(v3.0.0.9019) Fixes #229, #230, #227, #225

.github/ISSUE_TEMPLATE/1-bug-report.yml

@@ -42,7 +42,7 @@ body:
       multiple: false
       options:
         - ''
-        - Latest CRAN version (2.1.1)
-        - One of the latest GitHub versions (2.1.1.9xxx)
+        - Latest CRAN version (3.0.0)
+        - One of the latest GitHub versions (3.0.0.9xxx)
     validations:
       required: true

.github/prehooks/commit-msg

@@ -60,6 +60,7 @@ else
 fi
 
 git add data-raw/*
+git add data/*
 git add -u
 
 exit 0

.github/prehooks/pre-commit

@@ -51,6 +51,7 @@ if command -v Rscript > /dev/null; then
     currentpkg=$(Rscript -e "cat(pkgload::pkg_name())")
     echo "- Adding changed files in ./data-raw and ./man to this commit"
     git add data-raw/*
+    git add data/*
     git add man/*
     git add R/sysdata.rda
     git add NAMESPACE
@@ -67,6 +68,12 @@ echo ""
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 echo "Updating semantic versioning and date..."
 
+current_branch=$(git rev-parse --abbrev-ref HEAD)
+if [ "$current_branch" != "main" ]; then
+  echo "- Current branch is '$current_branch'; skipping version/date update (only runs on 'main')"
+else
+  # Version update logic begins here
+  
   # Get tags from remote and remove tags not on remote
   git fetch origin --prune --prune-tags --quiet
   currenttagfull=$(git describe --tags --abbrev=0)
@@ -110,8 +117,10 @@ echo ""
   
   # Save the version number for use in the commit-msg hook
   echo "${currentversion}" > .git/commit_version.tmp
+fi
 
 git add data-raw/*
+git add data/*
 git add -u
 
 exit 0

.github/workflows/check-old-tinytest.yaml

@@ -59,8 +59,15 @@ jobs:
 
     env:
       R_REMOTES_NO_ERRORS_FROM_WARNINGS: true
+      LANG: en_US.UTF-8
+      LC_ALL: en_US.UTF-8
 
     steps:
+      - name: Set up locales
+        run: |
+          sudo locale-gen en_US.UTF-8
+          sudo update-locale LANG=en_US.UTF-8
+
       - uses: actions/checkout@v4
 
       - uses: r-lib/actions/setup-r@v2

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
-Version: 2.1.1
-Date: 2023-10-20 16:05:16 UTC
-SHA: ca72a646d041f7f096c4e196e8ae2fb2b176019c
+Version: 3.0.0
+Date: 2025-06-01 16:52:53 UTC
+SHA: 79038fed2169a25a7fc067c80bb25d9d78be21d9

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9281
-Date: 2025-05-20
+Version: 3.0.0.9019
+Date: 2025-09-01
 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
@@ -51,6 +51,8 @@ Suggests:
   pillar,
   progress,
   readxl,
+  recipes,
+  rlang,
   rmarkdown,
   rstudioapi,
   rvest,

NAMESPACE

@@ -1,12 +1,14 @@
 # Generated by roxygen2: do not edit by hand
 
 S3method("!=",amr_selector)
+S3method("$",deprecated_amr_dataset)
 S3method("&",amr_selector)
 S3method("+",ab)
 S3method("+",amr_selector)
 S3method("==",amr_selector)
 S3method("[",ab)
 S3method("[",av)
+S3method("[",deprecated_amr_dataset)
 S3method("[",disk)
 S3method("[",mic)
 S3method("[",mo)
@@ -18,6 +20,7 @@ S3method("[<-",mo)
 S3method("[<-",sir)
 S3method("[[",ab)
 S3method("[[",av)
+S3method("[[",deprecated_amr_dataset)
 S3method("[[",disk)
 S3method("[[",mic)
 S3method("[[",mo)
@@ -40,6 +43,7 @@ S3method(any,amr_selector)
 S3method(any,amr_selector_any_all)
 S3method(as.data.frame,ab)
 S3method(as.data.frame,av)
+S3method(as.data.frame,deprecated_amr_dataset)
 S3method(as.data.frame,mic)
 S3method(as.data.frame,mo)
 S3method(as.double,mic)
@@ -93,6 +97,7 @@ S3method(print,av)
 S3method(print,bug_drug_combinations)
 S3method(print,custom_eucast_rules)
 S3method(print,custom_mdro_guideline)
+S3method(print,deprecated_amr_dataset)
 S3method(print,disk)
 S3method(print,mic)
 S3method(print,mo)
@@ -101,6 +106,8 @@ S3method(print,mo_uncertainties)
 S3method(print,pca)
 S3method(print,sir)
 S3method(print,sir_log)
+S3method(print,step_mic_log2)
+S3method(print,step_sir_numeric)
 S3method(quantile,mic)
 S3method(rep,ab)
 S3method(rep,av)
@@ -154,6 +161,10 @@ export(administrable_per_os)
 export(age)
 export(age_groups)
 export(all_antimicrobials)
+export(all_mic)
+export(all_mic_predictors)
+export(all_sir)
+export(all_sir_predictors)
 export(aminoglycosides)
 export(aminopenicillins)
 export(amr_class)
@@ -161,7 +172,6 @@ export(amr_distance_from_row)
 export(amr_selector)
 export(anti_join_microorganisms)
 export(antibiogram)
-export(antibiotics)
 export(antifungals)
 export(antimicrobials_equal)
 export(antimycobacterials)
@@ -348,6 +358,8 @@ export(sir_df)
 export(sir_interpretation_history)
 export(sir_predict)
 export(skewness)
+export(step_mic_log2)
+export(step_sir_numeric)
 export(streptogramins)
 export(sulfonamides)
 export(susceptibility)
@@ -384,6 +396,12 @@ if(getRversion() >= "3.0.0") S3method(pillar::type_sum, av)
 if(getRversion() >= "3.0.0") S3method(pillar::type_sum, mic)
 if(getRversion() >= "3.0.0") S3method(pillar::type_sum, mo)
 if(getRversion() >= "3.0.0") S3method(pillar::type_sum, sir)
+if(getRversion() >= "3.0.0") S3method(recipes::bake, step_mic_log2)
+if(getRversion() >= "3.0.0") S3method(recipes::bake, step_sir_numeric)
+if(getRversion() >= "3.0.0") S3method(recipes::prep, step_mic_log2)
+if(getRversion() >= "3.0.0") S3method(recipes::prep, step_sir_numeric)
+if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_mic_log2)
+if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_sir_numeric)
 if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, disk)
 if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, mic)
 if(getRversion() >= "3.0.0") S3method(skimr::get_skimmers, mo)

NEWS.md

@@ -1,50 +1,37 @@
-# AMR 2.1.1.9281
+# AMR 3.0.0.9019
 
-*(this beta version will eventually become v3.0. We're happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using [the instructions here](https://amr-for-r.org/#get-this-package).)*
+This is primarily a bugfix release, though we added one nice feature too.
+
+### New
+* Integration with the **tidymodels** framework to allow seamless use of MIC and SIR data in modelling pipelines via `recipes`
+  - `step_mic_log2()` to transform `<mic>` columns with log2, and `step_sir_numeric()` to convert `<sir>` columns to numeric
+  - New `tidyselect` helpers: `all_mic()`, `all_mic_predictors()`, `all_sir()`, `all_sir_predictors()`
+
+### Changed
+* Fixed a bug in `antibiogram()` for when no antimicrobials are set
+* Fixed a bug in `antibiogram()` to allow column names containing the `+` character (#222)
+* Fixed a bug in `as.ab()` for antimicrobial codes with a number in it if they are preceded by a space
+* Fixed a bug in `eucast_rules()` for using specific custom rules
+* Fixed a bug in `as.sir()` to allow any tidyselect language (#220)
+* Fixed a bug in `as.sir()` to pick right breakpoint when `uti = FALSE` (#216)
+* Fixed a bug in `ggplot_sir()` when using `combine_SI = FALSE` (#213)
+* Fixed a bug the `antimicrobials` data set to remove statins (#229)
+* Fixed a bug in `mdro()` to make sure all genes specified in arguments are acknowledges
+* Fixed ATC J01CR05 to map to piperacillin/tazobactam rather than piperacillin/sulbactam (#230)
+* Fixed all plotting to contain a separate colour for SDD (susceptible dose-dependent) (#223)
+* Fixed some specific Dutch translations for antimicrobials
+* Added all reasons in verbose output of `mdro()` (#227)
+* Added `names` to `age_groups()` so that custom names can be given (#215)
+* Added note to `as.sir()` to make it explicit when higher-level taxonomic breakpoints are used (#218)
+* Added antibiotic codes from the Comprehensive Antibiotic Resistance Database (CARD) to the `antimicrobials` data set (#225)
+* Updated Fosfomycin to be of antibiotic class 'Phosphonics' (#225)
+* Updated `random_mic()` and `random_disk()` to set skewedness of the distribution and allow multiple microorganisms
+
+
+# AMR 3.0.0
 
 This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the [University of Prince Edward Island's Atlantic Veterinary College](https://www.upei.ca/avc), Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.
 
-## tl;dr
-
-- Scope Expansion: One Health support (Human + Veterinary + Environmental microbiology).
-- Data Updates: 
-  - `antibiotics` renamed to `antimicrobials`.
-  - Veterinary antimicrobials and WHOCC codes added.
-  - MycoBank fungal taxonomy integrated (+20,000 fungi).
-- Breakpoints & Interpretations:
-  - CLSI/EUCAST 2024-2025 breakpoints added; EUCAST 2025 default.
-  - `as.sir()` supports NI/SDD levels; parallel computation enabled.
-  - Custom S/I/R/SDD/NI definitions allowed.
-  - Improved handling of capped MICs.
-- New Tools & Functions:
-  - WISCA antibiogram support (`antibiogram()`, `wisca()`).
-  - New ggplot2 extensions: `scale_*_mic()`, `scale_*_sir()`, `rescale_mic()`.
-  - New utility functions: `top_n_microorganisms()`, `mo_group_members()`, `mic_p50()`, `mic_p90()`.
-- Predictive Modelling:
-  - Full tidymodels compatibility for antimicrobial selectors.
-  - Deprecated `resistance_predict()` and `sir_predict()`.
-- Python Compatibility: AMR R package now runs in Python.
-- Selector Improvements:
-  * Added selectors (`isoxazolylpenicillins()`, `monobactams()`, `nitrofurans()`, `phenicols()`, `rifamycins()`, and `sulfonamides()`)
-  - Selectors renamed from `ab_*` to `amr_*`; old names deprecated.
-- MIC/Disks Handling:
-  - MIC strict comparisons, added levels.
-  - Disk diffusion range expanded (0–50 mm).
-- EUCAST Rules and MDROs:
-  - EUCAST v12–v15 rules implemented.
-  - Dutch MDRO 2024 guideline support in `mdro()`.
-- Infrastructure:
-  - New website: https://amr-for-r.org.
-  - Improved `vctrs` integration for tidyverse workflows.
-  - Dropped SAS `.xpt` file support.
-- Other Fixes & Enhancements:
-  - Support for 8 new languages, adding to a total of 28 languages.
-  - Faster microorganism identification.
-  - Improved antimicrobial and MIC handling.
-  - Extended documentation, additional contributors acknowledged.
-
-## Full Changelog
-
 ### Breaking
 * Dataset `antibiotics` has been renamed to `antimicrobials` as the data set contains more than just antibiotics. Using `antibiotics` will still work, but now returns a warning.
 * Removed all functions and references that used the deprecated `rsi` class, which were all replaced with their `sir` equivalents over two years ago.
@@ -165,7 +152,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
 
 ## Older Versions
 
-This changelog only contains changes from AMR v3.0 (March 2025) and later.
+This changelog only contains changes from AMR v3.0 (June 2025) and later.
 
 * For prior v2 versions, please see [our v2 archive](https://github.com/msberends/AMR/blob/v2.1.1/NEWS.md).
 * For prior v1 versions, please see [our v1 archive](https://github.com/msberends/AMR/blob/v1.8.2/NEWS.md).

R/aa_helper_functions.R

@@ -63,31 +63,6 @@ pm_left_join <- function(x, y, by = NULL, suffix = c(".x", ".y")) {
   merged
 }
 
-# support where() like tidyverse (this function will also be used when running `antibiogram()`):
-where <- function(fn) {
-  # based on https://github.com/nathaneastwood/poorman/blob/52eb6947e0b4430cd588976ed8820013eddf955f/R/where.R#L17-L32
-  if (!is.function(fn)) {
-    stop_("`", deparse(substitute(fn)), "()` is not a valid predicate function.")
-  }
-  df <- pm_select_env$.data
-  cols <- pm_select_env$get_colnames()
-  if (is.null(df)) {
-    df <- get_current_data("where", call = FALSE)
-    cols <- colnames(df)
-  }
-  preds <- unlist(lapply(
-    df,
-    function(x, fn) {
-      do.call("fn", list(x))
-    },
-    fn
-  ))
-  if (!is.logical(preds)) stop_("`where()` must be used with functions that return `TRUE` or `FALSE`.")
-  data_cols <- cols
-  cols <- data_cols[preds]
-  which(data_cols %in% cols)
-}
-
 # copied and slightly rewritten from {poorman} under permissive license (2021-10-15)
 # https://github.com/nathaneastwood/poorman, MIT licensed, Nathan Eastwood, 2020
 case_when_AMR <- function(...) {
@@ -544,7 +519,7 @@ word_wrap <- function(...,
     )
     msg <- paste0(parts, collapse = "`")
   }
-  msg <- gsub("`(.+?)`", font_grey_bg("\\1"), msg)
+  msg <- gsub("`(.+?)`", font_grey_bg("`\\1`"), msg)
 
   # clean introduced whitespace in between fullstops
   msg <- gsub("[.] +[.]", "..", msg)
@@ -814,7 +789,7 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
 
   # if object is missing, or another error:
   tryCatch(invisible(object),
-    error = function(e) AMR_env$meet_criteria_error_txt <- e$message
+    error = function(e) AMR_env$meet_criteria_error_txt <- conditionMessage(e)
   )
   if (!is.null(AMR_env$meet_criteria_error_txt)) {
     error_txt <- AMR_env$meet_criteria_error_txt
@@ -1244,7 +1219,9 @@ try_colour <- function(..., before, after, collapse = " ") {
   }
 }
 is_dark <- function() {
-  if (is.null(AMR_env$is_dark_theme)) {
+  AMR_env$current_theme <- tryCatch(getExportedValue("getThemeInfo", ns = asNamespace("rstudioapi"))()$editor, error = function(e) NULL)
+  if (!identical(AMR_env$current_theme, AMR_env$former_theme) || is.null(AMR_env$is_dark_theme)) {
+    AMR_env$former_theme <- AMR_env$current_theme
     AMR_env$is_dark_theme <- !has_colour() || tryCatch(isTRUE(getExportedValue("getThemeInfo", ns = asNamespace("rstudioapi"))()$dark), error = function(e) FALSE)
   }
   isTRUE(AMR_env$is_dark_theme)
@@ -1317,6 +1294,10 @@ font_green_bg <- function(..., collapse = " ") {
   # this is #3caea3 (picked to be colourblind-safe with other SIR colours)
   try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;79m", after = "\033[49m", collapse = collapse)
 }
+font_green_lighter_bg <- function(..., collapse = " ") {
+  # this is #8FD6C4 (picked to be colourblind-safe with other SIR colours)
+  try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;158m", after = "\033[49m", collapse = collapse)
+}
 font_purple_bg <- function(..., collapse = " ") {
   try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;89m", after = "\033[49m", collapse = collapse)
 }
@@ -1634,6 +1615,36 @@ get_n_cores <- function(max_cores = Inf) {
   n_cores
 }
 
+# Support `where()` if tidyselect not installed ----
+if (!is.null(import_fn("where", "tidyselect", error_on_fail = FALSE))) {
+  # tidyselect::where() exists, load the namespace to make `where()`s work across the package in default arguments
+  loadNamespace("tidyselect")
+} else {
+  where <- function(fn) {
+    # based on https://github.com/nathaneastwood/poorman/blob/52eb6947e0b4430cd588976ed8820013eddf955f/R/where.R#L17-L32
+    if (!is.function(fn)) {
+      stop_("`", deparse(substitute(fn)), "()` is not a valid predicate function.")
+    }
+    df <- pm_select_env$.data
+    cols <- pm_select_env$get_colnames()
+    if (is.null(df)) {
+      df <- get_current_data("where", call = FALSE)
+      cols <- colnames(df)
+    }
+    preds <- unlist(lapply(
+      df,
+      function(x, fn) {
+        do.call("fn", list(x))
+      },
+      fn
+    ))
+    if (!is.logical(preds)) stop_("`where()` must be used with functions that return `TRUE` or `FALSE`.")
+    data_cols <- cols
+    cols <- data_cols[preds]
+    which(data_cols %in% cols)
+  }
+}
+
 # Faster data.table implementations ----
 
 match <- function(x, table, ...) {
@@ -1653,52 +1664,6 @@ match <- function(x, table, ...) {
   }
 }
 
-# nolint start
-
-# Register S3 methods ----
-# copied from vctrs::s3_register by their permission:
-# https://github.com/r-lib/vctrs/blob/05968ce8e669f73213e3e894b5f4424af4f46316/R/register-s3.R
-s3_register <- function(generic, class, method = NULL) {
-  stopifnot(is.character(generic), length(generic) == 1)
-  stopifnot(is.character(class), length(class) == 1)
-  pieces <- strsplit(generic, "::")[[1]]
-  stopifnot(length(pieces) == 2)
-  package <- pieces[[1]]
-  generic <- pieces[[2]]
-  caller <- parent.frame()
-  get_method_env <- function() {
-    top <- topenv(caller)
-    if (isNamespace(top)) {
-      asNamespace(environmentName(top))
-    } else {
-      caller
-    }
-  }
-  get_method <- function(method, env) {
-    if (is.null(method)) {
-      get(paste0(generic, ".", class), envir = get_method_env())
-    } else {
-      method
-    }
-  }
-  method_fn <- get_method(method)
-  stopifnot(is.function(method_fn))
-  setHook(packageEvent(package, "onLoad"), function(...) {
-    ns <- asNamespace(package)
-    method_fn <- get_method(method)
-    registerS3method(generic, class, method_fn, envir = ns)
-  })
-  if (!isNamespaceLoaded(package)) {
-    return(invisible())
-  }
-  envir <- asNamespace(package)
-  if (exists(generic, envir)) {
-    registerS3method(generic, class, method_fn, envir = envir)
-  }
-  invisible()
-}
-
-
 # Support old R versions ----
 # these functions were not available in previous versions of R
 # see here for the full list: https://github.com/r-lib/backports

R/aa_helper_pm_functions.R

@@ -952,7 +952,19 @@ pm_select_env$get_nrow <- function() nrow(pm_select_env$.data)
 pm_select_env$get_ncol <- function() ncol(pm_select_env$.data)
 
 pm_select <- function(.data, ...) {
+  # col_pos <- pm_select_positions(.data, ..., .group_pos = TRUE),
+  col_pos <- tryCatch(pm_select_positions(.data, ..., .group_pos = TRUE), error = function(e) NULL)
+  if (is.null(col_pos)) {
+    # try with tidyverse
+    select_dplyr <- import_fn("select", "dplyr", error_on_fail = FALSE)
+    if (!is.null(select_dplyr)) {
+      col_pos <- which(colnames(.data) %in% colnames(select_dplyr(.data, ...)))
+    } else {
+      # this will throw an error as it did, but dplyr is not available, so no other option
       col_pos <- pm_select_positions(.data, ..., .group_pos = TRUE)
+    }
+  }
+
   map_names <- names(col_pos)
   map_names_length <- nchar(map_names)
   if (any(map_names_length == 0L)) {

R/ab.R

@@ -184,7 +184,8 @@ as.ab <- function(x, flag_multiple_results = TRUE, language = get_AMR_locale(),
   x_new[known_codes_cid] <- AMR_env$AB_lookup$ab[match(x[known_codes_cid], AMR_env$AB_lookup$cid)]
   previously_coerced <- x %in% AMR_env$ab_previously_coerced$x
   x_new[previously_coerced & is.na(x_new)] <- AMR_env$ab_previously_coerced$ab[match(x[is.na(x_new) & x %in% AMR_env$ab_previously_coerced$x], AMR_env$ab_previously_coerced$x)]
-  if (any(previously_coerced) && isTRUE(info) && message_not_thrown_before("as.ab", entire_session = TRUE)) {
+  previously_coerced_mention <- x %in% AMR_env$ab_previously_coerced$x & !x %in% AMR_env$AB_lookup$ab & !x %in% AMR_env$AB_lookup$generalised_name
+  if (any(previously_coerced_mention) && isTRUE(info) && message_not_thrown_before("as.ab", entire_session = TRUE)) {
     message_(
       "Returning previously coerced ",
       ifelse(length(unique(which(x[which(previously_coerced)] %in% x_bak_clean))) > 1, "value for an antimicrobial", "values for various antimicrobials"),
@@ -655,7 +656,9 @@ generalise_antibiotic_name <- function(x) {
   x <- trimws(gsub(" +", " ", x, perl = TRUE))
   # remove last couple of words if they numbers or units
   x <- gsub("( ([0-9]{3,}|U?M?C?G|L))+$", "", x, perl = TRUE)
-  # move HIGH to end
+  # remove whitespace prior to numbers if preceded by A-Z
+  x <- gsub("([A-Z]+) +([0-9]+)", "\\1\\2", x, perl = TRUE)
+  # move HIGH to the end
   x <- trimws(gsub("(.*) HIGH(.*)", "\\1\\2 HIGH", x, perl = TRUE))
   x
 }

R/ab_property.R

@@ -445,7 +445,7 @@ ab_validate <- function(x, property, ...) {
     # try to catch an error when inputting an invalid argument
     # so the 'call.' can be set to FALSE
     tryCatch(x[1L] %in% AMR_env$AB_lookup[1, property, drop = TRUE],
-      error = function(e) stop(e$message, call. = FALSE)
+      error = function(e) stop(conditionMessage(e), call. = FALSE)
     )
 
     if (!all(x %in% AMR_env$AB_lookup[, property, drop = TRUE])) {

R/age.R

@@ -128,9 +128,10 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
 
 #' Split Ages into Age Groups
 #'
-#' Split ages into age groups defined by the `split` argument. This allows for easier demographic (antimicrobial resistance) analysis.
+#' Split ages into age groups defined by the `split` argument. This allows for easier demographic (antimicrobial resistance) analysis. The function returns an ordered [factor].
 #' @param x Age, e.g. calculated with [age()].
 #' @param split_at Values to split `x` at - the default is age groups 0-11, 12-24, 25-54, 55-74 and 75+. See *Details*.
+#' @param names Optional names to be given to the various age groups.
 #' @param na.rm A [logical] to indicate whether missing values should be removed.
 #' @details To split ages, the input for the `split_at` argument can be:
 #'
@@ -152,6 +153,7 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
 #'
 #' # split into 0-19, 20-49 and 50+
 #' age_groups(ages, c(20, 50))
+#' age_groups(ages, c(20, 50), names = c("Under 20 years", "20 to 50 years", "Over 50 years"))
 #'
 #' # split into groups of ten years
 #' age_groups(ages, 1:10 * 10)
@@ -181,9 +183,10 @@ age <- function(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...) {
 #'     )
 #' }
 #' }
-age_groups <- function(x, split_at = c(12, 25, 55, 75), na.rm = FALSE) {
+age_groups <- function(x, split_at = c(0, 12, 25, 55, 75), names = NULL, na.rm = FALSE) {
   meet_criteria(x, allow_class = c("numeric", "integer"), is_positive_or_zero = TRUE, is_finite = TRUE)
   meet_criteria(split_at, allow_class = c("numeric", "integer", "character"), is_positive_or_zero = TRUE, is_finite = TRUE)
+  meet_criteria(names, allow_class = "character", allow_NULL = TRUE)
   meet_criteria(na.rm, allow_class = "logical", has_length = 1)
 
   if (any(x < 0, na.rm = TRUE)) {
@@ -208,7 +211,7 @@ age_groups <- function(x, split_at = c(12, 25, 55, 75), na.rm = FALSE) {
     split_at <- c(0, split_at)
   }
   split_at <- split_at[!is.na(split_at)]
-  stop_if(length(split_at) == 1, "invalid value for `split_at`") # only 0 is available
+  stop_if(length(split_at) == 1, "invalid value for `split_at`.") # only 0 is available
 
   # turn input values to 'split_at' indices
   y <- x
@@ -224,6 +227,11 @@ age_groups <- function(x, split_at = c(12, 25, 55, 75), na.rm = FALSE) {
 
   agegroups <- factor(lbls[y], levels = lbls, ordered = TRUE)
 
+  if (!is.null(names)) {
+    stop_ifnot(length(names) == length(levels(agegroups)), "`names` must have the same length as the number of age groups (", length(levels(agegroups)), ").")
+    levels(agegroups) <- names
+  }
+
   if (isTRUE(na.rm)) {
     agegroups <- agegroups[!is.na(agegroups)]
   }

R/amr_selectors.R

@@ -527,7 +527,7 @@ amr_selector <- function(filter,
   )
   call <- substitute(filter)
   agents <- tryCatch(AMR_env$AB_lookup[which(eval(call, envir = AMR_env$AB_lookup)), "ab", drop = TRUE],
-    error = function(e) stop_(e$message, call = -5)
+    error = function(e) stop_(conditionMessage(e), call = -5)
   )
   agents <- ab_in_data[ab_in_data %in% agents]
   message_agent_names(
@@ -640,7 +640,7 @@ not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, ver
         )
       }
     ),
-    error = function(e) stop_("in not_intrinsic_resistant(): ", e$message, call = FALSE)
+    error = function(e) stop_("in not_intrinsic_resistant(): ", conditionMessage(e), call = FALSE)
   )
 
   agents <- ab_in_data[ab_in_data %in% names(vars_df_R[which(vars_df_R)])]

R/antibiogram.R

@@ -40,6 +40,7 @@
 #'   - A combination of the above, using `c()`, e.g.:
 #'     - `c(aminoglycosides(), "AMP", "AMC")`
 #'     - `c(aminoglycosides(), carbapenems())`
+#'   - Column indices using numbers
 #'   - Combination therapy, indicated by using `"+"`, with or without [antimicrobial selectors][antimicrobial_selectors], e.g.:
 #'     - `"cipro + genta"`
 #'     - `"TZP+TOB"`
@@ -110,7 +111,7 @@
 #'
 #' There are various antibiogram types, as summarised by Klinker *et al.* (2021, \doi{10.1177/20499361211011373}), and they are all supported by [antibiogram()].
 #'
-#' For clinical coverage estimations, **use WISCA whenever possible**, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki *et al.* (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section *Explaining WISCA* on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
+#' For clinical coverage estimations, **use WISCA whenever possible**, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki *et al.* (2020, \doi{10.1001/jamanetworkopen.2019.21124}). See the section *Explaining WISCA* on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
 #'
 #' 1. **Traditional Antibiogram**
 #'
@@ -266,7 +267,7 @@
 #' For more background, interpretation, and examples, see [the WISCA vignette](https://amr-for-r.org/articles/WISCA.html).
 #' @source
 #' * Bielicki JA *et al.* (2016). **Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data** *Journal of Antimicrobial Chemotherapy* 71(3); \doi{10.1093/jac/dkv397}
-#' * Bielicki JA *et al.* (2020). **Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries** *JAMA Netw Open.* 3(2):e1921124; \doi{10.1001.jamanetworkopen.2019.21124}
+#' * Bielicki JA *et al.* (2020). **Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries** *JAMA Netw Open.* 3(2):e1921124; \doi{10.1001/jamanetworkopen.2019.21124}
 #' * Klinker KP *et al.* (2021). **Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms**. *Therapeutic Advances in Infectious Disease*, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
 #' * Barbieri E *et al.* (2021). **Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach** *Antimicrobial Resistance & Infection Control* May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
 #' * **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition**, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
@@ -452,7 +453,7 @@ antibiogram.default <- function(x,
     deprecation_warning("antibiotics", "antimicrobials", fn = "antibiogram", is_argument = TRUE)
     antimicrobials <- list(...)$antibiotics
   }
-  meet_criteria(antimicrobials, allow_class = "character", allow_NA = FALSE, allow_NULL = FALSE)
+  meet_criteria(antimicrobials, allow_class = c("character", "numeric", "integer"), allow_NA = FALSE, allow_NULL = FALSE)
   if (!is.function(mo_transform)) {
     meet_criteria(mo_transform, allow_class = "character", has_length = 1, is_in = c("name", "shortname", "gramstain", colnames(AMR::microorganisms)), allow_NULL = TRUE, allow_NA = TRUE)
   }
@@ -575,6 +576,15 @@ antibiogram.default <- function(x,
     }
     antimicrobials <- unlist(antimicrobials)
   } else {
+    existing_ab_combined_cols <- ab_trycatch[ab_trycatch %like% "[+]" & ab_trycatch %in% colnames(x)]
+    if (length(existing_ab_combined_cols) > 0 && !is.null(ab_transform)) {
+      ab_transform <- NULL
+      warning_(
+        "Detected column name(s) containing the '+' character, which conflicts with the expected syntax in `antibiogram()`: the '+' is used to combine separate antimicrobial agent columns (e.g., \"AMP+GEN\").\n\n",
+        "To avoid incorrectly guessing which antimicrobials this represents, `ab_transform` was automatically set to `NULL`.\n\n",
+        "If this is unintended, please rename the column(s) to avoid using '+' in the name, or set `ab_transform = NULL` explicitly to suppress this message."
+      )
+    }
     antimicrobials <- ab_trycatch
   }
 
@@ -1194,12 +1204,13 @@ retrieve_wisca_parameters <- function(wisca_model, ...) {
 #' @rawNamespace if(getRversion() >= "3.0.0") S3method(pillar::tbl_sum, antibiogram)
 tbl_sum.antibiogram <- function(x, ...) {
   dims <- paste(format(NROW(x), big.mark = ","), AMR_env$cross_icon, format(NCOL(x), big.mark = ","))
+  names(dims) <- "An Antibiogram"
   if (isTRUE(attributes(x)$wisca)) {
-    names(dims) <- paste0("An Antibiogram (WISCA / ", attributes(x)$conf_interval * 100, "% CI)")
+    dims <- c(dims, Type = paste0("WISCA with ", attributes(x)$conf_interval * 100, "% CI"))
   } else if (isTRUE(attributes(x)$formatting_type >= 13)) {
-    names(dims) <- paste0("An Antibiogram (non-WISCA / ", attributes(x)$conf_interval * 100, "% CI)")
+    dims <- c(dims, Type = paste0("Non-WISCA with ", attributes(x)$conf_interval * 100, "% CI"))
   } else {
-    names(dims) <- paste0("An Antibiogram (non-WISCA)")
+    dims <- c(dims, Type = paste0("Non-WISCA without CI"))
   }
   dims
 }

R/av_property.R

@@ -264,7 +264,7 @@ av_validate <- function(x, property, ...) {
     # try to catch an error when inputting an invalid argument
     # so the 'call.' can be set to FALSE
     tryCatch(x[1L] %in% AMR_env$AV_lookup[1, property, drop = TRUE],
-      error = function(e) stop(e$message, call. = FALSE)
+      error = function(e) stop(conditionMessage(e), call. = FALSE)
     )
 
     if (!all(x %in% AMR_env$AV_lookup[, property, drop = TRUE])) {

R/count.R

@@ -126,7 +126,7 @@ count_resistant <- function(..., only_all_tested = FALSE) {
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -139,7 +139,7 @@ count_susceptible <- function(..., only_all_tested = FALSE) {
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -152,7 +152,7 @@ count_S <- function(..., only_all_tested = FALSE) {
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -165,7 +165,7 @@ count_SI <- function(..., only_all_tested = FALSE) {
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -178,7 +178,7 @@ count_I <- function(..., only_all_tested = FALSE) {
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -191,7 +191,7 @@ count_IR <- function(..., only_all_tested = FALSE) {
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -204,7 +204,7 @@ count_R <- function(..., only_all_tested = FALSE) {
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -217,7 +217,7 @@ count_all <- function(..., only_all_tested = FALSE) {
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -240,6 +240,6 @@ count_df <- function(data,
       combine_SI = combine_SI,
       confidence_level = 0.95 # doesn't matter, will be removed
     ),
-    error = function(e) stop_(gsub("in sir_calc_df(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc_df(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }

R/custom_mdro_guideline.R

@@ -175,7 +175,7 @@ custom_mdro_guideline <- function(..., as_factor = TRUE) {
 
     # Value
     val <- tryCatch(eval(dots[[i]][[3]]), error = function(e) NULL)
-    stop_if(is.null(val), "rule ", i, " must return a valid value, it now returns an error: ", tryCatch(eval(dots[[i]][[3]]), error = function(e) e$message))
+    stop_if(is.null(val), "rule ", i, " must return a valid value, it now returns an error: ", tryCatch(eval(dots[[i]][[3]]), error = function(e) conditionMessage(e)))
     stop_if(length(val) > 1, "rule ", i, " must return a value of length 1, not ", length(val))
     out[[i]]$value <- as.character(val)
   }
@@ -254,7 +254,7 @@ run_custom_mdro_guideline <- function(df, guideline, info) {
   for (i in seq_len(n_dots)) {
     qry <- tryCatch(eval(parse(text = guideline[[i]]$query), envir = df, enclos = parent.frame()),
       error = function(e) {
-        AMR_env$err_msg <- e$message
+        AMR_env$err_msg <- conditionMessage(e)
         return("error")
       }
     )

R/data.R

@@ -29,7 +29,10 @@
 
 #' Data Sets with `r format(nrow(antimicrobials) + nrow(antivirals), big.mark = " ")` Antimicrobial Drugs
 #'
+#' @description
 #' Two data sets containing all antimicrobials and antivirals. Use [as.ab()] or one of the [`ab_*`][ab_property()] functions to retrieve values from the [antimicrobials] data set. Three identifiers are included in this data set: an antimicrobial ID (`ab`, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (`atc`) as defined by the WHO, and a Compound ID (`cid`) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.
+#'
+#' **The `antibiotics` data set has been renamed to `antimicrobials`. The old name will be removed in a future version.**
 #' @format
 #' ### For the [antimicrobials] data set: a [tibble][tibble::tibble] with `r nrow(antimicrobials)` observations and `r ncol(antimicrobials)` variables:
 #' - `ab`\cr antimicrobial ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. ***This is a unique identifier.***
@@ -88,6 +91,9 @@
 #' antivirals
 "antimicrobials"
 
+#' @rdname antimicrobials
+"antibiotics"
+
 #' @rdname antimicrobials
 "antivirals"
 
@@ -355,3 +361,15 @@
 #' @examples
 #' dosage
 "dosage"
+
+#' Data Set with `r format(nrow(esbl_isolates), big.mark = " ")` ESBL Isolates
+#'
+#' A data set containing `r format(nrow(esbl_isolates), big.mark = " ")` microbial isolates with MIC values of common antibiotics and a binary `esbl` column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with [tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.html).
+#' @format A [tibble][tibble::tibble] with `r format(nrow(esbl_isolates), big.mark = " ")` observations and `r ncol(esbl_isolates)` variables:
+#' - `esbl`\cr Logical indicator if the isolate is ESBL-producing
+#' - `genus`\cr Genus of the microorganism
+#' - `AMC:COL`\cr MIC values for 17 antimicrobial agents, transformed to class [`mic`] (see [as.mic()])
+#' @details See our [tidymodels integration][amr-tidymodels] for an example using this data set.
+#' @examples
+#' esbl_isolates
+"esbl_isolates"

R/eucast_rules.R

@@ -442,7 +442,7 @@ eucast_rules <- function(x,
     # big speed gain! only analyse unique rows:
     pm_distinct(`.rowid`, .keep_all = TRUE) %pm>%
     as.data.frame(stringsAsFactors = FALSE)
-  x[, col_mo] <- as.mo(as.character(x[, col_mo, drop = TRUE]), info = info)
+  x[, col_mo] <- as.mo(as.character(x[, col_mo, drop = TRUE]), info = FALSE)
   # rename col_mo to prevent interference with joined columns
   colnames(x)[colnames(x) == col_mo] <- ".col_mo"
   col_mo <- ".col_mo"
@@ -450,13 +450,20 @@ eucast_rules <- function(x,
   x <- left_join_microorganisms(x, by = col_mo, suffix = c("_oldcols", ""))
   x$gramstain <- mo_gramstain(x[, col_mo, drop = TRUE], language = NULL, info = FALSE)
   x$genus_species <- trimws(paste(x$genus, x$species))
-  if (isTRUE(info) && NROW(x) > 10000) {
+  if (isTRUE(info) && NROW(x.bak) > 10000) {
     message_("OK.", add_fn = list(font_green, font_bold), as_note = FALSE)
   }
 
   n_added <- 0
   n_changed <- 0
 
+  rule_current <- ""
+  rule_group_current <- ""
+  rule_group_previous <- ""
+  rule_next <- ""
+  rule_previous <- ""
+  rule_text <- ""
+
   # >>> Apply Other rules: enzyme inhibitors <<< ------------------------------------------
   if (any(c("all", "other") %in% rules)) {
     if (isTRUE(info)) {
@@ -617,31 +624,16 @@ eucast_rules <- function(x,
     eucast_rules_df <- eucast_rules_df %pm>%
       rbind_AMR(eucast_rules_df_total %pm>%
         subset(reference.rule_group %like% "breakpoint" & reference.version == version_breakpoints))
-    # eucast_rules_df <- subset(
-    #   eucast_rules_df,
-    #   reference.rule_group %unlike% "breakpoint" |
-    #     (reference.rule_group %like% "breakpoint" & reference.version == version_breakpoints)
-    # )
   }
   if (any(c("all", "expected_phenotypes") %in% rules)) {
     eucast_rules_df <- eucast_rules_df %pm>%
       rbind_AMR(eucast_rules_df_total %pm>%
         subset(reference.rule_group %like% "expected" & reference.version == version_expected_phenotypes))
-    # eucast_rules_df <- subset(
-    #   eucast_rules_df,
-    #   reference.rule_group %unlike% "expected" |
-    #     (reference.rule_group %like% "expected" & reference.version == version_expected_phenotypes)
-    # )
   }
   if (any(c("all", "expert") %in% rules)) {
     eucast_rules_df <- eucast_rules_df %pm>%
       rbind_AMR(eucast_rules_df_total %pm>%
         subset(reference.rule_group %like% "expert" & reference.version == version_expertrules))
-    # eucast_rules_df <- subset(
-    #   eucast_rules_df,
-    #   reference.rule_group %unlike% "expert" |
-    #     (reference.rule_group %like% "expert" & reference.version == version_expertrules)
-    # )
   }
   ## filter out AmpC de-repressed cephalosporin-resistant mutants ----
   # no need to filter on version number here - the rules contain these version number, so are inherently filtered
@@ -664,6 +656,9 @@ eucast_rules <- function(x,
   # we only hints on remaining rows in `eucast_rules_df`
   screening_abx <- as.character(AMR::antimicrobials$ab[which(AMR::antimicrobials$ab %like% "-S$")])
   screening_abx <- screening_abx[screening_abx %in% unique(unlist(strsplit(EUCAST_RULES_DF$and_these_antibiotics[!is.na(EUCAST_RULES_DF$and_these_antibiotics)], ", *")))]
+  if (isTRUE(info)) {
+    cat("\n")
+  }
   for (ab_s in screening_abx) {
     ab <- gsub("-S$", "", ab_s)
     if (ab %in% names(cols_ab) && !ab_s %in% names(cols_ab)) {
@@ -894,7 +889,9 @@ eucast_rules <- function(x,
     }
     for (i in seq_len(length(custom_rules))) {
       rule <- custom_rules[[i]]
-      rows <- which(eval(parse(text = rule$query), envir = x))
+      rows <- tryCatch(which(eval(parse(text = rule$query), envir = x)),
+        error = function(e) stop_(paste0(conditionMessage(e), font_red(" (check available data and compare with the custom rules set)")), call = FALSE)
+      )
       cols <- as.character(rule$result_group)
       cols <- c(
         cols[cols %in% colnames(x)], # direct column names
@@ -908,9 +905,8 @@ eucast_rules <- function(x,
         get_antibiotic_names(cols)
       )
       if (isTRUE(info)) {
-        # print rule
         cat(italicise_taxonomy(
-          word_wrap(format_custom_query_rule(rule$query, colours = FALSE),
+          word_wrap(rule_text,
             width = getOption("width") - 30,
             extra_indent = 6
           ),
@@ -1182,7 +1178,7 @@ edit_sir <- function(x,
             ifelse(length(rows) > 10, "...", ""),
             " while writing value '", to,
             "' to column(s) `", paste(cols, collapse = "`, `"),
-            "`:\n", e$message
+            "`:\n", conditionMessage(e)
           ),
           call. = FALSE
         )

R/g.test.R

@@ -72,7 +72,7 @@
 #'
 #' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use *G*-tests for each category, of course.
 #' @seealso [chisq.test()]
-#' @references 1. McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland. <http://www.biostathandbook.com/gtestgof.html>.
+#' @references 1. McDonald, J.H. 2014. **Handbook of Biological Statistics (3rd ed.)**. Sparky House Publishing, Baltimore, Maryland.
 #' @source The code for this function is identical to that of [chisq.test()], except that:
 #' - The calculation of the statistic was changed to \eqn{2 * sum(x * log(x / E))}
 #' - Yates' continuity correction was removed as it does not apply to a *G*-test

R/ggplot_sir.R

@@ -177,6 +177,7 @@ ggplot_sir <- function(data,
                        nrow = NULL,
                        colours = c(
                          S = "#3CAEA3",
+                         SDD = "#8FD6C4",
                          SI = "#3CAEA3",
                          I = "#F6D55C",
                          IR = "#ED553B",
@@ -205,7 +206,7 @@ ggplot_sir <- function(data,
   meet_criteria(minimum, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
   language <- validate_language(language)
   meet_criteria(nrow, allow_class = c("numeric", "integer"), has_length = 1, allow_NULL = TRUE, is_positive = TRUE, is_finite = TRUE)
-  meet_criteria(colours, allow_class = c("character", "logical"))
+  meet_criteria(colours, allow_class = c("character", "logical"), allow_NULL = TRUE)
   meet_criteria(datalabels, allow_class = "logical", has_length = 1)
   meet_criteria(datalabels.size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE)
   meet_criteria(datalabels.colour, allow_class = "character", has_length = 1)
@@ -245,7 +246,7 @@ ggplot_sir <- function(data,
     ) +
     theme_sir()
 
-  if (fill == "interpretation") {
+  if (fill == "interpretation" && !is.null(colours) && !isFALSE(colours)) {
     p <- suppressWarnings(p + scale_sir_colours(aesthetics = "fill", colours = colours))
   }
 

R/guess_ab_col.R

@@ -272,25 +272,24 @@ get_column_abx <- function(x,
       } else {
         message_(" WARNING.", add_fn = list(font_yellow, font_bold), as_note = FALSE)
       }
+
       for (i in seq_len(length(out))) {
-        if (isTRUE(verbose) && !names(out[i]) %in% names(duplicates)) {
+        if (isTRUE(verbose) && !out[i] %in% duplicates) {
           message_(
             "Using column '", font_bold(out[i]), "' as input for ", names(out)[i],
             " (", ab_name(names(out)[i], tolower = TRUE, language = NULL), ")."
           )
         }
-        if (names(out[i]) %in% names(duplicates)) {
-          already_set_as <- out[unname(out) == unname(out[i])][1L]
-          if (names(out)[i] != names(already_set_as)) {
-            warning_(
+        if (out[i] %in% duplicates) {
+          already_set_as <- out[which(out == out[i])[1L]]
+          if (names(out)[i] != already_set_as) {
+            message_(
               paste0(
                 "Column '", font_bold(out[i]), "' will not be used for ",
-                names(out)[i], " (", ab_name(names(out)[i], tolower = TRUE, language = NULL), ")",
-                ", as it is already set for ",
-                names(already_set_as), " (", ab_name(names(already_set_as), tolower = TRUE, language = NULL), ")"
+                names(out)[i], " (", suppressMessages(ab_name(names(out)[i], tolower = TRUE, language = NULL, fast_mode = TRUE)), ")",
+                ", as this antimicrobial has already been set."
               ),
-              add_fn = font_red,
-              immediate = verbose
+              add_fn = font_red
             )
           }
         }

R/mdro.R

@@ -41,7 +41,7 @@
 #' @inheritParams eucast_rules
 #' @param pct_required_classes Minimal required percentage of antimicrobial classes that must be available per isolate, rounded down. For example, with the default guideline, 17 antimicrobial classes must be available for *S. aureus*. Setting this `pct_required_classes` argument to `0.5` (default) means that for every *S. aureus* isolate at least 8 different classes must be available. Any lower number of available classes will return `NA` for that isolate.
 #' @param combine_SI A [logical] to indicate whether all values of S and I must be merged into one, so resistance is only considered when isolates are R, not I. As this is the default behaviour of the [mdro()] function, it follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. When using `combine_SI = FALSE`, resistance is considered when isolates are R or I.
-#' @param verbose A [logical] to turn Verbose mode on and off (default is off). In Verbose mode, the function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.
+#' @param verbose A [logical] to turn Verbose mode on and off (default is off). In Verbose mode, the function returns a data set with the MDRO results in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.
 #' @details
 #' These functions are context-aware. This means that the `x` argument can be left blank if used inside a [data.frame] call, see *Examples*.
 #'
@@ -174,48 +174,23 @@ mdro <- function(x = NULL,
   }
 
   # get gene values as TRUE/FALSE
-  if (is.character(esbl)) {
-    meet_criteria(esbl, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
-    esbl <- x[[esbl]]
-    meet_criteria(esbl, allow_class = "logical", allow_NA = TRUE)
-  } else if (length(esbl) == 1) {
-    esbl <- rep(esbl, NROW(x))
+  resolve_gene_var <- function(x, gene, varname) {
+    if (is.character(gene)) {
+      meet_criteria(gene, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
+      gene <- x[[gene]]
+      meet_criteria(gene, allow_class = "logical", allow_NA = TRUE)
+    } else if (length(gene) == 1) {
+      gene <- rep(gene, NROW(x))
     }
-  if (is.character(carbapenemase)) {
-    meet_criteria(carbapenemase, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
-    carbapenemase <- x[[carbapenemase]]
-    meet_criteria(carbapenemase, allow_class = "logical", allow_NA = TRUE)
-  } else if (length(carbapenemase) == 1) {
-    carbapenemase <- rep(carbapenemase, NROW(x))
-  }
-  if (is.character(mecA)) {
-    meet_criteria(mecA, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
-    mecA <- x[[mecA]]
-    meet_criteria(mecA, allow_class = "logical", allow_NA = TRUE)
-  } else if (length(mecA) == 1) {
-    mecA <- rep(mecA, NROW(x))
-  }
-  if (is.character(mecC)) {
-    meet_criteria(mecC, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
-    mecC <- x[[mecC]]
-    meet_criteria(mecC, allow_class = "logical", allow_NA = TRUE)
-  } else if (length(mecC) == 1) {
-    mecC <- rep(mecC, NROW(x))
-  }
-  if (is.character(vanA)) {
-    meet_criteria(vanA, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
-    vanA <- x[[vanA]]
-    meet_criteria(vanA, allow_class = "logical", allow_NA = TRUE)
-  } else if (length(vanA) == 1) {
-    vanA <- rep(vanA, NROW(x))
-  }
-  if (is.character(vanB)) {
-    meet_criteria(vanB, is_in = colnames(x), allow_NA = FALSE, has_length = 1)
-    vanB <- x[[vanB]]
-    meet_criteria(vanB, allow_class = "logical", allow_NA = TRUE)
-  } else if (length(vanB) == 1) {
-    vanB <- rep(vanB, NROW(x))
+    x[[varname]] <- gene
+    x
   }
+  x <- resolve_gene_var(x, esbl, "esbl")
+  x <- resolve_gene_var(x, carbapenemase, "carbapenemase")
+  x <- resolve_gene_var(x, mecA, "mecA")
+  x <- resolve_gene_var(x, mecC, "mecC")
+  x <- resolve_gene_var(x, vanA, "vanA")
+  x <- resolve_gene_var(x, vanB, "vanB")
 
   info.bak <- info
   # don't throw info's more than once per call
@@ -499,10 +474,11 @@ mdro <- function(x = NULL,
   if (!"AMP" %in% names(cols_ab) && "AMX" %in% names(cols_ab)) {
     # ampicillin column is missing, but amoxicillin is available
     if (isTRUE(info)) {
-      message_("Using column '", cols_ab[names(cols_ab) == "AMX"], "' as input for ampicillin since many MDRO rules depend on it.")
+      message_("Using column '", cols_ab[names(cols_ab) == "AMX"], "' as input for ampicillin since many MDRO rules depend on it.", add_fn = font_red)
     }
     cols_ab <- c(cols_ab, c(AMP = unname(cols_ab[names(cols_ab) == "AMX"])))
   }
+  cols_ab <- cols_ab[!duplicated(cols_ab)]
 
   # nolint start
   AMC <- cols_ab["AMC"]
@@ -771,7 +747,7 @@ mdro <- function(x = NULL,
         )
       }
       x[rows_to_change, "MDRO"] <<- to
-      x[rows_to_change, "reason"] <<- reason
+      x[rows_to_change, "reason"] <<- paste0(x[rows_to_change, "reason", drop = TRUE], "; ", reason)
       x[rows_not_to_change, "reason"] <<- "guideline criteria not met"
     }
   }
@@ -853,7 +829,7 @@ mdro <- function(x = NULL,
   x <- left_join_microorganisms(x, by = col_mo)
   x$MDRO <- ifelse(!is.na(x$genus), 1, NA_integer_)
   x$row_number <- seq_len(nrow(x))
-  x$reason <- NA_character_
+  x$reason <- ""
   x$all_nonsusceptible_columns <- ""
 
   if (guideline$code == "cmi2012") {
@@ -1489,7 +1465,7 @@ mdro <- function(x = NULL,
     if (length(ESBLs) > 0) {
       trans_tbl(
         2, # positive, unconfirmed
-        rows = which(x$order == "Enterobacterales" & x[[ESBLs[1]]] == "R" & x[[ESBLs[2]]] == "R" & is.na(esbl)),
+        rows = which(x$order == "Enterobacterales" & col_values(x, ESBLs[1]) == "R" & col_values(x, ESBLs[2]) == "R" & is.na(esbl)),
         cols = c(AMX %or% AMP, cephalosporins_3rd),
         any_all = "all",
         reason = "Enterobacterales: potential ESBL"
@@ -1497,7 +1473,7 @@ mdro <- function(x = NULL,
     }
     trans_tbl(
       3, # positive
-      rows = which(x$order == "Enterobacterales" & esbl == TRUE),
+      rows = which(x$order == "Enterobacterales" & x$esbl == TRUE),
       cols = "any",
       any_all = "any",
       reason = "Enterobacterales: ESBL"
@@ -1518,16 +1494,16 @@ mdro <- function(x = NULL,
     )
     trans_tbl(
       3,
-      rows = which(x$order == "Enterobacterales" & carbapenemase == TRUE),
+      rows = which(x$order == "Enterobacterales" & x$carbapenemase == TRUE),
       cols = "any",
       any_all = "any",
       reason = "Enterobacterales: carbapenemase"
     )
     trans_tbl(
       3,
-      rows = which(x[[SXT]] == "R" &
-        (x[[GEN]] == "R" | x[[TOB]] == "R" | x[[AMK]] == "R") &
-        (x[[CIP]] == "R" | x[[NOR]] == "R" | x[[LVX]] == "R") &
+      rows = which(col_values(x, SXT) == "R" &
+        (col_values(x, GEN) == "R" | col_values(x, TOB) == "R" | col_values(x, AMK) == "R") &
+        (col_values(x, CIP) == "R" | col_values(x, NOR) == "R" | col_values(x, LVX) == "R") &
         (x$genus %in% c("Enterobacter", "Providencia") | paste(x$genus, x$species) %in% c("Citrobacter freundii", "Klebsiella aerogenes", "Hafnia alvei", "Morganella morganii"))),
       cols = c(SXT, aminoglycosides, fluoroquinolones),
       any_all = "any",
@@ -1535,9 +1511,9 @@ mdro <- function(x = NULL,
     )
     trans_tbl(
       3,
-      rows = which(x[[SXT]] == "R" &
-        x[[GEN]] == "R" &
-        (x[[CIP]] == "R" | x[[NOR]] == "R" | x[[LVX]] == "R") &
+      rows = which(col_values(x, SXT) == "R" &
+        col_values(x, GEN) == "R" &
+        (col_values(x, CIP) == "R" | col_values(x, NOR) == "R" | col_values(x, LVX) == "R") &
         paste(x$genus, x$species) == "Serratia marcescens"),
       cols = c(SXT, aminoglycosides_serratia_marcescens, fluoroquinolones),
       any_all = "any",
@@ -1547,8 +1523,8 @@ mdro <- function(x = NULL,
     # Acinetobacter baumannii-calcoaceticus complex
     trans_tbl(
       3,
-      rows = which((x[[GEN]] == "R" | x[[TOB]] == "R" | x[[AMK]] == "R") &
-        (x[[CIP]] == "R" | x[[LVX]] == "R") &
+      rows = which((col_values(x, GEN) == "R" | col_values(x, TOB) == "R" | col_values(x, AMK) == "R") &
+        (col_values(x, CIP) == "R" | col_values(x, LVX) == "R") &
         x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"]),
       cols = c(aminoglycosides, CIP, LVX),
       any_all = "any",
@@ -1556,14 +1532,14 @@ mdro <- function(x = NULL,
     )
     trans_tbl(
       2, # unconfirmed
-      rows = which(x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"] & is.na(carbapenemase)),
+      rows = which(x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"] & is.na(x$carbapenemase)),
       cols = carbapenems,
       any_all = "any",
       reason = "A. baumannii-calcoaceticus complex: potential carbapenemase"
     )
     trans_tbl(
       3,
-      rows = which(x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"] & carbapenemase == TRUE),
+      rows = which(x[[col_mo]] %in% AMR::microorganisms.groups$mo[AMR::microorganisms.groups$mo_group_name == "Acinetobacter baumannii complex"] & x$carbapenemase == TRUE),
       cols = carbapenems,
       any_all = "any",
       reason = "A. baumannii-calcoaceticus complex: carbapenemase"
@@ -1573,6 +1549,7 @@ mdro <- function(x = NULL,
     x$psae <- 0
     x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, TOB) == "R") | NA_as_FALSE(col_values(x, AMK) == "R"), 1, 0)
     x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, IPM) == "R") | NA_as_FALSE(col_values(x, MEM) == "R"), 1, 0)
+    x$psae <- x$psae + ifelse(NA_as_FALSE(x$carbapenemase), 1, 0)
     x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, PIP) == "R") | NA_as_FALSE(col_values(x, TZP) == "R"), 1, 0)
     x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, CAZ) == "R") | NA_as_FALSE(col_values(x, CZA) == "R"), 1, 0)
     x$psae <- x$psae + ifelse(NA_as_FALSE(col_values(x, CIP) == "R") | NA_as_FALSE(col_values(x, NOR) == "R") | NA_as_FALSE(col_values(x, LVX) == "R"), 1, 0)
@@ -1601,26 +1578,26 @@ mdro <- function(x = NULL,
     )
     trans_tbl(
       3,
-      rows = which(x$genus == "Enterococcus" & x$species == "faecium" & (vanA == TRUE | vanB == TRUE)),
+      rows = which(x$genus == "Enterococcus" & x$species == "faecium" & (x$vanA == TRUE | x$vanB == TRUE)),
       cols = c(PEN, AMX, AMP, VAN),
       any_all = "any",
       reason = "E. faecium: vanA/vanB gene + penicillin group"
     )
 
-    # Staphylococcus aureus
+    # Staphylococcus aureus complex (= aureus, argenteus or schweitzeri)
     trans_tbl(
       2,
-      rows = which(x$genus == "Staphylococcus" & x$species == "aureus" & (is.na(mecA) | is.na(mecC))),
+      rows = which(x$genus == "Staphylococcus" & x$species %in% c("aureus", "argenteus", "schweitzeri") & (is.na(x$mecA) | is.na(x$mecC))),
       cols = c(AMC, TZP, FLC, OXA, FOX, FOX1),
       any_all = "any",
-      reason = "S. aureus: potential MRSA"
+      reason = "S. aureus complex: potential MRSA"
     )
     trans_tbl(
       3,
-      rows = which(x$genus == "Staphylococcus" & x$species == "aureus" & (mecA == TRUE | mecC == TRUE)),
+      rows = which(x$genus == "Staphylococcus" & x$species %in% c("aureus", "argenteus", "schweitzeri") & (x$mecA == TRUE | x$mecC == TRUE)),
       cols = "any",
       any_all = "any",
-      reason = "S. aureus: mecA/mecC gene"
+      reason = "S. aureus complex: mecA/mecC gene"
     )
 
     # Candida auris
@@ -1898,6 +1875,10 @@ mdro <- function(x = NULL,
     # fill in empty reasons
     x$reason[is.na(x$reason)] <- "not covered by guideline"
     x[rows_empty, "reason"] <- paste(x[rows_empty, "reason"], "(note: no available test results)")
+    # starting semicolons must be removed
+    x$reason <- trimws(gsub("^;", "", x$reason))
+    # if criteria were not met initially, but later they were, then they have a following semicolon; remove the initial lack of meeting criteria
+    x$reason <- trimws(gsub("guideline criteria not met;", "", x$reason, fixed = TRUE))
     # format data set
     colnames(x)[colnames(x) == col_mo] <- "microorganism"
     x$microorganism <- mo_name(x$microorganism, language = NULL)

R/mean_amr_distance.R

@@ -31,7 +31,7 @@
 #'
 #' Calculates a normalised mean for antimicrobial resistance between multiple observations, to help to identify similar isolates without comparing antibiograms by hand.
 #' @param x A vector of class [sir][as.sir()], [mic][as.mic()] or [disk][as.disk()], or a [data.frame] containing columns of any of these classes.
-#' @param ... Variables to select. Supports [tidyselect language][tidyselect::language] (such as `column1:column4` and `where(is.mic)`), and can thus also be [antimicrobial selectors][amr_selector()].
+#' @param ... Variables to select. Supports [tidyselect language][tidyselect::starts_with()] such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and can thus also be [antimicrobial selectors][amr_selector()].
 #' @param combine_SI A [logical] to indicate whether all values of S, SDD, and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant) - the default is `TRUE`.
 #' @details The mean AMR distance is effectively [the Z-score](https://en.wikipedia.org/wiki/Standard_score); a normalised numeric value to compare AMR test results which can help to identify similar isolates, without comparing antibiograms by hand.
 #'

R/mic.R

@@ -432,11 +432,17 @@ pillar_shaft.mic <- function(x, ...) {
   }
   crude_numbers <- as.double(x)
   operators <- gsub("[^<=>]+", "", as.character(x))
+  # colourise operators
   operators[!is.na(operators) & operators != ""] <- font_silver(operators[!is.na(operators) & operators != ""], collapse = NULL)
   out <- trimws(paste0(operators, trimws(format(crude_numbers))))
   out[is.na(x)] <- font_na(NA)
   # make trailing zeroes less visible
-  out[out %like% "[.]"] <- gsub("([.]?0+)$", font_silver("\\1"), out[out %like% "[.]"], perl = TRUE)
+  if (is_dark()) {
+    fn <- font_silver
+  } else {
+    fn <- font_white
+  }
+  out[out %like% "[.]"] <- gsub("([.]?0+)$", fn("\\1"), out[out %like% "[.]"], perl = TRUE)
   create_pillar_column(out, align = "right", width = max(nchar(font_stripstyle(out))))
 }
 

R/mo.R

@@ -276,12 +276,17 @@ as.mo <- function(x,
     AMR_env$mo_failures <- NULL
 
     # Laboratory systems: remove (translated) entries like "no growth", "not E. coli", etc.
-    x[trimws2(x) %like% translate_into_language("no .*growth", language = language)] <- NA_character_
-    x[trimws2(x) %like% paste0("^(", translate_into_language("no|not", language = language), ") ")] <- NA_character_
+    x[trimws2(x) %like% translate_AMR("no .*growth", language = language)] <- NA_character_
+    x[trimws2(x) %like% paste0("^(", translate_AMR("no|not", language = language), ") ")] <- NA_character_
 
     # groups are in our taxonomic table with a capital G
     x <- gsub(" group( |$)", " Group\\1", x, perl = TRUE)
 
+    # convert translations
+    x[x %like_case% "enter[o\u00F6]?[ck]o[ck](ken)?$"] <- gsub("(.* )?enter[o\u00F6]?[ck]o[ck](ken)?$", "enterococcus", x[x %like_case% "enter[o\u00F6]?[ck]o[ck](ken)?$"], perl = TRUE)
+    x[x %like_case% "strept[o\u00F6]?[ck]o[ck](ken)?$"] <- gsub("(.* )?strept[o\u00F6]?[ck]o[ck](ken)?$", "streptococcus", x[x %like_case% "strept[o\u00F6]?[ck]o[ck](ken)?$"], perl = TRUE)
+    x[x %like_case% "staph[yij]?[lo]*[ck]o[ck](ken)?$"] <- gsub("(.* )?staph[yij]?[lo]*[ck]o[ck](ken)?$", "staphylococcus", x[x %like_case% "staph[yij]?[lo]*[ck]o[ck](ken)?$"], perl = TRUE)
+
     # run over all unique leftovers
     x_unique <- unique(x[is.na(out) & !is.na(x)])
 
@@ -1065,6 +1070,8 @@ convert_colloquial_input <- function(x) {
   out[x %like_case% "mil+er+i gr"] <- "B_STRPT_MILL"
   out[x %like_case% "((strepto|^s).* viridans|^vgs[^a-z]*$)"] <- "B_STRPT_VIRI"
   out[x %like_case% "(viridans.* (strepto|^s).*|^vgs[^a-z]*$)"] <- "B_STRPT_VIRI"
+  out[x %like_case% "meningo[ck]o[ck](ken)?$"] <- "B_NESSR_MNNG"
+  out[x %like_case% "pneumo[ck]o[ck](ken)?$"] <- "B_STRPT_PNMN"
 
   # Salmonella in different languages, like "Salmonella grupo B"
   out[x %like_case% "salmonella.* [abcdefgh]$"] <- gsub(".*salmonella.* ([abcdefgh])$",
@@ -1179,7 +1186,7 @@ parse_and_convert <- function(x) {
         parsed <- gsub('"', "", parsed, fixed = TRUE)
         parsed
       },
-      error = function(e) stop(e$message, call. = FALSE)
+      error = function(e) stop(conditionMessage(e), call. = FALSE)
     ) # this will also be thrown when running `as.mo(no_existing_object)`
   }
   out <- trimws2(out)

R/mo_property.R

@@ -974,7 +974,7 @@ mo_validate <- function(x, property, language, keep_synonyms = keep_synonyms, ..
   # try to catch an error when inputting an invalid argument
   # so the 'call.' can be set to FALSE
   tryCatch(x[1L] %in% unlist(AMR_env$MO_lookup[1, property, drop = TRUE]),
-    error = function(e) stop(e$message, call. = FALSE)
+    error = function(e) stop(conditionMessage(e), call. = FALSE)
   )
 
   dots <- list(...)

R/pca.R

@@ -99,7 +99,7 @@ pca <- function(x,
     new_list <- list(0)
     for (i in seq_len(length(dots) - 1)) {
       new_list[[i]] <- tryCatch(eval(dots[[i + 1]], envir = x),
-        error = function(e) stop(e$message, call. = FALSE)
+        error = function(e) stop(conditionMessage(e), call. = FALSE)
       )
       if (length(new_list[[i]]) == 1) {
         if (is.character(new_list[[i]]) && new_list[[i]] %in% colnames(x)) {

R/plotting.R

@@ -90,6 +90,10 @@
 #'   autoplot(some_mic_values, mo = "Escherichia coli", ab = "cipro")
 #' }
 #' if (require("ggplot2")) {
+#'   autoplot(some_mic_values, mo = "Staph aureus", ab = "Ceftaroline", guideline = "CLSI")
+#' }
+#'
+#' if (require("ggplot2")) {
 #'   # support for 27 languages, various guidelines, and many options
 #'   autoplot(some_disk_values,
 #'     mo = "Escherichia coli", ab = "cipro",
@@ -146,7 +150,7 @@
 #'     aes(group, mic)
 #'   ) +
 #'     geom_boxplot() +
-#'     geom_violin(linetype = 2, colour = "grey", fill = NA) +
+#'     geom_violin(linetype = 2, colour = "grey30", fill = NA) +
 #'     scale_y_mic()
 #' }
 #' if (require("ggplot2")) {
@@ -158,7 +162,7 @@
 #'     aes(group, mic)
 #'   ) +
 #'     geom_boxplot() +
-#'     geom_violin(linetype = 2, colour = "grey", fill = NA) +
+#'     geom_violin(linetype = 2, colour = "grey30", fill = NA) +
 #'     scale_y_mic(mic_range = c(NA, 0.25))
 #' }
 #'
@@ -191,7 +195,7 @@
 #'     aes(x = group, y = mic, colour = sir)
 #'   ) +
 #'     theme_minimal() +
-#'     geom_boxplot(fill = NA, colour = "grey") +
+#'     geom_boxplot(fill = NA, colour = "grey30") +
 #'     geom_jitter(width = 0.25)
 #'
 #'   plain
@@ -377,6 +381,8 @@ create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
   args <- list(...)
   args[c("value", "labels", "limits")] <- NULL
 
+  colours_SIR <- expand_SIR_colours(colours_SIR, unname = FALSE)
+
   if (identical(aesthetics, "x")) {
     ggplot_fn <- ggplot2::scale_x_discrete
   } else {
@@ -385,24 +391,19 @@ create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
       args,
       list(
         aesthetics = aesthetics,
-        values = c(
-          S = colours_SIR[1],
-          SDD = colours_SIR[2],
-          I = colours_SIR[2],
-          R = colours_SIR[3],
-          NI = "grey30"
-        )
+        values = c(colours_SIR, NI = "grey30")
       )
     )
   }
   scale <- do.call(ggplot_fn, args)
 
   scale$labels <- function(x) {
-    stop_ifnot(all(x %in% c(levels(NA_sir_), NA)),
+    stop_ifnot(all(x %in% c(levels(NA_sir_), "SI", "IR", NA)),
       "Apply `scale_", aesthetics[1], "_sir()` to a variable of class 'sir', see `?as.sir`.",
       call = FALSE
     )
-    x <- as.character(as.sir(x))
+    x <- as.character(x)
+    x[!x %in% c("SI", "IR")] <- as.character(as.sir(x[!x %in% c("SI", "IR")]))
     if (!is.null(language)) {
       x[x == "S"] <- "(S) Susceptible"
       x[x == "SDD"] <- "(SDD) Susceptible dose-dependent"
@@ -412,6 +413,8 @@ create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
         x[x == "I"] <- "(I) Intermediate"
       }
       x[x == "R"] <- "(R) Resistant"
+      x[x == "SI"] <- "(S/I) Susceptible"
+      x[x == "IR"] <- "(I/R) Non-susceptible"
       x[x == "NI"] <- "(NI) Non-interpretable"
       x <- translate_AMR(x, language = language)
     }
@@ -419,7 +422,7 @@ create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
   }
   scale$limits <- function(x, ...) {
     # force SIR in the right order
-    as.character(sort(factor(x, levels = levels(NA_sir_))))
+    as.character(sort(factor(x, levels = c(levels(NA_sir_), "SI", "IR"))))
   }
 
   scale
@@ -427,11 +430,16 @@ create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
 
 #' @rdname plot
 #' @export
-scale_x_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+scale_x_sir <- function(colours_SIR = c(
+                          S = "#3CAEA3",
+                          SDD = "#8FD6C4",
+                          I = "#F6D55C",
+                          R = "#ED553B"
+                        ),
                         language = get_AMR_locale(),
                         eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
                         ...) {
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
   create_scale_sir(aesthetics = "x", colours_SIR = colours_SIR, language = language, eucast_I = eucast_I)
@@ -439,11 +447,16 @@ scale_x_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
 
 #' @rdname plot
 #' @export
-scale_colour_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+scale_colour_sir <- function(colours_SIR = c(
+                               S = "#3CAEA3",
+                               SDD = "#8FD6C4",
+                               I = "#F6D55C",
+                               R = "#ED553B"
+                             ),
                              language = get_AMR_locale(),
                              eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
                              ...) {
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
   args <- list(...)
@@ -463,11 +476,16 @@ scale_color_sir <- scale_colour_sir
 
 #' @rdname plot
 #' @export
-scale_fill_sir <- function(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+scale_fill_sir <- function(colours_SIR = c(
+                             S = "#3CAEA3",
+                             SDD = "#8FD6C4",
+                             I = "#F6D55C",
+                             R = "#ED553B"
+                           ),
                            language = get_AMR_locale(),
                            eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST",
                            ...) {
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(eucast_I, allow_class = "logical", has_length = 1)
   args <- list(...)
@@ -491,7 +509,12 @@ plot.mic <- function(x,
                      main = deparse(substitute(x)),
                      ylab = translate_AMR("Frequency", language = language),
                      xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
-                     colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+                     colours_SIR = c(
+                       S = "#3CAEA3",
+                       SDD = "#8FD6C4",
+                       I = "#F6D55C",
+                       R = "#ED553B"
+                     ),
                      language = get_AMR_locale(),
                      expand = TRUE,
                      include_PKPD = getOption("AMR_include_PKPD", TRUE),
@@ -503,16 +526,13 @@ plot.mic <- function(x,
   meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
   meet_criteria(ylab, allow_class = "character", has_length = 1)
   meet_criteria(xlab, allow_class = "character", has_length = 1)
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(expand, allow_class = "logical", has_length = 1)
 
   x <- as.mic(x) # make sure that currently implemented MIC levels are used
-
-  if (length(colours_SIR) == 1) {
-    colours_SIR <- rep(colours_SIR, 3)
-  }
   main <- gsub(" +", " ", paste0(main, collapse = " "))
+  colours_SIR <- expand_SIR_colours(colours_SIR)
 
   x <- plotrange_as_table(x, expand = expand)
   cols_sub <- plot_colours_subtitle_guideline(
@@ -549,13 +569,17 @@ plot.mic <- function(x,
       legend_col <- colours_SIR[1]
     }
     if (any(cols_sub$cols == colours_SIR[2] & cols_sub$count > 0)) {
-      legend_txt <- c(legend_txt, paste("(I)", plot_name_of_I(cols_sub$guideline)))
+      legend_txt <- c(legend_txt, "(SDD) Susceptible dose-dependent")
       legend_col <- c(legend_col, colours_SIR[2])
     }
     if (any(cols_sub$cols == colours_SIR[3] & cols_sub$count > 0)) {
-      legend_txt <- c(legend_txt, "(R) Resistant")
+      legend_txt <- c(legend_txt, paste("(I)", plot_name_of_I(cols_sub$guideline)))
       legend_col <- c(legend_col, colours_SIR[3])
     }
+    if (any(cols_sub$cols == colours_SIR[4] & cols_sub$count > 0)) {
+      legend_txt <- c(legend_txt, "(R) Resistant")
+      legend_col <- c(legend_col, colours_SIR[4])
+    }
 
     legend("top",
       x.intersp = 0.5,
@@ -580,7 +604,12 @@ barplot.mic <- function(height,
                         main = deparse(substitute(height)),
                         ylab = translate_AMR("Frequency", language = language),
                         xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
-                        colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+                        colours_SIR = c(
+                          S = "#3CAEA3",
+                          SDD = "#8FD6C4",
+                          I = "#F6D55C",
+                          R = "#ED553B"
+                        ),
                         language = get_AMR_locale(),
                         expand = TRUE,
                         ...) {
@@ -590,7 +619,7 @@ barplot.mic <- function(height,
   meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
   meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
   meet_criteria(guideline, allow_class = "character", has_length = 1)
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(expand, allow_class = "logical", has_length = 1)
 
@@ -622,7 +651,12 @@ autoplot.mic <- function(object,
                          title = deparse(substitute(object)),
                          ylab = translate_AMR("Frequency", language = language),
                          xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language = language),
-                         colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+                         colours_SIR = c(
+                           S = "#3CAEA3",
+                           SDD = "#8FD6C4",
+                           I = "#F6D55C",
+                           R = "#ED553B"
+                         ),
                          language = get_AMR_locale(),
                          expand = TRUE,
                          include_PKPD = getOption("AMR_include_PKPD", TRUE),
@@ -635,7 +669,7 @@ autoplot.mic <- function(object,
   meet_criteria(title, allow_class = "character", allow_NULL = TRUE)
   meet_criteria(ylab, allow_class = "character", has_length = 1)
   meet_criteria(xlab, allow_class = "character", has_length = 1)
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(expand, allow_class = "logical", has_length = 1)
 
@@ -646,6 +680,8 @@ autoplot.mic <- function(object,
     title <- gsub(" +", " ", paste0(title, collapse = " "))
   }
 
+  colours_SIR <- expand_SIR_colours(colours_SIR)
+
   object <- as.mic(object) # make sure that currently implemented MIC levels are used
   x <- plotrange_as_table(object, expand = expand)
   cols_sub <- plot_colours_subtitle_guideline(
@@ -665,12 +701,14 @@ autoplot.mic <- function(object,
   colnames(df) <- c("mic", "count")
   df$cols <- cols_sub$cols
   df$cols[df$cols == colours_SIR[1]] <- "(S) Susceptible"
-  df$cols[df$cols == colours_SIR[2]] <- paste("(I)", plot_name_of_I(cols_sub$guideline))
-  df$cols[df$cols == colours_SIR[3]] <- "(R) Resistant"
+  df$cols[df$cols == colours_SIR[2]] <- "(SDD) Susceptible dose-dependent"
+  df$cols[df$cols == colours_SIR[3]] <- paste("(I)", plot_name_of_I(cols_sub$guideline))
+  df$cols[df$cols == colours_SIR[4]] <- "(R) Resistant"
   df$cols <- factor(translate_into_language(df$cols, language = language),
     levels = translate_into_language(
       c(
         "(S) Susceptible",
+        "(SDD) Susceptible dose-dependent",
         paste("(I)", plot_name_of_I(cols_sub$guideline)),
         "(R) Resistant"
       ),
@@ -684,10 +722,10 @@ autoplot.mic <- function(object,
     vals <- c(
       "(S) Susceptible" = colours_SIR[1],
       "(SDD) Susceptible dose-dependent" = colours_SIR[2],
-      "(I) Susceptible, incr. exp." = colours_SIR[2],
-      "(I) Intermediate" = colours_SIR[2],
-      "(R) Resistant" = colours_SIR[3],
-      "(NI) Non-interpretable" = "grey"
+      "(I) Susceptible, incr. exp." = colours_SIR[3],
+      "(I) Intermediate" = colours_SIR[3],
+      "(R) Resistant" = colours_SIR[4],
+      "(NI) Non-interpretable" = "grey30"
     )
     names(vals) <- translate_into_language(names(vals), language = language)
     p <- p +
@@ -731,7 +769,12 @@ plot.disk <- function(x,
                       mo = NULL,
                       ab = NULL,
                       guideline = getOption("AMR_guideline", "EUCAST"),
-                      colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+                      colours_SIR = c(
+                        S = "#3CAEA3",
+                        SDD = "#8FD6C4",
+                        I = "#F6D55C",
+                        R = "#ED553B"
+                      ),
                       language = get_AMR_locale(),
                       expand = TRUE,
                       include_PKPD = getOption("AMR_include_PKPD", TRUE),
@@ -743,14 +786,12 @@ plot.disk <- function(x,
   meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
   meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
   meet_criteria(guideline, allow_class = "character", has_length = 1)
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(expand, allow_class = "logical", has_length = 1)
 
-  if (length(colours_SIR) == 1) {
-    colours_SIR <- rep(colours_SIR, 3)
-  }
   main <- gsub(" +", " ", paste0(main, collapse = " "))
+  colours_SIR <- expand_SIR_colours(colours_SIR)
 
   x <- plotrange_as_table(x, expand = expand)
   cols_sub <- plot_colours_subtitle_guideline(
@@ -783,12 +824,16 @@ plot.disk <- function(x,
   if (any(colours_SIR %in% cols_sub$cols)) {
     legend_txt <- character(0)
     legend_col <- character(0)
-    if (any(cols_sub$cols == colours_SIR[3] & cols_sub$count > 0)) {
+    if (any(cols_sub$cols == colours_SIR[4] & cols_sub$count > 0)) {
       legend_txt <- "(R) Resistant"
-      legend_col <- colours_SIR[3]
+      legend_col <- colours_SIR[4]
+    }
+    if (any(cols_sub$cols == colours_SIR[3] & cols_sub$count > 0)) {
+      legend_txt <- c(legend_txt, paste("(I)", plot_name_of_I(cols_sub$guideline)))
+      legend_col <- c(legend_col, colours_SIR[3])
     }
     if (any(cols_sub$cols == colours_SIR[2] & cols_sub$count > 0)) {
-      legend_txt <- c(legend_txt, paste("(I)", plot_name_of_I(cols_sub$guideline)))
+      legend_txt <- c(legend_txt, "(SDD) Susceptible dose-dependent")
       legend_col <- c(legend_col, colours_SIR[2])
     }
     if (any(cols_sub$cols == colours_SIR[1] & cols_sub$count > 0)) {
@@ -818,7 +863,12 @@ barplot.disk <- function(height,
                          mo = NULL,
                          ab = NULL,
                          guideline = getOption("AMR_guideline", "EUCAST"),
-                         colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+                         colours_SIR = c(
+                           S = "#3CAEA3",
+                           SDD = "#8FD6C4",
+                           I = "#F6D55C",
+                           R = "#ED553B"
+                         ),
                          language = get_AMR_locale(),
                          expand = TRUE,
                          ...) {
@@ -828,7 +878,7 @@ barplot.disk <- function(height,
   meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
   meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
   meet_criteria(guideline, allow_class = "character", has_length = 1)
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(expand, allow_class = "logical", has_length = 1)
 
@@ -858,7 +908,12 @@ autoplot.disk <- function(object,
                           ylab = translate_AMR("Frequency", language = language),
                           xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
                           guideline = getOption("AMR_guideline", "EUCAST"),
-                          colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+                          colours_SIR = c(
+                            S = "#3CAEA3",
+                            SDD = "#8FD6C4",
+                            I = "#F6D55C",
+                            R = "#ED553B"
+                          ),
                           language = get_AMR_locale(),
                           expand = TRUE,
                           include_PKPD = getOption("AMR_include_PKPD", TRUE),
@@ -871,7 +926,7 @@ autoplot.disk <- function(object,
   meet_criteria(mo, allow_class = c("mo", "character"), allow_NULL = TRUE)
   meet_criteria(ab, allow_class = c("ab", "character"), allow_NULL = TRUE)
   meet_criteria(guideline, allow_class = "character", has_length = 1)
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(expand, allow_class = "logical", has_length = 1)
 
@@ -882,6 +937,8 @@ autoplot.disk <- function(object,
     title <- gsub(" +", " ", paste0(title, collapse = " "))
   }
 
+  colours_SIR <- expand_SIR_colours(colours_SIR)
+
   x <- plotrange_as_table(object, expand = expand)
   cols_sub <- plot_colours_subtitle_guideline(
     x = x,
@@ -899,10 +956,10 @@ autoplot.disk <- function(object,
   df <- as.data.frame(x, stringsAsFactors = TRUE)
   colnames(df) <- c("disk", "count")
   df$cols <- cols_sub$cols
-
   df$cols[df$cols == colours_SIR[1]] <- "(S) Susceptible"
-  df$cols[df$cols == colours_SIR[2]] <- paste("(I)", plot_name_of_I(cols_sub$guideline))
-  df$cols[df$cols == colours_SIR[3]] <- "(R) Resistant"
+  df$cols[df$cols == colours_SIR[2]] <- "(SDD) Susceptible dose-dependent"
+  df$cols[df$cols == colours_SIR[3]] <- paste("(I)", plot_name_of_I(cols_sub$guideline))
+  df$cols[df$cols == colours_SIR[4]] <- "(R) Resistant"
   df$cols <- factor(translate_into_language(df$cols, language = language),
     levels = translate_into_language(
       c(
@@ -920,10 +977,10 @@ autoplot.disk <- function(object,
     vals <- c(
       "(S) Susceptible" = colours_SIR[1],
       "(SDD) Susceptible dose-dependent" = colours_SIR[2],
-      "(I) Susceptible, incr. exp." = colours_SIR[2],
-      "(I) Intermediate" = colours_SIR[2],
-      "(R) Resistant" = colours_SIR[3],
-      "(NI) Non-interpretable" = "grey"
+      "(I) Susceptible, incr. exp." = colours_SIR[3],
+      "(I) Intermediate" = colours_SIR[3],
+      "(R) Resistant" = colours_SIR[4],
+      "(NI) Non-interpretable" = "grey30"
     )
     names(vals) <- translate_into_language(names(vals), language = language)
     p <- p +
@@ -1024,22 +1081,26 @@ barplot.sir <- function(height,
                         main = deparse(substitute(height)),
                         xlab = translate_AMR("Antimicrobial Interpretation", language = language),
                         ylab = translate_AMR("Frequency", language = language),
-                        colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+                        colours_SIR = c(
+                          S = "#3CAEA3",
+                          SDD = "#8FD6C4",
+                          I = "#F6D55C",
+                          R = "#ED553B"
+                        ),
                         language = get_AMR_locale(),
                         expand = TRUE,
                         ...) {
   meet_criteria(xlab, allow_class = "character", has_length = 1)
   meet_criteria(main, allow_class = "character", has_length = 1, allow_NULL = TRUE)
   meet_criteria(ylab, allow_class = "character", has_length = 1)
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
   language <- validate_language(language)
   meet_criteria(expand, allow_class = "logical", has_length = 1)
 
-  if (length(colours_SIR) == 1) {
-    colours_SIR <- rep(colours_SIR, 3)
-  }
+  colours_SIR <- expand_SIR_colours(colours_SIR)
+
   # add SDD and N to colours
-  colours_SIR <- c(colours_SIR[1:2], colours_SIR[2], colours_SIR[3], "#888888")
+  colours_SIR <- c(colours_SIR, "grey30")
   main <- gsub(" +", " ", paste0(main, collapse = " "))
 
   x <- table(height)
@@ -1065,14 +1126,19 @@ autoplot.sir <- function(object,
                          title = deparse(substitute(object)),
                          xlab = translate_AMR("Antimicrobial Interpretation", language = language),
                          ylab = translate_AMR("Frequency", language = language),
-                         colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+                         colours_SIR = c(
+                           S = "#3CAEA3",
+                           SDD = "#8FD6C4",
+                           I = "#F6D55C",
+                           R = "#ED553B"
+                         ),
                          language = get_AMR_locale(),
                          ...) {
   stop_ifnot_installed("ggplot2")
   meet_criteria(title, allow_class = "character", allow_NULL = TRUE)
   meet_criteria(ylab, allow_class = "character", has_length = 1)
   meet_criteria(xlab, allow_class = "character", has_length = 1)
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
 
   if ("main" %in% names(list(...))) {
     title <- list(...)$main
@@ -1081,9 +1147,7 @@ autoplot.sir <- function(object,
     title <- gsub(" +", " ", paste0(title, collapse = " "))
   }
 
-  if (length(colours_SIR) == 1) {
-    colours_SIR <- rep(colours_SIR, 3)
-  }
+  colours_SIR <- expand_SIR_colours(colours_SIR)
 
   df <- as.data.frame(table(object), stringsAsFactors = TRUE)
   colnames(df) <- c("x", "n")
@@ -1095,9 +1159,9 @@ autoplot.sir <- function(object,
       values = c(
         "S" = colours_SIR[1],
         "SDD" = colours_SIR[2],
-        "I" = colours_SIR[2],
-        "R" = colours_SIR[3],
-        "NI" = "#888888"
+        "I" = colours_SIR[3],
+        "R" = colours_SIR[4],
+        "NI" = "grey30"
       ),
       limits = force
     ) +
@@ -1223,9 +1287,9 @@ plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_SIR, f
     cols[is.na(sir)] <- "#BEBEBE"
     cols[sir == "S"] <- colours_SIR[1]
     cols[sir == "SDD"] <- colours_SIR[2]
-    cols[sir == "I"] <- colours_SIR[2]
-    cols[sir == "R"] <- colours_SIR[3]
-    cols[sir == "NI"] <- "#888888"
+    cols[sir == "I"] <- colours_SIR[3]
+    cols[sir == "R"] <- colours_SIR[4]
+    cols[sir == "NI"] <- "grey30"
     sub <- bquote(.(abname) ~ "-" ~ italic(.(moname)) ~ .(guideline_txt))
   } else {
     cols <- "#BEBEBE"
@@ -1284,10 +1348,15 @@ scale_y_percent <- function(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.
 #' @export
 scale_sir_colours <- function(...,
                               aesthetics,
-                              colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B")) {
+                              colours_SIR = c(
+                                S = "#3CAEA3",
+                                SDD = "#8FD6C4",
+                                I = "#F6D55C",
+                                R = "#ED553B"
+                              )) {
   stop_ifnot_installed("ggplot2")
   meet_criteria(aesthetics, allow_class = "character", is_in = c("alpha", "colour", "color", "fill", "linetype", "shape", "size"))
-  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3))
+  meet_criteria(colours_SIR, allow_class = "character", has_length = c(1, 3, 4))
 
   if ("fill" %in% aesthetics && message_not_thrown_before("scale_sir_colours", "fill", entire_session = TRUE)) {
     warning_("Using `scale_sir_colours()` for the `fill` aesthetic has been superseded by `scale_fill_sir()`, please use that instead. This warning will be shown once per session.")
@@ -1296,67 +1365,48 @@ scale_sir_colours <- function(...,
     warning_("Using `scale_sir_colours()` for the `colour` aesthetic has been superseded by `scale_colour_sir()`, please use that instead. This warning will be shown once per session.")
   }
 
-  if (length(colours_SIR) == 1) {
-    colours_SIR <- rep(colours_SIR, 3)
-  }
-  # behaviour until AMR pkg v1.5.0 and also when coming from ggplot_sir()
   if ("colours" %in% names(list(...))) {
-    original_cols <- c(
-      S = colours_SIR[1],
-      SI = colours_SIR[1],
-      I = colours_SIR[2],
-      IR = colours_SIR[3],
-      R = colours_SIR[3]
-    )
-    colours <- replace(original_cols, names(list(...)$colours), list(...)$colours)
+    colours_SIR <- list(...)$colours
+  }
+
+  colours_SIR <- expand_SIR_colours(colours_SIR, unname = FALSE)
+
+  # behaviour when coming from ggplot_sir()
+  if ("colours" %in% names(list(...))) {
     # limits = force is needed in ggplot2 3.3.4 and 3.3.5, see here;
     # https://github.com/tidyverse/ggplot2/issues/4511#issuecomment-866185530
-    return(ggplot2::scale_fill_manual(values = colours, limits = force, aesthetics = aesthetics))
+    return(ggplot2::scale_fill_manual(values = colours_SIR, limits = force, aesthetics = aesthetics))
   }
   if (identical(unlist(list(...)), FALSE)) {
     return(invisible())
   }
 
-  names_susceptible <- c(
-    "S", "SI", "IS", "S+I", "I+S", "susceptible", "Susceptible",
-    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Susceptible"),
-      "replacement",
-      drop = TRUE
-    ])
-  )
+  colours_SIR <- unname(colours_SIR)
+
+  names_susceptible <- c("S", "SI", "IS", "S+I", "I+S", "susceptible", "Susceptible")
+  names_susceptible_dose_dep <- c("SDD", "susceptible dose-dependent", "Susceptible dose-dependent")
   names_incr_exposure <- c(
     "I", "intermediate", "increased exposure", "incr. exposure",
-    "Increased exposure", "Incr. exposure", "Susceptible, incr. exp.",
-    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Intermediate"),
-      "replacement",
-      drop = TRUE
-    ]),
-    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Susceptible, incr. exp."),
-      "replacement",
-      drop = TRUE
-    ])
-  )
-  names_resistant <- c(
-    "R", "IR", "RI", "R+I", "I+R", "resistant", "Resistant",
-    unique(TRANSLATIONS[which(TRANSLATIONS$pattern == "Resistant"),
-      "replacement",
-      drop = TRUE
-    ])
+    "Increased exposure", "Incr. exposure", "Susceptible, incr. exp."
   )
+  names_resistant <- c("R", "IR", "RI", "R+I", "I+R", "resistant", "Resistant")
 
   susceptible <- rep(colours_SIR[1], length(names_susceptible))
   names(susceptible) <- names_susceptible
-  incr_exposure <- rep(colours_SIR[2], length(names_incr_exposure))
+  susceptible_dose_dep <- rep(colours_SIR[2], length(names_susceptible_dose_dep))
+  names(susceptible_dose_dep) <- names_susceptible_dose_dep
+  incr_exposure <- rep(colours_SIR[3], length(names_incr_exposure))
   names(incr_exposure) <- names_incr_exposure
-  resistant <- rep(colours_SIR[3], length(names_resistant))
+  resistant <- rep(colours_SIR[4], length(names_resistant))
   names(resistant) <- names_resistant
 
-  original_cols <- c(susceptible, incr_exposure, resistant)
+  original_cols <- c(susceptible, susceptible_dose_dep, incr_exposure, resistant)
   dots <- c(...)
-  # replace S, I, R as colours: scale_sir_colours(mydatavalue = "S")
+  # replace S, SDD, I, R as colours: scale_sir_colours(mydatavalue = "S")
   dots[dots == "S"] <- colours_SIR[1]
-  dots[dots == "I"] <- colours_SIR[2]
-  dots[dots == "R"] <- colours_SIR[3]
+  dots[dots == "SDD"] <- colours_SIR[2]
+  dots[dots == "I"] <- colours_SIR[3]
+  dots[dots == "R"] <- colours_SIR[4]
   cols <- replace(original_cols, names(dots), dots)
   # limits = force is needed in ggplot2 3.3.4 and 3.3.5, see here;
   # https://github.com/tidyverse/ggplot2/issues/4511#issuecomment-866185530
@@ -1435,3 +1485,39 @@ labels_sir_count <- function(position = NULL,
     }
   )
 }
+
+expand_SIR_colours <- function(colours_SIR, unname = TRUE) {
+  sir_order <- c("S", "SDD", "I", "R", "SI", "IR")
+
+  if (is.null(names(colours_SIR))) {
+    if (length(colours_SIR) == 1) {
+      colours_SIR <- rep(colours_SIR, 4)
+    } else if (length(colours_SIR) == 3) {
+      # old method for AMR < 3.0.1 which allowed for 3 colours
+      # fill in green for SDD as extra colour
+      colours_SIR <- c(colours_SIR[1], colours_SIR[1], colours_SIR[2], colours_SIR[3])
+    }
+    if (length(colours_SIR) == 4) {
+      # add colours for SI (same as S) and IR (same as R)
+      colours_SIR <- c(colours_SIR[1:4], colours_SIR[1], colours_SIR[4])
+    }
+    names(colours_SIR) <- sir_order
+  } else {
+    # named input: match and reorder
+    stop_ifnot(
+      all(names(colours_SIR) %in% sir_order),
+      "Unknown names in `colours_SIR`. Expected any of: ", vector_or(levels(NA_sir_), quotes = FALSE, sort = FALSE), "."
+    )
+    if (length(colours_SIR) == 4) {
+      # add colours for SI (same as S) and IR (same as R)
+      colours_SIR <- c(colours_SIR[1:4], SI = unname(colours_SIR[1]), IR = unname(colours_SIR[4]))
+    }
+    colours_SIR <- colours_SIR[sir_order]
+  }
+
+  if (unname) {
+    colours_SIR <- unname(colours_SIR)
+  }
+
+  return(colours_SIR)
+}

R/proportion.R

@@ -237,7 +237,7 @@ resistance <- function(...,
       only_all_tested = only_all_tested,
       only_count = FALSE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -255,7 +255,7 @@ susceptibility <- function(...,
       only_all_tested = only_all_tested,
       only_count = FALSE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -283,7 +283,7 @@ sir_confidence_interval <- function(...,
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
   n <- tryCatch(
     sir_calc(...,
@@ -291,7 +291,7 @@ sir_confidence_interval <- function(...,
       only_all_tested = only_all_tested,
       only_count = TRUE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 
   if (x == 0) {
@@ -347,7 +347,7 @@ proportion_R <- function(...,
       only_all_tested = only_all_tested,
       only_count = FALSE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -365,7 +365,7 @@ proportion_IR <- function(...,
       only_all_tested = only_all_tested,
       only_count = FALSE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -383,7 +383,7 @@ proportion_I <- function(...,
       only_all_tested = only_all_tested,
       only_count = FALSE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -401,7 +401,7 @@ proportion_SI <- function(...,
       only_all_tested = only_all_tested,
       only_count = FALSE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -419,7 +419,7 @@ proportion_S <- function(...,
       only_all_tested = only_all_tested,
       only_count = FALSE
     ),
-    error = function(e) stop_(gsub("in sir_calc(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }
 
@@ -443,6 +443,6 @@ proportion_df <- function(data,
       combine_SI = combine_SI,
       confidence_level = confidence_level
     ),
-    error = function(e) stop_(gsub("in sir_calc_df(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc_df(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }

R/random.R

@@ -31,13 +31,17 @@
 #'
 #' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial drug, the generated results will reflect reality as much as possible.
 #' @param size Desired size of the returned vector. If used in a [data.frame] call or `dplyr` verb, will get the current (group) size if left blank.
-#' @param mo Any [character] that can be coerced to a valid microorganism code with [as.mo()].
+#' @param mo Any [character] that can be coerced to a valid microorganism code with [as.mo()]. Can be the same length as `size`.
 #' @param ab Any [character] that can be coerced to a valid antimicrobial drug code with [as.ab()].
 #' @param prob_SIR A vector of length 3: the probabilities for "S" (1st value), "I" (2nd value) and "R" (3rd value).
+#' @param skew Direction of skew for MIC or disk values, either `"right"` or `"left"`. A left-skewed distribution has the majority of the data on the right.
+#' @param severity Skew severity; higher values will increase the skewedness. Default is `2`; use `0` to prevent skewedness.
 #' @param ... Ignored, only in place to allow future extensions.
-#' @details The base \R function [sample()] is used for generating values.
-#'
-#' Generated values are based on the EUCAST `r max(as.integer(gsub("[^0-9]", "", subset(clinical_breakpoints, guideline %like% "EUCAST")$guideline)))` guideline as implemented in the [clinical_breakpoints] data set. To create specific generated values per bug or drug, set the `mo` and/or `ab` argument.
+#' @details
+#' Internally, MIC and disk zone values are sampled based on clinical breakpoints defined in the [clinical_breakpoints] data set. To create specific generated values per bug or drug, set the `mo` and/or `ab` argument. The MICs are sampled on a log2 scale and disks linearly, using weighted probabilities. The weights are based on the `skew` and `severity` arguments:
+#' * `skew = "right"` places more emphasis on lower MIC or higher disk values.
+#' * `skew = "left"` places more emphasis on higher MIC or lower disk values.
+#' * `severity` controls the exponential bias applied.
 #' @return class `mic` for [random_mic()] (see [as.mic()]) and class `disk` for [random_disk()] (see [as.disk()])
 #' @name random
 #' @rdname random
@@ -47,8 +51,13 @@
 #' random_disk(25)
 #' random_sir(25)
 #'
+#' # add more skewedness, make more realistic by setting a bug and/or drug:
+#' disks <- random_disk(100, severity = 2, mo = "Escherichia coli", ab = "CIP")
+#' plot(disks)
+#' # `plot()` and `ggplot2::autoplot()` allow for coloured bars if `mo` and `ab` are set
+#' plot(disks, mo = "Escherichia coli", ab = "CIP", guideline = "CLSI 2025")
+#'
 #' \donttest{
-#' # make the random generation more realistic by setting a bug and/or drug:
 #' random_mic(25, "Klebsiella pneumoniae") # range 0.0625-64
 #' random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
 #' random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4
@@ -57,26 +66,61 @@
 #' random_disk(25, "Klebsiella pneumoniae", "ampicillin") # range 11-17
 #' random_disk(25, "Streptococcus pneumoniae", "ampicillin") # range 12-27
 #' }
-random_mic <- function(size = NULL, mo = NULL, ab = NULL, ...) {
+random_mic <- function(size = NULL, mo = NULL, ab = NULL, skew = "right", severity = 1, ...) {
   meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
-  meet_criteria(mo, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(mo, allow_class = "character", has_length = c(1, size), allow_NULL = TRUE)
   meet_criteria(ab, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(skew, allow_class = "character", is_in = c("right", "left"), has_length = 1)
+  meet_criteria(severity, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
+
   if (is.null(size)) {
     size <- NROW(get_current_data(arg_name = "size", call = -3))
   }
-  random_exec("MIC", size = size, mo = mo, ab = ab)
+  if (length(mo) > 1) {
+    out <- rep(NA_mic_, length(size))
+    p <- progress_ticker(n = length(unique(mo)), n_min = 10, title = "Generating random MIC values")
+    for (mo_ in unique(mo)) {
+      p$tick()
+      out[which(mo == mo_)] <- random_exec("MIC", size = sum(mo == mo_), mo = mo_, ab = ab, skew = skew, severity = severity)
+    }
+    out <- as.mic(out, keep_operators = "none")
+    if (stats::runif(1) > 0.5 && length(unique(out)) > 1) {
+      out[out == min(out)] <- paste0("<=", out[out == min(out)])
+    }
+    if (stats::runif(1) > 0.5 && length(unique(out)) > 1) {
+      out[out == max(out) & out %unlike% "<="] <- paste0(">=", out[out == max(out) & out %unlike% "<="])
+    }
+
+    return(out)
+  } else {
+    random_exec("MIC", size = size, mo = mo, ab = ab, skew = skew, severity = severity)
+  }
 }
 
 #' @rdname random
 #' @export
-random_disk <- function(size = NULL, mo = NULL, ab = NULL, ...) {
+random_disk <- function(size = NULL, mo = NULL, ab = NULL, skew = "left", severity = 1, ...) {
   meet_criteria(size, allow_class = c("numeric", "integer"), has_length = 1, is_positive = TRUE, is_finite = TRUE, allow_NULL = TRUE)
-  meet_criteria(mo, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(mo, allow_class = "character", has_length = c(1, size), allow_NULL = TRUE)
   meet_criteria(ab, allow_class = "character", has_length = 1, allow_NULL = TRUE)
+  meet_criteria(skew, allow_class = "character", is_in = c("right", "left"), has_length = 1)
+  meet_criteria(severity, allow_class = c("numeric", "integer"), has_length = 1, is_positive_or_zero = TRUE, is_finite = TRUE)
+
   if (is.null(size)) {
     size <- NROW(get_current_data(arg_name = "size", call = -3))
   }
-  random_exec("DISK", size = size, mo = mo, ab = ab)
+  if (length(mo) > 1) {
+    out <- rep(NA_mic_, length(size))
+    p <- progress_ticker(n = length(unique(mo)), n_min = 10, title = "Generating random MIC values")
+    for (mo_ in unique(mo)) {
+      p$tick()
+      out[which(mo == mo_)] <- random_exec("DISK", size = sum(mo == mo_), mo = mo_, ab = ab, skew = skew, severity = severity)
+    }
+    out <- as.disk(out)
+    return(out)
+  } else {
+    random_exec("DISK", size = size, mo = mo, ab = ab, skew = skew, severity = severity)
+  }
 }
 
 #' @rdname random
@@ -90,78 +134,60 @@ random_sir <- function(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...) {
   sample(as.sir(c("S", "I", "R")), size = size, replace = TRUE, prob = prob_SIR)
 }
 
-random_exec <- function(method_type, size, mo = NULL, ab = NULL) {
-  df <- AMR::clinical_breakpoints %pm>%
-    pm_filter(guideline %like% "EUCAST") %pm>%
-    pm_arrange(pm_desc(guideline)) %pm>%
-    subset(guideline == max(guideline) &
-      method == method_type &
-      type == "human")
+
+random_exec <- function(method_type, size, mo = NULL, ab = NULL, skew = "right", severity = 1) {
+  df <- AMR::clinical_breakpoints %pm>% subset(method == method_type & type == "human")
 
   if (!is.null(mo)) {
-    mo_coerced <- as.mo(mo)
-    mo_include <- c(
-      mo_coerced,
-      as.mo(mo_genus(mo_coerced)),
-      as.mo(mo_family(mo_coerced)),
-      as.mo(mo_order(mo_coerced))
-    )
-    df_new <- df %pm>%
-      subset(mo %in% mo_include)
-    if (nrow(df_new) > 0) {
-      df <- df_new
-    } else {
-      warning_("in `random_", tolower(method_type), "()`: no rows found that match mo '", mo, "', ignoring argument `mo`")
-    }
+    mo_coerced <- as.mo(mo, info = FALSE)
+    mo_include <- c(mo_coerced, as.mo(mo_genus(mo_coerced)), as.mo(mo_family(mo_coerced)), as.mo(mo_order(mo_coerced)))
+    df_new <- df %pm>% subset(mo %in% mo_include)
+    if (nrow(df_new) > 0) df <- df_new
   }
 
   if (!is.null(ab)) {
     ab_coerced <- as.ab(ab)
-    df_new <- df %pm>%
-      subset(ab %in% ab_coerced)
-    if (nrow(df_new) > 0) {
-      df <- df_new
-    } else {
-      warning_("in `random_", tolower(method_type), "()`: no rows found that match ab '", ab, "' (", ab_name(ab_coerced, tolower = TRUE, language = NULL), "), ignoring argument `ab`")
-    }
+    df_new <- df %pm>% subset(ab %in% ab_coerced)
+    if (nrow(df_new) > 0) df <- df_new
   }
 
   if (method_type == "MIC") {
-    # set range
-    mic_range <- c(0.001, 0.002, 0.005, 0.010, 0.025, 0.0625, 0.125, 0.250, 0.5, 1, 2, 4, 8, 16, 32, 64, 128, 256)
+    lowest_mic <- min(df$breakpoint_S, na.rm = TRUE)
+    lowest_mic <- log2(lowest_mic) + sample(c(-3:2), 1)
+    lowest_mic <- 2^lowest_mic
+    highest_mic <- max(df$breakpoint_R, na.rm = TRUE)
+    highest_mic <- log2(highest_mic) + sample(c(-3:1), 1)
+    highest_mic <- max(lowest_mic * 2, 2^highest_mic)
 
-    # get highest/lowest +/- random 1 to 3 higher factors of two
-    max_range <- mic_range[min(
-      length(mic_range),
-      which(mic_range == max(df$breakpoint_R[!is.na(df$breakpoint_R)], na.rm = TRUE)) + sample(c(1:3), 1)
-    )]
-    min_range <- mic_range[max(
-      1,
-      which(mic_range == min(df$breakpoint_S, na.rm = TRUE)) - sample(c(1:3), 1)
-    )]
-
-    mic_range_new <- mic_range[mic_range <= max_range & mic_range >= min_range]
-    if (length(mic_range_new) == 0) {
-      mic_range_new <- mic_range
-    }
-    out <- as.mic(sample(mic_range_new, size = size, replace = TRUE))
-    # 50% chance that lowest will get <= and highest will get >=
+    out <- skewed_values(COMMON_MIC_VALUES, size = size, min = lowest_mic, max = highest_mic, skew = skew, severity = severity)
     if (stats::runif(1) > 0.5 && length(unique(out)) > 1) {
       out[out == min(out)] <- paste0("<=", out[out == min(out)])
     }
     if (stats::runif(1) > 0.5 && length(unique(out)) > 1) {
-      out[out == max(out)] <- paste0(">=", out[out == max(out)])
+      out[out == max(out) & out %unlike% "<="] <- paste0(">=", out[out == max(out) & out %unlike% "<="])
     }
-    return(out)
+    return(as.mic(out))
   } else if (method_type == "DISK") {
-    set_range <- seq(
-      from = as.integer(min(df$breakpoint_R[!is.na(df$breakpoint_R)], na.rm = TRUE) / 1.25),
-      to = as.integer(max(df$breakpoint_S, na.rm = TRUE) * 1.25),
+    disk_range <- seq(
+      from = floor(min(df$breakpoint_R[!is.na(df$breakpoint_R)], na.rm = TRUE) / 1.25),
+      to = ceiling(max(df$breakpoint_S[df$breakpoint_S != 50], na.rm = TRUE) * 1.25),
       by = 1
     )
-    out <- sample(set_range, size = size, replace = TRUE)
-    out[out < 6] <- sample(c(6:10), length(out[out < 6]), replace = TRUE)
-    out[out > 50] <- sample(c(40:50), length(out[out > 50]), replace = TRUE)
+    disk_range <- disk_range[disk_range >= 6 & disk_range <= 50]
+    out <- skewed_values(disk_range, size = size, min = min(disk_range), max = max(disk_range), skew = skew, severity = severity)
     return(as.disk(out))
   }
 }
+
+skewed_values <- function(values, size, min, max, skew = c("right", "left"), severity = 1) {
+  skew <- match.arg(skew)
+  range_vals <- values[values >= min & values <= max]
+  if (length(range_vals) < 2) range_vals <- values
+  ranks <- seq_along(range_vals)
+  weights <- switch(skew,
+    right = rev(ranks)^severity,
+    left = ranks^severity
+  )
+  weights <- weights / sum(weights)
+  sample(range_vals, size = size, replace = TRUE, prob = weights)
+}

R/sir.R

@@ -69,7 +69,9 @@
 #' @param reference_data A [data.frame] to be used for interpretation, which defaults to the [clinical_breakpoints] data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the [clinical_breakpoints] data set (same column names and column types). Please note that the `guideline` argument will be ignored when `reference_data` is manually set.
 #' @param threshold Maximum fraction of invalid antimicrobial interpretations of `x`, see *Examples*.
 #' @param conserve_capped_values Deprecated, use `capped_mic_handling` instead.
-#' @param ... For using on a [data.frame]: names of columns to apply [as.sir()] on (supports tidy selection such as `column1:column4`). Otherwise: arguments passed on to methods.
+#' @param ... For using on a [data.frame]: selection of columns to apply `as.sir()` to. Supports [tidyselect language][tidyselect::starts_with()] such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and can thus also be [antimicrobial selectors][amr_selector()] such as `as.sir(df, penicillins())`.
+#'
+#' Otherwise: arguments passed on to methods.
 #' @details
 #' *Note: The clinical breakpoints in this package were validated through, and imported from, [WHONET](https://whonet.org). The public use of this `AMR` package has been endorsed by both CLSI and EUCAST. See [clinical_breakpoints] for more information.*
 #'
@@ -225,9 +227,12 @@
 #'   df_wide %>% mutate_if(is.mic, as.sir)
 #'   df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
 #'   df_wide %>% mutate(across(where(is.mic), as.sir))
+#'
 #'   df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)
 #'   df_wide %>% mutate(across(amoxicillin:tobra, as.sir))
 #'
+#'   df_wide %>% mutate(across(aminopenicillins(), as.sir))
+#'
 #'   # approaches that all work with additional arguments:
 #'   df_long %>%
 #'     # given a certain data type, e.g. MIC values
@@ -722,8 +727,17 @@ as.sir.data.frame <- function(x,
   meet_criteria(info, allow_class = "logical", has_length = 1)
   meet_criteria(parallel, allow_class = "logical", has_length = 1)
   meet_criteria(max_cores, allow_class = c("numeric", "integer"), has_length = 1)
-
   x.bak <- x
+
+  if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
+    sel <- colnames(pm_select(x, ...))
+  } else {
+    sel <- colnames(x)
+  }
+  if (!is.null(col_mo)) {
+    sel <- sel[sel != col_mo]
+  }
+
   for (i in seq_len(ncol(x))) {
     # don't keep factors, overwriting them is hard
     if (is.factor(x[, i, drop = TRUE])) {
@@ -803,15 +817,6 @@ as.sir.data.frame <- function(x,
   }
 
   i <- 0
-  if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
-    sel <- colnames(pm_select(x, ...))
-  } else {
-    sel <- colnames(x)
-  }
-  if (!is.null(col_mo)) {
-    sel <- sel[sel != col_mo]
-  }
-
   ab_cols <- colnames(x)[vapply(FUN.VALUE = logical(1), x, function(y) {
     i <<- i + 1
     check <- is.mic(y) | is.disk(y)
@@ -863,7 +868,7 @@ as.sir.data.frame <- function(x,
     cl <- tryCatch(parallel::makeCluster(n_cores, type = "PSOCK"),
       error = function(e) {
         if (isTRUE(info)) {
-          message_("Could not create parallel cluster, using single-core computation. Error message: ", e$message, add_fn = font_red)
+          message_("Could not create parallel cluster, using single-core computation. Error message: ", conditionMessage(e), add_fn = font_red)
         }
         return(NULL)
       }
@@ -1135,7 +1140,6 @@ as_sir_method <- function(method_short,
   current_sir_interpretation_history <- NROW(AMR_env$sir_interpretation_history)
 
   if (isTRUE(info) && message_not_thrown_before("as.sir", "sir_interpretation_history")) {
-    message()
     message_("Run `sir_interpretation_history()` afterwards to retrieve a logbook with all details of the breakpoint interpretations.\n\n", add_fn = font_green)
   }
 
@@ -1553,7 +1557,7 @@ as_sir_method <- function(method_short,
       ))
 
     if (breakpoint_type == "animal") {
-      # 2025-03-13 for now, only strictly follow guideline for current host, no extrapolation
+      # 2025-03-13/ for now, only strictly follow guideline for current host, no extrapolation
       breakpoints_current <- breakpoints_current[which(breakpoints_current$host == host_current), , drop = FALSE]
     }
 
@@ -1651,26 +1655,23 @@ as_sir_method <- function(method_short,
       next
     }
 
-    # sort on host and taxonomic rank
-    # (this will e.g. prefer 'species' breakpoints over 'order' breakpoints)
-    if (is.na(uti_current)) {
-      breakpoints_current <- breakpoints_current %pm>%
-        #  `uti` is a column in the data set
-        # this will put UTI = FALSE first, then UTI = NA, then UTI = TRUE
-        pm_mutate(uti_index = ifelse(!is.na(uti) & uti == FALSE, 1,
-          ifelse(is.na(uti), 2,
-            3
-          )
-        )) %pm>%
-        # be as specific as possible (i.e. prefer species over genus):
-        pm_arrange(rank_index, uti_index)
-    } else if (uti_current == TRUE) {
-      breakpoints_current <- breakpoints_current %pm>%
-        subset(uti == TRUE) %pm>%
-        # be as specific as possible (i.e. prefer species over genus):
-        pm_arrange(rank_index)
+    # if the user explicitly set uti, keep only those rows
+    if (!is.na(uti_current)) {
+      breakpoints_current <- breakpoints_current[breakpoints_current$uti == uti_current, , drop = FALSE]
     }
 
+    # build a helper factor so FALSE < NA < TRUE
+    uti_index <- factor(
+      ifelse(is.na(breakpoints_current$uti), "NA",
+        as.character(breakpoints_current$uti)
+      ),
+      levels = c("FALSE", "NA", "TRUE")
+    )
+
+    # sort on host and taxonomic rank first, then by UTI
+    # (this will e.g. prefer 'species' breakpoints over 'order' breakpoints)
+    breakpoints_current <- breakpoints_current[order(breakpoints_current$rank_index, uti_index), , drop = FALSE]
+
     # throw messages for different body sites
     site <- breakpoints_current[1L, "site", drop = FALSE] # this is the one we'll take
     if (is.na(site)) {
@@ -1682,7 +1683,7 @@ as_sir_method <- function(method_short,
       # only UTI breakpoints available
       notes_current <- paste0(
         notes_current, "\n",
-        paste0("Breakpoints for ", font_bold(ab_formatted), " in ", mo_formatted, " are only available for (uncomplicated) urinary tract infections (UTI); assuming `uti = TRUE`.")
+        paste0("Breakpoints for ", font_bold(ab_formatted), " in ", mo_formatted, " are only available for (uncomplicated) urinary tract infections (UTI) - assuming `uti = TRUE`.")
       )
     } else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && any(is.na(uti_current)) && all(c(TRUE, FALSE) %in% breakpoints_current$uti, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteUTI", mo_current, ab_current)) {
       # both UTI and Non-UTI breakpoints available
@@ -1705,7 +1706,7 @@ as_sir_method <- function(method_short,
       new_sir <- rep(as.sir("R"), length(rows))
       notes_current <- paste0(
         notes_current, "\n",
-        paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, "")
+        paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, ".")
       )
     } else if (nrow(breakpoints_current) == 0) {
       # no rules available
@@ -1713,41 +1714,48 @@ as_sir_method <- function(method_short,
     } else {
       # then run the rules
       breakpoints_current <- breakpoints_current[1L, , drop = FALSE]
+      if (breakpoints_current$rank_index > 3) {
+        # we resort to a high-level taxonomic record since there are no breakpoint on genus (rank_index = 3) or lower, so note this
+        notes_current <- paste0(
+          "No genus- or species-level breakpoint available - applying higher taxonomic level instead.\n",
+          notes_current
+        )
+      }
 
       notes_current <- paste0(
         notes_current, "\n",
         ifelse(breakpoints_current$mo == "UNKNOWN" | breakpoints_current$ref_tbl %like% "PK.*PD",
-          "Some PK/PD breakpoints were applied - use `include_PKPD = FALSE` to prevent this",
+          "Some PK/PD breakpoints were applied - use `include_PKPD = FALSE` to prevent this.",
           ""
         ),
         "\n",
         ifelse(breakpoints_current$site %like% "screen" | breakpoints_current$ref_tbl %like% "screen",
-          "Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this",
+          "Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this.",
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[<][0-9]",
-          paste0("MIC values with the operator '<' are all considered 'S' since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values with the operator '<' are all considered 'S' since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[>][0-9]",
-          paste0("MIC values with the operator '>' are all considered 'R' since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values with the operator '>' are all considered 'R' since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^[><]=[0-9]" & as.double(values) > breakpoints_current$breakpoint_S & as.double(values) < breakpoints_current$breakpoint_R,
-          paste0("MIC values within the breakpoint guideline range with the operator '<=' or '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values within the breakpoint guideline range with the operator '<=' or '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^<=[0-9]" & as.double(values) == breakpoints_current$breakpoint_R,
-          paste0("MIC values at the R breakpoint with the operator '<=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values at the R breakpoint with the operator '<=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^>=[0-9]" & as.double(values) == breakpoints_current$breakpoint_S,
-          paste0("MIC values at the S breakpoint with the operator '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values at the S breakpoint with the operator '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         )
       )
@@ -1757,7 +1765,7 @@ as_sir_method <- function(method_short,
         notes_current <- paste0(
           notes_current, "\n",
           ifelse(!is.na(breakpoints_current$breakpoint_S) & is.na(breakpoints_current$breakpoint_R),
-            "NAs because of missing R breakpoints were substituted with R since substitute_missing_r_breakpoint = TRUE",
+            "NAs because of missing R breakpoints were substituted with R since substitute_missing_r_breakpoint = TRUE.",
             ""
           )
         )
@@ -1796,7 +1804,7 @@ as_sir_method <- function(method_short,
       }
 
       # write to verbose output
-      notes_current <- trimws2(notes_current)
+      notes_current <- gsub("\n\n", "\n", trimws2(notes_current), fixed = TRUE)
       notes_current[notes_current == ""] <- NA_character_
       out <- data.frame(
         # recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added
@@ -1904,11 +1912,11 @@ pillar_shaft.sir <- function(x, ...) {
     # colours will anyway not work when has_colour() == FALSE,
     # but then the indentation should also not be applied
     out[is.na(x)] <- font_grey("  NA")
-    out[x == "NI"] <- font_grey_bg(font_black("  NI "))
     out[x == "S"] <- font_green_bg("  S  ")
+    out[x == "SDD"] <- font_green_lighter_bg(" SDD ")
     out[x == "I"] <- font_orange_bg("  I  ")
-    out[x == "SDD"] <- font_orange_bg(" SDD ")
     out[x == "R"] <- font_rose_bg("  R  ")
+    out[x == "NI"] <- font_grey_bg(font_black("  NI "))
   }
   create_pillar_column(out, align = "left", width = 5)
 }

R/sir_calc.R

@@ -244,7 +244,7 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
   translate_ab <- get_translate_ab(translate_ab)
 
   data.bak <- data
-  # select only groups and antimicrobials
+  # select only groups and antibiotics
   if (is_null_or_grouped_tbl(data)) {
     data_has_groups <- TRUE
     groups <- get_group_names(data)
@@ -255,10 +255,12 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
   }
 
   data <- as.data.frame(data, stringsAsFactors = FALSE)
-  if (isTRUE(combine_SI)) {
+
   for (i in seq_len(ncol(data))) {
+    # transform SIR columns
     if (is.sir(data[, i, drop = TRUE])) {
       data[, i] <- as.character(data[, i, drop = TRUE])
+      if (isTRUE(combine_SI)) {
         if ("SDD" %in% data[, i, drop = TRUE] && message_not_thrown_before("sir_calc_df", combine_SI, entire_session = TRUE)) {
           message_("Note that `sir_calc_df()` will also count dose-dependent susceptibility, 'SDD', as 'SI' when `combine_SI = TRUE`. This note will be shown once for this session.", as_note = FALSE)
         }
@@ -364,7 +366,7 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
   } else {
     # don't use as.sir() here, as it would add the class 'sir' and we would like
     # the same data structure as output, regardless of input
-    if (out$value[out$interpretation == "SDD"] > 0) {
+    if (any(out$value[out$interpretation == "SDD"] > 0, na.rm = TRUE)) {
       out$interpretation <- factor(out$interpretation, levels = c("S", "SDD", "I", "R"), ordered = TRUE)
     } else {
       out$interpretation <- factor(out$interpretation, levels = c("S", "I", "R"), ordered = TRUE)

R/sir_df.R

@@ -47,6 +47,6 @@ sir_df <- function(data,
       combine_SI = combine_SI,
       confidence_level = confidence_level
     ),
-    error = function(e) stop_(gsub("in sir_calc_df(): ", "", e$message, fixed = TRUE), call = -5)
+    error = function(e) stop_(gsub("in sir_calc_df(): ", "", conditionMessage(e), fixed = TRUE), call = -5)
   )
 }

R/tidymodels.R

@@ -0,0 +1,265 @@
+#' AMR Extensions for Tidymodels
+#'
+#' This family of functions allows using AMR-specific data types such as `<mic>` and `<sir>` inside `tidymodels` pipelines.
+#' @inheritParams recipes::step_center
+#' @details
+#' You can read more in our online [AMR with tidymodels introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html).
+#'
+#' Tidyselect helpers include:
+#' - [all_mic()] and [all_mic_predictors()] to select `<mic>` columns
+#' - [all_sir()] and [all_sir_predictors()] to select `<sir>` columns
+#'
+#' Pre-processing pipeline steps include:
+#' - [step_mic_log2()] to convert MIC columns to numeric (via `as.numeric()`) and apply a log2 transform, to be used with [all_mic_predictors()]
+#' - [step_sir_numeric()] to convert SIR columns to numeric (via `as.numeric()`), to be used with [all_sir_predictors()]: `"S"` = 1, `"I"`/`"SDD"` = 2, `"R"` = 3. All other values are rendered `NA`. Keep this in mind for further processing, especially if the model does not allow for `NA` values.
+#'
+#' These steps integrate with `recipes::recipe()` and work like standard preprocessing steps. They are useful for preparing data for modelling, especially with classification models.
+#' @seealso [recipes::recipe()], [as.mic()], [as.sir()]
+#' @name amr-tidymodels
+#' @keywords internal
+#' @export
+#' @examples
+#' if (require("tidymodels")) {
+#'
+#'   # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
+#'   # Presence of ESBL genes was predicted based on raw MIC values.
+#'
+#'
+#'   # example data set in the AMR package
+#'   esbl_isolates
+#'
+#'   # Prepare a binary outcome and convert to ordered factor
+#'   data <- esbl_isolates %>%
+#'     mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+#'
+#'   # Split into training and testing sets
+#'   split <- initial_split(data)
+#'   training_data <- training(split)
+#'   testing_data <- testing(split)
+#'
+#'   # Create and prep a recipe with MIC log2 transformation
+#'   mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
+#'
+#'     # Optionally remove non-predictive variables
+#'     remove_role(genus, old_role = "predictor") %>%
+#'
+#'     # Apply the log2 transformation to all MIC predictors
+#'     step_mic_log2(all_mic_predictors()) %>%
+#'
+#'     # And apply the preparation steps
+#'     prep()
+#'
+#'   # View prepped recipe
+#'   mic_recipe
+#'
+#'   # Apply the recipe to training and testing data
+#'   out_training <- bake(mic_recipe, new_data = NULL)
+#'   out_testing <- bake(mic_recipe, new_data = testing_data)
+#'
+#'   # Fit a logistic regression model
+#'   fitted <- logistic_reg(mode = "classification") %>%
+#'     set_engine("glm") %>%
+#'     fit(esbl ~ ., data = out_training)
+#'
+#'   # Generate predictions on the test set
+#'   predictions <- predict(fitted, out_testing) %>%
+#'     bind_cols(out_testing)
+#'
+#'   # Evaluate predictions using standard classification metrics
+#'   our_metrics <- metric_set(accuracy, kap, ppv, npv)
+#'   metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+#'
+#'   # Show performance
+#'   metrics
+#' }
+all_mic <- function() {
+  x <- tidymodels_amr_select(levels(NA_mic_))
+  names(x)
+}
+
+#' @rdname amr-tidymodels
+#' @export
+all_mic_predictors <- function() {
+  x <- tidymodels_amr_select(levels(NA_mic_))
+  intersect(x, recipes::has_role("predictor"))
+}
+
+#' @rdname amr-tidymodels
+#' @export
+all_sir <- function() {
+  x <- tidymodels_amr_select(levels(NA_sir_))
+  names(x)
+}
+
+#' @rdname amr-tidymodels
+#' @export
+all_sir_predictors <- function() {
+  x <- tidymodels_amr_select(levels(NA_sir_))
+  intersect(x, recipes::has_role("predictor"))
+}
+
+#' @rdname amr-tidymodels
+#' @export
+step_mic_log2 <- function(
+    recipe,
+    ...,
+    role = NA,
+    trained = FALSE,
+    columns = NULL,
+    skip = FALSE,
+    id = recipes::rand_id("mic_log2")) {
+  recipes::add_step(
+    recipe,
+    step_mic_log2_new(
+      terms = rlang::enquos(...),
+      role = role,
+      trained = trained,
+      columns = columns,
+      skip = skip,
+      id = id
+    )
+  )
+}
+
+step_mic_log2_new <- function(terms, role, trained, columns, skip, id) {
+  recipes::step(
+    subclass = "mic_log2",
+    terms = terms,
+    role = role,
+    trained = trained,
+    columns = columns,
+    skip = skip,
+    id = id
+  )
+}
+
+#' @rawNamespace if(getRversion() >= "3.0.0") S3method(recipes::prep, step_mic_log2)
+prep.step_mic_log2 <- function(x, training, info = NULL, ...) {
+  col_names <- recipes::recipes_eval_select(x$terms, training, info)
+  recipes::check_type(training[, col_names], types = "ordered")
+  step_mic_log2_new(
+    terms = x$terms,
+    role = x$role,
+    trained = TRUE,
+    columns = col_names,
+    skip = x$skip,
+    id = x$id
+  )
+}
+
+#' @rawNamespace if(getRversion() >= "3.0.0") S3method(recipes::bake, step_mic_log2)
+bake.step_mic_log2 <- function(object, new_data, ...) {
+  recipes::check_new_data(object$columns, object, new_data)
+  for (col in object$columns) {
+    new_data[[col]] <- log2(as.numeric(as.mic(new_data[[col]])))
+  }
+  new_data
+}
+
+#' @export
+print.step_mic_log2 <- function(x, width = max(20, options()$width - 35), ...) {
+  title <- "Log2 transformation of MIC columns"
+  recipes::print_step(x$columns, x$terms, x$trained, title, width)
+  invisible(x)
+}
+
+#' @rawNamespace if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_mic_log2)
+tidy.step_mic_log2 <- function(x, ...) {
+  if (recipes::is_trained(x)) {
+    res <- tibble::tibble(terms = x$columns)
+  } else {
+    res <- tibble::tibble(terms = recipes::sel2char(x$terms))
+  }
+  res$id <- x$id
+  res
+}
+
+#' @rdname amr-tidymodels
+#' @export
+step_sir_numeric <- function(
+    recipe,
+    ...,
+    role = NA,
+    trained = FALSE,
+    columns = NULL,
+    skip = FALSE,
+    id = recipes::rand_id("sir_numeric")) {
+  recipes::add_step(
+    recipe,
+    step_sir_numeric_new(
+      terms = rlang::enquos(...),
+      role = role,
+      trained = trained,
+      columns = columns,
+      skip = skip,
+      id = id
+    )
+  )
+}
+
+step_sir_numeric_new <- function(terms, role, trained, columns, skip, id) {
+  recipes::step(
+    subclass = "sir_numeric",
+    terms = terms,
+    role = role,
+    trained = trained,
+    columns = columns,
+    skip = skip,
+    id = id
+  )
+}
+
+#' @rawNamespace if(getRversion() >= "3.0.0") S3method(recipes::prep, step_sir_numeric)
+prep.step_sir_numeric <- function(x, training, info = NULL, ...) {
+  col_names <- recipes::recipes_eval_select(x$terms, training, info)
+  recipes::check_type(training[, col_names], types = "ordered")
+  step_sir_numeric_new(
+    terms = x$terms,
+    role = x$role,
+    trained = TRUE,
+    columns = col_names,
+    skip = x$skip,
+    id = x$id
+  )
+}
+
+#' @rawNamespace if(getRversion() >= "3.0.0") S3method(recipes::bake, step_sir_numeric)
+bake.step_sir_numeric <- function(object, new_data, ...) {
+  recipes::check_new_data(object$columns, object, new_data)
+  for (col in object$columns) {
+    new_data[[col]] <- as.numeric(as.sir(new_data[[col]]))
+  }
+  new_data
+}
+
+#' @export
+print.step_sir_numeric <- function(x, width = max(20, options()$width - 35), ...) {
+  title <- "Numeric transformation of SIR columns"
+  recipes::print_step(x$columns, x$terms, x$trained, title, width)
+  invisible(x)
+}
+
+#' @rawNamespace if(getRversion() >= "3.0.0") S3method(recipes::tidy, step_sir_numeric)
+tidy.step_sir_numeric <- function(x, ...) {
+  if (recipes::is_trained(x)) {
+    res <- tibble::tibble(terms = x$columns)
+  } else {
+    res <- tibble::tibble(terms = recipes::sel2char(x$terms))
+  }
+  res$id <- x$id
+  res
+}
+
+tidymodels_amr_select <- function(check_vector) {
+  df <- get_current_data()
+  ind <- which(
+    vapply(
+      FUN.VALUE = logical(1),
+      df,
+      function(x) all(x %in% c(check_vector, NA), na.rm = TRUE) & any(x %in% check_vector),
+      USE.NAMES = TRUE
+    ),
+    useNames = TRUE
+  )
+  ind
+}

R/translate.R

@@ -258,6 +258,11 @@ translate_into_language <- function(from,
     return(from)
   }
 
+  if (only_affect_ab_names == TRUE) {
+    df_trans$pattern[df_trans$regular_expr == TRUE] <- paste0(df_trans$pattern[df_trans$regular_expr == TRUE], "$")
+    df_trans$pattern[df_trans$regular_expr == TRUE] <- gsub("$$", "$", df_trans$pattern[df_trans$regular_expr == TRUE], fixed = TRUE)
+  }
+
   lapply(
     # starting with longest pattern, since more general translations are shorter, such as 'Group'
     order(nchar(df_trans$pattern), decreasing = TRUE),

R/vctrs.R

@@ -30,7 +30,6 @@
 # These are all S3 implementations for the vctrs package,
 # that is used internally by tidyverse packages such as dplyr.
 # They are to convert AMR-specific classes to bare characters and integers.
-# All of them will be exported using s3_register() in R/zzz.R when loading the package.
 
 # see https://github.com/tidyverse/dplyr/issues/5955 why this is required
 

R/zz_deprecated.R

@@ -35,10 +35,35 @@
 #' @rdname AMR-deprecated
 NULL
 
-#' @rdname AMR-deprecated
-#' @usage NULL
+.amr_deprecation_warn <- function() {
+  deprecation_warning(old = "antibiotics", new = "antimicrobials", is_dataset = TRUE)
+  invisible(NULL)
+}
 #' @export
-"antibiotics"
+`[.deprecated_amr_dataset` <- function(x, ...) {
+  .amr_deprecation_warn()
+  NextMethod("[")
+}
+#' @export
+`[[.deprecated_amr_dataset` <- function(x, ...) {
+  .amr_deprecation_warn()
+  NextMethod("[[")
+}
+#' @export
+`$.deprecated_amr_dataset` <- function(x, name) {
+  .amr_deprecation_warn()
+  NextMethod("$")
+}
+#' @export
+print.deprecated_amr_dataset <- function(x, ...) {
+  .amr_deprecation_warn()
+  NextMethod("print")
+}
+#' @export
+as.data.frame.deprecated_amr_dataset <- function(x, ...) {
+  .amr_deprecation_warn()
+  NextMethod("as.data.frame")
+}
 
 # REMEMBER to search for `deprecation_warning` in the package code to find all instances.
 # currently deprecated arguments at least:

R/zzz.R

@@ -110,14 +110,6 @@ AMR_env$cross_icon <- if (isTRUE(base::l10n_info()$`UTF-8`)) "\u00d7" else "x"
     AB_LOOKUP <- create_AB_AV_lookup(AMR::antimicrobials)
   }
 
-  # deprecated antibiotics data set
-  makeActiveBinding("antibiotics", function() {
-    if (interactive()) {
-      deprecation_warning(old = "antibiotics", new = "antimicrobials", is_dataset = TRUE)
-    }
-    AMR::antimicrobials
-  }, env = asNamespace(pkgname))
-
   AMR_env$AB_lookup <- cbind(AMR::antimicrobials, AB_LOOKUP)
   AMR_env$AV_lookup <- cbind(AMR::antivirals, AV_LOOKUP)
 }
@@ -135,7 +127,7 @@ AMR_env$cross_icon <- if (isTRUE(base::l10n_info()$`UTF-8`)) "\u00d7" else "x"
           suppressWarnings(suppressMessages(add_custom_antimicrobials(x)))
           packageStartupMessage("OK.")
         },
-        error = function(e) packageStartupMessage("Failed: ", e$message)
+        error = function(e) packageStartupMessage("Failed: ", conditionMessage(e))
       )
     }
   }
@@ -151,7 +143,7 @@ AMR_env$cross_icon <- if (isTRUE(base::l10n_info()$`UTF-8`)) "\u00d7" else "x"
           suppressWarnings(suppressMessages(add_custom_microorganisms(x)))
           packageStartupMessage("OK.")
         },
-        error = function(e) packageStartupMessage("Failed: ", e$message)
+        error = function(e) packageStartupMessage("Failed: ", conditionMessage(e))
       )
     }
   }

_pkgdown.yml

@@ -234,6 +234,7 @@ reference:
     - "`antimicrobials`"
     - "`clinical_breakpoints`"
     - "`example_isolates`"
+    - "`esbl_isolates`"
     - "`microorganisms.codes`"
     - "`microorganisms.groups`"
     - "`intrinsic_resistant`"

cran-comments.md

@@ -1,5 +1,5 @@
-As with all previous >20 releases, some CHECKs might return a NOTE for *just* hitting the installation size limit, though its size has been brought down to a minimum in collaboration with CRAN maintainers previously.
+As with all previous >20 releases, some CHECKs on `oldrel` may return a `NOTE` for narrowly exceeding the installation size limit. This has been reduced to a minimum in prior coordination with CRAN maintainers and currently returns only an `INFO` on `release` and `devel`.
 
-We consider this a high-impact package: it was published in the Journal of Statistical Software (2022), is included in a CRAN Task View (Epidemiology), and is according to download stats (cranlogs) used in almost all countries in the world. If there is anything to note, please let us know up-front without directly archiving the current version. That said, we continually unit test our package extensively and have no reason to assume that anything is wrong.
+We treat this as a high-impact package: it was published in the *Journal of Statistical Software* (2022), is listed in the CRAN Task View "Epidemiology", and (based on cranlogs download statistics) is used globally. If there is anything to address, we would appreciate being informed before archiving the current version. We conduct extensive automated unit testing and have no indication of unresolved issues.
 
-Thanks for maintaining and hosting CRAN! It's empowering R and its use enormously!
+Thank you for your continued maintenance of CRAN, it plays a central role in the success and growth of the R ecosystem.

data-raw/_generate_python_wrapper.sh

@@ -56,7 +56,8 @@ os.makedirs(r_lib_path, exist_ok=True)
 os.environ['R_LIBS_SITE'] = r_lib_path
 
 from rpy2 import robjects
-from rpy2.robjects import pandas2ri
+from rpy2.robjects.conversion import localconverter
+from rpy2.robjects import default_converter, numpy2ri, pandas2ri
 from rpy2.robjects.packages import importr, isinstalled
 
 # Import base and utils
@@ -94,10 +95,9 @@ if r_amr_version != python_amr_version:
 print(f"AMR: Setting up R environment and AMR datasets...", flush=True)
 
 # Activate the automatic conversion between R and pandas DataFrames
-pandas2ri.activate()
-
+with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
     # example_isolates
-example_isolates = pandas2ri.rpy2py(robjects.r('''
+    example_isolates = robjects.r('''
     df <- AMR::example_isolates
     df[] <- lapply(df, function(x) {
         if (inherits(x, c("Date", "POSIXt", "factor"))) {
@@ -108,13 +108,13 @@ df[] <- lapply(df, function(x) {
     })
     df <- df[, !sapply(df, is.list)]
     df
-'''))
+    ''')
     example_isolates['date'] = pd.to_datetime(example_isolates['date'])
 
     # microorganisms
-microorganisms = pandas2ri.rpy2py(robjects.r('AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]'))
-antimicrobials = pandas2ri.rpy2py(robjects.r('AMR::antimicrobials[, !sapply(AMR::antimicrobials, is.list)]'))
-clinical_breakpoints = pandas2ri.rpy2py(robjects.r('AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]'))
+    microorganisms = robjects.r('AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]')
+    antimicrobials = robjects.r('AMR::antimicrobials[, !sapply(AMR::antimicrobials, is.list)]')
+    clinical_breakpoints = robjects.r('AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]')
 
 base.options(warn = 0)
 
@@ -129,16 +129,15 @@ echo "from .datasets import clinical_breakpoints" >> $init_file
 
 # Write header to the functions Python file, including the convert_to_python function
 cat <<EOL > "$functions_file"
+import functools
 import rpy2.robjects as robjects
 from rpy2.robjects.packages import importr
 from rpy2.robjects.vectors import StrVector, FactorVector, IntVector, FloatVector, DataFrame
-from rpy2.robjects import pandas2ri
+from rpy2.robjects.conversion import localconverter
+from rpy2.robjects import default_converter, numpy2ri, pandas2ri
 import pandas as pd
 import numpy as np
 
-# Activate automatic conversion between R data frames and pandas data frames
-pandas2ri.activate()
-
 # Import the AMR R package
 amr_r = importr('AMR')
 
@@ -156,10 +155,8 @@ def convert_to_python(r_output):
         return list(r_output)  # Convert to a Python list of integers or floats
     
     # Check if it's a pandas-compatible R data frame
-    elif isinstance(r_output, pd.DataFrame):
+    elif isinstance(r_output, (pd.DataFrame, DataFrame)):
         return r_output  # Return as pandas DataFrame (already converted by pandas2ri)
-    elif isinstance(r_output, DataFrame):
-        return pandas2ri.rpy2py(r_output) # Return as pandas DataFrame
 
     # Check if the input is a NumPy array and has a string data type
     if isinstance(r_output, np.ndarray) and np.issubdtype(r_output.dtype, np.str_):
@@ -167,6 +164,15 @@ def convert_to_python(r_output):
     
     # Fall-back
     return r_output
+
+def r_to_python(r_func):
+    """Decorator that runs an rpy2 function under a localconverter
+    and then applies convert_to_python to its output."""
+    @functools.wraps(r_func)
+    def wrapper(*args, **kwargs):
+        with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
+            return convert_to_python(r_func(*args, **kwargs))
+    return wrapper
 EOL
 
 # Directory where the .Rd files are stored (update path as needed)
@@ -246,10 +252,11 @@ for rd_file in "$rd_dir"/*.Rd; do
         gsub("FALSE", "False", func_args)
         gsub("NULL", "None", func_args)
 
-        # Write the Python function definition to the output file
+        # Write the Python function definition to the output file, using decorator
+        print "@r_to_python" >> "'"$functions_file"'"  
         print "def " func_name_py "(" func_args "):" >> "'"$functions_file"'"  
         print "    \"\"\"Please see our website of the R package for the full manual: https://amr-for-r.org\"\"\"" >> "'"$functions_file"'"  
-        print "    return convert_to_python(amr_r." func_name_py "(" func_args "))" >> "'"$functions_file"'"
+        print "    return amr_r." func_name_py "(" func_args ")" >> "'"$functions_file"'"  
 
         print "from .functions import " func_name_py >> "'"$init_file"'"
     }

data-raw/_pre_commit_checks.R

@@ -625,6 +625,11 @@ if (changed_md5(dosage)) {
   try(arrow::write_parquet(dosage, "data-raw/datasets/dosage.parquet"), silent = TRUE)
 }
 
+# Set `antibiotics` as a deprecated data set
+antibiotics <- structure(antimicrobials, class = c("deprecated_amr_dataset", class(antimicrobials)))
+usethis::use_data(antibiotics, internal = FALSE, overwrite = TRUE, compress = "xz", version = 2)
+rm(antibiotics)
+
 suppressMessages(reset_AMR_locale())
 
 devtools::load_all(quiet = TRUE)
@@ -658,7 +663,9 @@ if (files_changed()) {
 }
 
 # Update index.md and README.md -------------------------------------------
-if (files_changed("man/microorganisms.Rd") ||
+if (files_changed("README.Rmd") ||
+    files_changed("index.Rmd") ||
+    files_changed("man/microorganisms.Rd") ||
     files_changed("man/antimicrobials.Rd") ||
     files_changed("man/clinical_breakpoints.Rd") ||
     files_changed("man/antibiogram.Rd") ||

data-raw/_reproduction_scripts/reproduction_of_antimicrobials.R

@@ -948,6 +948,8 @@ get_atc_code <- function(ab) {
   # exception for imipenem
   if (ab_name == "imipenem") ab_name <- "imipenem/cilastatin"
   if (ab_name == "imipenem/relebactam") ab_name <- "imipenem/cilastatin/relebactam"
+  if (ab_name == "ceftaroline") ab_name <- "ceftaroline fosamil"
+  ab_name.bak <- ab_name
   if (ab_name %like% "/") {
     ab_name <- strsplit(ab_name, "[/ ]")[[1]]
   }
@@ -971,28 +973,33 @@ get_atc_code <- function(ab) {
   } else if (ab_name_full %like% " and " && ab_name_bla %in% atc_tbl[[2]]) {
     out <- atc_tbl[[1]][which(atc_tbl[[2]] == ab_name_bla)]
   } else {
+    if (any(atc_tbl_human$X1 %like% ab_name.bak, na.rm = TRUE)) {
+      message("returning NA, but DO MIND: ", ab_name.bak, " resembles ATC name(s) ", toString(atc_tbl_human$X1), appendLF = FALSE)
+    }
     out <- NA_character_
   }
   unique(out)
 }
 
+# update all:
+to_update <- 1:nrow(antimicrobials)
+# or just the empty ones:
+to_update <- which(sapply(antimicrobials$atc, function(x) length(x[!is.na(x)])) == 0)
+
 updated_atc <- lapply(seq_len(length(to_update)),
                       function(x) NA_character_)
 
-to_update <- 1:nrow(antimicrobials)
-# or just the empty ones:
-to_update <- which(sapply(antimicrobials$atc, length) == 0)
 
-# this takes around 10 minutes (some are skipped and go faster)
+# this takes around 10 minutes for the whole table (some ABx are skipped and go faster)
 for (i in to_update) {
-  message(percentage(i / length(updated_atc), digits = 1),
+  message(percentage(which(to_update == i) / length(updated_atc), digits = 1),
     " - Downloading ", antimicrobials$name[i],
     appendLF = FALSE
   )
   atcs <- get_atc_code(antimicrobials$name[i])
   if (length(atcs[!is.na(atcs)]) > 0) {
     updated_atc[[i]] <- atcs
-    message(" (", length(atcs[!is.na(atcs)]), " results: ", toString(atcs[!is.na(atcs)]), ")")
+    message(font_blue(paste0(" (", length(atcs[!is.na(atcs)]), " results: ", toString(atcs[!is.na(atcs)]), ")")))
     # let the WHO server rest for a second - they might have a limitation on the queries per second
     Sys.sleep(1)
   } else {
@@ -1001,7 +1008,8 @@ for (i in to_update) {
 }
 
 updated_atc <- lapply(updated_atc, function(x) sort(x[!is.na(x)]))
-antimicrobials$atc <- updated_atc
+antimicrobials$atc[to_update] <- updated_atc[to_update]
+
 # DO NOT FORGET TO UPDATE R/aa_globals.R!
 
 

data-raw/_reproduction_scripts/reproduction_of_microorganisms.R

@@ -288,7 +288,7 @@ for (page in LETTERS) {
   url <- paste0("https://lpsn.dsmz.de/genus?page=", page)
   x <- tryCatch(read_html(url),
                 error = function(e) {
-                  message("Waiting 10 seconds because of error: ", e$message)
+                  message("Waiting 10 seconds because of error: ", conditionMessage(e))
                   Sys.sleep(10)
                   read_html(url)
                 })

data-raw/_reproduction_scripts/reproduction_of_poorman.R

@@ -108,3 +108,18 @@ writeLines(contents, "R/aa_helper_pm_functions.R")
 
 # note: pm_left_join() will be overwritten by aaa_helper_functions.R, which contains a faster implementation
 # replace `res <- as.data.frame(res)` with  `res <- as.data.frame(res, stringsAsFactors = FALSE)`
+
+# after running, pm_select must be altered. The line:
+# col_pos <- pm_select_positions(.data, ..., .group_pos = TRUE)
+# ... must be replaced with this to support tidyselect functionality such as `starts_with()`:
+# col_pos <- tryCatch(pm_select_positions(.data, ..., .group_pos = TRUE), error = function(e) NULL)
+# if (is.null(col_pos)) {
+#   # try with tidyverse
+#   select_dplyr <- import_fn("select", "dplyr", error_on_fail = FALSE)
+#   if (!is.null(select_dplyr)) {
+#     col_pos <- which(colnames(.data) %in% colnames(select_dplyr(.data, ...)))
+#   } else {
+#     # this will throw an error as it did, but dplyr is not available, so no other option
+#     col_pos <- pm_select_positions(.data, ..., .group_pos = TRUE)
+#   }
+# }

data-raw/ab.md5

@@ -1 +1 @@
-959459b92fe6ff57c02bab08381a73a8
+d12f1c78feaecbb4d1631f9c735ad49b

data-raw/datasets/antimicrobials.txt

@@ -114,9 +114,9 @@
 "CRD"	5284529	"Cefroxadine"	"Cephalosporins (1st gen.)"	"J01DB11,QJ01DB11"	"Other beta-lactam antibacterials"	"First-generation cephalosporins"	"ceftix"	"cefroxadin,cefroxadino,cefroxadinum,oraspor"	2.1	"g"			"NA"
 "CFS"	656575	"Cefsulodin"	"Cephalosporins (3rd gen.)"	"J01DD03,QJ01DD03"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"cefsul,cfsl,cfsu"	"cefomonil,cefonomil,cefsulodine,cefsulodinhydrate,cefsulodino,cefsulodinum,pseudocef,pseudomonil,pyocefal,sulcephalosporin,takesulin,tilmapor,ulfaret"			4	"g"	"127-1,128-9,129-7,130-5,131-3,18892-0,25242-9,55647-2"
 "CSU"	68718	"Cefsumide"	"Cephalosporins (unclassified gen.)"	"NA"			"NA"	"cefsulmid,cefsumido,cefsumidum"					"NA"
-"CPT"	56841980	"Ceftaroline"	"Cephalosporins (5th gen.)"	"NA"			"ceftar,cfro"	"ceftaroine,teflaro,zinforo"					"73604-1,73605-8,73626-4,73627-2,73649-6,73650-4,74170-2"
+"CPT"	56841980	"Ceftaroline"	"Cephalosporins (5th gen.)"	"J01DI02,QJ01DI02"			"ceftar,cfro"	"ceftaroine,teflaro,zinforo"					"73604-1,73605-8,73626-4,73627-2,73649-6,73650-4,74170-2"
 "CPA"		"Ceftaroline/avibactam"	"Cephalosporins (5th gen.)"	"NA"			"NA"	"NA"					"73604-1,73626-4,73649-6"
-"CAZ"	5481173	"Ceftazidime"	"Cephalosporins (3rd gen.)"	"J01DD02,QJ01DD02"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"caz,cefta,ceftaz,cfta,cftz,taz,tz,xtz"	"ceftazimide,ceptaz,fortam,fortaz,fortum,glazidim,kefazim,modacin,pentacef,tazicef,tizime"			4	"g"	"101481-0,101482-8,101483-6,132-1,133-9,134-7,135-4,18893-8,21151-6,3449-6,35774-9,35775-6,35776-4,42352-5,55648-0,55649-8,55650-6,55651-4,58705-5,6995-5,73603-3,73625-6,73648-8,80960-8,87734-0,90850-9"
+"CAZ"	5481173	"Ceftazidime"	"Cephalosporins (3rd gen.)"	"J01DD02,QJ01DD02"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"caz,cef,cefta,ceftaz,cfta,cftz,taz,tz,xtz"	"ceftazimide,ceptaz,fortam,fortaz,fortum,glazidim,kefazim,modacin,pentacef,tazicef,tizime"			4	"g"	"101481-0,101482-8,101483-6,132-1,133-9,134-7,135-4,18893-8,21151-6,3449-6,35774-9,35775-6,35776-4,42352-5,55648-0,55649-8,55650-6,55651-4,58705-5,6995-5,73603-3,73625-6,73648-8,80960-8,87734-0,90850-9"
 "CZA"	90643431	"Ceftazidime/avibactam"	"Cephalosporins (3rd gen.)"	"J01DD52,QJ01DD52"			"cfav"	"avycaz,zavicefta"			6	"g"	"101483-6,73603-3,73625-6,73648-8,87734-0"
 "CCV"	9575352	"Ceftazidime/clavulanic acid"	"Cephalosporins (3rd gen.)"	"J01DD52,QJ01DD52"	"Other beta-lactam antibacterials"	"Third-generation cephalosporins"	"czcl,tazcla,xtzl"	"NA"			6	"g"	"NA"
 "CEM"	6537431	"Cefteram"	"Cephalosporins (3rd gen.)"	"J01DD18,QJ01DD18"			"cefter"	"cefterame,cefteramum,ceftetrame"	0.4	"g"			"100047-0,76144-5"
@@ -162,7 +162,7 @@
 "CYC"	6234	"Cycloserine"	"Oxazolidinones"	"J04AB01,QJ04AB01"	"Drugs for treatment of tuberculosis"	"Antibiotics"	"cycl,cyclos"	"cicloserina,closina,cyclorin,cycloserin,cycloserinum,farmiserina,levcicloserina,levcycloserine,levcycloserinum,micoserina,miroserina,miroseryn,novoserin,oxamicina,oxamycin,seromycin,tebemicina,wasserina"	0.75	"g"			"16702-3,18914-2,212-1,213-9,214-7,215-4,23608-3,25207-2,25208-0,25209-8,25251-0,3519-6,55667-0"
 "DAL"	23724878	"Dalbavancin"	"Glycopeptides"	"J01XA04,QJ01XA04"	"Other antibacterials"	"Glycopeptide antibacterials"	"dalb,dalbav"	"dalbavancina,dalvance,xydalba,zeven"			1.5	"g"	"41688-3,41689-1,41690-9,41734-5"
 "DAN"	71335	"Danofloxacin"	"Fluoroquinolones"	"QJ01MA92"			"danofl"	"advocin,danofloxacine,danofloxacino,danofloxacinum"					"73601-7,73623-1,73646-2"
-"DPS"	2955	"Dapsone"	"Other antibacterials"	"D10AX05,J04BA02,QD10AX05,QJ04BA02"	"Drugs for treatment of lepra"	"Drugs for treatment of lepra"	"NA"	"aczone,atrisone,avlosulfon,avlosulfone,avlosulphone,benzenamide,benzenamine,bissulfone,bissulphone,croysulfone,croysulphone,dapson,dapsona,dapsonum,daspone,diaphenylsulfon,diaphenylsulfone,diaphenylsulphon,diaphenylsulphone,diphenasone,diphone,disulfone,disulone,disulphone,dubronax,dumitone,eporal,medapsol,novophone,servidapson,sulfadione,sulfona,sulfonyldianiline,sulphadione,sulphonyldianiline,tarimyl,udolac,undolac"	50	"mg"			"51698-9,9747-7"
+"DPS"	2955	"Dapsone"	"Other antibacterials"	"D10AX05,J04BA02,QD10AX05,QJ04BA02"	"Drugs for treatment of lepra"	"Drugs for treatment of lepra"	"dao"	"aczone,atrisone,avlosulfon,avlosulfone,avlosulphone,benzenamide,benzenamine,bissulfone,bissulphone,croysulfone,croysulphone,dapson,dapsona,dapsonum,daspone,diaphenylsulfon,diaphenylsulfone,diaphenylsulphon,diaphenylsulphone,diphenasone,diphone,disulfone,disulone,disulphone,dubronax,dumitone,eporal,medapsol,novophone,servidapson,sulfadione,sulfona,sulfonyldianiline,sulphadione,sulphonyldianiline,tarimyl,udolac,undolac"	50	"mg"			"51698-9,9747-7"
 "DAP"	16134395	"Daptomycin"	"Other antibacterials"	"J01XX09,QJ01XX09"	"Other antibacterials"	"Other antibacterials"	"dap,dapt,dapt25,dapt50,daptom"	"cidecin,cubicin,dapcin,daptomicina,daptomycine,daptomycinum,deptomycin"			0.28	"g"	"35787-1,35788-9,35789-7,41691-7"
 "DFX"	487101	"Delafloxacin"	"Fluoroquinolones"	"J01MA23,QJ01MA23"			"NA"	"baxdela,delafloxacinum,quofenix"	0.9	"g"	0.6	"g"	"88885-9,90447-4,93790-4"
 "DLM"	6480466	"Delamanid"	"Antimycobacterials"	"J04AK06,QJ04AK06"	"Drugs for treatment of tuberculosis"	"Other drugs for treatment of tuberculosis"	"dela"	"deltyba"	0.2	"g"			"93851-4,96109-4"
@@ -184,7 +184,7 @@
 "ERV"	54726192	"Eravacycline"	"Tetracyclines"	"J01AA13,QJ01AA13"	"Tetracyclines"	"Tetracyclines"	"erav"	"xerava"			0.14	"g"	"100049-6,85423-2,93767-2"
 "ETP"	150610	"Ertapenem"	"Carbapenems"	"J01DH03,QJ01DH03"	"Other beta-lactam antibacterials"	"Carbapenems"	"erta,ertape,etp"	"ertapenemsalt,invanz"			1	"g"	"101486-9,35799-6,35800-2,35801-0,35802-8"
 "ERY"	12560	"Erythromycin"	"Macrolides/lincosamides"	"D10AF02,J01FA01,QD10AF02,QJ01FA01,QJ51FA01,QS01AA17,S01AA17"	"Macrolides, lincosamides and streptogramins"	"Macrolides"	"e,em,ery,ery32,eryt,eryth"	"abboticin,abomacetin,acneryne,acnesol,aknemycin,aknin,benzamycin,derimer,deripil,dotycin,dumotrycin,emgel,emuvin,emycin,endoeritrin,erecin,erisone,eritomicina,eritrocina,eritromicina,ermycin,eryacne,eryacnen,erycen,erycette,erycinum,eryderm,erydermer,erygel,eryhexal,erymax,erymed,erysafe,erytab,erythro,erythroderm,erythrogran,erythroguent,erythromast,erythromid,erythromycine,erythromycinum,erytop,erytrociclin,ilocaps,ilosone,iloticina,ilotycin,inderm,latotryd,lederpax,mephamycin,mercina,oftamolets,pantoderm,pantodrin,pantomicina,pharyngocin,primacine,propiocine,proterytrin,retcin,robimycin,sansac,spotex,staticin,stiemicyn,stiemycin,tiprocin,torlamicina,wemid"	2	"g"	1	"g"	"100050-4,11576-6,12298-6,16829-4,16830-2,18919-1,18920-9,20380-2,232-9,233-7,234-5,235-2,236-0,23633-1,237-8,238-6,239-4,25224-7,25275-9,3597-2,7009-4"
-"ETH"	14052	"Ethambutol"	"Antimycobacterials"	"J04AK02,QJ04AK02"	"Drugs for treatment of tuberculosis"	"Other drugs for treatment of tuberculosis"	"etha,ethamb"	"aethambutolum,dadibutol,diambutol,etambutol,etambutolo,ethambutolum,myambutol,purderal,servambutol,tibutol"	1.2	"g"	1.2	"g"	"100051-2,16841-9,18921-7,20381-0,23625-7,240-2,241-0,242-8,243-6,25187-6,25194-2,25195-9,25230-4,25404-5,3607-9,42645-2,42646-0,55154-9,55674-6,56025-0,7010-2,89491-5"
+"ETH"	14052	"Ethambutol"	"Antimycobacterials"	"J04AK02,QJ04AK02"	"Drugs for treatment of tuberculosis"	"Other drugs for treatment of tuberculosis"	"emb,etha,ethamb"	"aethambutolum,dadibutol,diambutol,etambutol,etambutolo,ethambutolum,myambutol,purderal,servambutol,tibutol"	1.2	"g"	1.2	"g"	"100051-2,16841-9,18921-7,20381-0,23625-7,240-2,241-0,242-8,243-6,25187-6,25194-2,25195-9,25230-4,25404-5,3607-9,42645-2,42646-0,55154-9,55674-6,56025-0,7010-2,89491-5"
 "ETI"	456476	"Ethambutol/isoniazid"	"Antimycobacterials"	"J04AM03,QJ04AM03"	"Drugs for treatment of tuberculosis"	"Combinations of drugs for treatment of tuberculosis"	"NA"	"NA"					"NA"
 "ETI1"	2761171	"Ethionamide"	"Antimycobacterials"	"J04AD03,QJ04AD03"	"Drugs for treatment of tuberculosis"	"Thiocarbamide derivatives"	"ethi,ethion"	"aethionamidum,aetina,aetiva,amidazin,amidazine,atina,ethimide,ethina,ethinamide,ethionamidum,ethioniamide,ethylisothiamide,ethyonomide,etimid,etiocidan,etionamid,etionamida,etionamide,etioniamid,etionid,etionizin,etionizina,etionizine,fatoliamid,iridocin,iridozin,isothin,isotiamida,itiocide,nicotion,nisotin,nizotin,rigenicid,sertinon,teberus,thianid,thianide,thioamide,thiodine,thiomid,thioniden,tianid,tiomid,trecator,trekator,trescatyl,trescazide,tubenamide,tubermin,tuberoid,tuberoson"	0.75	"g"			"16099-4,16845-0,18922-5,20382-8,23617-4,25183-5,25196-7,25198-3,25231-2,41693-3,42647-8,42648-6,7011-0,96110-2"
 "ETO"	6034	"Ethopabate"	"Other antibacterials"	"QP51AX17"			"NA"	"ethopabat"					"NA"
@@ -202,7 +202,7 @@
 "FLM"	3374	"Flumequine"	"Quinolones"	"J01MB07,QJ01MB07"	"Quinolone antibacterials"	"Other quinolones"	"flumeq"	"apurone,fantacin,flumequina,flumequino,flumequinum,flumigal,flumiquil,flumisol,flumix,imequyl"	1.2	"g"			"55675-3,55676-1,55677-9,55678-7"
 "FLR1"	71260	"Flurithromycin"	"Macrolides/lincosamides"	"J01FA14,QJ01FA14"	"Macrolides, lincosamides and streptogramins"	"Macrolides"	"NA"	"abbot,beritromicina,berythromycin,berythromycine,berythromycinum,flurithromycine,flurithromycinum,fluritromicina,fluritromycinum,flurizic,mizar"	0.75	"g"			"NA"
 "FFL"	214356	"Fosfluconazole"	"Antifungals/antimycotics"	"NA"			"NA"	"fosfluconazol,procif,prodif"					"NA"
-"FOS"	446987	"Fosfomycin"	"Other antibacterials"	"J01XX01,QJ01XX01,QS02AA17,S02AA17"	"Other antibacterials"	"Other antibacterials"	"ff,fm,fo,fof,fos,fosf,fosfom,fosmyc"	"fosfocina,fosfomicin,fosfomicina,fosfomycine,fosfomycinum,fosfonomycin,infectophos,phosphonemycin,phosphonomycin,veramina"	3	"g"	8	"g"	"25596-8,25653-7,35809-3,35810-1"
+"FOS"	446987	"Fosfomycin"	"Phosphonics"	"J01XX01,QJ01XX01,QS02AA17,S02AA17"	"Other antibacterials"	"Other antibacterials"	"ff,fm,fo,fof,fos,fosf,fosfom,fosmyc"	"fosfocina,fosfomicin,fosfomicina,fosfomycine,fosfomycinum,fosfonomycin,infectophos,phosphonemycin,phosphonomycin,veramina"	3	"g"	8	"g"	"25596-8,25653-7,35809-3,35810-1"
 "FMD"	572	"Fosmidomycin"	"Other antibacterials"	"NA"			"NA"	"fosmidomicina,fosmidomycina,fosmidomycine,fosmidomycinsalt,fosmidomycinum"					"NA"
 "FRM"	8378	"Framycetin"	"Aminoglycosides"	"D09AA01,QD09AA01,QJ01GB91,QR01AX08,QS01AA07,R01AX08,S01AA07"			"fram,framyc"	"actilin,actiline,antibiotique,bycomycin,enterfram,fradiomycin,fradiomycinum,framicetina,framidal,framycetine,framycetinum,framycin,framygen,francetin,jernadex,myacyne,mycerin,mycifradin,neobrettin,neolate,neomas,neomcin,neomicina,neomin,neomycine,neomycinum,nivemycin,soframycin,soframycine"					"18926-6,257-6,258-4,259-2,260-0,55679-5"
 "FUR"	6870646	"Furazidin"	"Other antibacterials"	"J01XE03,QJ01XE03"	"Other antibacterials"	"Nitrofuran derivatives"	"NA"	"akritoin,furagin,furaginum,furamag,furazidine,hydantoin"	0.3	"g"			"NA"
@@ -268,7 +268,7 @@
 "MET"	6087	"Meticillin"	"Beta-lactams/penicillins"	"J01CF03,QJ01CF03,QJ51CF03"	"Beta-lactam antibacterials, penicillins"	"Beta-lactamase resistant penicillins"	"methic,meti"	"belfacillin,celbenin,celpilline,cinopenil,dimocillin,estafcilina,flabelline,lucopenin,metacillin,methcillin,methicillin,methicillinanhydrous,methicillinhydrate,methicillinsalt,methicillinum,methycillin,meticilina,meticillina,meticilline,meticillinsalt,meticillinum,penaureus,penysol,staficyn,staphcillin,synticillin"			4	"g"	"NA"
 "MTP"	68590	"Metioprim"	"Other antibacterials"	"NA"			"NA"	"methioprim,metioprima,metioprime,metioprimum"					"NA"
 "MXT"	3047729	"Metioxate"	"Fluoroquinolones"	"NA"			"NA"	"metioxato,metioxatum"					"NA"
-"MTR"	4173	"Metronidazole"	"Other antibacterials"	"A01AB17,D06BX01,G01AF01,J01XD01,P01AB01,QA01AB17,QD06BX01,QG01AF01,QJ01XD01,QP51CA01"	"Other antibacterials"	"Imidazole derivatives"	"metr,metron,mnz"	"acromona,anagiardil,arilin,atrivyl,bexon,clont,danizol,deflamon,donnan,efloran,elyzol,entizol,eumin,flagemona,flagesol,flagil,flagyl,flazol,flegyl,florazole,fossyol,giatricol,gineflavir,givagil,hydroxydimetridazole,hydroxymetronidazole,izoklion,klion,klont,mepagyl,meronidal,metric,metrolag,metrolyl,metromidol,metronidazolo,metronidazolum,metroplex,metrotop,mexibol,monagyl,monasin,nalox,nidagyl,noritate,novonidazol,nuvessa,orvagil,polibiotic,protostat,rathimed,rosaced,rosased,sanatrichom,satric,takimetol,trichazol,trichex,trichobrol,trichocide,trichomol,trichopal,trichopol,tricocet,tricom,trikacide,trikamon,trikhopol,trikojol,trikozol,trimeks,trivazol,vagilen,vagimid,vandazole,vertisal,wagitran,zadstat,zidoval"	2	"g"	1.5	"g"	"10991-8,18946-4,326-9,327-7,328-5,329-3,7031-8"
+"MTR"	4173	"Metronidazole"	"Other antibacterials"	"A01AB17,D06BX01,G01AF01,J01XD01,P01AB01,QA01AB17,QD06BX01,QG01AF01,QJ01XD01,QP51CA01"	"Other antibacterials"	"Imidazole derivatives"	"metr,metron,mnz,mtz"	"acromona,anagiardil,arilin,atrivyl,bexon,clont,danizol,deflamon,donnan,efloran,elyzol,entizol,eumin,flagemona,flagesol,flagil,flagyl,flazol,flegyl,florazole,fossyol,giatricol,gineflavir,givagil,hydroxydimetridazole,hydroxymetronidazole,izoklion,klion,klont,mepagyl,meronidal,metric,metrolag,metrolyl,metromidol,metronidazolo,metronidazolum,metroplex,metrotop,mexibol,monagyl,monasin,nalox,nidagyl,noritate,novonidazol,nuvessa,orvagil,polibiotic,protostat,rathimed,rosaced,rosased,sanatrichom,satric,takimetol,trichazol,trichex,trichobrol,trichocide,trichomol,trichopal,trichopol,tricocet,tricom,trikacide,trikamon,trikhopol,trikojol,trikozol,trimeks,trivazol,vagilen,vagimid,vandazole,vertisal,wagitran,zadstat,zidoval"	2	"g"	1.5	"g"	"10991-8,18946-4,326-9,327-7,328-5,329-3,7031-8"
 "MEZ"	656511	"Mezlocillin"	"Beta-lactams/penicillins"	"J01CA10,QJ01CA10"	"Beta-lactam antibacterials, penicillins"	"Penicillins with extended spectrum"	"mez,mezl,mezlo,mz"	"baycipen,baypen,mezlin,mezlocilina,mezlocilline,mezlocillinsalt,mezlocillinum,multocillin"			6	"g"	"18947-2,330-1,331-9,332-7,333-5,3820-8,41702-2,54194-6,54195-3,54196-1"
 "MSU"		"Mezlocillin/sulbactam"	"Beta-lactams/penicillins"	"NA"			"mezsul"	"NA"					"54194-6,54195-3,54196-1"
 "MIF"	477468	"Micafungin"	"Antifungals/antimycotics"	"J02AX05,QJ02AX05"	"Antimycotics for systemic use"	"Other antimycotics for systemic use"	"mica,micafu"	"fungard,funguard,micafungina,micafunginsalt,mycamine"			0.1	"g"	"53812-4,58418-5,65340-2,85048-7"
@@ -339,7 +339,7 @@
 "PMR"	5284447	"Pimaricin"	"Antifungals/antimycotics"	"NA"			"natamycin"	"delvocid,delvolan,delvopos,mycophyt,myprozine,natacyn,natafucin,natajen,natamatrix,natamax,natamicina,natamycin,natamycine,natamycinum,pimafucin,pimaracin,pimaricine,pimarizin,synogil,tennecetin"					"NA"
 "PPA"	4831	"Pipemidic acid"	"Quinolones"	"J01MB04,QJ01MB04"	"Quinolone antibacterials"	"Other quinolones"	"pipaci,pipz,pizu"	"deblaston,dolcol,filtrax,karunomazin,memento,nuril,palin,pipedac,pipemid,pipemidate,pipemidic,pipemidicacid,pipram,pipurin,tractur,uromidin,urosten,uroval"	0.8	"g"			"NA"
 "PIP"	43672	"Piperacillin"	"Beta-lactams/penicillins"	"J01CA12,QJ01CA12"	"Beta-lactam antibacterials, penicillins"	"Penicillins with extended spectrum"	"pi,pip,pipc,pipe,pipera,pp"	"penmalin,pentcillin,peperacillin,peracin,piperacilina,piperacillina,piperacilline,piperacillinhydrate,piperacillinum,pipercillin,pipracil,tazocin"			14	"g"	"101490-1,101491-9,18969-6,18970-4,25268-4,3972-7,407-7,408-5,409-3,410-1,411-9,412-7,413-5,414-3,54197-9,54198-7,54199-5,55704-1,7043-3,7044-1"
-"PIS"		"Piperacillin/sulbactam"	"Beta-lactams/penicillins"	"J01CR05,QJ01CR05"			"NA"	"NA"			14	"g"	"54197-9,54198-7,54199-5,55704-1"
+"PIS"		"Piperacillin/sulbactam"	"Beta-lactams/penicillins"	"NA"			"NA"	"NA"			14	"g"	"54197-9,54198-7,54199-5,55704-1"
 "TZP"	461573	"Piperacillin/tazobactam"	"Beta-lactams/penicillins"	"J01CR05,QJ01CR05"	"Beta-lactam antibacterials, penicillins"	"Combinations of penicillins, incl. beta-lactamase inhibitors"	"p/t,piptaz,piptazo,pit,pita,pt,ptc,ptz,tzp"	"piptazobactam,tazonam,zobactin,zosyn"			14	"g"	"101491-9,18970-4,411-9,412-7,413-5,414-3,7044-1"
 "PRC"	71978	"Piridicillin"	"Beta-lactams/penicillins"	"NA"			"NA"	"NA"					"NA"
 "PRL"	157385	"Pirlimycin"	"Macrolides/lincosamides"	"QJ51FF90"			"pirlim"	"pirlimycina,pirlimycine,pirlimycinum,pirsue"					"35829-1,35830-9,35831-7"
@@ -370,7 +370,7 @@
 "RBC"	44631912	"Ribociclib"	"Antifungals/antimycotics"	"L01EF02,QL01EF02"	"Antimycotics for systemic use"	"Triazole derivatives"	"ribo"	"kisqali"	0.45	"g"			"NA"
 "RST"	33042	"Ribostamycin"	"Aminoglycosides"	"J01GB10,QJ01GB10"	"Aminoglycoside antibacterials"	"Other aminoglycosides"	"NA"	"exaluren,hetangmycin,ribastamin,ribostamicina,ribostamycine,ribostamycinum,vistamycin,xylostatin"			1	"g"	"NA"
 "RID1"	16659285	"Ridinilazole"	"Other antibacterials"	"NA"			"NA"	"ridinilazol"					"NA"
-"RIB"	135398743	"Rifabutin"	"Antimycobacterials"	"J04AB04,QJ04AB04"	"Drugs for treatment of tuberculosis"	"Antibiotics"	"ansamy,rifb"	"alfacid,ansamicin,ansamycins,ansatipin,ansatipine,assatipin,mycobutin,rifabutinum"	0.15	"g"			"100699-8,16100-0,16386-5,16387-3,19149-4,20386-9,23630-7,24032-5,25199-1,25200-7,25201-5,42655-1,42656-9,54183-9,96113-6"
+"RIB"	135398743	"Rifabutin"	"Antimycobacterials"	"J04AB04,QJ04AB04"	"Drugs for treatment of tuberculosis"	"Antibiotics"	"ansamy,rfb,rifb"	"alfacid,ansamicin,ansamycins,ansatipin,ansatipine,assatipin,mycobutin,rifabutinum"	0.15	"g"			"100699-8,16100-0,16386-5,16387-3,19149-4,20386-9,23630-7,24032-5,25199-1,25200-7,25201-5,42655-1,42656-9,54183-9,96113-6"
 "RIF"	135398735	"Rifampicin"	"Antimycobacterials"	"J04AB02,QJ04AB02,QJ54AB02"	"Drugs for treatment of tuberculosis"	"Antibiotics"	"rifa,rifamp"	"abrifam,archidyn,arficin,arzide,benemicin,doloresum,eremfat,famcin,fenampicin,rifadin,rifadine,rifagen,rifaldazin,rifaldazine,rifaldin,rifam,rifamor,rifampicina,rifampicine,rifampicinum,rifampin,rifamsolin,rifapiam,rifaprodin,rifcin,rifinah,rifobac,rifoldin,rifoldine,riforal,rimactan,rimactane,rimactazid,rimactizid,rimazid,sinerdol,tubocin"	0.6	"g"	0.6	"g"	"NA"
 "REI"	135483893	"Rifampicin/ethambutol/isoniazid"	"Antimycobacterials"	"J04AM07,QJ04AM07"	"Drugs for treatment of tuberculosis"	"Combinations of drugs for treatment of tuberculosis"	"NA"	"isonarif,rifamate,rifamazid"					"NA"
 "RFI"		"Rifampicin/isoniazid"	"Antimycobacterials"	"J04AM02,QJ04AM02"	"Drugs for treatment of tuberculosis"	"Combinations of drugs for treatment of tuberculosis"	"NA"	"NA"					"NA"
@@ -391,7 +391,6 @@
 "SRC"	54681908	"Sarecycline"	"Tetracyclines"	"J01AA14,QJ01AA14"	"Tetracyclines"	"Tetracyclines"	"NA"	"sareciclina,seysara"	0.1	"g"			"NA"
 "SRX"	9933415	"Sarmoxicillin"	"Beta-lactams/penicillins"	"NA"			"NA"	"sarmoxillina,sarmoxilline,sarmoxillinum"					"NA"
 "SEC"	71815	"Secnidazole"	"Other antibacterials"	"P01AB07"			"NA"	"flagentyl,secnidal,secnidazolum,secnil,sindose,solosec"	2	"g"			"NA"
-"SMF"		"Simvastatin/fenofibrate"	"Antimycobacterials"	"C10BA04,QC10BA04"	"Drugs for treatment of tuberculosis"	"Other drugs for treatment of tuberculosis"	"simv"	"NA"					"NA"
 "SIS"	36119	"Sisomicin"	"Aminoglycosides"	"J01GB08,QJ01GB08"	"Aminoglycoside antibacterials"	"Other aminoglycosides"	"siso,sisomy"	"rickamicin,salvamina,sisomicina,sisomicine,sisomicinum,sisomin,sisomycin,sissomicin,sizomycin"			0.24	"g"	"18979-5,447-3,448-1,449-9,450-7,55714-0"
 "SIT"	461399	"Sitafloxacin"	"Fluoroquinolones"	"J01MA21,QJ01MA21"			"sitafl"	"gracevit"	0.1	"g"			"NA"
 "SDA"	2724368	"Sodium aminosalicylate"	"Antimycobacterials"	"J04AA02,QJ04AA02"	"Drugs for treatment of tuberculosis"	"Aminosalicylic acid and derivatives"	"NA"	"bactylan,lepasen,monopas,tubersan"	14	"g"	14	"g"	"NA"
@@ -495,4 +494,4 @@
 "VOR"	71616	"Voriconazole"	"Antifungals/antimycotics"	"J02AC03,QJ02AC03"	"Antimycotics for systemic use"	"Triazole derivatives"	"vori,vorico,vrc"	"vfend,voriconazol,voriconazolum,voriconzole,vorikonazole"	0.4	"g"	0.4	"g"	"32379-0,35862-2,35863-0,38370-3,41199-1,41200-7,53902-3,73676-9,80553-1,80651-3"
 "XBR"	72144	"Xibornol"	"Other antibacterials"	"J01XX02,QJ01XX02"	"Other antibacterials"	"Other antibacterials"	"NA"	"bactacine,bracen,nanbacine,xibornolo,xibornolum"					"NA"
 "ZID"	77846445	"Zidebactam"	"Other antibacterials"	"NA"			"NA"	"zidebactamsalt"					"NA"
-"ZFD"		"Zoliflodacin"		"NA"			"NA"	"NA"					"NA"
+"ZFD"		"Zoliflodacin"		"NA"			"zol"	"NA"					"NA"

data-raw/read_EUCAST.R

@@ -283,7 +283,7 @@ for (i in 2:length(sheets_to_analyse)) {
         guideline_name = guideline_name
       )
     ),
-    error = function(e) message(e$message)
+    error = function(e) message(conditionMessage(e))
   )
 }
 

index.Rmd

@@ -28,8 +28,8 @@ AMR:::reset_all_thrown_messages()
 > Now available for Python too! [Click here](./articles/AMR_for_Python.html) to read more.
 
 <div style="display: flex; font-size: 0.8em;">
-  <p style="text-align:left; width: 50%;"><small><a href="https://amr-for-r.org/">https://amr-for-r.org</a></small></p>
-  <p style="text-align:right; width: 50%;"><small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small></p>
+  <p style="text-align:left; width: 50%;"><small><a href="https://amr-for-r.org/">amr-for-r.org</a></small></p>
+  <p style="text-align:right; width: 50%;"><small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">doi.org/10.18637/jss.v104.i03</a></small></p>
 </div>
 
 <a href="./reference/clinical_breakpoints.html#response-from-clsi-and-eucast"><img src="./endorsement_clsi_eucast.jpg" class="endorse_img" align="right" height="120" /></a>
@@ -133,7 +133,7 @@ ggplot(data.frame(mic = some_mic_values,
                   sir = interpretation),
        aes(x = group, y = mic, colour = sir)) +
   theme_minimal() +
-  geom_boxplot(fill = NA, colour = "grey") +
+  geom_boxplot(fill = NA, colour = "grey30") +
   geom_jitter(width = 0.25) +
   
   # NEW scale function: plot MIC values to x, y, colour or fill
@@ -192,7 +192,7 @@ out %>% set_ab_names(property = "atc")
 
 This package was intended as a comprehensive toolbox for integrated AMR data analysis. This package can be used for:
 
-  * Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](./reference/mo_property.html))
+  * Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature ([LPSN](https://lpsn.dsmz.de)) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](./reference/mo_property.html))
   * Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline ([manual](./reference/as.sir.html))
   * Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records ([manual](./reference/ab_from_text.html))
   * Determining first isolates to be used for AMR data analysis ([manual](./reference/first_isolate.html))

index.md

@@ -27,12 +27,12 @@
 
 <p style="text-align:left; width: 50%;">
 
-<small><a href="https://amr-for-r.org/">https://amr-for-r.org</a></small>
+<small><a href="https://amr-for-r.org/">amr-for-r.org</a></small>
 </p>
 
 <p style="text-align:right; width: 50%;">
 
-<small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small>
+<small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">doi.org/10.18637/jss.v104.i03</a></small>
 </p>
 
 </div>
@@ -171,14 +171,14 @@ example_isolates %>%
   select(bacteria,
          aminoglycosides(),
          carbapenems())
-#> ℹ Using column 'mo' as input for mo_fullname()
-#> ℹ Using column 'mo' as input for mo_is_gram_negative()
-#> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant()
+#> ℹ Using column 'mo' as input for `mo_fullname()`
+#> ℹ Using column 'mo' as input for `mo_is_gram_negative()`
+#> ℹ Using column 'mo' as input for `mo_is_intrinsic_resistant()`
 #> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant
 #>   Phenotypes' v1.2 (2023). This note will be shown once per session.
-#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
 #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
-#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
+#> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)
 #> # A tibble: 35 × 7
 #>    bacteria                     GEN   TOB   AMK   KAN   IPM   MEM  
 #>    <chr>                        <sir> <sir> <sir> <sir> <sir> <sir>
@@ -215,9 +215,9 @@ output format automatically (such as markdown, LaTeX, HTML, etc.).
 ``` r
 antibiogram(example_isolates,
             antimicrobials = c(aminoglycosides(), carbapenems()))
-#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
 #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
-#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
+#> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)
 ```
 
 | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
@@ -289,7 +289,7 @@ ggplot(data.frame(mic = some_mic_values,
                   sir = interpretation),
        aes(x = group, y = mic, colour = sir)) +
   theme_minimal() +
-  geom_boxplot(fill = NA, colour = "grey") +
+  geom_boxplot(fill = NA, colour = "grey30") +
   geom_jitter(width = 0.25) +
   
   # NEW scale function: plot MIC values to x, y, colour or fill
@@ -321,9 +321,9 @@ example_isolates %>%
 #> # A tibble: 3 × 5
 #>   ward       GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int
 #>   <chr>            <dbl> <chr>              <dbl> <chr>       
-#> 1 Clinical     0.2289362 0.205-0.254    0.3147503 0.284-0.347 
-#> 2 ICU          0.2902655 0.253-0.33     0.4004739 0.353-0.449 
-#> 3 Outpatient   0.2       0.131-0.285    0.3676471 0.254-0.493
+#> 1 Clinical         0.229 0.205-0.254        0.315 0.284-0.347 
+#> 2 ICU              0.290 0.253-0.33         0.400 0.353-0.449 
+#> 3 Outpatient       0.2   0.131-0.285        0.368 0.254-0.493
 ```
 
 Or use [antimicrobial
@@ -340,22 +340,22 @@ out <- example_isolates %>%
   # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
   summarise(across(c(aminoglycosides(), polymyxins()),
             resistance))
-#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
 #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
-#> ℹ For polymyxins() using column 'COL' (colistin)
+#> ℹ For `polymyxins()` using column 'COL' (colistin)
 #> Warning: There was 1 warning in `summarise()`.
 #> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.
 #> ℹ In group 3: `ward = "Outpatient"`.
 #> Caused by warning:
 #> ! Introducing NA: only 23 results available for KAN in group: ward =
-#> "Outpatient" (minimum = 30).
+#> "Outpatient" (`minimum` = 30).
 out
 #> # A tibble: 3 × 6
 #>   ward         GEN   TOB   AMK   KAN   COL
 #>   <chr>      <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 Clinical   0.2289362 0.3147503 0.6258993     1 0.7802956
-#> 2 ICU        0.2902655 0.4004739 0.6624473     1 0.8574144
-#> 3 Outpatient 0.2       0.3676471 0.6052632    NA 0.8888889
+#> 1 Clinical   0.229 0.315 0.626     1 0.780
+#> 2 ICU        0.290 0.400 0.662     1 0.857
+#> 3 Outpatient 0.2   0.368 0.605    NA 0.889
 ```
 
 ``` r
@@ -364,9 +364,9 @@ out %>% set_ab_names()
 #> # A tibble: 3 × 6
 #>   ward       gentamicin tobramycin amikacin kanamycin colistin
 #>   <chr>           <dbl>      <dbl>    <dbl>     <dbl>    <dbl>
-#> 1 Clinical    0.2289362  0.3147503 0.6258993         1 0.7802956
-#> 2 ICU         0.2902655  0.4004739 0.6624473         1 0.8574144
-#> 3 Outpatient  0.2        0.3676471 0.6052632        NA 0.8888889
+#> 1 Clinical        0.229      0.315    0.626         1    0.780
+#> 2 ICU             0.290      0.400    0.662         1    0.857
+#> 3 Outpatient      0.2        0.368    0.605        NA    0.889
 ```
 
 ``` r
@@ -375,9 +375,9 @@ out %>% set_ab_names(property = "atc")
 #> # A tibble: 3 × 6
 #>   ward       J01GB03 J01GB01 J01GB06 J01GB04 J01XB01
 #>   <chr>        <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-#> 1 Clinical   0.2289362 0.3147503 0.6258993       1 0.7802956
-#> 2 ICU        0.2902655 0.4004739 0.6624473       1 0.8574144
-#> 3 Outpatient 0.2       0.3676471 0.6052632      NA 0.8888889
+#> 1 Clinical     0.229   0.315   0.626       1   0.780
+#> 2 ICU          0.290   0.400   0.662       1   0.857
+#> 3 Outpatient   0.2     0.368   0.605      NA   0.889
 ```
 
 ## What else can you do with this package?
@@ -387,7 +387,7 @@ data analysis. This package can be used for:
 
 - Reference for the taxonomy of microorganisms, since the package
   contains all microbial (sub)species from the List of Prokaryotic names
-  with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the
+  with Standing in Nomenclature ([LPSN](https://lpsn.dsmz.de)) and the
   Global Biodiversity Information Facility
   ([GBIF](https://www.gbif.org))
   ([manual](./reference/mo_property.html))

man/AMR-deprecated.Rd

@@ -1,15 +1,10 @@
 % Generated by roxygen2: do not edit by hand
 % Please edit documentation in R/zz_deprecated.R
-\docType{data}
 \name{AMR-deprecated}
 \alias{AMR-deprecated}
-\alias{antibiotics}
 \alias{ab_class}
 \alias{ab_selector}
 \title{Deprecated Functions, Arguments, or Datasets}
-\format{
-An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 497 rows and 14 columns.
-}
 \usage{
 ab_class(...)
 
@@ -18,5 +13,4 @@ ab_selector(...)
 \description{
 These objects are so-called '\link{Deprecated}'. \strong{They will be removed in a future version of this package.} Using these will give a warning with the name of the alternative object it has been replaced by (if there is one).
 }
-\keyword{datasets}
 \keyword{internal}

man/age_groups.Rd

@@ -4,20 +4,23 @@
 \alias{age_groups}
 \title{Split Ages into Age Groups}
 \usage{
-age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)
+age_groups(x, split_at = c(0, 12, 25, 55, 75), names = NULL,
+  na.rm = FALSE)
 }
 \arguments{
 \item{x}{Age, e.g. calculated with \code{\link[=age]{age()}}.}
 
 \item{split_at}{Values to split \code{x} at - the default is age groups 0-11, 12-24, 25-54, 55-74 and 75+. See \emph{Details}.}
 
+\item{names}{Optional names to be given to the various age groups.}
+
 \item{na.rm}{A \link{logical} to indicate whether missing values should be removed.}
 }
 \value{
 Ordered \link{factor}
 }
 \description{
-Split ages into age groups defined by the \code{split} argument. This allows for easier demographic (antimicrobial resistance) analysis.
+Split ages into age groups defined by the \code{split} argument. This allows for easier demographic (antimicrobial resistance) analysis. The function returns an ordered \link{factor}.
 }
 \details{
 To split ages, the input for the \code{split_at} argument can be:
@@ -41,6 +44,7 @@ age_groups(ages, 50)
 
 # split into 0-19, 20-49 and 50+
 age_groups(ages, c(20, 50))
+age_groups(ages, c(20, 50), names = c("Under 20 years", "20 to 50 years", "Over 50 years"))
 
 # split into groups of ten years
 age_groups(ages, 1:10 * 10)

man/amr-tidymodels.Rd

@@ -0,0 +1,125 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/tidymodels.R
+\name{amr-tidymodels}
+\alias{amr-tidymodels}
+\alias{all_mic}
+\alias{all_mic_predictors}
+\alias{all_sir}
+\alias{all_sir_predictors}
+\alias{step_mic_log2}
+\alias{step_sir_numeric}
+\title{AMR Extensions for Tidymodels}
+\usage{
+all_mic()
+
+all_mic_predictors()
+
+all_sir()
+
+all_sir_predictors()
+
+step_mic_log2(recipe, ..., role = NA, trained = FALSE, columns = NULL,
+  skip = FALSE, id = recipes::rand_id("mic_log2"))
+
+step_sir_numeric(recipe, ..., role = NA, trained = FALSE, columns = NULL,
+  skip = FALSE, id = recipes::rand_id("sir_numeric"))
+}
+\arguments{
+\item{recipe}{A recipe object. The step will be added to the sequence of
+operations for this recipe.}
+
+\item{...}{One or more selector functions to choose variables for this step.
+See \code{\link[recipes:selections]{selections()}} for more details.}
+
+\item{role}{Not used by this step since no new variables are created.}
+
+\item{trained}{A logical to indicate if the quantities for preprocessing have
+been estimated.}
+
+\item{skip}{A logical. Should the step be skipped when the recipe is baked by
+\code{\link[recipes:bake]{bake()}}? While all operations are baked when \code{\link[recipes:prep]{prep()}} is run, some
+operations may not be able to be conducted on new data (e.g. processing the
+outcome variable(s)). Care should be taken when using \code{skip = TRUE} as it
+may affect the computations for subsequent operations.}
+
+\item{id}{A character string that is unique to this step to identify it.}
+}
+\description{
+This family of functions allows using AMR-specific data types such as \verb{<mic>} and \verb{<sir>} inside \code{tidymodels} pipelines.
+}
+\details{
+You can read more in our online \href{https://amr-for-r.org/articles/AMR_with_tidymodels.html}{AMR with tidymodels introduction}.
+
+Tidyselect helpers include:
+\itemize{
+\item \code{\link[=all_mic]{all_mic()}} and \code{\link[=all_mic_predictors]{all_mic_predictors()}} to select \verb{<mic>} columns
+\item \code{\link[=all_sir]{all_sir()}} and \code{\link[=all_sir_predictors]{all_sir_predictors()}} to select \verb{<sir>} columns
+}
+
+Pre-processing pipeline steps include:
+\itemize{
+\item \code{\link[=step_mic_log2]{step_mic_log2()}} to convert MIC columns to numeric (via \code{as.numeric()}) and apply a log2 transform, to be used with \code{\link[=all_mic_predictors]{all_mic_predictors()}}
+\item \code{\link[=step_sir_numeric]{step_sir_numeric()}} to convert SIR columns to numeric (via \code{as.numeric()}), to be used with \code{\link[=all_sir_predictors]{all_sir_predictors()}}: \code{"S"} = 1, \code{"I"}/\code{"SDD"} = 2, \code{"R"} = 3. All other values are rendered \code{NA}. Keep this in mind for further processing, especially if the model does not allow for \code{NA} values.
+}
+
+These steps integrate with \code{recipes::recipe()} and work like standard preprocessing steps. They are useful for preparing data for modelling, especially with classification models.
+}
+\examples{
+if (require("tidymodels")) {
+
+  # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
+  # Presence of ESBL genes was predicted based on raw MIC values.
+
+
+  # example data set in the AMR package
+  esbl_isolates
+
+  # Prepare a binary outcome and convert to ordered factor
+  data <- esbl_isolates \%>\%
+    mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+
+  # Split into training and testing sets
+  split <- initial_split(data)
+  training_data <- training(split)
+  testing_data <- testing(split)
+
+  # Create and prep a recipe with MIC log2 transformation
+  mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\%
+
+    # Optionally remove non-predictive variables
+    remove_role(genus, old_role = "predictor") \%>\%
+
+    # Apply the log2 transformation to all MIC predictors
+    step_mic_log2(all_mic_predictors()) \%>\%
+
+    # And apply the preparation steps
+    prep()
+
+  # View prepped recipe
+  mic_recipe
+
+  # Apply the recipe to training and testing data
+  out_training <- bake(mic_recipe, new_data = NULL)
+  out_testing <- bake(mic_recipe, new_data = testing_data)
+
+  # Fit a logistic regression model
+  fitted <- logistic_reg(mode = "classification") \%>\%
+    set_engine("glm") \%>\%
+    fit(esbl ~ ., data = out_training)
+
+  # Generate predictions on the test set
+  predictions <- predict(fitted, out_testing) \%>\%
+    bind_cols(out_testing)
+
+  # Evaluate predictions using standard classification metrics
+  our_metrics <- metric_set(accuracy, kap, ppv, npv)
+  metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+
+  # Show performance
+  metrics
+}
+}
+\seealso{
+\code{\link[recipes:recipe]{recipes::recipe()}}, \code{\link[=as.mic]{as.mic()}}, \code{\link[=as.sir]{as.sir()}}
+}
+\keyword{internal}

man/antibiogram.Rd

@@ -11,7 +11,7 @@
 \source{
 \itemize{
 \item Bielicki JA \emph{et al.} (2016). \strong{Selecting appropriate empirical antibiotic regimens for paediatric bloodstream infections: application of a Bayesian decision model to local and pooled antimicrobial resistance surveillance data} \emph{Journal of Antimicrobial Chemotherapy} 71(3); \doi{10.1093/jac/dkv397}
-\item Bielicki JA \emph{et al.} (2020). \strong{Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries} \emph{JAMA Netw Open.} 3(2):e1921124; \doi{10.1001.jamanetworkopen.2019.21124}
+\item Bielicki JA \emph{et al.} (2020). \strong{Evaluation of the coverage of 3 antibiotic regimens for neonatal sepsis in the hospital setting across Asian countries} \emph{JAMA Netw Open.} 3(2):e1921124; \doi{10.1001/jamanetworkopen.2019.21124}
 \item Klinker KP \emph{et al.} (2021). \strong{Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms}. \emph{Therapeutic Advances in Infectious Disease}, May 5;8:20499361211011373; \doi{10.1177/20499361211011373}
 \item Barbieri E \emph{et al.} (2021). \strong{Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach} \emph{Antimicrobial Resistance & Infection Control} May 1;10(1):74; \doi{10.1186/s13756-021-00939-2}
 \item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
@@ -56,6 +56,7 @@ retrieve_wisca_parameters(wisca_model, ...)
 \item \code{c(aminoglycosides(), "AMP", "AMC")}
 \item \code{c(aminoglycosides(), carbapenems())}
 }
+\item Column indices using numbers
 \item Combination therapy, indicated by using \code{"+"}, with or without \link[=antimicrobial_selectors]{antimicrobial selectors}, e.g.:
 \itemize{
 \item \code{"cipro + genta"}
@@ -163,7 +164,7 @@ Set \code{digits} (defaults to \code{0}) to alter the rounding of the susceptibi
 
 There are various antibiogram types, as summarised by Klinker \emph{et al.} (2021, \doi{10.1177/20499361211011373}), and they are all supported by \code{\link[=antibiogram]{antibiogram()}}.
 
-For clinical coverage estimations, \strong{use WISCA whenever possible}, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki \emph{et al.} (2020, \doi{10.1001.jamanetworkopen.2019.21124}). See the section \emph{Explaining WISCA} on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
+For clinical coverage estimations, \strong{use WISCA whenever possible}, since it provides more precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility, as has been shown by Bielicki \emph{et al.} (2020, \doi{10.1001/jamanetworkopen.2019.21124}). See the section \emph{Explaining WISCA} on this page. Do note that WISCA is pathogen-agnostic, meaning that the outcome is not stratied by pathogen, but rather by syndrome.
 \enumerate{
 \item \strong{Traditional Antibiogram}
 

man/antimicrobial_selectors.Rd

@@ -181,7 +181,7 @@ The \code{\link[=not_intrinsic_resistant]{not_intrinsic_resistant()}} function c
 \item \code{\link[=aminoglycosides]{aminoglycosides()}} can select: \cr amikacin (AMK), amikacin/fosfomycin (AKF), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB), and tobramycin-high (TOH)
 \item \code{\link[=aminopenicillins]{aminopenicillins()}} can select: \cr amoxicillin (AMX) and ampicillin (AMP)
 \item \code{\link[=antifungals]{antifungals()}} can select: \cr amorolfine (AMO), amphotericin B (AMB), amphotericin B-high (AMH), anidulafungin (ANI), butoconazole (BUT), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), flucytosine (FCT), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), oteseconazole (OTE), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC), and voriconazole (VOR)
-\item \code{\link[=antimycobacterials]{antimycobacterials()}} can select: \cr 4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), protionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), simvastatin/fenofibrate (SMF), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone (TAT), thioacetazone/isoniazid (THI1), tiocarlide (TCR), and viomycin (VIO)
+\item \code{\link[=antimycobacterials]{antimycobacterials()}} can select: \cr 4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), protionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone (TAT), thioacetazone/isoniazid (THI1), tiocarlide (TCR), and viomycin (VIO)
 \item \code{\link[=betalactams]{betalactams()}} can select: \cr amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), benzylpenicillin screening test (PEN-S), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), carumonam (CAR), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefalexin (LEX), cefaloridine (RID), cefalotin (CEP), cefamandole (MAN), cefapirin (HAP), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/enmetazobactam (FPE), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefiderocol (FDC), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime screening test (CTX-S), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening test (FOX-S), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), ceftriaxone/beta-lactamase inhibitor (CEB), cefuroxime (CXM), cefuroxime axetil (CXA), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), meticillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nafcillin (NAF), oxacillin (OXA), oxacillin screening test (OXA-S), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), pheneticillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC), ticarcillin/clavulanic acid (TCC), and tigemonam (TMN)
 \item \code{\link[=betalactams_with_inhibitor]{betalactams_with_inhibitor()}} can select: \cr amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin/sulbactam (SAM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), cefepime/amikacin (CFA), cefepime/clavulanic acid (CPC), cefepime/enmetazobactam (FPE), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefepime/zidebactam (FPZ), cefoperazone/sulbactam (CSL), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefpodoxime/clavulanic acid (CDC), ceftaroline/avibactam (CPA), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), ceftolozane/tazobactam (CZT), ceftriaxone/beta-lactamase inhibitor (CEB), imipenem/relebactam (IMR), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), mezlocillin/sulbactam (MSU), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), and ticarcillin/clavulanic acid (TCC)
 \item \code{\link[=carbapenems]{carbapenems()}} can select: \cr biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), and tebipenem (TBP)

man/antimicrobials.Rd

@@ -3,10 +3,11 @@
 \docType{data}
 \name{antimicrobials}
 \alias{antimicrobials}
+\alias{antibiotics}
 \alias{antivirals}
-\title{Data Sets with 617 Antimicrobial Drugs}
+\title{Data Sets with 616 Antimicrobial Drugs}
 \format{
-\subsection{For the \link{antimicrobials} data set: a \link[tibble:tibble]{tibble} with 497 observations and 14 variables:}{
+\subsection{For the \link{antimicrobials} data set: a \link[tibble:tibble]{tibble} with 496 observations and 14 variables:}{
 \itemize{
 \item \code{ab}\cr antimicrobial ID as used in this package (such as \code{AMC}), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. \emph{\strong{This is a unique identifier.}}
 \item \code{cid}\cr Compound ID as found in PubChem. \emph{\strong{This is a unique identifier.}}
@@ -49,6 +50,8 @@ LOINC:
 }
 }
 
+An object of class \code{deprecated_amr_dataset} (inherits from \code{tbl_df}, \code{tbl}, \code{data.frame}) with 496 rows and 14 columns.
+
 An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 120 rows and 11 columns.
 }
 \source{
@@ -61,10 +64,14 @@ An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) w
 \usage{
 antimicrobials
 
+antibiotics
+
 antivirals
 }
 \description{
 Two data sets containing all antimicrobials and antivirals. Use \code{\link[=as.ab]{as.ab()}} or one of the \code{\link[=ab_property]{ab_*}} functions to retrieve values from the \link{antimicrobials} data set. Three identifiers are included in this data set: an antimicrobial ID (\code{ab}, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (\code{atc}) as defined by the WHO, and a Compound ID (\code{cid}) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.
+
+\strong{The \code{antibiotics} data set has been renamed to \code{antimicrobials}. The old name will be removed in a future version.}
 }
 \details{
 Properties that are based on an ATC code are only available when an ATC is available. These properties are: \code{atc_group1}, \code{atc_group2}, \code{oral_ddd}, \code{oral_units}, \code{iv_ddd} and \code{iv_units}. Do note that ATC codes are not unique. For example, J01CR02 is officially the ATC code for "amoxicillin and beta-lactamase inhibitor". Consequently, these two items from the \link{antimicrobials} data set both return \code{"J01CR02"}:

man/as.sir.Rd

@@ -75,7 +75,9 @@ sir_interpretation_history(clean = FALSE)
 \arguments{
 \item{x}{Vector of values (for class \code{\link{mic}}: MIC values in mg/L, for class \code{\link{disk}}: a disk diffusion radius in millimetres).}
 
-\item{...}{For using on a \link{data.frame}: names of columns to apply \code{\link[=as.sir]{as.sir()}} on (supports tidy selection such as \code{column1:column4}). Otherwise: arguments passed on to methods.}
+\item{...}{For using on a \link{data.frame}: selection of columns to apply \code{as.sir()} to. Supports \link[tidyselect:starts_with]{tidyselect language} such as \code{where(is.mic)}, \code{starts_with(...)}, or \code{column1:column4}, and can thus also be \link[=amr_selector]{antimicrobial selectors} such as \code{as.sir(df, penicillins())}.
+
+Otherwise: arguments passed on to methods.}
 
 \item{threshold}{Maximum fraction of invalid antimicrobial interpretations of \code{x}, see \emph{Examples}.}
 
@@ -314,9 +316,12 @@ if (require("dplyr")) {
   df_wide \%>\% mutate_if(is.mic, as.sir)
   df_wide \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
   df_wide \%>\% mutate(across(where(is.mic), as.sir))
+
   df_wide \%>\% mutate_at(vars(amoxicillin:tobra), as.sir)
   df_wide \%>\% mutate(across(amoxicillin:tobra, as.sir))
 
+  df_wide \%>\% mutate(across(aminopenicillins(), as.sir))
+
   # approaches that all work with additional arguments:
   df_long \%>\%
     # given a certain data type, e.g. MIC values

man/custom_eucast_rules.Rd

@@ -103,7 +103,7 @@ These 35 antimicrobial groups are allowed in the rules (case-insensitive) and ca
 \item aminoglycosides\cr(amikacin, amikacin/fosfomycin, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, and tobramycin-high)
 \item aminopenicillins\cr(amoxicillin and ampicillin)
 \item antifungals\cr(amorolfine, amphotericin B, amphotericin B-high, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, and voriconazole)
-\item antimycobacterials\cr(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, and viomycin)
+\item antimycobacterials\cr(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, and viomycin)
 \item betalactams\cr(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, biapenem, carbenicillin, carindacillin, carumonam, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, oxacillin screening test, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid, and tigemonam)
 \item betalactams_with_inhibitor\cr(amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin/sulbactam, aztreonam/avibactam, aztreonam/nacubactam, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefoperazone/sulbactam, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefpodoxime/clavulanic acid, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, ceftolozane/tazobactam, ceftriaxone/beta-lactamase inhibitor, imipenem/relebactam, meropenem/nacubactam, meropenem/vaborbactam, mezlocillin/sulbactam, penicillin/novobiocin, penicillin/sulbactam, piperacillin/sulbactam, piperacillin/tazobactam, and ticarcillin/clavulanic acid)
 \item carbapenems\cr(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil, and tebipenem)

man/custom_mdro_guideline.Rd

@@ -99,7 +99,7 @@ All 35 antimicrobial selectors are supported for use in the rules:
 \item \code{\link[=aminoglycosides]{aminoglycosides()}} can select: \cr amikacin, amikacin/fosfomycin, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin, and tobramycin-high
 \item \code{\link[=aminopenicillins]{aminopenicillins()}} can select: \cr amoxicillin and ampicillin
 \item \code{\link[=antifungals]{antifungals()}} can select: \cr amorolfine, amphotericin B, amphotericin B-high, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, flucytosine, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, oteseconazole, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole, and voriconazole
-\item \code{\link[=antimycobacterials]{antimycobacterials()}} can select: \cr 4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, and viomycin
+\item \code{\link[=antimycobacterials]{antimycobacterials()}} can select: \cr 4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, morinamide, p-aminosalicylic acid, pretomanid, protionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone, thioacetazone/isoniazid, tiocarlide, and viomycin
 \item \code{\link[=betalactams]{betalactams()}} can select: \cr amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, benzylpenicillin screening test, biapenem, carbenicillin, carindacillin, carumonam, cefacetrile, cefaclor, cefadroxil, cefalexin, cefaloridine, cefalotin, cefamandole, cefapirin, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefiderocol, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime screening test, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening test, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/tazobactam, ceftriaxone, ceftriaxone/beta-lactamase inhibitor, cefuroxime, cefuroxime axetil, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, meticillin, mezlocillin, mezlocillin/sulbactam, nafcillin, oxacillin, oxacillin screening test, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, pheneticillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbenicillin, sultamicillin, talampicillin, tebipenem, temocillin, ticarcillin, ticarcillin/clavulanic acid, and tigemonam
 \item \code{\link[=betalactams_with_inhibitor]{betalactams_with_inhibitor()}} can select: \cr amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin/sulbactam, aztreonam/avibactam, aztreonam/nacubactam, cefepime/amikacin, cefepime/clavulanic acid, cefepime/enmetazobactam, cefepime/nacubactam, cefepime/tazobactam, cefepime/zidebactam, cefoperazone/sulbactam, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefpodoxime/clavulanic acid, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, ceftolozane/tazobactam, ceftriaxone/beta-lactamase inhibitor, imipenem/relebactam, meropenem/nacubactam, meropenem/vaborbactam, mezlocillin/sulbactam, penicillin/novobiocin, penicillin/sulbactam, piperacillin/sulbactam, piperacillin/tazobactam, and ticarcillin/clavulanic acid
 \item \code{\link[=carbapenems]{carbapenems()}} can select: \cr biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil, and tebipenem

man/esbl_isolates.Rd

@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{esbl_isolates}
+\alias{esbl_isolates}
+\title{Data Set with 500 ESBL Isolates}
+\format{
+A \link[tibble:tibble]{tibble} with 500 observations and 19 variables:
+\itemize{
+\item \code{esbl}\cr Logical indicator if the isolate is ESBL-producing
+\item \code{genus}\cr Genus of the microorganism
+\item \code{AMC:COL}\cr MIC values for 17 antimicrobial agents, transformed to class \code{\link{mic}} (see \code{\link[=as.mic]{as.mic()}})
+}
+}
+\usage{
+esbl_isolates
+}
+\description{
+A data set containing 500 microbial isolates with MIC values of common antibiotics and a binary \code{esbl} column for extended-spectrum beta-lactamase (ESBL) production. This data set contains randomised fictitious data but reflects reality and can be used to practise AMR-related machine learning, e.g., classification modelling with \href{https://amr-for-r.org/articles/AMR_with_tidymodels.html}{tidymodels}.
+}
+\details{
+See our \link[=amr-tidymodels]{tidymodels integration} for an example using this data set.
+}
+\examples{
+esbl_isolates
+}
+\keyword{datasets}

man/g.test.Rd

@@ -133,7 +133,7 @@ g.test(x)
 }
 \references{
 \enumerate{
-\item McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
+\item McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland.
 }
 }
 \seealso{

man/ggplot_sir.Rd

@@ -9,10 +9,10 @@ ggplot_sir(data, position = NULL, x = "antibiotic",
   fill = "interpretation", facet = NULL, breaks = seq(0, 1, 0.1),
   limits = NULL, translate_ab = "name", combine_SI = TRUE,
   minimum = 30, language = get_AMR_locale(), nrow = NULL, colours = c(S
-  = "#3CAEA3", SI = "#3CAEA3", I = "#F6D55C", IR = "#ED553B", R = "#ED553B"),
-  datalabels = TRUE, datalabels.size = 2.5, datalabels.colour = "grey15",
-  title = NULL, subtitle = NULL, caption = NULL,
-  x.title = "Antimicrobial", y.title = "Proportion", ...)
+  = "#3CAEA3", SDD = "#8FD6C4", SI = "#3CAEA3", I = "#F6D55C", IR = "#ED553B",
+  R = "#ED553B"), datalabels = TRUE, datalabels.size = 2.5,
+  datalabels.colour = "grey15", title = NULL, subtitle = NULL,
+  caption = NULL, x.title = "Antimicrobial", y.title = "Proportion", ...)
 
 geom_sir(position = NULL, x = c("antibiotic", "interpretation"),
   fill = "interpretation", translate_ab = "name", minimum = 30,

man/mdro.Rd

@@ -57,7 +57,7 @@ eucast_exceptional_phenotypes(x = NULL, only_sir_columns = any(is.sir(x)),
 
 \item{combine_SI}{A \link{logical} to indicate whether all values of S and I must be merged into one, so resistance is only considered when isolates are R, not I. As this is the default behaviour of the \code{\link[=mdro]{mdro()}} function, it follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. When using \code{combine_SI = FALSE}, resistance is considered when isolates are R or I.}
 
-\item{verbose}{A \link{logical} to turn Verbose mode on and off (default is off). In Verbose mode, the function does not return the MDRO results, but instead returns a data set in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.}
+\item{verbose}{A \link{logical} to turn Verbose mode on and off (default is off). In Verbose mode, the function returns a data set with the MDRO results in logbook form with extensive info about which isolates would be MDRO-positive, or why they are not.}
 
 \item{only_sir_columns}{A \link{logical} to indicate whether only antimicrobial columns must be included that were transformed to class \link[=as.sir]{sir} on beforehand. Defaults to \code{FALSE} if no columns of \code{x} have a class \link[=as.sir]{sir}.}
 

man/mean_amr_distance.Rd

@@ -18,7 +18,7 @@ amr_distance_from_row(amr_distance, row)
 \arguments{
 \item{x}{A vector of class \link[=as.sir]{sir}, \link[=as.mic]{mic} or \link[=as.disk]{disk}, or a \link{data.frame} containing columns of any of these classes.}
 
-\item{...}{Variables to select. Supports \link[tidyselect:language]{tidyselect language} (such as \code{column1:column4} and \code{where(is.mic)}), and can thus also be \link[=amr_selector]{antimicrobial selectors}.}
+\item{...}{Variables to select. Supports \link[tidyselect:starts_with]{tidyselect language} such as \code{where(is.mic)}, \code{starts_with(...)}, or \code{column1:column4}, and can thus also be \link[=amr_selector]{antimicrobial selectors}.}
 
 \item{combine_SI}{A \link{logical} to indicate whether all values of S, SDD, and I must be merged into one, so the input only consists of S+I vs. R (susceptible vs. resistant) - the default is \code{TRUE}.}
 

man/plot.Rd

@@ -33,25 +33,25 @@ scale_colour_mic(keep_operators = "edges", mic_range = NULL, ...)
 
 scale_fill_mic(keep_operators = "edges", mic_range = NULL, ...)
 
-scale_x_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
-  language = get_AMR_locale(), eucast_I = getOption("AMR_guideline",
-  "EUCAST") == "EUCAST", ...)
+scale_x_sir(colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R
+  = "#ED553B"), language = get_AMR_locale(),
+  eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST", ...)
 
-scale_colour_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
-  language = get_AMR_locale(), eucast_I = getOption("AMR_guideline",
-  "EUCAST") == "EUCAST", ...)
+scale_colour_sir(colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I =
+  "#F6D55C", R = "#ED553B"), language = get_AMR_locale(),
+  eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST", ...)
 
-scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
-  language = get_AMR_locale(), eucast_I = getOption("AMR_guideline",
-  "EUCAST") == "EUCAST", ...)
+scale_fill_sir(colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C",
+  R = "#ED553B"), language = get_AMR_locale(),
+  eucast_I = getOption("AMR_guideline", "EUCAST") == "EUCAST", ...)
 
 \method{plot}{mic}(x, mo = NULL, ab = NULL,
   guideline = getOption("AMR_guideline", "EUCAST"),
   main = deparse(substitute(x)), ylab = translate_AMR("Frequency", language
   = language),
   xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language =
-  language), colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
-  language = get_AMR_locale(), expand = TRUE,
+  language), colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R
+  = "#ED553B"), language = get_AMR_locale(), expand = TRUE,
   include_PKPD = getOption("AMR_include_PKPD", TRUE),
   breakpoint_type = getOption("AMR_breakpoint_type", "human"), ...)
 
@@ -60,8 +60,8 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
   title = deparse(substitute(object)), ylab = translate_AMR("Frequency",
   language = language),
   xlab = translate_AMR("Minimum Inhibitory Concentration (mg/L)", language =
-  language), colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
-  language = get_AMR_locale(), expand = TRUE,
+  language), colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R
+  = "#ED553B"), language = get_AMR_locale(), expand = TRUE,
   include_PKPD = getOption("AMR_include_PKPD", TRUE),
   breakpoint_type = getOption("AMR_breakpoint_type", "human"), ...)
 
@@ -69,8 +69,8 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
   ylab = translate_AMR("Frequency", language = language),
   xlab = translate_AMR("Disk diffusion diameter (mm)", language = language),
   mo = NULL, ab = NULL, guideline = getOption("AMR_guideline", "EUCAST"),
-  colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
-  language = get_AMR_locale(), expand = TRUE,
+  colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R =
+  "#ED553B"), language = get_AMR_locale(), expand = TRUE,
   include_PKPD = getOption("AMR_include_PKPD", TRUE),
   breakpoint_type = getOption("AMR_breakpoint_type", "human"), ...)
 
@@ -78,8 +78,8 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
   title = deparse(substitute(object)), ylab = translate_AMR("Frequency",
   language = language), xlab = translate_AMR("Disk diffusion diameter (mm)",
   language = language), guideline = getOption("AMR_guideline", "EUCAST"),
-  colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
-  language = get_AMR_locale(), expand = TRUE,
+  colours_SIR = c(S = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R =
+  "#ED553B"), language = get_AMR_locale(), expand = TRUE,
   include_PKPD = getOption("AMR_include_PKPD", TRUE),
   breakpoint_type = getOption("AMR_breakpoint_type", "human"), ...)
 
@@ -90,8 +90,8 @@ scale_fill_sir(colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
 
 \method{autoplot}{sir}(object, title = deparse(substitute(object)),
   xlab = translate_AMR("Antimicrobial Interpretation", language = language),
-  ylab = translate_AMR("Frequency", language = language),
-  colours_SIR = c("#3CAEA3", "#F6D55C", "#ED553B"),
+  ylab = translate_AMR("Frequency", language = language), colours_SIR = c(S
+  = "#3CAEA3", SDD = "#8FD6C4", I = "#F6D55C", R = "#ED553B"),
   language = get_AMR_locale(), ...)
 
 facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
@@ -99,8 +99,8 @@ facet_sir(facet = c("interpretation", "antibiotic"), nrow = NULL)
 scale_y_percent(breaks = function(x) seq(0, max(x, na.rm = TRUE), 0.1),
   limits = c(0, NA))
 
-scale_sir_colours(..., aesthetics, colours_SIR = c("#3CAEA3", "#F6D55C",
-  "#ED553B"))
+scale_sir_colours(..., aesthetics, colours_SIR = c(S = "#3CAEA3", SDD =
+  "#8FD6C4", I = "#F6D55C", R = "#ED553B"))
 
 theme_sir()
 
@@ -210,6 +210,10 @@ if (require("ggplot2")) {
   # when providing the microorganism and antibiotic, colours will show interpretations:
   autoplot(some_mic_values, mo = "Escherichia coli", ab = "cipro")
 }
+if (require("ggplot2")) {
+  autoplot(some_mic_values, mo = "Staph aureus", ab = "Ceftaroline", guideline = "CLSI")
+}
+
 if (require("ggplot2")) {
   # support for 27 languages, various guidelines, and many options
   autoplot(some_disk_values,
@@ -267,7 +271,7 @@ if (require("ggplot2")) {
     aes(group, mic)
   ) +
     geom_boxplot() +
-    geom_violin(linetype = 2, colour = "grey", fill = NA) +
+    geom_violin(linetype = 2, colour = "grey30", fill = NA) +
     scale_y_mic()
 }
 if (require("ggplot2")) {
@@ -279,7 +283,7 @@ if (require("ggplot2")) {
     aes(group, mic)
   ) +
     geom_boxplot() +
-    geom_violin(linetype = 2, colour = "grey", fill = NA) +
+    geom_violin(linetype = 2, colour = "grey30", fill = NA) +
     scale_y_mic(mic_range = c(NA, 0.25))
 }
 
@@ -312,7 +316,7 @@ if (require("ggplot2")) {
     aes(x = group, y = mic, colour = sir)
   ) +
     theme_minimal() +
-    geom_boxplot(fill = NA, colour = "grey") +
+    geom_boxplot(fill = NA, colour = "grey30") +
     geom_jitter(width = 0.25)
 
   plain

man/random.Rd

@@ -7,19 +7,25 @@
 \alias{random_sir}
 \title{Random MIC Values/Disk Zones/SIR Generation}
 \usage{
-random_mic(size = NULL, mo = NULL, ab = NULL, ...)
+random_mic(size = NULL, mo = NULL, ab = NULL, skew = "right",
+  severity = 1, ...)
 
-random_disk(size = NULL, mo = NULL, ab = NULL, ...)
+random_disk(size = NULL, mo = NULL, ab = NULL, skew = "left",
+  severity = 1, ...)
 
 random_sir(size = NULL, prob_SIR = c(0.33, 0.33, 0.33), ...)
 }
 \arguments{
 \item{size}{Desired size of the returned vector. If used in a \link{data.frame} call or \code{dplyr} verb, will get the current (group) size if left blank.}
 
-\item{mo}{Any \link{character} that can be coerced to a valid microorganism code with \code{\link[=as.mo]{as.mo()}}.}
+\item{mo}{Any \link{character} that can be coerced to a valid microorganism code with \code{\link[=as.mo]{as.mo()}}. Can be the same length as \code{size}.}
 
 \item{ab}{Any \link{character} that can be coerced to a valid antimicrobial drug code with \code{\link[=as.ab]{as.ab()}}.}
 
+\item{skew}{Direction of skew for MIC or disk values, either \code{"right"} or \code{"left"}. A left-skewed distribution has the majority of the data on the right.}
+
+\item{severity}{Skew severity; higher values will increase the skewedness. Default is \code{2}; use \code{0} to prevent skewedness.}
+
 \item{...}{Ignored, only in place to allow future extensions.}
 
 \item{prob_SIR}{A vector of length 3: the probabilities for "S" (1st value), "I" (2nd value) and "R" (3rd value).}
@@ -31,17 +37,25 @@ class \code{mic} for \code{\link[=random_mic]{random_mic()}} (see \code{\link[=a
 These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial drug, the generated results will reflect reality as much as possible.
 }
 \details{
-The base \R function \code{\link[=sample]{sample()}} is used for generating values.
-
-Generated values are based on the EUCAST 2025 guideline as implemented in the \link{clinical_breakpoints} data set. To create specific generated values per bug or drug, set the \code{mo} and/or \code{ab} argument.
+Internally, MIC and disk zone values are sampled based on clinical breakpoints defined in the \link{clinical_breakpoints} data set. To create specific generated values per bug or drug, set the \code{mo} and/or \code{ab} argument. The MICs are sampled on a log2 scale and disks linearly, using weighted probabilities. The weights are based on the \code{skew} and \code{severity} arguments:
+\itemize{
+\item \code{skew = "right"} places more emphasis on lower MIC or higher disk values.
+\item \code{skew = "left"} places more emphasis on higher MIC or lower disk values.
+\item \code{severity} controls the exponential bias applied.
+}
 }
 \examples{
 random_mic(25)
 random_disk(25)
 random_sir(25)
 
+# add more skewedness, make more realistic by setting a bug and/or drug:
+disks <- random_disk(100, severity = 2, mo = "Escherichia coli", ab = "CIP")
+plot(disks)
+# `plot()` and `ggplot2::autoplot()` allow for coloured bars if `mo` and `ab` are set
+plot(disks, mo = "Escherichia coli", ab = "CIP", guideline = "CLSI 2025")
+
 \donttest{
-# make the random generation more realistic by setting a bug and/or drug:
 random_mic(25, "Klebsiella pneumoniae") # range 0.0625-64
 random_mic(25, "Klebsiella pneumoniae", "meropenem") # range 0.0625-16
 random_mic(25, "Streptococcus pneumoniae", "meropenem") # range 0.0625-4

pkgdown/extra.css

@@ -41,7 +41,7 @@
 
   --bs-success: var(--amr-green-dark) !important;
   --bs-light: var(--amr-green-light) !important;
-  /* --bs-light was this: #128f76a6; that's success with 60% alpha */
+  /* --bs-light was this: #128f76a6; that's bs-success with 60% alpha */
   --bs-info: var(--amr-green-middle) !important;
   --bs-link-color: var(--amr-green-dark) !important;
   --bs-link-color-rgb: var(--amr-green-dark-rgb) !important;
@@ -104,6 +104,16 @@ body.amr-for-python * {
 .navbar .algolia-autocomplete .aa-dropdown-menu {
   background-color: var(--amr-green-dark) !important;
 }
+
+.version-main {
+  font-weight: bold;
+  color: var(--bs-navbar-brand-color);
+}
+.version-build {
+  font-weight: normal;
+  opacity: 0.75;
+  font-size: 0.85em;
+}
 input[type="search"] {
   color: var(--bs-tertiary-bg) !important;
   background-color: var(--amr-green-light) !important;
@@ -149,6 +159,7 @@ this shows on top of every sidebar to the right
   margin-top: 10px;
   border: 2px dashed var(--amr-green-dark);
   text-align: center;
+  background: var(--bs-body-bg);
 }
 .amr-gpt-assistant * {
   width: 90%;
@@ -179,6 +190,15 @@ this shows on top of every sidebar to the right
   }
 }
 
+.template-reference-topic h3,
+.template-reference-topic h3 code {
+  color: var(--amr-green-dark) !important;
+}
+.template-reference-topic h3 {
+  font-weight: normal;
+  margin-top: 2rem;
+}
+
 /* replace 'Developers' with 'Maintainers' */
 .developers h2 {
   display: none;

pkgdown/extra.js

@@ -29,10 +29,22 @@
 # ==================================================================== #
 */
 
-$(document).ready(function() {
+$(function () {
   // add GPT assistant info
   $('aside').prepend('<div class="amr-gpt-assistant"><a target="_blank" href="https://chat.amr-for-r.org"><img src="https://amr-for-r.org/AMRforRGPT.svg"></a></div>');
 
+  // split version number in navbar into main version and build number
+  $('.nav-text').each(function () {
+    const $el = $(this);
+    const text = $.trim($el.text());
+    const lastDotIndex = text.lastIndexOf('.');
+    if (lastDotIndex > -1) {
+      const main = text.substring(0, lastDotIndex);
+      const build = text.substring(lastDotIndex);
+      $el.html(`<span class="version-main">${main}</span><span class="version-build">${build}</span>`);
+    }
+  });
+
   // replace 'Developers' with 'Maintainers' on the main page, and "Contributors" on the Authors page
   $(".developers h2").text("Maintainers");
   $(".template-citation-authors h1:nth(0)").text("Contributors and Citation");

tests/testthat/test-data.R

@@ -71,6 +71,7 @@ test_that("test-data.R", {
   if (AMR:::pkg_is_available("tibble")) {
     # there should be no diacritics (i.e. non ASCII) characters in the datasets (CRAN policy)
     datasets <- data(package = "AMR", envir = asNamespace("AMR"))$results[, "Item", drop = TRUE]
+    datasets <- datasets[datasets != "antibiotics"]
     for (i in seq_len(length(datasets))) {
       dataset <- get(datasets[i], envir = asNamespace("AMR"))
       expect_identical(AMR:::dataset_UTF8_to_ASCII(dataset), dataset, info = datasets[i])

tests/testthat/test-mdro.R

@@ -48,7 +48,7 @@ test_that("test-mdro.R", {
   # example_isolates should have these finding using Dutch guidelines
   expect_equal(
     as.double(table(outcome)),
-    c(1977, 23, 0)
+    c(1977, 21, 2)
   )
 
   expect_equal(

tests/testthat/test-zzz.R

@@ -63,10 +63,12 @@ test_that("test-zzz.R", {
     "progress_bar" = "progress",
     "read_html" = "xml2",
     "right_join" = "dplyr",
+    "select" = "dplyr",
     "semi_join" = "dplyr",
     "showQuestion" = "rstudioapi",
     "symbol" = "cli",
     "tibble" = "tibble",
+    "where" = "tidyselect",
     "write.xlsx" = "openxlsx"
   )
 
@@ -127,6 +129,24 @@ test_that("test-zzz.R", {
     "type_sum" = "pillar",
     # readxl
     "read_excel" = "readxl",
+    # recipes
+    "add_step" = "recipes",
+    "bake" = "recipes",
+    "check_new_data" = "recipes",
+    "check_type" = "recipes",
+    "has_role" = "recipes",
+    "is_trained" = "recipes",
+    "prep" = "recipes",
+    "print_step" = "recipes",
+    "rand_id" = "recipes",
+    "recipe" = "recipes",
+    "recipes_eval_select" = "recipes",
+    "sel2char" = "recipes",
+    "step" = "recipes",
+    "step_center" = "recipes",
+    "tidy" = "recipes",
+    # rlang
+    "enquos" = "rlang",
     # rmarkdown
     "html_vignette" = "rmarkdown",
     # skimr

vignettes/AMR_with_tidymodels.Rmd

@@ -28,6 +28,13 @@ Antimicrobial resistance (AMR) is a global health crisis, and understanding resi
 
 In this post, we will explore how to use the `tidymodels` framework to predict resistance patterns in the `example_isolates` dataset in two examples.
 
+This post contains the following examples:
+
+1. Using Antimicrobial Selectors
+2. Predicting ESBL Presence Using Raw MICs
+3. Predicting AMR Over Time
+
+
 ## Example 1: Using Antimicrobial Selectors
 
 By leveraging the power of `tidymodels` and the `AMR` package, we’ll build a reproducible machine learning workflow to predict the Gramstain of the microorganism to two important antibiotic classes: aminoglycosides and beta-lactams.
@@ -208,10 +215,150 @@ This workflow is extensible to other antimicrobial classes and resistance patter
 
 ---
 
+## Example 2: Predicting ESBL Presence Using Raw MICs
 
-## Example 2: Predicting AMR Over Time
+In this second example, we demonstrate how to use `<mic>` columns directly in `tidymodels` workflows using AMR-specific recipe steps. This includes a transformation to `log2` scale using `step_mic_log2()`, which prepares MIC values for use in classification models.
 
-In this second example, we aim to predict antimicrobial resistance (AMR) trends over time using `tidymodels`. We will model resistance to three antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and ciprofloxacin `CIP`), based on historical data grouped by year and hospital ward.
+This approach and idea formed the basis for the publication [DOI: 10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703) to model the presence of extended-spectrum beta-lactamases (ESBL).
+
+### **Objective**
+
+Our goal is to:
+
+1. Use raw MIC values to predict whether a bacterial isolate produces ESBL.
+2. Apply AMR-aware preprocessing in a `tidymodels` recipe.
+3. Train a classification model and evaluate its predictive performance.
+
+### **Data Preparation**
+
+We use the `esbl_isolates` dataset that comes with the AMR package.
+
+```{r}
+# Load required libraries
+library(AMR)
+library(tidymodels)
+
+# View the esbl_isolates data set
+esbl_isolates
+
+# Prepare a binary outcome and convert to ordered factor
+data <- esbl_isolates %>%
+  mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+```
+
+**Explanation:**
+
+- `esbl_isolates`: Contains MIC test results and ESBL status for each isolate.
+- `mutate(esbl = ...)`: Converts the target column to an ordered factor for classification.
+
+### **Defining the Workflow**
+
+#### 1. Preprocessing with a Recipe
+
+We use our `step_mic_log2()` function to log2-transform MIC values, ensuring that MICs are numeric and properly scaled. All MIC predictors can easily and agnostically selected using the new `all_mic_predictors()`:
+
+```{r}
+# Split into training and testing sets
+set.seed(123)
+split <- initial_split(data)
+training_data <- training(split)
+testing_data <- testing(split)
+
+# Define the recipe
+mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
+  remove_role(genus, old_role = "predictor") %>%  # Remove non-informative variable
+  step_mic_log2(all_mic_predictors()) #%>%         # Log2 transform all MIC predictors
+ # prep()
+
+mic_recipe
+```
+
+**Explanation:**
+
+- `remove_role()`: Removes irrelevant variables like genus.
+- `step_mic_log2()`: Applies `log2(as.numeric(...))` to all MIC predictors in one go.
+- `prep()`: Finalises the recipe based on training data.
+
+#### 2. Specifying the Model
+
+We use a simple logistic regression to model ESBL presence, though recent models such as xgboost ([link to `parsnip` manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html)) could be much more precise.
+
+```{r}
+# Define the model
+model <- logistic_reg(mode = "classification") %>%
+  set_engine("glm")
+
+model
+```
+
+**Explanation:**
+
+- `logistic_reg()`: Specifies a binary classification model.
+- `set_engine("glm")`: Uses the base R GLM engine.
+
+#### 3. Building the Workflow
+
+```{r}
+# Create workflow
+workflow_model <- workflow() %>%
+  add_recipe(mic_recipe) %>%
+  add_model(model)
+
+workflow_model
+```
+
+### **Training and Evaluating the Model**
+
+```{r}
+# Fit the model
+fitted <- fit(workflow_model, training_data)
+
+# Generate predictions
+predictions <- predict(fitted, testing_data) %>%
+  bind_cols(testing_data)
+
+# Evaluate model performance
+our_metrics <- metric_set(accuracy, kap, ppv, npv)
+metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+
+metrics
+```
+
+**Explanation:**
+
+- `fit()`: Trains the model on the processed training data.
+- `predict()`: Produces predictions for unseen test data.
+- `metric_set()`: Allows evaluating multiple classification metrics.
+
+It appears we can predict ESBL gene presence with a positive predictive value (PPV) of `r round(metrics$.estimate[3], 3) * 100`% and a negative predictive value (NPV) of `r round(metrics$.estimate[4], 3) * 100` using a simplistic logistic regression model.
+
+### **Visualising Predictions**
+
+We can visualise predictions by comparing predicted and actual ESBL status.
+
+```{r}
+library(ggplot2)
+
+ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
+  geom_bar(position = "stack") +
+  labs(title = "Predicted vs Actual ESBL Status",
+       x = "Actual ESBL",
+       y = "Count") +
+  theme_minimal()
+```
+
+### **Conclusion**
+
+In this example, we showcased how the new `AMR`-specific recipe steps simplify working with `<mic>` columns in `tidymodels`. The `step_mic_log2()` transformation converts ordered MICs to log2-transformed numerics, improving compatibility with classification models.
+
+This pipeline enables realistic, reproducible, and interpretable modelling of antimicrobial resistance data.
+
+---
+
+
+## Example 3: Predicting AMR Over Time
+
+In this third example, we aim to predict antimicrobial resistance (AMR) trends over time using `tidymodels`. We will model resistance to three antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and ciprofloxacin `CIP`), based on historical data grouped by year and hospital ward.
 
 ### **Objective**
 

vignettes/welcome_to_AMR.Rmd

@@ -28,7 +28,7 @@ Note: to keep the package size as small as possible, we only include this vignet
 
 The `AMR` package is a peer-reviewed, [free and open-source](https://amr-for-r.org/#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. We are a team of [many different researchers](https://amr-for-r.org/authors.html) from around the globe to make this a successful and durable project!
 
-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}).
+This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
 
 After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](https://amr-for-r.org/reference/microorganisms.html) (updated June 2024) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](https://amr-for-r.org/reference/antimicrobials.html) 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 `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` 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). **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 [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).
 
@@ -36,7 +36,7 @@ The `AMR` package is available in `r AMR:::vector_and(vapply(FUN.VALUE = charact
 
 This package was intended as a comprehensive toolbox for integrated AMR data analysis. This package can be used for:
 
-  * Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](https://amr-for-r.org/reference/mo_property.html))
+  * Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature ([LPSN](https://lpsn.dsmz.de)) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](https://amr-for-r.org/reference/mo_property.html))
   * Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline ([manual](https://amr-for-r.org/reference/as.sir.html))
   * Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records ([manual](https://amr-for-r.org/reference/ab_from_text.html))
   * Determining first isolates to be used for AMR data analysis ([manual](https://amr-for-r.org/reference/first_isolate.html))