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(v3.0.1.9064) Documentation updates

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dr. M.S. (Matthijs) Berends
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DESCRIPTION
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Package: AMR Package: AMR
Version: 3.0.1.9063 Version: 3.0.1.9064
Date: 2026-06-24 Date: 2026-06-24
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)

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NEWS.md
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# AMR 3.0.1.9063 # AMR 3.0.1.9064
Planned as v3.1.0, end of June 2026. Planned as v3.1.0, end of June 2026.

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R/aa_amr-package.R
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#' #'
#' This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}). #' This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
#' #'
#' After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](https://amr-for-r.org/reference/microorganisms.html) (updated June 2024) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](https://amr-for-r.org/reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl). #' After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](https://amr-for-r.org/reference/microorganisms.html) (updated `r format(AMR:::TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](https://amr-for-r.org/reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).
#' #'
#' The `AMR` package is available in `r vector_and(vapply(FUN.VALUE = character(1), LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages. #' The `AMR` package is available in `r vector_and(vapply(FUN.VALUE = character(1), LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
#' @section Download Our Reference Data: #' @section Download Our Reference Data:

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R/data.R
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#' Data Set with `r format(nrow(microorganisms), big.mark = " ")` Taxonomic Records of Microorganisms #' Data Set with `r format(nrow(microorganisms), big.mark = " ")` Taxonomic Records of Microorganisms
#' #'
#' @description #' @description
#' A data set containing the full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date, TAXONOMY_VERSION$MycoBank$accessed_date))`**) of `r nr2char(length(unique(microorganisms$kingdom[!microorganisms$kingdom %like% "unknown"])))` kingdoms. This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()] and microorganism properties can be looked up using any of the [`mo_*`][mo_property()] functions. #' A data set containing the full microbial taxonomy (**last updated: `r documentation_date(max(TAXONOMY_VERSION$GBIF$accessed_date, TAXONOMY_VERSION$LPSN$accessed_date, TAXONOMY_VERSION$MycoBank$accessed_date))`**) of `r nr2char(length(unique(microorganisms$domain[!microorganisms$domain %like% "unknown"])))` domains. This data set is the backbone of this `AMR` package. MO codes can be looked up using [as.mo()] and microorganism properties can be looked up using any of the [`mo_*`][mo_property()] functions.
#' #'
#' This data set is carefully crafted, yet made 100% reproducible from public and authoritative taxonomic sources (using [this script](https://github.com/msberends/AMR/blob/main/data-raw/_reproduction_scripts/reproduction_of_microorganisms.R)), namely: *`r TAXONOMY_VERSION$LPSN$name`* for bacteria, *`r TAXONOMY_VERSION$MycoBank$name`* for fungi, and *`r TAXONOMY_VERSION$GBIF$name`* for all others taxons. #' This data set is carefully crafted, yet made 100% reproducible from public and authoritative taxonomic sources (using [this script](https://github.com/msberends/AMR/blob/main/data-raw/_reproduction_scripts/reproduction_of_microorganisms.R)), namely: *`r TAXONOMY_VERSION$LPSN$name`* for bacteria, *`r TAXONOMY_VERSION$MycoBank$name`* for fungi, and *`r TAXONOMY_VERSION$GBIF$name`* for all others taxons.
#' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = " ")` observations and `r ncol(microorganisms)` variables: #' @format A [tibble][tibble::tibble] with `r format(nrow(microorganisms), big.mark = " ")` observations and `r ncol(microorganisms)` variables:
@@ -110,8 +110,8 @@
#' - `domain`, `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism. Note that for fungi, *phylum* is used for their taxonomic *division*. Also, for fungi, *subkingdom* and *subdivision* were left out since they do not occur in the bacterial taxonomy. For all species outside the domains of Bacteria and Archaea, the `domain` and `kingdom` are identical. #' - `domain`, `kingdom`, `phylum`, `class`, `order`, `family`, `genus`, `species`, `subspecies`\cr Taxonomic rank of the microorganism. Note that for fungi, *phylum* is used for their taxonomic *division*. Also, for fungi, *subkingdom* and *subdivision* were left out since they do not occur in the bacterial taxonomy. For all species outside the domains of Bacteria and Archaea, the `domain` and `kingdom` are identical.
#' - `rank`\cr Text of the taxonomic rank of the microorganism, such as `"species"` or `"genus"` #' - `rank`\cr Text of the taxonomic rank of the microorganism, such as `"species"` or `"genus"`
#' - `ref`\cr Abbreviated authority citation for the nomenclatural act that established the current name combination, following ICNP conventions. For species described in their current genus (*sp. nov.*), this is the original description author(s) and year. For species transferred to a different genus (*comb. nov.*), this is the reclassification author(s) and year. Emendations are excluded. For synonyms, this is the authority under which the synonym was originally published. This field is directly retrieved from the source specified in the column `source`. Diacritics were removed to comply with CRAN, that only allows ASCII characters. #' - `ref`\cr Abbreviated authority citation for the nomenclatural act that established the current name combination, following ICNP conventions. For species described in their current genus (*sp. nov.*), this is the original description author(s) and year. For species transferred to a different genus (*comb. nov.*), this is the reclassification author(s) and year. Emendations are excluded. For synonyms, this is the authority under which the synonym was originally published. This field is directly retrieved from the source specified in the column `source`. Diacritics were removed to comply with CRAN, that only allows ASCII characters.
#' - `oxygen_tolerance` \cr Oxygen tolerance, either `r vector_or(microorganisms$oxygen_tolerance, documentation = TRUE)`. These data were retrieved from BacDive (see *Source*). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently `r round(length(microorganisms$oxygen_tolerance[which(!is.na(microorganisms$oxygen_tolerance))]) / nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]) * 100, 1)`% of all `r format_included_data_number(nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]))` bacteria in the data set contain an oxygen tolerance. #' - `oxygen_tolerance` \cr Oxygen tolerance, either `r vector_or(microorganisms$oxygen_tolerance, documentation = TRUE)`. These data were retrieved from BacDive (see *Source*). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently `r round(length(microorganisms$oxygen_tolerance[which(!is.na(microorganisms$oxygen_tolerance))]) / nrow(microorganisms[which(microorganisms$domain == "Bacteria"), ]) * 100, 1)`% of all `r format_included_data_number(nrow(microorganisms[which(microorganisms$domain == "Bacteria"), ]))` bacteria in the data set contain an oxygen tolerance.
#' - `morphology` \cr Morphology (cell shape), either `r vector_or(microorganisms$morphology, documentation = TRUE)`. These data were retrieved from BacDive (see *Source*). Genera that are clinically established as coccobacilli (the HACEK group and beyond, such as *Haemophilus* and *Acinetobacter*) are classified as such regardless of BacDive majority vote. Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus. Currently `r round(length(microorganisms$morphology[which(!is.na(microorganisms$morphology))]) / nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]) * 100, 1)`% of all `r format_included_data_number(nrow(microorganisms[which(microorganisms$kingdom == "Bacteria"), ]))` bacteria in the data set contain a morphology. #' - `morphology` \cr Morphology (cell shape), either `r vector_or(microorganisms$morphology, documentation = TRUE)`. These data were retrieved from BacDive (see *Source*). Genera that are clinically established as coccobacilli (the HACEK group and beyond, such as *Haemophilus* and *Acinetobacter*) are classified as such regardless of BacDive majority vote. Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus. Currently `r round(length(microorganisms$morphology[which(!is.na(microorganisms$morphology))]) / nrow(microorganisms[which(microorganisms$domain == "Bacteria"), ]) * 100, 1)`% of all `r format_included_data_number(nrow(microorganisms[which(microorganisms$domain == "Bacteria"), ]))` bacteria in the data set contain a morphology.
#' - `source`\cr Either `r vector_or(microorganisms$source, documentation = TRUE)` (see *Source*) #' - `source`\cr Either `r vector_or(microorganisms$source, documentation = TRUE)` (see *Source*)
#' - `lpsn`\cr Identifier ('Record number') of `r TAXONOMY_VERSION$LPSN$name`. This will be the first/highest LPSN identifier to keep one identifier per row. For example, *Acetobacter ascendens* has LPSN Record number 7864 and 11011. Only the first is available in the `microorganisms` data set. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$lpsn)))` records. #' - `lpsn`\cr Identifier ('Record number') of `r TAXONOMY_VERSION$LPSN$name`. This will be the first/highest LPSN identifier to keep one identifier per row. For example, *Acetobacter ascendens* has LPSN Record number 7864 and 11011. Only the first is available in the `microorganisms` data set. ***This is a unique identifier***, though available for only `r format_included_data_number(sum(!is.na(microorganisms$lpsn)))` records.
#' - `lpsn_parent`\cr LPSN identifier of the parent taxon #' - `lpsn_parent`\cr LPSN identifier of the parent taxon
@@ -131,12 +131,12 @@
#' #'
#' @section Included Taxa: #' @section Included Taxa:
#' Included taxonomic data from [LPSN](`r TAXONOMY_VERSION$LPSN$url`), [MycoBank](`r TAXONOMY_VERSION$MycoBank$url`), and [GBIF](`r TAXONOMY_VERSION$GBIF$url`) are: #' Included taxonomic data from [LPSN](`r TAXONOMY_VERSION$LPSN$url`), [MycoBank](`r TAXONOMY_VERSION$MycoBank$url`), and [GBIF](`r TAXONOMY_VERSION$GBIF$url`) are:
#' - All `r format_included_data_number(microorganisms[which(microorganisms$kingdom %in% c("Archeae", "Bacteria")), , drop = FALSE])` (sub)species from the kingdoms of Archaea and Bacteria #' - All `r format_included_data_number(microorganisms[which(microorganisms$domain %in% c("Archeae", "Bacteria")), , drop = FALSE])` (sub)species from the domains of Archaea and Bacteria
#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Fungi"), , drop = FALSE])` species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histoplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*). #' - `r format_included_data_number(microorganisms[which(microorganisms$domain == "Fungi"), , drop = FALSE])` species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of *Aspergillus*, *Candida*, *Cryptococcus*, *Histoplasma*, *Pneumocystis*, *Saccharomyces* and *Trichophyton*).
#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Protozoa"), , drop = FALSE])` (sub)species from the kingdom of Protozoa #' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Protozoa"), , drop = FALSE])` (sub)species from the kingdom of Protozoa
#' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), , drop = FALSE])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), "genus", drop = TRUE])` other relevant genera from the kingdom of Animalia (such as *Strongyloides* and *Taenia*) #' - `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), , drop = FALSE])` (sub)species from `r format_included_data_number(microorganisms[which(microorganisms$kingdom == "Animalia"), "genus", drop = TRUE])` other relevant genera from the kingdom of Animalia (such as *Strongyloides* and *Taenia*)
#' - All `r format_included_data_number(microorganisms[which(microorganisms$status != "accepted"), , drop = FALSE])` previously accepted names of all included (sub)species (these were taxonomically renamed) #' - All `r format_included_data_number(microorganisms[which(microorganisms$status != "accepted"), , drop = FALSE])` previously accepted names of all included (sub)species (these were taxonomically renamed)
#' - The complete taxonomic tree of all included (sub)species: from kingdom to subspecies #' - The complete taxonomic tree of all included (sub)species: from domain to subspecies
#' - The identifier of the parent taxons #' - The identifier of the parent taxons
#' - The year and first author of the related scientific publication #' - The year and first author of the related scientific publication
#' #'

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README.Rmd
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@@ -21,7 +21,7 @@ Please visit our comprehensive package website <https://amr-for-r.org> to read m
Overview: Overview:
* Provides an **all-in-one solution** for antimicrobial resistance (AMR) data analysis in a One Health approach * Provides an **all-in-one solution** for antimicrobial resistance (AMR) data analysis in a One Health approach
* Peer-reviewed, used in over 175 countries, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages * **Peer-reviewed**, used in over 175 countries, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
* Generates **antibiograms** - WISCA for empiric coverage estimates, or traditional/syndromic for AMR surveillance * Generates **antibiograms** - WISCA for empiric coverage estimates, or traditional/syndromic for AMR surveillance
* Provides the **full microbiological taxonomy** of `r AMR:::format_included_data_number(AMR::microorganisms)` distinct species and extensive info of `r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial drugs * Provides the **full microbiological taxonomy** of `r AMR:::format_included_data_number(AMR::microorganisms)` distinct species and extensive info of `r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial drugs
* Applies **CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`** and **EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`** clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation * Applies **CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`** and **EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`** clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation

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README.md
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@@ -10,7 +10,8 @@ Overview:
- Provides an **all-in-one solution** for antimicrobial resistance (AMR) - Provides an **all-in-one solution** for antimicrobial resistance (AMR)
data analysis in a One Health approach data analysis in a One Health approach
- Peer-reviewed, used in over 175 countries, available in 28 languages - **Peer-reviewed**, used in over 175 countries, available in 28
languages
- Generates **antibiograms** - WISCA for empiric coverage estimates, or - Generates **antibiograms** - WISCA for empiric coverage estimates, or
traditional/syndromic for AMR surveillance traditional/syndromic for AMR surveillance
- Provides the **full microbiological taxonomy** of ~97 000 distinct - Provides the **full microbiological taxonomy** of ~97 000 distinct

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_pkgdown.yml
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@@ -56,7 +56,7 @@ footer:
left: [devtext] left: [devtext]
right: [logo] right: [logo]
components: components:
devtext: '<code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE">GNU General Public License version 2.0 (GPL-2)</a>.<br>Developed at the <a target="_blank" href="https://www.rug.nl">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl">University Medical Center Groningen</a> in The Netherlands.' devtext: '<code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.'
logo: '<a target="_blank" href="https://www.rug.nl"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a>' logo: '<a target="_blank" href="https://www.rug.nl"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a>'
home: home:

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index.Rmd
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--- ---
output: github_document output: github_document
editor_options:
chunk_output_type: console
--- ---
<!-- index.md is generated from index.Rmd; please edit that file. --> <!-- index.md is generated from index.Rmd; please edit that file. -->
@@ -17,7 +19,7 @@ AMR:::reset_all_thrown_messages()
# The `AMR` Package for R <a href="https://amr-for-r.org/"><img src="./logo.svg" align="right" height="139" /></a> # The `AMR` Package for R <a href="https://amr-for-r.org/"><img src="./logo.svg" align="right" height="139" /></a>
* Provides an **all-in-one solution** for antimicrobial resistance (AMR) data analysis in a One Health approach * Provides an **all-in-one solution** for antimicrobial resistance (AMR) data analysis in a One Health approach
* Peer-reviewed, used in over 175 countries, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages * **Peer-reviewed**, used in over 175 countries, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
* Generates **antibiograms** - WISCA for empiric coverage estimates, or traditional/syndromic for AMR surveillance * Generates **antibiograms** - WISCA for empiric coverage estimates, or traditional/syndromic for AMR surveillance
* Provides the **full microbiological taxonomy** of `r AMR:::format_included_data_number(AMR::microorganisms)` distinct species and extensive info of `r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial drugs * Provides the **full microbiological taxonomy** of `r AMR:::format_included_data_number(AMR::microorganisms)` distinct species and extensive info of `r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial drugs
* Applies **CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`** and **EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`** clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation * Applies **CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`** and **EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`** clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation
@@ -25,7 +27,7 @@ AMR:::reset_all_thrown_messages()
* Integrates with **WHONET**, ATC, **EARS-Net**, PubChem, **LOINC**, **SNOMED CT**, and **NCBI** * Integrates with **WHONET**, ATC, **EARS-Net**, PubChem, **LOINC**, **SNOMED CT**, and **NCBI**
* 100% free of costs and dependencies, highly suitable for places with **limited resources** * 100% free of costs and dependencies, highly suitable for places with **limited resources**
> Now available for Python too! [Click here](./articles/AMR_for_Python.html) to read more. > Available for Python too! [Click here](./articles/AMR_for_Python.html) to read more.
<div style="display: flex; font-size: 0.8em;"> <div style="display: flex; font-size: 0.8em;">
<p style="text-align:left; width: 50%;"><small><a href="https://amr-for-r.org/">amr-for-r.org</a></small></p> <p style="text-align:left; width: 50%;"><small><a href="https://amr-for-r.org/">amr-for-r.org</a></small></p>
@@ -42,7 +44,7 @@ The `AMR` package is a peer-reviewed, [free and open-source](#copyright) R packa
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)). 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**](./reference/microorganisms.html) (updated June 2024) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](./reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl). After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](./reference/microorganisms.html) (updated `r format(AMR:::TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](./reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).
### Used in over 175 countries, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages ### Used in over 175 countries, available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages
@@ -84,24 +86,42 @@ With only having defined a row filter on Gram-negative bacteria with intrinsic r
### Generating antibiograms ### Generating antibiograms
The `AMR` package supports generating traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). The `AMR` package supports four types of antibiograms, with support for `r length(AMR:::LANGUAGES_SUPPORTED)` languages. If used inside [R Markdown](https://rmarkdown.rstudio.com) or [Quarto](https://quarto.org), the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).
If used inside [R Markdown](https://rmarkdown.rstudio.com) or [Quarto](https://quarto.org), the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.). **For empirical therapy guidance (i.e., coverage estimates), use WISCA** (Weighted-Incidence Syndromic Combination Antibiogram). When a clinician starts empirical treatment, the causative pathogen is unknown. The relevant question is not *"what percentage of E. coli is susceptible?"* but *"what is the probability that this regimen will cover whatever pathogen is causing the infection?"*. WISCA answers that question directly, weighting susceptibility by pathogen incidence and providing credible intervals via Bayesian simulation. See `vignette("WISCA")` for the full explanation.
```{r} ```{r}
antibiogram(example_isolates, wisca(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems()))
```
In combination antibiograms, it is clear that combined antimicrobials yield higher empiric coverage:
```{r}
antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain") minimum = 10) # Recommended threshold: >=30
``` ```
Like many other functions in this package, `antibiogram()` comes with support for `r length(AMR:::LANGUAGES_SUPPORTED)` languages that are often detected automatically based on system language: WISCA supports stratification by any clinical variable, so you can generate syndrome-specific or ward-specific coverage estimates:
```{r}
wisca(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
syndromic_group = "ward",
minimum = 10) # Recommended threshold: >=30
```
**For AMR surveillance**, traditional antibiograms remain the right tool for tracking resistance per species over time:
```{r}
antibiogram(example_isolates,
mo_transform = "gramstain",
antimicrobials = c("AMC", carbapenems(), "TZP"))
```
Combination antibiograms show the additional coverage gained by adding a second agent, stratified by species:
```{r}
antibiogram(example_isolates,
mo_transform = "gramstain",
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
```
Like many other functions in this package, `antibiogram()` and `wisca()` come with support for `r length(AMR:::LANGUAGES_SUPPORTED)` languages that are often detected automatically based on system language:
```{r} ```{r}
antibiogram(example_isolates, antibiogram(example_isolates,

93
index.md
View File

@@ -5,7 +5,8 @@
- Provides an **all-in-one solution** for antimicrobial resistance (AMR) - Provides an **all-in-one solution** for antimicrobial resistance (AMR)
data analysis in a One Health approach data analysis in a One Health approach
- Peer-reviewed, used in over 175 countries, available in 28 languages - **Peer-reviewed**, used in over 175 countries, available in 28
languages
- Generates **antibiograms** - WISCA for empiric coverage estimates, or - Generates **antibiograms** - WISCA for empiric coverage estimates, or
traditional/syndromic for AMR surveillance traditional/syndromic for AMR surveillance
- Provides the **full microbiological taxonomy** of ~97 000 distinct - Provides the **full microbiological taxonomy** of ~97 000 distinct
@@ -20,8 +21,8 @@
- 100% free of costs and dependencies, highly suitable for places with - 100% free of costs and dependencies, highly suitable for places with
**limited resources** **limited resources**
> Now available for Python too! [Click > Available for Python too! [Click here](./articles/AMR_for_Python.html)
> here](./articles/AMR_for_Python.html) to read more. > to read more.
<div style="display: flex; font-size: 0.8em;"> <div style="display: flex; font-size: 0.8em;">
@@ -63,7 +64,7 @@ formed the basis of two PhD theses ([DOI
[DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)). [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
After installing this package, R knows [**~97 000 distinct microbial After installing this package, R knows [**~97 000 distinct microbial
species**](./reference/microorganisms.html) (updated June 2024) and all species**](./reference/microorganisms.html) (updated May 2026) and all
[**~620 antimicrobial and antiviral [**~620 antimicrobial and antiviral
drugs**](./reference/antimicrobials.html) by name and code (including drugs**](./reference/antimicrobials.html) by name and code (including
ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all
@@ -205,41 +206,71 @@ make sure you get what you meant.
### Generating antibiograms ### Generating antibiograms
The `AMR` package supports generating traditional, combined, syndromic, The `AMR` package supports four types of antibiograms, with support for
and even weighted-incidence syndromic combination antibiograms (WISCA). 28 languages. If used inside [R Markdown](https://rmarkdown.rstudio.com)
or [Quarto](https://quarto.org), the table will be printed in the right
If used inside [R Markdown](https://rmarkdown.rstudio.com) or
[Quarto](https://quarto.org), the table will be printed in the right
output format automatically (such as markdown, LaTeX, HTML, etc.). output format automatically (such as markdown, LaTeX, HTML, etc.).
**For empirical therapy guidance (i.e., coverage estimates), use WISCA**
(Weighted-Incidence Syndromic Combination Antibiogram). When a clinician
starts empirical treatment, the causative pathogen is unknown. The
relevant question is not *“what percentage of E. coli is susceptible?”*
but *“what is the probability that this regimen will cover whatever
pathogen is causing the infection?”*. WISCA answers that question
directly, weighting susceptibility by pathogen incidence and providing
credible intervals via Bayesian simulation. See `vignette("WISCA")` for
the full explanation.
``` r
wisca(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
minimum = 10) # Recommended threshold: >=30
#> Warning: invalid microorganism code, NA generated
```
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|
| 70.1% (64.9-75.7%) | 93.6% (92.2-95%) | 89.8% (86.7-92.3%) |
WISCA supports stratification by any clinical variable, so you can
generate syndrome-specific or ward-specific coverage estimates:
``` r
wisca(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
syndromic_group = "ward",
minimum = 10) # Recommended threshold: >=30
#> Warning: invalid microorganism code, NA generated
```
| Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|:---|
| Clinical | 74.5% (69.3-80.1%) | 93.7% (92-95.1%) | 90.5% (87.1-93.1%) |
| ICU | 56.7% (48-65.5%) | 86.7% (83.4-89.8%) | 82.9% (78.2-87.3%) |
| Outpatient | 57.8% (46.4-69.7%) | 76.5% (70.1-82.2%) | 67.9% (57.9-77.5%) |
**For AMR surveillance**, traditional antibiograms remain the right tool
for tracking resistance per species over time:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems())) mo_transform = "gramstain",
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK antimicrobials = c("AMC", carbapenems(), "TZP"))
#> (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 | | Pathogen | Amoxicillin/clavulanic acid | Imipenem | Meropenem | Piperacillin/tazobactam |
|:---|:---|:---|:---|:---|:---|:---| |:---|:---|:---|:---|:---|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) | | Gram-negative | 76% (73-79%,N=726) | 99% (98-100%,N=631) | 100% (99-100%,N=626) | 88% (85-91%,N=641) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) | | Gram-positive | 76% (74-79%,N=1138) | 81% (75-85%,N=257) | 77% (70-82%,N=203) | 86% (82-89%,N=345) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
In combination antibiograms, it is clear that combined antimicrobials Combination antibiograms show the additional coverage gained by adding a
yield higher empiric coverage: second agent, stratified by species:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), mo_transform = "gramstain",
mo_transform = "gramstain") antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
``` ```
| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | | Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
@@ -247,9 +278,9 @@ antibiogram(example_isolates,
| Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) | | Gram-negative | 88% (85-91%,N=641) | 99% (97-99%,N=691) | 98% (97-99%,N=693) |
| Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) | | Gram-positive | 86% (82-89%,N=345) | 98% (96-98%,N=1044) | 95% (93-97%,N=550) |
Like many other functions in this package, `antibiogram()` comes with Like many other functions in this package, `antibiogram()` and `wisca()`
support for 28 languages that are often detected automatically based on come with support for 28 languages that are often detected automatically
system language: based on system language:
``` r ``` r
antibiogram(example_isolates, antibiogram(example_isolates,

2
man/AMR.Rd
View File

@@ -12,7 +12,7 @@ The \code{AMR} package is a peer-reviewed, \href{https://amr-for-r.org/#copyrigh
This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}). This work was published in the Journal of Statistical Software (Volume 104(3); \doi{10.18637/jss.v104.i03}) and formed the basis of two PhD theses (\doi{10.33612/diss.177417131} and \doi{10.33612/diss.192486375}).
After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated June 2024) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{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 \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}. After installing this package, R knows \href{https://amr-for-r.org/reference/microorganisms.html}{\strong{~97 000 distinct microbial species}} (updated May 2026) and all \href{https://amr-for-r.org/reference/antimicrobials.html}{\strong{~620 antimicrobial and antiviral drugs}} by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI 2011-2026 and EUCAST 2011-2026 are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). \strong{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 \href{https://www.rug.nl}{University of Groningen} and the \href{https://www.umcg.nl}{University Medical Center Groningen}.
The \code{AMR} package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages. The \code{AMR} package is available in English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, and Vietnamese. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
} }

10
man/microorganisms.Rd
View File

@@ -13,8 +13,8 @@ A \link[tibble:tibble]{tibble} with 96 982 observations and 28 variables:
\item \code{domain}, \code{kingdom}, \code{phylum}, \code{class}, \code{order}, \code{family}, \code{genus}, \code{species}, \code{subspecies}\cr Taxonomic rank of the microorganism. Note that for fungi, \emph{phylum} is used for their taxonomic \emph{division}. Also, for fungi, \emph{subkingdom} and \emph{subdivision} were left out since they do not occur in the bacterial taxonomy. For all species outside the domains of Bacteria and Archaea, the \code{domain} and \code{kingdom} are identical. \item \code{domain}, \code{kingdom}, \code{phylum}, \code{class}, \code{order}, \code{family}, \code{genus}, \code{species}, \code{subspecies}\cr Taxonomic rank of the microorganism. Note that for fungi, \emph{phylum} is used for their taxonomic \emph{division}. Also, for fungi, \emph{subkingdom} and \emph{subdivision} were left out since they do not occur in the bacterial taxonomy. For all species outside the domains of Bacteria and Archaea, the \code{domain} and \code{kingdom} are identical.
\item \code{rank}\cr Text of the taxonomic rank of the microorganism, such as \code{"species"} or \code{"genus"} \item \code{rank}\cr Text of the taxonomic rank of the microorganism, such as \code{"species"} or \code{"genus"}
\item \code{ref}\cr Abbreviated authority citation for the nomenclatural act that established the current name combination, following ICNP conventions. For species described in their current genus (\emph{sp. nov.}), this is the original description author(s) and year. For species transferred to a different genus (\emph{comb. nov.}), this is the reclassification author(s) and year. Emendations are excluded. For synonyms, this is the authority under which the synonym was originally published. This field is directly retrieved from the source specified in the column \code{source}. Diacritics were removed to comply with CRAN, that only allows ASCII characters. \item \code{ref}\cr Abbreviated authority citation for the nomenclatural act that established the current name combination, following ICNP conventions. For species described in their current genus (\emph{sp. nov.}), this is the original description author(s) and year. For species transferred to a different genus (\emph{comb. nov.}), this is the reclassification author(s) and year. Emendations are excluded. For synonyms, this is the authority under which the synonym was originally published. This field is directly retrieved from the source specified in the column \code{source}. Diacritics were removed to comply with CRAN, that only allows ASCII characters.
\item \code{oxygen_tolerance} \cr Oxygen tolerance, either \code{"aerobe"}, \code{"anaerobe"}, \code{"anaerobe/microaerophile"}, \code{"facultative anaerobe"}, \code{"likely facultative anaerobe"}, \code{"microaerophile"}, or NA. These data were retrieved from BacDive (see \emph{Source}). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently 1.3784 × 10\if{html}{\out{<sup>}}6\if{html}{\out{</sup>}}\% of all 2 bacteria in the data set contain an oxygen tolerance. \item \code{oxygen_tolerance} \cr Oxygen tolerance, either \code{"aerobe"}, \code{"anaerobe"}, \code{"anaerobe/microaerophile"}, \code{"facultative anaerobe"}, \code{"likely facultative anaerobe"}, \code{"microaerophile"}, or NA. These data were retrieved from BacDive (see \emph{Source}). Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus to guess the oxygen tolerance. Currently 59.5\% of all ~46 000 bacteria in the data set contain an oxygen tolerance.
\item \code{morphology} \cr Morphology (cell shape), either \code{"cocci"}, \code{"coccobacilli"}, \code{"filamentous"}, \code{"likely cocci"}, \code{"likely coccobacilli"}, \code{"likely filamentous"}, \code{"likely rods"}, \code{"likely spirilla"}, \code{"rods"}, \code{"spirilla"}, or NA. These data were retrieved from BacDive (see \emph{Source}). Genera that are clinically established as coccobacilli (the HACEK group and beyond, such as \emph{Haemophilus} and \emph{Acinetobacter}) are classified as such regardless of BacDive majority vote. Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus. Currently 1.3232 × 10\if{html}{\out{<sup>}}6\if{html}{\out{</sup>}}\% of all 2 bacteria in the data set contain a morphology. \item \code{morphology} \cr Morphology (cell shape), either \code{"cocci"}, \code{"coccobacilli"}, \code{"filamentous"}, \code{"likely cocci"}, \code{"likely coccobacilli"}, \code{"likely filamentous"}, \code{"likely rods"}, \code{"likely spirilla"}, \code{"rods"}, \code{"spirilla"}, or NA. These data were retrieved from BacDive (see \emph{Source}). Genera that are clinically established as coccobacilli (the HACEK group and beyond, such as \emph{Haemophilus} and \emph{Acinetobacter}) are classified as such regardless of BacDive majority vote. Items that contain "likely" are missing from BacDive and were extrapolated from other species within the same genus. Currently 57.1\% of all ~46 000 bacteria in the data set contain a morphology.
\item \code{source}\cr Either \code{"GBIF"}, \code{"LPSN"}, \code{"MycoBank"}, or \code{"manually added"} (see \emph{Source}) \item \code{source}\cr Either \code{"GBIF"}, \code{"LPSN"}, \code{"MycoBank"}, or \code{"manually added"} (see \emph{Source})
\item \code{lpsn}\cr Identifier ('Record number') of List of Prokaryotic names with Standing in Nomenclature (LPSN). This will be the first/highest LPSN identifier to keep one identifier per row. For example, \emph{Acetobacter ascendens} has LPSN Record number 7864 and 11011. Only the first is available in the \code{microorganisms} data set. \emph{\strong{This is a unique identifier}}, though available for only ~36 000 records. \item \code{lpsn}\cr Identifier ('Record number') of List of Prokaryotic names with Standing in Nomenclature (LPSN). This will be the first/highest LPSN identifier to keep one identifier per row. For example, \emph{Acetobacter ascendens} has LPSN Record number 7864 and 11011. Only the first is available in the \code{microorganisms} data set. \emph{\strong{This is a unique identifier}}, though available for only ~36 000 records.
\item \code{lpsn_parent}\cr LPSN identifier of the parent taxon \item \code{lpsn_parent}\cr LPSN identifier of the parent taxon
@@ -33,7 +33,7 @@ A \link[tibble:tibble]{tibble} with 96 982 observations and 28 variables:
microorganisms microorganisms
} }
\description{ \description{
A data set containing the full microbial taxonomy (\strong{last updated: 7th of May, 2026}) of 15 kingdoms. This data set is the backbone of this \code{AMR} package. MO codes can be looked up using \code{\link[=as.mo]{as.mo()}} and microorganism properties can be looked up using any of the \code{\link[=mo_property]{mo_*}} functions. A data set containing the full microbial taxonomy (\strong{last updated: 7th of May, 2026}) of seven domains. This data set is the backbone of this \code{AMR} package. MO codes can be looked up using \code{\link[=as.mo]{as.mo()}} and microorganism properties can be looked up using any of the \code{\link[=mo_property]{mo_*}} functions.
This data set is carefully crafted, yet made 100\% reproducible from public and authoritative taxonomic sources (using \href{https://github.com/msberends/AMR/blob/main/data-raw/_reproduction_scripts/reproduction_of_microorganisms.R}{this script}), namely: \emph{List of Prokaryotic names with Standing in Nomenclature (LPSN)} for bacteria, \emph{MycoBank} for fungi, and \emph{Global Biodiversity Information Facility (GBIF), via Catalogue of Life (COL)} for all others taxons. This data set is carefully crafted, yet made 100\% reproducible from public and authoritative taxonomic sources (using \href{https://github.com/msberends/AMR/blob/main/data-raw/_reproduction_scripts/reproduction_of_microorganisms.R}{this script}), namely: \emph{List of Prokaryotic names with Standing in Nomenclature (LPSN)} for bacteria, \emph{MycoBank} for fungi, and \emph{Global Biodiversity Information Facility (GBIF), via Catalogue of Life (COL)} for all others taxons.
} }
@@ -46,12 +46,12 @@ For example, \emph{Staphylococcus pettenkoferi} was described for the first time
Included taxonomic data from \href{https://lpsn.dsmz.de}{LPSN}, \href{https://www.mycobank.org}{MycoBank}, and \href{https://www.gbif.org}{GBIF} are: Included taxonomic data from \href{https://lpsn.dsmz.de}{LPSN}, \href{https://www.mycobank.org}{MycoBank}, and \href{https://www.gbif.org}{GBIF} are:
\itemize{ \itemize{
\item All 2 (sub)species from the kingdoms of Archaea and Bacteria \item All ~46 000 (sub)species from the domains of Archaea and Bacteria
\item ~36 000 species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histoplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}). \item ~36 000 species from the kingdom of Fungi. The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, like mushrooms). Because of this, not all fungi fit the scope of this package. Only relevant fungi are covered (such as all species of \emph{Aspergillus}, \emph{Candida}, \emph{Cryptococcus}, \emph{Histoplasma}, \emph{Pneumocystis}, \emph{Saccharomyces} and \emph{Trichophyton}).
\item ~11 000 (sub)species from the kingdom of Protozoa \item ~11 000 (sub)species from the kingdom of Protozoa
\item ~2 000 (sub)species from ~60 other relevant genera from the kingdom of Animalia (such as \emph{Strongyloides} and \emph{Taenia}) \item ~2 000 (sub)species from ~60 other relevant genera from the kingdom of Animalia (such as \emph{Strongyloides} and \emph{Taenia})
\item All ~31 000 previously accepted names of all included (sub)species (these were taxonomically renamed) \item All ~31 000 previously accepted names of all included (sub)species (these were taxonomically renamed)
\item The complete taxonomic tree of all included (sub)species: from kingdom to subspecies \item The complete taxonomic tree of all included (sub)species: from domain to subspecies
\item The identifier of the parent taxons \item The identifier of the parent taxons
\item The year and first author of the related scientific publication \item The year and first author of the related scientific publication
} }

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@@ -1,5 +1,5 @@
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36
vignettes/WISCA.Rmd
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@@ -143,6 +143,9 @@ data
# Add a synthetic syndrome column for demonstration # Add a synthetic syndrome column for demonstration
data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Non-UTI") data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Non-UTI")
# Keep only 10 most common microorganisms
data <- top_n_microorganisms(data, n = 10, property = "species")
``` ```
### Basic WISCA ### Basic WISCA
@@ -168,10 +171,11 @@ wisca(data,
Use `syndromic_group` to produce separate WISCA estimates per clinical stratum. You can pass a column name or any expression: Use `syndromic_group` to produce separate WISCA estimates per clinical stratum. You can pass a column name or any expression:
```{r} ```{r}
wisca(data, wisca_out <- wisca(data,
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"), antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
syndromic_group = "syndrome" syndromic_group = "syndrome"
) )
wisca_out
``` ```
The `AMR` package is available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages, which can all be used for the `wisca()` function too: The `AMR` package is available in `r length(AMR:::LANGUAGES_SUPPORTED)` languages, which can all be used for the `wisca()` function too:
@@ -191,6 +195,36 @@ Each row shows the estimated empirical coverage for a regimen, with a 95% credib
- **Overlapping credible intervals** mean there is no statistically significant difference in coverage. If a narrower-spectrum regimen overlaps with a broader one, the narrower-spectrum option can be preferred on stewardship grounds. - **Overlapping credible intervals** mean there is no statistically significant difference in coverage. If a narrower-spectrum regimen overlaps with a broader one, the narrower-spectrum option can be preferred on stewardship grounds.
- **Non-overlapping credible intervals** indicate a clinically meaningful difference in coverage. - **Non-overlapping credible intervals** indicate a clinically meaningful difference in coverage.
### Plotting
WISCA results can be visualised in several ways. All plot functions work on the output of `wisca()` (or `antibiogram(..., wisca = TRUE)`).
Below we use the `wisca_out` object that was generated above.
#### Coverage with credible intervals
The extended `autoplot()` method from the `ggplot2()` package produces a point-and-interval plot showing the coverage estimate and 95% credible interval for each regimen, grouped by syndromic stratum. This is the most direct way to compare regimens: overlapping intervals suggest clinical non-inferiority, non-overlapping intervals indicate a meaningful difference.
```{r, fig.width = 7.5, fig.height = 4.5}
ggplot2::autoplot(wisca_out)
```
#### Susceptibility vs. incidence weight
`wisca_plot()` produces a scatter plot of the Monte Carlo simulation draws, showing each pathogen's susceptibility (x-axis) against its incidence weight (y-axis) for each regimen. Each dot represents one of 1,000 simulated draws, so the spread reflects posterior uncertainty. This plot reveals *why* a regimen achieves its coverage: you can see which pathogens dominate the syndrome (high on the y-axis), how susceptible they are (position on the x-axis), and how uncertain both estimates are (spread of the cloud). The dashed vertical lines denote the point estimates, i.e., the coverage percentages. The ribbon behind the dashed lines denote the credible interval, which is 95% at default.
```{r, fig.width = 7.5, fig.height = 5}
wisca_plot(wisca_out)
```
#### Posterior coverage distributions
Setting `wisca_plot_type = "posterior_coverage"` shows the full posterior distribution of coverage for each regimen as a density curve. This is the most complete representation of what the Bayesian model produces: each curve shows the relative likelihood of each coverage value across all 1,000 simulations. Narrow, tall peaks indicate high certainty; wide, flat curves indicate greater uncertainty. Where two curves overlap, the regimens cannot be confidently distinguished.
```{r, fig.width = 7.5, fig.height = 4.5}
wisca_plot(wisca_out, wisca_plot_type = "posterior_coverage")
```
## Sensible defaults, which can be customised ## Sensible defaults, which can be customised
- `simulations = 1000`: number of Monte Carlo draws - `simulations = 1000`: number of Monte Carlo draws

2
vignettes/welcome_to_AMR.Rmd
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@@ -30,7 +30,7 @@ The `AMR` package is a peer-reviewed, [free and open-source](https://amr-for-r.o
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)). This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](https://amr-for-r.org/reference/microorganisms.html) (updated June 2024) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](https://amr-for-r.org/reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl). After installing this package, R knows [**`r AMR:::format_included_data_number(AMR::microorganisms)` distinct microbial species**](https://amr-for-r.org/reference/microorganisms.html) (updated `r format(AMR:::TAXONOMY_VERSION$GBIF$accessed_date, "%B %Y")`) and all [**`r AMR:::format_included_data_number(NROW(AMR::antimicrobials) + NROW(AMR::antivirals))` antimicrobial and antiviral drugs**](https://amr-for-r.org/reference/antimicrobials.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("CLSI", guideline))$guideline)))` and EUCAST `r min(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))`-`r max(as.integer(gsub("[^0-9]", "", subset(AMR::clinical_breakpoints, grepl("EUCAST", guideline))$guideline)))` are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the [University Medical Center Groningen](https://www.umcg.nl).
The `AMR` package is available in `r AMR:::vector_and(vapply(FUN.VALUE = character(1), AMR:::LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages. The `AMR` package is available in `r AMR:::vector_and(vapply(FUN.VALUE = character(1), AMR:::LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.