(v2.1.1.9163) cleanup

2025-07-09 14:21:51 +02:00 · 2025-02-27 14:04:29 +01:00
parent 68efddab3d
commit 07efc292bc
73 changed files with 2187 additions and 1715 deletions
--- a/.github/workflows/lintr.yaml
+++ b/.github/workflows/lintr.yaml
@ -56,9 +56,15 @@ jobs:
          extra-packages: |
            any::lintr
            any::cyclocomp
            any::roxygen2
            any::devtools
            any::usethis
      - name: Lint
        run: |
          # no not check these folders
          rm -rf data-raw
          rm -rf tests
          # old: lintr::lint_package(linters = lintr::with_defaults(line_length_linter = NULL, trailing_whitespace_linter = NULL, object_name_linter = NULL, cyclocomp_linter = NULL, object_length_linter = lintr::object_length_linter(length = 50L)), exclusions = list("R/aa_helper_pm_functions.R"))
          # now get ALL linters, not just default ones
          linters <- getNamespaceExports(asNamespace("lintr"))
@ -67,7 +73,7 @@ jobs:
          linters <- linters[!grepl("^(closed_curly|open_curly|paren_brace|semicolon_terminator|consecutive_stopifnot|no_tab|single_quotes|unnecessary_nested_if|unneeded_concatenation)_linter$", linters)]
          linters <- linters[linters != "linter"]
          # and the ones we find unnnecessary
-          linters <- linters[!grepl("^(extraction_operator|implicit_integer|line_length|object_name|nonportable_path|is)_linter$", linters)]
+          linters <- linters[!grepl("^(extraction_operator|implicit_integer|line_length|object_length|object_name|object_usage|nonportable_path|is)_linter$", linters)]
          # put the functions in a list
          linters_list <- lapply(linters, function(l) eval(parse(text = paste0("lintr::", l, "()")), envir = asNamespace("lintr")))
          names(linters_list) <- linters
--- a/4
+++ b/4
@ -1,6 +1,6 @@
 Package: AMR
-Version: 2.1.1.9160
+Version: 2.1.1.9163
-Date: 2025-02-26
+Date: 2025-02-27
 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
--- a/NEWS.md
+++ b/NEWS.md
@ -1,4 +1,4 @@
-# AMR 2.1.1.9160
+# AMR 2.1.1.9163
 *(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://msberends.github.io/AMR/#latest-development-version).)*
@ -62,6 +62,7 @@ This package now supports not only tools for AMR data analysis in clinical setti
  * Added Amorolfine (`AMO`, D01AE16), which is now also part of the `antifungals()` selector
  * Added Efflux (`EFF`), to allow mapping to AMRFinderPlus
  * Added Tigemonam (`TNM`), a monobactam
  * Added over 1,500 trade names
 * MICs
  * Added as valid levels: 4096, 6 powers of 0.0625, and 5 powers of 192 (192, 384, 576, 768, 960)
  * Fixed a bug in `as.mic()` that failed translation of scientifically formatted numbers
@ -76,12 +77,11 @@ This package now supports not only tools for AMR data analysis in clinical setti
 * `mo_info()` now contains an extra element `rank` and `group_members` (with the contents of the new `mo_group_members()` function)
 * Updated all ATC codes from WHOCC
 * Updated all antibiotic DDDs from WHOCC
 * Added over 1,500 trade names for antibiotics
 * Fix for using a manual value for `mo_transform` in `antibiogram()`
 * Fixed a bug for when `antibiogram()` returns an empty data set
 * Fix for mapping 'high level' antibiotics in `as.ab()` (amphotericin B-high, gentamicin-high, kanamycin-high, streptomycin-high, tobramycin-high)
 * Improved overall algorithm of `as.ab()` for better performance and accuracy, including the new function `as_reset_session()` to remove earlier coercions.
-* Improved overall algorithm of `as.mo()` for better performance and accuracy. Specifically:
+* Improved overall algorithm of `as.mo()` for better performance and accuracy, specifically:
  * More weight is given to genus and species combinations in cases where the subspecies is miswritten, so that the result will be the correct genus and species
  * Genera from the World Health Organization's (WHO) Priority Pathogen List now have the highest prevalence
 * Fixed a bug for `sir_confidence_interval()` when there are no isolates available
--- a/PythonPackage/AMR/AMR.egg-info/PKG-INFO
+++ b/PythonPackage/AMR/AMR.egg-info/PKG-INFO
@ -1,6 +1,6 @@
 Metadata-Version: 2.2
 Name: AMR
-Version: 2.1.1.9160
+Version: 2.1.1.9163
 Summary: A Python wrapper for the AMR R package
 Home-page: https://github.com/msberends/AMR
 Author: Matthijs Berends
--- a/PythonPackage/AMR/AMR/init.py
+++ b/PythonPackage/AMR/AMR/init.py
@ -28,8 +28,6 @@ from .functions import age_groups
 from .functions import antibiogram
 from .functions import wisca
 from .functions import retrieve_wisca_parameters
 from .functions import amr_class
 from .functions import amr_selector
 from .functions import aminoglycosides
 from .functions import aminopenicillins
 from .functions import antifungals
@ -61,6 +59,8 @@ from .functions import streptogramins
 from .functions import tetracyclines
 from .functions import trimethoprims
 from .functions import ureidopenicillins
 from .functions import amr_class
 from .functions import amr_selector
 from .functions import administrable_per_os
 from .functions import administrable_iv
 from .functions import not_intrinsic_resistant
--- a/PythonPackage/AMR/AMR/functions.py
+++ b/PythonPackage/AMR/AMR/functions.py
@ -114,12 +114,6 @@ def wisca(x, *args, **kwargs):
 def retrieve_wisca_parameters(wisca_model, *args, **kwargs):
    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
    return convert_to_python(amr_r.retrieve_wisca_parameters(wisca_model, *args, **kwargs))
 def amr_class(amr_class, *args, **kwargs):
    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
    return convert_to_python(amr_r.amr_class(amr_class, *args, **kwargs))
 def amr_selector(filter, *args, **kwargs):
    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
    return convert_to_python(amr_r.amr_selector(filter, *args, **kwargs))
 def aminoglycosides(only_sir_columns = False, *args, **kwargs):
    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
    return convert_to_python(amr_r.aminoglycosides(only_sir_columns = False, *args, **kwargs))
@ -213,6 +207,12 @@ def trimethoprims(only_sir_columns = False, *args, **kwargs):
 def ureidopenicillins(only_sir_columns = False, *args, **kwargs):
    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
    return convert_to_python(amr_r.ureidopenicillins(only_sir_columns = False, *args, **kwargs))
 def amr_class(amr_class, *args, **kwargs):
    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
    return convert_to_python(amr_r.amr_class(amr_class, *args, **kwargs))
 def amr_selector(filter, *args, **kwargs):
    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
    return convert_to_python(amr_r.amr_selector(filter, *args, **kwargs))
 def administrable_per_os(only_sir_columns = False, *args, **kwargs):
    """See our website of the R package for the manual: https://msberends.github.io/AMR/index.html"""
    return convert_to_python(amr_r.administrable_per_os(only_sir_columns = False, *args, **kwargs))
--- a/PythonPackage/AMR/dist/amr-2.1.1.9160-py3-none-any.whl
+++ b/PythonPackage/AMR/dist/amr-2.1.1.9160-py3-none-any.whl
--- a/PythonPackage/AMR/dist/amr-2.1.1.9160.tar.gz
+++ b/PythonPackage/AMR/dist/amr-2.1.1.9160.tar.gz
--- a/PythonPackage/AMR/dist/amr-2.1.1.9163-py3-none-any.whl
+++ b/PythonPackage/AMR/dist/amr-2.1.1.9163-py3-none-any.whl
--- a/PythonPackage/AMR/dist/amr-2.1.1.9163.tar.gz
+++ b/PythonPackage/AMR/dist/amr-2.1.1.9163.tar.gz
--- a/PythonPackage/AMR/setup.py
+++ b/PythonPackage/AMR/setup.py
@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 setup(
    name='AMR',
-    version='2.1.1.9160',
+    version='2.1.1.9163',
    packages=find_packages(),
    install_requires=[
        'rpy2',
--- a/R/aa_helper_functions.R
+++ b/R/aa_helper_functions.R
@ -512,21 +512,31 @@ word_wrap <- function(...,
  # format backticks
  if (pkg_is_available("cli") &&
    tryCatch(isTRUE(getExportedValue("ansi_has_hyperlink_support", ns = asNamespace("cli"))()), error = function(e) FALSE) &&
-      tryCatch(getExportedValue("isAvailable", ns = asNamespace("rstudioapi"))(), error = function(e) return(FALSE)) && 
+    tryCatch(getExportedValue("isAvailable", ns = asNamespace("rstudioapi"))(), error = function(e) {
-      tryCatch(getExportedValue("versionInfo", ns = asNamespace("rstudioapi"))()$version > "2023.6.0.0", error = function(e) return(FALSE))) {
+      return(FALSE)
    }) &&
    tryCatch(getExportedValue("versionInfo", ns = asNamespace("rstudioapi"))()$version > "2023.6.0.0", error = function(e) {
      return(FALSE)
    })) {
    # we are in a recent version of RStudio, so do something nice: add links to our help pages in the console.
    parts <- strsplit(msg, "`", fixed = TRUE)[[1]]
    cmds <- parts %in% paste0(ls(envir = asNamespace("AMR")), "()")
    # functions with a dot are not allowed: https://github.com/rstudio/rstudio/issues/11273#issuecomment-1156193252
    # lead them to the help page of our package
-    parts[cmds & parts %like% "[.]"] <- font_url(url = paste0("ide:help:AMR::", gsub("()", "", parts[cmds & parts %like% "[.]"], fixed = TRUE)),
+    parts[cmds & parts %like% "[.]"] <- font_url(
-                                                 txt = parts[cmds & parts %like% "[.]"])
+      url = paste0("ide:help:AMR::", gsub("()", "", parts[cmds & parts %like% "[.]"], fixed = TRUE)),
      txt = parts[cmds & parts %like% "[.]"]
    )
    # otherwise, give a 'click to run' popup
-    parts[cmds & parts %unlike% "[.]"] <- font_url(url = paste0("ide:run:AMR::", parts[cmds & parts %unlike% "[.]"]),
+    parts[cmds & parts %unlike% "[.]"] <- font_url(
-                                                   txt = parts[cmds & parts %unlike% "[.]"])
+      url = paste0("ide:run:AMR::", parts[cmds & parts %unlike% "[.]"]),
      txt = parts[cmds & parts %unlike% "[.]"]
    )
    # text starting with `?` must also lead to the help page
-    parts[parts %like% "^[?]"] <- font_url(url = paste0("ide:help:AMR::", gsub("()", "", gsub("^[?]", "", parts[parts %like% "^[?]"]), fixed = TRUE)),
+    parts[parts %like% "^[?]"] <- font_url(
-                                           txt = parts[parts %like% "^[?]"])
+      url = paste0("ide:help:AMR::", gsub("()", "", gsub("^[?]", "", parts[parts %like% "^[?]"]), fixed = TRUE)),
      txt = parts[parts %like% "^[?]"]
    )
    msg <- paste0(parts, collapse = "`")
  }
  msg <- gsub("`(.+?)`", font_grey_bg("\\1"), msg)
@ -965,9 +975,11 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
 ascertain_sir_classes <- function(x, obj_name) {
  sirs <- vapply(FUN.VALUE = logical(1), x, is.sir)
  if (!any(sirs, na.rm = TRUE)) {
-    warning_("the data provided in argument `", obj_name,
+    warning_(
      "the data provided in argument `", obj_name,
      "` should contain at least one column of class 'sir'. Eligible SIR column were now guessed. ",
-             "See `?as.sir`.")
+      "See `?as.sir`."
    )
    sirs_eligible <- is_sir_eligible(x)
    for (col in colnames(x)[sirs_eligible]) {
      x[[col]] <- as.sir(x[[col]])
@ -1322,8 +1334,10 @@ progress_ticker <- function(n = 1, n_min = 0, print = TRUE, clear = TRUE, title
      # a close()-method was also added, see below for that
      pb <- progress_bar$new(
        show_after = 0,
-        format = paste0(title,
+        format = paste0(
-                        ifelse(only_bar_percent == TRUE, "[:bar] :percent", "[:bar] :percent (:current/:total,:eta)")),
+          title,
          ifelse(only_bar_percent == TRUE, "[:bar] :percent", "[:bar] :percent (:current/:total,:eta)")
        ),
        clear = clear,
        total = n
      )
--- a/R/ab.R
+++ b/R/ab.R
@ -327,7 +327,6 @@ as.ab <- function(x, flag_multiple_results = TRUE, language = get_AMR_locale(),
    # More uncertain results ----
    if (fast_mode == FALSE) {
      ab_df <- AMR_env$AB_lookup
      ab_df$length_name <- nchar(ab_df$generalised_name)
      # now retrieve Levensthein distance for name, synonyms, and translated names
@ -335,23 +334,32 @@ as.ab <- function(x, flag_multiple_results = TRUE, language = get_AMR_locale(),
        ignore.case = FALSE,
        fixed = TRUE,
        costs = c(insertions = 1, deletions = 1, substitutions = 2),
-                                               counts = FALSE))
+        counts = FALSE
-      ab_df$lev_syn <- vapply(FUN.VALUE = double(1),
+      ))
      ab_df$lev_syn <- vapply(
        FUN.VALUE = double(1),
        ab_df$generalised_synonyms,
-                              function(y) ifelse(length(y[nchar(y) >= 5]) == 0,
+        function(y) {
          ifelse(length(y[nchar(y) >= 5]) == 0,
            999,
-                                                 min(as.double(utils::adist(x[i], y[nchar(y) >= 5],  ignore.case = FALSE,
+            min(as.double(utils::adist(x[i], y[nchar(y) >= 5],
              ignore.case = FALSE,
              fixed = TRUE,
              costs = c(insertions = 1, deletions = 1, substitutions = 2),
-                                                                            counts = FALSE)), na.rm = TRUE)),
+              counts = FALSE
-                              USE.NAMES = FALSE)
+            )), na.rm = TRUE)
          )
        },
        USE.NAMES = FALSE
      )
      if (!is.null(language) && language != "en") {
        ab_df$trans <- generalise_antibiotic_name(translate_AMR(ab_df$name, language = language))
        ab_df$lev_trans <- as.double(utils::adist(x[i], ab_df$trans,
          ignore.case = FALSE,
          fixed = TRUE,
          costs = c(insertions = 1, deletions = 1, substitutions = 2),
-                                                  counts = FALSE))
+          counts = FALSE
        ))
      } else {
        ab_df$lev_trans <- ab_df$lev_name
      }
@ -431,13 +439,17 @@ as.ab <- function(x, flag_multiple_results = TRUE, language = get_AMR_locale(),
          paste0(
            '"', x_uncertain, '" (assumed ',
            ab_name(AMR_env$ab_previously_coerced$ab[which(AMR_env$ab_previously_coerced$x_bak %in% x_uncertain)], language = NULL, tolower = TRUE),
-            ", ", AMR_env$ab_previously_coerced$ab[which(AMR_env$ab_previously_coerced$x_bak %in% x_uncertain)], ")"),
+            ", ", AMR_env$ab_previously_coerced$ab[which(AMR_env$ab_previously_coerced$x_bak %in% x_uncertain)], ")"
-          quotes = FALSE)
+          ),
          quotes = FALSE
        )
      } else {
        examples <- paste0(nr2char(length(x_uncertain)), " antimicrobials")
      }
-      message_("Antimicrobial translation was uncertain for ", examples,
+      message_(
-               ". If required, use `add_custom_antimicrobials()` to add custom entries.")
+        "Antimicrobial translation was uncertain for ", examples,
        ". If required, use `add_custom_antimicrobials()` to add custom entries."
      )
    }
  }
@ -476,8 +488,10 @@ pillar_shaft.ab <- function(x, ...) {
  # add the names to the drugs as mouse-over!
  if (tryCatch(isTRUE(getExportedValue("ansi_has_hyperlink_support", ns = asNamespace("cli"))()), error = function(e) FALSE)) {
-    out[!is.na(x)] <- font_url(url = paste0(x[!is.na(x)], ": ", ab_name(x[!is.na(x)])),
+    out[!is.na(x)] <- font_url(
-                               txt = out[!is.na(x)])
+      url = paste0(x[!is.na(x)], ": ", ab_name(x[!is.na(x)])),
      txt = out[!is.na(x)]
    )
  }
  create_pillar_column(out, align = "left", min_width = 4)
@ -494,12 +508,14 @@ type_sum.ab <- function(x, ...) {
 print.ab <- function(x, ...) {
  if (!is.null(attributes(x)$amr_selector)) {
    function_name <- attributes(x)$amr_selector
-    message_("This 'ab' vector was retrieved using `" , function_name, "()`, which should normally be used inside a `dplyr` verb or `data.frame` call, e.g.:\n",
+    message_(
      "This 'ab' vector was retrieved using `", function_name, "()`, which should normally be used inside a `dplyr` verb or `data.frame` call, e.g.:\n",
      "  ", AMR_env$bullet_icon, " your_data %>% select(", function_name, "())\n",
      "  ", AMR_env$bullet_icon, " your_data %>% select(column_a, column_b, ", function_name, "())\n",
      "  ", AMR_env$bullet_icon, " your_data %>% filter(any(", function_name, "() == \"R\"))\n",
      "  ", AMR_env$bullet_icon, " your_data[, ", function_name, "()]\n",
-             "  ", AMR_env$bullet_icon, " your_data[, c(\"column_a\", \"column_b\", ", function_name, "())]")
+      "  ", AMR_env$bullet_icon, " your_data[, c(\"column_a\", \"column_b\", ", function_name, "())]"
    )
  }
  cat("Class 'ab'\n")
  print(as.character(x), quote = FALSE)
--- a/R/amr_selectors.R
+++ b/R/amr_selectors.R
@ -231,57 +231,6 @@
 #'   dt[any(carbapenems() == "S"), penicillins(), with = FALSE]
 #' }
 #' }
 amr_class <- function(amr_class,
                      only_sir_columns = FALSE,
                      only_treatable = TRUE,
                      return_all = TRUE,
                      ...) {
  meet_criteria(amr_class, allow_class = "character", has_length = 1, allow_NULL = TRUE)
  meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
  meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
  meet_criteria(return_all, allow_class = "logical", has_length = 1)
  amr_select_exec(NULL, only_sir_columns = only_sir_columns, amr_class_args = amr_class, only_treatable = only_treatable, return_all = return_all)
 }
 #' @rdname antimicrobial_selectors
 #' @details The [amr_selector()] function can be used to internally filter the [antibiotics] data set on any results, see *Examples*. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
 #' @export
 amr_selector <- function(filter,
                         only_sir_columns = FALSE,
                         only_treatable = TRUE,
                         return_all = TRUE,
                         ...) {
  meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
  meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
  meet_criteria(return_all, allow_class = "logical", has_length = 1)
  # get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
  # but it only takes a couple of milliseconds
  vars_df <- get_current_data(arg_name = NA, call = -2)
  # to improve speed, get_column_abx() will only run once when e.g. in a select or group call
  ab_in_data <- get_column_abx(vars_df,
                               info = FALSE, only_sir_columns = only_sir_columns,
                               sort = FALSE, fn = "amr_selector", return_all = return_all
  )
  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)
  )
  agents <- ab_in_data[ab_in_data %in% agents]
  message_agent_names(
    function_name = "amr_selector",
    agents = agents,
    ab_group = NULL,
    examples = "",
    call = call
  )
  structure(unname(agents),
            class = c("amr_selector", "character")
  )
 }
 #' @rdname antimicrobial_selectors
 #' @export
 aminoglycosides <- function(only_sir_columns = FALSE, only_treatable = TRUE, return_all = TRUE, ...) {
  meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
  meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
@ -536,6 +485,57 @@ ureidopenicillins <- function(only_sir_columns = FALSE, return_all = TRUE, ...)
 #' @rdname antimicrobial_selectors
 #' @details The [administrable_per_os()] and [administrable_iv()] functions also rely on the [antibiotics] data set - antimicrobials will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in the [antibiotics] data set.
 #' @export
 amr_class <- function(amr_class,
                      only_sir_columns = FALSE,
                      only_treatable = TRUE,
                      return_all = TRUE,
                      ...) {
  meet_criteria(amr_class, allow_class = "character", has_length = 1, allow_NULL = TRUE)
  meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
  meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
  meet_criteria(return_all, allow_class = "logical", has_length = 1)
  amr_select_exec(NULL, only_sir_columns = only_sir_columns, amr_class_args = amr_class, only_treatable = only_treatable, return_all = return_all)
 }
 #' @rdname antimicrobial_selectors
 #' @details The [amr_selector()] function can be used to internally filter the [antibiotics] data set on any results, see *Examples*. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
 #' @export
 amr_selector <- function(filter,
                         only_sir_columns = FALSE,
                         only_treatable = TRUE,
                         return_all = TRUE,
                         ...) {
  meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
  meet_criteria(only_treatable, allow_class = "logical", has_length = 1)
  meet_criteria(return_all, allow_class = "logical", has_length = 1)
  # get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
  # but it only takes a couple of milliseconds
  vars_df <- get_current_data(arg_name = NA, call = -2)
  # to improve speed, get_column_abx() will only run once when e.g. in a select or group call
  ab_in_data <- get_column_abx(vars_df,
    info = FALSE, only_sir_columns = only_sir_columns,
    sort = FALSE, fn = "amr_selector", return_all = return_all
  )
  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)
  )
  agents <- ab_in_data[ab_in_data %in% agents]
  message_agent_names(
    function_name = "amr_selector",
    agents = agents,
    ab_group = NULL,
    examples = "",
    call = call
  )
  structure(unname(agents),
    class = c("amr_selector", "character")
  )
 }
 #' @rdname antimicrobial_selectors
 #' @export
 administrable_per_os <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
  meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
  meet_criteria(return_all, allow_class = "logical", has_length = 1)
@ -671,7 +671,8 @@ amr_select_exec <- function(function_name,
      only_sir_columns = only_sir_columns,
      sort = FALSE,
      fn = function_name,
-                                 return_all = return_all)
+      return_all = return_all
    )
  }
  # untreatable drugs
@ -772,7 +773,8 @@ amr_select_exec <- function(function_name,
 #' @noRd
 print.amr_selector <- function(x, ...) {
  warning_("It should never be needed to print an antimicrobial selector class. Are you using data.table? Then add the argument `with = FALSE`, see our examples at `?amr_selector`.",
-           immediate = TRUE)
+    immediate = TRUE
  )
  cat("Class 'amr_selector'\n")
  print(as.character(x), quote = FALSE)
 }
--- a/R/antibiogram.R
+++ b/R/antibiogram.R
@ -307,12 +307,14 @@
 #' antibiogram(example_isolates,
 #'   antibiotics = aminoglycosides(),
 #'   ab_transform = "atc",
-#'   mo_transform = "gramstain")
+#'   mo_transform = "gramstain"
 #' )
 #'
 #' antibiogram(example_isolates,
 #'   antibiotics = carbapenems(),
 #'   ab_transform = "name",
-#'   mo_transform = "name")
+#'   mo_transform = "name"
 #' )
 #'
 #'
 #' # Combined antibiogram -------------------------------------------------
@ -320,14 +322,16 @@
 #' # combined antibiotics yield higher empiric coverage
 #' antibiogram(example_isolates,
 #'   antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-#'   mo_transform = "gramstain")
+#'   mo_transform = "gramstain"
 #' )
 #'
 #' # names of antibiotics do not need to resemble columns exactly:
 #' antibiogram(example_isolates,
 #'   antibiotics = c("Cipro", "cipro + genta"),
 #'   mo_transform = "gramstain",
 #'   ab_transform = "name",
-#'   sep = " & ")
+#'   sep = " & "
 #' )
 #'
 #'
 #' # Syndromic antibiogram ------------------------------------------------
@ -335,7 +339,8 @@
 #' # the data set could contain a filter for e.g. respiratory specimens
 #' antibiogram(example_isolates,
 #'   antibiotics = c(aminoglycosides(), carbapenems()),
-#'   syndromic_group = "ward")
+#'   syndromic_group = "ward"
 #' )
 #'
 #' # now define a data set with only E. coli
 #' ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@ -348,7 +353,8 @@
 #'   syndromic_group = ifelse(ex1$ward == "ICU",
 #'     "UCI", "No UCI"
 #'   ),
-#'   language = "es")
+#'   language = "es"
 #' )
 #'
 #'
 #' # WISCA antibiogram ----------------------------------------------------
@ -357,7 +363,8 @@
 #' antibiogram(example_isolates,
 #'   antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
 #'   syndromic_group = "ward",
-#'   wisca = TRUE)
+#'   wisca = TRUE
 #' )
 #'
 #'
 #' # Print the output for R Markdown / Quarto -----------------------------
@ -365,7 +372,8 @@
 #' ureido <- antibiogram(example_isolates,
 #'   antibiotics = ureidopenicillins(),
 #'   syndromic_group = "ward",
-#'   wisca = TRUE)
+#'   wisca = TRUE
 #' )
 #'
 #' # in an Rmd file, you would just need to return `ureido` in a chunk,
 #' # but to be explicit here:
@ -378,11 +386,13 @@
 #'
 #' ab1 <- antibiogram(example_isolates,
 #'   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-#'   mo_transform = "gramstain")
+#'   mo_transform = "gramstain"
 #' )
 #' ab2 <- antibiogram(example_isolates,
 #'   antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
 #'   mo_transform = "gramstain",
-#'   syndromic_group = "ward")
+#'   syndromic_group = "ward"
 #' )
 #'
 #' if (requireNamespace("ggplot2")) {
 #'   ggplot2::autoplot(ab1)
@ -639,12 +649,14 @@ antibiogram.default <- function(x,
  }
  long_numeric <- out %pm>%
-    pm_summarise(coverage = p_susceptible,
+    pm_summarise(
      coverage = p_susceptible,
      lower_ci = lower_ci,
      upper_ci = upper_ci,
      n_total = n_total,
      n_tested = n_tested,
-                 n_susceptible = n_susceptible)
+      n_susceptible = n_susceptible
    )
  wisca_parameters <- data.frame()
@ -660,12 +672,14 @@ antibiogram.default <- function(x,
        pm_group_by(ab)
    }
    out_wisca <- out_wisca %pm>%
-      pm_summarise(coverage = NA_real_,
+      pm_summarise(
        coverage = NA_real_,
        lower_ci = NA_real_,
        upper_ci = NA_real_,
        n_total = sum(n_total, na.rm = TRUE),
        n_tested = sum(n_tested, na.rm = TRUE),
-                   n_susceptible = sum(n_susceptible, na.rm = TRUE))
+        n_susceptible = sum(n_susceptible, na.rm = TRUE)
      )
    out_wisca$p_susceptible <- out_wisca$n_susceptible / out_wisca$n_tested
    if (isTRUE(has_syndromic_group)) {
@ -688,17 +702,19 @@ antibiogram.default <- function(x,
      out_current <- out[i, , drop = FALSE]
      priors <- calculate_priors(out_current, combine_SI = combine_SI)
-      out$gamma_posterior[i] = priors$gamma_posterior
+      out$gamma_posterior[i] <- priors$gamma_posterior
-      out$beta_posterior1[i] = priors$beta_posterior_1
+      out$beta_posterior1[i] <- priors$beta_posterior_1
-      out$beta_posterior2[i] = priors$beta_posterior_2
+      out$beta_posterior2[i] <- priors$beta_posterior_2
    }
    wisca_parameters <- out
-    progress <- progress_ticker(n = length(unique(wisca_parameters$group)) * simulations,
+    progress <- progress_ticker(
      n = length(unique(wisca_parameters$group)) * simulations,
      n_min = 25,
      print = info,
-                                title = paste("Calculating WISCA for", length(unique(wisca_parameters$group)), "regimens"))
+      title = paste("Calculating WISCA for", length(unique(wisca_parameters$group)), "regimens")
    )
    on.exit(close(progress))
    # run WISCA
@ -837,7 +853,7 @@ antibiogram.default <- function(x,
  long_to_wide <- function(object) {
    if (wisca == TRUE) {
      # column `mo` has already been removed, but we create here a surrogate to make the stats::reshape() work since it needs an identifier
-      object$mo <- 1 #seq_len(NROW(object))
+      object$mo <- 1 # seq_len(NROW(object))
    }
    object <- object %pm>%
      # an unclassed data.frame is required for stats::reshape()
@ -930,7 +946,8 @@ antibiogram.default <- function(x,
    conf_interval = conf_interval,
    formatting_type = formatting_type,
    wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
-                   long_numeric = as_original_data_class(long_numeric, class(x)))
+    long_numeric = as_original_data_class(long_numeric, class(x))
  )
  rownames(out) <- NULL
  out
 }
@ -960,10 +977,12 @@ antibiogram.grouped_df <- function(x,
  stop_ifnot(is.null(syndromic_group), "`syndromic_group` must not be set if creating an antibiogram using a grouped tibble. The groups will become the variables over which the antimicrobials are calculated, making `syndromic_groups` redundant.", call = FALSE)
  groups <- attributes(x)$groups
  n_groups <- NROW(groups)
-  progress <- progress_ticker(n = n_groups,
+  progress <- progress_ticker(
    n = n_groups,
    n_min = 5,
    print = info,
-                              title = paste("Calculating AMR for", n_groups, "groups"))
+    title = paste("Calculating AMR for", n_groups, "groups")
  )
  on.exit(close(progress))
  out <- NULL
@ -994,7 +1013,8 @@ antibiogram.grouped_df <- function(x,
      simulations = simulations,
      conf_interval = conf_interval,
      interval_side = interval_side,
-                           info = FALSE)
+      info = FALSE
    )
    new_wisca_parameters <- attributes(new_out)$wisca_parameters
    new_long_numeric <- attributes(new_out)$long_numeric
@ -1045,7 +1065,8 @@ antibiogram.grouped_df <- function(x,
    conf_interval = conf_interval,
    formatting_type = formatting_type,
    wisca_parameters = as_original_data_class(wisca_parameters, class(x)),
-                   long_numeric = as_original_data_class(long_numeric, class(x)))
+    long_numeric = as_original_data_class(long_numeric, class(x))
  )
  rownames(out) <- NULL
  out
 }
@ -1069,7 +1090,8 @@ wisca <- function(x,
                  conf_interval = 0.95,
                  interval_side = "two-tailed",
                  info = interactive()) {
-  antibiogram(x = x,
+  antibiogram(
    x = x,
    antibiotics = antibiotics,
    ab_transform = ab_transform,
    mo_transform = NULL,
@ -1087,7 +1109,8 @@ wisca <- function(x,
    simulations = simulations,
    conf_interval = conf_interval,
    interval_side = interval_side,
-              info = info)
+    info = info
  )
 }
 #' @export
@ -1137,9 +1160,11 @@ tbl_format_footer.antibiogram <- function(x, ...) {
  if (NROW(x) == 0) {
    return(footer)
  }
-  c(footer, font_subtle(paste0("# Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,\n",
+  c(footer, font_subtle(paste0(
    "# Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,\n",
    "# or use it directly in R Markdown or ",
-                               font_url("https://quarto.org", "Quarto"), ", see ", word_wrap("?antibiogram"))))
+    font_url("https://quarto.org", "Quarto"), ", see ", word_wrap("?antibiogram")
  )))
 }
 #' @export
@ -1148,7 +1173,8 @@ plot.antibiogram <- function(x, ...) {
  df <- attributes(x)$long_numeric
  if (!"mo" %in% colnames(df)) {
    stop_("Plotting antibiograms using `plot()` is only possible if they were not created using dplyr groups. See `?antibiogram` for how to retrieve numeric values in a long format for advanced plotting.",
-          call = FALSE)
+      call = FALSE
    )
  }
  if ("syndromic_group" %in% colnames(df)) {
    # barplot in base R does not support facets - paste columns together
@ -1203,7 +1229,8 @@ autoplot.antibiogram <- function(object, ...) {
  df <- attributes(object)$long_numeric
  if (!"mo" %in% colnames(df)) {
    stop_("Plotting antibiograms using `autoplot()` is only possible if they were not created using dplyr groups. See `?antibiogram` for how to retrieve numeric values in a long format for advanced plotting.",
-          call = FALSE)
+      call = FALSE
    )
  }
  out <- ggplot2::ggplot(df,
    mapping = ggplot2::aes(
@ -1214,7 +1241,8 @@ autoplot.antibiogram <- function(object, ...) {
      } else {
        NULL
      }
-                         )) +
+    )
  ) +
    ggplot2::geom_col(position = ggplot2::position_dodge2(preserve = "single")) +
    ggplot2::facet_wrap("mo") +
    ggplot2::labs(
@ -1228,9 +1256,11 @@ autoplot.antibiogram <- function(object, ...) {
    )
  if (isTRUE(attributes(object)$wisca)) {
    out <- out +
-      ggplot2::geom_errorbar(mapping = ggplot2::aes(ymin = lower_ci * 100, ymax = upper_ci * 100),
+      ggplot2::geom_errorbar(
        mapping = ggplot2::aes(ymin = lower_ci * 100, ymax = upper_ci * 100),
        position = ggplot2::position_dodge2(preserve = "single"),
-                             width = 0.5)
+        width = 0.5
      )
  }
  out
 }
--- a/R/bug_drug_combinations.R
+++ b/R/bug_drug_combinations.R
@ -127,13 +127,15 @@ bug_drug_combinations <- function(x,
      # turn and merge everything
      pivot <- lapply(x_mo_filter, function(x) {
        m <- as.matrix(table(as.sir(x), useNA = "always"))
-        data.frame(S = m["S", ],
+        data.frame(
          S = m["S", ],
          SDD = m["SDD", ],
          I = m["I", ],
          R = m["R", ],
          NI = m["NI", ],
          na = m[which(is.na(rownames(m))), ],
-                   stringsAsFactors = FALSE)
+          stringsAsFactors = FALSE
        )
      })
      merged <- do.call(rbind_AMR, pivot)
      out_group <- data.frame(
@ -185,7 +187,7 @@ bug_drug_combinations <- function(x,
  out <- as_original_data_class(out, class(x.bak)) # will remove tibble groups
  out <- out %pm>% pm_arrange(mo, ab)
-  class(out) <- c("bug_drug_combinations", if(data_has_groups) "grouped" else NULL, class(out))
+  class(out) <- c("bug_drug_combinations", if (data_has_groups) "grouped" else NULL, class(out))
  rownames(out) <- NULL
  out
 }
--- a/R/custom_eucast_rules.R
+++ b/R/custom_eucast_rules.R
@ -169,7 +169,7 @@ custom_eucast_rules <- function(...) {
      "the result of rule ", i, " (the part after the `~`) must contain `==`, such as in `... ~ ampicillin == \"R\"`, see `?custom_eucast_rules`"
    )
    result_group <- as.character(result)[[2]]
-    result_group<- as.character(str2lang(result_group))
+    result_group <- as.character(str2lang(result_group))
    result_group <- result_group[result_group != "c"]
    result_group_agents <- character(0)
    for (j in seq_len(length(result_group))) {
@ -178,8 +178,10 @@ custom_eucast_rules <- function(...) {
        result_group[j] <- paste0(result_group[j], "s")
      }
      if (paste0("AB_", toupper(result_group[j])) %in% DEFINED_AB_GROUPS) {
-        result_group_agents <- c(result_group_agents,
+        result_group_agents <- c(
-                                 eval(parse(text = paste0("AB_", toupper(result_group[j]))), envir = asNamespace("AMR")))
+          result_group_agents,
          eval(parse(text = paste0("AB_", toupper(result_group[j]))), envir = asNamespace("AMR"))
        )
      } else {
        out_group <- tryCatch(
          suppressWarnings(as.ab(result_group[j],
--- a/R/custom_microorganisms.R
+++ b/R/custom_microorganisms.R
@ -252,8 +252,11 @@ add_custom_microorganisms <- function(x) {
      paste(abbreviate_mo(x$genus, 5),
        abbreviate_mo(x$species, 4, hyphen_as_space = TRUE),
        abbreviate_mo(x$subspecies, 4, hyphen_as_space = TRUE),
-            sep = "_"),
+        sep = "_"
-      whitespace = "_"))
+      ),
      whitespace = "_"
    )
  )
  stop_if(anyDuplicated(c(as.character(AMR_env$MO_lookup$mo), x$mo)), "MO codes must be unique and not match existing MO codes of the AMR package")
  # add to package ----
@ -309,19 +312,25 @@ abbreviate_mo <- function(x, minlength = 5, prefix = "", hyphen_as_space = FALSE
  }
  # keep a starting Latin ae
  suppressWarnings(
-    gsub("(\u00C6|\u00E6)+",
+    gsub(
      "(\u00C6|\u00E6)+",
      "AE",
      toupper(
-           paste0(prefix,
+        paste0(
          prefix,
          abbreviate(
            gsub("^ae",
              "\u00E6\u00E6",
              x,
-                         ignore.case = TRUE),
+              ignore.case = TRUE
            ),
            minlength = minlength,
            use.classes = TRUE,
            method = "both.sides",
            ...
-                  ))))
+          )
        )
      )
    )
  )
 }
--- a/R/eucast_rules.R
+++ b/R/eucast_rules.R
@ -462,10 +462,12 @@ eucast_rules <- function(x,
    if (isTRUE(info)) {
      cat(paste0("\n", font_grey(strrep("-", 0.95 * getOption("width", 100))), "\n"))
      cat(word_wrap(
-        paste0("Rules by the ",
+        paste0(
          "Rules by the ",
          font_bold(paste0("AMR package v", utils::packageDescription("AMR")$Version)),
          " (", format(as.Date(utils::packageDescription("AMR")$Date), format = "%Y"),
-               "), see `?eucast_rules`\n")
+          "), see `?eucast_rules`\n"
        )
      ))
      cat("\n\n")
    }
--- a/R/first_isolate.R
+++ b/R/first_isolate.R
@ -517,7 +517,8 @@ first_isolate <- function(x = NULL,
    if (icu_exclude == TRUE) {
      if (isTRUE(info)) {
        message_("Excluding ", format(sum(x$newvar_is_icu, na.rm = TRUE), decimal.mark = decimal.mark, big.mark = big.mark), " isolates from ICU.",
-                 add_fn = font_red)
+          add_fn = font_red
        )
      }
      x[which(x$newvar_is_icu), "newvar_first_isolate"] <- FALSE
    } else if (isTRUE(info)) {
@ -673,10 +674,12 @@ duplicated_antibiogram <- function(antibiogram, points_threshold, ignore_I, type
    return(FALSE)
  }
  # first sort on data availability - count the dots and order that ascending so that highest availability of SIR is on top
-  number_dots <- vapply(FUN.VALUE = integer(1),
+  number_dots <- vapply(
    FUN.VALUE = integer(1),
    antibiogram,
    function(x) sum(strsplit(x, "", fixed = TRUE)[[1]] == "."),
-                        USE.NAMES = FALSE)
+    USE.NAMES = FALSE
  )
  new_order <- order(number_dots, antibiogram)
  antibiogram.bak <- antibiogram
  antibiogram <- antibiogram[new_order]
@ -685,7 +688,8 @@ duplicated_antibiogram <- function(antibiogram, points_threshold, ignore_I, type
  out[1] <- FALSE
  out[2] <- antimicrobials_equal(antibiogram[1], antibiogram[2],
    ignore_I = ignore_I, points_threshold = points_threshold,
-                                 type = type)
+    type = type
  )
  if (length(antibiogram) == 2) {
    # fast return, no further check required
    return(out)
@ -702,11 +706,18 @@ duplicated_antibiogram <- function(antibiogram, points_threshold, ignore_I, type
  for (na in antibiogram[is.na(out)]) {
    # check if this antibiogram has any change with other antibiograms
    out[which(antibiogram == na)] <- all(
-      vapply(FUN.VALUE = logical(1),
+      vapply(
        FUN.VALUE = logical(1),
        antibiogram[!is.na(out) & antibiogram != na],
-             function(y) antimicrobials_equal(y = y, z = na,
+        function(y) {
          antimicrobials_equal(
            y = y, z = na,
            ignore_I = ignore_I, points_threshold = points_threshold,
-                                              type = type)))
+            type = type
          )
        }
      )
    )
  }
  out <- out[order(new_order)]
--- a/R/ggplot_sir.R
+++ b/R/ggplot_sir.R
@ -121,8 +121,10 @@
 #'   ) %>%
 #'     ggplot() +
 #'     geom_col(aes(x = x, y = y, fill = z)) +
-#'     scale_sir_colours(aesthetics = "fill",
+#'     scale_sir_colours(
-#'                       Value4 = "S", Value5 = "I", Value6 = "R")
+#'       aesthetics = "fill",
 #'       Value4 = "S", Value5 = "I", Value6 = "R"
 #'     )
 #' }
 #' if (require("ggplot2") && require("dplyr")) {
 #'   # resistance of ciprofloxacine per age group
--- a/R/mdro.R
+++ b/R/mdro.R
@ -799,11 +799,13 @@ mdro <- function(x = NULL,
      rows_not_to_change <- rows[!rows %in% c(rows_affected, rows_to_change)]
      rows_not_to_change <- rows_not_to_change[is.na(x[rows_not_to_change, "reason"])]
      if (is.null(reason)) {
-        reason <- paste0(any_all,
+        reason <- paste0(
          any_all,
          " of the required antibiotics ",
          ifelse(any_all == "any", "is", "are"),
          " R",
-                         ifelse(!isTRUE(combine_SI), " or I", ""))
+          ifelse(!isTRUE(combine_SI), " or I", "")
        )
      }
      x[rows_to_change, "MDRO"] <<- to
      x[rows_to_change, "reason"] <<- reason
@ -1848,7 +1850,7 @@ mdro <- function(x = NULL,
        " (3 required for MDR)"
      )
    } else {
-      #x[which(x$MDRO == 1), "reason"] <- "too few antibiotics are R"
+      # x[which(x$MDRO == 1), "reason"] <- "too few antibiotics are R"
    }
  }
--- a/R/mic.R
+++ b/R/mic.R
@ -39,18 +39,22 @@ VALID_MIC_LEVELS <- c(
 )
 VALID_MIC_LEVELS <- trimws(gsub("[.]?0+$", "", format(unique(sort(VALID_MIC_LEVELS)), scientific = FALSE), perl = TRUE))
 operators <- c("<", "<=", "", ">=", ">")
-VALID_MIC_LEVELS <- c(t(vapply(FUN.VALUE = character(length(VALID_MIC_LEVELS)),
+VALID_MIC_LEVELS <- c(t(vapply(
  FUN.VALUE = character(length(VALID_MIC_LEVELS)),
  c("<", "<=", "", ">=", ">"),
  paste0,
-                               VALID_MIC_LEVELS)))
+  VALID_MIC_LEVELS
-COMMON_MIC_VALUES <- c(0.0001, 0.0002, 0.0005,
+)))
 COMMON_MIC_VALUES <- c(
  0.0001, 0.0002, 0.0005,
  0.001, 0.002, 0.004, 0.008,
  0.016, 0.032, 0.064,
  0.125, 0.25, 0.5,
  1, 2, 4, 8,
  16, 32, 64,
  128, 256, 512,
-                       1024, 2048, 4096)
+  1024, 2048, 4096
 )
 #' Transform Input to Minimum Inhibitory Concentrations (MIC)
 #'
@ -165,7 +169,8 @@ as.mic <- function(x, na.rm = FALSE, keep_operators = "all") {
    if (!identical(levels(x), VALID_MIC_LEVELS)) {
      # might be from an older AMR version - just update MIC factor levels
      x <- set_clean_class(factor(as.character(x), levels = VALID_MIC_LEVELS, ordered = TRUE),
-                           new_class = c("mic", "ordered", "factor"))
+        new_class = c("mic", "ordered", "factor")
      )
    }
    return(x)
  }
@ -265,7 +270,8 @@ as.mic <- function(x, na.rm = FALSE, keep_operators = "all") {
  }
  set_clean_class(factor(x, levels = VALID_MIC_LEVELS, ordered = TRUE),
-                  new_class = c("mic", "ordered", "factor"))
+    new_class = c("mic", "ordered", "factor")
  )
 }
 #' @rdname as.mic
@ -294,10 +300,12 @@ rescale_mic <- function(x, mic_range, keep_operators = "edges", as.mic = TRUE) {
  } else if (is.mic(mic_range)) {
    mic_range <- as.character(mic_range)
  }
-  stop_ifnot(all(mic_range %in% c(VALID_MIC_LEVELS, NA)),
+  stop_ifnot(
    all(mic_range %in% c(VALID_MIC_LEVELS, NA)),
    "Values in `mic_range` must be valid MIC values. ",
    "The allowed range is ", format(as.double(as.mic(VALID_MIC_LEVELS)[1]), scientific = FALSE), " to ", format(as.double(as.mic(VALID_MIC_LEVELS)[length(VALID_MIC_LEVELS)]), scientific = FALSE), ". ",
-             "Unvalid: ", vector_and(mic_range[!mic_range %in% c(VALID_MIC_LEVELS, NA)], quotes = FALSE), ".")
+    "Unvalid: ", vector_and(mic_range[!mic_range %in% c(VALID_MIC_LEVELS, NA)], quotes = FALSE), "."
  )
  x <- as.mic(x)
  if (is.null(mic_range)) {
@ -328,7 +336,8 @@ rescale_mic <- function(x, mic_range, keep_operators = "edges", as.mic = TRUE) {
  expanded <- plotrange_as_table(x,
    expand = TRUE,
    keep_operators = ifelse(keep_operators == "edges", "none", keep_operators),
-                                 mic_range = mic_range)
+    mic_range = mic_range
  )
  if (keep_operators == "edges") {
    names(expanded)[1] <- paste0("<=", names(expanded)[1])
    names(expanded)[length(expanded)] <- paste0(">=", names(expanded)[length(expanded)])
@ -336,7 +345,8 @@ rescale_mic <- function(x, mic_range, keep_operators = "edges", as.mic = TRUE) {
  # MICs contain all MIC levels, so strip this to only existing levels and their intermediate values
  out <- factor(names(expanded),
    levels = names(expanded),
-                ordered = TRUE)
+    ordered = TRUE
  )
  # and only keep the ones in the data
  if (keep_operators == "edges") {
    out <- out[match(x, as.double(as.mic(out, keep_operators = "all")))]
@ -400,9 +410,10 @@ all_valid_mics <- function(x) {
 # will be exported using s3_register() in R/zzz.R
 pillar_shaft.mic <- function(x, ...) {
-  if(!identical(levels(x), VALID_MIC_LEVELS) && message_not_thrown_before("pillar_shaft.mic")) {
+  if (!identical(levels(x), VALID_MIC_LEVELS) && message_not_thrown_before("pillar_shaft.mic")) {
    warning_(AMR_env$sup_1_icon, " These columns contain an outdated or altered structure - convert with `as.mic()` to update",
-             call = FALSE)
+      call = FALSE
    )
  }
  crude_numbers <- as.double(x)
  operators <- gsub("[^<=>]+", "", as.character(x))
@ -416,7 +427,7 @@ pillar_shaft.mic <- function(x, ...) {
 # will be exported using s3_register() in R/zzz.R
 type_sum.mic <- function(x, ...) {
-  if(!identical(levels(x), VALID_MIC_LEVELS)) {
+  if (!identical(levels(x), VALID_MIC_LEVELS)) {
    paste0("mic", AMR_env$sup_1_icon)
  } else {
    "mic"
@ -428,7 +439,7 @@ type_sum.mic <- function(x, ...) {
 #' @noRd
 print.mic <- function(x, ...) {
  cat("Class 'mic'")
-  if(!identical(levels(x), VALID_MIC_LEVELS)) {
+  if (!identical(levels(x), VALID_MIC_LEVELS)) {
    cat(font_red(" with an outdated or altered structure - convert with `as.mic()` to update"))
  }
  cat("\n")
@ -649,5 +660,6 @@ Summary.mic <- function(..., na.rm = FALSE) {
  # NextMethod() cannot be called from an anonymous function (`...`), so we get() the generic directly:
  fn <- get(.Generic, envir = .GenericCallEnv)
  fn(as.double(c(...)),
-     na.rm = na.rm)
+    na.rm = na.rm
  )
 }
--- a/R/mo.R
+++ b/R/mo.R
@ -594,9 +594,11 @@ mo_reset_session <- function() {
 #' @rdname as.mo
 #' @export
 mo_cleaning_regex <- function() {
-  parts_to_remove <- c("e?spp([^a-z]+|$)", "e?ssp([^a-z]+|$)", "e?ss([^a-z]+|$)", "e?sp([^a-z]+|$)", "e?subsp", "sube?species", "e?species",
+  parts_to_remove <- c(
    "e?spp([^a-z]+|$)", "e?ssp([^a-z]+|$)", "e?ss([^a-z]+|$)", "e?sp([^a-z]+|$)", "e?subsp", "sube?species", "e?species",
    "biovar[a-z]*", "biotype", "serovar[a-z]*", "var([^a-z]+|$)", "serogr.?up[a-z]*",
-                       "titer", "dummy", "Ig[ADEGM]", " ?[a-z-]+[-](resistant|susceptible) ?")
+    "titer", "dummy", "Ig[ADEGM]", " ?[a-z-]+[-](resistant|susceptible) ?"
  )
  paste0(
    "(",
@ -605,7 +607,8 @@ mo_cleaning_regex <- function() {
    "([({]|\\[).+([})]|\\])",
    "|(^| )(",
    paste0(parts_to_remove[order(1 - nchar(parts_to_remove))], collapse = "|"),
-  "))")
+    "))"
  )
 }
 # UNDOCUMENTED METHODS ----------------------------------------------------
@ -660,9 +663,13 @@ pillar_shaft.mo <- function(x, ...) {
  # add the names to the bugs as mouse-over!
  if (tryCatch(isTRUE(getExportedValue("ansi_has_hyperlink_support", ns = asNamespace("cli"))()), error = function(e) FALSE)) {
-    out[!x %in% c("UNKNOWN", NA)] <- font_url(url = paste0(x[!x %in% c("UNKNOWN", NA)], ": ",
+    out[!x %in% c("UNKNOWN", NA)] <- font_url(
-                                                           mo_name(x[!x %in% c("UNKNOWN", NA)], keep_synonyms = TRUE)),
+      url = paste0(
-                                              txt = out[!x %in% c("UNKNOWN", NA)])
+        x[!x %in% c("UNKNOWN", NA)], ": ",
        mo_name(x[!x %in% c("UNKNOWN", NA)], keep_synonyms = TRUE)
      ),
      txt = out[!x %in% c("UNKNOWN", NA)]
    )
  }
  # make it always fit exactly
@ -1277,8 +1284,10 @@ repair_reference_df <- function(reference_df) {
 }
 get_mo_uncertainties <- function() {
-  remember <- list(uncertainties = AMR_env$mo_uncertainties,
+  remember <- list(
-                   failures = AMR_env$mo_failures)
+    uncertainties = AMR_env$mo_uncertainties,
    failures = AMR_env$mo_failures
  )
  # empty them, otherwise e.g. mo_shortname("Chlamydophila psittaci") will give 3 notes
  AMR_env$mo_uncertainties <- NULL
  AMR_env$mo_failures <- NULL
@ -1300,7 +1309,7 @@ synonym_mo_to_accepted_mo <- function(x, fill_in_accepted = FALSE, dataset = AMR
  out <- x
  is_still_synonym <- dataset$status[match(out, dataset$mo)] == "synonym"
  limit <- 0
-  while(any(is_still_synonym, na.rm = TRUE) && limit < 5) {
+  while (any(is_still_synonym, na.rm = TRUE) && limit < 5) {
    limit <- limit + 1
    # make sure to get the latest name, e.g. Fusarium pulicaris robiniae was first renamed to Fusarium roseum, then to Fusarium sambucinum
--- a/R/mo_property.R
+++ b/R/mo_property.R
@ -108,10 +108,12 @@
 #' mo_url("Klebsiella pneumoniae")
 #' mo_is_yeast(c("Candida", "Trichophyton", "Klebsiella"))
 #'
-#' mo_group_members(c("Streptococcus group A",
+#' mo_group_members(c(
 #'   "Streptococcus group A",
 #'   "Streptococcus group C",
 #'   "Streptococcus group G",
-#'                    "Streptococcus group L"))
+#'   "Streptococcus group L"
 #' ))
 #'
 #'
 #' # scientific reference -----------------------------------------------------
@ -442,11 +444,14 @@ mo_pathogenicity <- function(x, language = get_AMR_locale(), keep_synonyms = get
  kngd <- AMR_env$MO_lookup$kingdom[match(x.mo, AMR_env$MO_lookup$mo)]
  rank <- AMR_env$MO_lookup$rank[match(x.mo, AMR_env$MO_lookup$mo)]
-  out <- factor(case_when_AMR(prev <= 1.15 & kngd == "Bacteria" & rank != "genus" ~ "Pathogenic",
+  out <- factor(
    case_when_AMR(
      prev <= 1.15 & kngd == "Bacteria" & rank != "genus" ~ "Pathogenic",
      prev < 2 & kngd == "Fungi" ~ "Potentially pathogenic",
      prev == 2 & kngd == "Bacteria" ~ "Non-pathogenic",
      kngd == "Bacteria" ~ "Potentially pathogenic",
-                              TRUE ~ "Unknown"),
+      TRUE ~ "Unknown"
    ),
    levels = c("Pathogenic", "Potentially pathogenic", "Non-pathogenic", "Unknown"),
    ordered = TRUE
  )
@ -872,8 +877,10 @@ mo_info <- function(x, language = get_AMR_locale(), keep_synonyms = getOption("A
  info <- lapply(x, function(y) {
    c(
-      list(mo = as.character(y),
+      list(
-           rank = mo_rank(y, language = language, keep_synonyms = keep_synonyms)),
+        mo = as.character(y),
        rank = mo_rank(y, language = language, keep_synonyms = keep_synonyms)
      ),
      mo_taxonomy(y, language = language, keep_synonyms = keep_synonyms),
      list(
        status = mo_status(y, language = language, keep_synonyms = keep_synonyms),
@ -988,7 +995,6 @@ mo_validate <- function(x, property, language, keep_synonyms = keep_synonyms, ..
    } else {
      x <- AMR_env$MO_lookup[[property]][match(x, AMR_env$MO_lookup$mo)]
    }
  } else {
    # get microorganisms data set, but remove synonyms if keep_synonyms is FALSE
    mo_data_check <- AMR_env$MO_lookup[which(AMR_env$MO_lookup$status %in% if (isTRUE(keep_synonyms)) c("synonym", "accepted") else "accepted"), , drop = FALSE]
--- a/R/plotting.R
+++ b/R/plotting.R
@ -80,7 +80,6 @@
 #' some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
 #' some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))
 #'
 #' 
 #' \donttest{
 #' # Plotting using ggplot2's autoplot() for MIC, disk, and SIR -----------
 #' if (require("ggplot2")) {
@ -92,17 +91,23 @@
 #' }
 #' if (require("ggplot2")) {
 #'   # support for 20 languages, various guidelines, and many options
-#'   autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro",
+#'   autoplot(some_disk_values,
 #'     mo = "Escherichia coli", ab = "cipro",
 #'     guideline = "CLSI 2024", language = "no",
-#'            title = "Disk diffusion from the North")
+#'     title = "Disk diffusion from the North"
 #'   )
 #' }
 #'
 #'
 #' # Plotting using scale_x_mic() -----------------------------------------
 #' if (require("ggplot2")) {
-#'   mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
+#'   mic_plot <- ggplot(
-#'                                 counts = c(1, 1, 2, 2, 3, 3)),
+#'     data.frame(
-#'                      aes(mics, counts)) +
+#'       mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
 #'       counts = c(1, 1, 2, 2, 3, 3)
 #'     ),
 #'     aes(mics, counts)
 #'   ) +
 #'     geom_col()
 #'   mic_plot +
 #'     labs(title = "without scale_x_mic()")
@ -133,17 +138,25 @@
 #' some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
 #'
 #' if (require("ggplot2")) {
-#'   ggplot(data.frame(mic = some_mic_values,
+#'   ggplot(
-#'                     group = some_groups),
+#'     data.frame(
-#'          aes(group, mic)) +
+#'       mic = some_mic_values,
 #'       group = some_groups
 #'     ),
 #'     aes(group, mic)
 #'   ) +
 #'     geom_boxplot() +
 #'     geom_violin(linetype = 2, colour = "grey", fill = NA) +
 #'     scale_y_mic()
 #' }
 #' if (require("ggplot2")) {
-#'   ggplot(data.frame(mic = some_mic_values,
+#'   ggplot(
-#'                     group = some_groups),
+#'     data.frame(
-#'          aes(group, mic)) +
+#'       mic = some_mic_values,
 #'       group = some_groups
 #'     ),
 #'     aes(group, mic)
 #'   ) +
 #'     geom_boxplot() +
 #'     geom_violin(linetype = 2, colour = "grey", fill = NA) +
 #'     scale_y_mic(mic_range = c(NA, 0.25))
@ -152,9 +165,13 @@
 #'
 #' # Plotting using scale_x_sir() -----------------------------------------
 #' if (require("ggplot2")) {
-#'   ggplot(data.frame(x = c("I", "R", "S"),
+#'   ggplot(
-#'                     y = c(45,323, 573)),
+#'     data.frame(
-#'          aes(x, y)) +
+#'       x = c("I", "R", "S"),
 #'       y = c(45, 323, 573)
 #'     ),
 #'     aes(x, y)
 #'   ) +
 #'     geom_col() +
 #'     scale_x_sir()
 #' }
@ -162,12 +179,17 @@
 #'
 #' # Plotting using scale_y_mic() and scale_colour_sir() ------------------
 #' if (require("ggplot2")) {
-#'   plain <- ggplot(data.frame(mic = some_mic_values,
+#'   plain <- ggplot(
 #'     data.frame(
 #'       mic = some_mic_values,
 #'       group = some_groups,
 #'       sir = as.sir(some_mic_values,
 #'         mo = "E. coli",
-#'                                           ab = "cipro")),
+#'         ab = "cipro"
-#'                   aes(x = group, y = mic, colour = sir)) +
+#'       )
 #'     ),
 #'     aes(x = group, y = mic, colour = sir)
 #'   ) +
 #'     theme_minimal() +
 #'     geom_boxplot(fill = NA, colour = "grey") +
 #'     geom_jitter(width = 0.25)
@ -183,8 +205,10 @@
 #' if (require("ggplot2")) {
 #'   plain +
 #'     scale_y_mic(mic_range = c(0.005, 32), name = "Our MICs!") +
-#'     scale_colour_sir(language = "pt",
+#'     scale_colour_sir(
-#'                      name = "Support in 20 languages")
+#'       language = "pt",
 #'       name = "Support in 20 languages"
 #'     )
 #' }
 #' }
 #'
@ -203,7 +227,8 @@ NULL
 create_scale_mic <- function(aest, keep_operators, mic_range = NULL, ...) {
  ggplot_fn <- getExportedValue(paste0("scale_", aest, "_continuous"),
-                                ns = asNamespace("ggplot2"))
+    ns = asNamespace("ggplot2")
  )
  args <- list(...)
  breaks_set <- args$breaks
  limits_set <- args$limits
@ -264,7 +289,7 @@ create_scale_mic <- function(aest, keep_operators, mic_range = NULL, ...) {
      breaks <- tryCatch(scale$breaks(), error = function(e) NULL)
      if (!is.null(breaks)) {
        # for when breaks are set by the user
-        2 ^ breaks
+        2^breaks
      } else {
        self$mic_values_levels
      }
@ -338,20 +363,27 @@ create_scale_sir <- function(aesthetics, colours_SIR, language, eucast_I, ...) {
    ggplot_fn <- ggplot2::scale_x_discrete
  } else {
    ggplot_fn <- ggplot2::scale_discrete_manual
-    args <- c(args, 
+    args <- c(
-              list(aesthetics = aesthetics,
+      args,
-                   values = c(S = colours_SIR[1],
+      list(
        aesthetics = aesthetics,
        values = c(
          S = colours_SIR[1],
          SDD = colours_SIR[2],
          I = colours_SIR[2],
          R = colours_SIR[3],
-                              NI = "grey30")))
+          NI = "grey30"
        )
      )
    )
  }
  scale <- do.call(ggplot_fn, args)
  scale$labels <- function(x) {
    stop_ifnot(all(x %in% c(levels(NA_sir_), NA)),
      "Apply `scale_", aesthetics[1], "_sir()` to a variable of class 'sir', see `?as.sir`.",
-               call = FALSE)
+      call = FALSE
    )
    x <- as.character(as.sir(x))
    if (!is.null(language)) {
      x[x == "S"] <- "(S) Susceptible"
--- a/R/sir.R
+++ b/R/sir.R
@ -198,23 +198,35 @@
 #'     mutate_if(is.mic, as.sir,
 #'       mo = "bacteria",
 #'       ab = "antibiotic",
-#'               guideline = "CLSI")
+#'       guideline = "CLSI"
 #'     )
 #'   df_long %>%
-#'     mutate(across(where(is.mic),
+#'     mutate(across(
-#'                   function(x) as.sir(x,
+#'       where(is.mic),
 #'       function(x) {
 #'         as.sir(x,
 #'           mo = "bacteria",
 #'           ab = "antibiotic",
-#'                                      guideline = "CLSI")))
+#'           guideline = "CLSI"
 #'         )
 #'       }
 #'     ))
 #'   df_wide %>%
 #'     # given certain columns, e.g. from 'cipro' to 'genta'
 #'     mutate_at(vars(cipro:genta), as.sir,
 #'       mo = "bacteria",
-#'               guideline = "CLSI")
+#'       guideline = "CLSI"
 #'     )
 #'   df_wide %>%
-#'     mutate(across(cipro:genta,
+#'     mutate(across(
-#'                        function(x) as.sir(x,
+#'       cipro:genta,
 #'       function(x) {
 #'         as.sir(x,
 #'           mo = "bacteria",
-#'                                           guideline = "CLSI")))
+#'           guideline = "CLSI"
 #'         )
 #'       }
 #'     ))
 #'
 #'   # for veterinary breakpoints, add 'host':
 #'   df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
@ -224,36 +236,52 @@
 #'       mo = "bacteria",
 #'       ab = "antibiotic",
 #'       host = "animal_species",
-#'               guideline = "CLSI")
+#'       guideline = "CLSI"
 #'     )
 #'   df_long %>%
-#'     mutate(across(where(is.mic),
+#'     mutate(across(
-#'                   function(x) as.sir(x,
+#'       where(is.mic),
 #'       function(x) {
 #'         as.sir(x,
 #'           mo = "bacteria",
 #'           ab = "antibiotic",
 #'           host = "animal_species",
-#'                                      guideline = "CLSI")))
+#'           guideline = "CLSI"
 #'         )
 #'       }
 #'     ))
 #'   df_wide %>%
 #'     mutate_at(vars(cipro:genta), as.sir,
 #'       mo = "bacteria",
 #'       ab = "antibiotic",
 #'       host = "animal_species",
-#'               guideline = "CLSI")
+#'       guideline = "CLSI"
 #'     )
 #'   df_wide %>%
-#'     mutate(across(cipro:genta,
+#'     mutate(across(
-#'                        function(x) as.sir(x,
+#'       cipro:genta,
 #'       function(x) {
 #'         as.sir(x,
 #'           mo = "bacteria",
 #'           host = "animal_species",
-#'                                           guideline = "CLSI")))
+#'           guideline = "CLSI"
 #'         )
 #'       }
 #'     ))
 #'
 #'   # to include information about urinary tract infections (UTI)
-#'   data.frame(mo = "E. coli",
+#'   data.frame(
 #'     mo = "E. coli",
 #'     nitrofuratoin = c("<= 2", 32),
-#'              from_the_bladder = c(TRUE, FALSE)) %>%
+#'     from_the_bladder = c(TRUE, FALSE)
 #'   ) %>%
 #'     as.sir(uti = "from_the_bladder")
 #'
-#'   data.frame(mo = "E. coli",
+#'   data.frame(
 #'     mo = "E. coli",
 #'     nitrofuratoin = c("<= 2", 32),
-#'              specimen = c("urine", "blood")) %>%
+#'     specimen = c("urine", "blood")
 #'   ) %>%
 #'     as.sir() # automatically determines urine isolates
 #'
 #'   df_wide %>%
@ -326,16 +354,19 @@ as_sir_structure <- function(x,
                             method = NULL,
                             ref_tbl = NULL,
                             ref_breakpoints = NULL) {
-  out <- structure(factor(as.character(unlist(unname(x))),
+  out <- structure(
    factor(as.character(unlist(unname(x))),
      levels = c("S", "SDD", "I", "R", "NI"),
-                          ordered = TRUE),
+      ordered = TRUE
    ),
    guideline = guideline,
    mo = mo,
    ab = ab,
    method = method,
    ref_tbl = ref_tbl,
    ref_breakpoints = ref_breakpoints,
-                   class = c("sir", "ordered", "factor"))
+    class = c("sir", "ordered", "factor")
  )
 }
 #' @rdname as.sir
@ -1107,20 +1138,26 @@ as_sir_method <- function(method_short,
  same_ab <- generalise_antibiotic_name(ab) == generalise_antibiotic_name(agent_name)
  same_ab.bak <- generalise_antibiotic_name(ab.bak) == generalise_antibiotic_name(agent_name)
  agent_formatted[same_ab.bak] <- paste0(agent_formatted[same_ab.bak], " (", ab[same_ab.bak], ")")
-  agent_formatted[!same_ab.bak & !same_ab] <- paste0(agent_formatted[!same_ab.bak & !same_ab],
+  agent_formatted[!same_ab.bak & !same_ab] <- paste0(
    agent_formatted[!same_ab.bak & !same_ab],
    " (", ifelse(ab.bak[!same_ab.bak & !same_ab] == ab[!same_ab.bak & !same_ab],
      "",
-                                                                 paste0(ab[!same_ab.bak & !same_ab], ", ")),
+      paste0(ab[!same_ab.bak & !same_ab], ", ")
    ),
    agent_name[!same_ab.bak & !same_ab],
-                                                    ")")
+    ")"
  )
  # this intro text will also be printed in the progress bar if the `progress` package is installed
-  intro_txt <- paste0("Interpreting ", method_long, ": ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""),
+  intro_txt <- paste0(
    "Interpreting ", method_long, ": ", ifelse(isTRUE(list(...)$is_data.frame), "column ", ""),
    ifelse(length(unique(agent_formatted)) == 1, unique(agent_formatted), paste0(vector_and(agent_formatted, quotes = FALSE, sort = FALSE))),
    mo_var_found,
    ifelse(identical(reference_data, AMR::clinical_breakpoints),
      paste0(", ", font_bold(guideline_coerced)),
-                             ""),
+      ""
-                      "... ")
+    ),
    "... "
  )
  # prepare used arguments ----
  method <- method_short
@ -1163,7 +1200,7 @@ as_sir_method <- function(method_short,
    df$values <- as.disk(df$values)
  }
-  df_unique <- unique(df[ , c("mo", "ab", "uti", "host"), drop = FALSE])
+  df_unique <- unique(df[, c("mo", "ab", "uti", "host"), drop = FALSE])
  # get all breakpoints, use humans as backup for animals
  breakpoint_type_lookup <- breakpoint_type
@ -1203,9 +1240,12 @@ as_sir_method <- function(method_short,
    # apparently no breakpoints found
    message(
      paste0(font_rose_bg(" WARNING "), "\n"),
-      font_black(paste0("  ", AMR_env$bullet_icon, " No ", guideline_coerced, " ", method_coerced, " breakpoints available for ",
+      font_black(paste0(
        "  ", AMR_env$bullet_icon, " No ", guideline_coerced, " ", method_coerced, " breakpoints available for ",
        suppressMessages(suppressWarnings(ab_name(unique(ab_coerced), language = NULL, tolower = TRUE))),
-                        " (", unique(ab_coerced), ")."), collapse = "\n"))
+        " (", unique(ab_coerced), ")."
      ), collapse = "\n")
    )
    load_mo_uncertainties(metadata_mo)
    return(rep(NA_sir_, nrow(df)))
@ -1331,20 +1371,17 @@ as_sir_method <- function(method_short,
          # vancomycin can take human breakpoints in these hosts
          breakpoints_current <- breakpoints_current %pm>% subset(host == "human")
          notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", ab_formatted, " based on CLSI VET09."))
        } else if (host_current %in% c("dogs", "cats") && (mo_current_genus %in% c("B_AMYCS", "B_NOCRD", "B_CMPYL", "B_CRYNB", "B_ENTRC", "B_MYCBC", "B_PSDMN", "B_AERMN") | mo_current_class == "B_[CLS]_BTPRTBCT" | mo_current == "B_LISTR_MNCY")) {
          # dog breakpoints if no canine/feline
          # TODO do we still have dogs breakpoints at this point???
          breakpoints_current <- breakpoints_current %pm>% subset(host == "human") # WRONG
          notes_current <- c(notes_current, paste0("Using ", font_bold("human"), " breakpoints for ", mo_formatted, " based on CLSI VET09."))
        } else {
          # no specific CLSI solution for this, so only filter on current host (if no breakpoints available -> too bad)
          breakpoints_current <- breakpoints_current %pm>%
            subset(host == host_current)
        }
      }
    }
    if (NROW(breakpoints_current) == 0) {
@ -1383,7 +1420,9 @@ as_sir_method <- function(method_short,
        # 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>%
+            3
          )
        )) %pm>%
        # be as specific as possible (i.e. prefer species over genus):
        pm_arrange(rank_index, uti_index)
    } else if (uti_current == TRUE) {
--- a/R/sir_calc.R
+++ b/R/sir_calc.R
@ -254,7 +254,6 @@ sir_calc_df <- function(type, # "proportion", "count" or "both"
          if (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)
          }
        }
        data[, i] <- gsub("(I|S|SDD)", "SI", data[, i, drop = TRUE])
      }
--- a/R/top_n_microorganisms.R
+++ b/R/top_n_microorganisms.R
@ -42,15 +42,18 @@
 #' @examples
 #' # filter to the top 3 species:
 #' top_n_microorganisms(example_isolates,
-#'                      n = 3)
+#'   n = 3
 #' )
 #'
 #' # filter to any species in the top 5 genera:
 #' top_n_microorganisms(example_isolates,
-#'                      n = 5, property = "genus")
+#'   n = 5, property = "genus"
 #' )
 #'
 #' # filter to the top 3 species in each of the top 5 genera:
 #' top_n_microorganisms(example_isolates,
-#'                      n = 5, property = "genus", n_for_each = 3)
+#'   n = 5, property = "genus", n_for_each = 3
 #' )
 top_n_microorganisms <- function(x, n, property = "fullname", n_for_each = NULL, col_mo = NULL, ...) {
  meet_criteria(x, allow_class = "data.frame") # also checks dimensions to be >0
  meet_criteria(n, allow_class = c("numeric", "integer"), has_length = 1, is_finite = TRUE, is_positive = TRUE)
--- a/R/vctrs.R
+++ b/R/vctrs.R
@ -59,7 +59,7 @@ vec_cast.logical.amr_selector_any_all <- function(x, to, ...) {
 }
 # S3: ab ----
-vec_ptype2.ab.default <- function (x, y, ..., x_arg = "", y_arg = "") {
+vec_ptype2.ab.default <- function(x, y, ..., x_arg = "", y_arg = "") {
  x
 }
 vec_ptype2.ab.ab <- function(x, y, ...) {
@ -73,7 +73,7 @@ vec_cast.ab.character <- function(x, to, ...) {
 }
 # S3: av ----
-vec_ptype2.av.default <- function (x, y, ..., x_arg = "", y_arg = "") {
+vec_ptype2.av.default <- function(x, y, ..., x_arg = "", y_arg = "") {
  x
 }
 vec_ptype2.av.av <- function(x, y, ...) {
@ -87,7 +87,7 @@ vec_cast.av.character <- function(x, to, ...) {
 }
 # S3: mo ----
-vec_ptype2.mo.default <- function (x, y, ..., x_arg = "", y_arg = "") {
+vec_ptype2.mo.default <- function(x, y, ..., x_arg = "", y_arg = "") {
  x
 }
 vec_ptype2.mo.mo <- function(x, y, ...) {
@ -108,7 +108,7 @@ vec_ptype_full.disk <- function(x, ...) {
 vec_ptype_abbr.disk <- function(x, ...) {
  "dsk"
 }
-vec_ptype2.disk.default <- function (x, y, ..., x_arg = "", y_arg = "") {
+vec_ptype2.disk.default <- function(x, y, ..., x_arg = "", y_arg = "") {
  NA_disk_[0]
 }
 vec_ptype2.disk.disk <- function(x, y, ...) {
@ -137,7 +137,7 @@ vec_cast.disk.character <- function(x, to, ...) {
 }
 # S3: mic ----
-vec_ptype2.mic.default <- function (x, y, ..., x_arg = "", y_arg = "") {
+vec_ptype2.mic.default <- function(x, y, ..., x_arg = "", y_arg = "") {
  # this will make sure that currently implemented MIC levels are returned
  NA_mic_[0]
 }
@ -181,7 +181,7 @@ vec_arith.mic <- function(op, x, y, ...) {
 }
 # S3: sir ----
-vec_ptype2.sir.default <- function (x, y, ..., x_arg = "", y_arg = "") {
+vec_ptype2.sir.default <- function(x, y, ..., x_arg = "", y_arg = "") {
  NA_sir_[0]
 }
 vec_ptype2.sir.sir <- function(x, y, ...) {
--- a/data-raw/_pre_commit_checks.R
+++ b/data-raw/_pre_commit_checks.R
@ -627,12 +627,15 @@ suppressMessages(set_AMR_locale("English"))
 usethis::ui_info("Checking URLs for redirects")
 invisible(urlchecker::url_update("."))
 # Style pkg ---------------------------------------------------------------
 usethis::ui_info("Styling package")
 styler::style_pkg(include_roxygen_examples = FALSE,
                  exclude_dirs = list.dirs(full.names = FALSE, recursive = FALSE)[!list.dirs(full.names = FALSE, recursive = FALSE) %in% c("R", "tests")])
 # Document pkg ------------------------------------------------------------
 usethis::ui_info("Documenting package")
 suppressMessages(devtools::document(quiet = TRUE))
 # Finished ----------------------------------------------------------------
 usethis::ui_done("All done")
 suppressMessages(reset_AMR_locale())
--- a/data-raw/gpt_training_text_v2.1.1.9163.txt
+++ b/data-raw/gpt_training_text_v2.1.1.9163.txt
@ -1,6 +1,6 @@
 This knowledge base contains all context you must know about the AMR package for R. You are a GPT trained to be an assistant for the AMR package in R. You are an incredible R specialist, especially trained in this package and in the tidyverse.
-First and foremost, you are trained on version 2.1.1.9160. Remember this whenever someone asks which AMR package version you’re at.
+First and foremost, you are trained on version 2.1.1.9163. Remember this whenever someone asks which AMR package version you’re at.
 Below are the contents of the  file, the  file, and all the  files (documentation) in the package. Every file content is split using 100 hypens.
 ----------------------------------------------------------------------------------------------------
@ -1984,12 +1984,14 @@ antibiogram(example_isolates,
 antibiogram(example_isolates,
  antibiotics = aminoglycosides(),
  ab_transform = "atc",
-  mo_transform = "gramstain")
+  mo_transform = "gramstain"
 )
 antibiogram(example_isolates,
  antibiotics = carbapenems(),
  ab_transform = "name",
-  mo_transform = "name")
+  mo_transform = "name"
 )
 # Combined antibiogram -------------------------------------------------
@ -1997,14 +1999,16 @@ antibiogram(example_isolates,
 # combined antibiotics yield higher empiric coverage
 antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-  mo_transform = "gramstain")
+  mo_transform = "gramstain"
 )
 # names of antibiotics do not need to resemble columns exactly:
 antibiogram(example_isolates,
  antibiotics = c("Cipro", "cipro + genta"),
  mo_transform = "gramstain",
  ab_transform = "name",
-  sep = " & ")
+  sep = " & "
 )
 # Syndromic antibiogram ------------------------------------------------
@ -2012,7 +2016,8 @@ antibiogram(example_isolates,
 # the data set could contain a filter for e.g. respiratory specimens
 antibiogram(example_isolates,
  antibiotics = c(aminoglycosides(), carbapenems()),
-  syndromic_group = "ward")
+  syndromic_group = "ward"
 )
 # now define a data set with only E. coli
 ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@ -2025,7 +2030,8 @@ antibiogram(ex1,
  syndromic_group = ifelse(ex1$ward == "ICU",
    "UCI", "No UCI"
  ),
-  language = "es")
+  language = "es"
 )
 # WISCA antibiogram ----------------------------------------------------
@ -2034,7 +2040,8 @@ antibiogram(ex1,
 antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
  syndromic_group = "ward",
-  wisca = TRUE)
+  wisca = TRUE
 )
 # Print the output for R Markdown / Quarto -----------------------------
@ -2042,7 +2049,8 @@ antibiogram(example_isolates,
 ureido <- antibiogram(example_isolates,
  antibiotics = ureidopenicillins(),
  syndromic_group = "ward",
-  wisca = TRUE)
+  wisca = TRUE
 )
 # in an Rmd file, you would just need to return `ureido` in a chunk,
 # but to be explicit here:
@ -2055,11 +2063,13 @@ if (requireNamespace("knitr")) {
 ab1 <- antibiogram(example_isolates,
  antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-  mo_transform = "gramstain")
+  mo_transform = "gramstain"
 )
 ab2 <- antibiogram(example_isolates,
  antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
  mo_transform = "gramstain",
-  syndromic_group = "ward")
+  syndromic_group = "ward"
 )
 if (requireNamespace("ggplot2")) {
  ggplot2::autoplot(ab1)
@ -2181,8 +2191,6 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antimicrobial_selectors.Rd':
 % Please edit documentation in R/amr_selectors.R
 \name{antimicrobial_selectors}
 \alias{antimicrobial_selectors}
 \alias{amr_class}
 \alias{amr_selector}
 \alias{aminoglycosides}
 \alias{aminopenicillins}
 \alias{antifungals}
@ -2214,17 +2222,13 @@ THE PART HEREAFTER CONTAINS CONTENTS FROM FILE 'man/antimicrobial_selectors.Rd':
 \alias{tetracyclines}
 \alias{trimethoprims}
 \alias{ureidopenicillins}
 \alias{amr_class}
 \alias{amr_selector}
 \alias{administrable_per_os}
 \alias{administrable_iv}
 \alias{not_intrinsic_resistant}
 \title{Antimicrobial Selectors}
 \usage{
 amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
 amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
 aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
@ -2293,6 +2297,12 @@ trimethoprims(only_sir_columns = FALSE, return_all = TRUE, ...)
 ureidopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)
 amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
 amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
 administrable_per_os(only_sir_columns = FALSE, return_all = TRUE, ...)
 administrable_iv(only_sir_columns = FALSE, return_all = TRUE, ...)
@ -2301,8 +2311,6 @@ not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL,
  version_expertrules = 3.3, ...)
 }
 \arguments{
 \item{amr_class}{an antimicrobial class or a part of it, such as \code{"carba"} and \code{"carbapenems"}. The columns \code{group}, \code{atc_group1} and \code{atc_group2} of the \link{antibiotics} data set will be searched (case-insensitive) for this value.}
 \item{only_sir_columns}{a \link{logical} to indicate whether only columns of class \code{sir} must be selected (default is \code{FALSE}), see \code{\link[=as.sir]{as.sir()}}}
 \item{only_treatable}{a \link{logical} to indicate whether antimicrobial drugs should be excluded that are only for laboratory tests (default is \code{TRUE}), such as gentamicin-high (\code{GEH}) and imipenem/EDTA (\code{IPE})}
@ -2311,6 +2319,8 @@ not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL,
 \item{...}{ignored, only in place to allow future extensions}
 \item{amr_class}{an antimicrobial class or a part of it, such as \code{"carba"} and \code{"carbapenems"}. The columns \code{group}, \code{atc_group1} and \code{atc_group2} of the \link{antibiotics} data set will be searched (case-insensitive) for this value.}
 \item{filter}{an \link{expression} to be evaluated in the \link{antibiotics} data set, such as \code{name \%like\% "trim"}}
 \item{col_mo}{column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
@ -2339,10 +2349,10 @@ All columns in the data in which these functions are called will be searched for
 The \code{\link[=amr_class]{amr_class()}} function can be used to filter/select on a manually defined antimicrobial class. It searches for results in the \link{antibiotics} data set within the columns \code{group}, \code{atc_group1} and \code{atc_group2}.
 The \code{\link[=amr_selector]{amr_selector()}} function can be used to internally filter the \link{antibiotics} data set on any results, see \emph{Examples}. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
 The \code{\link[=administrable_per_os]{administrable_per_os()}} and \code{\link[=administrable_iv]{administrable_iv()}} functions also rely on the \link{antibiotics} data set - antimicrobials will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in the \link{antibiotics} data set.
 The \code{\link[=amr_selector]{amr_selector()}} function can be used to internally filter the \link{antibiotics} data set on any results, see \emph{Examples}. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
 The \code{\link[=not_intrinsic_resistant]{not_intrinsic_resistant()}} function can be used to only select antimicrobials that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of \emph{E. coli} and \emph{K. pneumoniae} and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies \href{https://www.eucast.org/expert_rules_and_expected_phenotypes}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021) to determine intrinsic resistance, using the \code{\link[=eucast_rules]{eucast_rules()}} function internally. Because of this determination, this function is quite slow in terms of performance.
 }
 \section{Full list of supported (antimicrobial) classes}{
@ -3540,23 +3550,35 @@ if (require("dplyr")) {
    mutate_if(is.mic, as.sir,
      mo = "bacteria",
      ab = "antibiotic",
-              guideline = "CLSI")
+      guideline = "CLSI"
    )
  df_long \%>\%
-    mutate(across(where(is.mic),
+    mutate(across(
-                  function(x) as.sir(x,
+      where(is.mic),
      function(x) {
        as.sir(x,
          mo = "bacteria",
          ab = "antibiotic",
-                                     guideline = "CLSI")))
+          guideline = "CLSI"
        )
      }
    ))
  df_wide \%>\%
    # given certain columns, e.g. from 'cipro' to 'genta'
    mutate_at(vars(cipro:genta), as.sir,
      mo = "bacteria",
-              guideline = "CLSI")
+      guideline = "CLSI"
    )
  df_wide \%>\%
-    mutate(across(cipro:genta,
+    mutate(across(
-                       function(x) as.sir(x,
+      cipro:genta,
      function(x) {
        as.sir(x,
          mo = "bacteria",
-                                          guideline = "CLSI")))
+          guideline = "CLSI"
        )
      }
    ))
  # for veterinary breakpoints, add 'host':
  df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
@ -3566,36 +3588,52 @@ if (require("dplyr")) {
      mo = "bacteria",
      ab = "antibiotic",
      host = "animal_species",
-              guideline = "CLSI")
+      guideline = "CLSI"
    )
  df_long \%>\%
-    mutate(across(where(is.mic),
+    mutate(across(
-                  function(x) as.sir(x,
+      where(is.mic),
      function(x) {
        as.sir(x,
          mo = "bacteria",
          ab = "antibiotic",
          host = "animal_species",
-                                     guideline = "CLSI")))
+          guideline = "CLSI"
        )
      }
    ))
  df_wide \%>\%
    mutate_at(vars(cipro:genta), as.sir,
      mo = "bacteria",
      ab = "antibiotic",
      host = "animal_species",
-              guideline = "CLSI")
+      guideline = "CLSI"
    )
  df_wide \%>\%
-    mutate(across(cipro:genta,
+    mutate(across(
-                       function(x) as.sir(x,
+      cipro:genta,
      function(x) {
        as.sir(x,
          mo = "bacteria",
          host = "animal_species",
-                                          guideline = "CLSI")))
+          guideline = "CLSI"
        )
      }
    ))
  # to include information about urinary tract infections (UTI)
-  data.frame(mo = "E. coli",
+  data.frame(
    mo = "E. coli",
    nitrofuratoin = c("<= 2", 32),
-             from_the_bladder = c(TRUE, FALSE)) \%>\%
+    from_the_bladder = c(TRUE, FALSE)
  ) \%>\%
    as.sir(uti = "from_the_bladder")
-  data.frame(mo = "E. coli",
+  data.frame(
    mo = "E. coli",
    nitrofuratoin = c("<= 2", 32),
-             specimen = c("urine", "blood")) \%>\%
+    specimen = c("urine", "blood")
  ) \%>\%
    as.sir() # automatically determines urine isolates
  df_wide \%>\%
@ -5624,8 +5662,10 @@ if (require("ggplot2") && require("dplyr")) {
  ) \%>\%
    ggplot() +
    geom_col(aes(x = x, y = y, fill = z)) +
-    scale_sir_colours(aesthetics = "fill",
+    scale_sir_colours(
-                      Value4 = "S", Value5 = "I", Value6 = "R")
+      aesthetics = "fill",
      Value4 = "S", Value5 = "I", Value6 = "R"
    )
 }
 if (require("ggplot2") && require("dplyr")) {
  # resistance of ciprofloxacine per age group
@ -7031,10 +7071,12 @@ mo_rank("Klebsiella pneumoniae")
 mo_url("Klebsiella pneumoniae")
 mo_is_yeast(c("Candida", "Trichophyton", "Klebsiella"))
-mo_group_members(c("Streptococcus group A",
+mo_group_members(c(
  "Streptococcus group A",
  "Streptococcus group C",
  "Streptococcus group G",
-                   "Streptococcus group L"))
+  "Streptococcus group L"
 ))
 # scientific reference -----------------------------------------------------
@ -7547,7 +7589,6 @@ some_mic_values <- random_mic(size = 100)
 some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
 some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))
 \donttest{
 # Plotting using ggplot2's autoplot() for MIC, disk, and SIR -----------
 if (require("ggplot2")) {
@ -7559,17 +7600,23 @@ if (require("ggplot2")) {
 }
 if (require("ggplot2")) {
  # support for 20 languages, various guidelines, and many options
-  autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro",
+  autoplot(some_disk_values,
    mo = "Escherichia coli", ab = "cipro",
    guideline = "CLSI 2024", language = "no",
-           title = "Disk diffusion from the North")
+    title = "Disk diffusion from the North"
  )
 }
 # Plotting using scale_x_mic() -----------------------------------------
 if (require("ggplot2")) {
-  mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
+  mic_plot <- ggplot(
-                                counts = c(1, 1, 2, 2, 3, 3)),
+    data.frame(
-                     aes(mics, counts)) +
+      mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
      counts = c(1, 1, 2, 2, 3, 3)
    ),
    aes(mics, counts)
  ) +
    geom_col()
  mic_plot +
    labs(title = "without scale_x_mic()")
@ -7600,17 +7647,25 @@ if (require("ggplot2")) {
 some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
 if (require("ggplot2")) {
-  ggplot(data.frame(mic = some_mic_values,
+  ggplot(
-                    group = some_groups),
+    data.frame(
-         aes(group, mic)) +
+      mic = some_mic_values,
      group = some_groups
    ),
    aes(group, mic)
  ) +
    geom_boxplot() +
    geom_violin(linetype = 2, colour = "grey", fill = NA) +
    scale_y_mic()
 }
 if (require("ggplot2")) {
-  ggplot(data.frame(mic = some_mic_values,
+  ggplot(
-                    group = some_groups),
+    data.frame(
-         aes(group, mic)) +
+      mic = some_mic_values,
      group = some_groups
    ),
    aes(group, mic)
  ) +
    geom_boxplot() +
    geom_violin(linetype = 2, colour = "grey", fill = NA) +
    scale_y_mic(mic_range = c(NA, 0.25))
@ -7619,9 +7674,13 @@ if (require("ggplot2")) {
 # Plotting using scale_x_sir() -----------------------------------------
 if (require("ggplot2")) {
-  ggplot(data.frame(x = c("I", "R", "S"),
+  ggplot(
-                    y = c(45,323, 573)),
+    data.frame(
-         aes(x, y)) +
+      x = c("I", "R", "S"),
      y = c(45, 323, 573)
    ),
    aes(x, y)
  ) +
    geom_col() +
    scale_x_sir()
 }
@ -7629,12 +7688,17 @@ if (require("ggplot2")) {
 # Plotting using scale_y_mic() and scale_colour_sir() ------------------
 if (require("ggplot2")) {
-  plain <- ggplot(data.frame(mic = some_mic_values,
+  plain <- ggplot(
    data.frame(
      mic = some_mic_values,
      group = some_groups,
      sir = as.sir(some_mic_values,
        mo = "E. coli",
-                                          ab = "cipro")),
+        ab = "cipro"
-                  aes(x = group, y = mic, colour = sir)) +
+      )
    ),
    aes(x = group, y = mic, colour = sir)
  ) +
    theme_minimal() +
    geom_boxplot(fill = NA, colour = "grey") +
    geom_jitter(width = 0.25)
@ -7650,8 +7714,10 @@ if (require("ggplot2")) {
 if (require("ggplot2")) {
  plain +
    scale_y_mic(mic_range = c(0.005, 32), name = "Our MICs!") +
-    scale_colour_sir(language = "pt",
+    scale_colour_sir(
-                     name = "Support in 20 languages")
+      language = "pt",
      name = "Support in 20 languages"
    )
 }
 }
@ -8247,15 +8313,18 @@ This function is useful for preprocessing data before creating \link[=antibiogra
 \examples{
 # filter to the top 3 species:
 top_n_microorganisms(example_isolates,
-                     n = 3)
+  n = 3
 )
 # filter to any species in the top 5 genera:
 top_n_microorganisms(example_isolates,
-                     n = 5, property = "genus")
+  n = 5, property = "genus"
 )
 # filter to the top 3 species in each of the top 5 genera:
 top_n_microorganisms(example_isolates,
-                     n = 5, property = "genus", n_for_each = 3)
+  n = 5, property = "genus", n_for_each = 3
 )
 }
 \seealso{
 \code{\link[=mo_property]{mo_property()}}, \code{\link[=as.mo]{as.mo()}}, \code{\link[=antibiogram]{antibiogram()}}
--- a/man/antibiogram.Rd
+++ b/man/antibiogram.Rd
@ -343,12 +343,14 @@ antibiogram(example_isolates,
 antibiogram(example_isolates,
  antibiotics = aminoglycosides(),
  ab_transform = "atc",
-  mo_transform = "gramstain")
+  mo_transform = "gramstain"
 )
 antibiogram(example_isolates,
  antibiotics = carbapenems(),
  ab_transform = "name",
-  mo_transform = "name")
+  mo_transform = "name"
 )
 # Combined antibiogram -------------------------------------------------
@ -356,14 +358,16 @@ antibiogram(example_isolates,
 # combined antibiotics yield higher empiric coverage
 antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-  mo_transform = "gramstain")
+  mo_transform = "gramstain"
 )
 # names of antibiotics do not need to resemble columns exactly:
 antibiogram(example_isolates,
  antibiotics = c("Cipro", "cipro + genta"),
  mo_transform = "gramstain",
  ab_transform = "name",
-  sep = " & ")
+  sep = " & "
 )
 # Syndromic antibiogram ------------------------------------------------
@ -371,7 +375,8 @@ antibiogram(example_isolates,
 # the data set could contain a filter for e.g. respiratory specimens
 antibiogram(example_isolates,
  antibiotics = c(aminoglycosides(), carbapenems()),
-  syndromic_group = "ward")
+  syndromic_group = "ward"
 )
 # now define a data set with only E. coli
 ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
@ -384,7 +389,8 @@ antibiogram(ex1,
  syndromic_group = ifelse(ex1$ward == "ICU",
    "UCI", "No UCI"
  ),
-  language = "es")
+  language = "es"
 )
 # WISCA antibiogram ----------------------------------------------------
@ -393,7 +399,8 @@ antibiogram(ex1,
 antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
  syndromic_group = "ward",
-  wisca = TRUE)
+  wisca = TRUE
 )
 # Print the output for R Markdown / Quarto -----------------------------
@ -401,7 +408,8 @@ antibiogram(example_isolates,
 ureido <- antibiogram(example_isolates,
  antibiotics = ureidopenicillins(),
  syndromic_group = "ward",
-  wisca = TRUE)
+  wisca = TRUE
 )
 # in an Rmd file, you would just need to return `ureido` in a chunk,
 # but to be explicit here:
@ -414,11 +422,13 @@ if (requireNamespace("knitr")) {
 ab1 <- antibiogram(example_isolates,
  antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
-  mo_transform = "gramstain")
+  mo_transform = "gramstain"
 )
 ab2 <- antibiogram(example_isolates,
  antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
  mo_transform = "gramstain",
-  syndromic_group = "ward")
+  syndromic_group = "ward"
 )
 if (requireNamespace("ggplot2")) {
  ggplot2::autoplot(ab1)
--- a/man/antimicrobial_selectors.Rd
+++ b/man/antimicrobial_selectors.Rd
@ -2,8 +2,6 @@
 % Please edit documentation in R/amr_selectors.R
 \name{antimicrobial_selectors}
 \alias{antimicrobial_selectors}
 \alias{amr_class}
 \alias{amr_selector}
 \alias{aminoglycosides}
 \alias{aminopenicillins}
 \alias{antifungals}
@ -35,17 +33,13 @@
 \alias{tetracyclines}
 \alias{trimethoprims}
 \alias{ureidopenicillins}
 \alias{amr_class}
 \alias{amr_selector}
 \alias{administrable_per_os}
 \alias{administrable_iv}
 \alias{not_intrinsic_resistant}
 \title{Antimicrobial Selectors}
 \usage{
 amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
 amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
 aminoglycosides(only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
@ -114,6 +108,12 @@ trimethoprims(only_sir_columns = FALSE, return_all = TRUE, ...)
 ureidopenicillins(only_sir_columns = FALSE, return_all = TRUE, ...)
 amr_class(amr_class, only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
 amr_selector(filter, only_sir_columns = FALSE, only_treatable = TRUE,
  return_all = TRUE, ...)
 administrable_per_os(only_sir_columns = FALSE, return_all = TRUE, ...)
 administrable_iv(only_sir_columns = FALSE, return_all = TRUE, ...)
@ -122,8 +122,6 @@ not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL,
  version_expertrules = 3.3, ...)
 }
 \arguments{
 \item{amr_class}{an antimicrobial class or a part of it, such as \code{"carba"} and \code{"carbapenems"}. The columns \code{group}, \code{atc_group1} and \code{atc_group2} of the \link{antibiotics} data set will be searched (case-insensitive) for this value.}
 \item{only_sir_columns}{a \link{logical} to indicate whether only columns of class \code{sir} must be selected (default is \code{FALSE}), see \code{\link[=as.sir]{as.sir()}}}
 \item{only_treatable}{a \link{logical} to indicate whether antimicrobial drugs should be excluded that are only for laboratory tests (default is \code{TRUE}), such as gentamicin-high (\code{GEH}) and imipenem/EDTA (\code{IPE})}
@ -132,6 +130,8 @@ not_intrinsic_resistant(only_sir_columns = FALSE, col_mo = NULL,
 \item{...}{ignored, only in place to allow future extensions}
 \item{amr_class}{an antimicrobial class or a part of it, such as \code{"carba"} and \code{"carbapenems"}. The columns \code{group}, \code{atc_group1} and \code{atc_group2} of the \link{antibiotics} data set will be searched (case-insensitive) for this value.}
 \item{filter}{an \link{expression} to be evaluated in the \link{antibiotics} data set, such as \code{name \%like\% "trim"}}
 \item{col_mo}{column name of the names or codes of the microorganisms (see \code{\link[=as.mo]{as.mo()}}) - the default is the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}
@ -160,10 +160,10 @@ All columns in the data in which these functions are called will be searched for
 The \code{\link[=amr_class]{amr_class()}} function can be used to filter/select on a manually defined antimicrobial class. It searches for results in the \link{antibiotics} data set within the columns \code{group}, \code{atc_group1} and \code{atc_group2}.
 The \code{\link[=amr_selector]{amr_selector()}} function can be used to internally filter the \link{antibiotics} data set on any results, see \emph{Examples}. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
 The \code{\link[=administrable_per_os]{administrable_per_os()}} and \code{\link[=administrable_iv]{administrable_iv()}} functions also rely on the \link{antibiotics} data set - antimicrobials will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in the \link{antibiotics} data set.
 The \code{\link[=amr_selector]{amr_selector()}} function can be used to internally filter the \link{antibiotics} data set on any results, see \emph{Examples}. It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
 The \code{\link[=not_intrinsic_resistant]{not_intrinsic_resistant()}} function can be used to only select antimicrobials that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of \emph{E. coli} and \emph{K. pneumoniae} and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies \href{https://www.eucast.org/expert_rules_and_expected_phenotypes}{'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3} (2021) to determine intrinsic resistance, using the \code{\link[=eucast_rules]{eucast_rules()}} function internally. Because of this determination, this function is quite slow in terms of performance.
 }
 \section{Full list of supported (antimicrobial) classes}{
--- a/man/as.sir.Rd
+++ b/man/as.sir.Rd
@ -265,23 +265,35 @@ if (require("dplyr")) {
    mutate_if(is.mic, as.sir,
      mo = "bacteria",
      ab = "antibiotic",
-              guideline = "CLSI")
+      guideline = "CLSI"
    )
  df_long \%>\%
-    mutate(across(where(is.mic),
+    mutate(across(
-                  function(x) as.sir(x,
+      where(is.mic),
      function(x) {
        as.sir(x,
          mo = "bacteria",
          ab = "antibiotic",
-                                     guideline = "CLSI")))
+          guideline = "CLSI"
        )
      }
    ))
  df_wide \%>\%
    # given certain columns, e.g. from 'cipro' to 'genta'
    mutate_at(vars(cipro:genta), as.sir,
      mo = "bacteria",
-              guideline = "CLSI")
+      guideline = "CLSI"
    )
  df_wide \%>\%
-    mutate(across(cipro:genta,
+    mutate(across(
-                       function(x) as.sir(x,
+      cipro:genta,
      function(x) {
        as.sir(x,
          mo = "bacteria",
-                                          guideline = "CLSI")))
+          guideline = "CLSI"
        )
      }
    ))
  # for veterinary breakpoints, add 'host':
  df_long$animal_species <- c("cats", "dogs", "horses", "cattle")
@ -291,36 +303,52 @@ if (require("dplyr")) {
      mo = "bacteria",
      ab = "antibiotic",
      host = "animal_species",
-              guideline = "CLSI")
+      guideline = "CLSI"
    )
  df_long \%>\%
-    mutate(across(where(is.mic),
+    mutate(across(
-                  function(x) as.sir(x,
+      where(is.mic),
      function(x) {
        as.sir(x,
          mo = "bacteria",
          ab = "antibiotic",
          host = "animal_species",
-                                     guideline = "CLSI")))
+          guideline = "CLSI"
        )
      }
    ))
  df_wide \%>\%
    mutate_at(vars(cipro:genta), as.sir,
      mo = "bacteria",
      ab = "antibiotic",
      host = "animal_species",
-              guideline = "CLSI")
+      guideline = "CLSI"
    )
  df_wide \%>\%
-    mutate(across(cipro:genta,
+    mutate(across(
-                       function(x) as.sir(x,
+      cipro:genta,
      function(x) {
        as.sir(x,
          mo = "bacteria",
          host = "animal_species",
-                                          guideline = "CLSI")))
+          guideline = "CLSI"
        )
      }
    ))
  # to include information about urinary tract infections (UTI)
-  data.frame(mo = "E. coli",
+  data.frame(
    mo = "E. coli",
    nitrofuratoin = c("<= 2", 32),
-             from_the_bladder = c(TRUE, FALSE)) \%>\%
+    from_the_bladder = c(TRUE, FALSE)
  ) \%>\%
    as.sir(uti = "from_the_bladder")
-  data.frame(mo = "E. coli",
+  data.frame(
    mo = "E. coli",
    nitrofuratoin = c("<= 2", 32),
-             specimen = c("urine", "blood")) \%>\%
+    specimen = c("urine", "blood")
  ) \%>\%
    as.sir() # automatically determines urine isolates
  df_wide \%>\%
--- a/man/ggplot_sir.Rd
+++ b/man/ggplot_sir.Rd
@ -139,8 +139,10 @@ if (require("ggplot2") && require("dplyr")) {
  ) \%>\%
    ggplot() +
    geom_col(aes(x = x, y = y, fill = z)) +
-    scale_sir_colours(aesthetics = "fill",
+    scale_sir_colours(
-                      Value4 = "S", Value5 = "I", Value6 = "R")
+      aesthetics = "fill",
      Value4 = "S", Value5 = "I", Value6 = "R"
    )
 }
 if (require("ggplot2") && require("dplyr")) {
  # resistance of ciprofloxacine per age group
--- a/man/mo_property.Rd
+++ b/man/mo_property.Rd
@ -262,10 +262,12 @@ mo_rank("Klebsiella pneumoniae")
 mo_url("Klebsiella pneumoniae")
 mo_is_yeast(c("Candida", "Trichophyton", "Klebsiella"))
-mo_group_members(c("Streptococcus group A",
+mo_group_members(c(
  "Streptococcus group A",
  "Streptococcus group C",
  "Streptococcus group G",
-                   "Streptococcus group L"))
+  "Streptococcus group L"
 ))
 # scientific reference -----------------------------------------------------
--- a/man/plot.Rd
+++ b/man/plot.Rd
@ -201,7 +201,6 @@ some_mic_values <- random_mic(size = 100)
 some_disk_values <- random_disk(size = 100, mo = "Escherichia coli", ab = "cipro")
 some_sir_values <- random_sir(50, prob_SIR = c(0.55, 0.05, 0.30))
 \donttest{
 # Plotting using ggplot2's autoplot() for MIC, disk, and SIR -----------
 if (require("ggplot2")) {
@ -213,17 +212,23 @@ if (require("ggplot2")) {
 }
 if (require("ggplot2")) {
  # support for 20 languages, various guidelines, and many options
-  autoplot(some_disk_values, mo = "Escherichia coli", ab = "cipro",
+  autoplot(some_disk_values,
    mo = "Escherichia coli", ab = "cipro",
    guideline = "CLSI 2024", language = "no",
-           title = "Disk diffusion from the North")
+    title = "Disk diffusion from the North"
  )
 }
 # Plotting using scale_x_mic() -----------------------------------------
 if (require("ggplot2")) {
-  mic_plot <- ggplot(data.frame(mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
+  mic_plot <- ggplot(
-                                counts = c(1, 1, 2, 2, 3, 3)),
+    data.frame(
-                     aes(mics, counts)) +
+      mics = as.mic(c(0.25, "<=4", 4, 8, 32, ">=32")),
      counts = c(1, 1, 2, 2, 3, 3)
    ),
    aes(mics, counts)
  ) +
    geom_col()
  mic_plot +
    labs(title = "without scale_x_mic()")
@ -254,17 +259,25 @@ if (require("ggplot2")) {
 some_groups <- sample(LETTERS[1:5], 20, replace = TRUE)
 if (require("ggplot2")) {
-  ggplot(data.frame(mic = some_mic_values,
+  ggplot(
-                    group = some_groups),
+    data.frame(
-         aes(group, mic)) +
+      mic = some_mic_values,
      group = some_groups
    ),
    aes(group, mic)
  ) +
    geom_boxplot() +
    geom_violin(linetype = 2, colour = "grey", fill = NA) +
    scale_y_mic()
 }
 if (require("ggplot2")) {
-  ggplot(data.frame(mic = some_mic_values,
+  ggplot(
-                    group = some_groups),
+    data.frame(
-         aes(group, mic)) +
+      mic = some_mic_values,
      group = some_groups
    ),
    aes(group, mic)
  ) +
    geom_boxplot() +
    geom_violin(linetype = 2, colour = "grey", fill = NA) +
    scale_y_mic(mic_range = c(NA, 0.25))
@ -273,9 +286,13 @@ if (require("ggplot2")) {
 # Plotting using scale_x_sir() -----------------------------------------
 if (require("ggplot2")) {
-  ggplot(data.frame(x = c("I", "R", "S"),
+  ggplot(
-                    y = c(45,323, 573)),
+    data.frame(
-         aes(x, y)) +
+      x = c("I", "R", "S"),
      y = c(45, 323, 573)
    ),
    aes(x, y)
  ) +
    geom_col() +
    scale_x_sir()
 }
@ -283,12 +300,17 @@ if (require("ggplot2")) {
 # Plotting using scale_y_mic() and scale_colour_sir() ------------------
 if (require("ggplot2")) {
-  plain <- ggplot(data.frame(mic = some_mic_values,
+  plain <- ggplot(
    data.frame(
      mic = some_mic_values,
      group = some_groups,
      sir = as.sir(some_mic_values,
        mo = "E. coli",
-                                          ab = "cipro")),
+        ab = "cipro"
-                  aes(x = group, y = mic, colour = sir)) +
+      )
    ),
    aes(x = group, y = mic, colour = sir)
  ) +
    theme_minimal() +
    geom_boxplot(fill = NA, colour = "grey") +
    geom_jitter(width = 0.25)
@ -304,8 +326,10 @@ if (require("ggplot2")) {
 if (require("ggplot2")) {
  plain +
    scale_y_mic(mic_range = c(0.005, 32), name = "Our MICs!") +
-    scale_colour_sir(language = "pt",
+    scale_colour_sir(
-                     name = "Support in 20 languages")
+      language = "pt",
      name = "Support in 20 languages"
    )
 }
 }
--- a/man/top_n_microorganisms.Rd
+++ b/man/top_n_microorganisms.Rd
@ -29,15 +29,18 @@ This function is useful for preprocessing data before creating \link[=antibiogra
 \examples{
 # filter to the top 3 species:
 top_n_microorganisms(example_isolates,
-                     n = 3)
+  n = 3
 )
 # filter to any species in the top 5 genera:
 top_n_microorganisms(example_isolates,
-                     n = 5, property = "genus")
+  n = 5, property = "genus"
 )
 # filter to the top 3 species in each of the top 5 genera:
 top_n_microorganisms(example_isolates,
-                     n = 5, property = "genus", n_for_each = 3)
+  n = 5, property = "genus", n_for_each = 3
 )
 }
 \seealso{
 \code{\link[=mo_property]{mo_property()}}, \code{\link[=as.mo]{as.mo()}}, \code{\link[=antibiogram]{antibiogram()}}
--- a/tests/testthat.R
+++ b/tests/testthat.R
@ -33,8 +33,11 @@ library(AMR)
 # add functions from the tinytest package (which we use for older R versions)
 expect_inherits <- function(x, y, ...) {
  expect(inherits(x, y),
-         failure_message = paste0("object has class ", paste0(class(x), collapse = "/"),
+    failure_message = paste0(
-                                  ", required is class ", paste0(y, collapse = "/")))
+      "object has class ", paste0(class(x), collapse = "/"),
      ", required is class ", paste0(y, collapse = "/")
    )
  )
 }
 expect_stdout <- expect_output
--- a/tests/testthat/test-ab.R
+++ b/tests/testthat/test-ab.R
@ -80,15 +80,19 @@ expect_equal(
 # based on Levenshtein distance
 expect_identical(ab_name("ceftazidim/avibactam", language = NULL), "Ceftazidime/avibactam")
-expect_identical(as.character(as.ab(c("gentamicine High Level",
+expect_identical(
  as.character(as.ab(c(
    "gentamicine High Level",
    "gentamicine High",
    "gentamicine (High Level)",
    "gentamicine (High)",
    "gentamicine HL",
    "gentamicine H-L",
    "gentamicine (HL)",
-                                      "gentamicine (H-L)"))),
+    "gentamicine (H-L)"
-                 rep("GEH", 8))
+  ))),
  rep("GEH", 8)
 )
 # assigning and subsetting
 x <- antibiotics$ab
--- a/tests/testthat/test-ab_property.R
+++ b/tests/testthat/test-ab_property.R
@ -96,4 +96,3 @@ if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
      set_ab_names(NIT:VAN) %>%
      colnames())))
 }
--- a/tests/testthat/test-age.R
+++ b/tests/testthat/test-age.R
@ -35,13 +35,14 @@ expect_equal(
  c(39, 34, 29)
 )
-expect_equal(age(
+expect_equal(
  age(
    x = c("2019-01-01", "2019-04-01", "2019-07-01"),
    reference = "2019-09-01",
    exact = TRUE
-),
+  ),
-c(0.6656393, 0.4191781, 0.1698630),
+  c(0.6656393, 0.4191781, 0.1698630),
-tolerance = 0.001
+  tolerance = 0.001
 )
 expect_error(age(
--- a/tests/testthat/test-antibiogram.R
+++ b/tests/testthat/test-antibiogram.R
@ -31,19 +31,22 @@
 # Traditional antibiogram ----------------------------------------------
 ab1 <- antibiogram(example_isolates,
-                   antibiotics = c(aminoglycosides(), carbapenems()))
+  antibiotics = c(aminoglycosides(), carbapenems())
 )
 ab2 <- antibiogram(example_isolates,
  antibiotics = aminoglycosides(),
  ab_transform = "atc",
  mo_transform = "gramstain",
-                   add_total_n = TRUE)
+  add_total_n = TRUE
 )
 ab3 <- antibiogram(example_isolates,
  antibiotics = carbapenems(),
  ab_transform = "ab",
  mo_transform = "name",
-                   formatting_type = 1)
+  formatting_type = 1
 )
 expect_inherits(ab1, "antibiogram")
 expect_inherits(ab2, "antibiogram")
@ -58,14 +61,16 @@ expect_equal(ab3$MEM, c(52, NA, 100, 100, NA))
 # combined antibiotics yield higher empiric coverage
 ab4 <- antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-                   mo_transform = "gramstain")
+  mo_transform = "gramstain"
 )
 ab5 <- antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB"),
  mo_transform = "gramstain",
  ab_transform = "name",
  sep = " & ",
-                   add_total_n = FALSE)
+  add_total_n = FALSE
 )
 expect_inherits(ab4, "antibiogram")
 expect_inherits(ab5, "antibiogram")
@ -78,7 +83,8 @@ expect_equal(colnames(ab5), c("Pathogen", "Piperacillin/tazobactam", "Piperacill
 ab6 <- antibiogram(example_isolates,
  antibiotics = c(aminoglycosides(), carbapenems()),
  syndromic_group = "ward",
-                   ab_transform = NULL)
+  ab_transform = NULL
 )
 # with a custom language, though this will be determined automatically
 # (i.e., this table will be in Dutch on Dutch systems)
@ -87,9 +93,11 @@ ab7 <- antibiogram(ex1,
  antibiotics = aminoglycosides(),
  ab_transform = "name",
  syndromic_group = ifelse(ex1$ward == "ICU",
-                                            "IC", "Geen IC"),
+    "IC", "Geen IC"
  ),
  language = "nl",
-                   add_total_n = TRUE)
+  add_total_n = TRUE
 )
 expect_inherits(ab6, "antibiogram")
 expect_inherits(ab7, "antibiogram")
@ -101,7 +109,8 @@ expect_equal(colnames(ab7), c("Syndroomgroep", "Pathogeen (N min-max)", "Amikaci
 # the data set could contain a filter for e.g. respiratory specimens
 ab8 <- suppressWarnings(antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
-                                    wisca = TRUE))
+  wisca = TRUE
 ))
 expect_inherits(ab8, "antibiogram")
 expect_equal(colnames(ab8), c("Piperacillin/tazobactam", "Piperacillin/tazobactam + Gentamicin", "Piperacillin/tazobactam + Tobramycin"))
--- a/tests/testthat/test-av_property.R
+++ b/tests/testthat/test-av_property.R
@ -34,7 +34,7 @@ expect_identical(av_cid("ACI"), as.integer(135398513))
 expect_inherits(av_tradenames("ACI"), "character")
 expect_inherits(av_tradenames(c("ACI", "ACI")), "list")
-expect_identical(av_group("ACI", language = NULL),"Nucleosides and nucleotides excl. reverse transcriptase inhibitors")
+expect_identical(av_group("ACI", language = NULL), "Nucleosides and nucleotides excl. reverse transcriptase inhibitors")
 expect_identical(av_name(135398513, language = NULL), "Aciclovir")
 expect_identical(av_name("J05AB01", language = NULL), "Aciclovir")
--- a/tests/testthat/test-custom_antimicrobials.R
+++ b/tests/testthat/test-custom_antimicrobials.R
@ -33,9 +33,11 @@ expect_message(as.ab("testab", info = TRUE))
 suppressMessages(
  add_custom_antimicrobials(
-    data.frame(ab = "TESTAB",
+    data.frame(
      ab = "TESTAB",
      name = "Test Antibiotic",
-               group = "Test Group")
+      group = "Test Group"
    )
  )
 )
--- a/tests/testthat/test-custom_microorganisms.R
+++ b/tests/testthat/test-custom_microorganisms.R
@ -27,14 +27,18 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #
-expect_identical(as.mo("Enterobacter asburiae/cloacae"),
+expect_identical(
-                 as.mo("Enterobacter asburiae"))
+  as.mo("Enterobacter asburiae/cloacae"),
  as.mo("Enterobacter asburiae")
 )
 suppressMessages(
  add_custom_microorganisms(
-    data.frame(mo = "ENT_ASB_CLO",
+    data.frame(
      mo = "ENT_ASB_CLO",
      genus = "Enterobacter",
-               species = "asburiae/cloacae")
+      species = "asburiae/cloacae"
    )
  )
 )
@ -44,8 +48,12 @@ expect_identical(mo_gramstain("ENT_ASB_CLO", language = NULL), "Gram-negative")
 if (getRversion() >= "3.3.0") {
  # until R 3.2, abbreviate() used a completely different algorithm, making these tests unreproducible
-  expect_identical(paste("B", AMR:::abbreviate_mo("Klebsiella"), AMR:::abbreviate_mo("pneumoniae", 4), sep = "_"),
+  expect_identical(
-                   as.character(as.mo("Klebsiella pneumoniae")))
+    paste("B", AMR:::abbreviate_mo("Klebsiella"), AMR:::abbreviate_mo("pneumoniae", 4), sep = "_"),
-  expect_identical(paste("B", AMR:::abbreviate_mo("Aerococcus"), AMR:::abbreviate_mo("urinae", 4), sep = "_"),
+    as.character(as.mo("Klebsiella pneumoniae"))
-                   as.character(as.mo("Aerococcus urinae")))
+  )
  expect_identical(
    paste("B", AMR:::abbreviate_mo("Aerococcus"), AMR:::abbreviate_mo("urinae", 4), sep = "_"),
    as.character(as.mo("Aerococcus urinae"))
  )
 }
--- a/tests/testthat/test-eucast_rules.R
+++ b/tests/testthat/test-eucast_rules.R
@ -117,15 +117,16 @@ if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
 }
 # azithromycin and clarythromycin must be equal to Erythromycin
-a <- suppressWarnings(as.sir(eucast_rules(data.frame(
+a <- suppressWarnings(as.sir(eucast_rules(
  data.frame(
    mo = example_isolates$mo,
    ERY = example_isolates$ERY,
    AZM = as.sir("R"),
    CLR = factor("R"),
    stringsAsFactors = FALSE
-),
+  ),
-version_expertrules = 3.1,
+  version_expertrules = 3.1,
-only_sir_columns = FALSE
+  only_sir_columns = FALSE
 )$CLR))
 b <- example_isolates$ERY
 expect_identical(
@ -160,7 +161,8 @@ expect_stdout(suppressWarnings(eucast_rules(example_isolates, verbose = TRUE, ru
 # AmpC de-repressed cephalo mutants
 expect_identical(
-  eucast_rules(data.frame(
+  eucast_rules(
    data.frame(
      mo = c("Escherichia coli", "Enterobacter cloacae"),
      cefotax = as.sir(c("S", "S"))
    ),
@ -171,7 +173,8 @@ expect_identical(
 )
 expect_identical(
-  eucast_rules(data.frame(
+  eucast_rules(
    data.frame(
      mo = c("Escherichia coli", "Enterobacter cloacae"),
      cefotax = as.sir(c("S", "S"))
    ),
@ -182,7 +185,8 @@ expect_identical(
 )
 expect_identical(
-  eucast_rules(data.frame(
+  eucast_rules(
    data.frame(
      mo = c("Escherichia coli", "Enterobacter cloacae"),
      cefotax = as.sir(c("S", "S"))
    ),
@ -208,12 +212,13 @@ expect_stdout(print(c(x, x)))
 expect_stdout(print(as.list(x, x)))
 # this custom rules makes 8 changes
-expect_equal(nrow(eucast_rules(example_isolates,
+expect_equal(
  nrow(eucast_rules(example_isolates,
    rules = "custom",
    custom_rules = x,
    info = FALSE,
    verbose = TRUE
-)),
+  )),
-8,
+  8,
-tolerance = 0.5
+  tolerance = 0.5
 )
--- a/tests/testthat/test-first_isolate.R
+++ b/tests/testthat/test-first_isolate.R
@ -46,17 +46,28 @@ expect_equal(
 )
 # for phenotype determination
-expect_equal(AMR:::duplicated_antibiogram("SSSS", points_threshold = 2, ignore_I = TRUE, type = "points"),
+expect_equal(
-             FALSE)
+  AMR:::duplicated_antibiogram("SSSS", points_threshold = 2, ignore_I = TRUE, type = "points"),
-expect_equal(AMR:::duplicated_antibiogram(c("RRR", "SSS"),
+  FALSE
-                                          points_threshold = 2, ignore_I = TRUE, type = "points"),
+)
-             c(FALSE, FALSE))
+expect_equal(
-expect_equal(AMR:::duplicated_antibiogram(c("RRR", "RRR", "SSS"),
+  AMR:::duplicated_antibiogram(c("RRR", "SSS"),
-                                          points_threshold = 2, ignore_I = TRUE, type = "points"),
+    points_threshold = 2, ignore_I = TRUE, type = "points"
-             c(FALSE, TRUE, FALSE))
+  ),
-expect_equal(AMR:::duplicated_antibiogram(c("RRR", "RSS", "SSS", "RSS", "RRR", "RRR", "SSS", "RSS", "RSR", "RRR"),
+  c(FALSE, FALSE)
-                                          points_threshold = 2, ignore_I = TRUE, type = "points"),
+)
-             c(FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE))
+expect_equal(
  AMR:::duplicated_antibiogram(c("RRR", "RRR", "SSS"),
    points_threshold = 2, ignore_I = TRUE, type = "points"
  ),
  c(FALSE, TRUE, FALSE)
 )
 expect_equal(
  AMR:::duplicated_antibiogram(c("RRR", "RSS", "SSS", "RSS", "RRR", "RRR", "SSS", "RSS", "RSR", "RRR"),
    points_threshold = 2, ignore_I = TRUE, type = "points"
  ),
  c(FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE, TRUE)
 )
 # Phenotype-based, using key antimicrobials
 expect_equal(
@ -89,7 +100,9 @@ expect_true(
      col_icu = example_isolates$ward == "ICU",
      info = TRUE,
      icu_exclude = TRUE
-  ), na.rm = TRUE) < 950
+    ),
    na.rm = TRUE
  ) < 950
 )
 # set 1500 random observations to be of specimen type 'Urine'
--- a/tests/testthat/test-ggplot_sir.R
+++ b/tests/testthat/test-ggplot_sir.R
@ -44,14 +44,18 @@ if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE) &&
      as.double()
  )
-  expect_inherits(example_isolates %>%
+  expect_inherits(
    example_isolates %>%
      select(AMC, CIP) %>%
      ggplot_sir(x = "interpretation", facet = "antibiotic"),
-                  "gg")
+    "gg"
-  expect_inherits(example_isolates %>%
+  )
  expect_inherits(
    example_isolates %>%
      select(AMC, CIP) %>%
      ggplot_sir(x = "antibiotic", facet = "interpretation"),
-                  "gg")
+    "gg"
  )
  expect_equal(
    (example_isolates %>%
--- a/tests/testthat/test-mdro.R
+++ b/tests/testthat/test-mdro.R
@ -55,7 +55,8 @@ expect_equal(
 # test Dutch P. aeruginosa MDRO
 expect_equal(
-  as.character(mdro(data.frame(
+  as.character(mdro(
    data.frame(
      mo = as.mo("P. aeruginosa"),
      cfta = "S",
      cipr = "S",
@ -72,7 +73,8 @@ expect_equal(
  "Negative"
 )
 expect_equal(
-  as.character(mdro(data.frame(
+  as.character(mdro(
    data.frame(
      mo = as.mo("P. aeruginosa"),
      cefta = "R",
      cipr = "R",
--- a/tests/testthat/test-mic.R
+++ b/tests/testthat/test-mic.R
@ -176,4 +176,3 @@ expect_true(as.mic("32") <= as.mic(32))
 expect_false(as.mic("32") <= as.mic("<32"))
 expect_true(as.mic("32") <= as.mic("<=32"))
 expect_false(as.mic("32") < as.mic("<=32"))
--- a/tests/testthat/test-mo_property.R
+++ b/tests/testthat/test-mo_property.R
@ -78,8 +78,10 @@ current_grampos_classes <- c(
  "Thermoleophilia",
  "Thermolithobacteria"
 )
-expect_identical(sort(unique(microorganisms[which(microorganisms$phylum %in% current_grampos_phyla), "class", drop = TRUE])),
+expect_identical(
-                 current_grampos_classes)
+  sort(unique(microorganisms[which(microorganisms$phylum %in% current_grampos_phyla), "class", drop = TRUE])),
  current_grampos_classes
 )
 expect_equal(mo_species("Escherichia coli"), "coli")
 expect_equal(mo_subspecies("Escherichia coli"), "")
@ -103,11 +105,15 @@ expect_inherits(mo_info(c("Escherichia coli", "Staphylococcus aureus")), "list")
 expect_true(length(mo_group_members("B_HACEK")) > 1)
 expect_inherits(mo_group_members(c("Candida albicans", "Escherichia coli")), "list")
-expect_identical(mo_oxygen_tolerance(c("Klebsiella pneumoniae", "Clostridioides difficile")),
+expect_identical(
-                 c("facultative anaerobe", "anaerobe"))
+  mo_oxygen_tolerance(c("Klebsiella pneumoniae", "Clostridioides difficile")),
  c("facultative anaerobe", "anaerobe")
 )
-expect_equal(as.character(table(mo_pathogenicity(example_isolates$mo))),
+expect_equal(
-             c("1911", "72", "1", "16"))
+  as.character(table(mo_pathogenicity(example_isolates$mo))),
  c("1911", "72", "1", "16")
 )
 expect_equal(mo_ref("Escherichia coli"), "Castellani et al., 1919")
 expect_equal(mo_authors("Escherichia coli"), "Castellani et al.")
@ -118,8 +124,10 @@ expect_true(mo_url("Candida albicans") %like% "mycobank.org")
 expect_true(mo_url("Escherichia coli") %like% "lpsn.dsmz.de")
 # test integrity of getting back full names
-expect_identical(microorganisms$fullname[microorganisms$fullname %unlike% "(Fungi|{)"],
+expect_identical(
-                 suppressWarnings(mo_fullname(microorganisms$fullname[microorganisms$fullname %unlike% "(Fungi|{)"], language = "en", keep_synonyms = TRUE)))
+  microorganisms$fullname[microorganisms$fullname %unlike% "(Fungi|{)"],
  suppressWarnings(mo_fullname(microorganisms$fullname[microorganisms$fullname %unlike% "(Fungi|{)"], language = "en", keep_synonyms = TRUE))
 )
 # check languages
 expect_equal(mo_type("Escherichia coli", language = "de"), "Bakterien")
@ -169,8 +177,10 @@ expect_identical(
 expect_true("Escherichia blattae" %in% mo_synonyms("Shimwellia blattae"))
 expect_true(is.list(mo_synonyms(rep("Shimwellia blattae", 2))))
-expect_identical(mo_current(c("Escherichia blattae", "Escherichia coli")),
+expect_identical(
-                 c("Shimwellia blattae", "Escherichia coli"))
+  mo_current(c("Escherichia blattae", "Escherichia coli")),
  c("Shimwellia blattae", "Escherichia coli")
 )
 expect_identical(mo_ref("Chlamydia psittaci"), "Garcia-Lopez et al., 2019")
 expect_identical(mo_ref("Chlamydophila psittaci", keep_synonyms = TRUE), "Everett et al., 1999")
--- a/tests/testthat/test-pca.R
+++ b/tests/testthat/test-pca.R
@ -27,7 +27,8 @@
 # how to conduct AMR data analysis: https://msberends.github.io/AMR/   #
 # ==================================================================== #
-resistance_data <- structure(list(
+resistance_data <- structure(
  list(
    order = c("Bacillales", "Enterobacterales", "Enterobacterales"),
    genus = c("Staphylococcus", "Escherichia", "Klebsiella"),
    AMC = c(0.00425, 0.13062, 0.10344),
@ -35,17 +36,18 @@ resistance_data <- structure(list(
    CTX = c(0.00000, 0.02396, 0.05172),
    TOB = c(0.02325, 0.02597, 0.10344),
    TMP = c(0.08387, 0.39141, 0.18367)
-),
+  ),
-class = c("grouped_df", "tbl_df", "tbl", "data.frame"),
+  class = c("grouped_df", "tbl_df", "tbl", "data.frame"),
-row.names = c(NA, -3L),
+  row.names = c(NA, -3L),
-groups = structure(list(
+  groups = structure(
    list(
      order = c("Bacillales", "Enterobacterales"),
      .rows = list(1L, 2:3)
-),
+    ),
-row.names = c(NA, -2L),
+    row.names = c(NA, -2L),
-class = c("tbl_df", "tbl", "data.frame"),
+    class = c("tbl_df", "tbl", "data.frame"),
-.drop = TRUE
+    .drop = TRUE
-)
+  )
 )
 pca_model <- pca(resistance_data)
 expect_inherits(pca_model, "pca")
--- a/tests/testthat/test-plotting.R
+++ b/tests/testthat/test-plotting.R
@ -38,7 +38,8 @@ if (AMR:::pkg_is_available("ggplot2", also_load = TRUE)) {
    scale_obj <- getExportedValue("ggplot2", scale_fn_name)()
    for (method in expected_methods) {
      expect_true(is.function(scale_obj[[method]]) || method %in% names(scale_obj),
-                  info = paste0("Method '", method, "' is missing in ggplot2::", scale_fn_name))
+        info = paste0("Method '", method, "' is missing in ggplot2::", scale_fn_name)
      )
    }
  }
@ -50,38 +51,64 @@ if (AMR:::pkg_is_available("ggplot2", also_load = TRUE)) {
    scale_obj <- getExportedValue("ggplot2", scale_fn_name)(aesthetics = aest)
    for (method in expected_methods) {
      expect_true(is.function(scale_obj[[method]]) || method %in% names(scale_obj),
-                  info = paste0("Method '", method, "' is missing in ggplot2::", scale_fn_name))
+        info = paste0("Method '", method, "' is missing in ggplot2::", scale_fn_name)
      )
    }
  }
  for (method in expected_methods) {
    expect_true(is.function(ggplot2::scale_x_discrete()[[method]]) || method %in% names(ggplot2::scale_x_discrete()),
-                info = paste0("Method '", method, "' is missing in ggplot2::", "scale_x_discrete"))
+      info = paste0("Method '", method, "' is missing in ggplot2::", "scale_x_discrete")
    )
  }
-  expect_inherits(ggplot(data.frame(count = c(1,2,3, 4),
+  expect_inherits(
-                                    sir = c("S", "I", "R", "SDD")),
+    ggplot(
-                         aes(x = sir, y = count, fill = sir)) +
+      data.frame(
        count = c(1, 2, 3, 4),
        sir = c("S", "I", "R", "SDD")
      ),
      aes(x = sir, y = count, fill = sir)
    ) +
      geom_col() +
      scale_x_sir(eucast_I = F, language = "el") +
      scale_fill_sir(eucast_I = T, language = "nl"),
-                  "gg")
+    "gg"
-  expect_inherits(ggplot(data.frame(mic = as.mic(c(2,4,8, 16)),
+  )
-                                    sir = as.sir(c("S", "I", "R", "SDD"))),
+  expect_inherits(
-                         aes(x = sir, y = mic)) +
+    ggplot(
      data.frame(
        mic = as.mic(c(2, 4, 8, 16)),
        sir = as.sir(c("S", "I", "R", "SDD"))
      ),
      aes(x = sir, y = mic)
    ) +
      geom_point() +
      scale_y_mic(),
-                  "gg")
+    "gg"
-  expect_inherits(ggplot(data.frame(mic = as.mic(c(2,4,8, 16)),
+  )
-                                    sir = as.sir(c("S", "I", "R", "SDD"))),
+  expect_inherits(
-                         aes(x = sir, y = mic)) +
+    ggplot(
      data.frame(
        mic = as.mic(c(2, 4, 8, 16)),
        sir = as.sir(c("S", "I", "R", "SDD"))
      ),
      aes(x = sir, y = mic)
    ) +
      geom_col() +
      scale_y_mic(),
-                  "gg")
+    "gg"
-  expect_inherits(ggplot(data.frame(mic = as.mic(c(2,4,8, 16)),
+  )
-                                    sir = as.sir(c("S", "I", "R", "SDD"))),
+  expect_inherits(
-                         aes(x = sir, y = mic)) +
+    ggplot(
      data.frame(
        mic = as.mic(c(2, 4, 8, 16)),
        sir = as.sir(c("S", "I", "R", "SDD"))
      ),
      aes(x = sir, y = mic)
    ) +
      geom_col() +
-                    scale_y_mic(mic_range = c(4,16)) +
+      scale_y_mic(mic_range = c(4, 16)) +
      scale_x_sir(),
-                  "gg")
+    "gg"
  )
 }
--- a/tests/testthat/test-sir.R
+++ b/tests/testthat/test-sir.R
@ -124,29 +124,45 @@ expect_equal(as.sir(c("", "-", NA, "NULL")), c(NA_sir_, NA_sir_, NA_sir_, NA_sir
 # Human -------------------------------------------------------------------
-mics <- as.mic(2 ^ c(-4:6)) # 0.0625 to 64 in factors of 2
+mics <- as.mic(2^c(-4:6)) # 0.0625 to 64 in factors of 2
-expect_identical(as.character(as.sir(mics, mo = "Enterobacterales", ab = "AMC", guideline = "EUCAST 2022",
+expect_identical(
-                                     uti = FALSE, include_PKPD = FALSE)),
+  as.character(as.sir(mics,
-                 c("S", "S", "S", "S", "S", "S", "S", "S", "R", "R", "R"))
+    mo = "Enterobacterales", ab = "AMC", guideline = "EUCAST 2022",
-expect_identical(as.character(as.sir(mics, mo = "Enterobacterales", ab = "AMC", guideline = "EUCAST 2022",
+    uti = FALSE, include_PKPD = FALSE
-                                     uti = TRUE, include_PKPD = FALSE)),
+  )),
-                 c("S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "R"))
+  c("S", "S", "S", "S", "S", "S", "S", "S", "R", "R", "R")
-expect_identical(as.character(as.sir(mics, mo = "Escherichia coli", ab = "AMC", guideline = "EUCAST 2022",
+)
-                                     uti = FALSE, include_PKPD = FALSE)),
+expect_identical(
-                 c("S", "S", "S", "S", "S", "S", "S", "S", "R", "R", "R"))
+  as.character(as.sir(mics,
    mo = "Enterobacterales", ab = "AMC", guideline = "EUCAST 2022",
    uti = TRUE, include_PKPD = FALSE
  )),
  c("S", "S", "S", "S", "S", "S", "S", "S", "S", "S", "R")
 )
 expect_identical(
  as.character(as.sir(mics,
    mo = "Escherichia coli", ab = "AMC", guideline = "EUCAST 2022",
    uti = FALSE, include_PKPD = FALSE
  )),
  c("S", "S", "S", "S", "S", "S", "S", "S", "R", "R", "R")
 )
 # test SIR using dplyr's mutate_if(...) and mutate(across(...))
 out1 <- as.sir(as.mic(c(0.256, 0.5, 1, 2)), mo = "Escherichia coli", ab = "ertapenem", guideline = "EUCAST 2023")
 expect_identical(out1, as.sir(c("S", "S", "R", "R")))
 if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
-  out2 <- data.frame(mo = "Escherichia coli",
+  out2 <- data.frame(
    mo = "Escherichia coli",
    ab = "ertapenem",
-                     some_mics = as.mic(c(0.256, 0.5, 1, 2))) %>% 
+    some_mics = as.mic(c(0.256, 0.5, 1, 2))
  ) %>%
    mutate(across(where(is.mic), function(x) as.sir(x, mo = "mo", ab = "ab", guideline = "EUCAST 2023"))) %>%
    pull(some_mics)
-  out3 <- data.frame(mo = "Escherichia coli",
+  out3 <- data.frame(
    mo = "Escherichia coli",
    ab = "ertapenem",
-                     some_mics = as.mic(c(0.256, 0.5, 1, 2))) %>% 
+    some_mics = as.mic(c(0.256, 0.5, 1, 2))
  ) %>%
    mutate_if(is.mic, as.sir, mo = "mo", ab = "ab", guideline = "EUCAST 2023") %>%
    pull(some_mics)
@ -155,7 +171,8 @@ if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
 }
 # S. pneumoniae/ampicillin in EUCAST 2020: 0.5-2 ug/ml (R is only > 2)
-expect_equal(suppressMessages(
+expect_equal(
  suppressMessages(
    as.character(
      as.sir(
        x = as.mic(c(0.125, 0.5, 1, 2, 4)),
@ -163,11 +180,13 @@ expect_equal(suppressMessages(
        ab = "AMP",
        guideline = "EUCAST 2020"
      )
-  )),
+    )
  ),
  c("S", "S", "I", "I", "R")
 )
 # S. pneumoniae/amoxicillin in CLSI 2019: 2-8 ug/ml (R is 8 and > 8)
-expect_equal(suppressMessages(
+expect_equal(
  suppressMessages(
    as.character(
      as.sir(
        x = as.mic(c(1, 2, 4, 8, 16)),
@ -175,7 +194,8 @@ expect_equal(suppressMessages(
        ab = "AMX",
        guideline = "CLSI 2019"
      )
-  )),
+    )
  ),
  c("S", "S", "I", "R", "R")
 )
@ -247,14 +267,22 @@ if (AMR:::pkg_is_available("dplyr", min_version = "1.0.0", also_load = TRUE)) {
    group_by(mo) %>%
    attributes() %>%
    .$groups
-  expect_equal(nrow(groups),
+  expect_equal(
-               90)
+    nrow(groups),
-  expect_equal(class(groups$.rows),
+    90
-               c("vctrs_list_of", "vctrs_vctr", "list"))
+  )
-  expect_equal(groups$.rows[[1]],
+  expect_equal(
-               c(101, 524, 1368))
+    class(groups$.rows),
-  expect_equal(example_isolates[c(101, 524, 1368), "mo", drop = TRUE],
+    c("vctrs_list_of", "vctrs_vctr", "list")
-               rep(groups$mo[1], 3))
+  )
  expect_equal(
    groups$.rows[[1]],
    c(101, 524, 1368)
  )
  expect_equal(
    example_isolates[c(101, 524, 1368), "mo", drop = TRUE],
    rep(groups$mo[1], 3)
  )
 }
 # frequency tables
 if (AMR:::pkg_is_available("cleaner")) {
@ -295,27 +323,35 @@ expect_message(as.sir(data.frame(
 )))
 # SDD vs I in CLSI 2024
-expect_identical(as.sir(as.mic(2 ^ c(-2:4)), mo = "Enterococcus faecium", ab = "Dapto", guideline = "CLSI 2024"),
+expect_identical(
-                 as.sir(c("SDD", "SDD", "SDD", "SDD", "SDD", "R", "R")))
+  as.sir(as.mic(2^c(-2:4)), mo = "Enterococcus faecium", ab = "Dapto", guideline = "CLSI 2024"),
-expect_identical(as.sir(as.mic(2 ^ c(-2:2)), mo = "Enterococcus faecium", ab = "Cipro
+  as.sir(c("SDD", "SDD", "SDD", "SDD", "SDD", "R", "R"))
 )
 expect_identical(
  as.sir(as.mic(2^c(-2:2)), mo = "Enterococcus faecium", ab = "Cipro
                        ", guideline = "CLSI 2024"),
-                 as.sir(c("S", "S", "S", "I", "R")))
+  as.sir(c("S", "S", "S", "I", "R"))
 )
 # Veterinary --------------------------------------------------------------
 sir_history <- sir_interpretation_history(clean = TRUE)
-mics <- as.mic(2 ^ c(-4:6)) # 0.0625 to 64 in factors of 2
+mics <- as.mic(2^c(-4:6)) # 0.0625 to 64 in factors of 2
-vet <- data.frame(animal = c(rep("cat", 3), rep("dogs", 3), "canine", "equine", "horse", "cattle", "bird"),
+vet <- data.frame(
  animal = c(rep("cat", 3), rep("dogs", 3), "canine", "equine", "horse", "cattle", "bird"),
  PRA = mics,
  FLR = mics,
-                  mo = mo_name(rep(c("B_ESCHR_COLI", "B_PSTRL_MLTC", "B_MNNHM_HMLY"), 4)[-1]))
+  mo = mo_name(rep(c("B_ESCHR_COLI", "B_PSTRL_MLTC", "B_MNNHM_HMLY"), 4)[-1])
 )
 out_vet <- as.sir(vet, host = vet$animal, guideline = "CLSI 2023")
 # host column name instead of values
-expect_identical(out_vet,
+expect_identical(
-                 as.sir(vet, host = "animal", guideline = "CLSI 2023"))
+  out_vet,
  as.sir(vet, host = "animal", guideline = "CLSI 2023")
 )
 # check outcomes
 expect_identical(out_vet$PRA, as.sir(c("S", NA, "S", NA, NA, "R", NA, NA, NA, "I", NA)))
@ -326,11 +362,15 @@ expect_identical(out_vet$PRA, rep(NA_sir_, 11))
 expect_identical(out_vet$FLR, as.sir(c("S", "S", NA, "S", "S", NA, "I", "R", NA, "R", "R")))
 sir_history <- sir_interpretation_history()
-expect_identical(sort(sir_history$host),
+expect_identical(
-                 c("cats",   "cats",   "cats",   "cats",   "cats",   "cats",   "cats",   "cats",   "cats",   "cats",   "cats",   "cats",   "cats",   "cats",
+  sort(sir_history$host),
  c(
    "cats", "cats", "cats", "cats", "cats", "cats", "cats", "cats", "cats", "cats", "cats", "cats", "cats", "cats",
    "cats", "cats", "cats", "cattle", "cattle", "cattle", "cattle", "cattle", "cattle", "cattle", "cattle", "cattle", "cattle", "dogs",
    "dogs", "dogs", "dogs", "dogs", "dogs", "dogs", "dogs", "dogs", "dogs", "dogs", "dogs", "dogs", "dogs", "dogs",
-                   "horse",  "horse",  "horse",  "horse",  "horse",  "horse",  "horse",  "horse",  "horse",  "poultry","poultry","poultry","poultry"))
+    "horse", "horse", "horse", "horse", "horse", "horse", "horse", "horse", "horse", "poultry", "poultry", "poultry", "poultry"
  )
 )
 # ECOFF -------------------------------------------------------------------
@ -340,4 +380,3 @@ expect_equal(
 )
 # old method
 expect_warning(as.sir(as.mic(2), "E. coli", "ampicillin", guideline = "EUCAST 2020", ecoff = TRUE))
--- a/tests/testthat/test-vctrs.R
+++ b/tests/testthat/test-vctrs.R
@ -29,11 +29,13 @@
 # extra tests for {vctrs} pkg support
 if (AMR:::pkg_is_available("tibble")) {
-  test <- tibble::tibble(ab = as.ab("CIP"),
+  test <- tibble::tibble(
    ab = as.ab("CIP"),
    mo = as.mo("Escherichia coli"),
    mic = as.mic(2),
    disk = as.disk(20),
-                         sir = as.sir("S"))
+    sir = as.sir("S")
  )
  check1 <- lapply(test, class)
  test[1, "ab"] <- "GEN"
  test[1, "mo"] <- "B_KLBSL_PNMN"
@ -46,8 +48,10 @@ if (AMR:::pkg_is_available("tibble")) {
  check2 <- lapply(test, class)
  expect_identical(check1, check2)
-  test <- tibble::tibble(cipro = as.sir("S"),
+  test <- tibble::tibble(
-                         variable = "test")
+    cipro = as.sir("S"),
    variable = "test"
  )
  expect_equal(nrow(test[quinolones() == "S", ]), 1)
  expect_equal(nrow(test[quinolones() == "R", ]), 0)
 }
--- a/tests/testthat/test-zzz.R
+++ b/tests/testthat/test-zzz.R
@ -146,14 +146,16 @@ for (i in seq_len(length(import_functions))) {
  fn <- names(import_functions)[i]
  pkg <- unname(import_functions[i])
  expect_true(pkg %in% suggests,
-              info = paste0("package `", pkg, "` is not in Suggests"))
+    info = paste0("package `", pkg, "` is not in Suggests")
  )
  # function should exist in foreign pkg namespace
  if (AMR:::pkg_is_available(pkg,
    also_load = FALSE,
    min_version = if (pkg == "dplyr") "1.0.0" else NULL
  )) {
    expect_true(!is.null(AMR:::import_fn(name = fn, pkg = pkg, error_on_fail = FALSE)),
-                info = paste0("Function does not exist (anymore): function `", pkg, "::", fn, "()`"))
+      info = paste0("Function does not exist (anymore): function `", pkg, "::", fn, "()`")
    )
  } else if (pkg != "rstudioapi") {
    warning("Package '", pkg, "' not available")
  }