USEPA · hillarymarler · Aug 6, 2024 · Jul 10, 2024 · Jul 10, 2024 · Jul 10, 2024
diff --git a/.gitignore b/.gitignore
@@ -23,3 +23,6 @@ test_function.R
 _snaps
 
 testing_log.txt
+
+# test data from AK
+AK_EPATADA_troubleshooting
diff --git a/R/ResultFlagsIndependent.R b/R/ResultFlagsIndependent.R
@@ -153,11 +153,11 @@ TADA_FlagMethod <- function(.data, clean = TRUE, flaggedonly = FALSE) {
 #' results are included in the output. When flaggedonly = TRUE, the dataframe
 #' will be filtered to include only the rows flagged as "Continuous" results.
 #' @param time_difference Numeric argument defining the maximum time difference
-#' in hours between measurements taken on the same day. This is used to search for
-#' continuous time series data (i.e., if there are multiple measurements on the same
-#' day within the selected time_difference, then the row will be flagged as
-#' continuous). The default time window is 4 hours. The time_difference can be
-#' adjusted by the user.
+#' in hours between measurements of the same TADA.ComparableDataIdentifier taken at the same
+#' latitude, longitude, and depth. This is used to search for
+#' continuous time series data (i.e., if there are multiple measurements within the selected
+#' time_difference, then the row will be flagged as continuous). The default time window is 4 hours.
+#' The time_difference can be adjusted by the user.
 #' @return The default is clean = FALSE and flaggedonly = FALSE.
 #' When clean = FALSE and flaggedonly = FALSE (default), a new column,
 #' "TADA.ContinuousData.Flag", is appended to the input data set which
@@ -194,24 +194,9 @@ TADA_FlagMethod <- function(.data, clean = TRUE, flaggedonly = FALSE) {
 #' # Remove continuous data in dataframe
 #' Data_Nutrients_UT_clean <- TADA_FlagContinuousData(Data_Nutrients_UT, clean = TRUE)
 #' unique(Data_Nutrients_UT_clean$TADA.ContinuousData.Flag)
-#'
-#' data(Data_R5_TADAPackageDemo)
-#'
-#' # Flag continuous data in new column titled "TADA.ContinuousData.Flag"
-#' Data_R5_TADAPackageDemo_flags <- TADA_FlagContinuousData(Data_R5_TADAPackageDemo, clean = FALSE)
-#' unique(Data_R5_TADAPackageDemo_flags$TADA.ContinuousData.Flag)
-#'
-#' # Show only rows flagged as continuous data
-#' Data_R5_TADAPackageDemo_flaggedonly <- TADA_FlagContinuousData(Data_R5_TADAPackageDemo, clean = FALSE, flaggedonly = TRUE)
-#'
-#' # Remove continuous data in dataframe
-#' Data_R5_TADAPackageDemo_clean <- TADA_FlagContinuousData(Data_R5_TADAPackageDemo, clean = TRUE)
-#' unique(Data_R5_TADAPackageDemo_clean$TADA.ContinuousData.Flag)
 #' }
 #'
 TADA_FlagContinuousData <- function(.data, clean = FALSE, flaggedonly = FALSE, time_difference = 4) {
-  # start.time <- Sys.time()
-
   # check .data is data.frame
   TADA_CheckType(.data, "data.frame", "Input object")
   # check clean is boolean
@@ -269,124 +254,100 @@ TADA_FlagContinuousData <- function(.data, clean = FALSE, flaggedonly = FALSE, t
     (SampleCollectionEquipmentName == "Probe/Sensor" & !is.na(ResultTimeBasisText)) |
     (SampleCollectionEquipmentName == "Probe/Sensor" & !is.na(StatisticalBaseCode)) |
     (SampleCollectionEquipmentName == "Probe/Sensor" & ResultValueTypeName == "Calculated") |
-    (SampleCollectionEquipmentName == "Probe/Sensor" & ResultValueTypeName == "Estimated")))
+    (SampleCollectionEquipmentName == "Probe/Sensor" & ResultValueTypeName == "Estimated"))) %>%
+    dplyr::mutate(TADA.ContinuousData.Flag = "Continuous")
 
   # everything not YET in cont dataframe
   noncont.data <- subset(.data, !.data$ResultIdentifier %in% cont.data$ResultIdentifier)
 
   # if time field is not NA, find time difference between results
+
   if (length(noncont.data) >= 1) {
-    for (i in 1:nrow(noncont.data)) {
-      if (!is.na(noncont.data$ActivityStartDateTime[i])) {
-        # find samples with the same date, lat/long, organization name, comparable data identifier, and depth
-        info_match <- which(
-          noncont.data$TADA.LatitudeMeasure == noncont.data$TADA.LatitudeMeasure[i] &
-            noncont.data$TADA.LongitudeMeasure == noncont.data$TADA.LongitudeMeasure[i] &
-            noncont.data$OrganizationIdentifier == noncont.data$OrganizationIdentifier[i] &
-            noncont.data$TADA.ComparableDataIdentifier == noncont.data$TADA.ComparableDataIdentifier[i] &
-            ((noncont.data$TADA.ActivityDepthHeightMeasure.MeasureValue == noncont.data$TADA.ActivityDepthHeightMeasure.MeasureValue[i]) | (is.na(noncont.data$TADA.ActivityDepthHeightMeasure.MeasureValue) & is.na(noncont.data$TADA.ActivityDepthHeightMeasure.MeasureValue[i]))) &
-            ((noncont.data$TADA.ResultDepthHeightMeasure.MeasureValue == noncont.data$TADA.ResultDepthHeightMeasure.MeasureValue[i]) | (is.na(noncont.data$TADA.ResultDepthHeightMeasure.MeasureValue) & is.na(noncont.data$TADA.ResultDepthHeightMeasure.MeasureValue[i]))) &
-            ((noncont.data$TADA.ActivityTopDepthHeightMeasure.MeasureValue == noncont.data$TADA.ActivityTopDepthHeightMeasure.MeasureValue[i]) | (is.na(noncont.data$TADA.ActivityTopDepthHeightMeasure.MeasureValue) & is.na(noncont.data$TADA.ActivityTopDepthHeightMeasure.MeasureValue[i]))) &
-            ((noncont.data$TADA.ActivityBottomDepthHeightMeasure.MeasureValue == noncont.data$TADA.ActivityBottomDepthHeightMeasure.MeasureValue[i]) | (is.na(noncont.data$TADA.ActivityBottomDepthHeightMeasure.MeasureValue) & is.na(noncont.data$TADA.ActivityBottomDepthHeightMeasure.MeasureValue[i]))) &
-            ((noncont.data$ActivityRelativeDepthName == noncont.data$ActivityRelativeDepthName[i]) | (is.na(noncont.data$ActivityRelativeDepthName) & is.na(noncont.data$ActivityRelativeDepthName[i])))
-        )
+    info_match <- noncont.data %>%
+      dplyr::group_by(
+        TADA.LatitudeMeasure, TADA.LongitudeMeasure,
+        OrganizationIdentifier, TADA.ComparableDataIdentifier,
+        TADA.ActivityDepthHeightMeasure.MeasureValue,
+        TADA.ResultDepthHeightMeasure.MeasureValue,
+        TADA.ActivityBottomDepthHeightMeasure.MeasureValue,
+        TADA.ActivityTopDepthHeightMeasure.MeasureValue,
+        ActivityRelativeDepthName
+      ) %>%
+      dplyr::mutate(n_records = length(TADA.ResultMeasureValue)) %>%
+      dplyr::mutate(group_id = dplyr::cur_group_id()) %>%
+      dplyr::filter(n_records > 1) %>%
+      dplyr::ungroup() %>%
+      dplyr::group_by(group_id) %>%
+      dplyr::arrange(ActivityStartDateTime, .by_group = TRUE) %>%
+      dplyr::mutate(
+        time_diff_lag = abs(difftime(ActivityStartDateTime, dplyr::lag(ActivityStartDateTime), units = "hours")),
+        time_diff_lead = abs(difftime(ActivityStartDateTime, dplyr::lead(ActivityStartDateTime), units = "hours"))
+      ) %>%
+      dplyr::ungroup()
 
-        time_diff <- abs(difftime(noncont.data$ActivityStartDateTime[i], noncont.data$ActivityStartDateTime[info_match], units = "hours"))
+    # find results where the time differences is <= time_difference (default is 4 hours)
+    within_window <- info_match %>%
+      dplyr::filter(time_diff_lead <= time_difference |
+        time_diff_lag <= time_difference)
 
-        # samples where the time differences is <= time_difference (default is 4 hours)
-        within_window <- info_match[time_diff <= time_difference]
+    rm(info_match)
 
-        # keep the samples with times within the window
-        info_match <- intersect(info_match, within_window)
+    # if matches are identified change flag to continuous
+    noncont.data <- noncont.data %>%
+      dplyr::mutate(TADA.ContinuousData.Flag = ifelse(ResultIdentifier %in% within_window$ResultIdentifier,
+        "Continuous", TADA.ContinuousData.Flag
+      ))
 
-        # if matches are identified change flag to continuous
-        if (length(info_match) >= 1) {
-          noncont.data$TADA.ContinuousData.Flag[info_match] <- "Continuous"
-        }
-      }
-    }
+    rm(within_window)
   }
 
-  # remove continuous results from noncont.data and create new df for these (more.cont.data)
-  more.cont.data <- noncont.data %>% dplyr::filter(TADA.ContinuousData.Flag == "Continuous")
-
-  # add additional continuous data (more.cont.data) to cont.data
-  all.cont.data <- plyr::rbind.fill(cont.data, more.cont.data)
-
-  # filter noncont.data to ONLY discrete results
-  noncont.data <- noncont.data %>% dplyr::filter(TADA.ContinuousData.Flag == "Discrete")
-
-  # if there is continuous data in the data set
-  # flag output
-  if (nrow(all.cont.data) != 0) {
-    # change contents of ContDataFlag column
-    all.cont.data$TADA.ContinuousData.Flag <- "Continuous"
-    # join all.cont.data to flag.data
-    flag.data <- plyr::rbind.fill(all.cont.data, noncont.data)
-
-    # flagged output, all data
-    if (clean == FALSE & flaggedonly == FALSE) {
-      flag.data <- TADA_OrderCols(flag.data)
-
-      # end.time <- Sys.time()
-      # time.taken <- round(end.time - start.time, 2)
-      # print(time.taken)
-
-      return(flag.data)
-    }
-
-    # clean output
-    if (clean == TRUE & flaggedonly == FALSE) {
-      # filter out invalid characteristic-unit-media combinations
-      clean.data <- dplyr::filter(flag.data, !(TADA.ContinuousData.Flag %in% "Continuous"))
+  flag.data <- cont.data %>%
+    dplyr::full_join(noncont.data, by = c(names(cont.data)))
 
-      # remove TADA.AggregatedContinuousData column
-      # clean.data <- dplyr::select(clean.data, -TADA.ContinuousData.Flag)
-      clean.data <- TADA_OrderCols(clean.data)
+  # flagged output, all data
+  if (clean == FALSE & flaggedonly == FALSE) {
+    flag.data <- TADA_OrderCols(flag.data)
 
-      # end.time <- Sys.time()
-      # time.taken <- round(end.time - start.time, 2)
-      # print(time.taken)
+    return(flag.data)
+  }
 
-      return(clean.data)
-    }
+  # clean output
+  if (clean == TRUE & flaggedonly == FALSE) {
+    # filter out invalid characteristic-unit-media combinations
+    clean.data <- flag.data %>%
+      dplyr::filter(!(TADA.ContinuousData.Flag %in% "Continuous")) %>%
+      dplyr::select(-TADA.ContinuousData.Flag) %>%
+      TADA_OrderCols()
 
-    # flagged output, only aggregated continuous data
-    if (clean == FALSE & flaggedonly == TRUE) {
-      # filter to show only invalid characteristic-unit-media combinations
+    return(clean.data)
+  }
 
-      onlycont.data <- dplyr::filter(flag.data, TADA.ContinuousData.Flag == "Continuous")
-      onlycont.data <- TADA_OrderCols(onlycont.data)
+  # flagged output, only aggregated continuous data
+  if (clean == FALSE & flaggedonly == TRUE) {
+    # filter to show only invalid characteristic-unit-media combinations
 
-      # end.time <- Sys.time()
-      # time.taken <- round(end.time - start.time, 2)
-      # print(time.taken)
+    onlycont.data <- flag.data %>%
+      dplyr::filter(TADA.ContinuousData.Flag == "Continuous") %>%
+      TADA_OrderCols()
 
-      return(onlycont.data)
-    }
+    return(onlycont.data)
   }
 
+
   # if no aggregated continuous data is in the data set
-  if (nrow(all.cont.data) == 0) {
+  if (nrow(flag.data[flag.data$TADA.ContinuousData.Flag == "Continuous", ]) == 0) {
     if (flaggedonly == FALSE) {
       print("No evidence of aggregated continuous data in your dataframe. Returning the input dataframe with TADA.ContinuousData.Flag column for tracking.")
       .data <- TADA_OrderCols(.data)
 
-      # end.time <- Sys.time()
-      # time.taken <- round(end.time - start.time, 2)
-      # print(time.taken)
-
       return(.data)
     }
 
     if (flaggedonly == TRUE) {
       print("This dataframe is empty because we did not find any aggregated continuous data in your dataframe")
 
-      all.cont.data <- TADA_OrderCols(all.cont.data)
-
-      # end.time <- Sys.time()
-      # time.taken <- round(end.time - start.time, 2)
-      # print(time.taken)
+      all.cont.data <- flag.data %>%
+        dplyr::filter(TADA.ContinuousData.Flag == "Continuous")
 
       return(all.cont.data)
     }

diff --git a/data/Data_6Tribes_5y_Harmonized.rda b/data/Data_6Tribes_5y_Harmonized.rda
diff --git a/data/Data_NCTCShepherdstown_HUC12.rda b/data/Data_NCTCShepherdstown_HUC12.rda
diff --git a/data/Data_Nutrients_UT.rda b/data/Data_Nutrients_UT.rda
diff --git a/data/Data_R5_TADAPackageDemo.rda b/data/Data_R5_TADAPackageDemo.rda
diff --git a/inst/extdata/AKAllotments.dbf b/inst/extdata/AKAllotments.dbf
diff --git a/inst/extdata/AKVillages.dbf b/inst/extdata/AKVillages.dbf
diff --git a/inst/extdata/AmericanIndian.dbf b/inst/extdata/AmericanIndian.dbf
diff --git a/inst/extdata/OKTribe.dbf b/inst/extdata/OKTribe.dbf
diff --git a/inst/extdata/OffReservation.dbf b/inst/extdata/OffReservation.dbf
diff --git a/inst/extdata/VATribe.dbf b/inst/extdata/VATribe.dbf
diff --git a/inst/extdata/WQXCharacteristicRef.csv b/inst/extdata/WQXCharacteristicRef.csv
@@ -28,6 +28,7 @@
 "(-)-1-Perfluoropropylethanol","Accepted",""
 "(-)-Perfluoro(2-propoxypropionic) acid","Accepted",""
 "(-)-cis-Permethrin","Accepted",""
+"(-)-delta9-THC-d3","Accepted",""
 "(-)-trans-4-(4-Fluorophenyl)-3-(4-hydroxy-3-methoxyphenoxymethyl)piperidine","Accepted",""
 "(-)-trans-Permethrin","Accepted",""
 "(.alpha.R,7.alpha.)-3-Hydroxy-.alpha.-((perfluorobutyl)propyl)-17-oxo-estra-1,3,5(10)-triene-7-decanoic acid","Accepted",""

diff --git a/inst/extdata/WQXMeasureQualifierCodeRef.csv b/inst/extdata/WQXMeasureQualifierCodeRef.csv
@@ -16,6 +16,7 @@
 "Result Measure Qualifier(MeasureQualifierCode)",822,"AP","The analyte was positively identified and the associated numerical value is the approximate concentration of the analyte in the sample. And serial dilution acceptance criteria not met","4/10/2020 6:07:22 PM","Pass"
 "Result Measure Qualifier(MeasureQualifierCode)",1121,"AR","counts outside acceptable range","8/16/2021 11:05:27 AM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",5,"B","Detection in blank, Analyte found in sample and associated blank","4/24/2008 8:28:30 AM","Suspect"
+"Result Measure Qualifier(MeasureQualifierCode)",1200,"B3","Target analyte detected in calibration blank at or above the method reporting limit","8/1/2024 7:33:52 PM","Not Reviewed"
 "Result Measure Qualifier(MeasureQualifierCode)",124,"BAC","Correction Factor, background","3/16/2016 1:04:24 PM","Pass"
 "Result Measure Qualifier(MeasureQualifierCode)",125,"BQL","Below Quantitation Limit","7/6/2016 4:09:14 PM","Non-Detect"
 "Result Measure Qualifier(MeasureQualifierCode)",126,"BRL","Below Reporting Limit","7/6/2016 4:09:17 PM","Non-Detect"
@@ -56,6 +57,7 @@
 "Result Measure Qualifier(MeasureQualifierCode)",19,"EE","Identifies compounds whose concentration exceed the calibration range addition of the instrument for that specific analysis.","8/28/2009 5:04:35 PM","Over-Detect"
 "Result Measure Qualifier(MeasureQualifierCode)",788,"EER","No Result Reported, entry error; Original value is known to be incorrect due to a data entry error.  The correct value could not be determined. No result value was reported","1/23/2020 2:12:06 PM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",59,"EFAI","Equipment failure","8/22/2013 1:44:00 PM","Suspect"
+"Result Measure Qualifier(MeasureQualifierCode)",1196,"EMCL","Value Exceeds Maximum Contaminant Level","8/1/2024 7:33:52 PM","Not Reviewed"
 "Result Measure Qualifier(MeasureQualifierCode)",137,"EMPC","Estimated Maximum Possible Concentration","3/16/2016 1:04:25 PM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",138,"ESD","Estimated Value, serial dilution difference","3/16/2016 1:04:25 PM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",139,"EST","Estimated Value, outside limit of precision","3/16/2016 1:04:25 PM","Suspect"
@@ -131,6 +133,8 @@
 "Result Measure Qualifier(MeasureQualifierCode)",161,"JCN","Sample Container Damaged, no sample lost","3/16/2016 1:04:25 PM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",162,"JCW","Sample Container Damaged, sample lost","3/16/2016 1:04:25 PM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",66,"K","Value below the detection Limit.  For BOD: depletion is less than 1.0","8/22/2013 1:44:00 PM","Non-Detect"
+"Result Measure Qualifier(MeasureQualifierCode)",1198,"K5","The dilution water D.O. depletion was > 0.2 mg/L","8/1/2024 7:33:52 PM","Not Reviewed"
+"Result Measure Qualifier(MeasureQualifierCode)",1197,"K6","Glucose/glutamic acid BOD was below method acceptance criteria","8/1/2024 7:33:52 PM","Not Reviewed"
 "Result Measure Qualifier(MeasureQualifierCode)",163,"KCF","Known Contamination, field","3/16/2016 1:04:25 PM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",1061,"KCX","Known Contamination, unknown","7/10/2020 12:31:13 PM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",18,"KK","True bacterial concentration is assumed to be less than the reported value. ","8/28/2009 5:04:14 PM","Suspect"
@@ -157,6 +161,7 @@
 "Result Measure Qualifier(MeasureQualifierCode)",834,"LTGTE","Result is less than the MQL but greater than or equal to the MDL","4/10/2020 6:07:23 PM","Non-Detect"
 "Result Measure Qualifier(MeasureQualifierCode)",33,"LVER","low calibration verification standard recovery, potential low bias","5/19/2010 11:24:47 AM","Pass"
 "Result Measure Qualifier(MeasureQualifierCode)",1188,"M3","Spike recovery value unusable since the analyte concentration in the sample is disproportionate to the spike level","7/18/2024 8:49:50 PM","Not Reviewed"
+"Result Measure Qualifier(MeasureQualifierCode)",1199,"M4","The analysis of the spiked sample required a dilution such that the spike recovery calculation does not provide useful information","8/1/2024 7:33:52 PM","Not Reviewed"
 "Result Measure Qualifier(MeasureQualifierCode)",168,"M6F","More Than 6 Flags Applied","3/16/2016 1:04:26 PM","Pass"
 "Result Measure Qualifier(MeasureQualifierCode)",813,"MDL","Method Detection Level","4/10/2020 6:07:22 PM","Suspect"
 "Result Measure Qualifier(MeasureQualifierCode)",1189,"MHA","Due to high levels of analyte in the sample, the MS/MSD calculation does not provide useful spike recovery information","7/18/2024 8:49:50 PM","Not Reviewed"

diff --git a/man/TADA_FlagContinuousData.Rd b/man/TADA_FlagContinuousData.Rd