Merge pull request #543 from USEPA/staging

Staging
USEPA · Nov 8, 2024 · 2420ddc · 2420ddc
2 parents 3cd90e2 + 851e168
commit 2420ddc
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diff --git a/R/CensoredDataSuite.R b/R/CensoredDataSuite.R
@@ -32,7 +32,7 @@
 #' 4) Non-Detect
 #' 5) Over-Detect
 #' 6) Other Condition/Limit Populated
-#' If user has not previously run TADA_FlagMeasureQualifierCode, this function will also run that 
+#' If user has not previously run TADA_FlagMeasureQualifierCode, this function will also run that
 #' function to add the columns TADA.MeasureQualifierCode.Flag and TADA.MeasureQualifierCode.Def
 #' because user-supplied Result Measure Qualifier codes are also used to ID censored results.
 #'
@@ -287,18 +287,20 @@ TADA_IDCensoredData <- function(.data) {
 #' # and in instances where the measurement is non-detect, set the result value
 #' # to half of the detection limit value. For over-detect measurements, retain
 #' # the detection limit value as the result value as-is.
-#' Data_Nutrients_UT_CensoredFlag <- TADA_SimpleCensoredMethods(Data_Nutrients_UT, 
-#'     nd_method = "multiplier",
-#'     nd_multiplier = 0.5,
-#'     od_method = "as-is",
-#'     od_multiplier = "null")
+#' Data_Nutrients_UT_CensoredFlag <- TADA_SimpleCensoredMethods(Data_Nutrients_UT,
+#'   nd_method = "multiplier",
+#'   nd_multiplier = 0.5,
+#'   od_method = "as-is",
+#'   od_multiplier = "null"
+#' )
 #'
 #' # Check for agreement between detection condition and detection limit type, and in instances where the measurement is non-detect, set the result value to a random value between 0 and the detection limit value. For over-detect measurements, retain the detection limit value as the result value as-is.
-#' Data_Nutrients_UT_CensoredFlag <- TADA_SimpleCensoredMethods(Data_Nutrients_UT, 
-#'     nd_method = "randombelowlimit",
-#'     nd_multiplier = "null",
-#'     od_method = "as-is",
-#'     od_multiplier = "null")
+#' Data_Nutrients_UT_CensoredFlag <- TADA_SimpleCensoredMethods(Data_Nutrients_UT,
+#'   nd_method = "randombelowlimit",
+#'   nd_multiplier = "null",
+#'   od_method = "as-is",
+#'   od_multiplier = "null"
+#' )
 #'
 TADA_SimpleCensoredMethods <- function(.data, nd_method = "multiplier", nd_multiplier = 0.5, od_method = "as-is", od_multiplier = "null") {
   # check .data has all of the required columns

diff --git a/R/CriteriaComparison.R b/R/CriteriaComparison.R
@@ -40,10 +40,9 @@
 #'
 TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
                                chloride = TRUE, salinity = TRUE, other_char = "null") {
-
   # create data frame to store pair refs
   pair.ref <- data.frame(matrix(ncol = 6, nrow = 0))
-  
+
   # name columns in pair.ref df
   colnames(pair.ref) <- c(
     "TADA.CharacteristicName", "TADA.ResultMeasure.MeasureUnitCode",
@@ -66,7 +65,8 @@ TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
       dplyr::select(
         TADA.CharacteristicName, TADA.ResultMeasure.MeasureUnitCode,
         TADA.MethodSpeciationName, TADA.ResultSampleFractionText,
-        NCount) %>%
+        NCount
+      ) %>%
       # retain only distinct rows
       dplyr::distinct() %>%
       # arrange from largest to smallest number of results
@@ -79,7 +79,6 @@ TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
 
 
   if (hardness == TRUE) {
-
     # create character reference from WQX characteristics containing "HARDNESS" in name
     char.ref <- TADA_GetCharacteristicRef() %>%
       dplyr::mutate(CharacteristicName = toupper(CharacteristicName)) %>%
@@ -99,7 +98,6 @@ TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
   }
 
   if (ph == TRUE) {
-
     # filter TADA df for pH results
     ph.ref <- .data %>%
       dplyr::filter(TADA.CharacteristicName == "PH") %>%
@@ -114,7 +112,6 @@ TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
   }
 
   if (temp == TRUE) {
-
     # filter TADA df for temperature results
     temp.ref <- .data %>%
       dplyr::filter(TADA.CharacteristicName %in% c("TEMPERATURE", "TEMPERATURE, WATER")) %>%
@@ -129,7 +126,6 @@ TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
   }
 
   if (salinity == TRUE) {
-
     # filter TADA df for salinity results
     salinity.ref <- .data %>%
       dplyr::filter(TADA.CharacteristicName %in% c("SALINITY")) %>%
@@ -145,7 +141,6 @@ TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
 
 
   if (chloride == TRUE) {
-
     # filter TADA df for chloride results
     chloride.ref <- .data %>%
       dplyr::filter(TADA.CharacteristicName %in% c("CHLORIDE")) %>%
@@ -155,7 +150,7 @@ TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
     # add chloride to pair.ref
     pair.ref <- rbind(pair.ref, chloride.ref)
 
-    #remove intermediate object
+    # remove intermediate object
     rm(chloride.ref)
   }
 
@@ -199,7 +194,7 @@ TADA_CreatePairRef <- function(.data, ph = TRUE, hardness = TRUE, temp = TRUE,
 #' @param .data TADA dataframe
 #'
 #' @param ref Write description of what columns need to be in this ref or null option
-#' 
+#'
 #' @param hours_range Numeric argument. The time difference allowed between the paired characteristic
 #' and the result.
 #

diff --git a/R/DataDiscoveryRetrieval.R b/R/DataDiscoveryRetrieval.R
@@ -54,12 +54,16 @@
 #' @examples
 #' \dontrun{
 #' # example for WI
-#' tada1 <- TADA_DataRetrieval(statecode = "WI", countycode = "Dane", 
-#'     characteristicName = "Phosphorus")
+#' tada1 <- TADA_DataRetrieval(
+#'   statecode = "WI", countycode = "Dane",
+#'   characteristicName = "Phosphorus"
+#' )
 #'
 #' # example for UT
-#' tada2 <- TADA_DataRetrieval(statecode = "UT",
-#'     characteristicName = c("Ammonia", "Nitrate", "Nitrogen"))
+#' tada2 <- TADA_DataRetrieval(
+#'   statecode = "UT",
+#'   characteristicName = c("Ammonia", "Nitrate", "Nitrogen")
+#' )
 #'
 #' # example for SC
 #' tada3 <- TADA_DataRetrieval(statecode = "SC", countycode = "Abbeville")

diff --git a/R/Figures.R b/R/Figures.R
@@ -1154,7 +1154,7 @@ TADA_TwoCharacteristicScatterplot <- function(.data, id_cols = "TADA.ComparableD
 #'
 #' @param group_col The column in the dataset used to identify the groups
 #'    plotted. Defaults to MonitoringLocationName. This input is flexible, and allows for the use of
-#'    other identifiers such as StateCode, CountyCode or user-created groups based on concatenation 
+#'    other identifiers such as StateCode, CountyCode or user-created groups based on concatenation
 #'    of other variables (e.g. characteristic name, site type, site name, year, organization, etc.)
 #'
 #' @param groups A vector of up to four identifiers from the id_cols column
@@ -1164,7 +1164,7 @@ TADA_TwoCharacteristicScatterplot <- function(.data, id_cols = "TADA.ComparableD
 #'   'Upper Red Lake: West', 'Upper Red Lake: West-Central', and 'Upper Red Lake: East Central'.
 #'
 #' @return A plotly scatterplot(s) figure with one x-axis (Date/Time) and a
-#'   left axis showing the units of a single TADA.ComparableDataIdentifier plotted on the same 
+#'   left axis showing the units of a single TADA.ComparableDataIdentifier plotted on the same
 #'   figure area with. Groups are identified by different colored circle markers and are displayed
 #'   in a legend.
 #'
@@ -1272,15 +1272,14 @@ TADA_GroupedScatterplot <- function(.data, group_col = "MonitoringLocationName",
       " ", group_col, "s by number of results will be plotted: ", groups.string, ".",
       sep = ""
     ))
-    
+
     # remove intermediate objects
     rm(groups.string, n.groups.plotted)
   }
 
   # check that groups are in group_col
   id <- unlist(unique(.data[, group_col]))
   if (any(!groups %in% id)) {
-
     # identify any groups missing from "groups" param
     missing.groups <- setdiff(groups, id)
 
@@ -1292,7 +1291,7 @@ TADA_GroupedScatterplot <- function(.data, group_col = "MonitoringLocationName",
       missing.groups.string, ". Revise param 'groups' before re-running function.",
       sep = ""
     )
-    
+
     # remove intermediate objects
     rm(missing.group, missing.groups.string, id)
   }
@@ -1311,7 +1310,7 @@ TADA_GroupedScatterplot <- function(.data, group_col = "MonitoringLocationName",
   param.data <- list()
   for (i in 1:length(unique(groups))) {
     param.data[[i]] <- subset(plot.data, plot.data[, group_col] %in% groups[i])
-    #assign(paste0("param", as.character(i)), subset(plot.data, plot.data[, group_col] %in% groups[i]))
+    # assign(paste0("param", as.character(i)), subset(plot.data, plot.data[, group_col] %in% groups[i]))
   }
 
   # create empty list to store scatterplots
@@ -1344,7 +1343,7 @@ TADA_GroupedScatterplot <- function(.data, group_col = "MonitoringLocationName",
     plot.data.y <- subset(plot.data, plot.data[, "TADA.ComparableDataIdentifier"] %in% unique(plot.data$TADA.ComparableDataIdentifier)[i])
     plot.data.y$name <- gsub("_NA", "", plot.data.y[, "TADA.ComparableDataIdentifier"])
     plot.data.y$name <- gsub("_", " ", plot.data.y$name)
-    
+
     scatterplot <-
       plotly::plot_ly(type = "scatter", mode = "markers") %>%
       plotly::layout(
@@ -1367,68 +1366,68 @@ TADA_GroupedScatterplot <- function(.data, group_col = "MonitoringLocationName",
         plot_bgcolor = "#e5ecf6",
         margin = mrg,
         legend = list(
-          title = list(text = paste0('<b>', group_col,'<b>'), x = 0.5, y= 100),
+          title = list(text = paste0("<b>", group_col, "<b>"), x = 0.5, y = 100),
           orientation = "h",
           xanchor = "center",
           x = 0.5
         )
       ) %>%
       # config options https://plotly.com/r/configuration-options/
       plotly::config(displaylogo = FALSE) # , displayModeBar = TRUE) # TRUE makes bar always visible
-    
+
     param <- list()
     for (j in 1:length(groups)) {
-      if ( length(groups) >= j){
-      param[[j]] <- subset(param.data[[j]], param.data[[j]][, "TADA.ComparableDataIdentifier"] %in% unique(plot.data$TADA.ComparableDataIdentifier)[i])
-
-      scatterplot <- scatterplot %>%
-        plotly::add_trace(
-          data = param[[j]],
-          x = ~ as.Date(ActivityStartDate),
-          y = ~TADA.ResultMeasureValue,
-          name = groups[j],
-          marker = list(
-            size = 10,
-            color = tada.pal[j, 1],
-            line = list(color = tada.pal[j, 2], width = 2)
-          ),
-          hoverinfo = "text",
-          hovertext = paste(
-            "Result:", paste0(param[[j]]$TADA.ResultMeasureValue, " ", param[[j]]$TADA.ResultMeasure.MeasureUnitCode), "<br>",
-            "Activity Start Date:", param[[j]]$ActivityStartDate, "<br>",
-            "Activity Start Date Time:", param[[j]]$ActivityStartDateTime, "<br>",
-            "Monitoring Location Name:", param[[j]]$MonitoringLocationName, "<br>",
-            "Media:", param[[j]]$TADA.ActivityMediaName, "<br>",
-            "Media Subdivision:", param[[j]]$ActivityMediaSubdivisionName, "<br>",
-            "Result Depth:", paste0(
-              param[[j]]$TADA.ResultDepthHeightMeasure.MeasureValue, " ",
-              param[[j]]$TADA.ResultDepthHeightMeasure.MeasureUnitCode
-            ), "<br>",
-            "Activity Relative Depth Name:", param[[j]]$ActivityRelativeDepthName, "<br>",
-            "Activity Depth:", paste0(
-              param[[j]]$TADA.ActivityDepthHeightMeasure.MeasureValue, " ",
-              param[[j]]$TADA.ActivityDepthHeightMeasure.MeasureUnitCode
-            ), "<br>",
-            "Activity Top Depth:", paste0(
-              param[[j]]$TADA.ActivityTopDepthHeightMeasure.MeasureValue, " ",
-              param[[j]]$TADA.ActivityTopDepthHeightMeasure.MeasureUnitCode
-            ), "<br>",
-            "Activity Bottom Depth:", paste0(
-              param[[j]]$TADA.ActivityBottomDepthHeightMeasure.MeasureValue, " ",
-              param[[j]]$TADA.ActivityBottomDepthHeightMeasure.MeasureUnitCode
-            ), "<br>"
+      if (length(groups) >= j) {
+        param[[j]] <- subset(param.data[[j]], param.data[[j]][, "TADA.ComparableDataIdentifier"] %in% unique(plot.data$TADA.ComparableDataIdentifier)[i])
+
+        scatterplot <- scatterplot %>%
+          plotly::add_trace(
+            data = param[[j]],
+            x = ~ as.Date(ActivityStartDate),
+            y = ~TADA.ResultMeasureValue,
+            name = groups[j],
+            marker = list(
+              size = 10,
+              color = tada.pal[j, 1],
+              line = list(color = tada.pal[j, 2], width = 2)
+            ),
+            hoverinfo = "text",
+            hovertext = paste(
+              "Result:", paste0(param[[j]]$TADA.ResultMeasureValue, " ", param[[j]]$TADA.ResultMeasure.MeasureUnitCode), "<br>",
+              "Activity Start Date:", param[[j]]$ActivityStartDate, "<br>",
+              "Activity Start Date Time:", param[[j]]$ActivityStartDateTime, "<br>",
+              "Monitoring Location Name:", param[[j]]$MonitoringLocationName, "<br>",
+              "Media:", param[[j]]$TADA.ActivityMediaName, "<br>",
+              "Media Subdivision:", param[[j]]$ActivityMediaSubdivisionName, "<br>",
+              "Result Depth:", paste0(
+                param[[j]]$TADA.ResultDepthHeightMeasure.MeasureValue, " ",
+                param[[j]]$TADA.ResultDepthHeightMeasure.MeasureUnitCode
+              ), "<br>",
+              "Activity Relative Depth Name:", param[[j]]$ActivityRelativeDepthName, "<br>",
+              "Activity Depth:", paste0(
+                param[[j]]$TADA.ActivityDepthHeightMeasure.MeasureValue, " ",
+                param[[j]]$TADA.ActivityDepthHeightMeasure.MeasureUnitCode
+              ), "<br>",
+              "Activity Top Depth:", paste0(
+                param[[j]]$TADA.ActivityTopDepthHeightMeasure.MeasureValue, " ",
+                param[[j]]$TADA.ActivityTopDepthHeightMeasure.MeasureUnitCode
+              ), "<br>",
+              "Activity Bottom Depth:", paste0(
+                param[[j]]$TADA.ActivityBottomDepthHeightMeasure.MeasureValue, " ",
+                param[[j]]$TADA.ActivityBottomDepthHeightMeasure.MeasureUnitCode
+              ), "<br>"
+            )
           )
-        )
       }
-    }  
+    }
 
     # create plots and store as list
     all_scatterplots[[i]] <- scatterplot
-    
+
     # rename scatterplots to reflect TADA.ComparbaleDataIdentifier (with NAs removed)
     names(all_scatterplots)[i] <- unique(TADA_CharStringRemoveNA(plot.data$TADA.ComparableDataIdentifier))[i]
   }
-  
+
   # filter to return one scatterplot, if only one was generated
   if (length(all_scatterplots) == 1) {
     all_scatterplots <- all_scatterplots[[1]]

diff --git a/R/Filtering.R b/R/Filtering.R
@@ -258,13 +258,17 @@ TADA_FieldValuesTable <- function(.data, field = "null", characteristicName = "n
 #' @examples
 #' data(Data_6Tribes_5y_Harmonized)
 #' # Returns data with ONLY surface water results retained and no TADA.UseForAnalysis.Flag column
-#' Data_6Tribes_Assessment1 <- TADA_AnalysisDataFilter(Data_6Tribes_5y_Harmonized, clean = TRUE, 
-#' surface_water = TRUE, ground_water = FALSE, sediment = FALSE)
+#' Data_6Tribes_Assessment1 <- TADA_AnalysisDataFilter(Data_6Tribes_5y_Harmonized,
+#'   clean = TRUE,
+#'   surface_water = TRUE, ground_water = FALSE, sediment = FALSE
+#' )
 #'
-#' # Returns data frame with ONLY surface water results identified as usable and adds 
+#' # Returns data frame with ONLY surface water results identified as usable and adds
 #' # TADA.UseForAnalysis.Flag column.
-#' Data_6Tribes_Assessment2 <- TADA_AnalysisDataFilter(Data_6Tribes_5y_Harmonized, clean = FALSE, 
-#' surface_water = TRUE, ground_water = FALSE, sediment = FALSE)
+#' Data_6Tribes_Assessment2 <- TADA_AnalysisDataFilter(Data_6Tribes_5y_Harmonized,
+#'   clean = FALSE,
+#'   surface_water = TRUE, ground_water = FALSE, sediment = FALSE
+#' )
 #' unique(Data_6Tribes_Assessment2$TADA.UseForAnalysis.Flag)
 #'
 TADA_AnalysisDataFilter <- function(.data,