* Data set `intrinsic_resistant`. This data set contains all bug-drug combinations where the 'bug' is intrinsic resistant to the 'drug' according to the latest EUCAST insights. It contains just two columns: `microorganism` and `antibiotic`.
* Big speed improvement for interpreting MIC values and disk zone diameters. When interpreting 5,000 MIC values of two antibiotics (10,000 values in total), our benchmarks showed a total run time going from 80.7-85.1 seconds to 1.8-2.0 seconds.
* Added parameter 'add_intrinsic_resistance' (defaults to `FALSE`), that considers intrinsic resistance according to EUCAST
* Added intelligent data cleaning to `as.disk()`, so numbers can also be extracted from text and decimal numbers will always be rounded up:
```r
as.disk(c("disk zone: 23.4 mm", 23.4))
#> Class <disk>
#> [1] 24 24
```
* Overall speed improvement by tweaking joining functions
#' Data set to interpret MIC and disk diffusion to R/SI values. Included guidelines are CLSI (2011-2019) and EUCAST (2011-2020). Use [as.rsi()] to transform MICs or disks measurements to R/SI values.
#' Data set to interpret MIC and disk diffusion to R/SI values. Included guidelines are CLSI (`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)))`) and EUCAST (`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)))`). Use [as.rsi()] to transform MICs or disks measurements to R/SI values.
#' @format A [`data.frame`] with `r format(nrow(rsi_translation), big.mark = ",")` observations and `r ncol(rsi_translation)` variables:
#' - `guideline`\cr Name of the guideline
#' - `method`\cr Either "MIC" or "DISK"
@ -234,6 +234,8 @@ catalogue_of_life <- list(
#' - `breakpoint_R`\cr Highest MIC value or lowest number of millimetres that leads to "R"
#' - `uti`\cr A logical value (`TRUE`/`FALSE`) to indicate whether the rule applies to a urinary tract infection (UTI)
#' @details The repository of this `AMR` package contains a file comprising this exact data set: <https://github.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt>. This file **allows for machine reading EUCAST and CLSI guidelines**, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. The file is updated automatically.
#' @param guideline defaults to the latest included EUCAST guideline, see Details for all options
#' @param conserve_capped_values a logical to indicate that MIC values starting with `">"` (but not `">="`) must always return "R" , and that MIC values starting with `"<"` (but not `"<="`) must always return "S"
#' @param add_intrinsic_resistance *(only useful when using a EUCAST guideline)* a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in *Klebsiella* species. Determination is based on the [intrinsic_resistant] data set, that itself is based on 'EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes', version `r EUCAST_VERSION_EXPERT_RULES`.
#' @param threshold maximum fraction of invalid antimicrobial interpretations of `x`, please see *Examples*
warning("Interpretation of ",font_bold(ab_name(ab,tolower=TRUE))," for some microorganisms is only available for (uncomplicated) urinary tract infections (UTI).\n Use parameter 'uti' to set which isolates are from urine. See ?as.rsi.",call.=FALSE)
warned<-TRUE
}
for (iinseq_len(length(x))){
if (isTRUE(add_intrinsic_resistance)){
if (!guideline_coerced%like%"EUCAST"){
warning("Using 'add_intrinsic_resistance' is only useful when using EUCAST guidelines, since the rules for intrinsic resistance are based on EUCAST.",call.=FALSE)
`AMR` is a free, open-source and independent R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. Our aim is to provide a standard for clean and reproducible antimicrobial resistance data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
<scriptsrc="../bootstrap-toc.js"></script><!-- Font Awesome icons --><linkrel="stylesheet"href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css"integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk="crossorigin="anonymous">
<p>This package contains a lot of reference data sets that are all reliable, up-to-date and free to download. You can even use them outside of R, for example to train your laboratory information system (LIS) about intrinsic resistance!</p>
<p>We included them in our <code>AMR</code> package, but also automatically ‘mirror’ them to our public repository in different software formats. On this page, we explain how to download them and how the structure of the data sets look like. The tab separated files <strong>allow for machine reading taxonomic data and EUCAST and CLSI interpretation guidelines</strong>, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI.</p>
<p><em>Note: Years and dates of updates mentioned on this page, are from on <code>AMR</code> package version 1.3.0.9004, online released on 16 August 2020. <strong>If you are reading this page from within R, please <ahref="https://msberends.github.io/AMR/articles/datasets.html">visit our website</a> for the latest update.</strong></em></p>
<p>Download the data set preferably in the software you use, so the data file already has the correct data structure. Below files were updated on 28 July 2020 20:52:40 CEST.</p>
<p>This data set is in R available as <code>antibiotics</code>, after you load the <code>AMR</code> package.</p>
<divid="source-1"class="section level4">
<h4class="hasAnchor">
<ahref="#source-1"class="anchor"></a>Source</h4>
<p>This data set contains all EARS-Net and ATC codes gathered from WHO and WHONET, and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.</p>
<ul>
<li>
<ahref="https://www.whocc.no/atc_ddd_index/">ATC/DDD index from WHO Collaborating Centre for Drug Statistics Methodology</a> (note: this may not be used for commercial purposes, but is frelly available from the WHO CC website for personal use)</li>
<li><ahref="https://pubchem.ncbi.nlm.nih.gov">PubChem by the US National Library of Medicine</a></li>
<p>Download the data set preferably in the software you use, so the data file already has the correct data structure. Below files were updated on 31 July 2020 12:12:13 CEST.</p>
<p>This data set is in R available as <code>antivirals</code>, after you load the <code>AMR</code> package.</p>
<divid="source-2"class="section level4">
<h4class="hasAnchor">
<ahref="#source-2"class="anchor"></a>Source</h4>
<p>This data set contains all ATC codes gathered from WHO and all compound IDs from PubChem. It also contains all brand names (synonyms) as found on PubChem and Defined Daily Doses (DDDs) for oral and parenteral administration.</p>
<ul>
<li>
<ahref="https://www.whocc.no/atc_ddd_index/">ATC/DDD index from WHO Collaborating Centre for Drug Statistics Methodology</a> (note: this may not be used for commercial purposes, but is frelly available from the WHO CC website for personal use)</li>
<li><ahref="https://pubchem.ncbi.nlm.nih.gov">PubChem by the US National Library of Medicine</a></li>
<p>Download the data set preferably in the software you use, so the data file already has the correct data structure. Below files were updated on 23 November 2019 19:03:43 CET.</p>
