(v2.1.1.9226) update intrinsic_resistant, unit test fixes

R/amr_selectors.R

@@ -612,9 +612,9 @@ administrable_iv <- function(only_sir_columns = FALSE, return_all = TRUE, ...) {
 
 #' @rdname antimicrobial_selectors
 #' @inheritParams eucast_rules
-#' @details The [not_intrinsic_resistant()] function can be used to only select antimicrobials that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of *E. coli* and *K. pneumoniae* and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies `r format_eucast_version_nr(names(EUCAST_VERSION_EXPERT_RULES[1]))` to determine intrinsic resistance, using the [eucast_rules()] function internally. Because of this determination, this function is quite slow in terms of performance.
+#' @details The [not_intrinsic_resistant()] function can be used to only select antimicrobials that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of *E. coli* and *K. pneumoniae* and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies `r format_eucast_version_nr(names(EUCAST_VERSION_EXPECTED_PHENOTYPES[1]))` to determine intrinsic resistance, using the [eucast_rules()] function internally. Because of this determination, this function is quite slow in terms of performance.
 #' @export
-not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, version_expertrules = 3.3, ...) {
+not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, version_expected_phenotypes = 1.2, ...) {
   meet_criteria(only_sir_columns, allow_class = "logical", has_length = 1)
   # get_current_data() has to run each time, for cases where e.g., filter() and select() are used in same call
   # but it only takes a couple of milliseconds
@@ -629,8 +629,9 @@ not_intrinsic_resistant <- function(only_sir_columns = FALSE, col_mo = NULL, ver
     sapply(
       eucast_rules(vars_df,
         col_mo = col_mo,
-        version_expertrules = version_expertrules,
-        rules = "expert",
+        version_expected_phenotypes = version_expected_phenotypes,
+        rules = "expected_phenotypes",
+        overwrite = TRUE,
         info = FALSE
       ),
       function(col) {

R/data.R

@@ -29,17 +29,17 @@
 
 #' Data Sets with `r format(nrow(antimicrobials) + nrow(antivirals), big.mark = " ")` Antimicrobial Drugs
 #'
-#' Two data sets containing all antimicrobials and antivirals. Use [as.ab()] or one of the [`ab_*`][ab_property()] functions to retrieve values from the [antimicrobials] data set. Three identifiers are included in this data set: an antimcrobial ID (`ab`, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (`atc`) as defined by the WHO, and a Compound ID (`cid`) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.
+#' Two data sets containing all antimicrobials and antivirals. Use [as.ab()] or one of the [`ab_*`][ab_property()] functions to retrieve values from the [antimicrobials] data set. Three identifiers are included in this data set: an antimicrobial ID (`ab`, primarily used in this package) as defined by WHONET/EARS-Net, an ATC code (`atc`) as defined by the WHO, and a Compound ID (`cid`) as found in PubChem. Other properties in this data set are derived from one or more of these codes. Note that some drugs have multiple ATC codes.
 #' @format
 #' ### For the [antimicrobials] data set: a [tibble][tibble::tibble] with `r nrow(antimicrobials)` observations and `r ncol(antimicrobials)` variables:
-#' - `ab`\cr antimcrobial ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. ***This is a unique identifier.***
+#' - `ab`\cr antimicrobial ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available. ***This is a unique identifier.***
 #' - `cid`\cr Compound ID as found in PubChem. ***This is a unique identifier.***
 #' - `name`\cr Official name as used by WHONET/EARS-Net or the WHO. ***This is a unique identifier.***
 #' - `group`\cr A short and concise group name, based on WHONET and WHOCC definitions
 #' - `atc`\cr ATC codes (Anatomical Therapeutic Chemical) as defined by the WHOCC, like `J01CR02`
 #' - `atc_group1`\cr Official pharmacological subgroup (3rd level ATC code) as defined by the WHOCC, like `"Macrolides, lincosamides and streptogramins"`
 #' - `atc_group2`\cr Official chemical subgroup (4th level ATC code) as defined by the WHOCC, like `"Macrolides"`
-#' - `abbr`\cr List of abbreviations as used in many countries, also for antimcrobial susceptibility testing (AST)
+#' - `abbr`\cr List of abbreviations as used in many countries, also for antimicrobial susceptibility testing (AST)
 #' - `synonyms`\cr Synonyms (often trade names) of a drug, as found in PubChem based on their compound ID
 #' - `oral_ddd`\cr Defined Daily Dose (DDD), oral treatment, currently available for `r sum(!is.na(AMR::antimicrobials$oral_ddd))` drugs
 #' - `oral_units`\cr Units of `oral_ddd`
@@ -238,7 +238,7 @@
 
 #' Data Set with `r format(nrow(WHONET), big.mark = " ")` Isolates - WHONET Example
 #'
-#' This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antimcrobial results are from our [example_isolates] data set. All patient names were created using online surname generators and are only in place for practice purposes.
+#' This example data set has the exact same structure as an export file from WHONET. Such files can be used with this package, as this example data set shows. The antimicrobial results are from our [example_isolates] data set. All patient names were created using online surname generators and are only in place for practice purposes.
 #' @format A [tibble][tibble::tibble] with `r format(nrow(WHONET), big.mark = " ")` observations and `r ncol(WHONET)` variables:
 #' - `Identification number`\cr ID of the sample
 #' - `Specimen number`\cr ID of the specimen
@@ -265,7 +265,7 @@
 #' - `Inducible clindamycin resistance`\cr Clindamycin can be induced?
 #' - `Comment`\cr Other comments
 #' - `Date of data entry`\cr [Date] this data was entered in WHONET
-#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.sir))` different antimicrobials. You can lookup the abbreviations in the [antimicrobials] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antimcrobial class, using [as.sir()].
+#' - `AMP_ND10:CIP_EE`\cr `r sum(vapply(FUN.VALUE = logical(1), WHONET, is.sir))` different antimicrobials. You can lookup the abbreviations in the [antimicrobials] data set, or use e.g. [`ab_name("AMP")`][ab_name()] to get the official name immediately. Before analysis, you should transform this to a valid antimicrobial class, using [as.sir()].
 #' @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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
 #' @examples
@@ -290,7 +290,7 @@
 #' - `site`\cr Body site for which the breakpoint must be applied, e.g. "Oral" or "Respiratory"
 #' - `mo`\cr Microbial ID, see [as.mo()]
 #' - `rank_index`\cr Taxonomic rank index of `mo` from 1 (subspecies/infraspecies) to 5 (unknown microorganism)
-#' - `ab`\cr Antimcrobial code as used by this package, EARS-Net and WHONET, see [as.ab()]
+#' - `ab`\cr Antimicrobial code as used by this package, EARS-Net and WHONET, see [as.ab()]
 #' - `ref_tbl`\cr Info about where the guideline rule can be found
 #' - `disk_dose`\cr Dose of the used disk diffusion method
 #' - `breakpoint_S`\cr Lowest MIC value or highest number of millimetres that leads to "S"
@@ -325,9 +325,11 @@
 #' Data set containing defined intrinsic resistance by EUCAST of all bug-drug combinations.
 #' @format A [tibble][tibble::tibble] with `r format(nrow(intrinsic_resistant), big.mark = " ")` observations and `r ncol(intrinsic_resistant)` variables:
 #' - `mo`\cr Microorganism ID
-#' - `ab`\cr Antimcrobial ID
+#' - `ab`\cr Antimicrobial ID
 #' @details
-#' This data set is based on `r format_eucast_version_nr(3.3)`.
+#' This data set is based on `r format_eucast_version_nr(names(EUCAST_VERSION_EXPECTED_PHENOTYPES[1]))`.
+#'
+#' This data set is internally used by [not_intrinsic_resistant()] (an [antimicrobial selector][antimicrobial_selectors]) and [mo_is_intrinsic_resistant()]
 #'
 #' ### Direct download
 #' 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, and Stata. Please visit [our website for the download links](https://msberends.github.io/AMR/articles/datasets.html). The actual files are of course available on [our GitHub repository](https://github.com/msberends/AMR/tree/main/data-raw).
@@ -341,7 +343,7 @@
 #'
 #' EUCAST breakpoints used in this package are based on the dosages in this data set. They can be retrieved with [eucast_dosage()].
 #' @format A [tibble][tibble::tibble] with `r format(nrow(dosage), big.mark = " ")` observations and `r ncol(dosage)` variables:
-#' - `ab`\cr Antimcrobial ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
+#' - `ab`\cr Antimicrobial ID as used in this package (such as `AMC`), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
 #' - `name`\cr Official name of the antimicrobial drug as used by WHONET/EARS-Net or the WHO
 #' - `type`\cr Type of the dosage, either `r vector_or(dosage$type)`
 #' - `dose`\cr Dose, such as "2 g" or "25 mg/kg"

R/eucast_rules.R

@@ -33,7 +33,7 @@
 
 format_eucast_version_nr <- function(version, markdown = TRUE) {
   # for documentation - adds title, version number, year and url in markdown language
-  lst <- c(EUCAST_VERSION_BREAKPOINTS, EUCAST_VERSION_EXPERT_RULES)
+  lst <- c(EUCAST_VERSION_BREAKPOINTS, EUCAST_VERSION_EXPECTED_PHENOTYPES, EUCAST_VERSION_EXPERT_RULES)
   version <- format(unique(version), nsmall = 1)
   txt <- character(0)
   for (i in seq_len(length(version))) {

R/mo_property.R

@@ -52,7 +52,7 @@
 #'
 #' Determination of yeasts ([mo_is_yeast()]) will be based on the taxonomic kingdom and class. *Budding yeasts* are yeasts that reproduce asexually through a process called budding, where a new cell develops from a small protrusion on the parent cell. Taxonomically, these are members of the phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) or Pichiomycetes. *True yeasts* quite specifically refers to yeasts in the underlying order Saccharomycetales (such as *Saccharomyces cerevisiae*). Thus, for all microorganisms that are member of the taxonomic class Saccharomycetes or Pichiomycetes, the function will return `TRUE`. It returns `FALSE` otherwise (or `NA` when the input is `NA` or the MO code is `UNKNOWN`).
 #'
-#' Determination of intrinsic resistance ([mo_is_intrinsic_resistant()]) will be based on the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(3.3)`. The [mo_is_intrinsic_resistant()] function can be vectorised over both argument `x` (input for microorganisms) and `ab` (input for antimicrobials).
+#' Determination of intrinsic resistance ([mo_is_intrinsic_resistant()]) will be based on the [intrinsic_resistant] data set, which is based on `r format_eucast_version_nr(names(EUCAST_VERSION_EXPECTED_PHENOTYPES[1]))`. The [mo_is_intrinsic_resistant()] function can be vectorised over both argument `x` (input for microorganisms) and `ab` (input for antimicrobials).
 #'
 #' Determination of bacterial oxygen tolerance ([mo_oxygen_tolerance()]) will be based on BacDive, see *Source*. The function [mo_is_anaerobic()] only returns `TRUE` if the oxygen tolerance is `"anaerobe"`, indicting an obligate anaerobic species or genus. It always returns `FALSE` for species outside the taxonomic kingdom of Bacteria.
 #'
@@ -591,7 +591,7 @@ mo_is_intrinsic_resistant <- function(x, ab, language = get_AMR_locale(), keep_s
   if (message_not_thrown_before("mo_is_intrinsic_resistant", "version.mo", entire_session = TRUE)) {
     message_(
       "Determining intrinsic resistance based on ",
-      format_eucast_version_nr(3.3, markdown = FALSE), ". ",
+      format_eucast_version_nr(1.2, markdown = FALSE), ". ",
       font_red("This note will be shown once per session.")
     )
   }

R/sir.R

@@ -659,6 +659,7 @@ as.sir.disk <- function(x,
     breakpoint_type = breakpoint_type,
     host = host,
     verbose = verbose,
+    info = info,
     ...
   )
 }