This package formed the basis of two PhD theses, of which the first was published and defended on 25 August 2021. Click here to read it: DOI 10.33612/diss.177417131.
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 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 ~70,000 distinct microbial species and all ~550 antibiotic, antimycotic and antiviral drugs by name and code (including ATC, 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 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 Foundation and University Medical Center Groningen. This R package is actively maintained and is free software (see Copyright).
AMR(for R), there’s always a knowledgeable microbiologist by your side!
# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr) example_isolates %>% mutate(bacteria = mo_fullname()) %>% filter(mo_is_gram_negative(), mo_is_intrinsic_resistant(ab = "cefotax")) %>% select(bacteria, aminoglycosides(), carbapenems())
With only having defined a row filter on Gram-negative bacteria with intrinsic resistance to cefotaxime (
mo_is_intrinsic_resistant()) and a column selection on two antibiotic groups (
carbapenems()), the reference data about all microorganisms and all antibiotics in the
AMR package make sure you get what you meant:
A base R equivalent would be, giving the exact same results:
This package can be used for:
This package is available here on the official R network (CRAN). 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.
The latest and unpublished development version can be installed from GitHub in two ways:
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/master/data-raw/AMR_latest.tar.gz
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.
This package contains all ~550 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.
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.
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.
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
AMXand 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
first_isolate()to identify the first isolates of every patient using guidelines from the CLSI (Clinical and Laboratory Standards Institute).
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.
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.
ab_tradenames()to look up values. The
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.
resistance()functions, or be even more specific with the
proportion_S()functions. Similarly, the number of isolates can be determined with the
count_all()functions. All these functions can be used with the
dplyrpackage (e.g. in conjunction with
geom_rsi(), a function made for the
It teaches the user how to use all the above actions.
example_isolatesdata set. This data set contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis.
WHONETdata 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.
This R package is free, open-source software and licensed under the GNU General Public License v2.0 (GPL-2). In a nutshell, this means that this package:
May be used for commercial purposes
May be used for private purposes
May not be used for patent purposes
May be modified, although:
May be distributed, although:
Comes with a LIMITATION of liability
Comes with NO warranty