @param dist_buffer Numeric. The maximum distance (in meters) two sites can be
-#'   from one another to be considered "nearby" and grouped together.
-#'
-#' @return Input dataframe with a TADA.MonitoringLocationIdentifier column that indicates
-#'   the nearby site groups each monitoring location belongs to.
+#' @param dist_buffer Numeric. The maximum distance (in meters) two sites can 
+#' be from one another to be considered "nearby" and grouped together.
+#' The default is 100m. 
+#'
+#' @return Input dataframe with a TADA.MonitoringLocationIdentifier column that 
+#' indicates the nearby site groups each monitoring location belongs to. Grouped 
+#' sites are concatenated in the TADA.MonitoringLocationIdentifier column 
+#' (e.g. "USGS-10010025","USGS-10010026" enclosed in square brackets []). 
+#' This JSON array is the new TADA monitoring location ID for the grouped sites.
+#' TADA.MonitoringLocationIdentifier can be leveraged to analyze data from 
+#' nearby sites together (as the same general location).
 #'
 #' @export
+#' 
+#' @examples
+#' GroupNearbySites_100m <- TADA_FindNearbySites(Data_Nutrients_UT)
+#' GroupNearbySites_10m <- TADA_FindNearbySites(Data_Nutrients_UT, dist_buffer = 10)
+#' 
 #'
 TADA_FindNearbySites <- function(.data, dist_buffer = 100) {
   # check .data is data.frame
@@ -1081,7 +1092,7 @@ TADA_AggregateMeasurements <- function(.data, grouping_cols = c("ActivityStartDa
 #' @param .data A TADA dataframe
 #' @param remove_na Boolean, Determines whether to keep TADA.ResultMeasureValues that are NA.
 #' Defaults to TRUE.
-#' @param clean Boolean. Determines whether to keep the Invalid rows in the dataset following each
+#' @param clean Boolean. Determines whether to keep the Suspect rows in the dataset following each
 #' flagging function. Defaults to TRUE.
 #'
 #' @return A TADA dataframe with the following flagging columns:TADA.ResultUnit.Flag,
@@ -1095,17 +1106,17 @@ TADA_AggregateMeasurements <- function(.data, grouping_cols = c("ActivityStartDa
 #' # Run flagging functions, keeping all rows
 #' Data_6Tribes_5y_ALL <- TADA_RunKeyFlagFunctions(Data_6Tribes_5y, remove_na = FALSE, clean = FALSE)
 #'
-#' # Run flagging functions, removing NA's and Invalid rows
+#' # Run flagging functions, removing NA's and Suspect rows
 #' Data_6Tribes_5y_CLEAN <- TADA_RunKeyFlagFunctions(Data_6Tribes_5y, remove_na = TRUE, clean = TRUE)
 TADA_RunKeyFlagFunctions <- function(.data, remove_na = TRUE, clean = TRUE) {
   if (remove_na == TRUE) {
     .data <- .data %>% dplyr::filter(!is.na(TADA.ResultMeasureValue))
   }
 
   if (clean == TRUE) {
-    .data <- TADA_FlagResultUnit(.data, clean = "invalid_only")
+    .data <- TADA_FlagResultUnit(.data, clean = "suspect_only")
     .data <- TADA_FlagFraction(.data, clean = TRUE)
-    .data <- TADA_FlagSpeciation(.data, clean = "invalid_only")
+    .data <- TADA_FlagSpeciation(.data, clean = "suspect_only")
     .data <- TADA_FindQCActivities(.data, clean = TRUE)
   } else {
     .data <- TADA_FlagResultUnit(.data, clean = "none")

diff --git a/R/WQXRefTables.R b/R/WQXRefTables.R
@@ -59,8 +59,8 @@ TADA_GetWQXCharValRef <- function() {
   WQXcharValRef <- raw.data %>%
     dplyr::mutate(TADA.WQXVal.Flag = dplyr::case_when(
       Status %in% notreviewed ~ "Not Reviewed",
-      Status %in% valid ~ "Valid",
-      Status %in% invalid ~ "Invalid",
+      Status %in% valid ~ "Pass",
+      Status %in% invalid ~ "Suspect",
       Status %in% nonstandard ~ "NonStandardized",
       Status %in% NA ~ "Not Reviewed",
       TRUE ~ as.character("Not Reviewed")

diff --git a/inst/WORDLIST b/inst/WORDLIST
@@ -246,6 +246,7 @@ StringA
 StringB
 SubstituteDeprecatedChars
 SummarizeColumn
+SuspectCoordinates
 TADA
 TADADataRetrieval
 TADAGeospatialRefLayers
@@ -281,9 +282,8 @@ WQXMonitoringLocationTypeName
 WQXRefTables
 WQXResultDetectionConditionRef
 WQXResultUnitConversion
-WQXResultUnitCoversion
-WQXTargetUnit
 WQXUnitConversionFactor
+WQXVal
 WQXcharValRef
 WQXunitRef
 WaterSciCon

diff --git a/man/TADA_FindNearbySites.Rd b/man/TADA_FindNearbySites.Rd
diff --git a/man/TADA_FlagAboveThreshold.Rd b/man/TADA_FlagAboveThreshold.Rd
diff --git a/man/TADA_FlagCoordinates.Rd b/man/TADA_FlagCoordinates.Rd