add MA dummy data and update docs and testing

R/format_data.R

@@ -47,7 +47,11 @@ format_pkncaconc_data <- function(ADNCA, group_columns, time_column = "AFRLT") {
 #'
 #' This function creates a pharmacokinetic dose dataset from the provided concentration data.
 #'
-#' @param ADNCA_conc A data frame containing the concentration data.
+#' @param pkncaconc_data A data frame containing the concentration data.
+#' @param group_columns A character vector specifying the columns to group by.
+#' @param dosno_column A character string specifying the dose number column.
+#' @param time_column A character string specifying the time column.
+#' @param since_lastdose_time_column A character string specifying the time since last dose column.
 #'
 #' @returns A data frame containing the dose data.
 #'
@@ -64,8 +68,7 @@ format_pkncadose_data <- function(pkncaconc_data,
                                   group_columns,
                                   dosno_column = NULL,
                                   time_column = "AFRLT",
-                                  since_lastdose_time_column = "ARRLT",
-                                  nominal_time = "NFRLT") {
+                                  since_lastdose_time_column = "ARRLT") {
 
   # Check: Dataset is not empty
   if (nrow(pkncaconc_data) == 0) {
@@ -86,7 +89,7 @@ format_pkncadose_data <- function(pkncaconc_data,
     group_columns <- c(group_columns, "TIME")
   }
 
-  dose_data <- pkncaconc_data %>%
+  pkncaconc_data %>%
     mutate(TIME = !!sym(time_column) - !!sym(since_lastdose_time_column)) %>%
     group_by(!!!syms(group_columns)) %>%
     arrange(!!sym(since_lastdose_time_column) < 0,
@@ -95,7 +98,6 @@ format_pkncadose_data <- function(pkncaconc_data,
     ungroup() %>%
     arrange(!!!syms(group_columns))
 
-  dose_data
 }
 
 #' Create Dose Intervals Dataset

R/general_lineplot.R

@@ -6,6 +6,7 @@
 #'
 #' @param data              A data frame containing the ADNCA dataset.
 #' @param selected_analytes A character vector of selected analytes to be included in the plot.
+#' @param selected_pcspec   A character vector of selected pcspec to be included in the plot.
 #' @param selected_usubjids A character vector of selected unique subject identifiers (USUBJIDs)
 #'                          to be included in the plot.
 #' @param colorby_var       A character string specifying the variable by which to color
@@ -22,7 +23,7 @@
 #' @details
 #' The function performs the following steps:a
 #' \itemize{
-#'   \item Filters the data based on the selected analytes and subjects.
+#'   \item Filters the data based on the selected analytes, matrices, and subjects.
 #'   \item Selects relevant columns and removes rows with missing concentration values.
 #'   \item Converts 'USUBJID', 'DOSNO', and 'DOSEA' to factors.
 #'   \item Filters the data by cycle if `time_scale` is "By Cycle".
@@ -36,6 +37,7 @@
 #'   # Example usage:
 #'   plot <- general_lineplot(data = adnca_data,
 #'                            selected_analytes = c("Analyte1", "Analyte2"),
+#'                            selected_pcspec = c("Spec1", "Spec2"),
 #'                            selected_usubjids = c("Subject1", "Subject2"),
 #'                            colorby_var = "DOSNO",
 #'                            time_scale = "By Cycle",

R/general_meanplot.R

@@ -7,6 +7,7 @@
 #' @param data              A data frame containing the ADNCA dataset.
 #' @param selected_studyids A character vector of selected study IDs to be included in the plot.
 #' @param selected_analytes A character vector of selected analytes to be included in the plot.
+#' @param selected_pcspecs  A character vector of selected pcspecs to be included in the plot.
 #' @param selected_cycles   A character vector or numeric vector of selected cycles to be
 #'                          included in the plot.
 #' @param id_variable       A character string specifying the variable by which to color the lines

R/lambda_slope_plot.R

@@ -9,6 +9,8 @@
 #' @param conc_pknca_df  Data frame containing the concentration data
 #'                      (default is `mydata$conc$data`).
 #' @param dosno         Numeric value representing the dose number (default is `profile`).
+#' @param analyte       Character value representing the analyte (default is `analyte`).
+#' @param pcspec        Character value representing the pcspec (default is `pcspec`).
 #' @param usubjid       Character value representing the unique subject identifier
 #'                      (default is `patient`).
 #' @param R2ADJTHRESHOL Numeric value representing the R-squared adjusted threshold for determining
@@ -37,6 +39,8 @@
 #'   # Example usage:
 #'   plot <- lambda_slope_plot(res_pknca_df = myres$result,
 #'                             conc_pknca_df = mydata$conc$data,
+#'                             analyte = "analyte_1",
+#'                             pcspec = "pcspec_1",
 #'                             dosno = 1,
 #'                             usubjid = "subject_1",
 #'                             R2ADJTHRESHOL = 0.7)

inst/shiny/modules/tab_data.R

@@ -71,7 +71,7 @@ tab_data_server <- function(id) {
     ADNCA <- reactiveVal(
       read.csv(
         system.file("shiny/data/DummyRO_ADNCA.csv", package = "aNCA"),
-        # na.strings = c("", "NA")
+        na.strings = c("", "NA")
       )
     )
 

inst/shiny/modules/tab_visuals.R

@@ -121,7 +121,7 @@ tab_visuals_ui <- function(id) {
           orderInput(
             ns("summary_groupby_source"),
             "Drag and drop these variables...",
-            items = c("STUDYID", "USUBJID", "DOSEA",),
+            items = c("STUDYID", "USUBJID", "DOSEA"),
             width = shiny::validateCssUnit("100%"),
             connect = ns("summary_groupby")
           ),

tests/testthat/test-general_lineplot.R

@@ -2,7 +2,8 @@
 sample_data <- data.frame(
   STUDYID = rep("Study1", 24),
   USUBJID = rep(c("Subject1", "Subject2", "Subject3", "Subject4"), each = 6),
-  ANALYTE = rep("Analyte1", 24),
+  ANALYTE = rep(c("Analyte1", "Analyte 2"), each = 12),
+  PCSPEC = rep(c("Spec1", "Spec2"), each = 12),
   DOSNO = rep(1, 24),
   EVID = rep(0, 24),
   NRRLT = rep(1:6, 4),
@@ -25,6 +26,7 @@ describe("general_lineplot functions correctly", {
       data = sample_data,
       selected_analytes = "Analyte1",
       selected_usubjids = c("Subject1", "Subject2"),
+      selected_pcspec = "Spec1",
       colorby_var = "DOSNO",
       time_scale = "By Cycle",
       yaxis_scale = "Linear",
@@ -39,6 +41,7 @@ describe("general_lineplot functions correctly", {
       data = empty_data,
       selected_analytes = "Analyte1",
       selected_usubjids = c("Subject1", "Subject2"),
+      selected_pcspec = "Spec1",
       colorby_var = "DOSNO",
       time_scale = "By Cycle",
       yaxis_scale = "Linear",
@@ -55,6 +58,7 @@ describe("general_lineplot functions correctly", {
         data = incomplete_data,
         selected_analytes = "Analyte1",
         selected_usubjids = c("Subject1", "Subject2"),
+        selected_pcspec = "Spec1",
         colorby_var = "DOSNO",
         time_scale = "By Cycle",
         yaxis_scale = "Linear",
@@ -69,6 +73,7 @@ describe("general_lineplot functions correctly", {
       data = sample_data,
       selected_analytes = "Analyte1",
       selected_usubjids = c("Subject1", "Subject2"),
+      selected_pcspec = "Spec1",
       colorby_var = "DOSNO",
       time_scale = "By Cycle",
       yaxis_scale = "Log",

tests/testthat/test-general_meanplot.R

@@ -1,20 +1,21 @@
 # Sample data for testing
 sample_data <- data.frame(
-  STUDYID = rep("Study1", 24),
-  USUBJID = rep(c("Subject1", "Subject2", "Subject3", "Subject4"), each = 6),
-  ANALYTE = rep("Analyte1", 24),
-  DOSNO = rep(1, 24),
-  EVID = rep(0, 24),
-  NRRLT = rep(1:6, 4),
-  AVAL = c(
+  STUDYID = rep("Study1", 48),
+  USUBJID = rep(c("Subject1", "Subject2", "Subject3", "Subject4"), each = 12),
+  ANALYTE = rep(c("Analyte1", "Analyte 2"), each = 24),
+  PCSPEC = rep(c("Spec1", "Spec2"), each = 24),
+  DOSNO = rep(1, 48),
+  EVID = rep(0, 48),
+  NRRLT = rep(1:6, 8),
+  AVAL = rep(c(
     10, 20, 30, 40, 50, 60,
     15, 25, 35, 45, 55, 65,
     12, 22, 32, 42, 52, 62,
     18, 28, 38, 48, 58, 68
-  ),
-  RRLTU = rep("hours", 24),
-  AVALU = rep("ng/mL", 24),
-  DOSEA = rep(35, 24)
+  ), 2),
+  RRLTU = rep("hours", 48),
+  AVALU = rep("ng/mL", 48),
+  DOSEA = rep(35, 48)
 )
 
 describe("general_meanplot functions correctly", {
@@ -23,6 +24,7 @@ describe("general_meanplot functions correctly", {
       data = sample_data,
       selected_studyids = "Study1",
       selected_analytes = "Analyte1",
+      selected_pcspecs = "Spec1",
       selected_cycles = 1
     )
     expect_s3_class(p, "ggplot")
@@ -34,6 +36,7 @@ describe("general_meanplot functions correctly", {
       data = empty_data,
       selected_studyids = "Study1",
       selected_analytes = "Analyte1",
+      selected_pcspecs = "Spec1",
       selected_cycles = 1
     )
     expect_s3_class(p, "ggplot")
@@ -47,6 +50,7 @@ describe("general_meanplot functions correctly", {
         data = incomplete_data,
         selected_studyids = "Study1",
         selected_analytes = "Analyte1",
+        selected_pcspecs = "Spec1",
         selected_cycles = 1
       ),
       "object 'AVAL' not found"
@@ -57,6 +61,7 @@ describe("general_meanplot functions correctly", {
       data = sample_data,
       selected_studyids = "Study1",
       selected_analytes = "Analyte1",
+      selected_pcspecs = "Spec1",
       selected_cycles = 1,
       plot_sd = TRUE
     )
@@ -72,6 +77,7 @@ describe("general_meanplot functions correctly", {
       data = sample_data,
       selected_studyids = "Study1",
       selected_analytes = "Analyte1",
+      selected_pcspecs = "Spec1",
       selected_cycles = 1,
       plot_ci = TRUE
     )
@@ -90,6 +96,7 @@ describe("general_meanplot functions correctly", {
       data = sample_data,
       selected_studyids = "Study1",
       selected_analytes = "Analyte1",
+      selected_pcspecs = "Spec1",
       selected_cycles = 1,
       plot_ylog = TRUE
     )