new rsi_translation

.github/workflows/check-current.yaml

@@ -52,9 +52,12 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          - {os: macOS-latest,   r: 'devel',   allowfail: true}
+          - {os: macOS-latest,   r: 'devel',   allowfail: false}
-          - {os: ubuntu-latest,  r: 'devel',   allowfail: true}
+          - {os: macOS-latest,   r: 'release', allowfail: false}
-          - {os: windows-latest, r: 'devel',   allowfail: true}
+          - {os: ubuntu-latest,  r: 'devel',   allowfail: false}
+          - {os: ubuntu-latest,  r: 'release', allowfail: false}
+          - {os: windows-latest, r: 'devel',   allowfail: false}
+          - {os: windows-latest, r: 'release', allowfail: false}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/check-old.yaml

@@ -50,23 +50,20 @@ jobs:
       fail-fast: false
       matrix:
         config:
-          # test all released versions of R >= 3.0, we support them all!
+          # test all old versions of R >= 3.0, we support them all!
-          - {os: macOS-latest,   r: '4.2',     allowfail: false}
+          # (for R-release and R-devel, see check-current.yaml)
           - {os: macOS-latest,   r: '4.1',     allowfail: false}
           - {os: macOS-latest,   r: '4.0',     allowfail: false}
           - {os: macOS-latest,   r: '3.6',     allowfail: false}
-          - {os: ubuntu-22.04,   r: '4.2',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '4.1',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '4.0',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.6',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           # R 3.5 returns a strange GC error when running examples, omit the checks for that
-          # - {os: ubuntu-22.04,   r: '3.5',     allowfail: true, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.4',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.3',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.2',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.1',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
           - {os: ubuntu-22.04,   r: '3.0',     allowfail: false, rspm: "https://packagemanager.rstudio.com/cran/__linux__/jammy/latest"}
-          - {os: windows-latest, r: '4.2',     allowfail: false}
           - {os: windows-latest, r: '4.1',     allowfail: false}
           - {os: windows-latest, r: '4.0',     allowfail: false}
           - {os: windows-latest, r: '3.6',     allowfail: false}

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: AMR
-Version: 1.8.2.9032
+Version: 1.8.2.9033
 Date: 2022-10-22
 Title: Antimicrobial Resistance Data Analysis
 Description: Functions to simplify and standardise antimicrobial resistance (AMR)

NEWS.md

@@ -1,4 +1,4 @@
-# AMR 1.8.2.9032
+# AMR 1.8.2.9033
 
 This version will eventually become v2.0! We're happy to reach a new major milestone soon!
 
@@ -11,13 +11,15 @@ This version will eventually become v2.0! We're happy to reach a new major miles
 * The `microorganisms.old` data set was removed, and all previously accepted names are now included in the `microorganisms` data set. A new column `status` contains `"accepted"` for currently accepted names and `"synonym"` for taxonomic synonyms; currently invalid names. All previously accepted names now have a microorganisms ID and - if available - an LPSN, GBIF and SNOMED CT identifier.
 * The MO matching score algorithm (`mo_matching_score()`) now counts deletions and substitutions as 2 instead of 1, which impacts the outcome of `as.mo()` and any `mo_*()` function
 * Argument `combine_IR` has been removed from this package (affecting functions `count_df()`, `proportion_df()`, and `rsi_df()` and some plotting functions), since it was replaced with `combine_SI` three years ago
+* Removal of interpretation guidelines older than 10 years, the oldest now included guidelines of EUCAST and CLSI are from 2013
 
 ### New
 * EUCAST 2022 and CLSI 2022 guidelines have been added for `as.rsi()`. EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations.
-* All new algorithm for `as.mo()` (and thus internally all `mo_*()` functions) while still following our original set-up as described in our paper (DOI 10.18637/jss.v104.i03).
+* All new algorithm for `as.mo()` (and thus all `mo_*()` functions) while still following our original set-up as described in our paper (DOI 10.18637/jss.v104.i03).
   * A new argument `keep_synonyms` allows to *not* correct for updated taxonomy, in favour of the now deleted argument `allow_uncertain`
   * It has increased tremendously in speed and returns generally more consequent results
   * Sequential coercion is now extremely fast as results are stored to the package environment, although coercion of unknown values must be run once per session. Previous results can be reset/removed with the new `mo_reset_session()` function.
+* Support for microorganism codes of the ASIan Antimicrobial Resistance Surveillance Network (ASIARS-Net) 
 * Function `rsi_confidence_interval()` to add confidence intervals in AMR calculation. This is also included in `rsi_df()` and `proportion_df()`
 * Function `mean_amr_distance()` to calculate the mean AMR distance. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand.
 * Function `rsi_interpretation_history()` to view the history of previous runs of `as.rsi()`. This returns a 'logbook' with the selected guideline, reference table and specific interpretation of each row in a data set on which `as.rsi()` was run.
@@ -28,6 +30,7 @@ This version will eventually become v2.0! We're happy to reach a new major miles
 * Support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. We are very grateful for the valuable input by our colleagues from other countries. The `AMR` package is now available in 16 languages. The automatic language determination will give a note at start-up on systems in supported languages.
 * Our data sets are now also continually exported to Apache Feather and Apache Parquet formats. You can find more info [in this article on our website](https://msberends.github.io/AMR/articles/datasets.html).
 * Support for using antibiotic selectors in scoped `dplyr` verbs (with or without `vars()`), such as in: `... %>% summarise_at(aminoglycosides(), resistance)`, see `resistance()`
+* Support for antimicrobial interpretation of anaerobic bacteria, by adding a 'placeholder' code `B_ANAER` to the `microorganisms` data set and add the breakpoints of anaerobics to the `rsi_interpretation` data set, which is used by `as.rsi()` when interpreting MIC and disk diffusion values
 
 ### Changed
 * Fix for using `as.rsi()` on certain EUCAST breakpoints for MIC values
@@ -46,6 +49,8 @@ This version will eventually become v2.0! We're happy to reach a new major miles
 * Black and white message texts are now reversed in colour if using an RStudio dark theme
 * `mo_snomed()` now returns class `character`, not `numeric` anymore (to make long SNOMED codes readable)
 * Fix for using `as.ab()` on `NA` values
+* Updated support for all WHONET 2022 microorganism codes 
+* Antimicrobial interpretation 'SDD' (susceptible dose-dependent, coined by CLSI) will be interpreted as 'I' to comply with EUCAST's 'I' in `as.rsi()`
 
 ### Other
 * New website to make use of the new Bootstrap 5 and pkgdown 2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions

R/aa_helper_functions.R

@@ -1083,8 +1083,7 @@ try_colour <- function(..., before, after, collapse = " ") {
 font_black <- function(..., collapse = " ") {
   before <- "\033[38;5;232m"
   after <- "\033[39m"
-  theme_info <- import_fn("getThemeInfo", "rstudioapi", error_on_fail = FALSE)
+  if (isTRUE(AMR_env$is_dark_theme)) {
-  if (!is.null(theme_info) && isTRUE(theme_info()$dark)) {
     # white
     before <- "\033[37m"
     after <- "\033[39m"
@@ -1094,8 +1093,7 @@ font_black <- function(..., collapse = " ") {
 font_white <- function(..., collapse = " ") {
   before <- "\033[37m"
   after <- "\033[39m"
-  theme_info <- import_fn("getThemeInfo", "rstudioapi", error_on_fail = FALSE)
+  if (isTRUE(AMR_env$is_dark_theme)) {
-  if (!is.null(theme_info) && isTRUE(theme_info()$dark)) {
     # black
     before <- "\033[38;5;232m"
     after <- "\033[39m"
@@ -1191,7 +1189,7 @@ progress_ticker <- function(n = 1, n_min = 0, print = TRUE, ...) {
       # so we use progress::progress_bar
       # a close() method was also added, see below this function
       pb <- progress_bar$new(
-        format = "(:spin) [:bar] :percent (:current/:total,:eta)",
+        format = "[:bar] :percent (:current/:total,:eta)",
         total = n
       )
     } else {

R/amr.R

@@ -42,7 +42,7 @@
 #'
 #' This package can be used for:
 #' - Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF)
-#' - Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
+#' - Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline from the last 10 years
 #' - Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records
 #' - Determining first isolates to be used for AMR data analysis
 #' - Calculating antimicrobial resistance

R/bug_drug_combinations.R

@@ -43,7 +43,6 @@
 #' @export
 #' @rdname bug_drug_combinations
 #' @return The function [bug_drug_combinations()] returns a [data.frame] with columns "mo", "ab", "S", "I", "R" and "total".
-#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, *Clinical and Laboratory Standards Institute (CLSI)*. <https://clsi.org/standards/products/microbiology/documents/m39/>.
 #' @examples
 #' \donttest{
 #' x <- bug_drug_combinations(example_isolates)

R/custom_eucast_rules.R

@@ -253,7 +253,7 @@ print.custom_eucast_rules <- function(x, ...) {
 }
 
 format_custom_query_rule <- function(query, colours = has_colour()) {
-  # font_black() is very expensive in RStudio because it checks if the theme is dark, so do it once:
+  # font_black() is a bit expensive so do it once:
   txt <- font_black("{text}")
   query <- gsub(" & ", sub("{text}", font_bold(" and "), txt, fixed = TRUE), query, fixed = TRUE)
   query <- gsub(" | ", sub("{text}", " or ", txt, fixed = TRUE), query, fixed = TRUE)

R/mo.R

@@ -236,6 +236,8 @@ as.mo <- function(x,
     # set up progress bar
     progress <- progress_ticker(n = length(x_unique), n_min = 10, print = info)
     on.exit(close(progress))
+    
+    msg <- character(0)
 
     # run it
     x_coerced <- vapply(FUN.VALUE = character(1), x_unique, function(x_search) {
@@ -249,12 +251,16 @@ as.mo <- function(x,
       x_search_cleaned <- x_out
       x_out <- tolower(x_out)
 
+      # first check if cleaning led to an exact result, case-insensitive
+      if (x_out %in% AMR_env$MO_lookup$fullname_lower) {
+        return(as.character(AMR_env$MO_lookup$mo[match(x_out, AMR_env$MO_lookup$fullname_lower)]))
+      }
+
       # input must not be too short
       if (nchar(x_out) < 3) {
         return("UNKNOWN")
       }
 
-
       # take out the parts, split by space
       x_parts <- strsplit(gsub("-", " ", x_out, fixed = TRUE), " ", fixed = TRUE)[[1]]
 
@@ -267,17 +273,13 @@ as.mo <- function(x,
         filtr <- which(AMR_env$MO_lookup$full_first %like_case% first_chars)
       } else if (nchar(x_out) == 4) {
         # no space and 4 characters - probably a code such as STAU or ESCO!
-        if (isTRUE(info)) {
+        msg <- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on ", vector_and(c(substr(x_out, 1, 2), substr(x_out, 3, 4)), sort = FALSE)))
-          message_("Input \"", x_search, "\" is assumed to be a microorganism code - trying to match on ", vector_and(c(substr(x_out, 1, 2), substr(x_out, 3, 4)), sort = FALSE))
-        }
         filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", substr(x_out, 1, 2), ".* ", substr(x_out, 3, 4)))
       } else if (nchar(x_out) <= 6) {
         # no space and 5-6 characters - probably a code such as STAAUR or ESCCOL!
         first_part <- paste0(substr(x_out, 1, 2), "[a-z]*", substr(x_out, 3, 3))
         second_part <- substr(x_out, 4, nchar(x_out))
-        if (isTRUE(info)) {
+        msg <- c(msg, paste0("Input \"", x_search, "\" was assumed to be a microorganism code - tried to match on ", vector_and(c(gsub("[a-z]*", "(...)", first_part, fixed = TRUE), second_part), sort = FALSE)))
-          message_("Input \"", x_search, "\" is assumed to be a microorganism code - trying to match on ", vector_and(c(gsub("[a-z]*", "(...)", first_part, fixed = TRUE), second_part), sort = FALSE))
-        }
         filtr <- which(AMR_env$MO_lookup$fullname_lower %like_case% paste0("(^| )", first_part, ".* ", second_part))
       } else {
         filtr <- which(AMR_env$MO_lookup$full_first == substr(x_out, 1, 1))
@@ -287,6 +289,7 @@ as.mo <- function(x,
       } else {
         mo_to_search <- AMR_env$MO_lookup$fullname[filtr]
       }
+      
       AMR_env$mo_to_search <- mo_to_search
       # determine the matching score on the original search value
       m <- mo_matching_score(x = x_search_cleaned, n = mo_to_search)
@@ -303,7 +306,7 @@ as.mo <- function(x,
 
       top_hits <- mo_to_search[order(m, decreasing = TRUE, na.last = NA)] # na.last = NA will remove the NAs
       if (length(top_hits) == 0) {
-        warning_("No hits found for \"", x_search, "\" with minimum_matching_score = ", ifelse(is.null(minimum_matching_score), paste0("NULL (=", round(min(minimum_matching_score_current, na.rm = TRUE), 3), ")"), minimum_matching_score), ". Try setting this value lower or even to 0.")
+        warning_("No hits found for \"", x_search, "\" with minimum_matching_score = ", ifelse(is.null(minimum_matching_score), paste0("NULL (=", round(min(minimum_matching_score_current, na.rm = TRUE), 3), ")"), minimum_matching_score), ". Try setting this value lower or even to 0.", call = FALSE)
         result_mo <- NA_character_
       } else {
         result_mo <- AMR_env$MO_lookup$mo[match(top_hits[1], AMR_env$MO_lookup$fullname)]
@@ -356,15 +359,18 @@ as.mo <- function(x,
         } else {
           examples <- paste0(nr2char(length(AMR_env$mo_uncertainties$original_input)), " microorganism", plural[1])
         }
-        msg <- paste0(
+        msg <- c(msg, paste0(
           "Microorganism translation was uncertain for ", examples,
           ". Run `mo_uncertainties()` to review ", plural[2], "."
-        )
+        ))
-        message_(msg)
+        
+        for (m in msg) {
+          message_(m)  
+        }
       }
     }
   } # end of loop over all yet unknowns
-
+  
   # Keep or replace synonyms ----
   gbif_matches <- AMR::microorganisms$gbif_renamed_to[match(out, AMR::microorganisms$mo)]
   gbif_matches[!gbif_matches %in% AMR::microorganisms$gbif] <- NA
@@ -383,7 +389,7 @@ as.mo <- function(x,
     }
   } else if (is.null(getOption("AMR_keep_synonyms")) && length(AMR_env$mo_renamed$old) > 0 && message_not_thrown_before("as.mo", "keep_synonyms_warning", entire_session = TRUE)) {
     # keep synonyms is TRUE, so check if any do have synonyms
-    warning_("Function `as.mo()` returned ", nr2char(length(unique(AMR_env$mo_renamed$old))), " old taxonomic name", ifelse(length(unique(AMR_env$mo_renamed$old)) > 1, "s", ""), ". Use `as.mo(..., keep_synonyms = FALSE)` to clean the input to currently accepted taxonomic names, or set the R option `AMR_keep_synonyms` to `FALSE`. This warning will be shown once per session.")
+    warning_("Function `as.mo()` returned ", nr2char(length(unique(AMR_env$mo_renamed$old))), " old taxonomic name", ifelse(length(unique(AMR_env$mo_renamed$old)) > 1, "s", ""), ". Use `as.mo(..., keep_synonyms = FALSE)` to clean the input to currently accepted taxonomic names, or set the R option `AMR_keep_synonyms` to `FALSE`. This warning will be shown once per session.", call = FALSE)
   }
 
   # Apply Becker ----
@@ -403,7 +409,7 @@ as.mo <- function(x,
         warning_("in `as.mo()`: Becker ", font_italic("et al."), " (2014, 2019, 2020) does not contain these species named after their publication: ",
           vector_and(font_italic(gsub("Staphylococcus", "S.", post_Becker, fixed = TRUE), collapse = NULL), quotes = FALSE),
           ". Categorisation to CoNS/CoPS was taken from the original scientific publication(s).",
-          immediate = TRUE
+          immediate = TRUE, call = FALSE
         )
       }
     }
@@ -442,7 +448,7 @@ as.mo <- function(x,
   out[is.na(out) & !is.na(x)] <- "UNKNOWN"
   AMR_env$mo_failures <- unique(x[out == "UNKNOWN" & !x %in% c("UNKNOWN", "con") & !x %like_case% "^[(]unknown [a-z]+[)]$" & !is.na(x)])
   if (length(AMR_env$mo_failures) > 0) {
-    warning_("The following input could not be coerced and was returned as \"UNKNOWN\": ", vector_and(AMR_env$mo_failures, quotes = TRUE), ".\nYou can retrieve this list with `mo_failures()`.")
+    warning_("The following input could not be coerced and was returned as \"UNKNOWN\": ", vector_and(AMR_env$mo_failures, quotes = TRUE), ".\nYou can retrieve this list with `mo_failures()`.", call = FALSE)
   }
 
   # Return class ----
@@ -542,10 +548,11 @@ pillar_shaft.mo <- function(x, ...) {
     mo_cols <- NULL
   }
 
-  if (!all(x %in% c(AMR::microorganisms$mo, NA)) ||
+  all_mos <- c(AMR::microorganisms$mo, NA)
-    (!is.null(df) && !all(unlist(df[, which(mo_cols), drop = FALSE]) %in% AMR::microorganisms$mo))) {
+  if (!all(x %in% all_mos) ||
+    (!is.null(df) && !all(unlist(df[, which(mo_cols), drop = FALSE]) %in% all_mos))) {
     # markup old mo codes
-    out[!x %in% AMR::microorganisms$mo] <- font_italic(font_na(x[!x %in% AMR::microorganisms$mo],
+    out[!x %in% all_mos] <- font_italic(font_na(x[!x %in% all_mos],
       collapse = NULL
     ),
     collapse = NULL
@@ -558,7 +565,7 @@ pillar_shaft.mo <- function(x, ...) {
     }
     warning_(
       col, " contains old MO codes (from a previous AMR package version). ",
-      "Please update your MO codes with `as.mo()`."
+      "Please update your MO codes with `as.mo()`.", call = FALSE
     )
   }
 
@@ -645,7 +652,7 @@ print.mo <- function(x, print.shortnames = FALSE, ...) {
   if (!all(x %in% c(AMR::microorganisms$mo, NA))) {
     warning_(
       "Some MO codes are from a previous AMR package version. ",
-      "Please update the MO codes with `as.mo()`."
+      "Please update the MO codes with `as.mo()`.", call = FALSE
     )
   }
   print.default(x, quote = FALSE)
@@ -911,11 +918,12 @@ convert_colloquial_input <- function(x) {
   # CoNS/CoPS in different languages (support for German, Dutch, Spanish, Portuguese)
   out[x %like_case% "([ck]oagulas[ea].negatie?[vf]|^[ck]o?ns[^a-z]*$)"] <- "B_STPHY_CONS"
   out[x %like_case% "([ck]oagulas[ea].positie?[vf]|^[ck]o?ps[^a-z]*$)"] <- "B_STPHY_COPS"
-
+  
   # Gram stains
-  out[x %like_case% "gram[ -]?neg.*|negatie?[vf]"] <- "B_GRAMN"
+  out[x %like_case% "gram[ -]?neg.*"] <- "B_GRAMN"
-  out[x %like_case% "gram[ -]?pos.*|positie?[vf]"] <- "B_GRAMP"
+  out[x %like_case% "gram[ -]?pos.*"] <- "B_GRAMP"
-
+  out[is.na(out) & x %like_case% "anaerob[a-z]+ (micro)?.*organism"] <- "B_ANAER"
+  
   # yeasts and fungi
   out[x %like_case% "^yeast?"] <- "F_YEAST"
   out[x %like_case% "^fung(us|i)"] <- "F_FUNGUS"
@@ -932,6 +940,10 @@ convert_colloquial_input <- function(x) {
   # unexisting names (xxx and con are WHONET codes)
   out[x %in% c("con", "other", "none", "unknown") | x %like_case% "virus"] <- "UNKNOWN"
 
+  # WHONET has a lot of E. coli and Vibrio cholerae names
+  out[x %like_case% "escherichia coli"] <- "B_ESCHR_COLI"
+  out[x %like_case% "vibrio cholerae"] <- "B_VIBRI_CHLR"
+
   out
 }
 

R/rsi.R

@@ -100,6 +100,12 @@
 #' @aliases rsi
 #' @export
 #' @seealso [as.mic()], [as.disk()], [as.mo()]
+#' @source
+#' For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:
+#' 
+#' - **M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data**, `r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/microbiology/documents/m39/>.
+#' - **M100 Performance Standard for Antimicrobial Susceptibility Testing**, `r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "CLSI")$guideline)))`, *Clinical and Laboratory Standards Institute* (CLSI). <https://clsi.org/standards/products/microbiology/documents/m100/>.
+#' - **Breakpoint tables for interpretation of MICs and zone diameters**, `r min(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(rsi_translation, guideline %like% "EUCAST")$guideline)))`, *European Committee on Antimicrobial Susceptibility Testing* (EUCAST). <https://www.eucast.org/clinical_breakpoints>.
 #' @inheritSection AMR Reference Data Publicly Available
 #' @examples
 #' example_isolates
@@ -332,11 +338,13 @@ as.rsi.default <- function(x, ...) {
     # set to capitals
     x <- toupper(x)
     x <- gsub("[^A-Z]+", "", x, perl = TRUE)
+    # CLSI uses SDD for "susceptible dose-dependent"
+    x <- gsub("SDD", "I", x, fixed = TRUE)
     # some labs now report "H" instead of "I" to not interfere with EUCAST prior to 2019
     x <- gsub("H", "I", x, fixed = TRUE)
-    # and MIPS uses D for Dose-dependent (which is I, but it will throw a note)
+    # MIPS uses D for Dose-dependent (which is I, but it will throw a note)
     x <- gsub("D", "I", x, fixed = TRUE)
-    # and MIPS uses U for "susceptible urine"
+    # MIPS uses U for "susceptible urine"
     x <- gsub("U", "S", x, fixed = TRUE)
     # in cases of "S;S" keep S, but in case of "S;I" make it NA
     x <- gsub("^S+$", "S", x)
@@ -368,6 +376,9 @@ as.rsi.default <- function(x, ...) {
       if (any(toupper(x.bak[!is.na(x.bak)]) == "D") && message_not_thrown_before("as.rsi", "D")) {
         warning_("in `as.rsi()`: 'D' (dose-dependent) was interpreted as 'I', following some laboratory systems")
       }
+      if (any(toupper(x.bak[!is.na(x.bak)]) == "SDD") && message_not_thrown_before("as.rsi", "SDD")) {
+        warning_("in `as.rsi()`: 'SDD' (susceptible dose-dependent, coined by CLSI) was interpreted as 'I' to comply with EUCAST's 'I'")
+      }
       if (any(toupper(x.bak[!is.na(x.bak)]) == "H") && message_not_thrown_before("as.rsi", "H")) {
         warning_("in `as.rsi()`: 'H' was interpreted as 'I', following some laboratory systems")
       }
@@ -875,9 +886,17 @@ as_rsi_method <- function(method_short,
         pm_filter(uti == FALSE) %pm>% # 'uti' is a column in rsi_translation
         pm_arrange(rank_index)
     }
-
+    
+    records_same_mo <- get_record[get_record$mo == get_record[1, "mo", drop = TRUE], , drop = FALSE]
+    if (message_not_thrown_before("as.rsi", "site", records_same_mo$mo[1]) && nrow(records_same_mo) > 1 && length(unique(records_same_mo$site)) > 1) {
+      warning_("in `as.rsi()`: assuming site '",
+               get_record[1L, "site", drop = FALSE], "' for ",
+               font_italic(suppressMessages(suppressWarnings(mo_name(records_same_mo$mo[1], language = NULL, keep_synonyms = FALSE)))),
+               call = FALSE)
+      rise_warning <- TRUE
+    }
     get_record <- get_record[1L, , drop = FALSE]
-
+    
     if (NROW(get_record) > 0) {
       if (is.na(x[i]) | (is.na(get_record$breakpoint_S) & is.na(get_record$breakpoint_R))) {
         new_rsi[i] <- NA_character_

R/zzz.R

@@ -68,6 +68,7 @@ AMR_env$rsi_interpretation_history <- data.frame(
 )
 AMR_env$has_data.table <- pkg_is_available("data.table", also_load = FALSE)
 AMR_env$custom_ab_codes <- character(0)
+AMR_env$is_dark_theme <- tryCatch(isTRUE(getExportedValue("getThemeInfo", ns = asNamespace("rstudioapi"))()$dark), error = function(e) FALSE)
 
 # determine info icon for messages
 utf8_supported <- isTRUE(base::l10n_info()$`UTF-8`)

data-raw/microorganisms.md5

@@ -1 +1 @@
-5ac1152c166d5d4f5763547d948fce79
+8c1fdbe23853d30840dc5d863bc761df

data-raw/reproduction_of_rsi_translation.R

@@ -35,144 +35,156 @@ library(readr)
 library(tidyr)
 library(AMR)
 
-# Install the WHONET software on Windows (http://www.whonet.org/software.html),
+# Install the WHONET 2022 software on Windows (http://www.whonet.org/software.html),
-# and copy the folder C:\WHONET\Codes to data-raw/WHONET/Codes
+# and copy the folder C:\WHONET\Resources to the data-raw/WHONET/ folder
-DRGLST <- read_tsv("data-raw/WHONET/Codes/DRGLST.txt", na = c("", "NA", "-"), show_col_types = FALSE)
-DRGLST1 <- read_tsv("data-raw/WHONET/Codes/DRGLST1.txt", na = c("", "NA", "-"), show_col_types = FALSE)
-ORGLIST <- read_tsv("data-raw/WHONET/Codes/ORGLIST.txt", na = c("", "NA", "-"), show_col_types = FALSE)
 
-# create data set for generic rules (i.e., AB-specific but not MO-specific)
+# Load source data ----
-rsi_generic <- DRGLST %>%
+whonet_organisms <- read_tsv("data-raw/WHONET/Resources/Organisms.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>% 
-  filter(CLSI == "X" | EUCST == "X") %>%
+  # remove old taxonomic names
-  select(ab = ANTIBIOTIC, disk_dose = POTENCY, matches("^(CLSI|EUCST)[0-9]")) %>%
+  filter(TAXONOMIC_STATUS == "C") %>% 
-  mutate(
+  transmute(ORGANISM_CODE = tolower(WHONET_ORG_CODE), ORGANISM) %>% 
-    ab = as.ab(ab),
+  # what's wrong here? 'sau' is both S. areus and S. aureus sp. aureus
-    across(matches("(CLSI|EUCST)"), as.double)
+  mutate(ORGANISM = if_else(ORGANISM_CODE == "sau", "Staphylococcus aureus", ORGANISM),
-  ) %>%
+         ORGANISM = if_else(ORGANISM_CODE == "pam", "Pasteurella multocida", ORGANISM))
-  pivot_longer(-c(ab, disk_dose), names_to = "method") %>%
+whonet_breakpoints <- read_tsv("data-raw/WHONET/Resources/Breakpoints.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>% 
-  separate(method, into = c("guideline", "method"), sep = "_") %>%
+  filter(BREAKPOINT_TYPE == "Human", GUIDELINES %in% c("CLSI", "EUCAST"))
-  mutate(method = ifelse(method %like% "D",
+whonet_antibiotics <- read_tsv("data-raw/WHONET/Resources/Antibiotics.txt", na = c("", "NA", "-"), show_col_types = FALSE) %>%
-    gsub("D", "DISK_", method, fixed = TRUE),
+  arrange(WHONET_ABX_CODE) %>%
-    gsub("M", "MIC_", method, fixed = TRUE)
+  distinct(WHONET_ABX_CODE, .keep_all = TRUE)
-  )) %>%
-  separate(method, into = c("method", "rsi"), sep = "_") %>%
-  # I is in the middle, so we only need R and S (saves data)
-  filter(rsi %in% c("R", "S")) %>%
-  pivot_wider(names_from = rsi, values_from = value) %>%
-  transmute(
-    guideline = gsub("([0-9]+)$", " 20\\1", gsub("EUCST", "EUCAST", guideline)),
-    method,
-    site = NA_character_,
-    mo = as.mo("UNKNOWN"),
-    ab,
-    ref_tbl = "Generic rules",
-    disk_dose,
-    breakpoint_S = S,
-    breakpoint_R = R,
-    uti = FALSE
-  ) %>%
-  filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)), !is.na(mo), !is.na(ab))
-rsi_generic
 
-# create data set for AB-specific and MO-specific rules
-rsi_specific <- DRGLST1 %>%
-  # only support guidelines for humans (for now)
-  filter(
-    HOST == "Human" & SITE_INF %unlike% "canine|feline",
-    # only CLSI and EUCAST
-    GUIDELINES %like% "(CLSI|EUCST)"
-  ) %>%
-  # get microorganism names from another WHONET table
-  mutate(ORG_CODE = tolower(ORG_CODE)) %>%
-  left_join(ORGLIST %>%
-    transmute(
-      ORG_CODE = tolower(ORG),
-      SCT_TEXT = case_when(
-        is.na(SCT_TEXT) & is.na(ORGANISM) ~ ORG_CODE,
-        is.na(SCT_TEXT) ~ ORGANISM,
-        TRUE ~ SCT_TEXT
-      )
-    ) %>%
-    # WHO for 'Generic'
-    bind_rows(tibble(ORG_CODE = "gen", SCT_TEXT = "Unknown")) %>%
-    # WHO for 'Enterobacterales'
-    bind_rows(tibble(ORG_CODE = "ebc", SCT_TEXT = "Enterobacterales"))) %>%
-  # still some manual cleaning required
-  filter(!SCT_TEXT %in% c("Anaerobic Actinomycetes")) %>%
-  transmute(
-    guideline = gsub("([0-9]+)$", " 20\\1", gsub("EUCST", "EUCAST", GUIDELINES)),
-    method = toupper(TESTMETHOD),
-    site = SITE_INF,
-    mo = as.mo(SCT_TEXT),
-    ab = as.ab(WHON5_CODE),
-    ref_tbl = REF_TABLE,
-    disk_dose = POTENCY,
-    breakpoint_S = as.double(ifelse(method == "DISK", DISK_S, MIC_S)),
-    breakpoint_R = as.double(ifelse(method == "DISK", DISK_R, MIC_R)),
-    uti = site %like% "(UTI|urinary|urine)"
-  ) %>%
-  filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)), !is.na(mo), !is.na(ab))
-rsi_specific
 
-rsi_translation <- rsi_generic %>%
+# Transform data ----
-  bind_rows(rsi_specific) %>%
+
-  # add the taxonomic rank index, used for sorting (so subspecies match first, order matches last)
+whonet_organisms <- whonet_organisms %>% 
-  mutate(
+  bind_rows(data.frame(ORGANISM_CODE = c("ebc", "cof"),
-    rank_index = case_when(
+                       ORGANISM = c("Enterobacterales", "Campylobacter")))
-      mo_rank(mo) %like% "(infra|sub)" ~ 1,
+
-      mo_rank(mo) == "species" ~ 2,
+breakpoints <- whonet_breakpoints %>% 
-      mo_rank(mo) == "genus" ~ 3,
+  mutate(ORGANISM_CODE = tolower(ORGANISM_CODE)) %>% 
-      mo_rank(mo) == "family" ~ 4,
+  left_join(whonet_organisms) %>%
-      mo_rank(mo) == "order" ~ 5,
+  filter(ORGANISM %unlike% "(^cdc |Gram.*variable|virus)")
-      TRUE ~ 6
+# this ones lack a MO name, they will become "UNKNOWN":
-    ),
+breakpoints %>% filter(is.na(ORGANISM)) %>% pull(ORGANISM_CODE) %>% unique()
-    .after = mo
+
-  ) %>%
+
+# Generate new lookup table for microorganisms ----
+
+new_mo_codes <- breakpoints %>%
+  distinct(ORGANISM_CODE, ORGANISM) %>% 
+  mutate(ORGANISM = ORGANISM %>% 
+           gsub("Issatchenkia orientalis", "Candida krusei", .) %>% 
+           gsub(", nutritionally variant", "", .) %>% 
+           gsub(", toxin-.*producing", "", .)) %>% 
+  mutate(mo = as.mo(ORGANISM, language = NULL, keep_synonyms = FALSE),
+         mo_name = mo_name(mo, language = NULL))
+
+
+# Update microorganisms.codes with the latest WHONET codes ----
+
+# these will be changed :
+new_mo_codes %>% mutate(code = toupper(ORGANISM_CODE)) %>% rename(mo_new = mo) %>% left_join(microorganisms.codes) %>% filter(mo != mo_new)
+
+microorganisms.codes <- microorganisms.codes %>% 
+  filter(!code %in% toupper(new_mo_codes$ORGANISM_CODE)) %>% 
+  bind_rows(new_mo_codes %>% transmute(code = toupper(ORGANISM_CODE), mo = mo) %>% filter(!is.na(mo))) %>% 
+  arrange(code) %>% 
+  as_tibble()
+usethis::use_data(microorganisms.codes, overwrite = TRUE, compress = "xz", version = 2)
+rm(microorganisms.codes)
+devtools::load_all()
+
+# update ASIARS-Net?
+asiarsnet <- read_tsv("data-raw/WHONET/Codes/ASIARS_Net_Organisms_ForwardLookup.txt")
+asiarsnet <- asiarsnet %>%
+  mutate(WHONET_Code = toupper(WHONET_Code)) %>%
+  left_join(whonet_organisms %>% mutate(WHONET_Code = toupper(ORGANISM_CODE))) %>% 
+  mutate(mo1 = as.mo(ORGANISM_CODE),
+         mo2 = as.mo(ORGANISM)) %>% 
+  mutate(mo = if_else(mo2 == "UNKNOWN" | is.na(mo2), mo1, mo2)) %>% 
+  filter(!is.na(mo))
+insert1 <- asiarsnet %>% transmute(code = WHONET_Code, mo)
+insert2 <- asiarsnet %>% transmute(code = as.character(ASIARS_Net_Code), mo)
+# these will be updated
+bind_rows(insert1, insert2) %>% rename(mo_new = mo) %>% left_join(microorganisms.codes) %>% filter(mo != mo_new)
+microorganisms.codes <- microorganisms.codes %>% 
+  filter(!code %in% c(insert1$code, insert2$code)) %>% 
+  bind_rows(insert1, insert2) %>% 
+  arrange(code)
+
+
+# Create new breakpoint table ----
+
+breakpoints_new <- breakpoints %>% 
+  # only last 10 years
+  filter(YEAR > as.double(format(Sys.Date(), "%Y")) - 10) %>% 
+  # "all" and "gen" (general) must become UNKNOWNs:
+  mutate(ORGANISM_CODE = if_else(ORGANISM_CODE %in% c("all", "gen"), "UNKNOWN", ORGANISM_CODE)) %>% 
+  transmute(guideline = paste(GUIDELINES, YEAR),
+            method = TEST_METHOD,
+            site = gsub("Urinary tract infection", "UTI", SITE_OF_INFECTION),
+            mo = as.mo(ORGANISM_CODE, keep_synonyms = FALSE),
+            rank_index = case_when(
+              mo_rank(mo) %like% "(infra|sub)" ~ 1,
+              mo_rank(mo) == "species" ~ 2,
+              mo_rank(mo) == "genus" ~ 3,
+              mo_rank(mo) == "family" ~ 4,
+              mo_rank(mo) == "order" ~ 5,
+              TRUE ~ 6
+            ),
+            ab = as.ab(WHONET_ABX_CODE),
+            ref_tbl = REFERENCE_TABLE,
+            disk_dose = POTENCY,
+            # keep disks within 6-50 mm
+            breakpoint_S = if_else(method == "DISK", S %>% pmax(6) %>% pmin(50), S),
+            breakpoint_R = if_else(method == "DISK", R %>% pmax(6) %>% pmin(50), R),
+            uti = SITE_OF_INFECTION %like% "(UTI|urinary|urine)") %>% 
+  # Greek symbols and EM dash symbols are not allowed by CRAN, so replace them with ASCII:
+  mutate(disk_dose = disk_dose %>% 
+           gsub("μ", "u", ., fixed = TRUE) %>% 
+           gsub("–", "-", ., fixed = TRUE)) %>% 
   arrange(desc(guideline), ab, mo, method) %>%
-  distinct(guideline, ab, mo, method, site, .keep_all = TRUE) %>%
+  filter(!(is.na(breakpoint_S) & is.na(breakpoint_R)) & !is.na(mo) & !is.na(ab)) %>% 
-  as.data.frame(stringsAsFactors = FALSE)
+  distinct(guideline, ab, mo, method, site, breakpoint_S, .keep_all = TRUE)
 
-# disks MUST be 6-50 mm, so correct where that is wrong:
+# clean disk zones and MICs
-rsi_translation[which(rsi_translation$method == "DISK" &
+breakpoints_new[which(breakpoints_new$method == "DISK"), "breakpoint_S"] <- as.double(as.disk(breakpoints_new[which(breakpoints_new$method == "DISK"), "breakpoint_S", drop = TRUE]))
-  (is.na(rsi_translation$breakpoint_S) | rsi_translation$breakpoint_S > 50)), "breakpoint_S"] <- 50
+breakpoints_new[which(breakpoints_new$method == "DISK"), "breakpoint_R"] <- as.double(as.disk(breakpoints_new[which(breakpoints_new$method == "DISK"), "breakpoint_R", drop = TRUE]))
-rsi_translation[which(rsi_translation$method == "DISK" &
+
-  (is.na(rsi_translation$breakpoint_R) | rsi_translation$breakpoint_R < 6)), "breakpoint_R"] <- 6
+# WHONET has no >1024 but instead uses 1025, 513, etc, so as.mic() cannot be used to clean.
+# instead, clean based on MIC factor levels 
 m <- unique(as.double(as.mic(levels(as.mic(1)))))
-rsi_translation[which(rsi_translation$method == "MIC" &
+breakpoints_new[which(breakpoints_new$method == "MIC" &
-  is.na(rsi_translation$breakpoint_S)), "breakpoint_S"] <- min(m)
+                        is.na(breakpoints_new$breakpoint_S)), "breakpoint_S"] <- min(m)
-rsi_translation[which(rsi_translation$method == "MIC" &
+breakpoints_new[which(breakpoints_new$method == "MIC" &
-  is.na(rsi_translation$breakpoint_R)), "breakpoint_R"] <- max(m)
+                        is.na(breakpoints_new$breakpoint_R)), "breakpoint_R"] <- max(m)
-
+# raise these one higher valid MIC factor level:
-# WHONET has no >1024 but instead uses 1025, 513, etc, so raise these one higher valid MIC factor level:
+breakpoints_new[which(breakpoints_new$breakpoint_R == 129), "breakpoint_R"] <- m[which(m == 128) + 1]
-rsi_translation[which(rsi_translation$breakpoint_R == 129), "breakpoint_R"] <- m[which(m == 128) + 1]
+breakpoints_new[which(breakpoints_new$breakpoint_R == 257), "breakpoint_R"] <- m[which(m == 256) + 1]
-rsi_translation[which(rsi_translation$breakpoint_R == 257), "breakpoint_R"] <- m[which(m == 256) + 1]
+breakpoints_new[which(breakpoints_new$breakpoint_R == 513), "breakpoint_R"] <- m[which(m == 512) + 1]
-rsi_translation[which(rsi_translation$breakpoint_R == 513), "breakpoint_R"] <- m[which(m == 512) + 1]
+breakpoints_new[which(breakpoints_new$breakpoint_R == 1025), "breakpoint_R"] <- m[which(m == 1024) + 1]
-rsi_translation[which(rsi_translation$breakpoint_R == 1025), "breakpoint_R"] <- m[which(m == 1024) + 1]
 
 # WHONET adds one log2 level to the R breakpoint for their software, e.g. in AMC in Enterobacterales:
 # EUCAST 2021 guideline: S <= 8 and R > 8
 #           WHONET file: S <= 8 and R >= 16
+breakpoints_new %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
 # this will make an MIC of 12 I, which should be R, so:
-eucast_mics <- which(rsi_translation$guideline %like% "EUCAST" &
+breakpoints_new <- breakpoints_new %>%
-  rsi_translation$method == "MIC" &
+  mutate(breakpoint_R = ifelse(guideline %like% "EUCAST" & method == "MIC" & log2(breakpoint_R) - log2(breakpoint_S) != 0,
-  log2(as.double(rsi_translation$breakpoint_R)) - log2(as.double(rsi_translation$breakpoint_S)) != 0 &
+                               pmax(breakpoint_S, breakpoint_R / 2),
-  !is.na(rsi_translation$breakpoint_R))
+                               breakpoint_R))
-old_R <- rsi_translation[eucast_mics, "breakpoint_R", drop = TRUE]
+# fix disks as well
-old_S <- rsi_translation[eucast_mics, "breakpoint_S", drop = TRUE]
+breakpoints_new <- breakpoints_new %>%
-new_R <- 2^(log2(old_R) - 1)
+  mutate(breakpoint_R = ifelse(guideline %like% "EUCAST" & method == "DISK" & breakpoint_S - breakpoint_R != 0,
-new_R[new_R < old_S | is.na(as.mic(new_R))] <- old_S[new_R < old_S | is.na(as.mic(new_R))]
+                               breakpoint_R + 1,
-rsi_translation[eucast_mics, "breakpoint_R"] <- new_R
+                               breakpoint_R))
-eucast_disks <- which(rsi_translation$guideline %like% "EUCAST" &
+# check again
-  rsi_translation$method == "DISK" &
+breakpoints_new %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
-  rsi_translation$breakpoint_S - rsi_translation$breakpoint_R != 0 &
+# compare with current version
-  !is.na(rsi_translation$breakpoint_R))
+rsi_translation %>% filter(guideline == "EUCAST 2022", ab == "AMC", mo == "B_[ORD]_ENTRBCTR", method == "MIC")
-rsi_translation[eucast_disks, "breakpoint_R"] <- rsi_translation[eucast_disks, "breakpoint_R", drop = TRUE] + 1
 
-# Greek symbols and EM dash symbols are not allowed by CRAN, so replace them with ASCII:
+# Save to package ----
-rsi_translation$disk_dose <- gsub("μ", "u", rsi_translation$disk_dose, fixed = TRUE)
-rsi_translation$disk_dose <- gsub("–", "-", rsi_translation$disk_dose, fixed = TRUE)
 
-# save to package
+rsi_translation <- breakpoints_new
 usethis::use_data(rsi_translation, overwrite = TRUE, compress = "xz")
 rm(rsi_translation)
 devtools::load_all(".")

data-raw/rsi.md5

@@ -1 +1 @@
-42c3626166f4521af288bb3d70a17271
+c7fbfa8e8b012a00c9e0de1476e28f99

index.md

@@ -6,7 +6,7 @@ The `AMR` package is a [free and open-source](#copyright) R package with [zero d
 
 This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
 
-After installing this package, R knows [**~49,000 distinct microbial species**](./reference/microorganisms.html) and all [**~570 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, WHONET/EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl), and is being [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
+After installing this package, R knows [**~49,000 distinct microbial species**](./reference/microorganisms.html) and all [**~570 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl), and is being [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
 
 ##### Used in 175 countries, translated to 16 languages
 
@@ -109,7 +109,7 @@ out %>% set_ab_names(property = "atc")
 This package was intended as a comprehensive toolbox for integrated AMR data analysis. This package can be used for:
 
   * Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](./reference/mo_property.html))
-  * Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines ([manual](./reference/as.rsi.html))
+  * Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline from the last 10 years ([manual](./reference/as.rsi.html))
   * Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records ([manual](./reference/ab_from_text.html))
   * Determining first isolates to be used for AMR data analysis ([manual](./reference/first_isolate.html))
   * Calculating antimicrobial resistance ([tutorial](./articles/AMR.html))

man/AMR.Rd

@@ -39,7 +39,7 @@ This package is fully independent of any other \R package and works on Windows,
 This package can be used for:
 \itemize{
 \item Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature (LPSN) and the Global Biodiversity Information Facility (GBIF)
-\item Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines
+\item Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline from the last 10 years
 \item Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records
 \item Determining first isolates to be used for AMR data analysis
 \item Calculating antimicrobial resistance

man/as.mic.Rd

@@ -33,7 +33,7 @@ Ordered \link{factor} with additional class \code{\link{mic}}, that in mathemati
 This transforms vectors to a new class \code{\link{mic}}, which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.
 }
 \details{
-To interpret MIC values as RSI values, use \code{\link[=as.rsi]{as.rsi()}} on MIC values. It supports guidelines from EUCAST (2011-2022) and CLSI (2011-2022).
+To interpret MIC values as RSI values, use \code{\link[=as.rsi]{as.rsi()}} on MIC values. It supports guidelines from EUCAST (2013-2022) and CLSI (2013-2022).
 
 This class for MIC values is a quite a special data type: formally it is an ordered \link{factor} with valid MIC values as \link{factor} levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:
 

man/as.rsi.Rd

@@ -15,6 +15,14 @@
 \format{
 An object of class \code{rsi} (inherits from \code{ordered}, \code{factor}) of length 1.
 }
+\source{
+For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:
+\itemize{
+\item \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data}, 2013-2022, \emph{Clinical and Laboratory Standards Institute} (CLSI). \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
+\item \strong{M100 Performance Standard for Antimicrobial Susceptibility Testing}, 2013-2022, \emph{Clinical and Laboratory Standards Institute} (CLSI). \url{https://clsi.org/standards/products/microbiology/documents/m100/}.
+\item \strong{Breakpoint tables for interpretation of MICs and zone diameters}, 2013-2022, \emph{European Committee on Antimicrobial Susceptibility Testing} (EUCAST). \url{https://www.eucast.org/clinical_breakpoints}.
+}
+}
 \usage{
 as.rsi(x, ...)
 
@@ -71,7 +79,7 @@ rsi_interpretation_history(clean = FALSE)
 
 \item{ab}{any (vector of) text that can be coerced to a valid antimicrobial code with \code{\link[=as.ab]{as.ab()}}}
 
-\item{guideline}{defaults to EUCAST 2022 (the latest implemented EUCAST guideline in the \link{rsi_translation} data set), supports EUCAST (2011-2022) and CLSI (2011-2022), see \emph{Details}}
+\item{guideline}{defaults to EUCAST 2022 (the latest implemented EUCAST guideline in the \link{rsi_translation} data set), supports EUCAST (2013-2022) and CLSI (2013-2022), see \emph{Details}}
 
 \item{uti}{(Urinary Tract Infection) A vector with \link{logical}s (\code{TRUE} or \code{FALSE}) to specify whether a UTI specific interpretation from the guideline should be chosen. For using \code{\link[=as.rsi]{as.rsi()}} on a \link{data.frame}, this can also be a column containing \link{logical}s or when left blank, the data set will be searched for a column 'specimen', and rows within this column containing 'urin' (such as 'urine', 'urina') will be regarded isolates from a UTI. See \emph{Examples}.}
 
@@ -122,7 +130,7 @@ For points 2, 3 and 4: Use \code{\link[=rsi_interpretation_history]{rsi_interpre
 
 \subsection{Supported Guidelines}{
 
-For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are EUCAST (2011-2022) and CLSI (2011-2022).
+For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are EUCAST (2013-2022) and CLSI (2013-2022).
 
 Thus, the \code{guideline} argument must be set to e.g., \code{"EUCAST 2022"} or \code{"CLSI 2022"}. By simply using \code{"EUCAST"} (the default) or \code{"CLSI"} as input, the latest included version of that guideline will automatically be selected. You can set your own data set using the \code{reference_data} argument. The \code{guideline} argument will then be ignored.
 }
@@ -134,7 +142,7 @@ After using \code{\link[=as.rsi]{as.rsi()}}, you can use the \code{\link[=eucast
 
 \subsection{Machine-Readable Interpretation Guidelines}{
 
-The repository of this package \href{https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt}{contains a machine-readable version} of all guidelines. This is a CSV file consisting of 20,369 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial agent and the microorganism. \strong{This allows for easy implementation of these rules in laboratory information systems (LIS)}. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
+The repository of this package \href{https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt}{contains a machine-readable version} of all guidelines. This is a CSV file consisting of 18,308 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial agent and the microorganism. \strong{This allows for easy implementation of these rules in laboratory information systems (LIS)}. Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
 }
 
 \subsection{Other}{

man/bug_drug_combinations.Rd

@@ -4,9 +4,6 @@
 \alias{bug_drug_combinations}
 \alias{format.bug_drug_combinations}
 \title{Determine Bug-Drug Combinations}
-\source{
-\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition}, 2022, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
-}
 \usage{
 bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)
 

man/microorganisms.Rd

@@ -3,9 +3,9 @@
 \docType{data}
 \name{microorganisms}
 \alias{microorganisms}
-\title{Data Set with 48,787 Microorganisms}
+\title{Data Set with 48,788 Microorganisms}
 \format{
-A \link[tibble:tibble]{tibble} with 48,787 observations and 22 variables:
+A \link[tibble:tibble]{tibble} with 48,788 observations and 22 variables:
 \itemize{
 \item \code{mo}\cr ID of microorganism as used by this package
 \item \code{fullname}\cr Full name, like \code{"Escherichia coli"}. For the taxonomic ranks genus, species and subspecies, this is the 'pasted' text of genus, species, and subspecies. For all taxonomic ranks higher than genus, this is the name of the taxon.

man/microorganisms.codes.Rd

@@ -3,9 +3,9 @@
 \docType{data}
 \name{microorganisms.codes}
 \alias{microorganisms.codes}
-\title{Data Set with 5,411 Common Microorganism Codes}
+\title{Data Set with 5,932 Common Microorganism Codes}
 \format{
-A \link[tibble:tibble]{tibble} with 5,411 observations and 2 variables:
+A \link[tibble:tibble]{tibble} with 5,932 observations and 2 variables:
 \itemize{
 \item \code{code}\cr Commonly used code of a microorganism
 \item \code{mo}\cr ID of the microorganism in the \link{microorganisms} data set

man/plot.Rd

@@ -126,7 +126,7 @@ Functions to plot classes \code{rsi}, \code{mic} and \code{disk}, with support f
 \details{
 The interpretation of "I" will be named "Increased exposure" for all EUCAST guidelines since 2019, and will be named "Intermediate" in all other cases.
 
-For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "EUCAST 2012", "EUCAST 2011", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014", "CLSI 2013", "CLSI 2012" and "CLSI 2011".
+For interpreting MIC values as well as disk diffusion diameters, supported guidelines to be used as input for the \code{guideline} argument are: "EUCAST 2022", "EUCAST 2021", "EUCAST 2020", "EUCAST 2019", "EUCAST 2018", "EUCAST 2017", "EUCAST 2016", "EUCAST 2015", "EUCAST 2014", "EUCAST 2013", "CLSI 2022", "CLSI 2021", "CLSI 2020", "CLSI 2019", "CLSI 2018", "CLSI 2017", "CLSI 2016", "CLSI 2015", "CLSI 2014" and "CLSI 2013".
 
 Simply using \code{"CLSI"} or \code{"EUCAST"} as input will automatically select the latest version of that guideline.
 }

man/rsi_translation.Rd

@@ -5,7 +5,7 @@
 \alias{rsi_translation}
 \title{Data Set for R/SI Interpretation}
 \format{
-A \link[tibble:tibble]{tibble} with 20,369 observations and 11 variables:
+A \link[tibble:tibble]{tibble} with 18,308 observations and 11 variables:
 \itemize{
 \item \code{guideline}\cr Name of the guideline
 \item \code{method}\cr Either "DISK" or "MIC"
@@ -24,7 +24,7 @@ A \link[tibble:tibble]{tibble} with 20,369 observations and 11 variables:
 rsi_translation
 }
 \description{
-Data set containing reference data to interpret MIC and disk diffusion to R/SI values, according to international guidelines. Currently implemented guidelines are EUCAST (2011-2022) and CLSI (2011-2022). Use \code{\link[=as.rsi]{as.rsi()}} to transform MICs or disks measurements to R/SI values.
+Data set containing reference data to interpret MIC and disk diffusion to R/SI values, according to international guidelines. Currently implemented guidelines are EUCAST (2013-2022) and CLSI (2013-2022). Use \code{\link[=as.rsi]{as.rsi()}} to transform MICs or disks measurements to R/SI values.
 }
 \details{
 Like all data sets in this package, this data set is publicly available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. Please visit \href{https://msberends.github.io/AMR/articles/datasets.html}{our website for the download links}. The actual files are of course available on \href{https://github.com/msberends/AMR/tree/main/data-raw}{our GitHub repository}.

vignettes/welcome_to_AMR.Rmd

@@ -30,7 +30,7 @@ The `AMR` package is a [free and open-source](https://msberends.github.io/AMR/#c
 
 This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
 
-After installing this package, R knows `r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species and all `r AMR:::format_included_data_number(rbind(AMR::antibiotics[, "atc", drop = FALSE], AMR::antivirals[, "atc", drop = FALSE]))` antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. It supports any data format, including WHONET/EARS-Net data.
+After installing this package, R knows `r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species and all `r AMR:::format_included_data_number(rbind(AMR::antibiotics[, "atc", drop = FALSE], AMR::antivirals[, "atc", drop = FALSE]))` antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid R/SI and MIC values. The integral breakpoint guidelines from CLSI and EUCAST are included from the last 10 years. It supports and can read any data format, including WHONET data.
 
 The `AMR` package is available in English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish and Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.