update sir to include interpretation details

.github/prehooks/pre-commit

@@ -68,49 +68,56 @@ echo ""
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 echo "Updating semantic versioning and date..."
 
-# Get tags from remote and remove tags not on remote
+current_branch=$(git rev-parse --abbrev-ref HEAD)
-git fetch origin --prune --prune-tags --quiet
+if [ "$current_branch" != "main" ]; then
-currenttagfull=$(git describe --tags --abbrev=0)
+  echo "- Current branch is '$current_branch'; skipping version/date update (only runs on 'main')"
-currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
-
-# Assume main branch to be 'main' or 'master'
-defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
-if [ "$currenttag" = "" ]; then
-  currenttag="0.0.1"
-  currentcommit=$(git rev-list --count ${defaultbranch})
-  echo "- No git tags found, creating one in format 'v(x).(y).(z)' - currently ${currentcommit} previous commits in '${defaultbranch}'"
 else
-  currentcommit=$(git rev-list --count ${currenttagfull}..${defaultbranch})
+  # Version update logic begins here
-  echo "- Latest tag is '${currenttagfull}', with ${currentcommit} previous commits in '${defaultbranch}'"
-fi
   
-# Combine tag and commit number
+  # Get tags from remote and remove tags not on remote
-currentversion="$currenttag.$((currentcommit + 9001))"
+  git fetch origin --prune --prune-tags --quiet
-echo "- ${currentpkg} pkg version set to ${currentversion}"
+  currenttagfull=$(git describe --tags --abbrev=0)
+  currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
   
-# Update version number and date in DESCRIPTION
+  # Assume main branch to be 'main' or 'master'
-sed -i -- "s/^Version: .*/Version: ${currentversion}/" DESCRIPTION
+  defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
-sed -i -- "s/^Date: .*/Date: $(date '+%Y-%m-%d')/" DESCRIPTION
+  if [ "$currenttag" = "" ]; then
-echo "- Updated version number and date in ./DESCRIPTION"
+    currenttag="0.0.1"
-rm -f DESCRIPTION--
+    currentcommit=$(git rev-list --count ${defaultbranch})
-git add DESCRIPTION
+    echo "- No git tags found, creating one in format 'v(x).(y).(z)' - currently ${currentcommit} previous commits in '${defaultbranch}'"
-
+  else
-# Update version number in NEWS.md
+    currentcommit=$(git rev-list --count ${currenttagfull}..${defaultbranch})
-if [ -e "NEWS.md" ]; then
+    echo "- Latest tag is '${currenttagfull}', with ${currentcommit} previous commits in '${defaultbranch}'"
-  if [ "$currentpkg" = "your" ]; then
-    currentpkg=""
   fi
-  sed -i -- "1s/.*/# ${currentpkg} ${currentversion}/" NEWS.md
-  echo "- Updated version number in ./NEWS.md"
-  rm -f NEWS.md--
-  git add NEWS.md
-else
-  echo "- No NEWS.md found!"
-fi
-echo ""
   
-# Save the version number for use in the commit-msg hook
+  # Combine tag and commit number
-echo "${currentversion}" > .git/commit_version.tmp
+  currentversion="$currenttag.$((currentcommit + 9001))"
+  echo "- ${currentpkg} pkg version set to ${currentversion}"
+  
+  # Update version number and date in DESCRIPTION
+  sed -i -- "s/^Version: .*/Version: ${currentversion}/" DESCRIPTION
+  sed -i -- "s/^Date: .*/Date: $(date '+%Y-%m-%d')/" DESCRIPTION
+  echo "- Updated version number and date in ./DESCRIPTION"
+  rm -f DESCRIPTION--
+  git add DESCRIPTION
+  
+  # Update version number in NEWS.md
+  if [ -e "NEWS.md" ]; then
+    if [ "$currentpkg" = "your" ]; then
+      currentpkg=""
+    fi
+    sed -i -- "1s/.*/# ${currentpkg} ${currentversion}/" NEWS.md
+    echo "- Updated version number in ./NEWS.md"
+    rm -f NEWS.md--
+    git add NEWS.md
+  else
+    echo "- No NEWS.md found!"
+  fi
+  echo ""
+  
+  # Save the version number for use in the commit-msg hook
+  echo "${currentversion}" > .git/commit_version.tmp
+fi
 
 git add data-raw/*
 git add data/*

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

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

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: AMR
-Version: 3.0.0.9004
+Version: 3.0.0.9017
-Date: 2025-06-13
+Date: 2025-07-28
 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

NAMESPACE

@@ -12,6 +12,7 @@ S3method("[",deprecated_amr_dataset)
 S3method("[",disk)
 S3method("[",mic)
 S3method("[",mo)
+S3method("[",sir)
 S3method("[<-",ab)
 S3method("[<-",av)
 S3method("[<-",disk)
@@ -24,6 +25,7 @@ S3method("[[",deprecated_amr_dataset)
 S3method("[[",disk)
 S3method("[[",mic)
 S3method("[[",mo)
+S3method("[[",sir)
 S3method("[[<-",ab)
 S3method("[[<-",av)
 S3method("[[<-",disk)
@@ -99,6 +101,7 @@ S3method(print,custom_eucast_rules)
 S3method(print,custom_mdro_guideline)
 S3method(print,deprecated_amr_dataset)
 S3method(print,disk)
+S3method(print,interpreted_sir)
 S3method(print,mic)
 S3method(print,mo)
 S3method(print,mo_renamed)

NEWS.md

@@ -1,16 +1,24 @@
-# AMR 3.0.0.9004
+# AMR 3.0.0.9017
+
+This is primarily a bugfix release, though we added one nice feature too.
 
 ### New
 * Integration with the **tidymodels** framework to allow seamless use of MIC and SIR data in modelling pipelines via `recipes`
   - `step_mic_log2()` to transform `<mic>` columns with log2, and `step_sir_numeric()` to convert `<sir>` columns to numeric
-  - `tidyselect` helpers: `all_mic()`, `all_mic_predictors()`, `all_sir()`, `all_sir_predictors()`
+  - New `tidyselect` helpers: `all_mic()`, `all_mic_predictors()`, `all_sir()`, `all_sir_predictors()`
-  - Enables seamless use of MIC and SIR data in modelling pipelines via `recipes`
 
 ### Changed
 * Fixed a bug in `antibiogram()` for when no antimicrobials are set
+* Fixed a bug in `antibiogram()` to allow column names containing the `+` character (#222)
 * Fixed a bug in `as.ab()` for antimicrobial codes with a number in it if they are preceded by a space
 * Fixed a bug in `eucast_rules()` for using specific custom rules
+* Fixed a bug in `as.sir()` to allow any tidyselect language (#220)
+* Fixed a bug in `as.sir()` to pick right breakpoint when `uti = FALSE` (#216)
+* Fixed a bug in `ggplot_sir()` when using `combine_SI = FALSE` (#213)
+* Fixed all plotting to contain a separate colour for SDD (susceptible dose-dependent) (#223)
 * Fixed some specific Dutch translations for antimicrobials
+* Added `names` to `age_groups()` so that custom names can be given (#215)
+* Added note to `as.sir()` to make it explicit when higher-level taxonomic breakpoints are used (#218)
 * Updated `random_mic()` and `random_disk()` to set skewedness of the distribution and allow multiple microorganisms
 
 

R/aa_helper_functions.R

@@ -63,31 +63,6 @@ pm_left_join <- function(x, y, by = NULL, suffix = c(".x", ".y")) {
   merged
 }
 
-# support where() like tidyverse (this function will also be used when running `antibiogram()`):
-where <- function(fn) {
-  # based on https://github.com/nathaneastwood/poorman/blob/52eb6947e0b4430cd588976ed8820013eddf955f/R/where.R#L17-L32
-  if (!is.function(fn)) {
-    stop_("`", deparse(substitute(fn)), "()` is not a valid predicate function.")
-  }
-  df <- pm_select_env$.data
-  cols <- pm_select_env$get_colnames()
-  if (is.null(df)) {
-    df <- get_current_data("where", call = FALSE)
-    cols <- colnames(df)
-  }
-  preds <- unlist(lapply(
-    df,
-    function(x, fn) {
-      do.call("fn", list(x))
-    },
-    fn
-  ))
-  if (!is.logical(preds)) stop_("`where()` must be used with functions that return `TRUE` or `FALSE`.")
-  data_cols <- cols
-  cols <- data_cols[preds]
-  which(data_cols %in% cols)
-}
-
 # copied and slightly rewritten from {poorman} under permissive license (2021-10-15)
 # https://github.com/nathaneastwood/poorman, MIT licensed, Nathan Eastwood, 2020
 case_when_AMR <- function(...) {
@@ -544,7 +519,7 @@ word_wrap <- function(...,
     )
     msg <- paste0(parts, collapse = "`")
   }
-  msg <- gsub("`(.+?)`", font_grey_bg("\\1"), msg)
+  msg <- gsub("`(.+?)`", font_grey_bg("`\\1`"), msg)
 
   # clean introduced whitespace in between fullstops
   msg <- gsub("[.] +[.]", "..", msg)
@@ -814,7 +789,7 @@ meet_criteria <- function(object, # can be literally `list(...)` for `allow_argu
 
   # if object is missing, or another error:
   tryCatch(invisible(object),
-    error = function(e) AMR_env$meet_criteria_error_txt <- e$message
+    error = function(e) AMR_env$meet_criteria_error_txt <- conditionMessage(e)
   )
   if (!is.null(AMR_env$meet_criteria_error_txt)) {
     error_txt <- AMR_env$meet_criteria_error_txt
@@ -1319,6 +1294,10 @@ font_green_bg <- function(..., collapse = " ") {
   # this is #3caea3 (picked to be colourblind-safe with other SIR colours)
   try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;79m", after = "\033[49m", collapse = collapse)
 }
+font_green_lighter_bg <- function(..., collapse = " ") {
+  # this is #8FD6C4 (picked to be colourblind-safe with other SIR colours)
+  try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;158m", after = "\033[49m", collapse = collapse)
+}
 font_purple_bg <- function(..., collapse = " ") {
   try_colour(font_black(..., collapse = collapse, adapt = FALSE), before = "\033[48;5;89m", after = "\033[49m", collapse = collapse)
 }
@@ -1636,6 +1615,36 @@ get_n_cores <- function(max_cores = Inf) {
   n_cores
 }
 
+# Support `where()` if tidyselect not installed ----
+if (!is.null(import_fn("where", "tidyselect", error_on_fail = FALSE))) {
+  # tidyselect::where() exists, load the namespace to make `where()`s work across the package in default arguments
+  loadNamespace("tidyselect")
+} else {
+  where <- function(fn) {
+    # based on https://github.com/nathaneastwood/poorman/blob/52eb6947e0b4430cd588976ed8820013eddf955f/R/where.R#L17-L32
+    if (!is.function(fn)) {
+      stop_("`", deparse(substitute(fn)), "()` is not a valid predicate function.")
+    }
+    df <- pm_select_env$.data
+    cols <- pm_select_env$get_colnames()
+    if (is.null(df)) {
+      df <- get_current_data("where", call = FALSE)
+      cols <- colnames(df)
+    }
+    preds <- unlist(lapply(
+      df,
+      function(x, fn) {
+        do.call("fn", list(x))
+      },
+      fn
+    ))
+    if (!is.logical(preds)) stop_("`where()` must be used with functions that return `TRUE` or `FALSE`.")
+    data_cols <- cols
+    cols <- data_cols[preds]
+    which(data_cols %in% cols)
+  }
+}
+
 # Faster data.table implementations ----
 
 match <- function(x, table, ...) {
@@ -1655,52 +1664,6 @@ match <- function(x, table, ...) {
   }
 }
 
-# nolint start
-
-# Register S3 methods ----
-# copied from vctrs::s3_register by their permission:
-# https://github.com/r-lib/vctrs/blob/05968ce8e669f73213e3e894b5f4424af4f46316/R/register-s3.R
-s3_register <- function(generic, class, method = NULL) {
-  stopifnot(is.character(generic), length(generic) == 1)
-  stopifnot(is.character(class), length(class) == 1)
-  pieces <- strsplit(generic, "::")[[1]]
-  stopifnot(length(pieces) == 2)
-  package <- pieces[[1]]
-  generic <- pieces[[2]]
-  caller <- parent.frame()
-  get_method_env <- function() {
-    top <- topenv(caller)
-    if (isNamespace(top)) {
-      asNamespace(environmentName(top))
-    } else {
-      caller
-    }
-  }
-  get_method <- function(method, env) {
-    if (is.null(method)) {
-      get(paste0(generic, ".", class), envir = get_method_env())
-    } else {
-      method
-    }
-  }
-  method_fn <- get_method(method)
-  stopifnot(is.function(method_fn))
-  setHook(packageEvent(package, "onLoad"), function(...) {
-    ns <- asNamespace(package)
-    method_fn <- get_method(method)
-    registerS3method(generic, class, method_fn, envir = ns)
-  })
-  if (!isNamespaceLoaded(package)) {
-    return(invisible())
-  }
-  envir <- asNamespace(package)
-  if (exists(generic, envir)) {
-    registerS3method(generic, class, method_fn, envir = envir)
-  }
-  invisible()
-}
-
-
 # Support old R versions ----
 # these functions were not available in previous versions of R
 # see here for the full list: https://github.com/r-lib/backports

R/aa_helper_pm_functions.R

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

R/ab.R

@@ -184,7 +184,8 @@ as.ab <- function(x, flag_multiple_results = TRUE, language = get_AMR_locale(),
   x_new[known_codes_cid] <- AMR_env$AB_lookup$ab[match(x[known_codes_cid], AMR_env$AB_lookup$cid)]
   previously_coerced <- x %in% AMR_env$ab_previously_coerced$x
   x_new[previously_coerced & is.na(x_new)] <- AMR_env$ab_previously_coerced$ab[match(x[is.na(x_new) & x %in% AMR_env$ab_previously_coerced$x], AMR_env$ab_previously_coerced$x)]
-  if (any(previously_coerced) && isTRUE(info) && message_not_thrown_before("as.ab", entire_session = TRUE)) {
+  previously_coerced_mention <- x %in% AMR_env$ab_previously_coerced$x & !x %in% AMR_env$AB_lookup$ab & !x %in% AMR_env$AB_lookup$generalised_name
+  if (any(previously_coerced_mention) && isTRUE(info) && message_not_thrown_before("as.ab", entire_session = TRUE)) {
     message_(
       "Returning previously coerced ",
       ifelse(length(unique(which(x[which(previously_coerced)] %in% x_bak_clean))) > 1, "value for an antimicrobial", "values for various antimicrobials"),

R/ab_property.R

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

R/age.R

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

R/amr_selectors.R

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

R/antibiogram.R

@@ -576,6 +576,15 @@ antibiogram.default <- function(x,
     }
     antimicrobials <- unlist(antimicrobials)
   } else {
+    existing_ab_combined_cols <- ab_trycatch[ab_trycatch %like% "[+]" & ab_trycatch %in% colnames(x)]
+    if (length(existing_ab_combined_cols) > 0 && !is.null(ab_transform)) {
+      ab_transform <- NULL
+      warning_(
+        "Detected column name(s) containing the '+' character, which conflicts with the expected syntax in `antibiogram()`: the '+' is used to combine separate antimicrobial agent columns (e.g., \"AMP+GEN\").\n\n",
+        "To avoid incorrectly guessing which antimicrobials this represents, `ab_transform` was automatically set to `NULL`.\n\n",
+        "If this is unintended, please rename the column(s) to avoid using '+' in the name, or set `ab_transform = NULL` explicitly to suppress this message."
+      )
+    }
     antimicrobials <- ab_trycatch
   }
 

R/av_property.R

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

R/count.R

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

R/custom_mdro_guideline.R

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

R/eucast_rules.R

@@ -1178,7 +1178,7 @@ edit_sir <- function(x,
             ifelse(length(rows) > 10, "...", ""),
             " while writing value '", to,
             "' to column(s) `", paste(cols, collapse = "`, `"),
-            "`:\n", e$message
+            "`:\n", conditionMessage(e)
           ),
           call. = FALSE
         )

R/ggplot_sir.R

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

R/mean_amr_distance.R

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

R/mo.R

@@ -1186,7 +1186,7 @@ parse_and_convert <- function(x) {
         parsed <- gsub('"', "", parsed, fixed = TRUE)
         parsed
       },
-      error = function(e) stop(e$message, call. = FALSE)
+      error = function(e) stop(conditionMessage(e), call. = FALSE)
     ) # this will also be thrown when running `as.mo(no_existing_object)`
   }
   out <- trimws2(out)

R/mo_property.R

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

R/pca.R

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

R/plotting.R

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

R/proportion.R

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

R/sir.R

@@ -69,7 +69,9 @@
 #' @param reference_data A [data.frame] to be used for interpretation, which defaults to the [clinical_breakpoints] data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the [clinical_breakpoints] data set (same column names and column types). Please note that the `guideline` argument will be ignored when `reference_data` is manually set.
 #' @param threshold Maximum fraction of invalid antimicrobial interpretations of `x`, see *Examples*.
 #' @param conserve_capped_values Deprecated, use `capped_mic_handling` instead.
-#' @param ... For using on a [data.frame]: names of columns to apply [as.sir()] on (supports tidy selection such as `column1:column4`). Otherwise: arguments passed on to methods.
+#' @param ... For using on a [data.frame]: selection of columns to apply `as.sir()` to. Supports [tidyselect language][tidyselect::starts_with()] such as `where(is.mic)`, `starts_with(...)`, or `column1:column4`, and can thus also be [antimicrobial selectors][amr_selector()] such as `as.sir(df, penicillins())`.
+#'
+#' Otherwise: arguments passed on to methods.
 #' @details
 #' *Note: The clinical breakpoints in this package were validated through, and imported from, [WHONET](https://whonet.org). The public use of this `AMR` package has been endorsed by both CLSI and EUCAST. See [clinical_breakpoints] for more information.*
 #'
@@ -225,9 +227,12 @@
 #'   df_wide %>% mutate_if(is.mic, as.sir)
 #'   df_wide %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
 #'   df_wide %>% mutate(across(where(is.mic), as.sir))
+#'
 #'   df_wide %>% mutate_at(vars(amoxicillin:tobra), as.sir)
 #'   df_wide %>% mutate(across(amoxicillin:tobra, as.sir))
 #'
+#'   df_wide %>% mutate(across(aminopenicillins(), as.sir))
+#'
 #'   # approaches that all work with additional arguments:
 #'   df_long %>%
 #'     # given a certain data type, e.g. MIC values
@@ -380,26 +385,15 @@ as.sir <- function(x, ...) {
   UseMethod("as.sir")
 }
 
-as_sir_structure <- function(x,
+as_sir_structure <- function(x) {
-                             guideline = NULL,
+  int <- attr(x, "interpretation_details")
-                             mo = NULL,
-                             ab = NULL,
-                             method = NULL,
-                             ref_tbl = NULL,
-                             ref_breakpoints = NULL) {
   structure(
     factor(as.character(unlist(unname(x))),
       levels = c("S", "SDD", "I", "R", "NI"),
       ordered = TRUE
     ),
-    # TODO for #170
+    interpretation_details = int,
-    # guideline = guideline,
+    class = c(if (!is.null(int)) "interpreted_sir" else NULL, "sir", "ordered", "factor")
-    # mo = mo,
-    # ab = ab,
-    # method = method,
-    # ref_tbl = ref_tbl,
-    # ref_breakpoints = ref_breakpoints,
-    class = c("sir", "ordered", "factor")
   )
 }
 
@@ -722,8 +716,17 @@ as.sir.data.frame <- function(x,
   meet_criteria(info, allow_class = "logical", has_length = 1)
   meet_criteria(parallel, allow_class = "logical", has_length = 1)
   meet_criteria(max_cores, allow_class = c("numeric", "integer"), has_length = 1)
-
   x.bak <- x
+
+  if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
+    sel <- colnames(pm_select(x, ...))
+  } else {
+    sel <- colnames(x)
+  }
+  if (!is.null(col_mo)) {
+    sel <- sel[sel != col_mo]
+  }
+
   for (i in seq_len(ncol(x))) {
     # don't keep factors, overwriting them is hard
     if (is.factor(x[, i, drop = TRUE])) {
@@ -803,15 +806,6 @@ as.sir.data.frame <- function(x,
   }
 
   i <- 0
-  if (tryCatch(length(list(...)) > 0, error = function(e) TRUE)) {
-    sel <- colnames(pm_select(x, ...))
-  } else {
-    sel <- colnames(x)
-  }
-  if (!is.null(col_mo)) {
-    sel <- sel[sel != col_mo]
-  }
-
   ab_cols <- colnames(x)[vapply(FUN.VALUE = logical(1), x, function(y) {
     i <<- i + 1
     check <- is.mic(y) | is.disk(y)
@@ -863,7 +857,7 @@ as.sir.data.frame <- function(x,
     cl <- tryCatch(parallel::makeCluster(n_cores, type = "PSOCK"),
       error = function(e) {
         if (isTRUE(info)) {
-          message_("Could not create parallel cluster, using single-core computation. Error message: ", e$message, add_fn = font_red)
+          message_("Could not create parallel cluster, using single-core computation. Error message: ", conditionMessage(e), add_fn = font_red)
         }
         return(NULL)
       }
@@ -1135,7 +1129,6 @@ as_sir_method <- function(method_short,
   current_sir_interpretation_history <- NROW(AMR_env$sir_interpretation_history)
 
   if (isTRUE(info) && message_not_thrown_before("as.sir", "sir_interpretation_history")) {
-    message()
     message_("Run `sir_interpretation_history()` afterwards to retrieve a logbook with all details of the breakpoint interpretations.\n\n", add_fn = font_green)
   }
 
@@ -1553,7 +1546,7 @@ as_sir_method <- function(method_short,
       ))
 
     if (breakpoint_type == "animal") {
-      # 2025-03-13 for now, only strictly follow guideline for current host, no extrapolation
+      # 2025-03-13/ for now, only strictly follow guideline for current host, no extrapolation
       breakpoints_current <- breakpoints_current[which(breakpoints_current$host == host_current), , drop = FALSE]
     }
 
@@ -1645,32 +1638,31 @@ as_sir_method <- function(method_short,
         breakpoint_S_R = vectorise_log_entry(NA_character_, length(rows)),
         stringsAsFactors = FALSE
       )
+      attr(new_sir, "interpretation_details") <- out
       out <- subset(out, !is.na(input_given))
       AMR_env$sir_interpretation_history <- rbind_AMR(AMR_env$sir_interpretation_history, out)
       notes <- c(notes, notes_current)
+      df[rows, "result"] <- new_sir
       next
     }
 
-    # sort on host and taxonomic rank
+    # if the user explicitly set uti, keep only those rows
-    # (this will e.g. prefer 'species' breakpoints over 'order' breakpoints)
+    if (!is.na(uti_current)) {
-    if (is.na(uti_current)) {
+      breakpoints_current <- breakpoints_current[breakpoints_current$uti == uti_current, , drop = FALSE]
-      breakpoints_current <- breakpoints_current %pm>%
-        #  `uti` is a column in the data set
-        # this will put UTI = FALSE first, then UTI = NA, then UTI = TRUE
-        pm_mutate(uti_index = ifelse(!is.na(uti) & uti == FALSE, 1,
-          ifelse(is.na(uti), 2,
-            3
-          )
-        )) %pm>%
-        # be as specific as possible (i.e. prefer species over genus):
-        pm_arrange(rank_index, uti_index)
-    } else if (uti_current == TRUE) {
-      breakpoints_current <- breakpoints_current %pm>%
-        subset(uti == TRUE) %pm>%
-        # be as specific as possible (i.e. prefer species over genus):
-        pm_arrange(rank_index)
     }
 
+    # build a helper factor so FALSE < NA < TRUE
+    uti_index <- factor(
+      ifelse(is.na(breakpoints_current$uti), "NA",
+        as.character(breakpoints_current$uti)
+      ),
+      levels = c("FALSE", "NA", "TRUE")
+    )
+
+    # sort on host and taxonomic rank first, then by UTI
+    # (this will e.g. prefer 'species' breakpoints over 'order' breakpoints)
+    breakpoints_current <- breakpoints_current[order(breakpoints_current$rank_index, uti_index), , drop = FALSE]
+
     # throw messages for different body sites
     site <- breakpoints_current[1L, "site", drop = FALSE] # this is the one we'll take
     if (is.na(site)) {
@@ -1682,7 +1674,7 @@ as_sir_method <- function(method_short,
       # only UTI breakpoints available
       notes_current <- paste0(
         notes_current, "\n",
-        paste0("Breakpoints for ", font_bold(ab_formatted), " in ", mo_formatted, " are only available for (uncomplicated) urinary tract infections (UTI); assuming `uti = TRUE`.")
+        paste0("Breakpoints for ", font_bold(ab_formatted), " in ", mo_formatted, " are only available for (uncomplicated) urinary tract infections (UTI) - assuming `uti = TRUE`.")
       )
     } else if (nrow(breakpoints_current) > 1 && length(unique(breakpoints_current$site)) > 1 && any(is.na(uti_current)) && all(c(TRUE, FALSE) %in% breakpoints_current$uti, na.rm = TRUE) && message_not_thrown_before("as.sir", "siteUTI", mo_current, ab_current)) {
       # both UTI and Non-UTI breakpoints available
@@ -1705,7 +1697,7 @@ as_sir_method <- function(method_short,
       new_sir <- rep(as.sir("R"), length(rows))
       notes_current <- paste0(
         notes_current, "\n",
-        paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, "")
+        paste0("Intrinsic resistance applied for ", ab_formatted, " in ", mo_formatted, ".")
       )
     } else if (nrow(breakpoints_current) == 0) {
       # no rules available
@@ -1713,41 +1705,48 @@ as_sir_method <- function(method_short,
     } else {
       # then run the rules
       breakpoints_current <- breakpoints_current[1L, , drop = FALSE]
+      if (breakpoints_current$rank_index > 3) {
+        # we resort to a high-level taxonomic record since there are no breakpoint on genus (rank_index = 3) or lower, so note this
+        notes_current <- paste0(
+          "No genus- or species-level breakpoint available - applying higher taxonomic level instead.\n",
+          notes_current
+        )
+      }
 
       notes_current <- paste0(
         notes_current, "\n",
         ifelse(breakpoints_current$mo == "UNKNOWN" | breakpoints_current$ref_tbl %like% "PK.*PD",
-          "Some PK/PD breakpoints were applied - use `include_PKPD = FALSE` to prevent this",
+          "Some PK/PD breakpoints were applied - use `include_PKPD = FALSE` to prevent this.",
           ""
         ),
         "\n",
         ifelse(breakpoints_current$site %like% "screen" | breakpoints_current$ref_tbl %like% "screen",
-          "Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this",
+          "Some screening breakpoints were applied - use `include_screening = FALSE` to prevent this.",
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[<][0-9]",
-          paste0("MIC values with the operator '<' are all considered 'S' since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values with the operator '<' are all considered 'S' since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "inverse") & as.character(values_bak) %like% "^[>][0-9]",
-          paste0("MIC values with the operator '>' are all considered 'R' since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values with the operator '>' are all considered 'R' since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^[><]=[0-9]" & as.double(values) > breakpoints_current$breakpoint_S & as.double(values) < breakpoints_current$breakpoint_R,
-          paste0("MIC values within the breakpoint guideline range with the operator '<=' or '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values within the breakpoint guideline range with the operator '<=' or '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^<=[0-9]" & as.double(values) == breakpoints_current$breakpoint_R,
-          paste0("MIC values at the R breakpoint with the operator '<=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values at the R breakpoint with the operator '<=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         ),
         "\n",
         ifelse(method == "mic" & capped_mic_handling %in% c("conservative", "standard") & as.character(values_bak) %like% "^>=[0-9]" & as.double(values) == breakpoints_current$breakpoint_S,
-          paste0("MIC values at the S breakpoint with the operator '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\""),
+          paste0("MIC values at the S breakpoint with the operator '>=' are considered 'NI' (non-interpretable) since capped_mic_handling = \"", capped_mic_handling, "\"."),
           ""
         )
       )
@@ -1757,7 +1756,7 @@ as_sir_method <- function(method_short,
         notes_current <- paste0(
           notes_current, "\n",
           ifelse(!is.na(breakpoints_current$breakpoint_S) & is.na(breakpoints_current$breakpoint_R),
-            "NAs because of missing R breakpoints were substituted with R since substitute_missing_r_breakpoint = TRUE",
+            "NAs because of missing R breakpoints were substituted with R since substitute_missing_r_breakpoint = TRUE.",
             ""
           )
         )
@@ -1796,7 +1795,7 @@ as_sir_method <- function(method_short,
       }
 
       # write to verbose output
-      notes_current <- trimws2(notes_current)
+      notes_current <- gsub("\n\n", "\n", trimws2(notes_current), fixed = TRUE)
       notes_current[notes_current == ""] <- NA_character_
       out <- data.frame(
         # recycling 1 to 2 rows does not always seem to work, which is why vectorise_log_entry() was added
@@ -1819,6 +1818,7 @@ as_sir_method <- function(method_short,
         breakpoint_S_R = vectorise_log_entry(paste0(breakpoints_current[, "breakpoint_S", drop = TRUE], "-", breakpoints_current[, "breakpoint_R", drop = TRUE]), length(rows)),
         stringsAsFactors = FALSE
       )
+      attr(new_sir, "interpretation_details") <- out
       out <- subset(out, !is.na(input_given))
       AMR_env$sir_interpretation_history <- rbind_AMR(AMR_env$sir_interpretation_history, out)
     }
@@ -1863,20 +1863,33 @@ as_sir_method <- function(method_short,
   new_part <- new_part[order(new_part$index), , drop = FALSE]
   AMR_env$sir_interpretation_history <- rbind_AMR(old_part, new_part)
 
-  df$result
+  as_sir_structure(df$result)
 }
 
 #' @rdname as.sir
+#' @param sir_values SIR values that were interpreted from MIC or disk diffusion values using [as.sir()].
 #' @param clean A [logical] to indicate whether previously stored results should be forgotten after returning the 'logbook' with results.
 #' @export
-sir_interpretation_history <- function(clean = FALSE) {
+sir_interpretation_history <- function(sir_values = NULL, clean = FALSE) {
+  # for AMR v3.0.0 and lower, the first argument was `clean`, so allow `sir_interpretation_history(TRUE)` to keep working
+  if (is.logical(sir_values) && missing(clean)) {
+    clean <- sir_values
+    sir_values <- NULL
+    warning_("For `sir_interpretation_history()`, the `clean` argument is no longer the first argument, please update your code to explicitly state 'clean': `sir_interpretation_history(clean = ", clean, ")`.")
+  }
+  meet_criteria(sir_values, allow_class = "sir", allow_NULL = TRUE)
   meet_criteria(clean, allow_class = "logical", has_length = 1)
-  out <- AMR_env$sir_interpretation_history
+
-  out <- out[which(!is.na(out$datetime)), , drop = FALSE]
+  if (!is.null(sir_values)) {
-  out$outcome <- as.sir(out$outcome)
+    out <- attr(sir_values, "interpretation_details")
-  out$site <- as.character(out$site)
+  } else {
-  if (isTRUE(clean)) {
+    out <- AMR_env$sir_interpretation_history
-    AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
+    out <- out[which(!is.na(out$datetime)), , drop = FALSE]
+    out$outcome <- as.sir(out$outcome)
+    out$site <- as.character(out$site)
+    if (isTRUE(clean)) {
+      AMR_env$sir_interpretation_history <- AMR_env$sir_interpretation_history[0, , drop = FALSE]
+    }
   }
   if (pkg_is_available("tibble")) {
     out <- import_fn("as_tibble", "tibble")(out)
@@ -1904,11 +1917,11 @@ pillar_shaft.sir <- function(x, ...) {
     # colours will anyway not work when has_colour() == FALSE,
     # but then the indentation should also not be applied
     out[is.na(x)] <- font_grey("  NA")
-    out[x == "NI"] <- font_grey_bg(font_black("  NI "))
     out[x == "S"] <- font_green_bg("  S  ")
+    out[x == "SDD"] <- font_green_lighter_bg(" SDD ")
     out[x == "I"] <- font_orange_bg("  I  ")
-    out[x == "SDD"] <- font_orange_bg(" SDD ")
     out[x == "R"] <- font_rose_bg("  R  ")
+    out[x == "NI"] <- font_grey_bg(font_black("  NI "))
   }
   create_pillar_column(out, align = "left", width = 5)
 }
@@ -2000,21 +2013,60 @@ get_skimmers.sir <- function(column) {
 #' @export
 #' @noRd
 print.sir <- function(x, ...) {
-  x_name <- deparse(substitute(x))
   cat("Class 'sir'\n")
-  # TODO for #170
-  # if (!is.null(attributes(x)$guideline) && !all(is.na(attributes(x)$guideline))) {
-  #   cat(font_blue(word_wrap("These values were interpreted using ",
-  #                           font_bold(vector_and(attributes(x)$guideline, quotes = FALSE)),
-  #                           " based on ",
-  #                           vector_and(attributes(x)$method, quotes = FALSE),
-  #                           " values. ",
-  #                           "Use `sir_interpretation_history(", x_name, ")` to return a full logbook.")))
-  #   cat("\n")
-  # }
   print(as.character(x), quote = FALSE)
 }
 
+#' @method print interpreted_sir
+#' @export
+#' @noRd
+print.interpreted_sir <- function(x, ...) {
+  cat("Class 'sir'\n")
+  print(as.character(x), quote = FALSE)
+
+  if (length(x) == 0) {
+    return(invisible())
+  }
+
+  int <- attr(x, "interpretation_details")
+  if (NROW(int) == 0) {
+    if (length(x) == 1) {
+      cat(font_blue(word_wrap("Source data were lost for this interpreted value.")))
+    } else {
+      cat(font_blue(word_wrap("Source data were lost for these interpreted values.")))
+    }
+  } else {
+    relevant_cols <- int[, c("guideline", "method", "ab", "mo"), drop = FALSE]
+    relevant_cols <- unique(relevant_cols)
+    vals1_plural <- ifelse(length(x) == 1, "This value was", "These values were")
+    vals2_plural <- ifelse(length(x) == 1, "value", "values")
+    method_fn <- ifelse(relevant_cols$method == "MIC", "MIC", "disk diffusion")
+    if (NROW(relevant_cols) == 1) {
+      in_host <- ifelse(relevant_cols$host == "human", "", paste0(" in ", relevant_cols$host))
+      cat(font_blue(word_wrap(
+        vals1_plural, " interpreted using ",
+        relevant_cols$guideline,
+        " based on the ",
+        method_fn,
+        " ", vals2_plural, " for ",
+        ab_name(relevant_cols$ab, language = NULL, info = FALSE, tolower = TRUE), " in ",
+        italicise_taxonomy(mo_name(relevant_cols$mo, language = NULL, info = FALSE), type = "ansi"),
+        in_host,
+        "."
+      )))
+    } else {
+      cat(font_blue(word_wrap(
+        vals1_plural, " interpreted using ",
+        vector_and(relevant_cols$guideline, quotes = FALSE),
+        " based on ",
+        vector_and(method_fn, quotes = FALSE),
+        " ", vals2_plural, "."
+      )))
+    }
+    cat(font_blue(word_wrap("\nUse `sir_interpretation_history()` on this object to return a full logbook.\n")))
+  }
+}
+
 
 #' @method as.double sir
 #' @export
@@ -2070,51 +2122,132 @@ summary.sir <- function(object, ...) {
   value
 }
 
+#' @method [ sir
+#' @export
+#' @noRd
+"[.sir" <- function(x, ...) {
+  y <- NextMethod()
+  det <- attr(x, "interpretation_details")
+  if (!is.null(det)) {
+    subset_idx <- seq_along(x)[...]
+    # safer than relying on implicit eval inside NextMethod()
+    attr(y, "interpretation_details") <- det[subset_idx, , drop = FALSE]
+  }
+  y
+}
+#' @method [[ sir
+#' @export
+#' @noRd
+"[[.sir" <- function(x, i, ...) {
+  if (length(i) != 1L) {
+    stop("attempt to select more than one element with [[.", call. = FALSE)
+  }
+  x[i] # calls `[.sir`, ensures attr alignment
+}
+
 #' @method [<- sir
 #' @export
 #' @noRd
 "[<-.sir" <- function(i, j, ..., value) {
   value <- as.sir(value)
   y <- NextMethod()
-  attributes(y) <- attributes(i)
+
+  old_det <- attr(i, "interpretation_details")
+  new_det <- attr(value, "interpretation_details")
+
+  len_y <- length(y)
+
+  # Neither i nor value have details -> do nothing
+  if (is.null(old_det) && is.null(new_det)) {
+    return(y)
+  }
+
+  # Start building full_det as copy of old_det or empty
+  full_det <- if (!is.null(old_det)) old_det else data.frame(row = seq_along(i))
+
+  # Ensure full_det has correct row count and order
+  if (nrow(full_det) != length(i)) {
+    attr(y, "interpretation_details") <- NULL
+    return(y)
+  }
+
+  # Which rows are being assigned?
+  assign_idx <- if (missing(j)) seq_along(i) else j
+  assign_idx <- as.integer(assign_idx)
+
+  # If new_det is missing or too short, fill it
+  if (is.null(new_det)) {
+    new_det <- data.frame(row = assign_idx)
+  } else if (nrow(new_det) != length(value)) {
+    new_det <- data.frame(row = assign_idx)
+  }
+
+  # Add temporary .row to track positions
+  full_det$.row <- seq_len(nrow(full_det))
+  new_det$.row <- assign_idx
+
+  # Replace old rows with new rows
+  full_det <- rbind(
+    subset(full_det, !.row %in% assign_idx),
+    new_det
+  )
+  full_det <- full_det[order(full_det$.row), , drop = FALSE]
+  full_det$.row <- NULL
+
+  # Clean up: ensure right number of rows
+  if (nrow(full_det) == len_y) {
+    attr(y, "interpretation_details") <- full_det
+  } else {
+    attr(y, "interpretation_details") <- NULL
+  }
+
   y
 }
 #' @method [[<- sir
 #' @export
 #' @noRd
 "[[<-.sir" <- function(i, j, ..., value) {
-  value <- as.sir(value)
+  if (!is.null(det) && length(i) == 1 && nrow(det) >= i) {
-  y <- NextMethod()
+    i[j] <- value
-  attributes(y) <- attributes(i)
+    i
-  y
+  } else {
+    NextMethod()
+  }
 }
 #' @method c sir
 #' @export
 #' @noRd
-c.sir <- function(...) {
+c.sir <- function(..., recursive = FALSE) {
-  lst <- list(...)
+  lst <- lapply(
+    list(...),
+    function(x) {
+      list(
+        values = as.character(x),
+        interpretation_details = attr(x, "interpretation_details")
+      )
+    }
+  )
+  x <- unlist(lapply(lst, `[[`, "values"), use.names = FALSE)
+  details <- lapply(lst, `[[`, "interpretation_details")
+  has_details <- vapply(details, is.data.frame, logical(1))
+  if (!any(has_details)) {
+    return(as_sir_structure(x))
+  }
 
-  # TODO for #170
+  # Pre-allocate details (no Map, no matrix allocation)
-  # guideline <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$guideline %or% NA_character_)
+  combined_details <- do.call(rbind, lapply(seq_along(details), function(i) {
-  # mo <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$mo %or% NA_character_)
+    d <- details[[i]]
-  # ab <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ab %or% NA_character_)
+    if (is.null(d)) {
-  # method <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$method %or% NA_character_)
+      # generate NA rows of correct length, but fast
-  # ref_tbl <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_tbl %or% NA_character_)
+      n <- length(details[[i]])
-  # ref_breakpoints <- vapply(FUN.VALUE = character(1), lst, function(x) attributes(x)$ref_breakpoints %or% NA_character_)
+      as.data.frame(matrix(NA, nrow = n, ncol = 0))
+    } else {
+      d
+    }
+  }))
 
-  out <- as.sir(unlist(lapply(list(...), as.character)))
+  attr(x, "interpretation_details") <- combined_details
-
+  as_sir_structure(x)
-  # TODO for #170
-  # if (!all(is.na(guideline))) {
-  #   attributes(out)$guideline <- guideline
-  #   attributes(out)$mo <- mo
-  #   attributes(out)$ab <- ab
-  #   attributes(out)$method <- method
-  #   attributes(out)$ref_tbl <- ref_tbl
-  #   attributes(out)$ref_breakpoints <- ref_breakpoints
-  # }
-
-  out
 }
 
 #' @method unique sir

R/sir_calc.R

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

R/sir_df.R

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

R/tidymodels.R

@@ -19,56 +19,59 @@
 #' @keywords internal
 #' @export
 #' @examples
-#' library(tidymodels)
+#' if (require("tidymodels")) {
 #'
-#' # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
+#'   # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
-#' # Presence of ESBL genes was predicted based on raw MIC values.
+#'   # Presence of ESBL genes was predicted based on raw MIC values.
 #'
 #'
-#' # example data set in the AMR package
+#'   # example data set in the AMR package
-#' esbl_isolates
+#'   esbl_isolates
 #'
-#' # Prepare a binary outcome and convert to ordered factor
+#'   # Prepare a binary outcome and convert to ordered factor
-#' data <- esbl_isolates %>%
+#'   data <- esbl_isolates %>%
-#'   mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+#'     mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
 #'
-#' # Split into training and testing sets
+#'   # Split into training and testing sets
-#' split <- initial_split(data)
+#'   split <- initial_split(data)
-#' training_data <- training(split)
+#'   training_data <- training(split)
-#' testing_data <- testing(split)
+#'   testing_data <- testing(split)
 #'
-#' # Create and prep a recipe with MIC log2 transformation
+#'   # Create and prep a recipe with MIC log2 transformation
-#' mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
+#'   mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
-#'   # Optionally remove non-predictive variables
-#'   remove_role(genus, old_role = "predictor") %>%
-#'   # Apply the log2 transformation to all MIC predictors
-#'   step_mic_log2(all_mic_predictors()) %>%
-#'   prep()
 #'
-#' # View prepped recipe
+#'     # Optionally remove non-predictive variables
-#' mic_recipe
+#'     remove_role(genus, old_role = "predictor") %>%
 #'
-#' # Apply the recipe to training and testing data
+#'     # Apply the log2 transformation to all MIC predictors
-#' out_training <- bake(mic_recipe, new_data = NULL)
+#'     step_mic_log2(all_mic_predictors()) %>%
-#' out_testing <- bake(mic_recipe, new_data = testing_data)
 #'
-#' # Fit a logistic regression model
+#'     # And apply the preparation steps
-#' fitted <- logistic_reg(mode = "classification") %>%
+#'     prep()
-#'   set_engine("glm") %>%
-#'   fit(esbl ~ ., data = out_training)
 #'
-#' # Generate predictions on the test set
+#'   # View prepped recipe
-#' predictions <- predict(fitted, out_testing) %>%
+#'   mic_recipe
-#'   bind_cols(out_testing)
 #'
-#' # Evaluate predictions using standard classification metrics
+#'   # Apply the recipe to training and testing data
-#' our_metrics <- metric_set(accuracy, kap, ppv, npv)
+#'   out_training <- bake(mic_recipe, new_data = NULL)
-#' metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+#'   out_testing <- bake(mic_recipe, new_data = testing_data)
 #'
-#' # Show performance:
+#'   # Fit a logistic regression model
-#' # - negative predictive value (NPV) of ~98%
+#'   fitted <- logistic_reg(mode = "classification") %>%
-#' # - positive predictive value (PPV) of ~94%
+#'     set_engine("glm") %>%
-#' metrics
+#'     fit(esbl ~ ., data = out_training)
+#'
+#'   # Generate predictions on the test set
+#'   predictions <- predict(fitted, out_testing) %>%
+#'     bind_cols(out_testing)
+#'
+#'   # Evaluate predictions using standard classification metrics
+#'   our_metrics <- metric_set(accuracy, kap, ppv, npv)
+#'   metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+#'
+#'   # Show performance
+#'   metrics
+#' }
 all_mic <- function() {
   x <- tidymodels_amr_select(levels(NA_mic_))
   names(x)

R/vctrs.R

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

R/zzz.R

@@ -127,7 +127,7 @@ AMR_env$cross_icon <- if (isTRUE(base::l10n_info()$`UTF-8`)) "\u00d7" else "x"
           suppressWarnings(suppressMessages(add_custom_antimicrobials(x)))
           packageStartupMessage("OK.")
         },
-        error = function(e) packageStartupMessage("Failed: ", e$message)
+        error = function(e) packageStartupMessage("Failed: ", conditionMessage(e))
       )
     }
   }
@@ -143,7 +143,7 @@ AMR_env$cross_icon <- if (isTRUE(base::l10n_info()$`UTF-8`)) "\u00d7" else "x"
           suppressWarnings(suppressMessages(add_custom_microorganisms(x)))
           packageStartupMessage("OK.")
         },
-        error = function(e) packageStartupMessage("Failed: ", e$message)
+        error = function(e) packageStartupMessage("Failed: ", conditionMessage(e))
       )
     }
   }

data-raw/_generate_python_wrapper.sh

@@ -56,7 +56,8 @@ os.makedirs(r_lib_path, exist_ok=True)
 os.environ['R_LIBS_SITE'] = r_lib_path
 
 from rpy2 import robjects
-from rpy2.robjects import pandas2ri
+from rpy2.robjects.conversion import localconverter
+from rpy2.robjects import default_converter, numpy2ri, pandas2ri
 from rpy2.robjects.packages import importr, isinstalled
 
 # Import base and utils
@@ -94,27 +95,26 @@ if r_amr_version != python_amr_version:
 print(f"AMR: Setting up R environment and AMR datasets...", flush=True)
 
 # Activate the automatic conversion between R and pandas DataFrames
-pandas2ri.activate()
+with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
+    # example_isolates
+    example_isolates = robjects.r('''
+    df <- AMR::example_isolates
+    df[] <- lapply(df, function(x) {
+        if (inherits(x, c("Date", "POSIXt", "factor"))) {
+            as.character(x)
+        } else {
+            x
+        }
+    })
+    df <- df[, !sapply(df, is.list)]
+    df
+    ''')
+    example_isolates['date'] = pd.to_datetime(example_isolates['date'])
 
-# example_isolates
+    # microorganisms
-example_isolates = pandas2ri.rpy2py(robjects.r('''
+    microorganisms = robjects.r('AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]')
-df <- AMR::example_isolates
+    antimicrobials = robjects.r('AMR::antimicrobials[, !sapply(AMR::antimicrobials, is.list)]')
-df[] <- lapply(df, function(x) {
+    clinical_breakpoints = robjects.r('AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]')
-    if (inherits(x, c("Date", "POSIXt", "factor"))) {
-        as.character(x)
-    } else {
-        x
-    }
-})
-df <- df[, !sapply(df, is.list)]
-df
-'''))
-example_isolates['date'] = pd.to_datetime(example_isolates['date'])
-
-# microorganisms
-microorganisms = pandas2ri.rpy2py(robjects.r('AMR::microorganisms[, !sapply(AMR::microorganisms, is.list)]'))
-antimicrobials = pandas2ri.rpy2py(robjects.r('AMR::antimicrobials[, !sapply(AMR::antimicrobials, is.list)]'))
-clinical_breakpoints = pandas2ri.rpy2py(robjects.r('AMR::clinical_breakpoints[, !sapply(AMR::clinical_breakpoints, is.list)]'))
 
 base.options(warn = 0)
 
@@ -129,16 +129,15 @@ echo "from .datasets import clinical_breakpoints" >> $init_file
 
 # Write header to the functions Python file, including the convert_to_python function
 cat <<EOL > "$functions_file"
+import functools
 import rpy2.robjects as robjects
 from rpy2.robjects.packages import importr
 from rpy2.robjects.vectors import StrVector, FactorVector, IntVector, FloatVector, DataFrame
-from rpy2.robjects import pandas2ri
+from rpy2.robjects.conversion import localconverter
+from rpy2.robjects import default_converter, numpy2ri, pandas2ri
 import pandas as pd
 import numpy as np
 
-# Activate automatic conversion between R data frames and pandas data frames
-pandas2ri.activate()
-
 # Import the AMR R package
 amr_r = importr('AMR')
 
@@ -156,10 +155,8 @@ def convert_to_python(r_output):
         return list(r_output)  # Convert to a Python list of integers or floats
     
     # Check if it's a pandas-compatible R data frame
-    elif isinstance(r_output, pd.DataFrame):
+    elif isinstance(r_output, (pd.DataFrame, DataFrame)):
         return r_output  # Return as pandas DataFrame (already converted by pandas2ri)
-    elif isinstance(r_output, DataFrame):
-        return pandas2ri.rpy2py(r_output) # Return as pandas DataFrame
 
     # Check if the input is a NumPy array and has a string data type
     if isinstance(r_output, np.ndarray) and np.issubdtype(r_output.dtype, np.str_):
@@ -167,6 +164,15 @@ def convert_to_python(r_output):
     
     # Fall-back
     return r_output
+
+def r_to_python(r_func):
+    """Decorator that runs an rpy2 function under a localconverter
+    and then applies convert_to_python to its output."""
+    @functools.wraps(r_func)
+    def wrapper(*args, **kwargs):
+        with localconverter(default_converter + numpy2ri.converter + pandas2ri.converter):
+            return convert_to_python(r_func(*args, **kwargs))
+    return wrapper
 EOL
 
 # Directory where the .Rd files are stored (update path as needed)
@@ -246,10 +252,11 @@ for rd_file in "$rd_dir"/*.Rd; do
         gsub("FALSE", "False", func_args)
         gsub("NULL", "None", func_args)
 
-        # Write the Python function definition to the output file
+        # Write the Python function definition to the output file, using decorator
+        print "@r_to_python" >> "'"$functions_file"'"  
         print "def " func_name_py "(" func_args "):" >> "'"$functions_file"'"  
         print "    \"\"\"Please see our website of the R package for the full manual: https://amr-for-r.org\"\"\"" >> "'"$functions_file"'"  
-        print "    return convert_to_python(amr_r." func_name_py "(" func_args "))" >> "'"$functions_file"'"
+        print "    return amr_r." func_name_py "(" func_args ")" >> "'"$functions_file"'"  
 
         print "from .functions import " func_name_py >> "'"$init_file"'"
     }

data-raw/_reproduction_scripts/reproduction_of_microorganisms.R

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

data-raw/_reproduction_scripts/reproduction_of_poorman.R

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

data-raw/read_EUCAST.R

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

index.Rmd

@@ -133,7 +133,7 @@ ggplot(data.frame(mic = some_mic_values,
                   sir = interpretation),
        aes(x = group, y = mic, colour = sir)) +
   theme_minimal() +
-  geom_boxplot(fill = NA, colour = "grey") +
+  geom_boxplot(fill = NA, colour = "grey30") +
   geom_jitter(width = 0.25) +
   
   # NEW scale function: plot MIC values to x, y, colour or fill

index.md

@@ -27,12 +27,12 @@
 
 <p style="text-align:left; width: 50%;">
 
-<small><a href="https://amr-for-r.org/">https://amr-for-r.org</a></small>
+<small><a href="https://amr-for-r.org/">amr-for-r.org</a></small>
 </p>
 
 <p style="text-align:right; width: 50%;">
 
-<small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small>
+<small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">doi.org/10.18637/jss.v104.i03</a></small>
 </p>
 
 </div>
@@ -171,14 +171,14 @@ example_isolates %>%
   select(bacteria,
          aminoglycosides(),
          carbapenems())
-#> ℹ Using column 'mo' as input for mo_fullname()
+#> ℹ Using column 'mo' as input for `mo_fullname()`
-#> ℹ Using column 'mo' as input for mo_is_gram_negative()
+#> ℹ Using column 'mo' as input for `mo_is_gram_negative()`
-#> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant()
+#> ℹ Using column 'mo' as input for `mo_is_intrinsic_resistant()`
 #> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant
 #>   Phenotypes' v1.2 (2023). This note will be shown once per session.
-#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
 #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
-#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
+#> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)
 #> # A tibble: 35 × 7
 #>    bacteria                     GEN   TOB   AMK   KAN   IPM   MEM  
 #>    <chr>                        <sir> <sir> <sir> <sir> <sir> <sir>
@@ -215,9 +215,9 @@ output format automatically (such as markdown, LaTeX, HTML, etc.).
 ``` r
 antibiogram(example_isolates,
             antimicrobials = c(aminoglycosides(), carbapenems()))
-#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
 #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
-#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
+#> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)
 ```
 
 | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
@@ -289,7 +289,7 @@ ggplot(data.frame(mic = some_mic_values,
                   sir = interpretation),
        aes(x = group, y = mic, colour = sir)) +
   theme_minimal() +
-  geom_boxplot(fill = NA, colour = "grey") +
+  geom_boxplot(fill = NA, colour = "grey30") +
   geom_jitter(width = 0.25) +
   
   # NEW scale function: plot MIC values to x, y, colour or fill
@@ -321,9 +321,9 @@ example_isolates %>%
 #> # A tibble: 3 × 5
 #>   ward       GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int
 #>   <chr>            <dbl> <chr>              <dbl> <chr>       
-#> 1 Clinical     0.2289362 0.205-0.254    0.3147503 0.284-0.347 
+#> 1 Clinical         0.229 0.205-0.254        0.315 0.284-0.347 
-#> 2 ICU          0.2902655 0.253-0.33     0.4004739 0.353-0.449 
+#> 2 ICU              0.290 0.253-0.33         0.400 0.353-0.449 
-#> 3 Outpatient   0.2       0.131-0.285    0.3676471 0.254-0.493
+#> 3 Outpatient       0.2   0.131-0.285        0.368 0.254-0.493
 ```
 
 Or use [antimicrobial
@@ -340,44 +340,44 @@ out <- example_isolates %>%
   # calculate AMR using resistance(), over all aminoglycosides and polymyxins:
   summarise(across(c(aminoglycosides(), polymyxins()),
             resistance))
-#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
 #>   (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
-#> ℹ For polymyxins() using column 'COL' (colistin)
+#> ℹ For `polymyxins()` using column 'COL' (colistin)
 #> Warning: There was 1 warning in `summarise()`.
 #> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.
 #> ℹ In group 3: `ward = "Outpatient"`.
 #> Caused by warning:
 #> ! Introducing NA: only 23 results available for KAN in group: ward =
-#> "Outpatient" (minimum = 30).
+#> "Outpatient" (`minimum` = 30).
 out
 #> # A tibble: 3 × 6
-#>   ward             GEN       TOB       AMK   KAN       COL
+#>   ward         GEN   TOB   AMK   KAN   COL
-#>   <chr>          <dbl>     <dbl>     <dbl> <dbl>     <dbl>
+#>   <chr>      <dbl> <dbl> <dbl> <dbl> <dbl>
-#> 1 Clinical   0.2289362 0.3147503 0.6258993     1 0.7802956
+#> 1 Clinical   0.229 0.315 0.626     1 0.780
-#> 2 ICU        0.2902655 0.4004739 0.6624473     1 0.8574144
+#> 2 ICU        0.290 0.400 0.662     1 0.857
-#> 3 Outpatient 0.2       0.3676471 0.6052632    NA 0.8888889
+#> 3 Outpatient 0.2   0.368 0.605    NA 0.889
 ```
 
 ``` r
 # transform the antibiotic columns to names:
 out %>% set_ab_names()
 #> # A tibble: 3 × 6
-#>   ward       gentamicin tobramycin  amikacin kanamycin  colistin
+#>   ward       gentamicin tobramycin amikacin kanamycin colistin
-#>   <chr>           <dbl>      <dbl>     <dbl>     <dbl>     <dbl>
+#>   <chr>           <dbl>      <dbl>    <dbl>     <dbl>    <dbl>
-#> 1 Clinical    0.2289362  0.3147503 0.6258993         1 0.7802956
+#> 1 Clinical        0.229      0.315    0.626         1    0.780
-#> 2 ICU         0.2902655  0.4004739 0.6624473         1 0.8574144
+#> 2 ICU             0.290      0.400    0.662         1    0.857
-#> 3 Outpatient  0.2        0.3676471 0.6052632        NA 0.8888889
+#> 3 Outpatient      0.2        0.368    0.605        NA    0.889
 ```
 
 ``` r
 # transform the antibiotic column to ATC codes:
 out %>% set_ab_names(property = "atc")
 #> # A tibble: 3 × 6
-#>   ward         J01GB03   J01GB01   J01GB06 J01GB04   J01XB01
+#>   ward       J01GB03 J01GB01 J01GB06 J01GB04 J01XB01
-#>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>     <dbl>
+#>   <chr>        <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
-#> 1 Clinical   0.2289362 0.3147503 0.6258993       1 0.7802956
+#> 1 Clinical     0.229   0.315   0.626       1   0.780
-#> 2 ICU        0.2902655 0.4004739 0.6624473       1 0.8574144
+#> 2 ICU          0.290   0.400   0.662       1   0.857
-#> 3 Outpatient 0.2       0.3676471 0.6052632      NA 0.8888889
+#> 3 Outpatient   0.2     0.368   0.605      NA   0.889
 ```
 
 ## What else can you do with this package?

man/age_groups.Rd

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

man/amr-tidymodels.Rd

@@ -65,56 +65,59 @@ Pre-processing pipeline steps include:
 These steps integrate with \code{recipes::recipe()} and work like standard preprocessing steps. They are useful for preparing data for modelling, especially with classification models.
 }
 \examples{
-library(tidymodels)
+if (require("tidymodels")) {
 
-# The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
+  # The below approach formed the basis for this paper: DOI 10.3389/fmicb.2025.1582703
-# Presence of ESBL genes was predicted based on raw MIC values.
+  # Presence of ESBL genes was predicted based on raw MIC values.
 
 
-# example data set in the AMR package
+  # example data set in the AMR package
-esbl_isolates
+  esbl_isolates
 
-# Prepare a binary outcome and convert to ordered factor
+  # Prepare a binary outcome and convert to ordered factor
-data <- esbl_isolates \%>\%
+  data <- esbl_isolates \%>\%
-  mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
+    mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
 
-# Split into training and testing sets
+  # Split into training and testing sets
-split <- initial_split(data)
+  split <- initial_split(data)
-training_data <- training(split)
+  training_data <- training(split)
-testing_data <- testing(split)
+  testing_data <- testing(split)
 
-# Create and prep a recipe with MIC log2 transformation
+  # Create and prep a recipe with MIC log2 transformation
-mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\%
+  mic_recipe <- recipe(esbl ~ ., data = training_data) \%>\%
-  # Optionally remove non-predictive variables
-  remove_role(genus, old_role = "predictor") \%>\%
-  # Apply the log2 transformation to all MIC predictors
-  step_mic_log2(all_mic_predictors()) \%>\%
-  prep()
 
-# View prepped recipe
+    # Optionally remove non-predictive variables
-mic_recipe
+    remove_role(genus, old_role = "predictor") \%>\%
 
-# Apply the recipe to training and testing data
+    # Apply the log2 transformation to all MIC predictors
-out_training <- bake(mic_recipe, new_data = NULL)
+    step_mic_log2(all_mic_predictors()) \%>\%
-out_testing <- bake(mic_recipe, new_data = testing_data)
 
-# Fit a logistic regression model
+    # And apply the preparation steps
-fitted <- logistic_reg(mode = "classification") \%>\%
+    prep()
-  set_engine("glm") \%>\%
-  fit(esbl ~ ., data = out_training)
 
-# Generate predictions on the test set
+  # View prepped recipe
-predictions <- predict(fitted, out_testing) \%>\%
+  mic_recipe
-  bind_cols(out_testing)
 
-# Evaluate predictions using standard classification metrics
+  # Apply the recipe to training and testing data
-our_metrics <- metric_set(accuracy, kap, ppv, npv)
+  out_training <- bake(mic_recipe, new_data = NULL)
-metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+  out_testing <- bake(mic_recipe, new_data = testing_data)
 
-# Show performance:
+  # Fit a logistic regression model
-# - negative predictive value (NPV) of ~98\%
+  fitted <- logistic_reg(mode = "classification") \%>\%
-# - positive predictive value (PPV) of ~94\%
+    set_engine("glm") \%>\%
-metrics
+    fit(esbl ~ ., data = out_training)
+
+  # Generate predictions on the test set
+  predictions <- predict(fitted, out_testing) \%>\%
+    bind_cols(out_testing)
+
+  # Evaluate predictions using standard classification metrics
+  our_metrics <- metric_set(accuracy, kap, ppv, npv)
+  metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
+
+  # Show performance
+  metrics
+}
 }
 \seealso{
 \code{\link[recipes:recipe]{recipes::recipe()}}, \code{\link[=as.mic]{as.mic()}}, \code{\link[=as.sir]{as.sir()}}

man/as.sir.Rd

@@ -70,12 +70,14 @@ is_sir_eligible(x, threshold = 0.05)
   language = get_AMR_locale(), verbose = FALSE, info = interactive(),
   parallel = FALSE, max_cores = -1, conserve_capped_values = NULL)
 
-sir_interpretation_history(clean = FALSE)
+sir_interpretation_history(sir_values = NULL, clean = FALSE)
 }
 \arguments{
 \item{x}{Vector of values (for class \code{\link{mic}}: MIC values in mg/L, for class \code{\link{disk}}: a disk diffusion radius in millimetres).}
 
-\item{...}{For using on a \link{data.frame}: names of columns to apply \code{\link[=as.sir]{as.sir()}} on (supports tidy selection such as \code{column1:column4}). Otherwise: arguments passed on to methods.}
+\item{...}{For using on a \link{data.frame}: selection of columns to apply \code{as.sir()} to. Supports \link[tidyselect:starts_with]{tidyselect language} such as \code{where(is.mic)}, \code{starts_with(...)}, or \code{column1:column4}, and can thus also be \link[=amr_selector]{antimicrobial selectors} such as \code{as.sir(df, penicillins())}.
+
+Otherwise: arguments passed on to methods.}
 
 \item{threshold}{Maximum fraction of invalid antimicrobial interpretations of \code{x}, see \emph{Examples}.}
 
@@ -145,6 +147,8 @@ The default \code{"standard"} setting ensures cautious handling of uncertain val
 
 \item{max_cores}{Maximum number of cores to use if \code{parallel = TRUE}. Use a negative value to subtract that number from the available number of cores, e.g. a value of \code{-2} on an 8-core machine means that at most 6 cores will be used. Defaults to \code{-1}. There will never be used more cores than variables to analyse. The available number of cores are detected using \code{\link[parallelly:availableCores]{parallelly::availableCores()}} if that package is installed, and base \R's \code{\link[parallel:detectCores]{parallel::detectCores()}} otherwise.}
 
+\item{sir_values}{SIR values that were interpreted from MIC or disk diffusion values using \code{\link[=as.sir]{as.sir()}}.}
+
 \item{clean}{A \link{logical} to indicate whether previously stored results should be forgotten after returning the 'logbook' with results.}
 }
 \value{
@@ -314,9 +318,12 @@ if (require("dplyr")) {
   df_wide \%>\% mutate_if(is.mic, as.sir)
   df_wide \%>\% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
   df_wide \%>\% mutate(across(where(is.mic), as.sir))
+
   df_wide \%>\% mutate_at(vars(amoxicillin:tobra), as.sir)
   df_wide \%>\% mutate(across(amoxicillin:tobra, as.sir))
 
+  df_wide \%>\% mutate(across(aminopenicillins(), as.sir))
+
   # approaches that all work with additional arguments:
   df_long \%>\%
     # given a certain data type, e.g. MIC values

man/ggplot_sir.Rd

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

man/mean_amr_distance.Rd

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

man/plot.Rd

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

pkgdown/extra.css

@@ -190,6 +190,15 @@ this shows on top of every sidebar to the right
   }
 }
 
+.template-reference-topic h3,
+.template-reference-topic h3 code {
+  color: var(--amr-green-dark) !important;
+}
+.template-reference-topic h3 {
+  font-weight: normal;
+  margin-top: 2rem;
+}
+
 /* replace 'Developers' with 'Maintainers' */
 .developers h2 {
   display: none;

tests/testthat/test-zzz.R

@@ -63,10 +63,12 @@ test_that("test-zzz.R", {
     "progress_bar" = "progress",
     "read_html" = "xml2",
     "right_join" = "dplyr",
+    "select" = "dplyr",
     "semi_join" = "dplyr",
     "showQuestion" = "rstudioapi",
     "symbol" = "cli",
     "tibble" = "tibble",
+    "where" = "tidyselect",
     "write.xlsx" = "openxlsx"
   )