Bug: Slope Selector Plots Incorrect

NAMESPACE

@@ -7,11 +7,10 @@ export(apply_filters)
 export(apply_labels)
 export(as_factor_preserve_label)
 export(calculate_summary_stats)
-export(check_slope_rule_overlap)
+export(create_duplicates)
 export(create_start_impute)
 export(export_cdisc)
 export(filter_breaks)
-export(filter_slopes)
 export(flexible_violinboxplot)
 export(format_pkncaconc_data)
 export(format_pkncadata_intervals)
@@ -62,14 +61,10 @@ importFrom(ggplot2,labs)
 importFrom(ggplot2,scale_x_continuous)
 importFrom(glue,glue)
 importFrom(grid,convertUnit)
-importFrom(logger,log_debug)
-importFrom(logger,log_error)
-importFrom(logger,log_fatal)
-importFrom(logger,log_info)
-importFrom(logger,log_trace)
-importFrom(logger,log_warn)
 importFrom(magrittr,`%>%`)
 importFrom(purrr,imap)
+importFrom(purrr,map)
+importFrom(purrr,reduce)
 importFrom(rlang,sym)
 importFrom(scales,breaks_log)
 importFrom(scales,label_log)

R/create_duplicates.R

@@ -0,0 +1,95 @@
+#' Create duplicates in concentration data with Predose and Last Values for Dosing Cycles
+#'
+#' This function duplicates and adjusts concentration data to ensure all dosing cycles have 
+#' complete predose and last concentration values. It is designed for use in pharmacokinetic 
+#' analyses where dosing intervals and concentration values need to be aligned for each dose.
+#'
+#' @param conc_data A data frame containing concentration data.
+#' @param groups A character vector of column names to use for grouping (e.g., c("USUBJID", "ANALYTE", "PCSPEC")).
+#' @param dosno Column name for the dose number (default: "DOSNO").
+#' @param arrlt Column name for time from the most recent dose (default: "ARRLT").
+#' @param afrlt Column name for time from the first dose (default: "AFRLT").
+#' @param nrrlt Column name for the numeric relative time (default: "NRRLT").
+#' @param nfrlt Column name for the nominal relative time (default: "NFRLT").
+#'
+#' @return A data frame with adjusted concentration data, including:
+#'   - Duplicated predose values assigned to the previous dose.
+#'   - Duplicated last values assigned to the next dose if predose values are missing.
+#'   - Sorted by the grouping variables and relative time.
+#'
+#' @examples
+#' # Example usage
+#' result <- adjust_concentration_data(conc_data, groups = c("USUBJID", "ANALYTE", "PCSPEC"), dosno = "DOSNO")
+#'
+#' @export
+create_duplicates <- function(conc_data,
+                                      groups = c("USUBJID", "ANALYTE", "PCSPEC"),
+                                      dosno = "DOSNO",
+                                      arrlt = "ARRLT",
+                                      afrlt = "AFRLT",
+                                      nrrlt = "NRRLT",
+                                      nfrlt = "NFRLT") {
+  # Helper to construct grouping keys
+  group_keys <- function(data, keys) {
+    data %>%
+      group_by(across(all_of(keys)))
+  }
+
+  # Step 1: Identify the dosing times (ARRLT == 0)
+  dose_times <- conc_data %>%
+    mutate(dose_time = .data[[afrlt]] - .data[[arrlt]]) %>%
+    select(all_of(groups), .data[[dosno]], dose_time) %>%
+    group_keys(c(groups, dosno)) %>%
+    summarize(dose_time = first(dose_time), .groups = "drop")
+
+  # Step 2: Calculate dosing intervals
+  dosing_intervals <- dose_times %>%
+    group_keys(groups) %>%
+    mutate(
+      interval_next = lead(dose_time) - dose_time,
+      interval_prev = dose_time - lag(dose_time),
+      interval_next = replace_na(interval_next, 0),
+      interval_prev = replace_na(interval_prev, 0),
+      next_dose = interval_next + dose_time
+    ) %>%
+    ungroup()
+
+  # Step 3: Duplicate predose values for the previous dose
+  predose_duplicates <- conc_data %>%
+    filter(.data[[arrlt]] <= 0, .data[[dosno]] > 1) %>%
+    left_join(dosing_intervals, by = c(dosno, groups)) %>%
+    mutate(
+      !!dosno := .data[[dosno]] - 1,
+      !!arrlt := .data[[arrlt]] + interval_prev,
+      !!nrrlt := .data[[nfrlt]] - interval_prev
+    ) %>%
+    select(-interval_next, -interval_prev)
+
+  # Step 4: Identify missing predose values for the next dose
+  missing_predose <- dose_times %>%
+    anti_join(
+      conc_data %>% filter(.data[[arrlt]] < 0),
+      by = c(groups, dosno)
+    )
+
+  # Step 5: Duplicate last value of the previous dose
+  last_values <- conc_data %>%
+    semi_join(missing_predose, by = c(groups, dosno)) %>%
+    group_keys(c(groups, dosno)) %>%
+    slice_tail(n = 1) %>%
+    ungroup() %>%
+    left_join(dosing_intervals, by = c(groups, dosno)) %>%
+    mutate(
+      !!dosno := .data[[dosno]] + 1,
+      !!arrlt := .data[[arrlt]] - interval_prev,
+      !!nrrlt := .data[[nrrlt]] + interval_prev
+    ) %>%
+    select(-interval_next, -interval_prev)
+
+  # Step 6: Combine all data
+  conc_data <- conc_data %>%
+    bind_rows(predose_duplicates, last_values) %>%
+    arrange(across(all_of(c(groups, dosno, arrlt))))
+
+  return(conc_data)
+}

R/format_data.R

@@ -150,7 +150,7 @@ format_pkncadata_intervals <- function(pknca_dose,
     arrange(!!sym(pknca_dose$columns$time)) %>%
     mutate(end = lead(as.numeric(!!sym(pknca_dose$columns$time)), default = Inf)) %>%
     ungroup() %>%
-    select(any_of(c("start", "end", unname(unlist(pknca_dose$columns$groups)), "DOSNO"))) %>%
+    select(any_of(c("start", "end", unname(unlist(pknca_dose$columns$groups)), "DOSNO", "ANALYTE"))) %>% #TODO: Gerardo adjust to remove hardcoding
 
     # Create logical columns with the TRUE and as names params argument
     mutate(!!!setNames(rep(TRUE, length(params)), params)) %>%