Fix 833 fixes

R/AE_Assess.R

@@ -123,13 +123,38 @@ AE_Assess <- function(
 
     # dfAnalyzed --------------------------------------------------------------
     if (strMethod == "NormalApprox") {
-      lData$dfAnalyzed <- gsm::Analyze_NormalApprox(lData$dfTransformed, strType = strType, bQuiet = bQuiet)
-      lData$dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(lData$dfTransformed, vThreshold = vThreshold, strType = strType, bQuiet = bQuiet)
+
+      lData$dfAnalyzed <- gsm::Analyze_NormalApprox(
+        dfTransformed = lData$dfTransformed,
+        strType = strType,
+        bQuiet = bQuiet
+        )
+
+      lData$dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(
+        dfTransformed = lData$dfTransformed,
+        vThreshold = vThreshold,
+        strType = strType,
+        bQuiet = bQuiet
+        )
+
     } else if (strMethod == "poisson") {
-      lData$dfAnalyzed <- gsm::Analyze_Poisson(lData$dfTransformed, bQuiet = bQuiet)
-      lData$dfBounds <- gsm::Analyze_Poisson_PredictBounds(lData$dfTransformed, vThreshold = vThreshold, bQuiet = bQuiet)
+
+      lData$dfAnalyzed <- gsm::Analyze_Poisson(
+        dfTransformed = lData$dfTransformed,
+        bQuiet = bQuiet
+        )
+
+      lData$dfBounds <- gsm::Analyze_Poisson_PredictBounds(
+        dfTransformed = lData$dfTransformed,
+        vThreshold = vThreshold,
+        bQuiet = bQuiet
+        )
     } else if (strMethod == "identity") {
-      lData$dfAnalyzed <- gsm::Analyze_Identity(lData$dfTransformed)
+
+      lData$dfAnalyzed <- gsm::Analyze_Identity(
+        dfTransformed = lData$dfTransformed
+        )
+
     }
 
     strAnalyzeFunction <- paste0("Analyze_", tools::toTitleCase(strMethod))

R/Analyze_NormalApprox.R

@@ -99,7 +99,7 @@ Analyze_NormalApprox <- function(
     arrange(.data$Score)
 
   if (!bQuiet) {
-    cli::cli_text("{.var OverallMetric}, {.var Factor}, {.var Score} columns created from normal approxiamtion")
+    cli::cli_text("{.var OverallMetric}, {.var Factor}, and {.var Score} columns created from normal approximation.")
   }
 
   return(dfAnalyzed)

R/Disp_Assess.R

@@ -127,8 +127,20 @@ Disp_Assess <- function(
 
     # dfAnalyzed --------------------------------------------------------------
     if (strMethod == "NormalApprox") {
-      lData$dfAnalyzed <- gsm::Analyze_NormalApprox(lData$dfTransformed, strType = strType, bQuiet = bQuiet)
-      lData$dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(lData$dfTransformed, vThreshold = vThreshold, strType = strType, bQuiet = bQuiet)
+
+      lData$dfAnalyzed <- gsm::Analyze_NormalApprox(
+        dfTransformed = lData$dfTransformed,
+        strType = strType,
+        bQuiet = bQuiet
+        )
+
+      lData$dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(
+        dfTransformed = lData$dfTransformed,
+        vThreshold = vThreshold,
+        strType = strType,
+        bQuiet = bQuiet
+        )
+
     } else if (strMethod == "fisher") {
       lData$dfAnalyzed <- gsm::Analyze_Fisher(lData$dfTransformed, bQuiet = bQuiet)
     } else if (strMethod == "identity") {

R/Flag_NormalApprox.R

@@ -41,6 +41,7 @@
 #'   strNumeratorCol = "Count",
 #'   strDenominatorCol = "Exposure"
 #' )
+#'
 #' dfAnalyzed <- Analyze_NormalApprox(dfTransformed, strType = "rate")
 #' dfFlagged <- Flag_NormalApprox(dfAnalyzed, vThreshold = c(-3, -2, 2, 3))
 #'

R/LB_Assess.R

@@ -121,9 +121,22 @@ LB_Assess <- function(
     if (!bQuiet) cli::cli_alert_success("{.fn Transform_Rate} returned output with {nrow(lData$dfTransformed)} rows.")
 
     # dfAnalyzed --------------------------------------------------------------
+
     if (strMethod == "NormalApprox") {
-      lData$dfAnalyzed <- gsm::Analyze_NormalApprox(lData$dfTransformed, strType = strType, bQuiet = bQuiet)
-      lData$dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(lData$dfTransformed, vThreshold = vThreshold, strType = strType, bQuiet = bQuiet)
+
+      lData$dfAnalyzed <- gsm::Analyze_NormalApprox(
+        dfTransformed = lData$dfTransformed,
+        strType = strType,
+        bQuiet = bQuiet
+        )
+
+      lData$dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(
+        dfTransformed = lData$dfTransformed,
+        vThreshold = vThreshold,
+        strType = strType,
+        bQuiet = bQuiet
+        )
+
     } else if (strMethod == "fisher") {
       lData$dfAnalyzed <- gsm::Analyze_Fisher(lData$dfTransformed, bQuiet = bQuiet)
     } else if (strMethod == "identity") {

R/PD_Assess.R

@@ -125,8 +125,20 @@ PD_Assess <- function(
 
     # dfAnalyzed --------------------------------------------------------------
     if (strMethod == "NormalApprox") {
-      lData$dfAnalyzed <- gsm::Analyze_NormalApprox(lData$dfTransformed, strType = strType, bQuiet = bQuiet)
-      lData$dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(lData$dfTransformed, vThreshold = vThreshold, strType = strType, bQuiet = bQuiet)
+
+      lData$dfAnalyzed <- gsm::Analyze_NormalApprox(
+        dfTransformed = lData$dfTransformed,
+        strType = strType,
+        bQuiet = bQuiet
+        )
+
+      lData$dfBounds <- gsm::Analyze_NormalApprox_PredictBounds(
+        dfTransformed = lData$dfTransformed,
+        vThreshold = vThreshold,
+        strType = strType,
+        bQuiet = bQuiet
+        )
+
     } else if (strMethod == "poisson") {
       lData$dfAnalyzed <- gsm::Analyze_Poisson(lData$dfTransformed, bQuiet = bQuiet)
       lData$dfBounds <- gsm::Analyze_Poisson_PredictBounds(lData$dfTransformed, vThreshold = vThreshold, bQuiet = bQuiet)

R/tests-assess_helpers.R

@@ -1,5 +1,142 @@
-test_logical_assess_parameters <- function(assess_function, dfInput) {
+test_valid_output_assess <- function(
+    assess_function,
+    dfInput
+) {
+  output <- assess_function(dfInput = dfInput)
+
+  testthat::expect_true(is.list(output))
+  testthat::expect_snapshot(names(output))
+  testthat::expect_snapshot(names(output$lData))
+  testthat::expect_snapshot(names(output$lCharts))
+  testthat::expect_true("data.frame" %in% class(output$lData$dfTransformed))
+  testthat::expect_true("data.frame" %in% class(output$lData$dfAnalyzed))
+  testthat::expect_true("data.frame" %in% class(output$lData$dfFlagged))
+  testthat::expect_true("data.frame" %in% class(output$lData$dfSummary))
+  testthat::expect_true(output$lChecks$status)
+
+  if (exists("dfBounds", where = output$lData)) {
+    testthat::expect_true("data.frame" %in% class(output$lData$dfBounds))
+  }
+
+}
+
+
+################################################################
+
+test_grouping_assess <- function(
+    assess_function,
+    dfInput
+) {
+  subsetGroupCols <- function(assessOutput) {
+    assessOutput[["lData"]][["dfSummary"]] %>% select("GroupID")
+  }
+  site <- assess_function(dfInput)
+  study <- assess_function(dfInput, strGroup = "Study")
+  country <- assess_function(dfInput, strGroup = "Country")
+  customGroup <- assess_function(dfInput, strGroup = "CustomGroup")
+
+  testthat::expect_snapshot(subsetGroupCols(site))
+  testthat::expect_snapshot(subsetGroupCols(study))
+  testthat::expect_snapshot(subsetGroupCols(country))
+  testthat::expect_snapshot(subsetGroupCols(customGroup))
+  testthat::expect_false(all(map_lgl(list(site, study, country, customGroup), ~ all(map_lgl(., ~ is_grouped_df(.))))))
+}
+
+################################################################
+
+test_invalid_data_assess <- function(
+    assess_function,
+    dfInput
+) {
+  testthat::expect_null(assess_function("Hi")[["lData"]])
+  testthat::expect_snapshot_error(assess_function(dfInput, strMethod = 123))
+  testthat::expect_snapshot_error(assess_function(dfInput, strMethod = "abacus"))
+  testthat::expect_snapshot_error(assess_function(dfInput, strMethod = c("identity", "poisson")))
+  testthat::expect_snapshot_error(assess_function(dfInput, vThreshold = "A"))
+  testthat::expect_snapshot_error(assess_function(dfInput, vThreshold = 1))
+  testthat::expect_error(assess_function(dfInput, strGroup = "something"))
+}
+
+################################################################
+
+test_invalid_data_assess_identity <- function(
+    assess_function,
+    dfInput
+) {
+  testthat::expect_null(assess_function("Hi")[["lData"]])
+  testthat::expect_error(assess_function(dfInput, nThreshold = FALSE), "nThreshold must be numeric")
+  testthat::expect_error(assess_function(dfInput, nThreshold = "A"), "nThreshold must be numeric")
+  testthat::expect_error(assess_function(dfInput, nThreshold = c(1, 1)), "nThreshold must be length 1")
+  testthat::expect_error(assess_function(dfInput, strGroup = "something"), "strGroup must be one of: Site, Study, Country, or CustomGroup")
+}
+
+################################################################
+
+test_missing_column_assess <- function(
+    assess_function,
+    dfInput
+) {
+  dfInput_test <- dfInput %>% select("SiteID")
+  missing_req_col <- assess_function(dfInput = dfInput_test)
+
+  testthat::expect_silent(missing_req_col)
+  testthat::expect_null(missing_req_col$lData)
+  testthat::expect_null(missing_req_col$lCharts)
+}
+
+################################################################
+
+test_invalid_mapping_assess <- function(
+    assess_function,
+    dfInput,
+    mapping
+) {
+  mapping_edited <- mapping
+  mapping_edited$dfInput$strSiteCol <- "cupcakes"
+  invalid_map <- dfInput %>% assess_function(lMapping = mapping_edited)
+
+  testthat::expect_null(invalid_map$lData)
+  testthat::expect_null(invalid_map$lCharts)
+  testthat::expect_equal(invalid_map[["lChecks"]][["dfInput"]][["tests_if"]][["has_expected_columns"]][["warning"]],
+                         "the following columns not found in df: cupcakes")
+}
+
+################################################################
+
+test_identity <- function(
+    assess_function,
+    dfInput
+) {
+  identity <- assess_function(dfInput, strMethod = "identity")
+
+  testthat::expect_error(assess_function(dfInput, strMethod = "identity"), NA)
+  testthat::expect_equal(names(identity$lCharts), c("barMetric", "barScore"))
+  testthat::expect_null(identity$lCharts$scatter)
+  testthat::expect_null(identity$lData$dfBounds)
+}
+
+################################################################
+
+test_NA_count <- function(
+    assess_function,
+    dfInput
+) {
+  dfInputNA <- dfInput
+  dfInputNA[1, "Count"] <- NA
+  count_check <- assess_function(dfInput = dfInputNA)
+
+  testthat::expect_silent(count_check)
+  testthat::expect_null(count_check$lData)
+  testthat::expect_null(count_check$lCharts)
+}
+
+################################################################
+
+test_logical_assess_parameters <- function(
+    assess_function,
+    dfInput
+) {
   testthat::expect_snapshot(
-    assessment <- assess_function(dfInput, bQuiet = FALSE)
+    assessOutput <- assess_function(dfInput = dfInput, bQuiet = FALSE)
   )
 }

_pkgdown.yml

@@ -12,9 +12,8 @@ reference:
      desc: Generate a visualization from the Assessment results
    - contents:
       - starts_with("Visualize_")
-      - Analyze_Binary_PredictBounds
+      - Analyze_NormalApprox_PredictBounds
       - Analyze_Poisson_PredictBounds
-      - Analyze_Rate_PredictBounds
 
    - title: Map
      desc: Generate input data from multiple clinical data standards (generally Raw+ or SDTM-)
@@ -24,11 +23,10 @@ reference:
    - title: Analyze
      desc: Conduct statistical analysis based on input data
    - contents:
-      - Analyze_Binary
+      - Analyze_NormalApprox
       - Analyze_Identity
       - Analyze_Fisher
       - Analyze_Poisson
-      - Analyze_Rate
 
    - title: Data Pipeline
      desc: Create site-level summary, analyzed, and flagged data for an assessment
@@ -37,7 +35,7 @@ reference:
       - Transform_Rate
       - Flag
       - Flag_Fisher
-      - Flag_Funnel
+      - Flag_NormalApprox
       - Flag_Poisson
       - Summarize