Used in 175 countries
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.
Data sets for download / own use
-02 December 2021
+05 December 2021
Source:vignettes/datasets.Rmd
datasets.Rmd
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AMR (for R)
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- How to conduct AMR data analysis -
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- How to apply EUCAST rules -
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- How to determine multi-drug resistance (MDR) -
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- How to conduct principal component analysis (PCA) for AMR -
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- How to import data from SPSS / SAS / Stata -
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- How to work with WHONET data -
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- Benchmarks -
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- Data sets for download / own use -
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- How to predict antimicrobial resistance -
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- Welcome to the `AMR` package -
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- How to conduct AMR data analysis +
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- How to apply EUCAST rules +
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- How to determine multi-drug resistance (MDR) +
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- How to conduct principal component analysis (PCA) for AMR +
- +
- How to import data from SPSS / SAS / Stata +
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- How to work with WHONET data +
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- Benchmarks +
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- Data sets for download / own use +
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- How to predict antimicrobial resistance +
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- Welcome to the `AMR` package +
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AMR (for R)
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Source Code
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Welcome to the
- Source:AMRpackagevignettes/welcome_to_AMR.Rmd+ Source:vignettes/welcome_to_AMR.Rmdwelcome_to_AMR.Rmd- Principal component analysis for AMR
All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this
-AMRpackage are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.This R package 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 formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.177417131) but is actively and durably maintained (see changelog)) by two public healthcare organisations in the Netherlands.
+This R package 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 formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.177417131) but is actively and durably maintained (see changelog)) by two public healthcare organisations in the Netherlands.
@@ -233,11 +235,13 @@ @@ -246,5 +250,7 @@ + + diff --git a/docs/authors.html b/docs/authors.html index 9636bc0c1..17ab41266 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -1,79 +1,12 @@ - - - - - - - -Citation and Authors • AMR (for R) - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Authors and Citation • AMR (for R) + + - - - - --- - -+-AMR (for R) - 1.7.1.9055 + 1.7.1.9058-+-
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- Source:inst/CITATION+++++ + +Authors
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Matthijs S. Berends. Author, maintainer. +
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Erwin E. A. Hassing. Author, contributor. +
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Judith M. Fonville. Contributor. +
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Eric H. L. C. M. Hazenberg. Contributor. +
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Bart C. Meijer. Contributor. +
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Rogier P. Schade. Contributor. +
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++++Citation
+ Source:inst/CITATIONBerends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2021). AMR - An R Package for Working with Antimicrobial Resistance Data. Journal of Statistical Software (accepted for publication), https://www.biorxiv.org/content/10.1101/810622, doi: 10.1101/810622.
@@ -253,9 +251,9 @@ Antimicrobial Resistance Data. Journal of Statistical Software (accepted for pub pages = {287}, year = {2021}, } -Luz, CF (2021). Data Science for Infection Management & Antimicrobial Stewardship (PhD thesis). University of Groningen, doi: 10.33612/diss.192486375.
+Luz, CF (2021). Data Science for Infection Management & Antimicrobial Stewardship (PhD thesis). University of Groningen, doi: 10.33612/diss.192486375.
@PhdThesis{, - title = {Data Science for Infection Management & Antimicrobial Stewardship}, + title = {Data Science for Infection Management & Antimicrobial Stewardship}, author = {C F Luz}, publisher = {University of Groningen}, school = {University of Groningen}, @@ -264,99 +262,26 @@ Antimicrobial Resistance Data. Journal of Statistical Software (accepted for pub year = {2021}, }--- -Authors
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Source Code
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--
-
AMR(for R)
++
-Update: The latest EUCAST guideline for intrinsic resistance (v3.3, October 2021) is now supported, and our taxonomy tables has been updated as well (LPSN, 5 October 2021).
--What is
-AMR(for R)?
+AMRis a free, open-source and independent R package (see 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.+What is
+AMR(for R)? +AMRis a free, open-source and independent R package (see 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 and all ~560 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. Antimicrobial names and group names are available in Danish, Dutch, English, French, German, Italian, Portuguese and Spanish.
-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 formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.177417131) but is actively and durably maintained by two public healthcare organisations in the Netherlands.
+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 formed the basis of two PhD theses (DOI 10.33612/diss.177417131 and DOI 10.33612/diss.177417131) but is actively and durably maintained by two public healthcare organisations in the Netherlands.
--- --With
+AMR(for R), there’s always a knowledgeable microbiologist by your side!+With
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) +library(AMR) +library(dplyr) -example_isolates %>% - mutate(bacteria = mo_fullname()) %>% - filter(mo_is_gram_negative(), - mo_is_intrinsic_resistant(ab = "cefotax")) %>% - select(bacteria, +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 (
@@ -328,25 +330,25 @@mo_is_gram_negative()andmo_is_intrinsic_resistant()) and a column selection on two antibiotic groups (aminoglycosides()andcarbapenems()), the reference data about all microorganisms and all antibiotics in theAMRpackage make sure you get what you meant:A base R equivalent would be, giving the exact same results:
+ c("bacteria", aminoglycosides(), carbapenems())]example_isolates$bacteria <- mo_fullname(example_isolates$mo) -example_isolates[which(mo_is_gram_negative() & +example_isolates[which(mo_is_gram_negative() & mo_is_intrinsic_resistant(ab = "cefotax")), - c("bacteria", aminoglycosides(), carbapenems())]--What can you do with this package?
++-What can you do with this package? +
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 and List of Prokaryotic names with Standing in Nomenclature (manual) +
- Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life and List of Prokaryotic names with Standing in Nomenclature (manual)
- Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines (manual)
- Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records (manual)
- Determining first isolates to be used for AMR data analysis (manual) @@ -364,74 +366,74 @@
- Principal component analysis for AMR (tutorial)
--Get this package
----Latest released version
-
-This package is available here on the official R network (CRAN). Install this package in R from CRAN by using the command:
++-Get this package +
++Latest released version +
+
+This package is available here on the official R network (CRAN). Install this package in R from CRAN by using the command:
+-
install.packages("AMR")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 development version.
--Latest development version
-
+
+Latest development version +
+ The latest and unpublished development version can be installed from GitHub in two ways:
--
+
-
Manually, using:
+-
install.packages("remotes") # if you haven't already -remotes::install_github("msberends/AMR")install.packages("remotes") # if you haven't already +remotes::install_github("msberends/AMR")
- -
-Automatically, using the rOpenSci R-universe platform, by adding our R-universe address to your list of repositories (‘repos’):
+Automatically, using the rOpenSci R-universe platform, by adding our R-universe address to your list of repositories (‘repos’):
--
options(repos = c(getOption("repos"), +options(repos = c(getOption("repos"), msberends = "https://msberends.r-universe.dev"))After this, you can install and update this
+AMRpackage like any official release (e.g., usinginstall.packages("AMR")or in RStudio via Tools > Check for Package Updates…).After this, you can install and update this
AMRpackage like any official release (e.g., usinginstall.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
+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
++-Get started +
To find out how to conduct AMR data analysis, please continue reading here to get started or click a link in the ‘How to’ menu.
--Short introduction
---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.+Short introduction +
++-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.
--Antimicrobial reference data
-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/.
++-Antimicrobial reference data +
+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.
--WHONET / EARS-Net
++-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
WHONETwith 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
++Overview of functions +
The
-AMRpackage 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:
-
@@ -444,21 +446,21 @@
-
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 (not the translation from MIC to R/SI values, useas.rsi()for that).
- - Use
first_isolate()to identify the first isolates of every patient using guidelines from the CLSI (Clinical and Laboratory Standards Institute). + - Use
eucast_rules()to apply EUCAST expert rules to isolates (not the translation from MIC to R/SI values, useas.rsi()for that).
+ - 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()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_genus(),mo_family(),mo_gramstain()or evenmo_phylum(). Usemo_snomed()to look up any SNOMED CT code associated with a microorganism. As all these function useas.mo()internally, they also use the same intelligent rules for determination. For example,mo_genus("MRSA")andmo_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 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()andab_tradenames()to look up values. Theab_*functions useas.ab()internally so they support the same intelligent rules to guess the most probable result. For example,ab_name("Fluclox"),ab_name("Floxapen")andab_name("J01CF05")will all return"Flucloxacillin". These functions can again be used to add new variables to your data.
+ - 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), 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_genus(),mo_family(),mo_gramstain()or evenmo_phylum(). Usemo_snomed()to look up any SNOMED CT code associated with a microorganism. As all these function useas.mo()internally, they also use the same intelligent rules for determination. For example,mo_genus("MRSA")andmo_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 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()andab_tradenames()to look up values. Theab_*functions useas.ab()internally so they support the same intelligent rules to guess the most probable result. For example,ab_name("Fluclox"),ab_name("Floxapen")andab_name("J01CF05")will all return"Flucloxacillin". These functions can again be used to add new variables to your data.
- Use
-
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()andresistance()functions, or be even more specific with theproportion_R(),proportion_IR(),proportion_I(),proportion_SI()andproportion_S()functions. Similarly, the number of isolates can be determined with thecount_resistant(),count_susceptible()andcount_all()functions. All these functions can be used with thedplyrpackage (e.g. in conjunction withsummarise())
+ - Calculate the microbial susceptibility or resistance (and even co-resistance) with the
susceptibility()andresistance()functions, or be even more specific with theproportion_R(),proportion_IR(),proportion_I(),proportion_SI()andproportion_S()functions. Similarly, the number of isolates can be determined with thecount_resistant(),count_susceptible()andcount_all()functions. All these functions can be used with thedplyrpackage (e.g. in conjunction withsummarise()) - Plot AMR results with
geom_rsi(), a function made for theggplot2package - Predict antimicrobial resistance for the nextcoming years using logistic regression models with the
resistance_predict()function
- Calculate the microbial susceptibility or resistance (and even co-resistance) with the
-
--Copyright
++@@ -563,5 +570,7 @@ + +Copyright +
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
@@ -509,52 +511,57 @@
- Download from CRAN at
https://cloud.r-project.org/package=AMR -
- - Browse source code at
https://github.com/msberends/AMR/ -
- - Report a bug at
https://github.com/msberends/AMR/issues -
+ - View on CRAN +
- Browse source code +
- Report a bug
-
-Matthijs S. Berends
Author, maintainer
- - Christian F. Luz
Author, contributor
- - Dennis Souverein
Author, contributor
+Matthijs S. Berends - Christian F. Luz
Author, contributor
+ - Dennis Souverein
Author, contributor - Erwin E. A. Hassing
Author, contributor
- - All authors... +
- More about authors...
-+ + + +Links
+Links
-
-
-+ +Developers
+Developers
Author, maintainer +
