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AMR (for R)

(TLDR - to find out how to conduct AMR analysis, please continue reading here to get started.

AMR is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. It supports any data format, including WHONET/EARS-Net data.

After installing this package, R knows almost all ~20,000 microorganisms and ~500 antibiotics by name and code, and knows all about valid RSI and MIC values.

Used to SPSS? Read our tutorial on how to import data from SPSS, SAS or Stata and learn in which ways R outclasses any of these statistical packages.

We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen, the Netherlands, and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is actively maintained and is free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation. Read the full license here.

This package can be used for:

  • Calculating antimicrobial resistance
  • Calculating empirical susceptibility of both mono therapy and combination therapy
  • Predicting future antimicrobial resistance using regression models
  • Getting properties for any microorganism (like Gram stain, species, genus or family)
  • Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name)
  • Plotting antimicrobial resistance
  • Determining first isolates to be used for AMR analysis
  • Applying EUCAST rules
  • Determining multi-drug resistant organisms (MDRO)
  • Descriptive statistics: frequency tables, kurtosis and skewness

This package is ready-to-use for a professional environment by specialists in the following fields:

Medical Microbiology

  • Epidemiologists (both clinical microbiological and research)
  • Research Microbiologists
  • Biomedical Researchers
  • Research Pharmacologists
  • Data Scientists / Data Analysts

Veterinary Microbiology

  • Research Veterinarians
  • Veterinary Epidemiologists

Microbial Ecology

  • Soil Microbiologists
  • Extremophile Researchers
  • Astrobiologists

Developers

  • Package developers for R
  • Software developers
  • Web application / Shiny developers

Get this package

Latest released version

This package is available on the official R network (CRAN), which has a peer-reviewed submission process. Install this package in R with:

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.

Latest development version

The latest and unpublished development version can be installed with (precaution: may be unstable):

install.packages("devtools")
devtools::install_gitlab("msberends/AMR")

Get started

To find out how to conduct AMR analysis, please continue reading here to get started or click the links in the 'How to' menu.

Short introduction

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.

Antimicrobial reference data

WHO Collaborating Centre for Drug Statistics Methodology

This package contains all ~500 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) 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.

Read more about the data from WHOCC in our manual.

Microbial (taxonomic) reference data

This package contains the complete taxonomic tree of almost all microorganisms from the authoritative and comprehensive Catalogue of Life (www.catalogueoflife.org). This data is updated annually - check the included version with catalogue_of_life_version().

Included are:

  • All ~55,000 species from the kingdoms of Archaea, Bacteria, Protozoa and Viruses
  • All ~3,000 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales and Schizosaccharomycetales. The kingdom of Fungi is a very large taxon with almost 300,000 different species, of which most are not microbial. Including everything tremendously slows down our algortihms, and not all fungi fit the scope of this package. By only including the aforementioned taxonomic orders, the most relevant species are covered (like genera Aspergillus, Candida, Pneumocystis, Saccharomyces and Trichophyton).
  • All ~15,000 previously accepted names of species that have been taxonomically renamed
  • The responsible author(s) and year of scientific publication

The Catalogue of Life (www.catalogueoflife.org) is the most comprehensive and authoritative global index of species currently available. It holds essential information on the names, relationships and distributions of over 1.6 million species. The Catalogue of Life is used to support the major biodiversity and conservation information services such as the Global Biodiversity Information Facility (GBIF), Encyclopedia of Life (EoL) and the International Union for Conservation of Nature Red List. It is recognised by the Convention on Biological Diversity as a significant component of the Global Taxonomy Initiative and a contribution to Target 1 of the Global Strategy for Plant Conservation.

Read more about the data from the Catalogue of Life in our manual.

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 artificial intelligence to guess results that you would expect:

    • Use as.mo() to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of Klebsiella pneumoniae is "B_KLBSL_PNE" (B stands for Bacteria) and the ID of S. aureus is "B_STPHY_AUR". 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 artificial intelligence (AI) on the included ITIS data set, consisting of almost 20,000 microorganisms. It is very fast, 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).
    • Use as.rsi() to transform 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".
    • Use as.atc() to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values "Furabid", "Furadantin", "nitro" all return the ATC code of Nitrofurantoine.

  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.
    • Use 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.
    • Use mdro() (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
    • The data set microorganisms contains the complete taxonomic tree of almost 20,000 microorganisms (bacteria, fungi/yeasts and protozoa). Furthermore, the colloquial name and Gram stain 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(). As they use as.mo() internally, they also use artificial intelligence. For example, mo_genus("MRSA") and 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 almost 500 antimicrobial drugs with their ATC code, EARS-Net code, common LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains hundreds of trade names. Use functions like atc_name() and atc_tradenames() to look up values. The atc_* functions use as.atc() internally so they support AI to guess your expected result. For example, atc_name("Fluclox"), atc_name("Floxapen") and atc_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 resistance (and even co-resistance) of microbial isolates with the portion_R(), portion_IR(), portion_I(), portion_SI() and portion_S() functions. Similarly, the number of isolates can be determined with the count_R(), count_IR(), count_I(), count_SI() and count_S() 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
    • Conduct descriptive statistics to enhance base R: calculate kurtosis(), skewness() and create frequency tables with freq()

  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 septic_patients data set. This data set contains:
        • 2,000 blood culture isolates from anonymised septic patients between 2001 and 2017 in the Northern Netherlands
        • Results of 40 antibiotics (each antibiotic in its own column) with a total ~40,000 antimicrobial results
        • Real and genuine data
      • The 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.

Partners

The development of this package is part of, related to, or made possible by: