Bug fix: prevent PKNCA_impute_method_FALSE error from YAML impute_c0 uploaded issue #1266

NEWS.md

@@ -79,6 +79,7 @@
 * DOSNOA computation fixed for specimen-level grouping — urine-only data no longer gets incorrect dose numbering (#1116)
 * Dose-aware AUCint parameters now share the same PPTESTCD as their non-dose-aware counterparts in CDISC exports, with `PPANMETH` indicating the analytical method. Internal PPTESTCDs renamed from misleading `D` suffix (e.g. `AUCINTD`) to lowercase `da` suffix (e.g. `AUCINTda`). Fixed wrong PPTEST label for `AUCINTD` which said "Normalized by Dose" (#1242)
 * Optional settings (`slope_rules`, `int_parameters`, `ratio_table`) are now normalized to `NULL` when empty, instead of persisting as 0-row data frames throughout the app and settings pipeline (#1262)
+* Fixed `PKNCA_impute_method_FALSE` error when YAML settings contain `impute_c0: no` — the `impute` column is now initialized before BLQ imputation to prevent name collision with the boolean function parameter (#1266)
 * Interval-specific parameters (`aucint.*`, `cav.int.*`) excluded from the Parameter Selection matrix — they require finite sub-intervals and must be configured via Partial Interval Calculations (#1309)
 
 ### Ratio Calculations

R/PKNCA.R

@@ -898,6 +898,18 @@ remove_pp_not_requested <- function(pknca_res) {
     pknca_res$data$intervals$impute <- NA_character_
   }
 
+  # Match on PKNCA groups + interval IDs only; exclude keep_interval_cols
+  # to avoid type-mismatch errors on joins.
+  pknca_groups <- unique(c(
+    group_vars(pknca_res$data$conc),
+    group_vars(pknca_res$data$dose)
+  ))
+  match_cols <- unique(c(
+    pknca_groups,
+    "start", "end", "ATPTREF", "DOSNOA", "type_interval"
+  ))
+  match_cols <- intersect(match_cols, names(pknca_res$data$intervals))
+
   # Reshape intervals, filter
   params_not_requested <- pknca_res$data$intervals %>%
     pivot_longer(
@@ -906,7 +918,7 @@ remove_pp_not_requested <- function(pknca_res) {
       values_to = "is_requested"
     ) %>%
     mutate(PPTESTCD = translate_terms(PPTESTCD, "PKNCA", "PPTESTCD")) %>%
-    group_by(across(c(-impute, -is_requested))) %>%
+    group_by(across(all_of(c(match_cols, "PPTESTCD")))) %>%
     summarise(
       is_requested = any(is_requested),
       .groups = "drop"

R/intervals.R

@@ -290,6 +290,12 @@ update_main_intervals <- function(
     data <- create_start_impute(data)
   }
 
+  # Prevent mutate() from resolving `impute` to the function parameter instead
+  # of the column (causes PKNCA_impute_method_FALSE error when impute=FALSE).
+  if (!"impute" %in% names(data$intervals)) {
+    data$intervals$impute <- NA_character_
+  }
+
   ############################################
   # Define a BLQ imputation method for PKNCA
   # and apply it only for non-observational parameters

R/ratio_calculations.R

@@ -130,6 +130,12 @@ calculate_ratios.data.frame <- function(
   )
   group_cols <- setdiff(colnames(data), extra_res_cols)
 
+  # Coerce YAML-sourced character values to match data column types for joins.
+  ref_groups <- .coerce_group_types(ref_groups, data)
+  if (!is.null(test_groups)) {
+    test_groups <- .coerce_group_types(test_groups, data)
+  }
+
   # Define the reference and test data based on the parameters and groups
   df_ref <- as.data.frame(data)[data$PPTESTCD == ref_parameter, , drop = FALSE]
   df_ref <- merge(df_ref, ref_groups)
@@ -468,6 +474,34 @@ calculate_ratio_app <- function(
   list(test_groups = test_groups, ref_groups = ref_groups)
 }
 
+#' Coerce group column types to match the corresponding columns in data.
+#'
+#' When group specifications are sourced from YAML settings, all values
+#' are character strings. If the PKNCA result data has those columns as
+#' numeric (e.g. AGE), joins will fail with incompatible type errors.
+#' This helper converts group columns to match the data column types.
+#'
+#' @param groups A data.frame of group values (from `.parse_ratio_groups`).
+#' @param data A data.frame with the PKNCA result data.
+#' @returns A data.frame with column types coerced to match `data`.
+#' @noRd
+.coerce_group_types <- function(groups, data) {
+  for (col in intersect(names(groups), names(data))) {
+    groups[[col]] <- utils::type.convert(groups[[col]], as.is = TRUE)
+    data_type <- class(data[[col]])[1]
+    group_type <- class(groups[[col]])[1]
+    if (data_type == group_type) next
+    if (data_type == "factor") {
+      groups[[col]] <- factor(groups[[col]], levels = levels(data[[col]]))
+    } else if (data_type %in% c("numeric", "integer")) {
+      groups[[col]] <- as.numeric(groups[[col]])
+    } else if (data_type == "character") {
+      groups[[col]] <- as.character(groups[[col]])
+    }
+  }
+  groups
+}
+
 #' Filter result data to keep only the specific interval rows for interval parameters.
 #'
 #' For non-interval parameters, all rows are kept. For interval parameters,

inst/shiny/modules/tab_nca/setup/settings.R

@@ -588,6 +588,7 @@ settings_server <- function(id, data, adnca_data, settings_override) {
 
   if (!is.null(settings$int_parameters)) {
     int_parameters(settings$int_parameters)
+    reset_reactable_memory()
     refresh_reactable(refresh_reactable() + 1)
   }
 

inst/shiny/modules/tab_nca/setup/slope_selector.R

@@ -262,7 +262,7 @@ slope_selector_server <- function( # nolint
       manual_slopes(click_result$manual_slopes)
 
       # render rectable anew #
-      shinyjs::runjs("memory = {};") # needed to properly reset reactable.extras widgets
+      reset_reactable_memory()
       refresh_reactable(refresh_reactable() + 1)
     })
 

tests/testthat/test-ratio_calculations.R

@@ -574,3 +574,49 @@ describe("calculate_ratio_app with interval parameters", {
     expect_true(nrow(ratios) > 0)
   })
 })
+
+describe(".coerce_group_types", {
+  it("coerces character column to numeric when data column is numeric", {
+    groups <- data.frame(AGE = "25", stringsAsFactors = FALSE)
+    data <- data.frame(AGE = 25, USUBJID = "S1")
+    result <- .coerce_group_types(groups, data)
+    expect_type(result$AGE, "double")
+    expect_equal(result$AGE, 25)
+  })
+
+  it("coerces character column to factor with correct levels when data column is factor", {
+    groups <- data.frame(RACE = "WHITE", stringsAsFactors = FALSE)
+    data <- data.frame(
+      RACE = factor(c("WHITE", "ASIAN", "BLACK")),
+      USUBJID = "S1", stringsAsFactors = FALSE
+    )
+    result <- .coerce_group_types(groups, data)
+    expect_s3_class(result$RACE, "factor")
+    expect_setequal(levels(result$RACE), c("WHITE", "ASIAN", "BLACK"))
+  })
+
+  it("leaves already-matching types unchanged", {
+    groups <- data.frame(AGE = 25, RACE = "WHITE", stringsAsFactors = FALSE)
+    data <- data.frame(AGE = 30, RACE = "ASIAN", USUBJID = "S1",
+                       stringsAsFactors = FALSE)
+    result <- .coerce_group_types(groups, data)
+    # AGE is already numeric → unchanged
+    expect_equal(result$AGE, 25)
+    # RACE is character, data$RACE is character → unchanged
+    expect_equal(result$RACE, "WHITE")
+  })
+
+  it("handles mixed columns: some coerced, some unchanged", {
+    groups <- data.frame(AGE = "25", RACE = "WHITE", SEX = "M",
+                         stringsAsFactors = FALSE)
+    data <- data.frame(AGE = 30, RACE = "ASIAN", SEX = "F", USUBJID = "S1",
+                       stringsAsFactors = FALSE)
+    result <- .coerce_group_types(groups, data)
+    # AGE: character → numeric
+    expect_type(result$AGE, "double")
+    expect_equal(result$AGE, 25)
+    # RACE, SEX: character → character (already matching)
+    expect_equal(result$RACE, "WHITE")
+    expect_equal(result$SEX, "M")
+  })
+})