(v3.0.1.9086) new eucast update flow

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 3.0.1.9085
-Date: 2026-07-09
+Version: 3.0.1.9086
+Date: 2026-08-13
 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 3.0.1.9085
+# AMR 3.0.1.9086
 
 Planned as v3.1.0, end of June 2026.
 

R/tidymodels.R

@@ -120,14 +120,13 @@ all_disk_predictors <- function() {
 #' @rdname amr-tidymodels
 #' @export
 step_mic_log2 <- function(
-  recipe,
-  ...,
-  role = NA,
-  trained = FALSE,
-  columns = NULL,
-  skip = FALSE,
-  id = recipes::rand_id("mic_log2")
-) {
+    recipe,
+    ...,
+    role = NA,
+    trained = FALSE,
+    columns = NULL,
+    skip = FALSE,
+    id = recipes::rand_id("mic_log2")) {
   recipes::add_step(
     recipe,
     step_mic_log2_new(
@@ -196,14 +195,13 @@ tidy.step_mic_log2 <- function(x, ...) {
 #' @rdname amr-tidymodels
 #' @export
 step_sir_numeric <- function(
-  recipe,
-  ...,
-  role = NA,
-  trained = FALSE,
-  columns = NULL,
-  skip = FALSE,
-  id = recipes::rand_id("sir_numeric")
-) {
+    recipe,
+    ...,
+    role = NA,
+    trained = FALSE,
+    columns = NULL,
+    skip = FALSE,
+    id = recipes::rand_id("sir_numeric")) {
   recipes::add_step(
     recipe,
     step_sir_numeric_new(

data-raw/_reproduction_scripts/reproduction_of_clinical_breakpoints_eucast.R

@@ -0,0 +1,725 @@
+# ==================================================================== #
+# TITLE:                                                               #
+# AMR: An R Package for Working with Antimicrobial Resistance Data     #
+#                                                                      #
+# SOURCE CODE:                                                         #
+# https://github.com/msberends/AMR                                     #
+#                                                                      #
+# PLEASE CITE THIS SOFTWARE AS:                                        #
+# Berends MS, Luz CF, Friedrich AW, et al. (2022).                     #
+# AMR: An R Package for Working with Antimicrobial Resistance Data.    #
+# Journal of Statistical Software, 104(3), 1-31.                       #
+# https://doi.org/10.18637/jss.v104.i03                                #
+#                                                                      #
+# Developed at the University of Groningen and the University Medical  #
+# Center Groningen in The Netherlands, in collaboration with many      #
+# colleagues from around the world, see our website.                   #
+#                                                                      #
+# This R package is free software; you can freely use and distribute   #
+# it for both personal and commercial purposes under the terms of the  #
+# GNU General Public License version 2.0 (GNU GPL-2), as published by  #
+# the Free Software Foundation.                                        #
+# We created this package for both routine data analysis and academic  #
+# research and it was publicly released in the hope that it will be    #
+# useful, but it comes WITHOUT ANY WARRANTY OR LIABILITY.              #
+#                                                                      #
+# Visit our website for the full manual and a complete tutorial about  #
+# how to conduct AMR data analysis: https://amr-for-r.org              #
+# ==================================================================== #
+
+# Generic parser for EUCAST Clinical Breakpoint Table xlsx files.
+# Works for any organism sheet by auto-detecting:
+#   - Header rows (via "MIC breakpoint" in col 2)
+#   - Sub-header rows (via "S " pattern in col 2, one row below header)
+#   - Organism names (row 1 for single-organism sheets, or discovered above
+#     each header row for multi-organism sheets like Anaerobic bacteria)
+#   - Data rows (between sub-header and next header/end)
+#   - Notes (col 9, resolved per-row via superscript references)
+#
+# Uses tidyxl to parse rich text cells, separating base values from
+# superscript footnote references that EUCAST uses for notes.
+#
+# Output mimics the AMR::clinical_breakpoints structure with added columns:
+#   version, is_screening, note
+
+
+library(tidyxl)
+library(dplyr, warn.conflicts = FALSE)
+library(purrr)
+devtools::load_all()
+
+# ==============================================================================
+# 1. Rich text parser
+# ==============================================================================
+split_rich_text <- function(fmt_list) {
+  map_dfr(fmt_list, function(fmt) {
+    if (is.null(fmt) || nrow(fmt) == 0)
+      return(tibble(base_text = NA_character_, note_super = NA_character_))
+    is_super <- !is.na(fmt$vertAlign) & fmt$vertAlign == "superscript"
+    tibble(
+      base_text  = paste0(fmt$character[!is_super], collapse = ""),
+      note_super = paste0(fmt$character[is_super],  collapse = "")
+    )
+  })
+}
+
+# ==============================================================================
+# 2. Notes block parser
+# ==============================================================================
+parse_notes_block <- function(txt) {
+  if (is.na(txt) || txt == "") return(list())
+  
+  txt <- gsub("\r\n", "\n", txt)
+  txt <- gsub("\r", "\n", txt)
+  txt <- gsub("\u00A0", " ", txt)
+  
+  find_keys <- function(pattern, txt) {
+    m <- gregexpr(pattern, txt, perl = TRUE)[[1]]
+    if (m[1] == -1)
+      return(data.frame(pos = integer(0), key = character(0),
+                        mlen = integer(0), stringsAsFactors = FALSE))
+    lens <- attr(m, "match.length")
+    keys <- gsub("[.\\s]+$", "", trimws(substring(txt, m, m + lens - 1)))
+    data.frame(pos = as.integer(m), key = keys, mlen = as.integer(lens),
+               stringsAsFactors = FALSE)
+  }
+  
+  # Combined key: 1/A. or 5/A.
+  r1 <- find_keys("(?:^|(?<=\\n)|(?<=\\.))\\s*\\d+/[A-Z]\\.\\s*", txt)
+  # Numbered with dot: "1." "2."
+  r2 <- find_keys("(?:^|(?<=\\n)|(?<=\\.))\\s*\\d+\\.\\s+", txt)
+  # Lettered: "A." followed by uppercase (not species names like "C. difficile")
+  r3 <- find_keys("(?:^|(?<=\\n)|(?<=\\.)|(?<=\\s))\\s*[A-Z]\\.\\s+(?=[A-Z])", txt)
+  # Numbered without dot (e.g. C. difficile style): digit space uppercase
+  r4 <- find_keys("(?:^|(?<=\\n))\\d+\\s+(?=[A-Z])", txt)
+  
+  all_keys <- rbind(r1, r2, r3, r4)
+  if (nrow(all_keys) == 0) return(list())
+  all_keys <- all_keys[order(all_keys$pos), ]
+  
+  # Remove overlapping matches (keep longer/more specific)
+  keep <- rep(TRUE, nrow(all_keys))
+  for (i in seq_len(nrow(all_keys))) {
+    if (!keep[i]) next
+    for (j in seq_len(nrow(all_keys))) {
+      if (i == j || !keep[j]) next
+      if (all_keys$pos[j] >= all_keys$pos[i] &&
+          all_keys$pos[j] < all_keys$pos[i] + all_keys$mlen[i]) {
+        if (nchar(all_keys$key[i]) >= nchar(all_keys$key[j])) {
+          keep[j] <- FALSE
+        } else {
+          keep[i] <- FALSE
+        }
+      }
+    }
+  }
+  all_keys <- all_keys[keep, ]
+  
+  # Build note lookup, allowing multiple bodies per key
+  notes <- list()
+  add_note <- function(key, body) {
+    if (is.null(notes[[key]])) {
+      notes[[key]] <<- body
+    } else if (!body %in% notes[[key]]) {
+      notes[[key]] <<- c(notes[[key]], body)
+    }
+  }
+  
+  for (i in seq_len(nrow(all_keys))) {
+    body_start <- all_keys$pos[i] + all_keys$mlen[i]
+    body_end <- if (i < nrow(all_keys)) all_keys$pos[i + 1] - 1 else nchar(txt)
+    body <- trimws(substr(txt, body_start, body_end))
+    key <- all_keys$key[i]
+    
+    if (grepl("/", key)) {
+      subkeys <- unlist(strsplit(key, "/"))
+      for (sk in subkeys) add_note(sk, body)
+      add_note(key, body)
+    } else {
+      add_note(key, body)
+    }
+  }
+  notes
+}
+
+# ==============================================================================
+# 3. Note resolver
+# ==============================================================================
+resolve_notes <- function(notes_list, mic_super, disk_super) {
+  nl <- notes_list
+  if (is.null(nl) || length(nl) == 0) return(NA_character_)
+  
+  mic_refs <- character(0)
+  disk_refs <- character(0)
+  if (!is.na(mic_super) && mic_super != "")
+    mic_refs <- trimws(unlist(strsplit(mic_super, ",")))
+  if (!is.na(disk_super) && disk_super != "")
+    disk_refs <- trimws(unlist(strsplit(disk_super, ",")))
+  all_refs <- unique(c(mic_refs, disk_refs))
+  if (length(all_refs) == 0) return(NA_character_)
+  
+  # Detect combined keys (e.g. "1/A")
+  combined_keys <- grep("/", names(nl), value = TRUE)
+  used_combined <- character(0)
+  consumed_refs <- character(0)
+  
+  for (ck in combined_keys) {
+    parts <- unlist(strsplit(ck, "/"))
+    if (all(parts %in% all_refs)) {
+      used_combined <- c(used_combined, ck)
+      consumed_refs <- c(consumed_refs, parts)
+    }
+  }
+  
+  remaining_refs <- setdiff(all_refs, consumed_refs)
+  
+  parts_out <- character(0)
+  bodies_seen <- character(0)
+  
+  # Combined notes
+  for (ck in used_combined) {
+    bodies <- nl[[ck]]
+    if (!is.null(bodies)) {
+      for (body in bodies) {
+        if (!body %in% bodies_seen) {
+          parts_out <- c(parts_out, paste0("[", ck, "] ", body))
+          bodies_seen <- c(bodies_seen, body)
+        }
+      }
+    }
+  }
+  
+  # Remaining individual notes: prefer duplicated over missing
+  for (ref in remaining_refs) {
+    if (!is.null(nl[[ref]])) {
+      for (body in nl[[ref]]) {
+        # Include all notes, even if body was already seen under a combined key
+        entry <- paste0("[", ref, "] ", body)
+        if (!entry %in% parts_out) {
+          parts_out <- c(parts_out, entry)
+        }
+      }
+    }
+  }
+  
+  if (length(parts_out) == 0) return(NA_character_)
+  paste(parts_out, collapse = " | ")
+}
+
+# ==============================================================================
+# 4. Cell helpers
+# ==============================================================================
+get_cell <- function(df, r, c) {
+  hit <- df[df$row == r & df$col == c, ]
+  if (nrow(hit) == 0) return(list(base = NA_character_, super = NA_character_))
+  list(base = hit$base_text[1], super = hit$note_super[1])
+}
+
+parse_bp <- function(txt) {
+  if (is.na(txt) || txt == "") return(NA_real_)
+  t <- trimws(txt)
+  # if (toupper(t) %in% c("NOTE", "IE", "IP", "NA", "-")) return(NA_real_)
+  if (toupper(t) %in% c("NA", "-")) return(NA_real_)
+  t <- gsub("^\\((.+)\\)$", "\\1", t)
+  # suppressWarnings(as.numeric(t))
+  t
+}
+
+is_screening_fn <- function(txt) {
+  if (is.na(txt)) return(FALSE)
+  grepl("^\\(.+\\)$", trimws(txt))
+}
+
+format_disk_dose <- function(dose) {
+  if (is.na(dose) || dose == "") return(NA_character_)
+  d <- trimws(dose)
+  if (grepl("mcg|unit", d, ignore.case = TRUE)) return(d)
+  d <- gsub("-", "/", d)
+  paste0(d, " mcg")
+}
+
+# ==============================================================================
+# 5. Detect version and guideline from workbook
+# ==============================================================================
+detect_version <- function(cells) {
+  # Version string is typically in col 5+ of row 1 (or sometimes col 9)
+  # e.g. "EUCAST Clinical Breakpoint Tables v. 13.1, valid from 2023-06-29"
+  version_cells <- cells |>
+    filter(row == 1, !is_blank, col >= 5) |>
+    mutate(text = coalesce(character, ""))
+  version_text <- paste(version_cells$text, collapse = " ")
+  
+  version <- regmatches(version_text,
+                        regexpr("v\\.?\\s*[\\d.]+", version_text, perl = TRUE))
+  version <- gsub("v\\.?\\s*", "", version)
+  if (length(version) == 0) version <- NA_character_
+  
+  major <- suppressWarnings(as.integer(sub("\\..*", "", version)))
+  guideline <- if (!is.na(major)) paste0("EUCAST ", major + 2010) else NA_character_
+  
+  list(version = version, guideline = guideline)
+}
+
+# ==============================================================================
+# 6. Main parser: parse a single sheet
+# ==============================================================================
+parse_sheet <- function(xlsx_path, sheet_name) {
+  cells <- xlsx_cells(xlsx_path, sheets = sheet_name)
+  
+  # --- Detect version ---
+  ver <- detect_version(cells)
+  
+  # --- Parse rich text for cols A:I ---
+  target <- cells |> filter(col >= 1, col <= 9, !is_blank)
+  rich <- split_rich_text(target$character_formatted)
+  
+  parsed <- target |>
+    bind_cols(rich) |>
+    mutate(
+      base_text = case_when(
+        !is.na(base_text) & base_text != "" ~ trimws(base_text),
+        !is.na(character)                   ~ trimws(character),
+        !is.na(numeric)                     ~ as.character(numeric),
+        TRUE                                ~ NA_character_
+      ),
+      note_super = if_else(is.na(note_super) | note_super == "",
+                           NA_character_, note_super)
+    ) |>
+    select(row, col, base_text, note_super)
+  
+  # --- Detect header rows (col 2 contains "MIC breakpoint") ---
+  header_rows <- parsed |>
+    filter(col == 2, grepl("MIC breakpoint", base_text, fixed = TRUE)) |>
+    pull(row) |>
+    sort()
+  
+  if (length(header_rows) == 0) {
+    message("  No MIC breakpoint headers found in sheet '", sheet_name, "', skipping")
+    return(NULL)
+  }
+  
+  # Sub-header rows: one row below each header (contains "S " pattern)
+  sub_header_rows <- header_rows + 1
+  
+  # --- Detect sheet type: single-organism vs multi-organism ---
+  # Multi-organism: col A at header rows contains "Antimicrobial agent"
+  header_col_a <- parsed |>
+    filter(row %in% header_rows, col == 1) |>
+    pull(base_text)
+  
+  is_multi_organism <- all(grepl("Antimicrobial agent", header_col_a, fixed = TRUE))
+  
+  # --- Detect organisms and their table ranges ---
+  max_data_row <- max(parsed$row)
+  
+  if (is_multi_organism) {
+    # Multi-organism: discover organism names above each header row.
+    # Organism name is a standalone row (col A only, no data in B:H)
+    # found between the previous table's end and this header row.
+    rows_with_b <- parsed |> filter(col == 2) |> pull(row)
+    
+    tables <- list()
+    for (i in seq_along(header_rows)) {
+      hr <- header_rows[i]
+      shr <- sub_header_rows[i]
+      
+      # Search backwards from the header row for the organism name
+      search_from <- if (i == 1) 1 else sub_header_rows[i - 1] + 1
+      candidates <- parsed |>
+        filter(col == 1, row >= search_from, row < hr,
+               !row %in% rows_with_b) |>
+        # Exclude known non-organism text patterns
+        filter(!grepl("Antimicrobial|MIC determination|Disk diffusion|Breakpoints for|Expert Rules|For abbreviations|For species|Examples of|haze|Isolated|Ignore haemolysis|Numbered|Lettered|Medium:|Inoculum:|Incubation:|Reading:|Quality control:|See disk",
+                      base_text))
+      
+      # Take the last candidate (closest to the header)
+      if (nrow(candidates) > 0) {
+        org_row <- candidates |> slice_max(row, n = 1)
+        organism <- org_row$base_text
+      } else {
+        # Fallback: use sheet name
+        organism <- sheet_name
+      }
+      
+      # Data range: from sub_header + 1 to the row before the next organism
+      # or end of data
+      first_data <- shr + 1
+      if (i < length(header_rows)) {
+        # End before the next organism name (or header)
+        last_data <- header_rows[i + 1] - 1
+        # Walk back to find actual last data row
+        while (last_data >= first_data) {
+          has_data <- nrow(parsed |> filter(row == last_data, col %in% 2:8)) > 0
+          if (has_data) break
+          last_data <- last_data - 1
+        }
+      } else {
+        last_data <- max_data_row
+        while (last_data >= first_data) {
+          has_data <- nrow(parsed |> filter(row == last_data, col %in% 2:8)) > 0
+          if (has_data) break
+          last_data <- last_data - 1
+        }
+      }
+      
+      # Notes cell: col 9, in the data range (usually at first_data, merged)
+      note_cell <- parsed |> filter(col == 9, row >= first_data, row <= last_data) |>
+        slice_min(row, n = 1)
+      note_text <- if (nrow(note_cell) > 0) note_cell$base_text[1] else NA_character_
+      notes_parsed <- parse_notes_block(note_text)
+      
+      tables[[i]] <- list(organism = organism, first_data = first_data,
+                          last_data = last_data, notes = notes_parsed)
+    }
+  } else {
+    # Single-organism: organism name from row 1, col A
+    org_cell <- parsed |> filter(row == 1, col == 1)
+    organism <- if (nrow(org_cell) > 0) org_cell$base_text[1] else sheet_name
+    # Clean: remove trailing * or whitespace
+    organism <- gsub("[*]+$", "", trimws(organism))
+    
+    # Notes: col 9, within each class section
+    # For single-organism sheets, there is one notes block per class.
+    # Notes cell is at the header row or first data row of each class.
+    tables <- list()
+    for (i in seq_along(header_rows)) {
+      hr <- header_rows[i]
+      shr <- sub_header_rows[i]
+      first_data <- shr + 1
+      
+      if (i < length(header_rows)) {
+        last_data <- header_rows[i + 1] - 1
+        while (last_data >= first_data) {
+          has_data <- nrow(parsed |> filter(row == last_data, col %in% 2:8)) > 0
+          if (has_data) break
+          last_data <- last_data - 1
+        }
+      } else {
+        last_data <- max_data_row
+        while (last_data >= first_data) {
+          has_data <- nrow(parsed |> filter(row == last_data, col %in% 2:8)) > 0
+          if (has_data) break
+          last_data <- last_data - 1
+        }
+      }
+      
+      # Notes: look in col 9 from header row through end of section
+      note_cells <- parsed |>
+        filter(col == 9, row >= hr, row <= last_data,
+               !grepl("^Notes", base_text))
+      note_text <- if (nrow(note_cells) > 0) {
+        paste(note_cells$base_text, collapse = "\n")
+      } else {
+        NA_character_
+      }
+      notes_parsed <- parse_notes_block(note_text)
+      
+      tables[[i]] <- list(organism = organism, first_data = first_data,
+                          last_data = last_data, notes = notes_parsed)
+    }
+  }
+  
+  # --- Extract breakpoint rows ---
+  results <- vector("list", 500)
+  idx <- 0L
+  
+  for (tbl in tables) {
+    org <- tbl$organism
+    nl  <- tbl$notes
+    
+    for (r in tbl$first_data:tbl$last_data) {
+      agent <- get_cell(parsed, r, 1)
+      if (is.na(agent$base)) next
+      # Skip category headers (rows that have col A text but no data in B:H)
+      has_any_data <- any(!is.na(c(
+        get_cell(parsed, r, 2)$base,
+        get_cell(parsed, r, 3)$base,
+        get_cell(parsed, r, 5)$base,
+        get_cell(parsed, r, 6)$base,
+        get_cell(parsed, r, 7)$base
+      )))
+      if (!has_any_data) next
+      
+      mic_s   <- get_cell(parsed, r, 2)
+      mic_r   <- get_cell(parsed, r, 3)
+      mic_atu <- get_cell(parsed, r, 4)
+      disk_dose_cell <- get_cell(parsed, r, 5)
+      disk_s  <- get_cell(parsed, r, 6)
+      disk_r  <- get_cell(parsed, r, 7)
+      disk_atu <- get_cell(parsed, r, 8)
+      
+      note_text <- resolve_notes(nl, mic_s$super, disk_s$super)
+      
+      # MIC row
+      s_val <- parse_bp(mic_s$base)
+      r_val <- parse_bp(mic_r$base)
+      if (!is.na(s_val) || !is.na(r_val)) {
+        idx <- idx + 1L
+        results[[idx]] <- tibble(
+          guideline    = ver$guideline,
+          version      = ver$version,
+          type         = "human",
+          host         = "human",
+          method       = "MIC",
+          site         = NA_character_,
+          mo           = org,
+          rank_index   = NA_integer_,
+          ab           = agent$base,
+          ref_tbl      = org,
+          disk_dose    = NA_character_,
+          breakpoint_S = s_val,
+          breakpoint_R = r_val,
+          uti          = FALSE,
+          is_SDD       = FALSE,
+          is_screening = is_screening_fn(mic_s$base),
+          note         = note_text
+        )
+      }
+      
+      # DISK row
+      s_val <- parse_bp(disk_s$base)
+      r_val <- parse_bp(disk_r$base)
+      if (!is.na(s_val) || !is.na(r_val)) {
+        idx <- idx + 1L
+        results[[idx]] <- tibble(
+          guideline    = ver$guideline,
+          version      = ver$version,
+          type         = "human",
+          host         = "human",
+          method       = "DISK",
+          site         = NA_character_,
+          mo           = org,
+          rank_index   = NA_integer_,
+          ab           = agent$base,
+          ref_tbl      = org,
+          disk_dose    = format_disk_dose(disk_dose_cell$base),
+          breakpoint_S = s_val,
+          breakpoint_R = r_val,
+          uti          = FALSE,
+          is_SDD       = FALSE,
+          is_screening = is_screening_fn(disk_s$base),
+          note         = note_text
+        )
+      }
+    }
+  }
+  
+  if (idx == 0) return(NULL)
+  bind_rows(results[seq_len(idx)])
+}
+
+# ==============================================================================
+# 7. Parse "Topical agents" sheet (transposed layout)
+# ==============================================================================
+parse_topical_sheet <- function(xlsx_path) {
+  cells <- xlsx_cells(xlsx_path, sheets = "Topical agents")
+  
+  ver <- detect_version(cells)
+  
+  target <- cells |> filter(!is_blank)
+  rich <- split_rich_text(target$character_formatted)
+  parsed <- target |>
+    bind_cols(rich) |>
+    mutate(
+      base_text = case_when(
+        !is.na(base_text) & base_text != "" ~ trimws(base_text),
+        !is.na(character)                   ~ trimws(character),
+        !is.na(numeric)                     ~ as.character(numeric),
+        TRUE                                ~ NA_character_
+      ),
+      note_super = if_else(is.na(note_super) | note_super == "",
+                           NA_character_, note_super)
+    ) |>
+    select(row, col, base_text, note_super)
+  
+  # Antimicrobial names from row 6, cols 4:18
+  ab_cols <- 4:18
+  ab_info <- list()
+  for (col_idx in ab_cols) {
+    name_cell <- get_cell(parsed, 6, col_idx)
+    dose_cell <- get_cell(parsed, 10, col_idx)
+    if (is.na(name_cell$base)) next
+    ab_info[[as.character(col_idx)]] <- list(
+      name = gsub("\r\n", " ", name_cell$base),
+      name_super = name_cell$super,
+      dose = if (!is.na(dose_cell$base) && !dose_cell$base %in% c("-", "ND"))
+        paste0(dose_cell$base, " mcg") else NA_character_
+    )
+  }
+  
+  # Organism rows: col 1 from row 11 onwards, excluding "Notes"
+  org_cells <- parsed |>
+    filter(col == 1, row >= 11, !grepl("^Notes", base_text))
+  
+  organism_pairs <- list()
+  for (i in seq_len(nrow(org_cells))) {
+    mic_row <- org_cells$row[i]
+    disk_row <- mic_row + 1
+    mic_check <- get_cell(parsed, mic_row, 2)
+    disk_check <- get_cell(parsed, disk_row, 2)
+    if (is.na(mic_check$base) || !grepl("MIC", mic_check$base)) next
+    if (is.na(disk_check$base) || !grepl("Zone", disk_check$base)) next
+    organism_pairs[[length(organism_pairs) + 1]] <- list(
+      organism = gsub("\r\n", " ", org_cells$base_text[i]),
+      mic_row = mic_row, disk_row = disk_row
+    )
+  }
+  
+  # Notes: find in col 1, rows after the last organism data row
+  last_data_row <- if (length(organism_pairs) > 0)
+    max(vapply(organism_pairs, function(x) x$disk_row, numeric(1))) else 0L
+  notes_cells <- parsed |>
+    filter(col == 1, row > last_data_row, !grepl("^Notes$", base_text))
+  notes_text <- if (nrow(notes_cells) > 0)
+    paste(notes_cells$base_text, collapse = "\n") else ""
+  if (is.na(notes_text)) notes_text <- ""
+  notes_lookup <- parse_notes_block(notes_text)
+  
+  results <- vector("list", 200)
+  idx <- 0L
+  
+  for (od in organism_pairs) {
+    for (col_idx in ab_cols) {
+      cc <- as.character(col_idx)
+      if (is.null(ab_info[[cc]])) next
+      ab_entry <- ab_info[[cc]]
+      ab_name <- ab_entry$name
+      ab_super <- ab_entry$name_super
+      ab_dose <- ab_entry$dose
+      
+      # MIC
+      mic_cell <- get_cell(parsed, od$mic_row, col_idx)
+      mic_val <- parse_bp(mic_cell$base)
+      # Combine superscripts from the cell and the agent name
+      all_supers <- c(mic_cell$super, ab_super)
+      all_supers <- all_supers[!is.na(all_supers) & all_supers != ""]
+      mic_note <- resolve_notes(notes_lookup, paste(all_supers, collapse = ","), NA)
+      
+      if (!is.na(mic_val)) {
+        idx <- idx + 1L
+        results[[idx]] <- tibble(
+          guideline = ver$guideline, version = ver$version,
+          type = "human", host = "human", method = "MIC",
+          site = "Topical", mo = od$organism, rank_index = NA_integer_,
+          ab = ab_name, ref_tbl = "Topical agents",
+          disk_dose = NA_character_,
+          breakpoint_S = mic_val, breakpoint_R = mic_val,
+          uti = FALSE, is_SDD = FALSE, is_screening = TRUE,
+          note = mic_note
+        )
+      }
+      
+      # DISK
+      disk_cell <- get_cell(parsed, od$disk_row, col_idx)
+      disk_val <- parse_bp(disk_cell$base)
+      all_supers_d <- c(disk_cell$super, ab_super)
+      all_supers_d <- all_supers_d[!is.na(all_supers_d) & all_supers_d != ""]
+      disk_note <- resolve_notes(notes_lookup, NA, paste(all_supers_d, collapse = ","))
+      
+      if (!is.na(disk_val)) {
+        idx <- idx + 1L
+        results[[idx]] <- tibble(
+          guideline = ver$guideline, version = ver$version,
+          type = "human", host = "human", method = "DISK",
+          site = "Topical", mo = od$organism, rank_index = NA_integer_,
+          ab = ab_name, ref_tbl = "Topical agents",
+          disk_dose = ab_dose,
+          breakpoint_S = disk_val, breakpoint_R = disk_val,
+          uti = FALSE, is_SDD = FALSE, is_screening = TRUE,
+          note = disk_note
+        )
+      }
+    }
+  }
+  
+  if (idx == 0) return(NULL)
+  bind_rows(results[seq_len(idx)])
+}
+
+# ==============================================================================
+# 8. Parse all data sheets
+# ==============================================================================
+parse_workbook <- function(xlsx_path,
+                           skip_sheets = c("Content", "Changes", "Notes",
+                                           "Guidance", "Dosages",
+                                           "Technical uncertainty",
+                                           "Non-species related breakpoints",
+                                           "PKPD breakpoints",
+                                           "PK PD breakpoints")) {
+  all_sheets <- xlsx_sheet_names(xlsx_path)
+  data_sheets <- setdiff(all_sheets, skip_sheets)
+  
+  results <- list()
+  for (s in data_sheets) {
+    message("Parsing ", basename(xlsx_path), ": ", s)
+    res <- tryCatch({
+      if (s == "Topical agents") {
+        parse_topical_sheet(xlsx_path)
+      } else {
+        parse_sheet(xlsx_path, s)
+      }
+    },
+    error = function(e) {
+      message("  ERROR: ", conditionMessage(e))
+      NULL
+    })
+    if (!is.null(res) && nrow(res) > 0) {
+      res$sheet <- s
+      results[[s]] <- res
+      message("  -> ", nrow(res), " rows")
+    }
+  }
+  
+  bind_rows(results)
+}
+
+# ==============================================================================
+# 8. Run
+# ==============================================================================
+
+breakpoint_files <- list.files(path = "data-raw",
+                               pattern = "breakpoint_table.*xlsx",
+                               full.names = TRUE,
+                               recursive = FALSE,
+                               ignore.case = TRUE)
+breakpoint_files <- breakpoint_files[breakpoint_files %unlike% "dosages"]
+
+breakpoints_eucast <- tibble()
+
+for (xlsx_path in breakpoint_files) {
+  
+  message("Parsing ", basename(xlsx_path))
+  result <- suppressMessages(parse_workbook(xlsx_path))
+  
+  # cat("\n============================\n")
+  cat("Total rows:", nrow(result), "\n\n")
+  # glimpse(result)
+  
+  # cat("\n--- Rows per sheet ---\n")
+  # result |> count(sheet) |> print(n = 40)
+  
+  # cat("\n--- Rows per organism (top 20) ---\n")
+  # result |> count(mo) |> arrange(desc(n)) |> head(20) |> print()
+  
+  breakpoints_eucast <- breakpoints_eucast |>
+    bind_rows(result)
+  
+  # saveRDS(result, "/home/claude/eucast_all_breakpoints.rds")
+  # write.csv(result, "/home/claude/eucast_all_breakpoints.csv", row.names = FALSE)
+  # cat("\nSaved.\n")
+}
+
+breakpoints_eucast_raw <- breakpoints_eucast
+
+saveRDS(breakpoints_eucast_raw, "data-raw/breakpoints_eucast_raw.rds")
+write.csv(breakpoints_eucast_raw, "data-raw/breakpoints_eucast_raw.csv", row.names = FALSE)
+
+breakpoints_eucast <- breakpoints_eucast_raw |>
+  filter(!breakpoint_S %in% c("NOTE", "IE", "IP", "NA", "-"),
+         !breakpoint_R %in% c("NOTE", "IE", "IP", "NA", "-")) |>
+  mutate(breakpoint_S = as.numeric(breakpoint_S),
+         breakpoint_R = as.numeric(breakpoint_R),
+         uti = uti | ab %like_case% "UTI") |>
+  filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)))
+
+breakpoints_eucast %>% count(guideline, version)

man/AMR.Rd

@@ -12,7 +12,7 @@ The \code{AMR} package is a peer-reviewed, \href{https://amr-for-r.org/#copyrigh
 
 This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
 
-After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated mei 2026) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}.
+After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated May 2026) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{It was designed to work in any setting, including those with very limited resources}. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}.
 
 The \code{AMR} package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
 }

man/g.test.Rd

@@ -46,7 +46,7 @@ A list with class \code{"htest"} containing the following
     \code{(observed - expected) / sqrt(expected)}.}
   \item{stdres}{standardized residuals,
     \code{(observed - expected) / sqrt(V)}, where \code{V} is the
-    residual cell variance {(\if{html}{\out{<a href="#reference+chisq.test.Rd+R+3AAgresti+3A2007" class="citation">}}Agresti 2007\if{html}{\out{</a>}}, section 2.4.5)}
+    residual cell variance (Agresti, 2007, section 2.4.5
     for the case where \code{x} is a matrix, \code{n * p * (1 - p)} otherwise).}
 }
 \description{

man/ggplot_pca.Rd

@@ -59,9 +59,8 @@ ggplot_pca(
   }
 
 \item{pc.biplot}{
-    If true, use what {\if{html}{\cite{}\out{<a href="#reference+biplot.princomp.Rd+R+3AGabriel+3A1971" class="citation">}}Gabriel (1971)\if{html}{\out{</a>}}} refers to as a
-    \dQuote{principal component biplot},
-    with \code{lambda = 1} and observations scaled up by sqrt(n) and
+    If true, use what Gabriel (1971) refers to as a "principal component
+    biplot", with \code{lambda = 1} and observations scaled up by sqrt(n) and
     variables scaled down by sqrt(n).  Then inner products between
     variables approximate covariances and distances between observations
     approximate Mahalanobis distance.