USEPA · cristinamullin · Jan 9, 2023 · Nov 22, 2022 · Nov 22, 2022 · Dec 9, 2022
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: TADA
 Type: Package
-Title: Tools for Automated Data Assessment R Package
+Title: Tools for Automated Data Analysis R Package
 Version: 0.0.1
 Organization: U.S. Environmental Protection Agency
 Authors@R: 
@@ -9,11 +9,11 @@ Authors@R:
            role = c("aut", "cre"),
            email = "[email protected]",
            comment = c(ORCID = "0000-0002-0615-6087")),
+    person(given = "Michelle",
+           family = "Thawley",
+           role = "aut"),
     person(given = "Jacob",
            family = "Greif",
-           role = "aut"), 
-           person(given = "Michelle",
-           family = "Thawley",
            role = "aut"),
     person(given = "Laura",
            family = "Shumway",

diff --git a/R/DataDiscoveryRetrieval.R b/R/DataDiscoveryRetrieval.R
@@ -113,6 +113,10 @@ TADAdataRetrieval <- function(statecode = "null",
   # Retrieve all 3 profiles
   results.DR <- dataRetrieval::readWQPdata(WQPquery,
                                            ignore_attributes = TRUE)
+  #check if any results are available
+  if ((nrow(results.DR) > 0) == FALSE) {
+    stop("Your WQP query returned no results (no data available). Try a different query. Removing some of your query filters OR broadening your search area may help.")
+  }
 
   narrow.DR <- dataRetrieval::readWQPdata(WQPquery, 
                                           dataProfile = "narrowResult", 
@@ -256,7 +260,9 @@ readWQPwebservice <- function(webservice) {
 #'
 #' @examples 
 #' \dontrun{
-#' tada2 <- TADABigdataRetrieval(startDate = "2019-01-01", endDate = "2021-12-31", characteristicName = "Temperature, water", siteType = "Stream")
+#' tada2 <- TADABigdataRetrieval(startDate = "2019-01-01", e
+#' ndDate = "2021-12-31", characteristicName = "Temperature, water", 
+#' siteType = "Stream")
 #' }
 #' 
 

diff --git a/R/Filtering.R b/R/Filtering.R
@@ -12,6 +12,12 @@ TADA.env <- new.env()
 #'
 #' @export
 #'
+#' @examples 
+#' # Load example dataset:
+#' data(Nutrients_Utah)
+#' 
+#' # Create a table of fields and count of unique values in each field:
+#' Fields_Nutrients_Utah <- FilterFields(Nutrients_Utah)
 
 FilterFields <- function(.data) {
   # check .data is data.frame
@@ -90,6 +96,12 @@ FilterFields <- function(.data) {
 #'
 #' @export
 #'
+#' @examples 
+#' # Load example dataset:
+#' data(Nutrients_Utah)
+#' 
+#' # Create table and pie chart of "Hydrologic Condition" unique values and counts:
+#' FieldReview_HydrologicCondition <- FilterFieldReview(field = "HydrologicCondition", Nutrients_Utah)
 
 FilterFieldReview <- function(field, .data) {
   # if provided, check .data is data.frame
@@ -136,7 +148,14 @@ FilterFieldReview <- function(field, .data) {
 #' @return A list of unique characteristics and their counts
 #'
 #' @export
-#'
+#' 
+#' @examples 
+#' # Load example dataset:
+#' data(Nutrients_Utah)
+#' 
+#' # Create a list of parameters in the dataset and the number of records of each paramter:
+#' ParameterList <- FilterParList(Nutrients_Utah)
+
 FilterParList <- function(.data) {
   # count the frequency of each value in CharactersticName field
   ParValueCount <- data.frame(table(list(.data$CharacteristicName)))
@@ -162,7 +181,13 @@ FilterParList <- function(.data) {
 #' subset by a parameter.
 #'
 #' @export
-#'
+#' 
+#' @examples 
+#' # Load example dataset:
+#' data(Nutrients_Utah)
+#' 
+#' # Create list of fields for parameter "AMMONIA" with number of unique values in each field:
+#' AmmoniaFields <- FilterParFields(Nutrients_Utah, parameter = "AMMONIA")
 
 FilterParFields <- function(.data, parameter) {
   # check .data is data.frame
@@ -241,7 +266,8 @@ FilterParFields <- function(.data, parameter) {
     "ResultAnalyticalMethod.MethodName",
     "ResultAnalyticalMethod.MethodIdentifier",
     "ResultAnalyticalMethod.MethodIdentifierContext",
-    "AssemblageSampledName", "DetectionQuantitationLimitTypeName"
+    "AssemblageSampledName", "DetectionQuantitationLimitTypeName", 
+    "MonitoringLocationIdentifier"
   )]
 
 
@@ -261,7 +287,13 @@ FilterParFields <- function(.data, parameter) {
 #' @return A table and pie chart of unique values in the selected field.
 #'
 #' @export
-#'
+#' 
+#' @examples 
+#' # Load example dataset:
+#' data(Nutrients_Utah)
+#' 
+#' # Create table and pie chart of monitoring locations for the parameter "AMMONIA" in dataframe:
+#' AmmoniaMonitoringLocations <- FilterParFieldReview(field = "MonitoringLocationIdentifier", Nutrients_Utah, parameter = "AMMONIA")
 
 FilterParFieldReview <- function(field, .data, parameter) {
   # if provided, check .data is data.frame