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# `AMR` (for R) <img src="./logo.svg" align="right"/>
# The `AMR` Package for R <a href="https://msberends.github.io/AMR/"><img src="./logo.svg" align="right" height="139" /></a>
> Update May 2022: EUCAST 2022 and CLSI 2022 guidelines have been added for `as.rsi()`!
>
> Updates to the AMR interpretation rules (such as by EUCAST and CLSI), the microbial taxonomy, and the antibiotic dosages will all be updated every 6 to 12 months.
### Introduction
### What is `AMR` (for R)?
The `AMR` package is a [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) 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 AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. The rationale behind this package was scientifically described in the Journal of Statistical Software (Volume xx, Issue xx; [DOI 10.18637/jss.v000.i00](https://doi.org/10.18637/jss.v000.i00) *- waiting for copy-editing to finish*).
`AMR` is a free, open-source and independent [R package](https://www.r-project.org) (see [Copyright](#copyright)) 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 AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting.
After installing this package, R knows [**~71,000 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). This R package 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)) but is [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
After installing this package, R knows [**~71,000 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.
##### Used in 175 countries, translated to 16 languages
The `AMR` package is available in <img src="lang_da.svg" style="height: 11px !important; vertical-align: initial !important;"> Danish, <img src="lang_nl.svg" style="height: 11px !important; vertical-align: initial !important;"> Dutch, <img src="lang_en.svg" style="height: 11px !important; vertical-align: initial !important;"> English, <img src="lang_fr.svg" style="height: 11px !important; vertical-align: initial !important;"> French, <img src="lang_de.svg" style="height: 11px !important; vertical-align: initial !important;"> German, <img src="lang_it.svg" style="height: 11px !important; vertical-align: initial !important;"> Italian, <img src="lang_pt.svg" style="height: 11px !important; vertical-align: initial !important;"> Portuguese, <img src="lang_ru.svg" style="height: 11px !important; vertical-align: initial !important;"> Russian, <img src="lang_es.svg" style="height: 11px !important; vertical-align: initial !important;"> Spanish and <img src="lang_sv.svg" style="height: 11px !important; vertical-align: initial !important;"> Swedish. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
<a href="./countries_large.png"><img src="./countries.png" target="_blank" align="right" style="max-width: 300px;" /></a>
This package is [fully independent of any other R package](https://en.wikipedia.org/wiki/Dependency_hell) and 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). This R package 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)) but is [actively and durably maintained](./news) by two public healthcare organisations in the Netherlands.
Since its first public release in early 2018, this R package has been used in almost all countries in the world. Click the map to enlarge and to see the country names.
<div class="main-content" style="display: inline-block;">
<p>
<a href="./countries_large.png" target="_blank"><img src="./countries.png" class="countries_map"></a>
<strong>Used in 175 countries</strong><br>
Since its first public release in early 2018, this R package has been used in almost all countries in the world. Click the map to enlarge and to see the country names.</p>
</div>
The `AMR` package is available in <img src="lang_en.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> English, <img src="lang_zh.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Chinese, <img src="lang_da.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Danish, <img src="lang_nl.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Dutch, <img src="lang_fr.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> French, <img src="lang_de.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> German, <img src="lang_el.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Greek, <img src="lang_it.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Italian, <img src="lang_ja.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Japanese, <img src="lang_pl.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Polish, <img src="lang_pt.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Portuguese, <img src="lang_ru.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Russian, <img src="lang_es.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Spanish, <img src="lang_sv.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Swedish, <img src="lang_tr.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Turkish, and <img src="lang_uk.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
##### With `AMR` (for R), there's always a knowledgeable microbiologist by your side!
### Practical examples
#### Filtering and selecting data
```r
# AMR works great with dplyr, but it's not required or neccesary
@ -52,7 +47,7 @@ With only having defined a row filter on Gram-negative bacteria with intrinsic r
|*Pseudomonas aeruginosa* | S | S | S | R | | S |
|*Pseudomonas aeruginosa* | S | S | S | R | S | S |
A base R equivalent would be, giving the exact same results:
A base R equivalent would be:
```r
example_isolates$bacteria <- mo_fullname(example_isolates$mo)
@ -61,21 +56,58 @@ example_isolates[which(mo_is_gram_negative() &
c("bacteria", aminoglycosides(), carbapenems())]
```
#### Partners
This base R snippet will work in any version of R since April 2013 (R-3.0).
The development of this package is part of, related to, or made possible by:
#### Calculating resistance per group
<div align="center">
<a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.png" class="partner_logo"></a>
<a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.png" class="partner_logo"></a>
<a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation"><img src="./logo_certe.png" class="partner_logo"></a>
<a href="https://www.deutschland-nederland.eu" title="EurHealth-1-Health"><img src="./logo_eh1h.png" class="partner_logo"></a>
<a href="https://www.deutschland-nederland.eu" title="INTERREG"><img src="./logo_interreg.png" class="partner_logo"></a>
</div>
```r
library(AMR)
library(dplyr)
### What can you do with this package?
out <- example_isolates %>%
# group by hospital code:
group_by(hospital_id) %>%
# calculate AMR using resistance(), over all aminoglycosides
# and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()),
resistance))
out
```
This package can be used for:
| hospital_id | GEN | TOB | AMK | KAN | COL |
|:------------|:-----:|:-----:|:-----:|:-----:|:-----:|
| A | 0.203 | 0.328 | 0.657 | 1 | 0.853 |
| B | 0.274 | 0.367 | 0.645 | 1 | 0.845 |
| C | 0.212 | 0.342 | 0.582 | 1 | 0.865 |
| D | 0.251 | 0.332 | 0.642 | 1 | 0.745 |
```r
# transform the antibiotic columns to names:
out %>% set_ab_names()
```
| hospital_id | gentamicin | tobramycin | amikacin | kanamycin | colistin |
|:------------|:----------:|:----------:|:---------|:---------:|:---------:|
| A | 0.203 | 0.328 | 0.657 | 1 | 0.853 |
| B | 0.274 | 0.367 | 0.645 | 1 | 0.845 |
| C | 0.212 | 0.342 | 0.582 | 1 | 0.865 |
| D | 0.251 | 0.332 | 0.642 | 1 | 0.745 |
```r
# transform the antibiotic column to ATC codes:
out %>% set_ab_names(property = "atc")
```
| hospital_id | J01GB03 | J01GB01 | J01GB06 | J01GB04 | J01XB01 |
|:------------|:----------:|:----------:|:---------|:---------:|:---------:|
| A | 0.203 | 0.328 | 0.657 | 1 | 0.853 |
| B | 0.274 | 0.367 | 0.645 | 1 | 0.845 |
| C | 0.212 | 0.342 | 0.582 | 1 | 0.865 |
| D | 0.251 | 0.332 | 0.642 | 1 | 0.745 |
### What else can you do with this package?
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 [Catalogue of Life](http://www.catalogueoflife.org) and [List of Prokaryotic names with Standing in Nomenclature](https://lpsn.dsmz.de) ([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))
@ -96,7 +128,7 @@ This package can be used for:
### Get this package
#### Latest released version
#### Latest official version
[![CRAN](https://www.r-pkg.org/badges/version-ago/AMR)](https://cran.r-project.org/package=AMR)
[![CRANlogs](https://cranlogs.r-pkg.org/badges/grand-total/AMR)](https://cran.r-project.org/package=AMR)
@ -111,13 +143,14 @@ It will be downloaded and installed automatically. For RStudio, click on the men
**Note:** Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version.
#### Latest development version
[![R-code-check](https://github.com/msberends/AMR/workflows/R-code-check/badge.svg?branch=main)](https://codecov.io/gh/msberends/AMR?branch=main)
[![CodeFactor](https://www.codefactor.io/repository/github/msberends/amr/badge)](https://www.codefactor.io/repository/github/msberends/amr)
[![Codecov](https://codecov.io/gh/msberends/AMR/branch/main/graph/badge.svg)](https://codecov.io/gh/msberends/AMR?branch=main)
Please read our [Developer Guideline here](https://github.com/msberends/AMR/wiki/Developer-Guideline).
The latest and unpublished development version can be installed from GitHub in two ways:
1. Manually, using:
@ -136,66 +169,21 @@ The latest and unpublished development version can be installed from GitHub in t
After this, you can install and update this `AMR` package like any official release (e.g., using `install.packages("AMR")` or in RStudio via *Tools* > *Check for Package Updates...*).
You can also download the latest build from our repository: <https://github.com/msberends/AMR/raw/main/data-raw/AMR_latest.tar.gz>
### Get started
To find out how to conduct AMR data analysis, please [continue reading here to get started](./articles/AMR.html) or click a link in the ['How to' menu](https://msberends.github.io/AMR/articles/).
### Short introduction
### Partners
#### Microbial (taxonomic) reference data
The development of this package is part of, related to, or made possible by:
This package contains the complete taxonomic tree of almost all ~71,000 microorganisms from the authoritative and comprehensive Catalogue of Life (CoL, [www.catalogueoflife.org](http://www.catalogueoflife.org)), supplemented by data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, [lpsn.dsmz.de](https://lpsn.dsmz.de)). This supplementation is needed until the [CoL+ project](https://github.com/Sp2000/colplus) is finished, which we await. With `catalogue_of_life_version()` can be checked which version of the CoL is included in this package.
Read more about which data from the Catalogue of Life [in our manual](./reference/catalogue_of_life.html).
#### Antimicrobial reference data
This package contains **all ~570 antibiotic, antimycotic and antiviral drugs** and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the [Pharmaceuticals Community Register of the European Commission](https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm).
**NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.**
Read more about the data from WHOCC [in our manual](./reference/WHOCC.html).
#### WHONET / EARS-Net
We support WHONET and EARS-Net data. Exported files from WHONET can be imported into R and can be analysed easily using this package. For education purposes, we created an [example data set `WHONET`](./reference/WHONET.html) with the exact same structure as a WHONET export file. Furthermore, this package also contains a [data set antibiotics](./reference/antibiotics.html) with all EARS-Net antibiotic abbreviations, and knows almost all WHONET abbreviations for microorganisms. When using WHONET data as input for analysis, all input parameters will be set automatically.
Read our tutorial about [how to work with WHONET data here](./articles/WHONET.html).
#### Overview of functions
The `AMR` package basically does four important things:
1. It **cleanses existing data** by providing new *classes* for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:
* Use `as.mo()` to get a microbial ID. The IDs are human readable for the trained eye - the ID of *Klebsiella pneumoniae* is "B_KLBSL_PNMN" (B stands for Bacteria) and the ID of *S. aureus* is "B_STPHY_AURS". The function takes almost any text as input that looks like the name or code of a microorganism like "E. coli", "esco" or "esccol" and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our [benchmarks](./articles/benchmarks.html). Moreover, it can group *Staphylococci* into coagulase negative and positive (CoNS and CoPS, see [source](./reference/as.mo.html#source)) and can categorise *Streptococci* into Lancefield groups (like beta-haemolytic *Streptococcus* Group B, [source](./reference/as.mo.html#source)).
* Use `as.ab()` to get an antibiotic ID. Like microbial IDs, these IDs are also human readable based on those used by EARS-Net. For example, the ID of amoxicillin is `AMX` and the ID of gentamicin is `GEN`. The `as.ab()` function also uses intelligent rules to find results like accepting misspelling, trade names and abbrevations used in many laboratory systems. For instance, the values "Furabid", "Furadantin", "nitro" all return the ID of Nitrofurantoine. To accomplish this, the package contains a database with most LIS codes, official names, trade names, ATC codes, defined daily doses (DDD) and drug categories of antibiotics.
* Use `as.rsi()` to get antibiotic interpretations based on raw MIC values (in mg/L) or disk diffusion values (in mm), or transform existing values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like "<=0.002; S" (combined MIC/RSI) will result in "S".
* Use `as.mic()` to cleanse your MIC values. It produces a so-called factor (called *ordinal* in SPSS) with valid MIC values as levels. A value like "<=0.002; S" (combined MIC/RSI) will result in "<=0.002".
2. It **enhances existing data** and **adds new data** from data sets included in this package.
* Use `eucast_rules()` to apply [EUCAST expert rules to isolates](https://www.eucast.org/expert_rules_and_intrinsic_resistance/) (not the translation from MIC to R/SI values, use `as.rsi()` for that).
* Use `first_isolate()` to identify the first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute).
* You can also identify first *weighted* isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
* Use `mdro()` to determine which micro-organisms are multi-drug resistant organisms (MDRO). It supports a variety of international guidelines, such as the MDR-paper by Magiorakos *et al.* (2012, [PMID 21793988](https://www.ncbi.nlm.nih.gov/pubmed/?term=21793988)), the exceptional phenotype definitions of EUCAST and the WHO guideline on multi-drug resistant TB. It also supports the national guidelines of the Netherlands and Germany.
* The [data set microorganisms](./reference/microorganisms.html) contains the complete taxonomic tree of ~70,000 microorganisms. Furthermore, some colloquial names and all Gram stains are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like `mo_genus()`, `mo_family()`, `mo_gramstain()` or even `mo_phylum()`. Use `mo_snomed()` to look up any SNOMED CT code associated with a microorganism. As all these function use `as.mo()` internally, they also use the same intelligent rules for determination. For example, `mo_genus("MRSA")` and `mo_genus("S. aureus")` will both return `"Staphylococcus"`. They also come with support for German, Danish, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
* The [data set antibiotics](./reference/antibiotics.html) contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, LOINC code, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. Use functions like `ab_name()`, `ab_group()`, `ab_atc()`, `ab_loinc()` and `ab_tradenames()` to look up values. The `ab_*` functions use `as.ab()` internally so they support the same intelligent rules to guess the most probable result. For example, `ab_name("Fluclox")`, `ab_name("Floxapen")` and `ab_name("J01CF05")` will all return `"Flucloxacillin"`. These functions can again be used to add new variables to your data.
3. It **analyses the data** with convenient functions that use well-known methods.
* Calculate the microbial susceptibility or resistance (and even co-resistance) with the `susceptibility()` and `resistance()` functions, or be even more specific with the `proportion_R()`, `proportion_IR()`, `proportion_I()`, `proportion_SI()` and `proportion_S()` functions. Similarly, the *number* of isolates can be determined with the `count_resistant()`, `count_susceptible()` and `count_all()` functions. All these functions can be used with the `dplyr` package (e.g. in conjunction with `summarise()`)
* Plot AMR results with `geom_rsi()`, a function made for the `ggplot2` package
* Predict antimicrobial resistance for the nextcoming years using logistic regression models with the `resistance_predict()` function
4. It **teaches the user** how to use all the above actions.
* Aside from this website with many tutorials, the package itself contains extensive help pages with many examples for all functions.
* The package also contains example data sets:
* The [`example_isolates` data set](./reference/example_isolates.html). This data set contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis.
* The [`WHONET` data set](./reference/WHONET.html). This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET [on its tutorial page](./articles/WHONET.html).
<div align="center">
<a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.png" class="partner_logo"></a>
<a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.png" class="partner_logo"></a>
<a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation"><img src="./logo_certe.png" class="partner_logo"></a>
<a href="https://www.deutschland-nederland.eu" title="EurHealth-1-Health"><img src="./logo_eh1h.png" class="partner_logo"></a>
<a href="https://www.deutschland-nederland.eu" title="INTERREG"><img src="./logo_interreg.png" class="partner_logo"></a>
</div>
### Copyright