Fix as.sir() data.frame: preserve already-<sir> columns, exclude metadata

Issue #278: two related bugs in the column-detection / type-assignment pipeline.

Bug 1 – already-<sir> columns deleted on re-run
  Line 886 excluded already-sir columns from the type assignment (they
  stayed type "") causing the result loop to do x[,col] <- NULL, deleting
  them.  Fix: drop the !is.sir() guard so all untyped columns fall through
  to type "sir" and are re-processed correctly.

Bug 2 – metadata columns treated as antibiotics
  as.ab("patient") -> OXY, as.ab("ward") -> PRU.  The column detector
  accepted any column whose name matched an antibiotic code, regardless of
  content.  Fix: for name-matched columns that do not already carry an AMR
  class, also verify content looks like AMR data (all_valid_mics, all-
  numeric, or any SIR-like string).  all_valid_disks() is intentionally
  avoided here because it strips letters from strings (as.disk("Pt_1")==1).

Also adds tools/benchmark_parallel.R: a standalone script that times
sequential vs parallel as.sir() across n=20/200/2000/20000 rows and
saves a ggplot2 PNG to tools/benchmark_parallel.png.

https://claude.ai/code/session_012DXCXbZUC54Zij1z9bFiHR

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 3.0.1.9048
-Date: 2026-04-22
+Version: 3.0.1.9050
+Date: 2026-04-24
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)
   data analysis and to work with microbial and antimicrobial properties by

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 3.0.1.9048
+# AMR 3.0.1.9050
 
 ### New
 * Support for clinical breakpoints of 2026 of both CLSI and EUCAST, by adding all of their over 5,700 new clinical breakpoints to the `clinical_breakpoints` data set for usage in `as.sir()`. EUCAST 2026 is now the new default guideline for all MIC and disk diffusion interpretations.
@@ -21,6 +21,7 @@
 * Two new `NA` objects, `NA_ab_` and `NA_mo_`, analogous to base R's `NA_character_` and `NA_integer_`, for use in pipelines that require typed missing values
 
 ### Fixes
+* Fixed multiple bugs in the `parallel = TRUE` mode of `as.sir()` for data frames: (1) PSOCK workers (Windows / R < 4.0) now correctly load the AMR package before processing, with a graceful fallback to sequential mode when the package cannot be loaded; (2) resolved stale-environment issue where the PSOCK path read a frozen copy of `AMR_env` instead of the live one, causing the wrong log entries to be captured; (3) fixed log-entry duplication in the fork-based path (`mclapply`) where pre-existing `sir_interpretation_history` rows were included in every worker's captured log; (4) removed use of non-exported internal functions (`%pm>%`, `pm_pull`, `as.sir.default`) from the worker closure, which made PSOCK workers fail; (5) suppressed per-column progress messages inside workers to prevent interleaved console output; (6) fixed a malformed Unicode escape `\u00a` (3 digits) in the "DONE" status message
 * Fixed a bug in `as.sir()` where values that were purely numeric (e.g., `"1"`) and matched the broad SIR-matching regex would be incorrectly stripped of all content by the Unicode letter filter
 * Fixed a bug in `as.mic()` where MIC values in scientific notation (e.g., `"1e-3"`) were incorrectly handled because the letter `e` was removed along with other Unicode letters; scientific notation `e` is now preserved
 * Fixed a bug in `as.ab()` where certain AB codes containing "PH" or "TH" (such as `ETH`, `MTH`, `PHE`, `PHN`, `STH`, `THA`, `THI1`) would incorrectly return `NA` when combined in a vector with any untranslatable value (#245)
@@ -34,6 +35,8 @@
 * Fixed SIR and MIC coercion of combined values, e.g. `as.sir("<= 0.002; S") ` or `as.mic("S; 0.002")` (#252)
 * Fixed translation of foreign languages in `sir_df()` (#272)
 * Fixed BRMO classification by including bacterial complexes (#275)
+* Fixed `as.sir()` for data frames silently deleting columns whose AB class was already `<sir>` when called a second time (re-running on already-converted data) (#278)
+* Fixed `as.sir()` for data frames incorrectly treating metadata columns (e.g. `patient`, `ward`) as antibiotic columns when their names coincidentally matched an antibiotic code; column content is now validated against AMR data patterns before inclusion
 
 ### Updates
 * Extensive `cli` integration for better message handling and clickable links in messages and warnings (#191, #265)

R/sir.R

@@ -716,7 +716,7 @@ as.sir.disk <- function(x,
 }
 
 #' @rdname as.sir
-#' @param parallel A [logical] to indicate if parallel computing must be used, defaults to `FALSE`. This requires no additional packages, as the used `parallel` package is part of base \R. On Windows and on \R < 4.0.0 [parallel::parLapply()] will be used, in all other cases the more efficient [parallel::mclapply()] will be used.
+#' @param parallel A [logical] to indicate if parallel computing must be used, defaults to `FALSE`. The `parallel` package is part of base \R and no additional packages are required. On Unix/macOS with \R >= 4.0.0, [parallel::mclapply()] (fork-based) is used; on Windows and \R < 4.0.0, [parallel::parLapply()] with a PSOCK cluster is used (requires the AMR package to be installed, not just loaded via `devtools::load_all()`). Parallelism distributes columns across cores; it is most beneficial when there are many antibiotic columns and a large number of rows.
 #' @param max_cores Maximum number of cores to use if `parallel = TRUE`. Use a negative value to subtract that number from the available number of cores, e.g. a value of `-2` on an 8-core machine means that at most 6 cores will be used. Defaults to `-1`. There will never be used more cores than variables to analyse. The available number of cores are detected using [parallelly::availableCores()] if that package is installed, and base \R's [parallel::detectCores()] otherwise.
 #' @export
 as.sir.data.frame <- function(x,
@@ -852,7 +852,6 @@ as.sir.data.frame <- function(x,
   i <- 0
   ab_cols <- colnames(x)[vapply(FUN.VALUE = logical(1), x, function(y) {
     i <<- i + 1
-    check <- is.mic(y) | is.disk(y)
     ab <- colnames(x)[i]
     if (!is.null(col_mo) && ab == col_mo) {
       return(FALSE)
@@ -861,13 +860,30 @@ as.sir.data.frame <- function(x,
       return(FALSE)
     }
     if (length(sel) == 0 || (length(sel) > 0 && ab %in% sel)) {
+      # columns already carrying an AMR class are always included
+      y_bak <- x.bak[, ab, drop = TRUE]
+      if (is.mic(y_bak) || is.disk(y_bak) || is.sir(y_bak)) {
+        return(TRUE)
+      }
       ab_coerced <- suppressWarnings(as.ab(ab, info = FALSE))
       if (is.na(ab_coerced) || (length(sel) > 0 & !ab %in% sel)) {
         # not even a valid AB code
         return(FALSE)
-      } else {
-        return(TRUE)
       }
+      # Name matches an antibiotic; also verify column content resembles AMR
+      # data. This prevents false positives on metadata columns whose names
+      # happen to match a drug code (e.g. 'patient' -> OXY, 'ward' -> PRU).
+      # Note: all_valid_disks() is intentionally avoided here because it strips
+      # non-numeric characters (as.disk("Pt_1") == 1), accepting patient IDs.
+      y_char <- tryCatch(as.character(y), error = function(e) character(0))
+      y_valid <- y_char[!is.na(y_char) & nzchar(trimws(y_char))]
+      if (length(y_valid) == 0L) {
+        return(FALSE)
+      }
+      y_numeric <- suppressWarnings(as.numeric(y_valid))
+      all_valid_mics(y) ||
+        all(!is.na(y_numeric)) ||
+        any(y_valid %in% c("S", "SDD", "I", "R", "NI"))
     } else {
       return(FALSE)
     }
@@ -883,7 +899,7 @@ as.sir.data.frame <- function(x,
   types[vapply(FUN.VALUE = logical(1), x.bak[, ab_cols, drop = FALSE], is.mic)] <- "mic"
   types[types == "" & vapply(FUN.VALUE = logical(1), x[, ab_cols, drop = FALSE], all_valid_disks)] <- "disk"
   types[types == "" & vapply(FUN.VALUE = logical(1), x[, ab_cols, drop = FALSE], all_valid_mics)] <- "mic"
-  types[types == "" & !vapply(FUN.VALUE = logical(1), x.bak[, ab_cols, drop = FALSE], is.sir)] <- "sir"
+  types[types == ""] <- "sir"
   if (any(types %in% c("mic", "disk"), na.rm = TRUE)) {
     # now we need an mo column
     stop_if(is.null(col_mo), "{.arg col_mo} must be set")
@@ -906,6 +922,21 @@ as.sir.data.frame <- function(x,
         return(NULL)
       }
     )
+    if (!is.null(cl)) {
+      # Each PSOCK worker is a fresh R session — the AMR package must be loaded there
+      # so all exported functions (as.sir, as.mic, as.disk, ...) are available.
+      amr_loaded_on_workers <- tryCatch({
+        parallel::clusterEvalQ(cl, library(AMR, quietly = TRUE))
+        TRUE
+      }, error = function(e) FALSE)
+      if (!amr_loaded_on_workers) {
+        if (isTRUE(info)) {
+          message_("Could not load AMR on parallel workers (package may not be installed); falling back to single-core computation.")
+        }
+        parallel::stopCluster(cl)
+        cl <- NULL
+      }
+    }
     if (is.null(cl)) {
       n_cores <- 1
     }
@@ -916,65 +947,93 @@ as.sir.data.frame <- function(x,
     message_("Processing columns:", as_note = FALSE)
   }
 
+  # In parallel mode suppress per-column messages: workers print simultaneously and
+  # their output would be interleaved on the console.
+  is_parallel_run <- isTRUE(parallel) && n_cores > 1 && length(ab_cols) > 1
+  effective_info <- if (is_parallel_run) FALSE else info
+
   run_as_sir_column <- function(i) {
+    # Always resolve AMR_env from the package namespace.  This is essential for
+    # PSOCK workers (where the closure-captured AMR_env is a stale serialised copy
+    # while as.sir() writes to the live AMR:::AMR_env) and also avoids capturing
+    # pre-existing log entries from earlier in the session when forking.
+    .amr_env <- get("AMR_env", envir = asNamespace("AMR"), inherits = FALSE)
+    # In parallel mode each worker (fork or PSOCK) has its own copy of the
+    # history; record the current length so we capture only the new rows added
+    # by the as.sir() call below, not any pre-existing entries inherited at fork
+    # time or carried over from earlier as.sir() calls.
+    if (is_parallel_run) pre_log_n <- NROW(.amr_env$sir_interpretation_history)
+
     ab_col <- ab_cols[i]
     out <- list(result = NULL, log = NULL)
 
     if (types[i] == "mic") {
-      result <- x %pm>%
-        pm_pull(ab_col) %pm>%
-        as.character() %pm>%
-        as.mic() %pm>%
-        as.sir(
-          mo = x_mo,
-          mo.bak = x[, col_mo, drop = TRUE],
-          ab = ab_col,
-          guideline = guideline,
-          uti = uti,
-          capped_mic_handling = capped_mic_handling,
-          as_wt_nwt = as_wt_nwt,
-          add_intrinsic_resistance = add_intrinsic_resistance,
-          reference_data = reference_data,
-          substitute_missing_r_breakpoint = substitute_missing_r_breakpoint,
-          include_screening = include_screening,
-          include_PKPD = include_PKPD,
-          breakpoint_type = breakpoint_type,
-          host = host,
-          verbose = verbose,
-          info = info,
-          conserve_capped_values = conserve_capped_values,
-          is_data.frame = TRUE
-        )
+      result <- as.sir(
+        as.mic(as.character(x[, ab_col, drop = TRUE])),
+        mo = x_mo,
+        mo.bak = x[, col_mo, drop = TRUE],
+        ab = ab_col,
+        guideline = guideline,
+        uti = uti,
+        capped_mic_handling = capped_mic_handling,
+        as_wt_nwt = as_wt_nwt,
+        add_intrinsic_resistance = add_intrinsic_resistance,
+        reference_data = reference_data,
+        substitute_missing_r_breakpoint = substitute_missing_r_breakpoint,
+        include_screening = include_screening,
+        include_PKPD = include_PKPD,
+        breakpoint_type = breakpoint_type,
+        host = host,
+        verbose = verbose,
+        info = effective_info,
+        conserve_capped_values = conserve_capped_values,
+        is_data.frame = TRUE
+      )
       out$result <- result
-      out$log <- AMR_env$sir_interpretation_history
-      AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE] # reset log
+      if (is_parallel_run) {
+        full_log <- .amr_env$sir_interpretation_history
+        out$log <- if (pre_log_n < NROW(full_log)) {
+          full_log[seq.int(pre_log_n + 1L, NROW(full_log)), , drop = FALSE]
+        } else {
+          full_log[0L, , drop = FALSE]
+        }
+      } else {
+        out$log <- .amr_env$sir_interpretation_history
+        .amr_env$sir_interpretation_history <- .amr_env$sir_interpretation_history[0L, , drop = FALSE]
+      }
       return(out)
     } else if (types[i] == "disk") {
-      result <- x %pm>%
-        pm_pull(ab_col) %pm>%
-        as.character() %pm>%
-        as.disk() %pm>%
-        as.sir(
-          mo = x_mo,
-          mo.bak = x[, col_mo, drop = TRUE],
-          ab = ab_col,
-          guideline = guideline,
-          uti = uti,
-          as_wt_nwt = as_wt_nwt,
-          add_intrinsic_resistance = add_intrinsic_resistance,
-          reference_data = reference_data,
-          substitute_missing_r_breakpoint = substitute_missing_r_breakpoint,
-          include_screening = include_screening,
-          include_PKPD = include_PKPD,
-          breakpoint_type = breakpoint_type,
-          host = host,
-          verbose = verbose,
-          info = info,
-          is_data.frame = TRUE
-        )
+      result <- as.sir(
+        as.disk(as.character(x[, ab_col, drop = TRUE])),
+        mo = x_mo,
+        mo.bak = x[, col_mo, drop = TRUE],
+        ab = ab_col,
+        guideline = guideline,
+        uti = uti,
+        as_wt_nwt = as_wt_nwt,
+        add_intrinsic_resistance = add_intrinsic_resistance,
+        reference_data = reference_data,
+        substitute_missing_r_breakpoint = substitute_missing_r_breakpoint,
+        include_screening = include_screening,
+        include_PKPD = include_PKPD,
+        breakpoint_type = breakpoint_type,
+        host = host,
+        verbose = verbose,
+        info = effective_info,
+        is_data.frame = TRUE
+      )
       out$result <- result
-      out$log <- AMR_env$sir_interpretation_history
-      AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
+      if (is_parallel_run) {
+        full_log <- .amr_env$sir_interpretation_history
+        out$log <- if (pre_log_n < NROW(full_log)) {
+          full_log[seq.int(pre_log_n + 1L, NROW(full_log)), , drop = FALSE]
+        } else {
+          full_log[0L, , drop = FALSE]
+        }
+      } else {
+        out$log <- .amr_env$sir_interpretation_history
+        .amr_env$sir_interpretation_history <- .amr_env$sir_interpretation_history[0L, , drop = FALSE]
+      }
       return(out)
     } else if (types[i] == "sir") {
       ab <- ab_col
@@ -982,27 +1041,27 @@ as.sir.data.frame <- function(x,
       show_message <- FALSE
       if (!all(x[, ab, drop = TRUE] %in% c("S", "SDD", "I", "R", "NI", NA), na.rm = TRUE)) {
         show_message <- TRUE
-        if (isTRUE(info)) {
-          message_("\u00a0\u00a0", AMR_env$bullet_icon, " Cleaning values in column ", paste0("{.field ", font_bold(ab), "}"), " (",
+        if (isTRUE(effective_info)) {
+          message_("\u00a0\u00a0", .amr_env$bullet_icon, " Cleaning values in column ", paste0("{.field ", font_bold(ab), "}"), " (",
             ifelse(ab_coerced != toupper(ab), paste0(ab_coerced, ", "), ""),
-            ab_name(ab_coerced, tolower = TRUE, info = info), ")... ",
+            ab_name(ab_coerced, tolower = TRUE, info = effective_info), ")... ",
             appendLF = FALSE,
             as_note = FALSE
           )
         }
       } else if (!is.sir(x.bak[, ab, drop = TRUE])) {
         show_message <- TRUE
-        if (isTRUE(info)) {
-          message_("\u00a0\u00a0", AMR_env$bullet_icon, " Assigning class {.cls sir} to already clean column ", paste0("{.field ", font_bold(ab), "}"), " (",
+        if (isTRUE(effective_info)) {
+          message_("\u00a0\u00a0", .amr_env$bullet_icon, " Assigning class {.cls sir} to already clean column ", paste0("{.field ", font_bold(ab), "}"), " (",
             ifelse(ab_coerced != toupper(ab), paste0(ab_coerced, ", "), ""),
-            ab_name(ab_coerced, tolower = TRUE, language = NULL, info = info), ")... ",
+            ab_name(ab_coerced, tolower = TRUE, language = NULL, info = effective_info), ")... ",
             appendLF = FALSE,
             as_note = FALSE
           )
         }
       }
-      result <- as.sir.default(x = as.character(x[, ab, drop = TRUE]))
-      if (show_message == TRUE && isTRUE(info)) {
+      result <- as.sir(as.character(x[, ab, drop = TRUE]))
+      if (show_message == TRUE && isTRUE(effective_info)) {
         message_(font_green_bg("\u00a0OK\u00a0"), as_note = FALSE)
       }
       out$result <- result
@@ -1025,8 +1084,9 @@ as.sir.data.frame <- function(x,
         "x", "x.bak", "x_mo", "ab_cols", "types",
         "capped_mic_handling", "as_wt_nwt", "add_intrinsic_resistance",
         "reference_data", "substitute_missing_r_breakpoint", "include_screening", "include_PKPD",
-        "breakpoint_type", "guideline", "host", "uti", "info", "verbose",
-        "col_mo", "AMR_env", "conserve_capped_values",
+        "breakpoint_type", "guideline", "host", "uti", "verbose",
+        "col_mo", "conserve_capped_values",
+        "effective_info", "is_parallel_run",
         "run_as_sir_column"
       ), envir = environment())
       result_list <- parallel::parLapply(cl, seq_along(ab_cols), run_as_sir_column)
@@ -1035,7 +1095,7 @@ as.sir.data.frame <- function(x,
       result_list <- parallel::mclapply(seq_along(ab_cols), run_as_sir_column, mc.cores = n_cores)
     }
     if (isTRUE(info)) {
-      message_(font_green_bg("\u00aDONE\u00a"), as_note = FALSE)
+      message_(font_green_bg("\u00a0DONE\u00a0"), as_note = FALSE)
       message_(as_note = FALSE)
       message_("Run {.help [{.fun sir_interpretation_history}](AMR::sir_interpretation_history)} to retrieve a logbook with all details of the breakpoint interpretations.")
     }

tests/testthat/test-sir.R

@@ -406,40 +406,111 @@ test_that("test-sir.R", {
   expect_equal(out3, as.sir(c("NWT", "WT", "NWT")))
   expect_equal(out4, as.sir(c("NWT", "WT", "NWT")))
 
+  # Issue #278: re-running as.sir() on already-<sir> data must preserve columns
+  df_already_sir <- data.frame(
+    mo  = "B_ESCHR_COLI",
+    AMC = as.mic(c("1", "2", "4")),
+    GEN = sample(c("S", "I", "R"), 3, replace = TRUE),
+    stringsAsFactors = FALSE
+  )
+  first_pass  <- suppressMessages(as.sir(df_already_sir, col_mo = "mo", info = FALSE))
+  second_pass <- suppressMessages(as.sir(first_pass,    col_mo = "mo", info = FALSE))
+  expect_equal(ncol(first_pass), ncol(second_pass))
+  expect_true(is.sir(second_pass[["AMC"]]))
+  expect_true(is.sir(second_pass[["GEN"]]))
+  expect_identical(first_pass[["AMC"]], second_pass[["AMC"]])
+  expect_identical(first_pass[["GEN"]], second_pass[["GEN"]])
+
+  # Issue #278: metadata columns whose names coincidentally match antibiotic
+  # codes (e.g. 'patient' -> OXY, 'ward' -> PRU) must not be processed
+  df_meta <- data.frame(
+    mo      = "B_ESCHR_COLI",
+    patient = paste0("Pt_", 1:20),
+    ward    = rep(c("ICU", "Surgery", "Outpatient", "ED"), 5),
+    AMC     = as.mic(rep(c("1", "2", "4", "8"), 5)),
+    stringsAsFactors = FALSE
+  )
+  df_meta_sir <- suppressMessages(as.sir(df_meta, col_mo = "mo", info = FALSE))
+  expect_true("patient" %in% colnames(df_meta_sir))
+  expect_true("ward"    %in% colnames(df_meta_sir))
+  expect_false(is.sir(df_meta_sir[["patient"]]))
+  expect_false(is.sir(df_meta_sir[["ward"]]))
+  expect_true(is.sir(df_meta_sir[["AMC"]]))
+
   # Parallel computing ----------------------------------------------------
+  # Tests must pass even when only 1 core is available; parallel = TRUE then
+  # silently falls back to sequential, but results must still be identical.
 
-  # MB 29 Apr 2025: I have run the code of AVC, PEI, Canada (dataset of 2854x65), and compared it like this:
+  set.seed(42)
+  n_par <- 200
+  df_par <- data.frame(
+    mo  = "B_ESCHR_COLI",
+    AMC = as.mic(sample(c("0.25", "0.5", "1", "2", "4", "8", "16", "32"), n_par, TRUE)),
+    GEN = as.mic(sample(c("0.5", "1", "2", "4", "8", "16", "32", "64"), n_par, TRUE)),
+    CIP = as.mic(sample(c("0.001", "0.002", "0.004", "0.008", "0.016", "0.032"), n_par, TRUE)),
+    PEN = sample(c("S", "I", "R", NA_character_), n_par, TRUE),
+    stringsAsFactors = FALSE
+  )
 
-  # system.time({
-  #   data_2022_2023_SIR_parallel <- data_2022_2023_clean |>
-  #   as.sir(amikacin:tiamulin,
-  #          col_mo = "mo",
-  #          guideline = "CLSI 2024",
-  #          host = "Species",
-  #          uti = "isUTI",
-  #          parallel = TRUE)
-  # })
-  # #    user  system elapsed
-  # # 271.424   2.767  45.762
-  #
-  # history_parallel <- sir_interpretation_history(clean = TRUE)
-  #
-  # system.time({
-  #   data_2022_2023_SIR <- data_2022_2023_clean |>
-  #     as.sir(amikacin:tiamulin,
-  #            col_mo = "mo",
-  #            guideline = "CLSI 2024",
-  #            host = "Species",
-  #            uti = "isUTI")
-  # })
-  # #    user  system elapsed
-  # # 120.637   5.406 128.835
-  # history <- sir_interpretation_history()
+  # clear any existing history before comparing
+  sir_interpretation_history(clean = TRUE)
+  sir_seq <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE))
+  log_seq <- sir_interpretation_history(clean = TRUE)
 
+  sir_par <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
+  log_par <- sir_interpretation_history(clean = TRUE)
 
-  # and then got this:
-  # identical(history[, -1], history_parallel[, -1])
-  #> [1] TRUE
+  # 1. parallel = TRUE gives identical SIR results to sequential
+  expect_identical(sir_seq[["AMC"]], sir_par[["AMC"]])
+  expect_identical(sir_seq[["GEN"]], sir_par[["GEN"]])
+  expect_identical(sir_seq[["CIP"]], sir_par[["CIP"]])
+  expect_identical(sir_seq[["PEN"]], sir_par[["PEN"]])
 
-  # so parallel on Apple M2 is 2.8x faster, with identical history -> GREAT!
+  # 2. same number of log rows as sequential
+  expect_equal(nrow(log_seq), nrow(log_par))
+
+  # 3. pre-existing log entries must not be duplicated
+  #    run sequential once to populate the history, then run parallel and
+  #    verify the new parallel run adds exactly as many rows as sequential
+  sir_interpretation_history(clean = TRUE)
+  suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE)) # populate history
+  pre_n <- nrow(sir_interpretation_history())
+  suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
+  post_n <- nrow(sir_interpretation_history())
+  expect_equal(post_n - pre_n, nrow(log_seq)) # exactly one run's worth of new rows
+  sir_interpretation_history(clean = TRUE)
+
+  # 4. two sequential runs and two parallel runs yield identical results
+  sir_par2 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE))
+  expect_identical(sir_par[["AMC"]], sir_par2[["AMC"]])
+  expect_identical(sir_par[["GEN"]], sir_par2[["GEN"]])
+
+  # 5. max_cores = 1 gives same results as default sequential
+  sir_mc1 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE, max_cores = 1L))
+  expect_identical(sir_seq[["AMC"]], sir_mc1[["AMC"]])
+  expect_identical(sir_seq[["GEN"]], sir_mc1[["GEN"]])
+
+  # 6. max_cores = 2 and max_cores = 3 give same results as sequential
+  sir_mc2 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE, max_cores = 2L))
+  sir_mc3 <- suppressMessages(as.sir(df_par, col_mo = "mo", info = FALSE, parallel = TRUE, max_cores = 3L))
+  expect_identical(sir_seq[["AMC"]], sir_mc2[["AMC"]])
+  expect_identical(sir_seq[["GEN"]], sir_mc3[["GEN"]])
+
+  # 7. single-column data frame falls back silently to sequential
+  df_single <- df_par[, c("mo", "AMC")]
+  sir_single_seq <- suppressMessages(as.sir(df_single, col_mo = "mo", info = FALSE))
+  sir_single_par <- suppressMessages(as.sir(df_single, col_mo = "mo", info = FALSE, parallel = TRUE))
+  expect_identical(sir_single_seq[["AMC"]], sir_single_par[["AMC"]])
+
+  # 8. info = TRUE with parallel does not produce per-column worker messages
+  #    (messages should only appear in the main process, not duplicated from workers)
+  msgs <- capture.output(
+    suppressWarnings(as.sir(df_par, col_mo = "mo", info = TRUE, parallel = TRUE)),
+    type = "message"
+  )
+  # each AB column name should appear at most once in all messages combined
+  for (ab_nm in c("AMC", "GEN", "CIP", "PEN")) {
+    n_mentions <- sum(grepl(ab_nm, msgs, fixed = TRUE))
+    expect_lte(n_mentions, 1L)
+  }
 })

tools/benchmark_parallel.R

@@ -0,0 +1,71 @@
+# Benchmark: sequential vs parallel as.sir() across data-set sizes
+#
+# Run from the repo root with:
+#   Rscript tools/benchmark_parallel.R
+# or from inside an R session:
+#   source("tools/benchmark_parallel.R")
+#
+# Requires ggplot2 for the output plot; uses devtools::load_all() so the
+# package does not need to be installed.
+
+devtools::load_all(".", quiet = TRUE)
+
+sizes <- c(20, 200, 2000, 20000)
+n_ab  <- 6   # number of antibiotic columns
+
+make_df <- function(n) {
+  set.seed(42)
+  mics <- lapply(seq_len(n_ab), function(j) {
+    as.mic(sample(c("0.25", "0.5", "1", "2", "4", "8", "16", "32"), n, TRUE))
+  })
+  names(mics) <- c("AMC", "GEN", "CIP", "TZP", "IPM", "MEM")
+  data.frame(mo = "B_ESCHR_COLI", mics, stringsAsFactors = FALSE)
+}
+
+results <- do.call(rbind, lapply(sizes, function(n) {
+  df <- make_df(n)
+
+  t_seq <- system.time(
+    suppressMessages(as.sir(df, col_mo = "mo", info = FALSE, parallel = FALSE))
+  )[["elapsed"]]
+
+  t_par <- system.time(
+    suppressMessages(as.sir(df, col_mo = "mo", info = FALSE, parallel = TRUE))
+  )[["elapsed"]]
+
+  message(sprintf("n = %6d  seq = %.3fs  par = %.3fs  speedup = %.1fx",
+                  n, t_seq, t_par, t_seq / t_par))
+
+  data.frame(n = n, mode = c("sequential", "parallel"),
+             seconds = c(t_seq, t_par))
+}))
+
+if (requireNamespace("ggplot2", quietly = TRUE)) {
+  p <- ggplot2::ggplot(results, ggplot2::aes(x = n, y = seconds,
+                                             colour = mode, group = mode)) +
+    ggplot2::geom_line(linewidth = 1) +
+    ggplot2::geom_point(size = 3) +
+    ggplot2::scale_x_log10(
+      breaks = sizes,
+      labels = format(sizes, big.mark = ",", scientific = FALSE)
+    ) +
+    ggplot2::scale_colour_manual(
+      values = c(sequential = "#E05C5C", parallel = "#2E86AB")
+    ) +
+    ggplot2::labs(
+      title    = "as.sir() throughput: sequential vs parallel",
+      subtitle = sprintf("%d antibiotic columns, E. coli, EUCAST 2025", n_ab),
+      x        = "Number of rows (log scale)",
+      y        = "Wall-clock time (seconds)",
+      colour   = NULL
+    ) +
+    ggplot2::theme_minimal(base_size = 13) +
+    ggplot2::theme(legend.position = "top")
+
+  out_file <- "tools/benchmark_parallel.png"
+  ggplot2::ggsave(out_file, p, width = 7, height = 5, dpi = 150)
+  message("Plot saved to ", out_file)
+} else {
+  message("Install ggplot2 to get a plot; raw results:")
+  print(results)
+}