AMR (for R)?
(To find out how to conduct AMR analysis, please continue reading here to get started.)
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.
After installing this package, R knows ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, EARS-NET, 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 is fully independent of any other R package and works on Windows, macOS and Linux with all versions of R since R-3.0.0 (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, in collaboration with non-profit organisations Certe Medical Diagnostics and Advice and University Medical Center Groningen. This R package is actively maintained and is free software (see Copyright).
Used in more than 100 countries
Since its first public release in early 2018, this package has been downloaded from more than 100 countries (source: CRAN logs). Click the map to enlarge, to see the names of the countries.
The development of this package is part of, related to, or made possible by:
Get this package
Latest released version
This package is available here on the official R network (CRAN), which has a peer-reviewed submission process. Install this package in R from CRAN by using the command:
It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install.
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
The latest and unpublished development version can be installed from GitHub using:
Microbial (taxonomic) reference data
This package contains the complete taxonomic tree of almost all ~70,000 microorganisms from the authoritative and comprehensive Catalogue of Life (CoL, www.catalogueoflife.org), supplemented by data from the List of Prokaryotic names with Standing in Nomenclature (LPSN, lpsn.dsmz.de). This supplementation is needed until the CoL+ project 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.
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 with the exact same structure as a WHONET export file. Furthermore, this package also contains a data set antibiotics 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.
Overview of functions
AMR package basically does four important things:
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:
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. Moreover, it can group Staphylococci into coagulase negative and positive (CoNS and CoPS, see source) and can categorise Streptococci into Lancefield groups (like beta-haemolytic Streptococcus Group B, source).
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
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.
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”.
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”.
It enhances existing data and adds new data from data sets included in this package.
eucast_rules() to apply EUCAST expert rules to isolates (not the translation from MIC to R/SI values, use
as.rsi() for that).
first_isolate() to identify the first isolates of every patient using guidelines from the CLSI (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.
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), 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 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_gramstain() or even
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("S. aureus") will both return
"Staphylococcus". They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
- The data set antibiotics 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_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("J01CF05") will all return
"Flucloxacillin". These functions can again be used to add new variables to your data.
It analyses the data with convenient functions that use well-known methods.
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:
example_isolates data set. This data set contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR analysis.
WHONET data set. 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.