# The `AMR` Package for R - 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 20 languages - Generates **antibiograms** - traditional, combined, syndromic, and even WISCA - Provides the **full microbiological taxonomy** of ~79 000 distinct species and extensive info of ~620 antimicrobial drugs - Applies **CLSI 2011-2025** and **EUCAST 2011-2025** clinical and veterinary breakpoints, and ECOFFs, for MIC and disk zone interpretation - Corrects for duplicate isolates, **calculates** and **predicts** AMR per antimicrobial class - 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** > Now available for Python too! [Click > here](./articles/AMR_for_Python.html) to read more.

https://amr-for-r.org

https://doi.org/10.18637/jss.v104.i03

------------------------------------------------------------------------ ### Introduction The `AMR` package is a peer-reviewed, [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. We are a team of [many different researchers](./authors.html) from around the globe to make this a successful and durable project! 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 [**~79 000 distinct microbial species**](./reference/microorganisms.html) (updated June 2024) and all [**~620 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 2011-2025 and EUCAST 2011-2025 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 20 languages 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. With the help of contributors from all corners of the world, the `AMR` package is available in English, Czech, Chinese, Danish, Dutch, Finnish, French, German, Greek, Italian, Japanese, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swedish, Turkish, and Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages. ### Practical examples #### Filtering and selecting data One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antimicrobial columns. This can be done using the so-called [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html), which work in base R, `dplyr` and `data.table`. ``` r # AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr, warn.conflicts = FALSE) example_isolates %>% mutate(bacteria = mo_fullname()) %>% # filtering functions for microorganisms: filter(mo_is_gram_negative(), mo_is_intrinsic_resistant(ab = "cefotax")) %>% # antimicrobial selectors: select(bacteria, aminoglycosides(), carbapenems()) #> ℹ Using column 'mo' as input for mo_fullname() #> ℹ Using column 'mo' as input for mo_is_gram_negative() #> ℹ Using column 'mo' as input for mo_is_intrinsic_resistant() #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 35 × 7 #> bacteria GEN TOB AMK KAN IPM MEM #> #> 1 Pseudomonas aeruginosa  I   S   NA  R   S   NA #> 2 Pseudomonas aeruginosa  I   S   NA  R   S   NA #> 3 Pseudomonas aeruginosa  I   S   NA  R   S   NA #> 4 Pseudomonas aeruginosa  S   S   S   R   NA  S  #> 5 Pseudomonas aeruginosa  S   S   S   R   S   S  #> 6 Pseudomonas aeruginosa  S   S   S   R   S   S  #> 7 Stenotrophomonas maltophilia  R   R   R   R   R   R  #> 8 Pseudomonas aeruginosa  S   S   S   R   NA  S  #> 9 Pseudomonas aeruginosa  S   S   S   R   NA  S  #> 10 Pseudomonas aeruginosa  S   S   S   R   S   S  #> # ℹ 25 more rows ``` With only having defined a row filter on Gram-negative bacteria with intrinsic resistance to cefotaxime (`mo_is_gram_negative()` and `mo_is_intrinsic_resistant()`) and a column selection on two antibiotic groups (`aminoglycosides()` and `carbapenems()`), the reference data about [all microorganisms](./reference/microorganisms.html) and [all antimicrobials](./reference/antimicrobials.html) in the `AMR` package make sure you get what you meant. #### Generating antibiograms The `AMR` package supports generating traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). 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.). ``` r antibiogram(example_isolates, antimicrobials = c(aminoglycosides(), carbapenems())) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ 502 combinations had less than minimum = 30 results and were ignored ``` | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin | |:---|:---|:---|:---|:---|:---|:---| | 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) | | E. coli | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | | 100% (99-100%,N=418) | 97% (96-99%,N=462) | | E. faecalis | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | | 0% (0-9%,N=39) | | K. pneumoniae | | 90% (79-96%,N=58) | 100% (93-100%,N=51) | | 100% (93-100%,N=53) | 90% (79-96%,N=58) | | P. aeruginosa | | 100% (88-100%,N=30) | | 0% (0-12%,N=30) | | 100% (88-100%,N=30) | | P. mirabilis | | 94% (80-99%,N=34) | 94% (79-99%,N=32) | | | 94% (80-99%,N=34) | | S. aureus | | 99% (97-100%,N=233) | | | | 98% (92-100%,N=86) | | S. epidermidis | 0% (0-8%,N=44) | 79% (71-85%,N=163) | | 0% (0-8%,N=44) | | 51% (40-61%,N=89) | | S. hominis | | 92% (84-97%,N=80) | | | | 85% (74-93%,N=62) | | S. pneumoniae | 0% (0-3%,N=117) | 0% (0-3%,N=117) | | 0% (0-3%,N=117) | | 0% (0-3%,N=117) | In combination antibiograms, it is clear that combined antimicrobials yield higher empiric coverage: ``` r antibiogram(example_isolates, antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"), mo_transform = "gramstain") #> ℹ 3 combinations had less than minimum = 30 results and were ignored ``` | Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin | |:---|:---|:---|:---| | 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) | Like many other functions in this package, `antibiogram()` comes with support for 20 languages that are often detected automatically based on system language: ``` r antibiogram(example_isolates, antimicrobials = c("cipro", "tobra", "genta"), # any arbitrary name or code will work mo_transform = "gramstain", ab_transform = "name", language = "uk") # Ukrainian #> ℹ 3 combinations had less than minimum = 30 results and were ignored ``` | Збудник | Гентаміцин | Тобраміцин | Ципрофлоксацин | |:--------------|:--------------------|:-------------------|:-------------------| | Грамнегативні | 96% (95-98%,N=684) | 96% (94-97%,N=686) | 91% (88-93%,N=684) | | Грампозитивні | 63% (60-66%,N=1170) | 34% (31-38%,N=665) | 77% (74-80%,N=724) | #### Interpreting and plotting MIC and SIR values The `AMR` package allows interpretation of MIC and disk diffusion values based on CLSI and EUCAST. Moreover, the `ggplot2` package is extended with new scale functions, to allow plotting of log2-distributed MIC values and SIR values. ``` r library(ggplot2) library(AMR) # generate some random values some_mic_values <- random_mic(size = 100) some_groups <- sample(LETTERS[1:5], 20, replace = TRUE) interpretation <- as.sir(some_mic_values, guideline = "EUCAST 2024", mo = "E. coli", # or any code or name resembling a known species ab = "Cipro") # or any code or name resembling an antibiotic # create the plot ggplot(data.frame(mic = some_mic_values, group = some_groups, sir = interpretation), aes(x = group, y = mic, colour = sir)) + theme_minimal() + geom_boxplot(fill = NA, colour = "grey") + geom_jitter(width = 0.25) + # NEW scale function: plot MIC values to x, y, colour or fill scale_y_mic() + # NEW scale function: write out S/I/R in any of the 20 supported languages # and set colourblind-friendly colours scale_colour_sir() ``` #### Calculating resistance per group For a manual approach, you can use the `resistance` or `susceptibility()` function: ``` r example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance() for gentamicin and tobramycin # and get their 95% confidence intervals using sir_confidence_interval(): summarise(across(c(GEN, TOB), list(total_R = resistance, conf_int = function(x) sir_confidence_interval(x, collapse = "-")))) #> # A tibble: 3 × 5 #> ward GEN_total_R GEN_conf_int TOB_total_R TOB_conf_int #> #> 1 Clinical 0.2289362 0.205-0.254 0.3147503 0.284-0.347 #> 2 ICU 0.2902655 0.253-0.33 0.4004739 0.353-0.449 #> 3 Outpatient 0.2 0.131-0.285 0.3676471 0.254-0.493 ``` Or use [antimicrobial selectors](https://amr-for-r.org/reference/antimicrobial_selectors.html) to select a series of antibiotic columns: ``` r library(AMR) library(dplyr) out <- example_isolates %>% # group by ward: group_by(ward) %>% # calculate AMR using resistance(), over all aminoglycosides and polymyxins: summarise(across(c(aminoglycosides(), polymyxins()), resistance)) #> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB' (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For polymyxins() using column 'COL' (colistin) #> Warning: There was 1 warning in `summarise()`. #> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`. #> ℹ In group 3: `ward = "Outpatient"`. #> Caused by warning: #> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" (minimum = 30). out #> # A tibble: 3 × 6 #> ward GEN TOB AMK KAN COL #> #> 1 Clinical 0.2289362 0.3147503 0.6258993 1 0.7802956 #> 2 ICU 0.2902655 0.4004739 0.6624473 1 0.8574144 #> 3 Outpatient 0.2 0.3676471 0.6052632 NA 0.8888889 ``` ``` r # transform the antibiotic columns to names: out %>% set_ab_names() #> # A tibble: 3 × 6 #> ward gentamicin tobramycin amikacin kanamycin colistin #> #> 1 Clinical 0.2289362 0.3147503 0.6258993 1 0.7802956 #> 2 ICU 0.2902655 0.4004739 0.6624473 1 0.8574144 #> 3 Outpatient 0.2 0.3676471 0.6052632 NA 0.8888889 ``` ``` r # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = "atc") #> # A tibble: 3 × 6 #> ward J01GB03 J01GB01 J01GB06 J01GB04 J01XB01 #> #> 1 Clinical 0.2289362 0.3147503 0.6258993 1 0.7802956 #> 2 ICU 0.2902655 0.4004739 0.6624473 1 0.8574144 #> 3 Outpatient 0.2 0.3676471 0.6052632 NA 0.8888889 ``` ### 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 List of Prokaryotic names with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](./reference/mo_property.html)) - Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline ([manual](./reference/as.sir.html)) - Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records ([manual](./reference/ab_from_text.html)) - Determining first isolates to be used for AMR data analysis ([manual](./reference/first_isolate.html)) - Calculating antimicrobial resistance ([tutorial](./articles/AMR.html)) - Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) ([tutorial](./articles/MDR.html)) - Calculating (empirical) susceptibility of both mono therapy and combination therapies ([tutorial](./articles/AMR.html)) - Apply AMR function in predictive modelling ([tutorial](./articles/AMR_with_tidymodels.html)) - Getting properties for any microorganism (like Gram stain, species, genus or family) ([manual](./reference/mo_property.html)) - Getting properties for any antimicrobial (like name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name) ([manual](./reference/ab_property.html)) - Plotting antimicrobial resistance ([tutorial](./articles/AMR.html)) - Applying EUCAST expert rules ([manual](./reference/eucast_rules.html)) - Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code ([manual](./reference/mo_property.html)) - Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code ([manual](./reference/ab_property.html)) - Machine reading the EUCAST and CLSI guidelines from 2011-2021 to translate MIC values and disk diffusion diameters to SIR ([link](./articles/datasets.html)) - Principal component analysis for AMR ([tutorial](./articles/PCA.html)) ### Get this package #### 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) This package is available [here on the official R network (CRAN)](https://cran.r-project.org/package=AMR). Install this package in R from CRAN by using the command: ``` r install.packages("AMR") ``` 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 beta version. #### Latest beta version [![check-old](https://github.com/msberends/AMR/actions/workflows/check-old-tinytest.yaml/badge.svg?branch=main)](https://github.com/msberends/AMR/actions/workflows/check-old-tinytest.yaml) [![check-recent](https://github.com/msberends/AMR/actions/workflows/check-current-testthat.yaml/badge.svg?branch=main)](https://github.com/msberends/AMR/actions/workflows/check-current-testthat.yaml) [![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). To install the latest and unpublished beta version: ``` r install.packages("AMR", repos = "beta.amr-for-r.org") # if this does not work, try to install directly from GitHub using the 'remotes' package: remotes::install_github("msberends/AMR") ``` ### 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](./articles/). ### Partners The initial development of this package was part of, related to, or made possible by the following non-profit organisations and initiatives:
### Copyright This R package is free, open-source software and licensed under the [GNU General Public License v2.0 (GPL-2)](./LICENSE-text.html). 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: - Modifications **must** be released under the same license when distributing the package - Changes made to the code **must** be documented - May be distributed, although: - Source code **must** be made available when the package is distributed - A copy of the license and copyright notice **must** be included with the package. - Comes with a LIMITATION of liability - Comes with NO warranty