Irene dev2

.Rbuildignore

@@ -1,2 +1,3 @@
 ^.*\.Rproj$
 ^\.Rproj\.user$
+test-space.R

DESCRIPTION

@@ -30,7 +30,7 @@ Imports:
     measurements,
     statip,
     stringr
-RoxygenNote: 7.2.0
+RoxygenNote: 7.3.1
 Suggests: 
     testthat,
     roxygen2

NAMESPACE

@@ -4,32 +4,44 @@ export(BMIDiversityMetricsPlot)
 export(BMIFormatted)
 export(BMIFunctionalMetricsPlot)
 export(BMIGeneralMetricsPlot)
+export(BMIHabitMetricsPlot)
 export(BMILong)
+export(BMIMetricsLong)
+export(BMISensitivityMetricsPlot)
+export(BMISpecies)
 export(BMITaxonomicMetricsPlot)
-export(BMIToleranceMetricsPlot)
+export(BenchmarkConsistencyPlot)
 export(ChannelCharacteristics)
-export(ChannelFLow)
+export(ChannelFlow)
 export(ChannelSubstrate)
-export(ChemANC)
+export(ChemFormatted)
 export(ChemLakeANCPlot)
 export(ChemLakeIonPlot)
+export(ChemLakeIonSplitPlot)
 export(ChemLakeNutrientBarPlot)
 export(ChemLakeNutrientPlot)
+export(ChemLakeNutrientSplitPlot)
 export(ChemStreamANCPlot)
 export(ChemStreamIonPlot)
+export(ChemStreamIonSplitPlot)
 export(ChemStreamNutrientBarPlot)
 export(ChemStreamNutrientPlot)
+export(ChemStreamNutrientSplitPlot)
 export(CloseDatabaseConnection)
 export(FormatPlot)
 export(GetRawData)
 export(GetSiteName)
 export(LakeSurfaceElevation)
 export(LakeWqMedian)
-export(OpenDatabaseConnection)
+export(LoadStreamsAndLakes)
 export(PlotBenchmarkElevation)
 export(PlotLakeSurfaceElevation)
+export(PlotStringComparisons)
+export(ReadAndFilterData)
+export(ReadCSV)
 export(SaveDataToCsv)
 export(StreamWqMedian)
+export(StringSurveyElevation)
 export(SurveyPointElevation)
 export(WqDailyMean)
 export(WqDailyMeanLong)
@@ -38,9 +50,10 @@ export(WqPlotPHDepthProfile)
 export(WqPlotSpCondDepthProfile)
 export(WqPlotTemperatureDepthProfile)
 export(expect_dataframe_equal)
+export(fetchAndWrangleAGOL)
 export(getMDLLookup)
 export(qcBMIDiscrepancies)
-export(qcBenchmarkElevation)
+export(qcBenchmarkConsistency)
 export(qcChemFieldBlanks)
 export(qcChemFieldDupes)
 export(qcChemFlags)
@@ -72,6 +85,7 @@ export(qcWqCompletenessPlot)
 export(qcWqGrades)
 export(qcWqGradesLong)
 export(qcWqGradesPlot)
+export(writeBMI)
 importFrom(magrittr,"%<>%")
 importFrom(magrittr,"%>%")
 importFrom(stats,median)

R/calculations.R

@@ -25,99 +25,93 @@ MaxDQF <- function(flags) {
 
 #' Calculate median values for each water quality parameter for each lake visit.
 #'
-#' @param conn Database connection generated from call to \code{OpenDatabaseConnection()}. Ignored if \code{data.source} is \code{"local"}.
-#' @param path.to.data The directory containing the csv data exports generated from \code{SaveDataToCsv()}. Ignored if \code{data.source} is \code{"database"}.
 #' @param park Optional. Four-letter park code to filter on, e.g. "GRBA".
 #' @param site Optional. Site code to filter on, e.g. "GRBA_L_DEAD0".
 #' @param field.season Optional. Field season name to filter on, e.g. "2019".
-#' @param data.source Character string indicating whether to access data in the live database (\code{"database"}, default) or to use data saved locally (\code{"local"}). In order to access the most up-to-date data, it is recommended that you select \code{"database"} unless you are working offline or your code will be shared with someone who doesn't have access to the database.
 #'
-#' @return A tibble with columns for park, field season, site code, visit date, and the median values, flags, and counts for temperature, specific conductance, pH, and dissolved oxygen.
+#' @return A tibble
 #' @export
 #'
-LakeWqMedian <- function(conn, path.to.data, park, site, field.season, data.source = "database") {
-  temp <- ReadAndFilterData(conn = conn, path.to.data = path.to.data, park = park, site = site, field.season = field.season, data.source = data.source, data.name = "WaterQualityTemperature")
-  spcond <- ReadAndFilterData(conn = conn, path.to.data = path.to.data, park = park, site = site, field.season = field.season, data.source = data.source, data.name = "WaterQualitySpCond")
-  ph <- ReadAndFilterData(conn = conn, path.to.data = path.to.data, park = park, site = site, field.season = field.season, data.source = data.source, data.name = "WaterQualitypH")
-  do <- ReadAndFilterData(conn = conn, path.to.data = path.to.data, park = park, site = site, field.season = field.season, data.source = data.source, data.name = "WaterQualityDO")
+LakeWqMedian <- function(park, site, field.season) {
+  temp <- ReadAndFilterData(park = park, site = site, field.season = field.season, data.name = "WaterQualityTemperature")
+  spcond <- ReadAndFilterData(park = park, site = site, field.season = field.season, data.name = "WaterQualitySpCond")
+  ph <- ReadAndFilterData(park = park, site = site, field.season = field.season, data.name = "WaterQualitypH")
+  do <- ReadAndFilterData(park = park, site = site, field.season = field.season, data.name = "WaterQualityDO")
 
-  wq.visits <- ReadAndFilterData(conn = conn, path.to.data = path.to.data, park = park, site = site, field.season = field.season, data.source = data.source, data.name = "Visit")
+  wq.visits <- ReadAndFilterData(park = park, site = site, field.season = field.season, data.name = "Visit")
 
-  temp.med <- temp %>%
-    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) %>%
-    dplyr::filter(MonitoringStatus == "Sampled") %>%
-    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, MeasurementDepth_m, Flag, FlagNote, DPL) %>%
+  temp.med <- temp |>
+    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) |>
+    dplyr::filter(MonitoringStatus == "Sampled") |>
+    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, MeasurementDepth_m, Flag, FlagNote, DPL) |>
     dplyr::summarise(TemperatureMedian_C = median(WaterTemperature_C, na.rm = TRUE),
-                     TemperatureCount = sum(!is.na(WaterTemperature_C))) %>%
-    dplyr::rename(TemperatureFlag = Flag) %>%
+                     TemperatureCount = sum(!is.na(WaterTemperature_C))) |>
+    dplyr::rename(TemperatureFlag = Flag) |>
     dplyr::arrange(SiteCode, VisitDate)
 
-  spcond.med <- spcond %>%
-    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) %>%
-    dplyr::filter(MonitoringStatus == "Sampled") %>%
-    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, MeasurementDepth_m, Flag, FlagNote, DPL) %>%
+  spcond.med <- spcond |>
+    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) |>
+    dplyr::filter(MonitoringStatus == "Sampled") |>
+    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, MeasurementDepth_m, Flag, FlagNote, DPL) |>
     dplyr::summarise(SpCondMedian_microS_per_cm = median(SpecificConductance_microS_per_cm, na.rm = TRUE),
-                     SpCondCount = sum(!is.na(SpecificConductance_microS_per_cm))) %>%
-    dplyr::rename(SpCondFlag = Flag) %>%
+                     SpCondCount = sum(!is.na(SpecificConductance_microS_per_cm))) |>
+    dplyr::rename(SpCondFlag = Flag) |>
     dplyr::arrange(SiteCode, VisitDate)
 
-  ph.med <- ph %>%
-    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) %>%
-    dplyr::filter(MonitoringStatus == "Sampled") %>%
-    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, MeasurementDepth_m, Flag, FlagNote, DPL) %>%
+  ph.med <- ph |>
+    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) |>
+    dplyr::filter(MonitoringStatus == "Sampled") |>
+    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, MeasurementDepth_m, Flag, FlagNote, DPL) |>
     dplyr::summarise(pHMedian = median(pH, na.rm = TRUE),
-                     pHCount = sum(!is.na(pH))) %>%
-    dplyr::rename(pHFlag = Flag) %>%
+                     pHCount = sum(!is.na(pH))) |>
+    dplyr::rename(pHFlag = Flag) |>
     dplyr::arrange(SiteCode, VisitDate)
 
-  do.med <- do %>%
-    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) %>%
-    dplyr::filter(MonitoringStatus == "Sampled") %>%
-    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, MeasurementDepth_m, Flag, FlagNote, DPL) %>%
+  do.med <- do |>
+    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) |>
+    dplyr::filter(MonitoringStatus == "Sampled") |>
+    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, MeasurementDepth_m, Flag, FlagNote, DPL) |>
     dplyr::summarise(DOMedian_percent = median(DissolvedOxygen_percent), DOMedian_mg_per_L = median(DissolvedOxygen_mg_per_L),
                      DOPercentCount = sum(!is.na(DissolvedOxygen_percent)),
-                     DOmgLCount = sum(!is.na(DissolvedOxygen_mg_per_L))) %>%
-    dplyr::rename(DOFlag = Flag) %>%
+                     DOmgLCount = sum(!is.na(DissolvedOxygen_mg_per_L))) |>
+    dplyr::rename(DOFlag = Flag) |>
     dplyr::arrange(SiteCode, VisitDate)
 
-  wq.med <- temp.med %>%
-    dplyr::full_join(spcond.med, by = c("Park", "FieldSeason", "SiteCode", "VisitDate", "VisitType", "SampleFrame", "MeasurementDepth_m", "FlagNote", "DPL")) %>%
-    dplyr::full_join(ph.med, by = c("Park", "FieldSeason", "SiteCode", "VisitDate", "VisitType", "SampleFrame", "MeasurementDepth_m", "FlagNote", "DPL")) %>%
-    dplyr::full_join(do.med, by = c("Park", "FieldSeason", "SiteCode", "VisitDate", "VisitType", "SampleFrame", "MeasurementDepth_m", "FlagNote", "DPL")) %>%
+  wq.med <- temp.med |>
+    dplyr::full_join(spcond.med, by = c("Park", "FieldSeason", "SiteCode", "VisitDate", "VisitType", "SampleFrame", "MeasurementDepth_m", "FlagNote", "DPL")) |>
+    dplyr::full_join(ph.med, by = c("Park", "FieldSeason", "SiteCode", "VisitDate", "VisitType", "SampleFrame", "MeasurementDepth_m", "FlagNote", "DPL")) |>
+    dplyr::full_join(do.med, by = c("Park", "FieldSeason", "SiteCode", "VisitDate", "VisitType", "SampleFrame", "MeasurementDepth_m", "FlagNote", "DPL")) |>
     dplyr::ungroup()
 
   return(wq.med)
 }
 
 #' Calculate median values for each water quality parameter for each stream visit.
 #'
-#' @param conn Database connection generated from call to \code{OpenDatabaseConnection()}. Ignored if \code{data.source} is \code{"local"}.
-#' @param path.to.data The directory containing the csv data exports generated from \code{SaveDataToCsv()}. Ignored if \code{data.source} is \code{"database"}.
 #' @param park Optional. Four-letter park code to filter on, e.g. "GRBA".
 #' @param site Optional. Site code to filter on, e.g. "GRBA_L_DEAD0".
 #' @param field.season Optional. Field season name to filter on, e.g. "2019".
-#' @param data.source Character string indicating whether to access data in the live database (\code{"database"}, default) or to use data saved locally (\code{"local"}). In order to access the most up-to-date data, it is recommended that you select \code{"database"} unless you are working offline or your code will be shared with someone who doesn't have access to the database.
 #'
-#' @return A tibble with columns for park, field season, site code, visit date, and the median values, flags, and counts for temperature, specific conductance, pH, and dissolved oxygen.
+#' @return A tibble
 #' @export
 #'
-StreamWqMedian <- function(conn, path.to.data, park, site, field.season, data.source = "database") {
-  stream_wq <- ReadAndFilterData(conn = conn, path.to.data = path.to.data, park = park, site = site, field.season = field.season, data.source = data.source, data.name = "WQStreamXSection")
-  wq.visits <- ReadAndFilterData(conn = conn, path.to.data = path.to.data, park = park, site = site, field.season = field.season, data.source = data.source, data.name = "Visit")
+StreamWqMedian <- function(park, site, field.season) {
+  stream_wq <- ReadAndFilterData(park = park, site = site, field.season = field.season, data.name = "WQStreamXSection")
+  wq.visits <- ReadAndFilterData(park = park, site = site, field.season = field.season, data.name = "Visit")
 
-  stream_wq_med <- stream_wq %>%
-    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate, MonitoringStatus), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) %>%
-    dplyr::filter(MonitoringStatus == "Sampled") %>%
-    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, pHFlag, DOFlag, SpCondFlag, TemperatureFlag, FlagNote, DPL) %>%
+  stream_wq_med <- stream_wq |>
+    dplyr::left_join(dplyr::select(wq.visits, SampleFrame, Park, FieldSeason, SiteCode, VisitDate, MonitoringStatus), by = c("Park", "FieldSeason", "SiteCode", "VisitDate")) |>
+    dplyr::filter(MonitoringStatus == "Sampled") |>
+    dplyr::group_by(Park, FieldSeason, SiteCode, VisitDate, VisitType, SampleFrame, pHFlag, DOFlag, SpCondFlag, TemperatureFlag, FlagNote, DPL) |>
     dplyr::summarise(TemperatureMedian_C = median(WaterTemperature_C),
                      TemperatureCount = sum(!is.na(WaterTemperature_C)),
                      pHMedian = median(pH),
                      pHCount = sum(!is.na(pH)),
                      DOMedian_mg_per_L = median(DissolvedOxygen_mg_per_L),
                      DOmgLCount = sum(!is.na(DissolvedOxygen_mg_per_L)),
                      SpCondMedian_microS_per_cm = median(SpecificConductance_microS_per_cm),
-                     SpCondCount = sum(!is.na(SpecificConductance_microS_per_cm))) %>%
-    dplyr::arrange(SiteCode, VisitDate) %>%
+                     SpCondCount = sum(!is.na(SpecificConductance_microS_per_cm))) |>
+    dplyr::arrange(SiteCode, VisitDate) |>
     dplyr::ungroup()