Refactor summary report generation: improve formatting and update example paths

R/amRdataPlots.R

@@ -4,17 +4,21 @@
 #'   standardized AMR metadata.
 #' @param out_path Character string. Directory where the Markdown report is written.
 #'
-#' @return Writes a structured, human‑readable summary report to
-#'   "<out_path>/amr_metadata_summary.md".
+#' @return Invisibly returns the path to the written Markdown summary report
+#'   ("<out_path>/amr_metadata_summary.md").
 #'
 #' @examples
+#' \dontrun{
 #' generateSummary(
-#'   metadata_parquet = "results/metadata.parquet",
-#'   out_path = "results/"
+#'   metadata_parquet = "data/metadata.parquet",
+#'   out_path = "data/"
 #' )
+#' }
 #'
 #' @export
-generateSummary <- function(metadata_parquet, out_path) {
+generateSummary <- function(metadata_parquet, 
+  out_path
+) {
   # Little helper to apply distinct + non-empty + sorted vector
   clean_distinct <- function(df, col) {
     df |>
@@ -60,7 +64,9 @@ generateSummary <- function(metadata_parquet, out_path) {
     dplyr::pull()
 
   # Core summaries
-  TotalEntryCount <- metadata |> dplyr::count()
+  TotalEntryCount <- metadata |> 
+    dplyr::count()
+
   CleanEntryCount <- metadata |>
     dplyr::distinct(genome.genome_id) |>
     dplyr::count()
@@ -172,7 +178,7 @@ ResPropbyDrugClass <- drug_class_calls |>
   # Header
   write_new(
     md_path,
-    sprintf("# AMR summary report for *%s*", Species_name)
+    sprintf("# AMR summary report for *%s*", paste(Species_name, collapse = ", "))
   )
 
   # Basic stats
@@ -218,18 +224,14 @@ ResPropbyDrugClass <- drug_class_calls |>
   append_lines(md_path, c("## Isolation sources", "", md_tbl(SourceCount), "", ""))
   append_lines(md_path, c("## Hosts", "", md_tbl(HostCount), "", ""))
 
-
-  # Hosts as a simple list
-  # if (length(Host)) {
-  #   append_lines(md_path, c("## Hosts", "", paste0("- ", Host), "", ""))
-  # }
+invisible(md_path)
 }
 
 
 #' Write all summary plots to file(s)
 #'
-#' Expects `metadata_parquet` to be the output of `runDataProcessing()`'s
-#' `cleanData()` step (or an export of the resulting `metadata` table), since
+#' Expects `metadata_parquet` to be the output of
+#' `cleanMetaData()` step (or an export of the resulting `metadata` table), since
 #' `drug_abbr`, `drug_class`, and `num_resistant_classes` are only populated
 #' after that step joins in the reference drug tables.
 #'
@@ -238,9 +240,18 @@ ResPropbyDrugClass <- drug_class_calls |>
 #'
 #' @return Invisibly returns the path to the written PDF (all plots as
 #'   separate pages of one multi-page file).
+#' 
+#' @examples
+#' \dontrun{
+#' generatePlots(
+#'   metadata_parquet = "data/metadata.parquet",
+#'   out_path = "data/"
+#' )
+#' }
 #' @export
 generatePlots <- function(metadata_parquet,
-                          out_path) {
+                          out_path
+                        ) {
   if (!dir.exists(out_path)) {
     dir.create(out_path, showWarnings = FALSE, recursive = TRUE)
   }

R/utils_colors.R

@@ -3,7 +3,7 @@
 
 # AMR phenotype palette (R/S/I plus full-word + lowercase variants so it works
 # regardless of how the column is encoded). Susceptible is intentionally
-# neutral grey so Resistant amber stands out as the signal of interest.
+# a muted blue so Resistant amber stands out as the signal of interest.
 PHENOTYPE_COLORS <- c(
   R = "#d4872a", Resistant = "#d4872a", resistant = "#d4872a",
   S = "#5b8db8", Susceptible = "#5b8db8", susceptible = "#5b8db8",