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v0.3.0
Executable
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^.*\.Rproj$
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^\.Rproj\.user$
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.travis.yml
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.zenodo.json
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^cran-comments\.md$
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^appveyor\.yml$
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Executable
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.Rproj.user
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.Rhistory
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.RData
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.Ruserdata
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AMR.Rproj
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tests/testthat/Rplots.pdf
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inst/doc
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/src/*.o
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/src/*.o-*
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/src/*.d
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/src/*.so
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*.dll
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vignettes/*.R
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.DS_Store
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^cran-comments\.md$
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Executable
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# Setting up R deps
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language: r
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jobs:
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include:
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- r: 3.1
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os: linux
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warnings_are_errors: false
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- r: 3.2
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os: linux
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- r: 3.2
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os: osx
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- r: 3.3
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os: linux
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- r: 3.3
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os: osx
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- r: 3.4
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os: linux
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- r: 3.4
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os: osx
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- r: release
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os: linux
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- r: release
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os: osx
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- r: devel
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os: linux
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warnings_are_errors: false
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# matrix:
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# allow_failures:
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# - r: 3.1
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# - r: devel
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r_packages: covr
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cache: packages
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cran: https://cran.rstudio.com
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before_install:
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# needed for clipboard function:
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# - if [ $TRAVIS_OS_NAME = linux ]; then sudo apt-get -qq update; fi
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# - if [ $TRAVIS_OS_NAME = linux ]; then sudo apt-get install -y xclip; fi
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- if [ $TRAVIS_OS_NAME = osx ]; then brew install xclip; fi
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- if [ $TRAVIS_OS_NAME = osx ]; then brew install libgit2; fi
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# postrun
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after_success:
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# only run on osx, so clipboard functions can be checked
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- if [ $TRAVIS_OS_NAME = osx ]; then Rscript -e 'covr::codecov()'; fi
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notifications:
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email:
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recipients:
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- m.s.berends@umcg.nl
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# - c.f.luz@umcg.nl
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on_success: change
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on_failure: change
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{
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"description": "<p>This R package contains functions to make <strong>microbiological, epidemiological data analysis easier</strong>. It allows the use of some new classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).</p>\n\n<p>With <code>AMR</code> you can also:</p>\n\n<ul>\n\t<li>Conduct AMR analysis with the <code>rsi</code> function, that can also be used with the <code>dplyr</code> package (e.g. in conjunction with <code>summarise</code>) to calculate the resistance percentages (and even co-resistance) of different antibiotic columns of a table</li>\n\t<li>Predict antimicrobial resistance for the nextcoming years with the <code>rsi_predict</code> function</li>\n\t<li>Apply <a href="http://www.eucast.org/expert_rules_and_intrinsic_resistance/">EUCAST rules to isolates</a> with the <code>EUCAST_rules</code> function</li>\n\t<li>Identify first isolates of every patient <a href="https://clsi.org/standards/products/microbiology/documents/m39/">using guidelines from the CLSI</a> (Clinical and Laboratory Standards Institute) with the <code>first_isolate</code> function</li>\n\t<li>Get antimicrobial ATC properties from the WHO Collaborating Centre for Drug Statistics Methodology (<a href="https://www.whocc.no/atc_ddd_methodology/who_collaborating_centre/">WHOCC</a>), to be able to:\n\t<ul>\n\t\t<li>Translate antibiotic codes (like <em>AMOX</em>), official names (like <em>amoxicillin</em>) and even trade names (like <em>Amoxil</em> or <em>Trimox</em>) to an <a href="https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no">ATC code</a> (like <em>J01CA04</em>) and vice versa with the <code>abname</code> function</li>\n\t\t<li>Get the latest antibiotic properties like hierarchic groups and <a href="https://en.wikipedia.org/wiki/Defined_daily_dose">defined daily dose</a> (DDD) with units and administration form from the WHOCC website with the <code>atc_property</code> function</li>\n\t</ul>\n\t</li>\n\t<li>Create frequency tables with the <code>freq</code> function</li>\n</ul>\n\n<p>And it contains:</p>\n\n<ul>\n\t<li>A recent data set with ~2500 human pathogenic microorganisms, including family, genus, species, gram stain and aerobic/anaerobic</li>\n\t<li>A recent data set with all antibiotics as defined by the <a href="https://www.whocc.no/atc_ddd_methodology/who_collaborating_centre/">WHOCC</a>, including ATC code, official name and DDD's</li>\n\t<li>An example data set <code>septic_patients</code>, consisting of 2000 blood culture isolates from anonymised septic patients between 2001 and 2017.</li>\n</ul>\n\n<p>With the <code>MDRO</code> function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: <a href="https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E">https://github.com/msberends/AMR/issues/new</a>.</p>\n",
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"license": "GPL-2.0",
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"title": "AMR: An R package to simplify the analysis and prediction of Antimicrobial Resistance and work with antibiotic properties by using evidence-based methods.",
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"version": "0.3.0",
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"upload_type": "software",
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"publication_date": "2018-08-18",
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"creators": [
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{
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"orcid": "0000-0001-7620-1800",
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"affiliation": "University of Groningen, University Medical Center Groningen, Department of Medical Microbiology, Groningen, The Netherlands",
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"name": "Matthijs S. Berends"
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},
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||||
{
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"orcid": "0000-0001-5809-5995",
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"affiliation": "University of Groningen, University Medical Center Groningen, Department of Medical Microbiology, Groningen, The Netherlands",
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"name": "Christian F. Luz"
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}
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],
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"access_right": "open",
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"related_identifiers": [
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{
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"scheme": "url",
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"identifier": "https://github.com/msberends/AMR",
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"relation": "isSupplementTo"
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},
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{
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"scheme": "doi",
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||||
"identifier": "10.5281/zenodo.1305355",
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"relation": "isVersionOf"
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}
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]
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}
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<!DOCTYPE html>
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<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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<head>
|
||||
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
|
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<meta charset="utf-8">
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<meta http-equiv="X-UA-Compatible" content="IE=edge">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<title>Page not found (404) • AMR (for R)</title>
|
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<!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="https://amr-for-r.org/favicon-96x96.png">
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<link rel="icon" type="”image/svg+xml”" href="https://amr-for-r.org/favicon.svg">
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<link rel="apple-touch-icon" sizes="180x180" href="https://amr-for-r.org/apple-touch-icon.png">
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<link rel="icon" sizes="any" href="https://amr-for-r.org/favicon.ico">
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<link rel="manifest" href="https://amr-for-r.org/site.webmanifest">
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<script src="https://amr-for-r.org/deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<link href="https://amr-for-r.org/deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet">
|
||||
<script src="https://amr-for-r.org/deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="https://amr-for-r.org/deps/Lato-0.4.10/font.css" rel="stylesheet">
|
||||
<link href="https://amr-for-r.org/deps/Fira_Code-0.4.10/font.css" rel="stylesheet">
|
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<link href="https://amr-for-r.org/deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet">
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<link href="https://amr-for-r.org/deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
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<script src="https://amr-for-r.org/deps/headroom-0.11.0/headroom.min.js"></script><script src="https://amr-for-r.org/deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="https://amr-for-r.org/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="https://amr-for-r.org/deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="https://amr-for-r.org/deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="https://amr-for-r.org/deps/search-1.0.0/fuse.min.js"></script><script src="https://amr-for-r.org/deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="https://amr-for-r.org/pkgdown.js"></script><link href="https://amr-for-r.org/extra.css" rel="stylesheet">
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<script src="https://amr-for-r.org/extra.js"></script><meta property="og:title" content="Page not found (404)">
|
||||
<meta property="og:image" content="https://amr-for-r.org/logo.svg">
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<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous">
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<script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script>
|
||||
</head>
|
||||
<body>
|
||||
<a href="https://amr-for-r.org/#main" class="visually-hidden-focusable">Skip to contents</a>
|
||||
|
||||
|
||||
<nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
|
||||
|
||||
<a class="navbar-brand me-2" href="https://amr-for-r.org/index.html">AMR (for R)</a>
|
||||
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9065</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
<span class="navbar-toggler-icon"></span>
|
||||
</button>
|
||||
|
||||
<div id="navbar" class="collapse navbar-collapse ms-3">
|
||||
<ul class="navbar-nav me-auto">
|
||||
<li class="nav-item dropdown">
|
||||
<button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
|
||||
<ul class="dropdown-menu" aria-labelledby="dropdown-how-to">
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
|
||||
<li><a class="dropdown-item" href="https://amr-for-r.org/reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
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||||
<li><a class="dropdown-item" href="https://amr-for-r.org/reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
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</ul>
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||||
</li>
|
||||
<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
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<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
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<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/authors.html"><span class="fa fa-users"></span> Authors</a></li>
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</ul>
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<ul class="navbar-nav">
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<li class="nav-item"><form class="form-inline" role="search">
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<input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json">
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</form></li>
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<li class="nav-item"><a class="nav-link" href="https://amr-for-r.org/news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
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<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
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</ul>
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</div>
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</div>
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</nav><div class="container template-title-body">
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<div class="row">
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<main id="main" class="col-md-9"><div class="page-header">
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<img src="https://amr-for-r.org/logo.svg" class="logo" alt=""><h1>Page not found (404)</h1>
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</div>
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Content not found. Please use links in the navbar.
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</main>
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</div>
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||||
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||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
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</div>
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<div class="pkgdown-footer-right">
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<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
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</div>
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</footer>
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</div>
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</body>
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</html>
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@@ -1,3 +0,0 @@
|
||||
Content not found. Please use links in the navbar.
|
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# Page not found (404)
|
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-443
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<!DOCTYPE html>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>CLAUDE.md — AMR R Package • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="CLAUDE.md — AMR R Package"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
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<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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<a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
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<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9065</small>
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||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
||||
<span class="navbar-toggler-icon"></span>
|
||||
</button>
|
||||
|
||||
<div id="navbar" class="collapse navbar-collapse ms-3">
|
||||
<ul class="navbar-nav me-auto"><li class="nav-item dropdown">
|
||||
<button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
|
||||
<ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
|
||||
<li><a class="dropdown-item" href="reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
|
||||
<li><a class="dropdown-item" href="articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
|
||||
<li><a class="dropdown-item" href="articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
|
||||
<li><a class="dropdown-item" href="reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
|
||||
<li><a class="dropdown-item" href="articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
|
||||
<li><a class="dropdown-item" href="reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
|
||||
<li><a class="dropdown-item" href="articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
|
||||
<li><a class="dropdown-item" href="articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
|
||||
<li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
|
||||
<li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
|
||||
<li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
|
||||
</ul></li>
|
||||
<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
|
||||
<li class="nav-item"><a class="nav-link" href="reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
|
||||
<li class="nav-item"><a class="nav-link" href="authors.html"><span class="fa fa-users"></span> Authors</a></li>
|
||||
</ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
|
||||
<input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
|
||||
<li class="nav-item"><a class="nav-link" href="news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
|
||||
<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
|
||||
</ul></div>
|
||||
|
||||
|
||||
</div>
|
||||
</nav><div class="container template-title-body">
|
||||
<div class="row">
|
||||
<main id="main" class="col-md-9"><div class="page-header">
|
||||
<img src="logo.svg" class="logo" alt=""><h1>CLAUDE.md — AMR R Package</h1>
|
||||
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/CLAUDE.md" class="external-link"><code>CLAUDE.md</code></a></small>
|
||||
</div>
|
||||
|
||||
<div id="claudemd--amr-r-package" class="section level1">
|
||||
|
||||
<p>This file provides context for Claude Code when working in this repository.</p>
|
||||
<div class="section level2">
|
||||
<h2 id="project-overview">Project Overview<a class="anchor" aria-label="anchor" href="#project-overview"></a></h2>
|
||||
<p><strong>AMR</strong> is a zero-dependency R package for antimicrobial resistance (AMR) data analysis using a One Health approach. It is peer-reviewed, used in 175+ countries, and supports 28 languages.</p>
|
||||
<p>Key capabilities: - SIR (Susceptible/Intermediate/Resistant) classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints - Antibiogram generation: traditional, combined, syndromic, and WISCA - Microorganism taxonomy database (~79,000 species) - Antimicrobial drug database (~620 drugs) - Multi-drug resistant organism (MDRO) classification - First-isolate identification - Minimum Inhibitory Concentration (MIC) and disk diffusion handling - Multilingual output (28 languages)</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="common-commands">Common Commands<a class="anchor" aria-label="anchor" href="#common-commands"></a></h2>
|
||||
<p>All commands run inside an R session:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)</span></span>
|
||||
<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">document</span><span class="op">(</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Run all tests</span></span>
|
||||
<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">test</span><span class="op">(</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Full package check (CRAN-level: docs + tests + checks)</span></span>
|
||||
<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu">check</span><span class="op">(</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Build pkgdown website locally</span></span>
|
||||
<span><span class="fu">pkgdown</span><span class="fu">::</span><span class="fu"><a href="https://pkgdown.r-lib.org/reference/build_site.html" class="external-link">build_site</a></span><span class="op">(</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Code coverage report</span></span>
|
||||
<span><span class="fu">covr</span><span class="fu">::</span><span class="fu"><a href="http://covr.r-lib.org/reference/package_coverage.html" class="external-link">package_coverage</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
|
||||
<p>From the shell:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="co"># CRAN check from parent directory</span></span>
|
||||
<span id="cb2-2"><a href="#cb2-2" tabindex="-1"></a><span class="ex">R</span> CMD check AMR</span></code></pre></div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="repository-structure">Repository Structure<a class="anchor" aria-label="anchor" href="#repository-structure"></a></h2>
|
||||
<pre><code>R/ # All R source files (62 files, ~28,000 lines)
|
||||
man/ # Auto-generated .Rd documentation (do not edit manually)
|
||||
tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
|
||||
data/ # Pre-compiled .rda datasets
|
||||
data-raw/ # Scripts used to generate data/ files
|
||||
vignettes/ # Rmd vignette articles
|
||||
inst/ # Installed files (translations, etc.)
|
||||
_pkgdown.yml # pkgdown website configuration</code></pre>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="r-source-file-conventions">R Source File Conventions<a class="anchor" aria-label="anchor" href="#r-source-file-conventions"></a></h2>
|
||||
<p><strong>Naming conventions in <code>R/</code>:</strong></p>
|
||||
<table class="table"><thead><tr><th>Prefix/Name</th>
|
||||
<th>Purpose</th>
|
||||
</tr></thead><tbody><tr><td><code>aa_*.R</code></td>
|
||||
<td>Loaded first (helpers, globals, options, package docs)</td>
|
||||
</tr><tr><td><code>zz_deprecated.R</code></td>
|
||||
<td>Deprecated function wrappers</td>
|
||||
</tr><tr><td><code>zzz.R</code></td>
|
||||
<td>
|
||||
<code>.onLoad</code> / <code>.onAttach</code> initialization</td>
|
||||
</tr></tbody></table><p><strong>Key source files:</strong></p>
|
||||
<ul><li>
|
||||
<code>aa_helper_functions.R</code> / <code>aa_helper_pm_functions.R</code> — internal utility functions (large; ~63 KB and ~37 KB)</li>
|
||||
<li>
|
||||
<code>aa_globals.R</code> — global constants and breakpoint lookup structures</li>
|
||||
<li>
|
||||
<code>aa_options.R</code> — <code>amr_options()</code> / <code>get_AMR_option()</code> system</li>
|
||||
<li>
|
||||
<code>mo.R</code> / <code>mo_property.R</code> — microorganism lookup and properties</li>
|
||||
<li>
|
||||
<code>ab.R</code> / <code>ab_property.R</code> — antimicrobial drug functions</li>
|
||||
<li>
|
||||
<code>av.R</code> / <code>av_property.R</code> — antiviral drug functions</li>
|
||||
<li>
|
||||
<code>sir.R</code> / <code>sir_calc.R</code> / <code>sir_df.R</code> — SIR classification engine</li>
|
||||
<li>
|
||||
<code>mic.R</code> / <code>disk.R</code> — MIC and disk diffusion classes</li>
|
||||
<li>
|
||||
<code>antibiogram.R</code> — antibiogram generation (traditional, combined, syndromic, WISCA)</li>
|
||||
<li>
|
||||
<code>first_isolate.R</code> — first-isolate identification algorithms</li>
|
||||
<li>
|
||||
<code>mdro.R</code> — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)</li>
|
||||
<li>
|
||||
<code>amr_selectors.R</code> — tidyselect helpers for selecting AMR columns</li>
|
||||
<li>
|
||||
<code>interpretive_rules.R</code> / <code>custom_eucast_rules.R</code> — clinical interpretation rules</li>
|
||||
<li>
|
||||
<code>translate.R</code> — 28-language translation system</li>
|
||||
<li>
|
||||
<code>ggplot_sir.R</code> / <code>ggplot_pca.R</code> / <code>plotting.R</code> — visualisation functions</li>
|
||||
</ul></div>
|
||||
<div class="section level2">
|
||||
<h2 id="custom-s3-classes">Custom S3 Classes<a class="anchor" aria-label="anchor" href="#custom-s3-classes"></a></h2>
|
||||
<p>The package defines five S3 classes with full print/format/plot/vctrs support:</p>
|
||||
<table class="table"><thead><tr><th>Class</th>
|
||||
<th>Created by</th>
|
||||
<th>Represents</th>
|
||||
</tr></thead><tbody><tr><td><code><mo></code></td>
|
||||
<td><code><a href="reference/as.mo.html">as.mo()</a></code></td>
|
||||
<td>Microorganism code</td>
|
||||
</tr><tr><td><code><ab></code></td>
|
||||
<td><code><a href="reference/as.ab.html">as.ab()</a></code></td>
|
||||
<td>Antimicrobial drug code</td>
|
||||
</tr><tr><td><code><av></code></td>
|
||||
<td><code><a href="reference/as.av.html">as.av()</a></code></td>
|
||||
<td>Antiviral drug code</td>
|
||||
</tr><tr><td><code><sir></code></td>
|
||||
<td><code><a href="reference/as.sir.html">as.sir()</a></code></td>
|
||||
<td>SIR value (S/I/R/SDD)</td>
|
||||
</tr><tr><td><code><mic></code></td>
|
||||
<td><code><a href="reference/as.mic.html">as.mic()</a></code></td>
|
||||
<td>Minimum inhibitory concentration</td>
|
||||
</tr><tr><td><code><disk></code></td>
|
||||
<td><code><a href="reference/as.disk.html">as.disk()</a></code></td>
|
||||
<td>Disk diffusion diameter</td>
|
||||
</tr></tbody></table></div>
|
||||
<div class="section level2">
|
||||
<h2 id="data-files">Data Files<a class="anchor" aria-label="anchor" href="#data-files"></a></h2>
|
||||
<p>Pre-compiled in <code>data/</code> (do not edit directly; regenerate via <code>data-raw/</code> scripts):</p>
|
||||
<table class="table"><colgroup><col width="50%"><col width="50%"></colgroup><thead><tr><th>File</th>
|
||||
<th>Contents</th>
|
||||
</tr></thead><tbody><tr><td><code>microorganisms.rda</code></td>
|
||||
<td>~79,000 microbial species with full taxonomy</td>
|
||||
</tr><tr><td><code>antimicrobials.rda</code></td>
|
||||
<td>~620 antimicrobial drugs with ATC codes</td>
|
||||
</tr><tr><td><code>antivirals.rda</code></td>
|
||||
<td>Antiviral drugs</td>
|
||||
</tr><tr><td><code>clinical_breakpoints.rda</code></td>
|
||||
<td>EUCAST + CLSI breakpoints (2011–2025)</td>
|
||||
</tr><tr><td><code>intrinsic_resistant.rda</code></td>
|
||||
<td>Intrinsic resistance patterns</td>
|
||||
</tr><tr><td><code>example_isolates.rda</code></td>
|
||||
<td>Example AMR dataset for documentation/testing</td>
|
||||
</tr><tr><td><code>WHONET.rda</code></td>
|
||||
<td>Example WHONET-format dataset</td>
|
||||
</tr></tbody></table></div>
|
||||
<div class="section level2">
|
||||
<h2 id="zero-dependency-design">Zero-Dependency Design<a class="anchor" aria-label="anchor" href="#zero-dependency-design"></a></h2>
|
||||
<p>The package has <strong>no <code>Imports</code></strong> in <code>DESCRIPTION</code>. All optional integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.) are listed in <code>Suggests</code> and guarded with:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="kw">if</span> <span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">"pkg"</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span> <span class="va">...</span> <span class="op">}</span></span></code></pre></div>
|
||||
<p>Never add packages to <code>Imports</code>. If new functionality requires an external package, add it to <code>Suggests</code> and guard usage appropriately.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="testing">Testing<a class="anchor" aria-label="anchor" href="#testing"></a></h2>
|
||||
<ul><li>
|
||||
<strong>Framework:</strong> <code>testthat</code> (R ≥ 3.1); legacy <code>tinytest</code> used for R 3.0–3.6 CI</li>
|
||||
<li>
|
||||
<strong>Test files:</strong> <code>tests/testthat/test-*.R</code>
|
||||
</li>
|
||||
<li>
|
||||
<strong>Helpers:</strong> <code>tests/testthat/helper-functions.R</code>
|
||||
</li>
|
||||
<li>
|
||||
<strong>CI matrix:</strong> GitHub Actions across Windows / macOS / Linux × R devel / release / oldrel-1 through oldrel-4</li>
|
||||
<li>
|
||||
<strong>Coverage:</strong> <code>covr</code> (some files excluded: <code>atc_online.R</code>, <code>mo_source.R</code>, <code>translate.R</code>, <code>resistance_predict.R</code>, <code>zz_deprecated.R</code>, helper files, <code>zzz.R</code>)</li>
|
||||
</ul></div>
|
||||
<div class="section level2">
|
||||
<h2 id="documentation">Documentation<a class="anchor" aria-label="anchor" href="#documentation"></a></h2>
|
||||
<ul><li>All exported functions use <strong>roxygen2</strong> blocks (<code>RoxygenNote: 7.3.3</code>, markdown enabled)</li>
|
||||
<li>Run <code>devtools::document()</code> after any change to roxygen comments</li>
|
||||
<li>Never edit files in <code>man/</code> directly — they are auto-generated</li>
|
||||
<li>Vignettes live in <code>vignettes/</code> as <code>.Rmd</code> files</li>
|
||||
<li>The pkgdown website is configured in <code>_pkgdown.yml</code>
|
||||
</li>
|
||||
</ul></div>
|
||||
<div class="section level2">
|
||||
<h2 id="versioning">Versioning<a class="anchor" aria-label="anchor" href="#versioning"></a></h2>
|
||||
<p>Version format: <code>major.minor.patch.dev</code> (e.g., <code>3.0.1.9021</code>)</p>
|
||||
<ul><li>Development versions use a <code>.9xxx</code> suffix</li>
|
||||
<li>Stable CRAN releases drop the dev suffix (e.g., <code>3.0.1</code>)</li>
|
||||
<li>
|
||||
<code>NEWS.md</code> uses sections <strong>New</strong>, <strong>Fixes</strong>, <strong>Updates</strong> with GitHub issue references (<code>#NNN</code>)</li>
|
||||
</ul><div class="section level3">
|
||||
<h3 id="version-and-date-bump-required-for-every-pr">Version and date bump required for every PR<a class="anchor" aria-label="anchor" href="#version-and-date-bump-required-for-every-pr"></a></h3>
|
||||
<p>All PRs are <strong>squash-merged</strong>, so each PR lands as exactly <strong>one commit</strong> on the default branch. Version numbers are kept in sync with the cumulative commit count since the last released tag. Therefore <strong>exactly one version bump is allowed per PR</strong>, regardless of how many intermediate commits are made on the branch.</p>
|
||||
<div class="section level4">
|
||||
<h4 id="computing-the-correct-version-number">Computing the correct version number<a class="anchor" aria-label="anchor" href="#computing-the-correct-version-number"></a></h4>
|
||||
<p><strong>First, ensure <code>git</code> and <code>gh</code> are installed</strong> — both are required for the version computation and for pushing changes. Install them if missing before doing anything else:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="fu">which</span> git <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> git</span>
|
||||
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="fu">which</span> gh <span class="kw">||</span> <span class="ex">apt-get</span> install <span class="at">-y</span> gh</span>
|
||||
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="co"># Also ensure all tags are fetched so git describe works</span></span>
|
||||
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="fu">git</span> fetch <span class="at">--tags</span></span></code></pre></div>
|
||||
<p>Then run the following from the repo root to determine the version string to use:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="va">currenttag</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0 <span class="kw">|</span> <span class="fu">sed</span> <span class="st">'s/v//'</span><span class="va">)</span></span>
|
||||
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="va">currenttagfull</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0<span class="va">)</span></span>
|
||||
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="va">defaultbranch</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> branch <span class="kw">|</span> <span class="fu">cut</span> <span class="at">-c</span> 3- <span class="kw">|</span> <span class="fu">grep</span> <span class="at">-E</span> <span class="st">'^master$|^main$'</span><span class="va">)</span></span>
|
||||
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="fu">git</span> fetch origin <span class="va">${defaultbranch}</span> <span class="at">--quiet</span></span>
|
||||
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a><span class="va">currentcommit</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> rev-list <span class="at">--count</span> <span class="va">${currenttagfull}</span>..origin/<span class="va">${defaultbranch})</span></span>
|
||||
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="va">currentversion</span><span class="op">=</span><span class="st">"</span><span class="va">${currenttag}</span><span class="st">.</span><span class="va">$((currentcommit</span> <span class="op">+</span> <span class="dv">9001</span> <span class="op">+</span> <span class="dv">1</span><span class="va">))</span><span class="st">"</span></span>
|
||||
<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a><span class="bu">echo</span> <span class="st">"</span><span class="va">$currentversion</span><span class="st">"</span></span></code></pre></div>
|
||||
<p>The <code>+ 1</code> accounts for the fact that this PR’s squash commit is not yet on the default branch. Set <strong>both</strong> of these files to the resulting version string (and only once per PR, even across multiple commits):</p>
|
||||
<ol style="list-style-type: decimal"><li><p><strong><code>DESCRIPTION</code></strong> — the <code>Version:</code> field</p></li>
|
||||
<li>
|
||||
<p><strong><code>NEWS.md</code></strong> — <strong>only replace line 1</strong> (the <code># AMR <version></code> heading) with the new version number; do <strong>not</strong> create a new section. <code>NEWS.md</code> is a <strong>continuous log</strong> for the entire current <code>x.y.z.9nnn</code> development series: all changes since the last stable release accumulate under that single heading. After updating line 1, append the new change as a bullet under the appropriate sub-heading (<code>### New</code>, <code>### Fixes</code>, or <code>### Updates</code>).</p>
|
||||
<p>Style rules for <code>NEWS.md</code> entries:</p>
|
||||
<ul><li>Be <strong>extremely concise</strong> — one short line per item</li>
|
||||
<li>Do <strong>not</strong> end with a full stop (period)</li>
|
||||
<li>No verbose explanations; just the essential fact</li>
|
||||
</ul></li>
|
||||
</ol><p>If <code>git describe</code> fails (e.g. no tags exist in the environment), fall back to reading the current version from <code>DESCRIPTION</code> and adding 1 to the last numeric component — but only if no bump has already been made in this PR.</p>
|
||||
</div>
|
||||
<div class="section level4">
|
||||
<h4 id="date-field">Date field<a class="anchor" aria-label="anchor" href="#date-field"></a></h4>
|
||||
<p>The <code>Date:</code> field in <code>DESCRIPTION</code> must reflect the date of the <strong>last commit to the PR</strong> (not the first), in ISO format. Update it with every commit so it is always current:</p>
|
||||
<pre><code><span><span class="va">Date</span><span class="op">:</span> <span class="fl">2026</span><span class="op">-</span><span class="fl">03</span><span class="op">-</span><span class="fl">07</span></span></code></pre>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="internal-state">Internal State<a class="anchor" aria-label="anchor" href="#internal-state"></a></h2>
|
||||
<p>The package uses a private <code>AMR_env</code> environment (created in <code>aa_globals.R</code>) for caching expensive lookups (e.g., microorganism matching scores, breakpoint tables). This avoids re-computation within a session.</p>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
</nav></aside></div>
|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
||||
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
|
||||
</div>
|
||||
|
||||
</footer></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body></html>
|
||||
|
||||
@@ -1,238 +0,0 @@
|
||||
# CLAUDE.md — AMR R Package
|
||||
|
||||
This file provides context for Claude Code when working in this
|
||||
repository.
|
||||
|
||||
## Project Overview
|
||||
|
||||
**AMR** is a zero-dependency R package for antimicrobial resistance
|
||||
(AMR) data analysis using a One Health approach. It is peer-reviewed,
|
||||
used in 175+ countries, and supports 28 languages.
|
||||
|
||||
Key capabilities: - SIR (Susceptible/Intermediate/Resistant)
|
||||
classification using EUCAST 2011–2025 and CLSI 2011–2025 breakpoints -
|
||||
Antibiogram generation: traditional, combined, syndromic, and WISCA -
|
||||
Microorganism taxonomy database (~79,000 species) - Antimicrobial drug
|
||||
database (~620 drugs) - Multi-drug resistant organism (MDRO)
|
||||
classification - First-isolate identification - Minimum Inhibitory
|
||||
Concentration (MIC) and disk diffusion handling - Multilingual output
|
||||
(28 languages)
|
||||
|
||||
## Common Commands
|
||||
|
||||
All commands run inside an R session:
|
||||
|
||||
``` r
|
||||
|
||||
# Rebuild documentation (roxygen2 → .Rd files + NAMESPACE)
|
||||
devtools::document()
|
||||
|
||||
# Run all tests
|
||||
devtools::test()
|
||||
|
||||
# Full package check (CRAN-level: docs + tests + checks)
|
||||
devtools::check()
|
||||
|
||||
# Build pkgdown website locally
|
||||
pkgdown::build_site()
|
||||
|
||||
# Code coverage report
|
||||
covr::package_coverage()
|
||||
```
|
||||
|
||||
From the shell:
|
||||
|
||||
``` bash
|
||||
# CRAN check from parent directory
|
||||
R CMD check AMR
|
||||
```
|
||||
|
||||
## Repository Structure
|
||||
|
||||
R/ # All R source files (62 files, ~28,000 lines)
|
||||
man/ # Auto-generated .Rd documentation (do not edit manually)
|
||||
tests/testthat/ # testthat test files (test-*.R) and helper-functions.R
|
||||
data/ # Pre-compiled .rda datasets
|
||||
data-raw/ # Scripts used to generate data/ files
|
||||
vignettes/ # Rmd vignette articles
|
||||
inst/ # Installed files (translations, etc.)
|
||||
_pkgdown.yml # pkgdown website configuration
|
||||
|
||||
## R Source File Conventions
|
||||
|
||||
**Naming conventions in `R/`:**
|
||||
|
||||
| Prefix/Name | Purpose |
|
||||
|-------------------|--------------------------------------------------------|
|
||||
| `aa_*.R` | Loaded first (helpers, globals, options, package docs) |
|
||||
| `zz_deprecated.R` | Deprecated function wrappers |
|
||||
| `zzz.R` | `.onLoad` / `.onAttach` initialization |
|
||||
|
||||
**Key source files:**
|
||||
|
||||
- `aa_helper_functions.R` / `aa_helper_pm_functions.R` — internal
|
||||
utility functions (large; ~63 KB and ~37 KB)
|
||||
- `aa_globals.R` — global constants and breakpoint lookup structures
|
||||
- `aa_options.R` — `amr_options()` / `get_AMR_option()` system
|
||||
- `mo.R` / `mo_property.R` — microorganism lookup and properties
|
||||
- `ab.R` / `ab_property.R` — antimicrobial drug functions
|
||||
- `av.R` / `av_property.R` — antiviral drug functions
|
||||
- `sir.R` / `sir_calc.R` / `sir_df.R` — SIR classification engine
|
||||
- `mic.R` / `disk.R` — MIC and disk diffusion classes
|
||||
- `antibiogram.R` — antibiogram generation (traditional, combined,
|
||||
syndromic, WISCA)
|
||||
- `first_isolate.R` — first-isolate identification algorithms
|
||||
- `mdro.R` — MDRO classification (EUCAST, CLSI, CDC, custom guidelines)
|
||||
- `amr_selectors.R` — tidyselect helpers for selecting AMR columns
|
||||
- `interpretive_rules.R` / `custom_eucast_rules.R` — clinical
|
||||
interpretation rules
|
||||
- `translate.R` — 28-language translation system
|
||||
- `ggplot_sir.R` / `ggplot_pca.R` / `plotting.R` — visualisation
|
||||
functions
|
||||
|
||||
## Custom S3 Classes
|
||||
|
||||
The package defines five S3 classes with full print/format/plot/vctrs
|
||||
support:
|
||||
|
||||
| Class | Created by | Represents |
|
||||
|----|----|----|
|
||||
| `<mo>` | [`as.mo()`](https://amr-for-r.org/reference/as.mo.md) | Microorganism code |
|
||||
| `<ab>` | [`as.ab()`](https://amr-for-r.org/reference/as.ab.md) | Antimicrobial drug code |
|
||||
| `<av>` | [`as.av()`](https://amr-for-r.org/reference/as.av.md) | Antiviral drug code |
|
||||
| `<sir>` | [`as.sir()`](https://amr-for-r.org/reference/as.sir.md) | SIR value (S/I/R/SDD) |
|
||||
| `<mic>` | [`as.mic()`](https://amr-for-r.org/reference/as.mic.md) | Minimum inhibitory concentration |
|
||||
| `<disk>` | [`as.disk()`](https://amr-for-r.org/reference/as.disk.md) | Disk diffusion diameter |
|
||||
|
||||
## Data Files
|
||||
|
||||
Pre-compiled in `data/` (do not edit directly; regenerate via
|
||||
`data-raw/` scripts):
|
||||
|
||||
| File | Contents |
|
||||
|----------------------------|-----------------------------------------------|
|
||||
| `microorganisms.rda` | ~79,000 microbial species with full taxonomy |
|
||||
| `antimicrobials.rda` | ~620 antimicrobial drugs with ATC codes |
|
||||
| `antivirals.rda` | Antiviral drugs |
|
||||
| `clinical_breakpoints.rda` | EUCAST + CLSI breakpoints (2011–2025) |
|
||||
| `intrinsic_resistant.rda` | Intrinsic resistance patterns |
|
||||
| `example_isolates.rda` | Example AMR dataset for documentation/testing |
|
||||
| `WHONET.rda` | Example WHONET-format dataset |
|
||||
|
||||
## Zero-Dependency Design
|
||||
|
||||
The package has **no `Imports`** in `DESCRIPTION`. All optional
|
||||
integrations (ggplot2, dplyr, data.table, tidymodels, cli, crayon, etc.)
|
||||
are listed in `Suggests` and guarded with:
|
||||
|
||||
``` r
|
||||
|
||||
if (requireNamespace("pkg", quietly = TRUE)) { ... }
|
||||
```
|
||||
|
||||
Never add packages to `Imports`. If new functionality requires an
|
||||
external package, add it to `Suggests` and guard usage appropriately.
|
||||
|
||||
## Testing
|
||||
|
||||
- **Framework:** `testthat` (R ≥ 3.1); legacy `tinytest` used for R
|
||||
3.0–3.6 CI
|
||||
- **Test files:** `tests/testthat/test-*.R`
|
||||
- **Helpers:** `tests/testthat/helper-functions.R`
|
||||
- **CI matrix:** GitHub Actions across Windows / macOS / Linux × R devel
|
||||
/ release / oldrel-1 through oldrel-4
|
||||
- **Coverage:** `covr` (some files excluded: `atc_online.R`,
|
||||
`mo_source.R`, `translate.R`, `resistance_predict.R`,
|
||||
`zz_deprecated.R`, helper files, `zzz.R`)
|
||||
|
||||
## Documentation
|
||||
|
||||
- All exported functions use **roxygen2** blocks (`RoxygenNote: 7.3.3`,
|
||||
markdown enabled)
|
||||
- Run `devtools::document()` after any change to roxygen comments
|
||||
- Never edit files in `man/` directly — they are auto-generated
|
||||
- Vignettes live in `vignettes/` as `.Rmd` files
|
||||
- The pkgdown website is configured in `_pkgdown.yml`
|
||||
|
||||
## Versioning
|
||||
|
||||
Version format: `major.minor.patch.dev` (e.g., `3.0.1.9021`)
|
||||
|
||||
- Development versions use a `.9xxx` suffix
|
||||
- Stable CRAN releases drop the dev suffix (e.g., `3.0.1`)
|
||||
- `NEWS.md` uses sections **New**, **Fixes**, **Updates** with GitHub
|
||||
issue references (`#NNN`)
|
||||
|
||||
### Version and date bump required for every PR
|
||||
|
||||
All PRs are **squash-merged**, so each PR lands as exactly **one
|
||||
commit** on the default branch. Version numbers are kept in sync with
|
||||
the cumulative commit count since the last released tag. Therefore
|
||||
**exactly one version bump is allowed per PR**, regardless of how many
|
||||
intermediate commits are made on the branch.
|
||||
|
||||
#### Computing the correct version number
|
||||
|
||||
**First, ensure `git` and `gh` are installed** — both are required for
|
||||
the version computation and for pushing changes. Install them if missing
|
||||
before doing anything else:
|
||||
|
||||
``` bash
|
||||
which git || apt-get install -y git
|
||||
which gh || apt-get install -y gh
|
||||
# Also ensure all tags are fetched so git describe works
|
||||
git fetch --tags
|
||||
```
|
||||
|
||||
Then run the following from the repo root to determine the version
|
||||
string to use:
|
||||
|
||||
``` bash
|
||||
currenttag=$(git describe --tags --abbrev=0 | sed 's/v//')
|
||||
currenttagfull=$(git describe --tags --abbrev=0)
|
||||
defaultbranch=$(git branch | cut -c 3- | grep -E '^master$|^main$')
|
||||
git fetch origin ${defaultbranch} --quiet
|
||||
currentcommit=$(git rev-list --count ${currenttagfull}..origin/${defaultbranch})
|
||||
currentversion="${currenttag}.$((currentcommit + 9001 + 1))"
|
||||
echo "$currentversion"
|
||||
```
|
||||
|
||||
The `+ 1` accounts for the fact that this PR’s squash commit is not yet
|
||||
on the default branch. Set **both** of these files to the resulting
|
||||
version string (and only once per PR, even across multiple commits):
|
||||
|
||||
1. **`DESCRIPTION`** — the `Version:` field
|
||||
|
||||
2. **`NEWS.md`** — **only replace line 1** (the `# AMR <version>`
|
||||
heading) with the new version number; do **not** create a new
|
||||
section. `NEWS.md` is a **continuous log** for the entire current
|
||||
`x.y.z.9nnn` development series: all changes since the last stable
|
||||
release accumulate under that single heading. After updating line 1,
|
||||
append the new change as a bullet under the appropriate sub-heading
|
||||
(`### New`, `### Fixes`, or `### Updates`).
|
||||
|
||||
Style rules for `NEWS.md` entries:
|
||||
|
||||
- Be **extremely concise** — one short line per item
|
||||
- Do **not** end with a full stop (period)
|
||||
- No verbose explanations; just the essential fact
|
||||
|
||||
If `git describe` fails (e.g. no tags exist in the environment), fall
|
||||
back to reading the current version from `DESCRIPTION` and adding 1 to
|
||||
the last numeric component — but only if no bump has already been made
|
||||
in this PR.
|
||||
|
||||
#### Date field
|
||||
|
||||
The `Date:` field in `DESCRIPTION` must reflect the date of the **last
|
||||
commit to the PR** (not the first), in ISO format. Update it with every
|
||||
commit so it is always current:
|
||||
|
||||
Date: 2026-03-07
|
||||
|
||||
## Internal State
|
||||
|
||||
The package uses a private `AMR_env` environment (created in
|
||||
`aa_globals.R`) for caching expensive lookups (e.g., microorganism
|
||||
matching scores, breakpoint tables). This avoids re-computation within a
|
||||
session.
|
||||
Executable
+70
@@ -0,0 +1,70 @@
|
||||
Package: AMR
|
||||
Version: 0.3.0
|
||||
Date: 2018-08-14
|
||||
Title: Antimicrobial Resistance Analysis
|
||||
Authors@R: c(
|
||||
person(
|
||||
given = c("Matthijs", "S."),
|
||||
family = "Berends",
|
||||
email = "m.s.berends@umcg.nl",
|
||||
role = c("aut", "cre"),
|
||||
comment = c(ORCID = "0000-0001-7620-1800")),
|
||||
person(
|
||||
given = c("Christian", "F."),
|
||||
family = "Luz",
|
||||
email = "c.f.luz@umcg.nl",
|
||||
role = c("aut", "rev"),
|
||||
comment = c(ORCID = "0000-0001-5809-5995")),
|
||||
person(
|
||||
given = c("Erwin", "E.A."),
|
||||
family = "Hassing",
|
||||
email = "e.hassing@certe.nl",
|
||||
role = "ctb"),
|
||||
person(
|
||||
given = "Corinna",
|
||||
family = "Glasner",
|
||||
email = "c.glasner@umcg.nl",
|
||||
role = "ths",
|
||||
comment = c(ORCID = "0000-0003-1241-1328")),
|
||||
person(
|
||||
given = c("Alex", "W."),
|
||||
family = "Friedrich",
|
||||
email = "alex.friedrich@umcg.nl",
|
||||
role = "ths",
|
||||
comment = c(ORCID = "0000-0003-4881-038X")),
|
||||
person(
|
||||
given = "Bhanu",
|
||||
family = "Sinha",
|
||||
email = "b.sinha@umcg.nl",
|
||||
role = "ths",
|
||||
comment = c(ORCID = "0000-0003-1634-0010")))
|
||||
Description: Functions to simplify the analysis of Antimicrobial Resistance (AMR)
|
||||
of microbial isolates, by using new S3 classes and applying EUCAST expert rules
|
||||
on antibiograms according to Leclercq (2013)
|
||||
<doi:10.1111/j.1469-0691.2011.03703.x>.
|
||||
Depends:
|
||||
R (>= 3.1.0)
|
||||
Imports:
|
||||
backports,
|
||||
clipr,
|
||||
curl,
|
||||
dplyr (>= 0.7.0),
|
||||
xml2 (>= 1.0.0),
|
||||
knitr (>= 1.0.0),
|
||||
readr,
|
||||
rvest (>= 0.3.2),
|
||||
tibble
|
||||
Suggests:
|
||||
testthat (>= 1.0.2),
|
||||
covr (>= 3.0.1),
|
||||
rmarkdown,
|
||||
rstudioapi,
|
||||
tidyr,
|
||||
ggplot2
|
||||
VignetteBuilder: knitr
|
||||
URL: https://github.com/msberends/AMR
|
||||
BugReports: https://github.com/msberends/AMR/issues
|
||||
License: GPL-2 | file LICENSE
|
||||
Encoding: UTF-8
|
||||
LazyData: true
|
||||
RoxygenNote: 6.1.0
|
||||
@@ -0,0 +1,339 @@
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 2, June 1991
|
||||
|
||||
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
Preamble
|
||||
|
||||
The licenses for most software are designed to take away your
|
||||
freedom to share and change it. By contrast, the GNU General Public
|
||||
License is intended to guarantee your freedom to share and change free
|
||||
software--to make sure the software is free for all its users. This
|
||||
General Public License applies to most of the Free Software
|
||||
Foundation's software and to any other program whose authors commit to
|
||||
using it. (Some other Free Software Foundation software is covered by
|
||||
the GNU Lesser General Public License instead.) You can apply it to
|
||||
your programs, too.
|
||||
|
||||
When we speak of free software, we are referring to freedom, not
|
||||
price. Our General Public Licenses are designed to make sure that you
|
||||
have the freedom to distribute copies of free software (and charge for
|
||||
this service if you wish), that you receive source code or can get it
|
||||
if you want it, that you can change the software or use pieces of it
|
||||
in new free programs; and that you know you can do these things.
|
||||
|
||||
To protect your rights, we need to make restrictions that forbid
|
||||
anyone to deny you these rights or to ask you to surrender the rights.
|
||||
These restrictions translate to certain responsibilities for you if you
|
||||
distribute copies of the software, or if you modify it.
|
||||
|
||||
For example, if you distribute copies of such a program, whether
|
||||
gratis or for a fee, you must give the recipients all the rights that
|
||||
you have. You must make sure that they, too, receive or can get the
|
||||
source code. And you must show them these terms so they know their
|
||||
rights.
|
||||
|
||||
We protect your rights with two steps: (1) copyright the software, and
|
||||
(2) offer you this license which gives you legal permission to copy,
|
||||
distribute and/or modify the software.
|
||||
|
||||
Also, for each author's protection and ours, we want to make certain
|
||||
that everyone understands that there is no warranty for this free
|
||||
software. If the software is modified by someone else and passed on, we
|
||||
want its recipients to know that what they have is not the original, so
|
||||
that any problems introduced by others will not reflect on the original
|
||||
authors' reputations.
|
||||
|
||||
Finally, any free program is threatened constantly by software
|
||||
patents. We wish to avoid the danger that redistributors of a free
|
||||
program will individually obtain patent licenses, in effect making the
|
||||
program proprietary. To prevent this, we have made it clear that any
|
||||
patent must be licensed for everyone's free use or not licensed at all.
|
||||
|
||||
The precise terms and conditions for copying, distribution and
|
||||
modification follow.
|
||||
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
|
||||
|
||||
0. This License applies to any program or other work which contains
|
||||
a notice placed by the copyright holder saying it may be distributed
|
||||
under the terms of this General Public License. The "Program", below,
|
||||
refers to any such program or work, and a "work based on the Program"
|
||||
means either the Program or any derivative work under copyright law:
|
||||
that is to say, a work containing the Program or a portion of it,
|
||||
either verbatim or with modifications and/or translated into another
|
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language. (Hereinafter, translation is included without limitation in
|
||||
the term "modification".) Each licensee is addressed as "you".
|
||||
|
||||
Activities other than copying, distribution and modification are not
|
||||
covered by this License; they are outside its scope. The act of
|
||||
running the Program is not restricted, and the output from the Program
|
||||
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|
||||
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|
||||
Whether that is true depends on what the Program does.
|
||||
|
||||
1. You may copy and distribute verbatim copies of the Program's
|
||||
source code as you receive it, in any medium, provided that you
|
||||
conspicuously and appropriately publish on each copy an appropriate
|
||||
copyright notice and disclaimer of warranty; keep intact all the
|
||||
notices that refer to this License and to the absence of any warranty;
|
||||
and give any other recipients of the Program a copy of this License
|
||||
along with the Program.
|
||||
|
||||
You may charge a fee for the physical act of transferring a copy, and
|
||||
you may at your option offer warranty protection in exchange for a fee.
|
||||
|
||||
2. You may modify your copy or copies of the Program or any portion
|
||||
of it, thus forming a work based on the Program, and copy and
|
||||
distribute such modifications or work under the terms of Section 1
|
||||
above, provided that you also meet all of these conditions:
|
||||
|
||||
a) You must cause the modified files to carry prominent notices
|
||||
stating that you changed the files and the date of any change.
|
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|
||||
b) You must cause any work that you distribute or publish, that in
|
||||
whole or in part contains or is derived from the Program or any
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||||
part thereof, to be licensed as a whole at no charge to all third
|
||||
parties under the terms of this License.
|
||||
|
||||
c) If the modified program normally reads commands interactively
|
||||
when run, you must cause it, when started running for such
|
||||
interactive use in the most ordinary way, to print or display an
|
||||
announcement including an appropriate copyright notice and a
|
||||
notice that there is no warranty (or else, saying that you provide
|
||||
a warranty) and that users may redistribute the program under
|
||||
these conditions, and telling the user how to view a copy of this
|
||||
License. (Exception: if the Program itself is interactive but
|
||||
does not normally print such an announcement, your work based on
|
||||
the Program is not required to print an announcement.)
|
||||
|
||||
These requirements apply to the modified work as a whole. If
|
||||
identifiable sections of that work are not derived from the Program,
|
||||
and can be reasonably considered independent and separate works in
|
||||
themselves, then this License, and its terms, do not apply to those
|
||||
sections when you distribute them as separate works. But when you
|
||||
distribute the same sections as part of a whole which is a work based
|
||||
on the Program, the distribution of the whole must be on the terms of
|
||||
this License, whose permissions for other licensees extend to the
|
||||
entire whole, and thus to each and every part regardless of who wrote it.
|
||||
|
||||
Thus, it is not the intent of this section to claim rights or contest
|
||||
your rights to work written entirely by you; rather, the intent is to
|
||||
exercise the right to control the distribution of derivative or
|
||||
collective works based on the Program.
|
||||
|
||||
In addition, mere aggregation of another work not based on the Program
|
||||
with the Program (or with a work based on the Program) on a volume of
|
||||
a storage or distribution medium does not bring the other work under
|
||||
the scope of this License.
|
||||
|
||||
3. You may copy and distribute the Program (or a work based on it,
|
||||
under Section 2) in object code or executable form under the terms of
|
||||
Sections 1 and 2 above provided that you also do one of the following:
|
||||
|
||||
a) Accompany it with the complete corresponding machine-readable
|
||||
source code, which must be distributed under the terms of Sections
|
||||
1 and 2 above on a medium customarily used for software interchange; or,
|
||||
|
||||
b) Accompany it with a written offer, valid for at least three
|
||||
years, to give any third party, for a charge no more than your
|
||||
cost of physically performing source distribution, a complete
|
||||
machine-readable copy of the corresponding source code, to be
|
||||
distributed under the terms of Sections 1 and 2 above on a medium
|
||||
customarily used for software interchange; or,
|
||||
|
||||
c) Accompany it with the information you received as to the offer
|
||||
to distribute corresponding source code. (This alternative is
|
||||
allowed only for noncommercial distribution and only if you
|
||||
received the program in object code or executable form with such
|
||||
an offer, in accord with Subsection b above.)
|
||||
|
||||
The source code for a work means the preferred form of the work for
|
||||
making modifications to it. For an executable work, complete source
|
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code means all the source code for all modules it contains, plus any
|
||||
associated interface definition files, plus the scripts used to
|
||||
control compilation and installation of the executable. However, as a
|
||||
special exception, the source code distributed need not include
|
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anything that is normally distributed (in either source or binary
|
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form) with the major components (compiler, kernel, and so on) of the
|
||||
operating system on which the executable runs, unless that component
|
||||
itself accompanies the executable.
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||||
|
||||
If distribution of executable or object code is made by offering
|
||||
access to copy from a designated place, then offering equivalent
|
||||
access to copy the source code from the same place counts as
|
||||
distribution of the source code, even though third parties are not
|
||||
compelled to copy the source along with the object code.
|
||||
|
||||
4. You may not copy, modify, sublicense, or distribute the Program
|
||||
except as expressly provided under this License. Any attempt
|
||||
otherwise to copy, modify, sublicense or distribute the Program is
|
||||
void, and will automatically terminate your rights under this License.
|
||||
However, parties who have received copies, or rights, from you under
|
||||
this License will not have their licenses terminated so long as such
|
||||
parties remain in full compliance.
|
||||
|
||||
5. You are not required to accept this License, since you have not
|
||||
signed it. However, nothing else grants you permission to modify or
|
||||
distribute the Program or its derivative works. These actions are
|
||||
prohibited by law if you do not accept this License. Therefore, by
|
||||
modifying or distributing the Program (or any work based on the
|
||||
Program), you indicate your acceptance of this License to do so, and
|
||||
all its terms and conditions for copying, distributing or modifying
|
||||
the Program or works based on it.
|
||||
|
||||
6. Each time you redistribute the Program (or any work based on the
|
||||
Program), the recipient automatically receives a license from the
|
||||
original licensor to copy, distribute or modify the Program subject to
|
||||
these terms and conditions. You may not impose any further
|
||||
restrictions on the recipients' exercise of the rights granted herein.
|
||||
You are not responsible for enforcing compliance by third parties to
|
||||
this License.
|
||||
|
||||
7. If, as a consequence of a court judgment or allegation of patent
|
||||
infringement or for any other reason (not limited to patent issues),
|
||||
conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot
|
||||
distribute so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you
|
||||
may not distribute the Program at all. For example, if a patent
|
||||
license would not permit royalty-free redistribution of the Program by
|
||||
all those who receive copies directly or indirectly through you, then
|
||||
the only way you could satisfy both it and this License would be to
|
||||
refrain entirely from distribution of the Program.
|
||||
|
||||
If any portion of this section is held invalid or unenforceable under
|
||||
any particular circumstance, the balance of the section is intended to
|
||||
apply and the section as a whole is intended to apply in other
|
||||
circumstances.
|
||||
|
||||
It is not the purpose of this section to induce you to infringe any
|
||||
patents or other property right claims or to contest validity of any
|
||||
such claims; this section has the sole purpose of protecting the
|
||||
integrity of the free software distribution system, which is
|
||||
implemented by public license practices. Many people have made
|
||||
generous contributions to the wide range of software distributed
|
||||
through that system in reliance on consistent application of that
|
||||
system; it is up to the author/donor to decide if he or she is willing
|
||||
to distribute software through any other system and a licensee cannot
|
||||
impose that choice.
|
||||
|
||||
This section is intended to make thoroughly clear what is believed to
|
||||
be a consequence of the rest of this License.
|
||||
|
||||
8. If the distribution and/or use of the Program is restricted in
|
||||
certain countries either by patents or by copyrighted interfaces, the
|
||||
original copyright holder who places the Program under this License
|
||||
may add an explicit geographical distribution limitation excluding
|
||||
those countries, so that distribution is permitted only in or among
|
||||
countries not thus excluded. In such case, this License incorporates
|
||||
the limitation as if written in the body of this License.
|
||||
|
||||
9. The Free Software Foundation may publish revised and/or new versions
|
||||
of the General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the Program
|
||||
specifies a version number of this License which applies to it and "any
|
||||
later version", you have the option of following the terms and conditions
|
||||
either of that version or of any later version published by the Free
|
||||
Software Foundation. If the Program does not specify a version number of
|
||||
this License, you may choose any version ever published by the Free Software
|
||||
Foundation.
|
||||
|
||||
10. If you wish to incorporate parts of the Program into other free
|
||||
programs whose distribution conditions are different, write to the author
|
||||
to ask for permission. For software which is copyrighted by the Free
|
||||
Software Foundation, write to the Free Software Foundation; we sometimes
|
||||
make exceptions for this. Our decision will be guided by the two goals
|
||||
of preserving the free status of all derivatives of our free software and
|
||||
of promoting the sharing and reuse of software generally.
|
||||
|
||||
NO WARRANTY
|
||||
|
||||
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
|
||||
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
|
||||
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
|
||||
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
|
||||
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
|
||||
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
|
||||
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
|
||||
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
|
||||
REPAIR OR CORRECTION.
|
||||
|
||||
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
|
||||
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
|
||||
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
|
||||
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
|
||||
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
|
||||
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
|
||||
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGES.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
|
||||
How to Apply These Terms to Your New Programs
|
||||
|
||||
If you develop a new program, and you want it to be of the greatest
|
||||
possible use to the public, the best way to achieve this is to make it
|
||||
free software which everyone can redistribute and change under these terms.
|
||||
|
||||
To do so, attach the following notices to the program. It is safest
|
||||
to attach them to the start of each source file to most effectively
|
||||
convey the exclusion of warranty; and each file should have at least
|
||||
the "copyright" line and a pointer to where the full notice is found.
|
||||
|
||||
{description}
|
||||
Copyright (C) {year} {fullname}
|
||||
|
||||
This program is free software; you can redistribute it and/or modify
|
||||
it under the terms of the GNU General Public License as published by
|
||||
the Free Software Foundation; either version 2 of the License, or
|
||||
(at your option) any later version.
|
||||
|
||||
This program is distributed in the hope that it will be useful,
|
||||
but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
GNU General Public License for more details.
|
||||
|
||||
You should have received a copy of the GNU General Public License along
|
||||
with this program; if not, write to the Free Software Foundation, Inc.,
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
|
||||
|
||||
Also add information on how to contact you by electronic and paper mail.
|
||||
|
||||
If the program is interactive, make it output a short notice like this
|
||||
when it starts in an interactive mode:
|
||||
|
||||
Gnomovision version 69, Copyright (C) year name of author
|
||||
Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
|
||||
This is free software, and you are welcome to redistribute it
|
||||
under certain conditions; type `show c' for details.
|
||||
|
||||
The hypothetical commands `show w' and `show c' should show the appropriate
|
||||
parts of the General Public License. Of course, the commands you use may
|
||||
be called something other than `show w' and `show c'; they could even be
|
||||
mouse-clicks or menu items--whatever suits your program.
|
||||
|
||||
You should also get your employer (if you work as a programmer) or your
|
||||
school, if any, to sign a "copyright disclaimer" for the program, if
|
||||
necessary. Here is a sample; alter the names:
|
||||
|
||||
Yoyodyne, Inc., hereby disclaims all copyright interest in the program
|
||||
`Gnomovision' (which makes passes at compilers) written by James Hacker.
|
||||
|
||||
{signature of Ty Coon}, 1 April 1989
|
||||
Ty Coon, President of Vice
|
||||
|
||||
This General Public License does not permit incorporating your program into
|
||||
proprietary programs. If your program is a subroutine library, you may
|
||||
consider it more useful to permit linking proprietary applications with the
|
||||
library. If this is what you want to do, use the GNU Lesser General
|
||||
Public License instead of this License.
|
||||
@@ -1,319 +0,0 @@
|
||||
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||||
</nav><div class="container template-title-body">
|
||||
<div class="row">
|
||||
<main id="main" class="col-md-9"><div class="page-header">
|
||||
<img src="logo.svg" class="logo" alt=""><h1>License</h1>
|
||||
|
||||
</div>
|
||||
|
||||
<pre>GNU GENERAL PUBLIC LICENSE
|
||||
Version 2, June 1991
|
||||
|
||||
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
|
||||
|
||||
This R package, with package name 'AMR':
|
||||
- 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
|
||||
|
||||
END OF THE SUMMARY
|
||||
|
||||
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
|
||||
|
||||
0. This License applies to any program or other work which contains
|
||||
a notice placed by the copyright holder saying it may be distributed
|
||||
under the terms of this General Public License. The "Program", below,
|
||||
refers to any such program or work, and a "work based on the Program"
|
||||
means either the Program or any derivative work under copyright law:
|
||||
that is to say, a work containing the Program or a portion of it,
|
||||
either verbatim or with modifications and/or translated into another
|
||||
language. (Hereinafter, translation is included without limitation in
|
||||
the term "modification".) Each licensee is addressed as "you".
|
||||
|
||||
Activities other than copying, distribution and modification are not
|
||||
covered by this License; they are outside its scope. The act of
|
||||
running the Program is not restricted, and the output from the Program
|
||||
is covered only if its contents constitute a work based on the
|
||||
Program (independent of having been made by running the Program).
|
||||
Whether that is true depends on what the Program does.
|
||||
|
||||
1. You may copy and distribute verbatim copies of the Program's
|
||||
source code as you receive it, in any medium, provided that you
|
||||
conspicuously and appropriately publish on each copy an appropriate
|
||||
copyright notice and disclaimer of warranty; keep intact all the
|
||||
notices that refer to this License and to the absence of any warranty;
|
||||
and give any other recipients of the Program a copy of this License
|
||||
along with the Program.
|
||||
|
||||
You may charge a fee for the physical act of transferring a copy, and
|
||||
you may at your option offer warranty protection in exchange for a fee.
|
||||
|
||||
2. You may modify your copy or copies of the Program or any portion
|
||||
of it, thus forming a work based on the Program, and copy and
|
||||
distribute such modifications or work under the terms of Section 1
|
||||
above, provided that you also meet all of these conditions:
|
||||
|
||||
a) You must cause the modified files to carry prominent notices
|
||||
stating that you changed the files and the date of any change.
|
||||
|
||||
b) You must cause any work that you distribute or publish, that in
|
||||
whole or in part contains or is derived from the Program or any
|
||||
part thereof, to be licensed as a whole at no charge to all third
|
||||
parties under the terms of this License.
|
||||
|
||||
c) If the modified program normally reads commands interactively
|
||||
when run, you must cause it, when started running for such
|
||||
interactive use in the most ordinary way, to print or display an
|
||||
announcement including an appropriate copyright notice and a
|
||||
notice that there is no warranty (or else, saying that you provide
|
||||
a warranty) and that users may redistribute the program under
|
||||
these conditions, and telling the user how to view a copy of this
|
||||
License. (Exception: if the Program itself is interactive but
|
||||
does not normally print such an announcement, your work based on
|
||||
the Program is not required to print an announcement.)
|
||||
|
||||
These requirements apply to the modified work as a whole. If
|
||||
identifiable sections of that work are not derived from the Program,
|
||||
and can be reasonably considered independent and separate works in
|
||||
themselves, then this License, and its terms, do not apply to those
|
||||
sections when you distribute them as separate works. But when you
|
||||
distribute the same sections as part of a whole which is a work based
|
||||
on the Program, the distribution of the whole must be on the terms of
|
||||
this License, whose permissions for other licensees extend to the
|
||||
entire whole, and thus to each and every part regardless of who wrote it.
|
||||
|
||||
Thus, it is not the intent of this section to claim rights or contest
|
||||
your rights to work written entirely by you; rather, the intent is to
|
||||
exercise the right to control the distribution of derivative or
|
||||
collective works based on the Program.
|
||||
|
||||
In addition, mere aggregation of another work not based on the Program
|
||||
with the Program (or with a work based on the Program) on a volume of
|
||||
a storage or distribution medium does not bring the other work under
|
||||
the scope of this License.
|
||||
|
||||
3. You may copy and distribute the Program (or a work based on it,
|
||||
under Section 2) in object code or executable form under the terms of
|
||||
Sections 1 and 2 above provided that you also do one of the following:
|
||||
|
||||
a) Accompany it with the complete corresponding machine-readable
|
||||
source code, which must be distributed under the terms of Sections
|
||||
1 and 2 above on a medium customarily used for software interchange; or,
|
||||
|
||||
b) Accompany it with a written offer, valid for at least three
|
||||
years, to give any third party, for a charge no more than your
|
||||
cost of physically performing source distribution, a complete
|
||||
machine-readable copy of the corresponding source code, to be
|
||||
distributed under the terms of Sections 1 and 2 above on a medium
|
||||
customarily used for software interchange; or,
|
||||
|
||||
c) Accompany it with the information you received as to the offer
|
||||
to distribute corresponding source code. (This alternative is
|
||||
allowed only for noncommercial distribution and only if you
|
||||
received the program in object code or executable form with such
|
||||
an offer, in accord with Subsection b above.)
|
||||
|
||||
The source code for a work means the preferred form of the work for
|
||||
making modifications to it. For an executable work, complete source
|
||||
code means all the source code for all modules it contains, plus any
|
||||
associated interface definition files, plus the scripts used to
|
||||
control compilation and installation of the executable. However, as a
|
||||
special exception, the source code distributed need not include
|
||||
anything that is normally distributed (in either source or binary
|
||||
form) with the major components (compiler, kernel, and so on) of the
|
||||
operating system on which the executable runs, unless that component
|
||||
itself accompanies the executable.
|
||||
|
||||
If distribution of executable or object code is made by offering
|
||||
access to copy from a designated place, then offering equivalent
|
||||
access to copy the source code from the same place counts as
|
||||
distribution of the source code, even though third parties are not
|
||||
compelled to copy the source along with the object code.
|
||||
|
||||
4. You may not copy, modify, sublicense, or distribute the Program
|
||||
except as expressly provided under this License. Any attempt
|
||||
otherwise to copy, modify, sublicense or distribute the Program is
|
||||
void, and will automatically terminate your rights under this License.
|
||||
However, parties who have received copies, or rights, from you under
|
||||
this License will not have their licenses terminated so long as such
|
||||
parties remain in full compliance.
|
||||
|
||||
5. You are not required to accept this License, since you have not
|
||||
signed it. However, nothing else grants you permission to modify or
|
||||
distribute the Program or its derivative works. These actions are
|
||||
prohibited by law if you do not accept this License. Therefore, by
|
||||
modifying or distributing the Program (or any work based on the
|
||||
Program), you indicate your acceptance of this License to do so, and
|
||||
all its terms and conditions for copying, distributing or modifying
|
||||
the Program or works based on it.
|
||||
|
||||
6. Each time you redistribute the Program (or any work based on the
|
||||
Program), the recipient automatically receives a license from the
|
||||
original licensor to copy, distribute or modify the Program subject to
|
||||
these terms and conditions. You may not impose any further
|
||||
restrictions on the recipients' exercise of the rights granted herein.
|
||||
You are not responsible for enforcing compliance by third parties to
|
||||
this License.
|
||||
|
||||
7. If, as a consequence of a court judgment or allegation of patent
|
||||
infringement or for any other reason (not limited to patent issues),
|
||||
conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot
|
||||
distribute so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you
|
||||
may not distribute the Program at all. For example, if a patent
|
||||
license would not permit royalty-free redistribution of the Program by
|
||||
all those who receive copies directly or indirectly through you, then
|
||||
the only way you could satisfy both it and this License would be to
|
||||
refrain entirely from distribution of the Program.
|
||||
|
||||
If any portion of this section is held invalid or unenforceable under
|
||||
any particular circumstance, the balance of the section is intended to
|
||||
apply and the section as a whole is intended to apply in other
|
||||
circumstances.
|
||||
|
||||
It is not the purpose of this section to induce you to infringe any
|
||||
patents or other property right claims or to contest validity of any
|
||||
such claims; this section has the sole purpose of protecting the
|
||||
integrity of the free software distribution system, which is
|
||||
implemented by public license practices. Many people have made
|
||||
generous contributions to the wide range of software distributed
|
||||
through that system in reliance on consistent application of that
|
||||
system; it is up to the author/donor to decide if he or she is willing
|
||||
to distribute software through any other system and a licensee cannot
|
||||
impose that choice.
|
||||
|
||||
This section is intended to make thoroughly clear what is believed to
|
||||
be a consequence of the rest of this License.
|
||||
|
||||
8. If the distribution and/or use of the Program is restricted in
|
||||
certain countries either by patents or by copyrighted interfaces, the
|
||||
original copyright holder who places the Program under this License
|
||||
may add an explicit geographical distribution limitation excluding
|
||||
those countries, so that distribution is permitted only in or among
|
||||
countries not thus excluded. In such case, this License incorporates
|
||||
the limitation as if written in the body of this License.
|
||||
|
||||
9. The Free Software Foundation may publish revised and/or new versions
|
||||
of the General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the Program
|
||||
specifies a version number of this License which applies to it and "any
|
||||
later version", you have the option of following the terms and conditions
|
||||
either of that version or of any later version published by the Free
|
||||
Software Foundation. If the Program does not specify a version number of
|
||||
this License, you may choose any version ever published by the Free Software
|
||||
Foundation.
|
||||
|
||||
10. If you wish to incorporate parts of the Program into other free
|
||||
programs whose distribution conditions are different, write to the author
|
||||
to ask for permission. For software which is copyrighted by the Free
|
||||
Software Foundation, write to the Free Software Foundation; we sometimes
|
||||
make exceptions for this. Our decision will be guided by the two goals
|
||||
of preserving the free status of all derivatives of our free software and
|
||||
of promoting the sharing and reuse of software generally.
|
||||
|
||||
NO WARRANTY
|
||||
|
||||
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
|
||||
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
|
||||
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
|
||||
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
|
||||
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
|
||||
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
|
||||
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
|
||||
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
|
||||
REPAIR OR CORRECTION.
|
||||
|
||||
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
|
||||
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
|
||||
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
|
||||
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
|
||||
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
|
||||
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
|
||||
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGES.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
</pre>
|
||||
|
||||
</main></div>
|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
||||
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
|
||||
</div>
|
||||
|
||||
</footer></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body></html>
|
||||
|
||||
-250
@@ -1,250 +0,0 @@
|
||||
# License
|
||||
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
Version 2, June 1991
|
||||
|
||||
Copyright (C) 1989, 1991 Free Software Foundation, Inc., <http://fsf.org/>
|
||||
51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA
|
||||
Everyone is permitted to copy and distribute verbatim copies
|
||||
of this license document, but changing it is not allowed.
|
||||
|
||||
A SUMMARY OF THIS LICENSE BY THE ORIGINAL AUTHORS OF THE AMR R PACKAGE
|
||||
|
||||
This R package, with package name 'AMR':
|
||||
- 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
|
||||
|
||||
END OF THE SUMMARY
|
||||
|
||||
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION
|
||||
|
||||
0. This License applies to any program or other work which contains
|
||||
a notice placed by the copyright holder saying it may be distributed
|
||||
under the terms of this General Public License. The "Program", below,
|
||||
refers to any such program or work, and a "work based on the Program"
|
||||
means either the Program or any derivative work under copyright law:
|
||||
that is to say, a work containing the Program or a portion of it,
|
||||
either verbatim or with modifications and/or translated into another
|
||||
language. (Hereinafter, translation is included without limitation in
|
||||
the term "modification".) Each licensee is addressed as "you".
|
||||
|
||||
Activities other than copying, distribution and modification are not
|
||||
covered by this License; they are outside its scope. The act of
|
||||
running the Program is not restricted, and the output from the Program
|
||||
is covered only if its contents constitute a work based on the
|
||||
Program (independent of having been made by running the Program).
|
||||
Whether that is true depends on what the Program does.
|
||||
|
||||
1. You may copy and distribute verbatim copies of the Program's
|
||||
source code as you receive it, in any medium, provided that you
|
||||
conspicuously and appropriately publish on each copy an appropriate
|
||||
copyright notice and disclaimer of warranty; keep intact all the
|
||||
notices that refer to this License and to the absence of any warranty;
|
||||
and give any other recipients of the Program a copy of this License
|
||||
along with the Program.
|
||||
|
||||
You may charge a fee for the physical act of transferring a copy, and
|
||||
you may at your option offer warranty protection in exchange for a fee.
|
||||
|
||||
2. You may modify your copy or copies of the Program or any portion
|
||||
of it, thus forming a work based on the Program, and copy and
|
||||
distribute such modifications or work under the terms of Section 1
|
||||
above, provided that you also meet all of these conditions:
|
||||
|
||||
a) You must cause the modified files to carry prominent notices
|
||||
stating that you changed the files and the date of any change.
|
||||
|
||||
b) You must cause any work that you distribute or publish, that in
|
||||
whole or in part contains or is derived from the Program or any
|
||||
part thereof, to be licensed as a whole at no charge to all third
|
||||
parties under the terms of this License.
|
||||
|
||||
c) If the modified program normally reads commands interactively
|
||||
when run, you must cause it, when started running for such
|
||||
interactive use in the most ordinary way, to print or display an
|
||||
announcement including an appropriate copyright notice and a
|
||||
notice that there is no warranty (or else, saying that you provide
|
||||
a warranty) and that users may redistribute the program under
|
||||
these conditions, and telling the user how to view a copy of this
|
||||
License. (Exception: if the Program itself is interactive but
|
||||
does not normally print such an announcement, your work based on
|
||||
the Program is not required to print an announcement.)
|
||||
|
||||
These requirements apply to the modified work as a whole. If
|
||||
identifiable sections of that work are not derived from the Program,
|
||||
and can be reasonably considered independent and separate works in
|
||||
themselves, then this License, and its terms, do not apply to those
|
||||
sections when you distribute them as separate works. But when you
|
||||
distribute the same sections as part of a whole which is a work based
|
||||
on the Program, the distribution of the whole must be on the terms of
|
||||
this License, whose permissions for other licensees extend to the
|
||||
entire whole, and thus to each and every part regardless of who wrote it.
|
||||
|
||||
Thus, it is not the intent of this section to claim rights or contest
|
||||
your rights to work written entirely by you; rather, the intent is to
|
||||
exercise the right to control the distribution of derivative or
|
||||
collective works based on the Program.
|
||||
|
||||
In addition, mere aggregation of another work not based on the Program
|
||||
with the Program (or with a work based on the Program) on a volume of
|
||||
a storage or distribution medium does not bring the other work under
|
||||
the scope of this License.
|
||||
|
||||
3. You may copy and distribute the Program (or a work based on it,
|
||||
under Section 2) in object code or executable form under the terms of
|
||||
Sections 1 and 2 above provided that you also do one of the following:
|
||||
|
||||
a) Accompany it with the complete corresponding machine-readable
|
||||
source code, which must be distributed under the terms of Sections
|
||||
1 and 2 above on a medium customarily used for software interchange; or,
|
||||
|
||||
b) Accompany it with a written offer, valid for at least three
|
||||
years, to give any third party, for a charge no more than your
|
||||
cost of physically performing source distribution, a complete
|
||||
machine-readable copy of the corresponding source code, to be
|
||||
distributed under the terms of Sections 1 and 2 above on a medium
|
||||
customarily used for software interchange; or,
|
||||
|
||||
c) Accompany it with the information you received as to the offer
|
||||
to distribute corresponding source code. (This alternative is
|
||||
allowed only for noncommercial distribution and only if you
|
||||
received the program in object code or executable form with such
|
||||
an offer, in accord with Subsection b above.)
|
||||
|
||||
The source code for a work means the preferred form of the work for
|
||||
making modifications to it. For an executable work, complete source
|
||||
code means all the source code for all modules it contains, plus any
|
||||
associated interface definition files, plus the scripts used to
|
||||
control compilation and installation of the executable. However, as a
|
||||
special exception, the source code distributed need not include
|
||||
anything that is normally distributed (in either source or binary
|
||||
form) with the major components (compiler, kernel, and so on) of the
|
||||
operating system on which the executable runs, unless that component
|
||||
itself accompanies the executable.
|
||||
|
||||
If distribution of executable or object code is made by offering
|
||||
access to copy from a designated place, then offering equivalent
|
||||
access to copy the source code from the same place counts as
|
||||
distribution of the source code, even though third parties are not
|
||||
compelled to copy the source along with the object code.
|
||||
|
||||
4. You may not copy, modify, sublicense, or distribute the Program
|
||||
except as expressly provided under this License. Any attempt
|
||||
otherwise to copy, modify, sublicense or distribute the Program is
|
||||
void, and will automatically terminate your rights under this License.
|
||||
However, parties who have received copies, or rights, from you under
|
||||
this License will not have their licenses terminated so long as such
|
||||
parties remain in full compliance.
|
||||
|
||||
5. You are not required to accept this License, since you have not
|
||||
signed it. However, nothing else grants you permission to modify or
|
||||
distribute the Program or its derivative works. These actions are
|
||||
prohibited by law if you do not accept this License. Therefore, by
|
||||
modifying or distributing the Program (or any work based on the
|
||||
Program), you indicate your acceptance of this License to do so, and
|
||||
all its terms and conditions for copying, distributing or modifying
|
||||
the Program or works based on it.
|
||||
|
||||
6. Each time you redistribute the Program (or any work based on the
|
||||
Program), the recipient automatically receives a license from the
|
||||
original licensor to copy, distribute or modify the Program subject to
|
||||
these terms and conditions. You may not impose any further
|
||||
restrictions on the recipients' exercise of the rights granted herein.
|
||||
You are not responsible for enforcing compliance by third parties to
|
||||
this License.
|
||||
|
||||
7. If, as a consequence of a court judgment or allegation of patent
|
||||
infringement or for any other reason (not limited to patent issues),
|
||||
conditions are imposed on you (whether by court order, agreement or
|
||||
otherwise) that contradict the conditions of this License, they do not
|
||||
excuse you from the conditions of this License. If you cannot
|
||||
distribute so as to satisfy simultaneously your obligations under this
|
||||
License and any other pertinent obligations, then as a consequence you
|
||||
may not distribute the Program at all. For example, if a patent
|
||||
license would not permit royalty-free redistribution of the Program by
|
||||
all those who receive copies directly or indirectly through you, then
|
||||
the only way you could satisfy both it and this License would be to
|
||||
refrain entirely from distribution of the Program.
|
||||
|
||||
If any portion of this section is held invalid or unenforceable under
|
||||
any particular circumstance, the balance of the section is intended to
|
||||
apply and the section as a whole is intended to apply in other
|
||||
circumstances.
|
||||
|
||||
It is not the purpose of this section to induce you to infringe any
|
||||
patents or other property right claims or to contest validity of any
|
||||
such claims; this section has the sole purpose of protecting the
|
||||
integrity of the free software distribution system, which is
|
||||
implemented by public license practices. Many people have made
|
||||
generous contributions to the wide range of software distributed
|
||||
through that system in reliance on consistent application of that
|
||||
system; it is up to the author/donor to decide if he or she is willing
|
||||
to distribute software through any other system and a licensee cannot
|
||||
impose that choice.
|
||||
|
||||
This section is intended to make thoroughly clear what is believed to
|
||||
be a consequence of the rest of this License.
|
||||
|
||||
8. If the distribution and/or use of the Program is restricted in
|
||||
certain countries either by patents or by copyrighted interfaces, the
|
||||
original copyright holder who places the Program under this License
|
||||
may add an explicit geographical distribution limitation excluding
|
||||
those countries, so that distribution is permitted only in or among
|
||||
countries not thus excluded. In such case, this License incorporates
|
||||
the limitation as if written in the body of this License.
|
||||
|
||||
9. The Free Software Foundation may publish revised and/or new versions
|
||||
of the General Public License from time to time. Such new versions will
|
||||
be similar in spirit to the present version, but may differ in detail to
|
||||
address new problems or concerns.
|
||||
|
||||
Each version is given a distinguishing version number. If the Program
|
||||
specifies a version number of this License which applies to it and "any
|
||||
later version", you have the option of following the terms and conditions
|
||||
either of that version or of any later version published by the Free
|
||||
Software Foundation. If the Program does not specify a version number of
|
||||
this License, you may choose any version ever published by the Free Software
|
||||
Foundation.
|
||||
|
||||
10. If you wish to incorporate parts of the Program into other free
|
||||
programs whose distribution conditions are different, write to the author
|
||||
to ask for permission. For software which is copyrighted by the Free
|
||||
Software Foundation, write to the Free Software Foundation; we sometimes
|
||||
make exceptions for this. Our decision will be guided by the two goals
|
||||
of preserving the free status of all derivatives of our free software and
|
||||
of promoting the sharing and reuse of software generally.
|
||||
|
||||
NO WARRANTY
|
||||
|
||||
11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY
|
||||
FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN
|
||||
OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES
|
||||
PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED
|
||||
OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
|
||||
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS
|
||||
TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE
|
||||
PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING,
|
||||
REPAIR OR CORRECTION.
|
||||
|
||||
12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
|
||||
WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR
|
||||
REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES,
|
||||
INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING
|
||||
OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED
|
||||
TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY
|
||||
YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER
|
||||
PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE
|
||||
POSSIBILITY OF SUCH DAMAGES.
|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
@@ -0,0 +1,173 @@
|
||||
# Generated by roxygen2: do not edit by hand
|
||||
|
||||
S3method(as.data.frame,bactid)
|
||||
S3method(as.data.frame,frequency_tbl)
|
||||
S3method(as.double,mic)
|
||||
S3method(as.integer,mic)
|
||||
S3method(as.numeric,mic)
|
||||
S3method(as.vector,frequency_tbl)
|
||||
S3method(as_tibble,frequency_tbl)
|
||||
S3method(barplot,mic)
|
||||
S3method(barplot,rsi)
|
||||
S3method(format,frequency_tbl)
|
||||
S3method(hist,frequency_tbl)
|
||||
S3method(kurtosis,data.frame)
|
||||
S3method(kurtosis,default)
|
||||
S3method(kurtosis,matrix)
|
||||
S3method(plot,frequency_tbl)
|
||||
S3method(plot,mic)
|
||||
S3method(plot,rsi)
|
||||
S3method(print,bactid)
|
||||
S3method(print,frequency_tbl)
|
||||
S3method(print,mic)
|
||||
S3method(print,rsi)
|
||||
S3method(pull,bactid)
|
||||
S3method(skewness,data.frame)
|
||||
S3method(skewness,default)
|
||||
S3method(skewness,matrix)
|
||||
S3method(summary,mic)
|
||||
S3method(summary,rsi)
|
||||
export("%like%")
|
||||
export(BRMO)
|
||||
export(EUCAST_exceptional_phenotypes)
|
||||
export(EUCAST_rules)
|
||||
export(MDRO)
|
||||
export(MRGN)
|
||||
export(abname)
|
||||
export(anti_join_microorganisms)
|
||||
export(as.bactid)
|
||||
export(as.mic)
|
||||
export(as.rsi)
|
||||
export(atc_ddd)
|
||||
export(atc_groups)
|
||||
export(atc_property)
|
||||
export(clipboard_export)
|
||||
export(clipboard_import)
|
||||
export(facet_rsi)
|
||||
export(first_isolate)
|
||||
export(freq)
|
||||
export(frequency_tbl)
|
||||
export(full_join_microorganisms)
|
||||
export(g.test)
|
||||
export(geom_rsi)
|
||||
export(ggplot_rsi)
|
||||
export(guess_atc)
|
||||
export(guess_bactid)
|
||||
export(inner_join_microorganisms)
|
||||
export(interpretive_reading)
|
||||
export(is.bactid)
|
||||
export(is.mic)
|
||||
export(is.rsi)
|
||||
export(key_antibiotics)
|
||||
export(key_antibiotics_equal)
|
||||
export(kurtosis)
|
||||
export(left_join_microorganisms)
|
||||
export(like)
|
||||
export(mo_property)
|
||||
export(n_rsi)
|
||||
export(p.symbol)
|
||||
export(portion_I)
|
||||
export(portion_IR)
|
||||
export(portion_R)
|
||||
export(portion_S)
|
||||
export(portion_SI)
|
||||
export(portion_df)
|
||||
export(ratio)
|
||||
export(resistance_predict)
|
||||
export(right_join_microorganisms)
|
||||
export(rsi)
|
||||
export(rsi_predict)
|
||||
export(scale_rsi_colours)
|
||||
export(scale_y_percent)
|
||||
export(semi_join_microorganisms)
|
||||
export(skewness)
|
||||
export(theme_rsi)
|
||||
export(top_freq)
|
||||
exportMethods(as.data.frame.bactid)
|
||||
exportMethods(as.data.frame.frequency_tbl)
|
||||
exportMethods(as.double.mic)
|
||||
exportMethods(as.integer.mic)
|
||||
exportMethods(as.numeric.mic)
|
||||
exportMethods(as.vector.frequency_tbl)
|
||||
exportMethods(as_tibble.frequency_tbl)
|
||||
exportMethods(barplot.mic)
|
||||
exportMethods(barplot.rsi)
|
||||
exportMethods(format.frequency_tbl)
|
||||
exportMethods(hist.frequency_tbl)
|
||||
exportMethods(kurtosis)
|
||||
exportMethods(kurtosis.data.frame)
|
||||
exportMethods(kurtosis.default)
|
||||
exportMethods(kurtosis.matrix)
|
||||
exportMethods(plot.frequency_tbl)
|
||||
exportMethods(plot.mic)
|
||||
exportMethods(plot.rsi)
|
||||
exportMethods(print.bactid)
|
||||
exportMethods(print.frequency_tbl)
|
||||
exportMethods(print.mic)
|
||||
exportMethods(print.rsi)
|
||||
exportMethods(pull.bactid)
|
||||
exportMethods(skewness)
|
||||
exportMethods(skewness.data.frame)
|
||||
exportMethods(skewness.default)
|
||||
exportMethods(skewness.matrix)
|
||||
exportMethods(summary.mic)
|
||||
exportMethods(summary.rsi)
|
||||
importFrom(clipr,read_clip_tbl)
|
||||
importFrom(clipr,write_clip)
|
||||
importFrom(curl,nslookup)
|
||||
importFrom(dplyr,"%>%")
|
||||
importFrom(dplyr,arrange)
|
||||
importFrom(dplyr,arrange_at)
|
||||
importFrom(dplyr,as_tibble)
|
||||
importFrom(dplyr,between)
|
||||
importFrom(dplyr,bind_rows)
|
||||
importFrom(dplyr,case_when)
|
||||
importFrom(dplyr,desc)
|
||||
importFrom(dplyr,everything)
|
||||
importFrom(dplyr,filter)
|
||||
importFrom(dplyr,group_by)
|
||||
importFrom(dplyr,group_by_at)
|
||||
importFrom(dplyr,group_vars)
|
||||
importFrom(dplyr,if_else)
|
||||
importFrom(dplyr,lag)
|
||||
importFrom(dplyr,left_join)
|
||||
importFrom(dplyr,mutate)
|
||||
importFrom(dplyr,n_distinct)
|
||||
importFrom(dplyr,progress_estimated)
|
||||
importFrom(dplyr,pull)
|
||||
importFrom(dplyr,row_number)
|
||||
importFrom(dplyr,select)
|
||||
importFrom(dplyr,slice)
|
||||
importFrom(dplyr,summarise)
|
||||
importFrom(dplyr,summarise_if)
|
||||
importFrom(dplyr,tibble)
|
||||
importFrom(dplyr,top_n)
|
||||
importFrom(grDevices,boxplot.stats)
|
||||
importFrom(graphics,axis)
|
||||
importFrom(graphics,barplot)
|
||||
importFrom(graphics,hist)
|
||||
importFrom(graphics,plot)
|
||||
importFrom(graphics,text)
|
||||
importFrom(knitr,kable)
|
||||
importFrom(readr,locale)
|
||||
importFrom(readr,parse_guess)
|
||||
importFrom(rvest,html_children)
|
||||
importFrom(rvest,html_node)
|
||||
importFrom(rvest,html_nodes)
|
||||
importFrom(rvest,html_table)
|
||||
importFrom(stats,complete.cases)
|
||||
importFrom(stats,fivenum)
|
||||
importFrom(stats,glm)
|
||||
importFrom(stats,lm)
|
||||
importFrom(stats,mad)
|
||||
importFrom(stats,pchisq)
|
||||
importFrom(stats,predict)
|
||||
importFrom(stats,sd)
|
||||
importFrom(tibble,tibble)
|
||||
importFrom(utils,View)
|
||||
importFrom(utils,browseVignettes)
|
||||
importFrom(utils,installed.packages)
|
||||
importFrom(utils,object.size)
|
||||
importFrom(utils,read.delim)
|
||||
importFrom(utils,write.table)
|
||||
importFrom(xml2,read_html)
|
||||
@@ -0,0 +1,121 @@
|
||||
# 0.3.0
|
||||
**Published on CRAN: 2018-08-14**
|
||||
|
||||
#### New
|
||||
* **BREAKING**: `rsi_df` was removed in favour of new functions `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` to selectively calculate resistance or susceptibility. These functions are 20 to 30 times faster than the old `rsi` function. The old function still works, but is deprecated.
|
||||
* New function `portion_df` to get all portions of S, I and R of a data set with antibiotic columns, with support for grouped variables
|
||||
* **BREAKING**: the methodology for determining first weighted isolates was changed. The antibiotics that are compared between isolates (call *key antibiotics*) to include more first isolates (afterwards called first *weighted* isolates) are now as follows:
|
||||
* Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole
|
||||
* Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
|
||||
* Gram-negative: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
|
||||
* Support for `ggplot2`
|
||||
* New functions `geom_rsi`, `facet_rsi`, `scale_y_percent`, `scale_rsi_colours` and `theme_rsi`
|
||||
* New wrapper function `ggplot_rsi` to apply all above functions on a data set:
|
||||
* `septic_patients %>% select(tobr, gent) %>% ggplot_rsi` will show portions of S, I and R immediately in a pretty plot
|
||||
* Support for grouped variables, see `?ggplot_rsi`
|
||||
* Determining bacterial ID:
|
||||
* New functions `as.bactid` and `is.bactid` to transform/ look up microbial ID's.
|
||||
* The existing function `guess_bactid` is now an alias of `as.bactid`
|
||||
* New Becker classification for *Staphylococcus* to categorise them into Coagulase Negative *Staphylococci* (CoNS) and Coagulase Positve *Staphylococci* (CoPS)
|
||||
* New Lancefield classification for *Streptococcus* to categorise them into Lancefield groups
|
||||
* For convience, new descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
|
||||
* Function `g.test` to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test), which use is the same as `chisq.test`
|
||||
* Function `ratio` to transform a vector of values to a preset ratio
|
||||
* For example: `ratio(c(10, 500, 10), ratio = "1:2:1")` would return `130, 260, 130`
|
||||
* Support for Addins menu in RStudio to quickly insert `%in%` or `%like%` (and give them keyboard shortcuts), or to view the datasets that come with this package
|
||||
* Function `p.symbol` to transform p values to their related symbols: `0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1`
|
||||
* Functions `clipboard_import` and `clipboard_export` as helper functions to quickly copy and paste from/to software like Excel and SPSS. These functions use the `clipr` package, but are a little altered to also support headless Linux servers (so you can use it in RStudio Server)
|
||||
* New for frequency tables (function `freq`):
|
||||
* A vignette to explain its usage
|
||||
* Support for `rsi` (antimicrobial resistance) to use as input
|
||||
* Support for `table` to use as input: `freq(table(x, y))`
|
||||
* Support for existing functions `hist` and `plot` to use a frequency table as input: `hist(freq(df$age))`
|
||||
* Support for `as.vector`, `as.data.frame`, `as_tibble` and `format`
|
||||
* Support for quasiquotation: `freq(mydata, mycolumn)` is the same as `mydata %>% freq(mycolumn)`
|
||||
* Function `top_freq` function to return the top/below *n* items as vector
|
||||
* Header of frequency tables now also show Mean Absolute Deviaton (MAD) and Interquartile Range (IQR)
|
||||
* Possibility to globally set the default for the amount of items to print, with `options(max.print.freq = n)` where *n* is your preset value
|
||||
|
||||
#### Changed
|
||||
* Improvements for forecasting with `resistance_predict` and added more examples
|
||||
* More antibiotics added as parameters for EUCAST rules
|
||||
* Updated version of the `septic_patients` data set to better reflect the reality
|
||||
* Pretty printing for tibbles removed as it is not really the scope of this package
|
||||
* Printing of `mic` and `rsi` classes now returns all values - use `freq` to check distributions
|
||||
* Improved speed of key antibiotics comparison for determining first isolates
|
||||
* Column names for the `key_antibiotics` function are now generic: 6 for broadspectrum ABs, 6 for Gram-positive specific and 6 for Gram-negative specific ABs
|
||||
* Speed improvement for the `abname` function
|
||||
* `%like%` now supports multiple patterns
|
||||
* Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the parameter `toConsole` is not longer needed.
|
||||
* Fix for `freq` where the class of an item would be lost
|
||||
* Small translational improvements to the `septic_patients` dataset and the column `bactid` now has the new class `"bactid"`
|
||||
* Small improvements to the `microorganisms` dataset (especially for *Salmonella*) and the column `bactid` now has the new class `"bactid"`
|
||||
* Combined MIC/RSI values will now be coerced by the `rsi` and `mic` functions:
|
||||
* `as.rsi("<=0.002; S")` will return `S`
|
||||
* `as.mic("<=0.002; S")` will return `<=0.002`
|
||||
* Now possible to coerce MIC values with a space between operator and value, i.e. `as.mic("<= 0.002")` now works
|
||||
* Classes `rsi` and `mic` do not add the attribute `package.version` anymore
|
||||
* Added `"groups"` option for `atc_property(..., property)`. It will return a vector of the ATC hierarchy as defined by the [WHO](https://www.whocc.no/atc/structure_and_principles/). The new function `atc_groups` is a convenient wrapper around this.
|
||||
* Build-in host check for `atc_property` as it requires the host set by `url` to be responsive
|
||||
* Improved `first_isolate` algorithm to exclude isolates where bacteria ID or genus is unavailable
|
||||
* Fix for warning *hybrid evaluation forced for row_number* ([`924b62`](https://github.com/tidyverse/dplyr/commit/924b62)) from the `dplyr` package v0.7.5 and above
|
||||
* Support for empty values and for 1 or 2 columns as input for `guess_bactid` (now called `as.bactid`)
|
||||
* So `yourdata %>% select(genus, species) %>% as.bactid()` now also works
|
||||
* Other small fixes
|
||||
|
||||
#### Other
|
||||
* Added integration tests (check if everything works as expected) for all releases of R 3.1 and higher
|
||||
* Linux and macOS: https://travis-ci.org/msberends/AMR
|
||||
* Windows: https://ci.appveyor.com/project/msberends/amr
|
||||
* Added thesis advisors to DESCRIPTION file
|
||||
|
||||
# 0.2.0
|
||||
**Published on CRAN: 2018-05-03**
|
||||
|
||||
#### New
|
||||
* Full support for Windows, Linux and macOS
|
||||
* Full support for old R versions, only R-3.0.0 (April 2013) or later is needed (needed packages may have other dependencies)
|
||||
* Function `n_rsi` to count cases where antibiotic test results were available, to be used in conjunction with `dplyr::summarise`, see ?rsi
|
||||
* Function `guess_bactid` to **determine the ID** of a microorganism based on genus/species or known abbreviations like MRSA
|
||||
* Function `guess_atc` to **determine the ATC** of an antibiotic based on name, trade name, or known abbreviations
|
||||
* Function `freq` to create **frequency tables**, with additional info in a header
|
||||
* Function `MDRO` to **determine Multi Drug Resistant Organisms (MDRO)** with support for country-specific guidelines.
|
||||
* Suggest your own via [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E)
|
||||
* [Exceptional resistances defined by EUCAST](http://www.eucast.org/expert_rules_and_intrinsic_resistance) are also supported instead of countries alone
|
||||
* Functions `BRMO` and `MRGN` are wrappers for Dutch and German guidelines, respectively
|
||||
* New algorithm to determine weighted isolates, can now be `"points"` or `"keyantibiotics"`, see `?first_isolate`
|
||||
* New print format for `tibble`s and `data.table`s
|
||||
|
||||
#### Changed
|
||||
* Fixed `rsi` class for vectors that contain only invalid antimicrobial interpretations
|
||||
* Renamed dataset `ablist` to `antibiotics`
|
||||
* Renamed dataset `bactlist` to `microorganisms`
|
||||
* Added common abbreviations and trade names to the `antibiotics` dataset
|
||||
* Added more microorganisms to the `microorganisms` dataset
|
||||
* Added analysis examples on help page of dataset `septic_patients`
|
||||
* Added support for character vector in `join` functions
|
||||
* Added warnings when a join results in more rows after than before the join
|
||||
* Altered `%like%` to make it case insensitive
|
||||
* For parameters of functions `first_isolate` and `EUCAST_rules` column names are now case-insensitive
|
||||
* Functions `as.rsi` and `as.mic` now add the package name and version as attributes
|
||||
|
||||
#### Other
|
||||
* Expanded `README.md` with more examples
|
||||
* Added [ORCID](https://orcid.org) of authors to DESCRIPTION file
|
||||
* Added unit testing with the `testthat` package
|
||||
* Added build tests for Linux and macOS using Travis CI (https://travis-ci.org/msberends/AMR)
|
||||
* Added line coverage checking using CodeCov (https://codecov.io/gh/msberends/AMR/tree/master/R)
|
||||
|
||||
# 0.1.1
|
||||
**Published on CRAN: 2018-03-14**
|
||||
|
||||
* `EUCAST_rules` applies for amoxicillin even if ampicillin is missing
|
||||
* Edited column names to comply with GLIMS, the laboratory information system
|
||||
* Added more valid MIC values
|
||||
* Renamed 'Daily Defined Dose' to 'Defined Daily Dose'
|
||||
* Added barplots for `rsi` and `mic` classes
|
||||
|
||||
# 0.1.0
|
||||
**Published on CRAN: 2018-02-22**
|
||||
|
||||
* First submission to CRAN.
|
||||
+163
@@ -0,0 +1,163 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Name of an antibiotic
|
||||
#'
|
||||
#' Convert antibiotic codes (from a laboratory information system like MOLIS or GLIMS) to a (trivial) antibiotic name or ATC code, or vice versa. This uses the data from \code{\link{antibiotics}}.
|
||||
#' @param abcode a code or name, like \code{"AMOX"}, \code{"AMCL"} or \code{"J01CA04"}
|
||||
#' @param from,to type to transform from and to. See \code{\link{antibiotics}} for its column names. WIth \code{from = "guess"} the from will be guessed from \code{"atc"}, \code{"molis"} and \code{"umcg"}. When using \code{to = "atc"}, the ATC code will be searched using \code{\link{guess_atc}}.
|
||||
#' @param textbetween text to put between multiple returned texts
|
||||
#' @param tolower return output as lower case with function \code{\link{tolower}}.
|
||||
#' @keywords ab antibiotics
|
||||
#' @source \code{\link{antibiotics}}
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% pull
|
||||
#' @examples
|
||||
#' abname("AMCL")
|
||||
#' # "amoxicillin and enzyme inhibitor"
|
||||
#'
|
||||
#' # It is quite flexible at default (having `from = "guess"`)
|
||||
#' abname(c("amox", "J01CA04", "Trimox", "dispermox", "Amoxil"))
|
||||
#' # "Amoxicillin" "Amoxicillin" "Amoxicillin" "Amoxicillin" "Amoxicillin"
|
||||
#'
|
||||
#' # Multiple antibiotics can be combined with "+".
|
||||
#' # The second antibiotic will be set to lower case when `tolower` was not set:
|
||||
#' abname("AMCL+GENT", textbetween = "/")
|
||||
#' # "amoxicillin and enzyme inhibitor/gentamicin"
|
||||
#'
|
||||
#' abname(c("AMCL", "GENT"))
|
||||
#' # "Amoxicillin and beta-lactamase inhibitor" "Gentamicin"
|
||||
#'
|
||||
#' abname("AMCL", to = "trivial_nl")
|
||||
#' # "Amoxicilline/clavulaanzuur"
|
||||
#'
|
||||
#' abname("AMCL", to = "atc")
|
||||
#' # "J01CR02"
|
||||
#'
|
||||
#' # specific codes for University Medical Center Groningen (UMCG):
|
||||
#' abname("J01CR02", from = "atc", to = "umcg")
|
||||
#' # "AMCL"
|
||||
abname <- function(abcode,
|
||||
from = c("guess", "atc", "molis", "umcg"),
|
||||
to = 'official',
|
||||
textbetween = ' + ',
|
||||
tolower = FALSE) {
|
||||
|
||||
if (length(to) != 1L) {
|
||||
stop('`to` must be of length 1', call. = FALSE)
|
||||
}
|
||||
|
||||
if (to == "atc") {
|
||||
return(guess_atc(abcode))
|
||||
}
|
||||
|
||||
#antibiotics <- AMR::antibiotics
|
||||
abx <- AMR::antibiotics
|
||||
|
||||
from <- from[1]
|
||||
# if (from == "guess") {
|
||||
# for (i in 1:3) {
|
||||
# if (abcode[1] %in% (antibiotics %>% pull(i))) {
|
||||
# from <- colnames(antibiotics)[i]
|
||||
# }
|
||||
# }
|
||||
# if (from == "guess") {
|
||||
# from <- "umcg"
|
||||
# }
|
||||
# }
|
||||
|
||||
colnames(abx) <- colnames(abx) %>% tolower()
|
||||
from <- from %>% tolower()
|
||||
to <- to %>% tolower()
|
||||
|
||||
if (!(from %in% colnames(abx) | from == "guess") |
|
||||
!to %in% colnames(abx)) {
|
||||
stop(paste0('Invalid `from` or `to`. Choose one of ',
|
||||
colnames(abx) %>% paste(collapse = ", "), '.'), call. = FALSE)
|
||||
}
|
||||
|
||||
abcode <- as.character(abcode)
|
||||
abcode.bak <- abcode
|
||||
|
||||
for (i in 1:length(abcode)) {
|
||||
if (abcode[i] %like% "[+]") {
|
||||
# support for multiple ab's with +
|
||||
parts <- trimws(strsplit(abcode[i], split = "+", fixed = TRUE)[[1]])
|
||||
ab1 <- abname(parts[1], from = from, to = to)
|
||||
ab2 <- abname(parts[2], from = from, to = to)
|
||||
if (missing(tolower)) {
|
||||
ab2 <- tolower(ab2)
|
||||
}
|
||||
abcode[i] <- paste0(ab1, textbetween, ab2)
|
||||
next
|
||||
}
|
||||
if (from %in% c("atc", "guess")) {
|
||||
if (abcode[i] %in% abx$atc) {
|
||||
abcode[i] <- abx[which(abx$atc == abcode[i]),] %>% pull(to)
|
||||
next
|
||||
}
|
||||
}
|
||||
if (from %in% c("molis", "guess")) {
|
||||
if (abcode[i] %in% abx$molis) {
|
||||
abcode[i] <- abx[which(abx$molis == abcode[i]),] %>% pull(to)
|
||||
next
|
||||
}
|
||||
}
|
||||
if (from %in% c("umcg", "guess")) {
|
||||
if (abcode[i] %in% abx$umcg) {
|
||||
abcode[i] <- abx[which(abx$umcg == abcode[i]),] %>% pull(to)
|
||||
next
|
||||
}
|
||||
}
|
||||
if (from %in% c("trade_name", "guess")) {
|
||||
if (abcode[i] %in% abx$trade_name) {
|
||||
abcode[i] <- abx[which(abx$trade_name == abcode[i]),] %>% pull(to)
|
||||
next
|
||||
}
|
||||
if (sum(abx$trade_name %like% abcode[i]) > 0) {
|
||||
abcode[i] <- abx[which(abx$trade_name %like% abcode[i]),] %>% pull(to)
|
||||
next
|
||||
}
|
||||
}
|
||||
|
||||
if (from != "guess") {
|
||||
# when not found, try any `from`
|
||||
abcode[i] <- abx[which(abx[,from] == abcode[i]),] %>% pull(to) %>% .[1]
|
||||
}
|
||||
|
||||
# when nothing found, try first chars of official name
|
||||
# if (is.na(abcode[i])) {
|
||||
# abcode[i] <- antibiotics %>%
|
||||
# filter(official %like% paste0('^', abcode.bak[i])) %>%
|
||||
# pull(to) %>%
|
||||
# .[1]
|
||||
# next
|
||||
# }
|
||||
|
||||
if (is.na(abcode[i]) | length(abcode[i] == 0)) {
|
||||
abcode[i] <- abcode.bak[i]
|
||||
warning('Code "', abcode.bak[i], '" not found in antibiotics list.', call. = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
if (tolower == TRUE) {
|
||||
abcode <- abcode %>% tolower()
|
||||
}
|
||||
|
||||
abcode
|
||||
}
|
||||
@@ -0,0 +1,289 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Properties of an ATC code
|
||||
#'
|
||||
#' Gets data from the WHO to determine properties of an ATC (e.g. an antibiotic) like name, defined daily dose (DDD) or standard unit. \cr \strong{This function requires an internet connection.}
|
||||
#' @param atc_code a character or character vector with ATC code(s) of antibiotic(s)
|
||||
#' @param property property of an ATC code. Valid values are \code{"ATC"}, \code{"Name"}, \code{"DDD"}, \code{"U"} (\code{"unit"}), \code{"Adm.R"}, \code{"Note"} and \code{groups}. For this last option, all hierarchical groups of an ATC code will be returned, see Examples.
|
||||
#' @param administration type of administration when using \code{property = "Adm.R"}, see Details
|
||||
#' @param url url of website of the WHO. The sign \code{\%s} can be used as a placeholder for ATC codes.
|
||||
#' @param ... parameters to pass on to \code{atc_property}
|
||||
#' @details
|
||||
#' Options for parameter \code{administration}:
|
||||
#' \itemize{
|
||||
#' \item{\code{"Implant"}}{ = Implant}
|
||||
#' \item{\code{"Inhal"}}{ = Inhalation}
|
||||
#' \item{\code{"Instill"}}{ = Instillation}
|
||||
#' \item{\code{"N"}}{ = nasal}
|
||||
#' \item{\code{"O"}}{ = oral}
|
||||
#' \item{\code{"P"}}{ = parenteral}
|
||||
#' \item{\code{"R"}}{ = rectal}
|
||||
#' \item{\code{"SL"}}{ = sublingual/buccal}
|
||||
#' \item{\code{"TD"}}{ = transdermal}
|
||||
#' \item{\code{"V"}}{ = vaginal}
|
||||
#' }
|
||||
#'
|
||||
#' Abbreviations of return values when using \code{property = "U"} (unit):
|
||||
#' \itemize{
|
||||
#' \item{\code{"g"}}{ = gram}
|
||||
#' \item{\code{"mg"}}{ = milligram}
|
||||
#' \item{\code{"mcg"}}{ = microgram}
|
||||
#' \item{\code{"U"}}{ = unit}
|
||||
#' \item{\code{"TU"}}{ = thousand units}
|
||||
#' \item{\code{"MU"}}{ = million units}
|
||||
#' \item{\code{"mmol"}}{ = millimole}
|
||||
#' \item{\code{"ml"}}{ = milliliter (e.g. eyedrops)}
|
||||
#' }
|
||||
#' @export
|
||||
#' @rdname atc_property
|
||||
#' @importFrom dplyr %>% progress_estimated
|
||||
#' @importFrom xml2 read_html
|
||||
#' @importFrom rvest html_children html_node html_nodes html_table
|
||||
#' @importFrom curl nslookup
|
||||
#' @source \url{https://www.whocc.no/atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/}
|
||||
#' @examples
|
||||
#' \donttest{
|
||||
#' # What's the ATC of amoxicillin?
|
||||
#' guess_atc("Amoxicillin")
|
||||
#' # [1] "J01CA04"
|
||||
#'
|
||||
#' # oral DDD (Defined Daily Dose) of amoxicillin
|
||||
#' atc_property("J01CA04", "DDD", "O")
|
||||
#' # parenteral DDD (Defined Daily Dose) of amoxicillin
|
||||
#' atc_property("J01CA04", "DDD", "P")
|
||||
#'
|
||||
#' atc_property("J01CA04", property = "groups") # search hierarchical groups of amoxicillin
|
||||
#' # [1] "ANTIINFECTIVES FOR SYSTEMIC USE"
|
||||
#' # [2] "ANTIBACTERIALS FOR SYSTEMIC USE"
|
||||
#' # [3] "BETA-LACTAM ANTIBACTERIALS, PENICILLINS"
|
||||
#' # [4] "Penicillins with extended spectrum"
|
||||
#' }
|
||||
atc_property <- function(atc_code,
|
||||
property,
|
||||
administration = 'O',
|
||||
url = 'https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no') {
|
||||
|
||||
# check active network interface, from https://stackoverflow.com/a/5078002/4575331
|
||||
has_internet <- function(url) {
|
||||
# extract host from given url
|
||||
# https://www.whocc.no/atc_ddd_index/ -> www.whocc.no
|
||||
url <- url %>%
|
||||
gsub("^(http://|https://)", "", .) %>%
|
||||
strsplit('/', fixed = TRUE) %>%
|
||||
unlist() %>%
|
||||
.[1]
|
||||
!is.null(curl::nslookup(url, error = FALSE))
|
||||
}
|
||||
# check for connection using the ATC of amoxicillin
|
||||
if (!has_internet(url = url)) {
|
||||
message("The URL could not be reached.")
|
||||
return(rep(NA, length(atc_code)))
|
||||
}
|
||||
|
||||
if (length(property) != 1L) {
|
||||
stop('`property` must be of length 1', call. = FALSE)
|
||||
}
|
||||
if (length(administration) != 1L) {
|
||||
stop('`administration` must be of length 1', call. = FALSE)
|
||||
}
|
||||
|
||||
# also allow unit as property
|
||||
if (property %like% 'unit') {
|
||||
property <- 'U'
|
||||
}
|
||||
|
||||
# validation of properties
|
||||
valid_properties <- c("ATC", "Name", "DDD", "U", "Adm.R", "Note", "groups")
|
||||
valid_properties.bak <- valid_properties
|
||||
|
||||
property <- tolower(property)
|
||||
valid_properties <- tolower(valid_properties)
|
||||
|
||||
if (!property %in% valid_properties) {
|
||||
stop('Invalid `property`, use one of ', paste(valid_properties.bak, collapse = ", "), '.')
|
||||
}
|
||||
|
||||
if (property == 'ddd') {
|
||||
returnvalue <- rep(NA_real_, length(atc_code))
|
||||
} else if (property == 'groups') {
|
||||
returnvalue <- list()
|
||||
} else {
|
||||
returnvalue <- rep(NA_character_, length(atc_code))
|
||||
}
|
||||
|
||||
progress <- progress_estimated(n = length(atc_code))
|
||||
|
||||
for (i in 1:length(atc_code)) {
|
||||
|
||||
progress$tick()$print()
|
||||
|
||||
atc_url <- sub('%s', atc_code[i], url, fixed = TRUE)
|
||||
|
||||
if (property == "groups") {
|
||||
tbl <- xml2::read_html(atc_url) %>%
|
||||
rvest::html_node("#content") %>%
|
||||
rvest::html_children() %>%
|
||||
rvest::html_node("a")
|
||||
|
||||
# get URLS of items
|
||||
hrefs <- tbl %>% rvest::html_attr("href")
|
||||
# get text of items
|
||||
texts <- tbl %>% rvest::html_text()
|
||||
# select only text items where URL like "code="
|
||||
texts <- texts[grepl("?code=", tolower(hrefs), fixed = TRUE)]
|
||||
# last one is antibiotics, skip it
|
||||
texts <- texts[1:length(texts) - 1]
|
||||
returnvalue <- c(list(texts), returnvalue)
|
||||
|
||||
} else {
|
||||
tbl <- xml2::read_html(atc_url) %>%
|
||||
rvest::html_nodes('table') %>%
|
||||
rvest::html_table(header = TRUE) %>%
|
||||
as.data.frame(stringsAsFactors = FALSE)
|
||||
|
||||
# case insensitive column names
|
||||
colnames(tbl) <- tolower(colnames(tbl)) %>% gsub('^atc.*', 'atc', .)
|
||||
|
||||
if (length(tbl) == 0) {
|
||||
warning('ATC not found: ', atc_code[i], '. Please check ', atc_url, '.', call. = FALSE)
|
||||
returnvalue[i] <- NA
|
||||
next
|
||||
}
|
||||
|
||||
if (property %in% c('atc', 'name')) {
|
||||
# ATC and name are only in first row
|
||||
returnvalue[i] <- tbl[1, property]
|
||||
} else {
|
||||
if (!'adm.r' %in% colnames(tbl) | is.na(tbl[1, 'adm.r'])) {
|
||||
returnvalue[i] <- NA
|
||||
next
|
||||
} else {
|
||||
for (j in 1:nrow(tbl)) {
|
||||
if (tbl[j, 'adm.r'] == administration) {
|
||||
returnvalue[i] <- tbl[j, property]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (property == "groups" & length(returnvalue) == 1) {
|
||||
returnvalue <- returnvalue[[1]]
|
||||
}
|
||||
|
||||
returnvalue
|
||||
}
|
||||
|
||||
#' @rdname atc_property
|
||||
#' @export
|
||||
atc_groups <- function(atc_code, ...) {
|
||||
atc_property(atc_code = atc_code, property = "groups", ...)
|
||||
}
|
||||
|
||||
#' @rdname atc_property
|
||||
#' @export
|
||||
atc_ddd <- function(atc_code, ...) {
|
||||
atc_property(atc_code = atc_code, property = "ddd", ...)
|
||||
}
|
||||
|
||||
|
||||
#' Find ATC code based on antibiotic property
|
||||
#'
|
||||
#' Use this function to determine the ATC code of one or more antibiotics. The dataset \code{\link{antibiotics}} will be searched for abbreviations, official names and trade names.
|
||||
#' @param x character vector to determine \code{ATC} code
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% filter slice pull
|
||||
#' @details In the ATC classification system, the active substances are classified in a hierarchy with five different levels. The system has fourteen main anatomical/pharmacological groups or 1st levels. Each ATC main group is divided into 2nd levels which could be either pharmacological or therapeutic groups. The 3rd and 4th levels are chemical, pharmacological or therapeutic subgroups and the 5th level is the chemical substance. The 2nd, 3rd and 4th levels are often used to identify pharmacological subgroups when that is considered more appropriate than therapeutic or chemical subgroups.
|
||||
#' Source: \url{https://www.whocc.no/atc/structure_and_principles/}
|
||||
#' @return Character (vector).
|
||||
#' @seealso \code{\link{antibiotics}} for the dataframe that is being used to determine ATC's.
|
||||
#' @examples
|
||||
#' # These examples all return "J01FA01", the ATC code of Erythromycin:
|
||||
#' guess_atc("J01FA01")
|
||||
#' guess_atc("Erythromycin")
|
||||
#' guess_atc("eryt")
|
||||
#' guess_atc("ERYT")
|
||||
#' guess_atc("ERY")
|
||||
#' guess_atc("Erythrocin") # Trade name
|
||||
#' guess_atc("Eryzole") # Trade name
|
||||
#' guess_atc("Pediamycin") # Trade name
|
||||
guess_atc <- function(x) {
|
||||
|
||||
# use this later to further fill AMR::antibiotics
|
||||
|
||||
# drug <- "Ciprofloxacin"
|
||||
# url <- xml2::read_html(paste0("https://www.ncbi.nlm.nih.gov/pccompound?term=", drug)) %>%
|
||||
# html_nodes(".rslt") %>%
|
||||
# .[[1]] %>%
|
||||
# html_nodes(".title a") %>%
|
||||
# html_attr("href") %>%
|
||||
# gsub("/compound/", "/rest/pug_view/data/compound/", ., fixed = TRUE) %>%
|
||||
# paste0("/XML/?response_type=display")
|
||||
# synonyms <- url %>%
|
||||
# read_xml() %>%
|
||||
# xml_contents() %>% .[[6]] %>%
|
||||
# xml_contents() %>% .[[8]] %>%
|
||||
# xml_contents() %>% .[[3]] %>%
|
||||
# xml_contents() %>% .[[3]] %>%
|
||||
# xml_contents() %>%
|
||||
# paste() %>%
|
||||
# .[. %like% "StringValueList"] %>%
|
||||
# gsub("[</]+StringValueList[>]", "", .)
|
||||
|
||||
|
||||
for (i in 1:length(x)) {
|
||||
|
||||
# first try atc
|
||||
found <- AMR::antibiotics %>% filter(atc == x[i])
|
||||
|
||||
if (nrow(found) == 0) {
|
||||
# try abbreviation of molis and glims
|
||||
found <- AMR::antibiotics %>% filter(tolower(molis) == tolower(x[i]) | tolower(umcg) == tolower(x[i]))
|
||||
}
|
||||
|
||||
if (nrow(found) == 0) {
|
||||
# try exact official name
|
||||
found <- AMR::antibiotics[which(tolower(AMR::antibiotics$official) == tolower(x[i])),]
|
||||
}
|
||||
|
||||
if (nrow(found) == 0) {
|
||||
# try trade name
|
||||
found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$trade_name, ")") %like% x[i]),]
|
||||
}
|
||||
|
||||
if (nrow(found) == 0) {
|
||||
# try abbreviation
|
||||
found <- AMR::antibiotics[which(paste0("(", AMR::antibiotics$abbr, ")") %like% x[i]),]
|
||||
}
|
||||
# if (nrow(found) == 0) {
|
||||
# # loosely try official name
|
||||
# found <- AMR::antibiotics[which(AMR::antibiotics$official %like% x[i]),]
|
||||
# }
|
||||
|
||||
if (nrow(found) != 0) {
|
||||
x[i] <- found %>%
|
||||
slice(1) %>%
|
||||
pull(atc)
|
||||
} else {
|
||||
x[i] <- NA
|
||||
}
|
||||
}
|
||||
x
|
||||
}
|
||||
+396
@@ -0,0 +1,396 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Transform to bacteria ID
|
||||
#'
|
||||
#' Use this function to determine a valid ID based on a genus (and species). This input can be a full name (like \code{"Staphylococcus aureus"}), an abbreviated name (like \code{"S. aureus"}), or just a genus. You could also \code{\link{select}} a genus and species column, zie Examples.
|
||||
#' @param x a character vector or a dataframe with one or two columns
|
||||
#' @param Becker a logical to indicate whether \emph{Staphylococci} should be categorised into Coagulase Negative \emph{Staphylococci} ("CoNS") and Coagulase Positive \emph{Staphylococci} ("CoPS") instead of their own species, according to Karsten Becker \emph{et al.} [1]. This excludes \emph{Staphylococcus aureus} at default, use \code{Becker = "all"} to also categorise \emph{S. aureus} as "CoPS".
|
||||
#' @param Lancefield a logical to indicate whether beta-haemolytic \emph{Streptococci} should be categorised into Lancefield groups instead of their own species, according to Rebecca C. Lancefield [2]. These \emph{Streptococci} will be categorised in their first group, i.e. \emph{Streptococcus dysgalactiae} will be group C, although officially it was also categorised into groups G and L. Groups D and E will be ignored, since they are \emph{Enterococci}.
|
||||
#' @rdname as.bactid
|
||||
#' @keywords bactid Becker becker Lancefield lancefield guess
|
||||
#' @details \code{guess_bactid} is an alias of \code{as.bactid}.
|
||||
#'
|
||||
#' Some exceptions have been built in to get more logical results, based on prevalence of human pathogens. These are:
|
||||
#' \itemize{
|
||||
#' \item{\code{"E. coli"} will return the ID of \emph{Escherichia coli} and not \emph{Entamoeba coli}, although the latter would alphabetically come first}
|
||||
#' \item{\code{"H. influenzae"} will return the ID of \emph{Haemophilus influenzae} and not \emph{Haematobacter influenzae}}
|
||||
#' \item{Something like \code{"p aer"} will return the ID of \emph{Pseudomonas aeruginosa} and not \emph{Pasteurella aerogenes}}
|
||||
#' \item{Something like \code{"stau"} or \code{"staaur"} will return the ID of \emph{Staphylococcus aureus} and not \emph{Staphylococcus auricularis}}
|
||||
#' }
|
||||
#' Moreover, this function also supports ID's based on only Gram stain, when the species is not known. \cr
|
||||
#' For example, \code{"Gram negative rods"} and \code{"GNR"} will both return the ID of a Gram negative rod: \code{GNR}.
|
||||
#' @source
|
||||
#' [1] Becker K \emph{et al.} \strong{Coagulase-Negative Staphylococci}. 2014. Clin Microbiol Rev. 27(4): 870–926. \cr
|
||||
#' \url{https://dx.doi.org/10.1128/CMR.00109-13} \cr
|
||||
#' [2] Lancefield RC \strong{A serological differentiation of human and other groups of hemolytic streptococci}. 1933. J Exp Med. 57(4): 571–95. \cr
|
||||
#' \url{https://dx.doi.org/10.1084/jem.57.4.571}
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% filter pull
|
||||
#' @return Character (vector) with class \code{"bactid"}. Unknown values will return \code{NA}.
|
||||
#' @seealso \code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
|
||||
#' @examples
|
||||
#' # These examples all return "STAAUR", the ID of S. aureus:
|
||||
#' as.bactid("stau")
|
||||
#' as.bactid("STAU")
|
||||
#' as.bactid("staaur")
|
||||
#' as.bactid("S. aureus")
|
||||
#' as.bactid("S aureus")
|
||||
#' as.bactid("Staphylococcus aureus")
|
||||
#' as.bactid("MRSA") # Methicillin Resistant S. aureus
|
||||
#' as.bactid("VISA") # Vancomycin Intermediate S. aureus
|
||||
#' as.bactid("VRSA") # Vancomycin Resistant S. aureus
|
||||
#'
|
||||
#' guess_bactid("S. epidermidis") # will remain species: STAEPI
|
||||
#' guess_bactid("S. epidermidis", Becker = TRUE) # will not remain species: STACNS
|
||||
#'
|
||||
#' guess_bactid("S. pyogenes") # will remain species: STCAGA
|
||||
#' guess_bactid("S. pyogenes", Lancefield = TRUE) # will not remain species: STCGRA
|
||||
#'
|
||||
#' \dontrun{
|
||||
#' df$bactid <- as.bactid(df$microorganism_name)
|
||||
#'
|
||||
#' # the select function of tidyverse is also supported:
|
||||
#' library(dplyr)
|
||||
#' df$bactid <- df %>%
|
||||
#' select(microorganism_name) %>%
|
||||
#' guess_bactid()
|
||||
#'
|
||||
#' # and can even contain 2 columns, which is convenient for genus/species combinations:
|
||||
#' df$bactid <- df %>%
|
||||
#' select(genus, species) %>%
|
||||
#' guess_bactid()
|
||||
#'
|
||||
#' # same result:
|
||||
#' df <- df %>%
|
||||
#' mutate(bactid = guess_bactid(paste(genus, species)))
|
||||
#' }
|
||||
as.bactid <- function(x, Becker = FALSE, Lancefield = FALSE) {
|
||||
|
||||
failures <- character(0)
|
||||
|
||||
if (NCOL(x) == 2) {
|
||||
# support tidyverse selection like: df %>% select(colA, colB)
|
||||
# paste these columns together
|
||||
x_vector <- vector("character", NROW(x))
|
||||
for (i in 1:NROW(x)) {
|
||||
x_vector[i] <- paste(pull(x[i,], 1), pull(x[i,], 2), sep = " ")
|
||||
}
|
||||
x <- x_vector
|
||||
} else {
|
||||
if (NCOL(x) > 2) {
|
||||
stop('`x` can be 2 columns at most', call. = FALSE)
|
||||
}
|
||||
|
||||
# support tidyverse selection like: df %>% select(colA)
|
||||
if (!is.vector(x)) {
|
||||
x <- pull(x, 1)
|
||||
}
|
||||
}
|
||||
|
||||
x.fullbackup <- x
|
||||
# remove dots and other non-text in case of "E. coli" except spaces
|
||||
x <- gsub("[^a-zA-Z0-9 ]+", "", x)
|
||||
# but spaces before and after should be omitted
|
||||
x <- trimws(x, which = "both")
|
||||
x.backup <- x
|
||||
# replace space by regex sign
|
||||
x_withspaces <- gsub(" ", ".* ", x, fixed = TRUE)
|
||||
x <- gsub(" ", ".*", x, fixed = TRUE)
|
||||
# for species
|
||||
x_species <- paste(x, 'species')
|
||||
# add start en stop regex
|
||||
x <- paste0('^', x, '$')
|
||||
x_withspaces <- paste0('^', x_withspaces, '$')
|
||||
|
||||
for (i in 1:length(x)) {
|
||||
|
||||
if (Becker == TRUE | Becker == "all") {
|
||||
mo <- suppressWarnings(guess_bactid(x.fullbackup[i]))
|
||||
if (mo %like% '^STA') {
|
||||
# See Source. It's this figure:
|
||||
# https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187637/figure/F3/
|
||||
species <- left_join_microorganisms(mo)$species
|
||||
if (species %in% c("arlettae", "auricularis", "capitis",
|
||||
"caprae", "carnosus", "cohnii", "condimenti",
|
||||
"devriesei", "epidermidis", "equorum",
|
||||
"fleurettii", "gallinarum", "haemolyticus",
|
||||
"hominis", "jettensis", "kloosii", "lentus",
|
||||
"lugdunensis", "massiliensis", "microti",
|
||||
"muscae", "nepalensis", "pasteuri", "petrasii",
|
||||
"pettenkoferi", "piscifermentans", "rostri",
|
||||
"saccharolyticus", "saprophyticus", "sciuri",
|
||||
"stepanovicii", "simulans", "succinus",
|
||||
"vitulinus", "warneri", "xylosus")) {
|
||||
x[i] <- "STACNS"
|
||||
next
|
||||
} else if ((Becker == "all" & species == "aureus")
|
||||
| species %in% c("simiae", "agnetis", "chromogenes",
|
||||
"delphini", "felis", "lutrae",
|
||||
"hyicus", "intermedius",
|
||||
"pseudintermedius", "pseudointermedius",
|
||||
"schleiferi")) {
|
||||
x[i] <- "STACPS"
|
||||
next
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (Lancefield == TRUE) {
|
||||
mo <- suppressWarnings(guess_bactid(x.fullbackup[i]))
|
||||
if (mo %like% '^STC') {
|
||||
# See Source
|
||||
species <- left_join_microorganisms(mo)$species
|
||||
if (species == "pyogenes") {
|
||||
x[i] <- "STCGRA"
|
||||
next
|
||||
}
|
||||
if (species == "agalactiae") {
|
||||
x[i] <- "STCGRB"
|
||||
next
|
||||
}
|
||||
if (species %in% c("equisimilis", "equi",
|
||||
"zooepidemicus", "dysgalactiae")) {
|
||||
x[i] <- "STCGRC"
|
||||
next
|
||||
}
|
||||
if (species == "anginosus") {
|
||||
x[i] <- "STCGRF"
|
||||
next
|
||||
}
|
||||
if (species == "sanguis") {
|
||||
x[i] <- "STCGRH"
|
||||
next
|
||||
}
|
||||
if (species == "salivarius") {
|
||||
x[i] <- "STCGRK"
|
||||
next
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (identical(x.backup[i], "")) {
|
||||
# empty values
|
||||
x[i] <- NA
|
||||
failures <- c(failures, x.fullbackup[i])
|
||||
next
|
||||
}
|
||||
if (x.fullbackup[i] %in% AMR::microorganisms$bactid) {
|
||||
# is already a valid bactid
|
||||
x[i] <- x.fullbackup[i]
|
||||
next
|
||||
}
|
||||
if (x.backup[i] %in% AMR::microorganisms$bactid) {
|
||||
# is already a valid bactid
|
||||
x[i] <- x.backup[i]
|
||||
next
|
||||
}
|
||||
|
||||
if (tolower(x[i]) == '^e.*coli$') {
|
||||
# avoid detection of Entamoeba coli in case of E. coli
|
||||
x[i] <- 'ESCCOL'
|
||||
next
|
||||
}
|
||||
if (tolower(x[i]) == '^h.*influenzae$') {
|
||||
# avoid detection of Haematobacter influenzae in case of H. influenzae
|
||||
x[i] <- 'HAEINF'
|
||||
next
|
||||
}
|
||||
if (tolower(x[i]) == '^st.*au$'
|
||||
| tolower(x[i]) == '^stau$'
|
||||
| tolower(x[i]) == '^staaur$') {
|
||||
# avoid detection of Staphylococcus auricularis in case of S. aureus
|
||||
x[i] <- 'STAAUR'
|
||||
next
|
||||
}
|
||||
if (tolower(x[i]) == '^p.*aer$') {
|
||||
# avoid detection of Pasteurella aerogenes in case of Pseudomonas aeruginosa
|
||||
x[i] <- 'PSEAER'
|
||||
next
|
||||
}
|
||||
if (tolower(x[i]) %like% 'coagulase negative'
|
||||
| tolower(x[i]) %like% 'cns'
|
||||
| tolower(x[i]) %like% 'cons') {
|
||||
# coerce S. coagulase negative, also as CNS and CoNS
|
||||
x[i] <- 'STACNS'
|
||||
next
|
||||
}
|
||||
|
||||
# translate known trivial names to genus+species
|
||||
if (!is.na(x.backup[i])) {
|
||||
if (toupper(x.backup[i]) == 'MRSA'
|
||||
| toupper(x.backup[i]) == 'VISA'
|
||||
| toupper(x.backup[i]) == 'VRSA') {
|
||||
x[i] <- 'STAAUR'
|
||||
next
|
||||
}
|
||||
if (toupper(x.backup[i]) == 'MRSE') {
|
||||
x[i] <- 'STAEPI'
|
||||
next
|
||||
}
|
||||
if (toupper(x.backup[i]) == 'VRE') {
|
||||
x[i] <- 'ENC'
|
||||
next
|
||||
}
|
||||
if (toupper(x.backup[i]) == 'MRPA') {
|
||||
# multi resistant P. aeruginosa
|
||||
x[i] <- 'PSEAER'
|
||||
next
|
||||
}
|
||||
if (toupper(x.backup[i]) %in% c('PISP', 'PRSP', 'VISP', 'VRSP')) {
|
||||
# peni R, peni I, vanco I, vanco R: S. pneumoniae
|
||||
x[i] <- 'STCPNE'
|
||||
next
|
||||
}
|
||||
}
|
||||
|
||||
# let's try the ID's first
|
||||
found <- AMR::microorganisms[which(AMR::microorganisms$bactid == x.backup[i]),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# now try exact match
|
||||
found <- AMR::microorganisms[which(AMR::microorganisms$fullname == x[i]),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# try any match keeping spaces
|
||||
found <- AMR::microorganisms[which(AMR::microorganisms$fullname %like% x_withspaces[i]),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# try any match diregarding spaces
|
||||
found <- AMR::microorganisms[which(AMR::microorganisms$fullname %like% x[i]),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# try exact match of only genus, with 'species' attached
|
||||
# (this prevents Streptococcus from becoming Peptostreptococcus, since "p" < "s")
|
||||
found <- AMR::microorganisms[which(AMR::microorganisms$fullname == x_species[i]),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# try any match of only genus, with 'species' attached
|
||||
found <- AMR::microorganisms[which(AMR::microorganisms$fullname %like% x_species[i]),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# search for GLIMS code
|
||||
found <- AMR::microorganisms.umcg[which(toupper(AMR::microorganisms.umcg$mocode) == toupper(x.backup[i])),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# try splitting of characters and then find ID
|
||||
# like esco = E. coli, klpn = K. pneumoniae, stau = S. aureus
|
||||
x_split <- x
|
||||
x_length <- nchar(x.backup[i])
|
||||
x_split[i] <- paste0(x.backup[i] %>% substr(1, x_length / 2) %>% trimws(),
|
||||
'.* ',
|
||||
x.backup[i] %>% substr((x_length / 2) + 1, x_length) %>% trimws())
|
||||
found <- AMR::microorganisms[which(AMR::microorganisms$fullname %like% paste0('^', x_split[i])),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
|
||||
# try any match with text before and after original search string
|
||||
# so "negative rods" will be "GNR"
|
||||
if (x.backup[i] %like% "^Gram") {
|
||||
x.backup[i] <- gsub("^Gram", "", x.backup[i], ignore.case = TRUE)
|
||||
# remove leading and trailing spaces again
|
||||
x.backup[i] <- trimws(x.backup[i], which = "both")
|
||||
}
|
||||
if (!is.na(x.backup[i])) {
|
||||
found <- AMR::microorganisms[which(AMR::microorganisms$fullname %like% x.backup[i]),]$bactid
|
||||
if (length(found) > 0) {
|
||||
x[i] <- found[1L]
|
||||
next
|
||||
}
|
||||
}
|
||||
|
||||
# not found
|
||||
x[i] <- NA_character_
|
||||
failures <- c(failures, x.fullbackup[i])
|
||||
|
||||
}
|
||||
|
||||
failures <- failures[!failures %in% c(NA, NULL, NaN)]
|
||||
if (length(failures) > 0) {
|
||||
warning("These values could not be coerced to a valid bactid: ",
|
||||
paste('"', unique(failures), '"', sep = "", collapse = ', '),
|
||||
".",
|
||||
call. = FALSE)
|
||||
}
|
||||
class(x) <- "bactid"
|
||||
attr(x, 'package') <- 'AMR'
|
||||
x
|
||||
}
|
||||
|
||||
#' @rdname as.bactid
|
||||
#' @export
|
||||
guess_bactid <- as.bactid
|
||||
|
||||
#' @rdname as.bactid
|
||||
#' @export
|
||||
is.bactid <- function(x) {
|
||||
identical(class(x), "bactid")
|
||||
}
|
||||
|
||||
#' @exportMethod print.bactid
|
||||
#' @export
|
||||
#' @noRd
|
||||
print.bactid <- function(x, ...) {
|
||||
cat("Class 'bactid'\n")
|
||||
print.default(as.character(x), quote = FALSE)
|
||||
}
|
||||
|
||||
#' @exportMethod as.data.frame.bactid
|
||||
#' @export
|
||||
#' @noRd
|
||||
as.data.frame.bactid <- function (x, ...) {
|
||||
# same as as.data.frame.character but with removed stringsAsFactors
|
||||
nm <- paste(deparse(substitute(x), width.cutoff = 500L),
|
||||
collapse = " ")
|
||||
if (!"nm" %in% names(list(...))) {
|
||||
as.data.frame.vector(x, ..., nm = nm)
|
||||
} else {
|
||||
as.data.frame.vector(x, ...)
|
||||
}
|
||||
}
|
||||
|
||||
#' @exportMethod pull.bactid
|
||||
#' @export
|
||||
#' @importFrom dplyr pull
|
||||
#' @noRd
|
||||
pull.bactid <- function(.data, ...) {
|
||||
pull(as.data.frame(.data), ...)
|
||||
}
|
||||
Executable
+405
@@ -0,0 +1,405 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Class 'rsi'
|
||||
#'
|
||||
#' This transforms a vector to a new class \code{rsi}, which is an ordered factor with levels \code{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
|
||||
#' @rdname as.rsi
|
||||
#' @param x vector
|
||||
#' @return Ordered factor with new class \code{rsi} and new attribute \code{package}
|
||||
#' @keywords rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
#' @seealso \code{\link{as.mic}}
|
||||
#' @examples
|
||||
#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
|
||||
#' rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
|
||||
#' is.rsi(rsi_data)
|
||||
#'
|
||||
#' # this can also coerce combined MIC/RSI values:
|
||||
#' as.rsi("<= 0.002; S") # will return S
|
||||
#'
|
||||
#' plot(rsi_data) # for percentages
|
||||
#' barplot(rsi_data) # for frequencies
|
||||
#' freq(rsi_data) # frequency table with informative header
|
||||
as.rsi <- function(x) {
|
||||
if (is.rsi(x)) {
|
||||
x
|
||||
} else {
|
||||
|
||||
x <- x %>% unlist()
|
||||
x.bak <- x
|
||||
|
||||
na_before <- x[is.na(x) | x == ''] %>% length()
|
||||
# remove all spaces
|
||||
x <- gsub(' +', '', x)
|
||||
# remove all MIC-like values: numbers, operators and periods
|
||||
x <- gsub('[0-9.,;:<=>]+', '', x)
|
||||
# disallow more than 3 characters
|
||||
x[nchar(x) > 3] <- NA
|
||||
# set to capitals
|
||||
x <- toupper(x)
|
||||
# remove all invalid characters
|
||||
x <- gsub('[^RSI]+', '', x)
|
||||
# in cases of "S;S" keep S, but in case of "S;I" make it NA
|
||||
x <- gsub('^S+$', 'S', x)
|
||||
x <- gsub('^I+$', 'I', x)
|
||||
x <- gsub('^R+$', 'R', x)
|
||||
x[!x %in% c('S', 'I', 'R')] <- NA
|
||||
na_after <- x[is.na(x) | x == ''] %>% length()
|
||||
|
||||
if (na_before != na_after) {
|
||||
list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
|
||||
unique() %>%
|
||||
sort()
|
||||
list_missing <- paste0('"', list_missing , '"', collapse = ", ")
|
||||
warning(na_after - na_before, ' results truncated (',
|
||||
round(((na_after - na_before) / length(x)) * 100),
|
||||
'%) that were invalid antimicrobial interpretations: ',
|
||||
list_missing, call. = FALSE)
|
||||
}
|
||||
|
||||
x <- x %>% factor(levels = c("S", "I", "R"), ordered = TRUE)
|
||||
class(x) <- c('rsi', 'ordered', 'factor')
|
||||
attr(x, 'package') <- 'AMR'
|
||||
x
|
||||
}
|
||||
}
|
||||
|
||||
#' @rdname as.rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
is.rsi <- function(x) {
|
||||
class(x) %>% identical(c('rsi', 'ordered', 'factor'))
|
||||
}
|
||||
|
||||
#' @exportMethod print.rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
#' @noRd
|
||||
print.rsi <- function(x, ...) {
|
||||
cat("Class 'rsi'\n")
|
||||
print(as.character(x), quote = FALSE)
|
||||
}
|
||||
|
||||
#' @exportMethod summary.rsi
|
||||
#' @export
|
||||
#' @noRd
|
||||
summary.rsi <- function(object, ...) {
|
||||
x <- object
|
||||
c(
|
||||
"Mode" = 'rsi',
|
||||
"<NA>" = sum(is.na(x)),
|
||||
"Sum S" = sum(x == "S", na.rm = TRUE),
|
||||
"Sum IR" = sum(x %in% c("I", "R"), na.rm = TRUE),
|
||||
"-Sum R" = sum(x == "R", na.rm = TRUE),
|
||||
"-Sum I" = sum(x == "I", na.rm = TRUE)
|
||||
)
|
||||
}
|
||||
|
||||
#' @exportMethod plot.rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
|
||||
#' @importFrom graphics plot text
|
||||
#' @noRd
|
||||
plot.rsi <- function(x, ...) {
|
||||
x_name <- deparse(substitute(x))
|
||||
|
||||
data <- data.frame(x = x,
|
||||
y = 1,
|
||||
stringsAsFactors = TRUE) %>%
|
||||
group_by(x) %>%
|
||||
summarise(n = sum(y)) %>%
|
||||
filter(!is.na(x)) %>%
|
||||
mutate(s = round((n / sum(n)) * 100, 1))
|
||||
data$x <- factor(data$x, levels = c('S', 'I', 'R'), ordered = TRUE)
|
||||
|
||||
ymax <- if_else(max(data$s) > 95, 105, 100)
|
||||
|
||||
plot(x = data$x,
|
||||
y = data$s,
|
||||
lwd = 2,
|
||||
col = c('green', 'orange', 'red'),
|
||||
ylim = c(0, ymax),
|
||||
ylab = 'Percentage',
|
||||
xlab = 'Antimicrobial Interpretation',
|
||||
main = paste('Susceptibility Analysis of', x_name),
|
||||
axes = FALSE,
|
||||
...)
|
||||
# x axis
|
||||
axis(side = 1, at = 1:n_distinct(data$x), labels = levels(data$x), lwd = 0)
|
||||
# y axis, 0-100%
|
||||
axis(side = 2, at = seq(0, 100, 5))
|
||||
|
||||
text(x = data$x,
|
||||
y = data$s + 4,
|
||||
labels = paste0(data$s, '% (n = ', data$n, ')'))
|
||||
}
|
||||
|
||||
|
||||
#' @exportMethod barplot.rsi
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% group_by summarise filter mutate if_else n_distinct
|
||||
#' @importFrom graphics barplot axis
|
||||
#' @noRd
|
||||
barplot.rsi <- function(height, ...) {
|
||||
x <- height
|
||||
x_name <- deparse(substitute(height))
|
||||
|
||||
data <- data.frame(rsi = x, cnt = 1) %>%
|
||||
group_by(rsi) %>%
|
||||
summarise(cnt = sum(cnt)) %>%
|
||||
droplevels()
|
||||
|
||||
barplot(table(x),
|
||||
col = c('green3', 'orange2', 'red3'),
|
||||
xlab = 'Antimicrobial Interpretation',
|
||||
main = paste('Susceptibility Analysis of', x_name),
|
||||
ylab = 'Frequency',
|
||||
axes = FALSE,
|
||||
...)
|
||||
# y axis, 0-100%
|
||||
axis(side = 2, at = seq(0, max(data$cnt) + max(data$cnt) * 1.1, by = 25))
|
||||
}
|
||||
|
||||
#' Class 'mic'
|
||||
#'
|
||||
#' This transforms a vector to a new class \code{mic}, which is an ordered factor with valid MIC values as levels. Invalid MIC values will be translated as \code{NA} with a warning.
|
||||
#' @rdname as.mic
|
||||
#' @param x vector
|
||||
#' @param na.rm a logical indicating whether missing values should be removed
|
||||
#' @return Ordered factor with new class \code{mic} and new attribute \code{package}
|
||||
#' @keywords mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
#' @seealso \code{\link{as.rsi}}
|
||||
#' @examples
|
||||
#' mic_data <- as.mic(c(">=32", "1.0", "1", "1.00", 8, "<=0.128", "8", "16", "16"))
|
||||
#' is.mic(mic_data)
|
||||
#'
|
||||
#' # this can also coerce combined MIC/RSI values:
|
||||
#' as.mic("<=0.002; S") # will return <=0.002
|
||||
#'
|
||||
#' plot(mic_data)
|
||||
#' barplot(mic_data)
|
||||
#' freq(mic_data)
|
||||
as.mic <- function(x, na.rm = FALSE) {
|
||||
if (is.mic(x)) {
|
||||
x
|
||||
} else {
|
||||
x <- x %>% unlist()
|
||||
if (na.rm == TRUE) {
|
||||
x <- x[!is.na(x)]
|
||||
}
|
||||
x.bak <- x
|
||||
|
||||
# comma to period
|
||||
x <- gsub(',', '.', x, fixed = TRUE)
|
||||
# remove space between operator and number ("<= 0.002" -> "<=0.002")
|
||||
x <- gsub('(<|=|>) +', '\\1', x)
|
||||
# starting dots must start with 0
|
||||
x <- gsub('^[.]+', '0.', x)
|
||||
# <=0.2560.512 should be 0.512
|
||||
x <- gsub('.*[.].*[.]', '0.', x)
|
||||
# remove ending .0
|
||||
x <- gsub('[.]+0$', '', x)
|
||||
# remove all after last digit
|
||||
x <- gsub('[^0-9]+$', '', x)
|
||||
# remove last zeroes
|
||||
x <- gsub('[.]?0+$', '', x)
|
||||
# force to be character
|
||||
x <- as.character(x)
|
||||
|
||||
# these are alllowed MIC values and will become factor levels
|
||||
lvls <- c("<0.002", "<=0.002", "0.002", ">=0.002", ">0.002",
|
||||
"<0.003", "<=0.003", "0.003", ">=0.003", ">0.003",
|
||||
"<0.004", "<=0.004", "0.004", ">=0.004", ">0.004",
|
||||
"<0.006", "<=0.006", "0.006", ">=0.006", ">0.006",
|
||||
"<0.008", "<=0.008", "0.008", ">=0.008", ">0.008",
|
||||
"<0.012", "<=0.012", "0.012", ">=0.012", ">0.012",
|
||||
"<0.0125", "<=0.0125", "0.0125", ">=0.0125", ">0.0125",
|
||||
"<0.016", "<=0.016", "0.016", ">=0.016", ">0.016",
|
||||
"<0.023", "<=0.023", "0.023", ">=0.023", ">0.023",
|
||||
"<0.025", "<=0.025", "0.025", ">=0.025", ">0.025",
|
||||
"<0.03", "<=0.03", "0.03", ">=0.03", ">0.03",
|
||||
"<0.032", "<=0.032", "0.032", ">=0.032", ">0.032",
|
||||
"<0.047", "<=0.047", "0.047", ">=0.047", ">0.047",
|
||||
"<0.05", "<=0.05", "0.05", ">=0.05", ">0.05",
|
||||
"<0.054", "<=0.054", "0.054", ">=0.054", ">0.054",
|
||||
"<0.06", "<=0.06", "0.06", ">=0.06", ">0.06",
|
||||
"<0.0625", "<=0.0625", "0.0625", ">=0.0625", ">0.0625",
|
||||
"<0.063", "<=0.063", "0.063", ">=0.063", ">0.063",
|
||||
"<0.064", "<=0.064", "0.064", ">=0.064", ">0.064",
|
||||
"<0.09", "<=0.09", "0.09", ">=0.09", ">0.09",
|
||||
"<0.094", "<=0.094", "0.094", ">=0.094", ">0.094",
|
||||
"<0.12", "<=0.12", "0.12", ">=0.12", ">0.12",
|
||||
"<0.125", "<=0.125", "0.125", ">=0.125", ">0.125",
|
||||
"<0.128", "<=0.128", "0.128", ">=0.128", ">0.128",
|
||||
"<0.16", "<=0.16", "0.16", ">=0.16", ">0.16",
|
||||
"<0.19", "<=0.19", "0.19", ">=0.19", ">0.19",
|
||||
"<0.23", "<=0.23", "0.23", ">=0.23", ">0.23",
|
||||
"<0.25", "<=0.25", "0.25", ">=0.25", ">0.25",
|
||||
"<0.256", "<=0.256", "0.256", ">=0.256", ">0.256",
|
||||
"<0.28", "<=0.28", "0.28", ">=0.28", ">0.28",
|
||||
"<0.30", "<=0.30", "0.30", ">=0.30", ">0.30",
|
||||
"<0.32", "<=0.32", "0.32", ">=0.32", ">0.32",
|
||||
"<0.36", "<=0.36", "0.36", ">=0.36", ">0.36",
|
||||
"<0.38", "<=0.38", "0.38", ">=0.38", ">0.38",
|
||||
"<0.5", "<=0.5", "0.5", ">=0.5", ">0.5",
|
||||
"<0.512", "<=0.512", "0.512", ">=0.512", ">0.512",
|
||||
"<0.64", "<=0.64", "0.64", ">=0.64", ">0.64",
|
||||
"<0.75", "<=0.75", "0.75", ">=0.75", ">0.75",
|
||||
"<1", "<=1", "1", ">=1", ">1",
|
||||
"<1.5", "<=1.5", "1.5", ">=1.5", ">1.5",
|
||||
"<2", "<=2", "2", ">=2", ">2",
|
||||
"<3", "<=3", "3", ">=3", ">3",
|
||||
"<4", "<=4", "4", ">=4", ">4",
|
||||
"<5", "<=5", "5", ">=5", ">5",
|
||||
"<6", "<=6", "6", ">=6", ">6",
|
||||
"<7", "<=7", "7", ">=7", ">7",
|
||||
"<8", "<=8", "8", ">=8", ">8",
|
||||
"<10", "<=10", "10", ">=10", ">10",
|
||||
"<12", "<=12", "12", ">=12", ">12",
|
||||
"<16", "<=16", "16", ">=16", ">16",
|
||||
"<20", "<=20", "20", ">=20", ">20",
|
||||
"<24", "<=24", "24", ">=24", ">24",
|
||||
"<32", "<=32", "32", ">=32", ">32",
|
||||
"<40", "<=40", "40", ">=40", ">40",
|
||||
"<48", "<=48", "48", ">=48", ">48",
|
||||
"<64", "<=64", "64", ">=64", ">64",
|
||||
"<80", "<=80", "80", ">=80", ">80",
|
||||
"<96", "<=96", "96", ">=96", ">96",
|
||||
"<128", "<=128", "128", ">=128", ">128",
|
||||
"<160", "<=160", "160", ">=160", ">160",
|
||||
"<256", "<=256", "256", ">=256", ">256",
|
||||
"<320", "<=320", "320", ">=320", ">320",
|
||||
"<512", "<=512", "512", ">=512", ">512",
|
||||
"<1024", "<=1024", "1024", ">=1024", ">1024")
|
||||
|
||||
na_before <- x[is.na(x) | x == ''] %>% length()
|
||||
x[!x %in% lvls] <- NA
|
||||
na_after <- x[is.na(x) | x == ''] %>% length()
|
||||
|
||||
if (na_before != na_after) {
|
||||
list_missing <- x.bak[is.na(x) & !is.na(x.bak) & x.bak != ''] %>%
|
||||
unique() %>%
|
||||
sort()
|
||||
list_missing <- paste0('"', list_missing , '"', collapse = ", ")
|
||||
warning(na_after - na_before, ' results truncated (',
|
||||
round(((na_after - na_before) / length(x)) * 100),
|
||||
'%) that were invalid MICs: ',
|
||||
list_missing, call. = FALSE)
|
||||
}
|
||||
|
||||
x <- factor(x = x,
|
||||
levels = lvls,
|
||||
ordered = TRUE)
|
||||
class(x) <- c('mic', 'ordered', 'factor')
|
||||
attr(x, 'package') <- 'AMR'
|
||||
x
|
||||
}
|
||||
}
|
||||
|
||||
#' @rdname as.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
is.mic <- function(x) {
|
||||
class(x) %>% identical(c('mic', 'ordered', 'factor'))
|
||||
}
|
||||
|
||||
#' @exportMethod as.double.mic
|
||||
#' @export
|
||||
#' @noRd
|
||||
as.double.mic <- function(x, ...) {
|
||||
as.double(gsub('(<|=|>)+', '', as.character(x)))
|
||||
}
|
||||
|
||||
#' @exportMethod as.integer.mic
|
||||
#' @export
|
||||
#' @noRd
|
||||
as.integer.mic <- function(x, ...) {
|
||||
as.integer(gsub('(<|=|>)+', '', as.character(x)))
|
||||
}
|
||||
|
||||
#' @exportMethod as.numeric.mic
|
||||
#' @export
|
||||
#' @noRd
|
||||
as.numeric.mic <- function(x, ...) {
|
||||
as.numeric(gsub('(<|=|>)+', '', as.character(x)))
|
||||
}
|
||||
|
||||
#' @exportMethod print.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% tibble group_by summarise pull
|
||||
#' @noRd
|
||||
print.mic <- function(x, ...) {
|
||||
cat("Class 'mic'\n")
|
||||
print(as.character(x), quote = FALSE)
|
||||
}
|
||||
|
||||
#' @exportMethod summary.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>%
|
||||
#' @noRd
|
||||
summary.mic <- function(object, ...) {
|
||||
x <- object
|
||||
n_total <- x %>% length()
|
||||
x <- x[!is.na(x)]
|
||||
n <- x %>% length()
|
||||
lst <- c('mic',
|
||||
n_total - n,
|
||||
sort(x)[1] %>% as.character(),
|
||||
sort(x)[n] %>% as.character())
|
||||
names(lst) <- c("Mode", "<NA>", "Min.", "Max.")
|
||||
lst
|
||||
}
|
||||
|
||||
#' @exportMethod plot.mic
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% group_by summarise
|
||||
#' @importFrom graphics plot text
|
||||
#' @noRd
|
||||
plot.mic <- function(x, ...) {
|
||||
x_name <- deparse(substitute(x))
|
||||
create_barplot_mic(x, x_name, ...)
|
||||
}
|
||||
|
||||
#' @exportMethod barplot.mic
|
||||
#' @export
|
||||
#' @importFrom graphics barplot axis
|
||||
#' @noRd
|
||||
barplot.mic <- function(height, ...) {
|
||||
x_name <- deparse(substitute(height))
|
||||
create_barplot_mic(height, x_name, ...)
|
||||
}
|
||||
|
||||
#' @importFrom graphics barplot axis
|
||||
#' @importFrom dplyr %>% group_by summarise
|
||||
create_barplot_mic <- function(x, x_name, ...) {
|
||||
data <- data.frame(mic = x, cnt = 1) %>%
|
||||
group_by(mic) %>%
|
||||
summarise(cnt = sum(cnt)) %>%
|
||||
droplevels()
|
||||
barplot(table(droplevels(x)),
|
||||
ylab = 'Frequency',
|
||||
xlab = 'MIC value',
|
||||
main = paste('MIC values of', x_name),
|
||||
axes = FALSE,
|
||||
...)
|
||||
axis(2, seq(0, max(data$cnt)))
|
||||
}
|
||||
+165
@@ -0,0 +1,165 @@
|
||||
#' Import/export from clipboard
|
||||
#'
|
||||
#' @description These are helper functions around \code{\link{read.table}} and \code{\link{write.table}} to import from and export to clipboard with support for Windows, Linux and macOS.
|
||||
#'
|
||||
#' The data will be read and written as tab-separated by default, which makes it possible to copy and paste from other software like Excel and SPSS without further transformation.
|
||||
#'
|
||||
#' This also supports automatic column type transformation, with AMR classes \code{\link{as.rsi}} and \code{\link{as.mic}}.
|
||||
#' @rdname clipboard
|
||||
#' @name clipboard
|
||||
#' @inheritParams base::data.frame
|
||||
#' @inheritParams utils::read.table
|
||||
#' @inheritParams utils::write.table
|
||||
#' @inheritParams readr::locale
|
||||
#' @param startrow \emph{n}th row to start importing from. When \code{header = TRUE}, the import will start on row \code{startrow} \emph{below} the header.
|
||||
#' @param as_vector a logical value indicating whether data consisting of only one column should be imported as vector using \code{\link[dplyr]{pull}}. This will strip off the header.
|
||||
#' @param guess_col_types a logical value indicating whether column types should be guessed and transformed automatically with \code{\link[readr]{parse_guess}} from the \code{readr} package. Besides, the antimicrobial classes in this AMR package (\code{\link{as.rsi}} and \code{\link{as.mic}}) are also supported.
|
||||
#' @param remove_ASCII_escape_char remove ASCII escape character
|
||||
#' @param info print info to console
|
||||
#' @keywords clipboard clipboard_import clipboard_export import export
|
||||
#' @importFrom dplyr %>% pull as_tibble
|
||||
#' @importFrom clipr read_clip_tbl write_clip
|
||||
#' @importFrom utils read.delim write.table object.size
|
||||
#' @importFrom readr parse_guess locale
|
||||
#' @details
|
||||
#' The parameter \code{stringsAsFactors} defaults to \code{FALSE}, as opposed to most base \R methods.
|
||||
#'
|
||||
#' The parameters \code{date_format} and \code{time_format} also support generic date and time formats like \code{"dd-mm-yyyy"} like Excel.
|
||||
#'
|
||||
#' \if{html}{
|
||||
#' \strong{Example for copying from Excel:}
|
||||
#' \out{<div style="text-align: left">}\figure{clipboard_copy.png}\out{</div>}
|
||||
#' \cr
|
||||
#' \strong{And pasting in R:} \cr
|
||||
#' \cr
|
||||
#' \code{> data <- clipboard_import()} \cr
|
||||
#' \code{> data} \cr
|
||||
#' \out{<div style="text-align: left">}\figure{clipboard_paste.png}\out{</div>}
|
||||
#' \cr
|
||||
#' \strong{The resulting data contains the right RSI-classes:} \cr
|
||||
#' \cr
|
||||
#' \code{> data$amox} \cr
|
||||
#' \out{<div style="text-align: left">}\figure{clipboard_rsi.png}\out{</div>}
|
||||
#' }
|
||||
#' @export
|
||||
#' @examples
|
||||
#' \dontrun{
|
||||
#'
|
||||
#' df1 <- data.frame(a = letters[1:12],
|
||||
#' b = runif(n = 12, min = 1000, max = 2000),
|
||||
#' stringsAsFactors = FALSE)
|
||||
#' clipboard_export(df1)
|
||||
#' df2 <- clipboard_import()
|
||||
#' identical(df1, df2)
|
||||
#'
|
||||
#' # send frequency table to clipboard (e.g. for pasting in Excel)
|
||||
#' septic_patients %>%
|
||||
#' freq(age) %>%
|
||||
#' format() %>% # this will format the percentages
|
||||
#' clipboard_export()
|
||||
#' }
|
||||
clipboard_import <- function(sep = '\t',
|
||||
quote = "",
|
||||
header = TRUE,
|
||||
dec = ".",
|
||||
na = c("", "NA", "NULL"),
|
||||
stringsAsFactors = FALSE,
|
||||
startrow = 1,
|
||||
as_vector = TRUE,
|
||||
guess_col_types = TRUE,
|
||||
date_names = 'en',
|
||||
date_format = '%Y-%m-%d',
|
||||
time_format = '%H:%M',
|
||||
remove_ASCII_escape_char = FALSE,
|
||||
tz = "UTC",
|
||||
encoding = "UTF-8",
|
||||
info = TRUE) {
|
||||
|
||||
if (!clipr::clipr_available() & Sys.info()['sysname'] == "Linux") {
|
||||
# try to support on X11, by setting the R variable DISPLAY
|
||||
Sys.setenv(DISPLAY = "localhost:10.0")
|
||||
}
|
||||
|
||||
# this will fail when clipr is (still) not available
|
||||
import_tbl <- clipr::read_clip_tbl(file = file,
|
||||
sep = sep,
|
||||
quote = quote,
|
||||
header = header,
|
||||
strip.white = TRUE,
|
||||
dec = dec,
|
||||
na.strings = na,
|
||||
encoding = 'UTF-8',
|
||||
stringsAsFactors = stringsAsFactors)
|
||||
|
||||
# use tibble, so column types will be translated correctly
|
||||
import_tbl <- as_tibble(import_tbl)
|
||||
|
||||
if (startrow > 1) {
|
||||
# would else lose column headers
|
||||
import_tbl <- import_tbl[startrow:NROW(import_tbl),]
|
||||
}
|
||||
|
||||
colnames(import_tbl) <- gsub('[.]+', '_', colnames(import_tbl))
|
||||
|
||||
if (guess_col_types == TRUE) {
|
||||
if (info == TRUE) {
|
||||
cat('Transforming data by guessing column types...')
|
||||
}
|
||||
import_tbl <- tbl_parse_guess(tbl = import_tbl,
|
||||
date_names = date_names,
|
||||
date_format = date_format,
|
||||
time_format = time_format,
|
||||
decimal_mark = dec,
|
||||
tz = tz,
|
||||
encoding = encoding,
|
||||
remove_ASCII_escape_char = remove_ASCII_escape_char,
|
||||
na = na)
|
||||
if (info == TRUE) {
|
||||
cat('OK\n')
|
||||
}
|
||||
}
|
||||
|
||||
if (NCOL(import_tbl) == 1 & as_vector == TRUE) {
|
||||
import_tbl <- import_tbl %>% pull(1)
|
||||
}
|
||||
|
||||
# and transform back to data.frame
|
||||
import_tbl <- as.data.frame(import_tbl, stringsAsFactors = stringsAsFactors)
|
||||
|
||||
if (info == TRUE) {
|
||||
cat("Successfully imported from clipboard:", NROW(import_tbl), "obs. of", NCOL(import_tbl), "variables.\n")
|
||||
}
|
||||
|
||||
import_tbl
|
||||
|
||||
}
|
||||
|
||||
#' @rdname clipboard
|
||||
#' @importFrom dplyr %>% pull as_tibble
|
||||
#' @export
|
||||
clipboard_export <- function(x,
|
||||
sep = '\t',
|
||||
dec = ".",
|
||||
na = "",
|
||||
header = TRUE,
|
||||
info = TRUE) {
|
||||
|
||||
if (!clipr::clipr_available() & Sys.info()['sysname'] == "Linux") {
|
||||
# try to support on X11, by setting the R variable DISPLAY
|
||||
Sys.setenv(DISPLAY = "localhost:10.0")
|
||||
}
|
||||
|
||||
clipr::write_clip(content = x,
|
||||
na = na,
|
||||
sep = sep,
|
||||
row.names = FALSE,
|
||||
col.names = header,
|
||||
dec = dec,
|
||||
quote = FALSE)
|
||||
|
||||
if (info == TRUE) {
|
||||
cat("Successfully exported to clipboard:", NROW(x), "obs. of", NCOL(x), "variables.\n")
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
@@ -0,0 +1,336 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Dataset with 420 antibiotics
|
||||
#'
|
||||
#' A dataset containing all antibiotics with a J0 code, with their DDD's. Properties were downloaded from the WHO, see Source.
|
||||
#' @format A data.frame with 420 observations and 18 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{atc}}{ATC code, like \code{J01CR02}}
|
||||
#' \item{\code{molis}}{MOLIS code, like \code{amcl}}
|
||||
#' \item{\code{umcg}}{UMCG code, like \code{AMCL}}
|
||||
#' \item{\code{abbr}}{Abbreviation as used by many countries, to be used for \code{\link{guess_atc}}}
|
||||
#' \item{\code{official}}{Official name by the WHO, like \code{"Amoxicillin and enzyme inhibitor"}}
|
||||
#' \item{\code{official_nl}}{Official name in the Netherlands, like \code{"Amoxicilline met enzymremmer"}}
|
||||
#' \item{\code{trivial_nl}}{Trivial name in Dutch, like \code{"Amoxicilline/clavulaanzuur"}}
|
||||
#' \item{\code{trade_name}}{Trade name as used by many countries, to be used for \code{\link{guess_atc}}}
|
||||
#' \item{\code{oral_ddd}}{Defined Daily Dose (DDD), oral treatment}
|
||||
#' \item{\code{oral_units}}{Units of \code{ddd_units}}
|
||||
#' \item{\code{iv_ddd}}{Defined Daily Dose (DDD), parenteral treatment}
|
||||
#' \item{\code{iv_units}}{Units of \code{iv_ddd}}
|
||||
#' \item{\code{atc_group1}}{ATC group, like \code{"Macrolides, lincosamides and streptogramins"}}
|
||||
#' \item{\code{atc_group2}}{Subgroup of \code{atc_group1}, like \code{"Macrolides"}}
|
||||
#' \item{\code{atc_group1_nl}}{ATC group in Dutch, like \code{"Macroliden, lincosamiden en streptograminen"}}
|
||||
#' \item{\code{atc_group2_nl}}{Subgroup of \code{atc_group1} in Dutch, like \code{"Macroliden"}}
|
||||
#' \item{\code{useful_gramnegative}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
|
||||
#' \item{\code{useful_grampositive}}{\code{FALSE} if not useful according to EUCAST, \code{NA} otherwise (see Source)}
|
||||
#' }
|
||||
#' @source - World Health Organization: \url{https://www.whocc.no/atc_ddd_index/} \cr - EUCAST - Expert rules intrinsic exceptional V3.1 \cr - MOLIS (LIS of Certe): \url{https://www.certe.nl} \cr - GLIMS (LIS of UMCG): \url{https://www.umcg.nl}
|
||||
#' @seealso \code{\link{microorganisms}}
|
||||
# abbr and trade_name created with:
|
||||
# https://hs.unr.edu/Documents/dhs/chs/NVPHTC/antibiotic_refeference_guide.pdf
|
||||
# antibiotics %>%
|
||||
# mutate(abbr =
|
||||
# case_when(
|
||||
# official == 'Amikacin' ~ 'Ak|AN|AMI|AMK',
|
||||
# official == 'Amoxicillin' ~ 'AMX|AMOX|AC',
|
||||
# official == 'Amoxicillin and beta-lactamase inhibitor' ~ 'AUG|A/C|XL|AML',
|
||||
# official == 'Ampicillin' ~ 'AM|AMP',
|
||||
# official == 'Ampicillin and beta-lactamase inhibitor' ~ 'A/S|SAM|AMS|AB',
|
||||
# official == 'Azithromycin' ~ 'Azi|AZM|AZ',
|
||||
# official == 'Azlocillin' ~ 'AZ|AZL',
|
||||
# official == 'Aztreonam' ~ 'Azt|ATM|AT|AZM',
|
||||
# official == 'Carbenicillin' ~ 'Cb|BAR',
|
||||
# official == 'Cefaclor' ~ 'Ccl|CEC|Cfr|FAC|CF',
|
||||
# official == 'Cefadroxil' ~ 'CFR|FAD',
|
||||
# official == 'Cefazolin' ~ 'Cfz|CZ|FAZ|KZ',
|
||||
# official == 'Cefdinir' ~ 'Cdn|CDR|DIN|CD|CFD',
|
||||
# official == 'Cefditoren' ~ 'CDN',
|
||||
# official == 'Cefepime' ~ 'Cpe|FEP|PM|CPM',
|
||||
# official == 'Cefixime' ~ 'Cfe|DCFM|FIX|IX',
|
||||
# official == 'Cefoperazone' ~ 'Cfp|CPZ|PER|FOP|CP',
|
||||
# official == 'Cefotaxime' ~ 'Cft|CTX|TAX|FOT|CT',
|
||||
# official == 'Cefotetan' ~ 'Ctn|CTT|CTE|TANS|CN',
|
||||
# official == 'Cefoxitin' ~ 'Cfx|FOX|CX|FX',
|
||||
# official == 'Cefpodoxime' ~ 'Cpd|POD|PX',
|
||||
# official == 'Cefprozil' ~ 'Cpz|CPR|FP',
|
||||
# official == 'Ceftaroline' ~ 'CPT',
|
||||
# official == 'Ceftazidime' ~ 'Caz|TAZ|TZ',
|
||||
# official == 'Ceftibuten' ~ 'CTB|TIB|CB',
|
||||
# official == 'Ceftizoxime' ~ 'Cz|ZOX|CZX|CZ|CTZ|TIZ',
|
||||
# official == 'Ceftriaxone' ~ 'Cax|CRO|CTR|FRX|AXO|TX',
|
||||
# official == 'Cefuroxime' ~ 'Crm|CXM|CFX|ROX|FUR|XM',
|
||||
# official == 'Cephalexin' ~ 'CN|LX|CFL',
|
||||
# official == 'Cephalothin' ~ 'Cf',
|
||||
# official == 'Chloramphenicol' ~ 'C|CHL|CL',
|
||||
# official == 'Ciprofloxacin' ~ 'Cp|CIP|CI',
|
||||
# official == 'Clarithromycin' ~ 'Cla|CLR|CLM|CH',
|
||||
# official == 'Clindamycin' ~ 'Cd|CC|CM|CLI|DA',
|
||||
# official == 'Colistin' ~ 'CL|CS|CT',
|
||||
# official == 'Daptomycin' ~ 'Dap',
|
||||
# official == 'Doxycycline' ~ 'Dox',
|
||||
# official == 'Doripenem' ~ 'DOR|Dor',
|
||||
# official == 'Ertapenem' ~ 'Etp',
|
||||
# official == 'Erythromycin' ~ 'E|ERY|EM',
|
||||
# official == 'Fosfomycin' ~ 'FOS|FF|FO|FM',
|
||||
# official == 'Flucloxacillin' ~ 'CLOX',
|
||||
# official == 'Gentamicin' ~ 'Gm|CN|GEN',
|
||||
# official == 'Imipenem' ~ 'Imp|IPM|IMI|IP',
|
||||
# official == 'Kanamycin' ~ 'K|KAN|HLK|KM',
|
||||
# official == 'Levofloxacin' ~ 'Lvx|LEV|LEVO|LE',
|
||||
# official == 'Linezolid' ~ 'Lzd|LNZ|LZ',
|
||||
# official == 'Lomefloxacin' ~ 'Lmf|LOM',
|
||||
# official == 'Meropenem' ~ 'Mer|MEM|MERO|MRP|MP',
|
||||
# official == 'Metronidazole' ~ 'MNZ',
|
||||
# official == 'Mezlocillin' ~ 'Mz|MEZ',
|
||||
# official == 'Minocycline' ~ 'Min|MI|MN|MNO|MC|MH',
|
||||
# official == 'Moxifloxacin' ~ 'Mox|MXF',
|
||||
# official == 'Mupirocin' ~ 'MUP',
|
||||
# official == 'Nafcillin' ~ 'Naf|NF',
|
||||
# official == 'Nalidixic acid' ~ 'NA|NAL',
|
||||
# official == 'Nitrofurantoin' ~ 'Fd|F/M|FT|NIT|NI|F',
|
||||
# official == 'Norfloxacin' ~ 'Nxn|NOR|NX',
|
||||
# official == 'Ofloxacin' ~ 'Ofl|OFX|OF',
|
||||
# official == 'Oxacillin' ~ 'Ox|OXS|OXA',
|
||||
# official == 'Benzylpenicillin' ~ 'P|PEN|PV',
|
||||
# official == 'Penicillins, combinations with other antibacterials' ~ 'P|PEN|PV',
|
||||
# official == 'Piperacillin' ~ 'Pi|PIP|PP',
|
||||
# official == 'Piperacillin and beta-lactamase inhibitor' ~ 'PT|TZP|PTZ|P/T|PTc',
|
||||
# official == 'Polymyxin B' ~ 'PB',
|
||||
# official == 'Quinupristin/dalfopristin' ~ 'Syn|Q/D|QDA|RP',
|
||||
# official == 'Rifampin' ~ 'Rif|RA|RI|RD',
|
||||
# official == 'Spectinomycin' ~ 'SPT|SPE|SC',
|
||||
# official == 'Streptomycin' ~ 'S|STR',
|
||||
# official == 'Teicoplanin' ~ 'Tei|TEC|TPN|TP|TPL',
|
||||
# official == 'Telavancin' ~ 'TLV',
|
||||
# official == 'Telithromcyin' ~ 'Tel',
|
||||
# official == 'Tetracycline' ~ 'Te|TET|TC',
|
||||
# official == 'Ticarcillin' ~ 'Ti|TIC|TC',
|
||||
# official == 'Ticarcillin and beta-lactamase inhibitor' ~ 'Tim|T/C|TCC|TLc',
|
||||
# official == 'Tigecycline' ~ 'TGC',
|
||||
# official == 'Tobramycin' ~ 'To|NN|TM|TOB',
|
||||
# official == 'Trimethoprim' ~ 'T|TMP|TR|W',
|
||||
# official == 'Sulfamethoxazole and trimethoprim' ~ 'T/S|SXT|SxT|TS|COT',
|
||||
# official == 'Vancomycin' ~ 'Va|VAN',
|
||||
# TRUE ~ NA_character_),
|
||||
#
|
||||
# trade_name =
|
||||
# case_when(
|
||||
# official == 'Amikacin' ~ 'Amikin',
|
||||
# official == 'Amoxicillin' ~ 'Amoxil|Dispermox|Larotid|Trimox',
|
||||
# official == 'Amoxicillin and beta-lactamase inhibitor' ~ 'Augmentin',
|
||||
# official == 'Ampicillin' ~ 'Pfizerpen-A|Principen',
|
||||
# official == 'Ampicillin and beta-lactamase inhibitor' ~ 'Unasyn',
|
||||
# official == 'Azithromycin' ~ 'Zithromax',
|
||||
# official == 'Azlocillin' ~ 'Azlin',
|
||||
# official == 'Aztreonam' ~ 'Azactam',
|
||||
# official == 'Carbenicillin' ~ 'Geocillin',
|
||||
# official == 'Cefaclor' ~ 'Ceclor',
|
||||
# official == 'Cefadroxil' ~ 'Duricef',
|
||||
# official == 'Cefazolin' ~ 'Ancef',
|
||||
# official == 'Cefdinir' ~ 'Omnicef',
|
||||
# official == 'Cefditoren' ~ 'Spectracef',
|
||||
# official == 'Cefepime' ~ 'Maxipime',
|
||||
# official == 'Cefixime' ~ 'Suprax',
|
||||
# official == 'Cefoperazone' ~ 'Cefobid',
|
||||
# official == 'Cefotaxime' ~ 'Claforan',
|
||||
# official == 'Cefotetan' ~ 'Cefotan',
|
||||
# official == 'Cefoxitin' ~ 'Mefoxin',
|
||||
# official == 'Cefpodoxime' ~ 'Vantin',
|
||||
# official == 'Cefprozil' ~ 'Cefzil',
|
||||
# official == 'Ceftaroline' ~ 'Teflaro',
|
||||
# official == 'Ceftazidime' ~ 'Fortaz|Tazicef|Tazidime',
|
||||
# official == 'Ceftibuten' ~ 'Cedax',
|
||||
# official == 'Ceftizoxime' ~ 'Cefizox',
|
||||
# official == 'Ceftriaxone' ~ 'Rocephin',
|
||||
# official == 'Cefuroxime' ~ 'Ceftin|Zinacef',
|
||||
# official == 'Cephalexin' ~ 'Keflex|Panixine',
|
||||
# official == 'Cephalothin' ~ 'Keflin',
|
||||
# official == 'Chloramphenicol' ~ 'Chloromycetin',
|
||||
# official == 'Ciprofloxacin' ~ 'Cipro|Ciloxan|Ciproxin',
|
||||
# official == 'Clarithromycin' ~ 'Biaxin',
|
||||
# official == 'Clindamycin' ~ 'Cleocin|Clinda-Derm|Clindagel|Clindesse|Clindets|Evoclin',
|
||||
# official == 'Colistin' ~ 'Coly-Mycin',
|
||||
# official == 'Daptomycin' ~ 'Cubicin',
|
||||
# official == 'Doxycycline' ~ 'Doryx|Monodox|Vibramycin|Atridox|Oracea|Periostat|Vibra-Tabs',
|
||||
# official == 'Doripenem' ~ 'Doribax',
|
||||
# official == 'Ertapenem' ~ 'Invanz',
|
||||
# official == 'Erythromycin' ~ 'Eryc|EryPed|Erythrocin|E-Base|E-Glades|E-Mycin|E.E.S.|Ery-Tab|Eryderm|Erygel|Erythra-derm|Eryzole|Pediamycin',
|
||||
# official == 'Fosfomycin' ~ 'Monurol',
|
||||
# official == 'Flucloxacillin' ~ 'Flopen|Floxapen|Fluclox|Sesamol|Softapen|Staphylex',
|
||||
# official == 'Gentamicin' ~ 'Garamycin|Genoptic',
|
||||
# official == 'Imipenem' ~ 'Primaxin',
|
||||
# official == 'Kanamycin' ~ 'Kantrex',
|
||||
# official == 'Levofloxacin' ~ 'Levaquin|Quixin',
|
||||
# official == 'Linezolid' ~ 'Zyvox',
|
||||
# official == 'Lomefloxacin' ~ 'Maxaquin',
|
||||
# official == 'Meropenem' ~ 'Merrem',
|
||||
# official == 'Metronidazole' ~ 'Flagyl|MetroGel|MetroCream|MetroLotion',
|
||||
# official == 'Mezlocillin' ~ 'Mezlin',
|
||||
# official == 'Minocycline' ~ 'Arestin|Solodyn',
|
||||
# official == 'Moxifloxacin' ~ 'Avelox|Vigamox',
|
||||
# official == 'Mupirocin' ~ 'Bactroban|Centany',
|
||||
# official == 'Nafcillin' ~ 'Unipen',
|
||||
# official == 'Nalidixic acid' ~ 'NegGram',
|
||||
# official == 'Nitrofurantoin' ~ 'Furadantin|Macrobid|Macrodantin',
|
||||
# official == 'Norfloxacin' ~ 'Noroxin',
|
||||
# official == 'Ofloxacin' ~ 'Floxin|Ocuflox|Ophthalmic',
|
||||
# official == 'Oxacillin' ~ 'Bactocill',
|
||||
# official == 'Benzylpenicillin' ~ 'Permapen|Pfizerpen|Veetids',
|
||||
# official == 'Penicillins, combinations with other antibacterials' ~ 'Permapen|Pfizerpen|Veetids',
|
||||
# official == 'Piperacillin' ~ 'Pipracil',
|
||||
# official == 'Piperacillin and beta-lactamase inhibitor' ~ 'Zosyn',
|
||||
# official == 'Polymyxin B' ~ 'Poly-RX',
|
||||
# official == 'Quinupristin/dalfopristin' ~ 'Synercid',
|
||||
# official == 'Rifampin' ~ 'Rifadin|Rifamate|Rimactane',
|
||||
# official == 'Spectinomycin' ~ 'Trobicin',
|
||||
# official == 'Streptomycin' ~ 'Streptomycin Sulfate',
|
||||
# official == 'Teicoplanin' ~ 'Targocid',
|
||||
# official == 'Telavancin' ~ 'Vibativ',
|
||||
# official == 'Telithromcyin' ~ 'Ketek',
|
||||
# official == 'Tetracycline' ~ 'Sumycin|Bristacycline|Tetrex',
|
||||
# official == 'Ticarcillin' ~ 'Ticar',
|
||||
# official == 'Ticarcillin and beta-lactamase inhibitor' ~ 'Timentin',
|
||||
# official == 'Tigecycline' ~ 'Tygacil',
|
||||
# official == 'Tobramycin' ~ 'Tobi|Aktob|Tobre',
|
||||
# official == 'Trimethoprim' ~ 'Primsol|Proloprim',
|
||||
# official == 'Sulfamethoxazole and trimethoprim' ~ 'Bactrim|Septra|Sulfatrim',
|
||||
# official == 'Vancomycin' ~ 'Vancocin|Vancomycin Hydrochloride',
|
||||
# TRUE ~ NA_character_)
|
||||
# )
|
||||
# last two columns created with:
|
||||
# antibiotics %>%
|
||||
# mutate(useful_gramnegative =
|
||||
# if_else(
|
||||
# atc_group1 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
|
||||
# atc_group2 %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)' |
|
||||
# official %like% '(fusidic|glycopeptide|macrolide|lincosamide|daptomycin|linezolid)',
|
||||
# FALSE,
|
||||
# NA
|
||||
# ),
|
||||
# useful_grampositive =
|
||||
# if_else(
|
||||
# atc_group1 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
|
||||
# atc_group2 %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)' |
|
||||
# official %like% '(aztreonam|temocillin|polymyxin|colistin|nalidixic)',
|
||||
# FALSE,
|
||||
# NA
|
||||
# )
|
||||
# )
|
||||
"antibiotics"
|
||||
|
||||
#' Dataset with ~2500 microorganisms
|
||||
#'
|
||||
#' A dataset containing 2464 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
|
||||
#' @format A data.frame with 2464 observations and 12 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{bactid}}{ID of microorganism}
|
||||
#' \item{\code{bactsys}}{Bactsyscode of microorganism}
|
||||
#' \item{\code{family}}{Family name of microorganism}
|
||||
#' \item{\code{genus}}{Genus name of microorganism, like \code{"Echerichia"}}
|
||||
#' \item{\code{species}}{Species name of microorganism, like \code{"coli"}}
|
||||
#' \item{\code{subspecies}}{Subspecies name of bio-/serovar of microorganism, like \code{"EHEC"}}
|
||||
#' \item{\code{fullname}}{Full name, like \code{"Echerichia coli (EHEC)"}}
|
||||
#' \item{\code{type}}{Type of microorganism, like \code{"Bacteria"} and \code{"Fungus/yeast"}}
|
||||
#' \item{\code{gramstain}}{Gram of microorganism, like \code{"Negative rods"}}
|
||||
#' \item{\code{aerobic}}{Logical whether bacteria is aerobic}
|
||||
#' \item{\code{type_nl}}{Type of microorganism in Dutch, like \code{"Bacterie"} and \code{"Schimmel/gist"}}
|
||||
#' \item{\code{gramstain_nl}}{Gram of microorganism in Dutch, like \code{"Negatieve staven"}}
|
||||
#' }
|
||||
# source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
|
||||
# new <- microorganisms %>% filter(genus == "Bacteroides") %>% .[1,]
|
||||
# new[1, 'bactid'] <- "DIAPNU"
|
||||
# new[1, 'bactsys'] <- "DIAPNU"
|
||||
# new[1, 'family'] <- "Veillonellaceae"
|
||||
# new[1, 'genus'] <- "Dialister"
|
||||
# new[1, 'species'] <- "pneumosintes"
|
||||
# new[1, 'subspecies'] <- NA
|
||||
# new[1, 'fullname'] <- paste(new[1, 'genus'], new[1, 'species'])
|
||||
# microorganisms <- microorganisms %>% bind_rows(new) %>% arrange(bactid)
|
||||
#' @seealso \code{\link{guess_bactid}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}
|
||||
"microorganisms"
|
||||
|
||||
#' Translation table for UMCG with ~1100 microorganisms
|
||||
#'
|
||||
#' A dataset containing all bacteria codes of UMCG MMB. These codes can be joined to data with an ID from \code{\link{microorganisms}$bactid} (using \code{\link{left_join_microorganisms}}). GLIMS codes can also be translated to valid \code{bactid}'s with \code{\link{guess_bactid}}.
|
||||
#' @format A data.frame with 1090 observations and 2 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{mocode}}{Code of microorganism according to UMCG MMB}
|
||||
#' \item{\code{bactid}}{Code of microorganism in \code{\link{microorganisms}}}
|
||||
#' }
|
||||
# source MOLIS (LIS of Certe) - \url{https://www.certe.nl} \cr \cr GLIMS (LIS of UMCG) - \url{https://www.umcg.nl}
|
||||
#' @seealso \code{\link{guess_bactid}} \code{\link{microorganisms}}
|
||||
"microorganisms.umcg"
|
||||
|
||||
#' Dataset with 2000 blood culture isolates of septic patients
|
||||
#'
|
||||
#' An anonymised dataset containing 2000 microbial blood culture isolates with their full antibiograms found in septic patients in 4 different hospitals in the Netherlands, between 2001 and 2017. It is true, genuine data. This \code{data.frame} can be used to practice AMR analysis. For examples, press F1.
|
||||
#' @format A data.frame with 2000 observations and 49 variables:
|
||||
#' \describe{
|
||||
#' \item{\code{date}}{date of receipt at the laboratory}
|
||||
#' \item{\code{hospital_id}}{ID of the hospital, from A to D}
|
||||
#' \item{\code{ward_icu}}{logical to determine if ward is an intensive care unit}
|
||||
#' \item{\code{ward_clinical}}{logical to determine if ward is a regular clinical ward}
|
||||
#' \item{\code{ward_outpatient}}{logical to determine if ward is an outpatient clinic}
|
||||
#' \item{\code{age}}{age of the patient}
|
||||
#' \item{\code{sex}}{sex of the patient}
|
||||
#' \item{\code{patient_id}}{ID of the patient, first 10 characters of an SHA hash containing irretrievable information}
|
||||
#' \item{\code{bactid}}{ID of microorganism, see \code{\link{microorganisms}}}
|
||||
#' \item{\code{peni:rifa}}{40 different antibiotics with class \code{rsi} (see \code{\link{as.rsi}}); these column names occur in \code{\link{antibiotics}} data set and can be translated with \code{\link{abname}}}
|
||||
#' }
|
||||
# source MOLIS (LIS of Certe) - \url{https://www.certe.nl}
|
||||
#' @examples
|
||||
#' # ----------- #
|
||||
#' # PREPARATION #
|
||||
#' # ----------- #
|
||||
#'
|
||||
#' # Save this example dataset to an object, so we can edit it:
|
||||
#' my_data <- septic_patients
|
||||
#'
|
||||
#' # load the dplyr package to make data science A LOT easier
|
||||
#' library(dplyr)
|
||||
#'
|
||||
#' # Add first isolates to our dataset:
|
||||
#' my_data <- my_data %>%
|
||||
#' mutate(first_isolates = first_isolate(my_data, "date", "patient_id", "bactid"))
|
||||
#'
|
||||
#' # -------- #
|
||||
#' # ANALYSIS #
|
||||
#' # -------- #
|
||||
#'
|
||||
#' # 1. Get the amoxicillin resistance percentages (p)
|
||||
#' # and numbers (n) of E. coli, divided by hospital:
|
||||
#'
|
||||
#' my_data %>%
|
||||
#' filter(bactid == guess_bactid("E. coli"),
|
||||
#' first_isolates == TRUE) %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(n = n_rsi(amox),
|
||||
#' p = portion_IR(amox))
|
||||
#'
|
||||
#'
|
||||
#' # 2. Get the amoxicillin/clavulanic acid resistance
|
||||
#' # percentages of E. coli, trend over the years:
|
||||
#'
|
||||
#' my_data %>%
|
||||
#' filter(bactid == guess_bactid("E. coli"),
|
||||
#' first_isolates == TRUE) %>%
|
||||
#' group_by(year = format(date, "%Y")) %>%
|
||||
#' summarise(n = n_rsi(amcl),
|
||||
#' p = portion_IR(amcl, minimum = 20))
|
||||
"septic_patients"
|
||||
Executable
+732
@@ -0,0 +1,732 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' EUCAST expert rules
|
||||
#'
|
||||
#' Apply expert rules (like intrinsic resistance), as defined by the European Committee on Antimicrobial Susceptibility Testing (EUCAST, \url{http://eucast.org}), see \emph{Source}.
|
||||
#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
|
||||
#' @param col_bactid column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{microorganisms$bactid}, see \code{\link{microorganisms}}
|
||||
#' @param info print progress
|
||||
#' @param amcl,amik,amox,ampi,azit,azlo,aztr,cefa,cfep,cfot,cfox,cfra,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,mezl,mino,moxi,nali,neom,neti,nitr,norf,novo,oflo,peni,pita,poly,pris,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. Use \code{NA} to skip a column, like \code{tica = NA}. Non-existing columns will anyway be skipped. See the Antibiotics section for an explanation of the abbreviations.
|
||||
#' @param ... parameters that are passed on to \code{EUCAST_rules}
|
||||
#' @section Abbrevations of antibiotics:
|
||||
#' Abbrevations of the column containing antibiotics:
|
||||
#'
|
||||
#' \strong{amcl}: amoxicillin and beta-lactamase inhibitor (\emph{J01CR02}),
|
||||
#' \strong{amik}: amikacin (\emph{J01GB06}),
|
||||
#' \strong{amox}: amoxicillin (\emph{J01CA04}),
|
||||
#' \strong{ampi}: ampicillin (\emph{J01CA01}),
|
||||
#' \strong{azit}: azithromycin (\emph{J01FA10}),
|
||||
#' \strong{azlo}: azlocillin (\emph{J01CA09}),
|
||||
#' \strong{aztr}: aztreonam (\emph{J01DF01}),
|
||||
#' \strong{cefa}: cefaloridine (\emph{J01DB02}),
|
||||
#' \strong{cfep}: cefepime (\emph{J01DE01}),
|
||||
#' \strong{cfot}: cefotaxime (\emph{J01DD01}),
|
||||
#' \strong{cfox}: cefoxitin (\emph{J01DC01}),
|
||||
#' \strong{cfra}: cefradine (\emph{J01DB09}),
|
||||
#' \strong{cfta}: ceftazidime (\emph{J01DD02}),
|
||||
#' \strong{cftr}: ceftriaxone (\emph{J01DD04}),
|
||||
#' \strong{cfur}: cefuroxime (\emph{J01DC02}),
|
||||
#' \strong{chlo}: chloramphenicol (\emph{J01BA01}),
|
||||
#' \strong{cipr}: ciprofloxacin (\emph{J01MA02}),
|
||||
#' \strong{clar}: clarithromycin (\emph{J01FA09}),
|
||||
#' \strong{clin}: clindamycin (\emph{J01FF01}),
|
||||
#' \strong{clox}: flucloxacillin (\emph{J01CF05}),
|
||||
#' \strong{coli}: colistin (\emph{J01XB01}),
|
||||
#' \strong{czol}: cefazolin (\emph{J01DB04}),
|
||||
#' \strong{dapt}: daptomycin (\emph{J01XX09}),
|
||||
#' \strong{doxy}: doxycycline (\emph{J01AA02}),
|
||||
#' \strong{erta}: ertapenem (\emph{J01DH03}),
|
||||
#' \strong{eryt}: erythromycin (\emph{J01FA01}),
|
||||
#' \strong{fosf}: fosfomycin (\emph{J01XX01}),
|
||||
#' \strong{fusi}: fusidic acid (\emph{J01XC01}),
|
||||
#' \strong{gent}: gentamicin (\emph{J01GB03}),
|
||||
#' \strong{imip}: imipenem and cilastatin (\emph{J01DH51}),
|
||||
#' \strong{kana}: kanamycin (\emph{J01GB04}),
|
||||
#' \strong{levo}: levofloxacin (\emph{J01MA12}),
|
||||
#' \strong{linc}: lincomycin (\emph{J01FF02}),
|
||||
#' \strong{line}: linezolid (\emph{J01XX08}),
|
||||
#' \strong{mero}: meropenem (\emph{J01DH02}),
|
||||
#' \strong{mezl}: mezlocillin (\emph{J01CA10}),
|
||||
#' \strong{mino}: minocycline (\emph{J01AA08}),
|
||||
#' \strong{moxi}: moxifloxacin (\emph{J01MA14}),
|
||||
#' \strong{nali}: nalidixic acid (\emph{J01MB02}),
|
||||
#' \strong{neom}: neomycin (\emph{J01GB05}),
|
||||
#' \strong{neti}: netilmicin (\emph{J01GB07}),
|
||||
#' \strong{nitr}: nitrofurantoin (\emph{J01XE01}),
|
||||
#' \strong{norf}: norfloxacin (\emph{J01MA06}),
|
||||
#' \strong{novo}: novobiocin (an ATCvet code: \emph{QJ01XX95}),
|
||||
#' \strong{oflo}: ofloxacin (\emph{J01MA01}),
|
||||
#' \strong{peni}: penicillins, combinations with other antibacterials (\emph{J01RA01}),
|
||||
#' \strong{pita}: piperacillin and beta-lactamase inhibitor (\emph{J01CR05}),
|
||||
#' \strong{poly}: polymyxin B (\emph{J01XB02}),
|
||||
#' \strong{pris}: pristinamycin (\emph{J01FG01}),
|
||||
#' \strong{qida}: quinupristin/dalfopristin (\emph{J01FG02}),
|
||||
#' \strong{rifa}: rifampicin (\emph{J04AB02}),
|
||||
#' \strong{roxi}: roxithromycin (\emph{J01FA06}),
|
||||
#' \strong{siso}: sisomicin (\emph{J01GB08}),
|
||||
#' \strong{teic}: teicoplanin (\emph{J01XA02}),
|
||||
#' \strong{tetr}: tetracycline (\emph{J01AA07}),
|
||||
#' \strong{tica}: ticarcillin (\emph{J01CA13}),
|
||||
#' \strong{tige}: tigecycline (\emph{J01AA12}),
|
||||
#' \strong{tobr}: tobramycin (\emph{J01GB01}),
|
||||
#' \strong{trim}: trimethoprim (\emph{J01EA01}),
|
||||
#' \strong{trsu}: sulfamethoxazole and trimethoprim (\emph{J01EE01}),
|
||||
#' \strong{vanc}: vancomycin (\emph{J01XA01}).
|
||||
#' @keywords interpretive eucast reading resistance
|
||||
#' @rdname EUCAST
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% left_join select
|
||||
#' @return table with edited variables of antibiotics.
|
||||
#' @source
|
||||
#' EUCAST Expert Rules Version 2.0: \cr
|
||||
#' Leclercq et al. \strong{EUCAST expert rules in antimicrobial susceptibility testing.} \emph{Clin Microbiol Infect.} 2013;19(2):141-60. \cr
|
||||
#' \url{https://doi.org/10.1111/j.1469-0691.2011.03703.x} \cr
|
||||
#' \cr
|
||||
#' EUCAST Expert Rules Version 3.1 (Intrinsic Resistance and Exceptional Phenotypes Tables): \cr
|
||||
#' \url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}
|
||||
#' @examples
|
||||
#' a <- EUCAST_rules(septic_patients)
|
||||
#' a <- data.frame(bactid = c("STAAUR", # Staphylococcus aureus
|
||||
#' "ENCFAE", # Enterococcus faecalis
|
||||
#' "ESCCOL", # Escherichia coli
|
||||
#' "KLEPNE", # Klebsiella pneumoniae
|
||||
#' "PSEAER"), # Pseudomonas aeruginosa
|
||||
#' vanc = "-", # Vancomycin
|
||||
#' amox = "-", # Amoxicillin
|
||||
#' coli = "-", # Colistin
|
||||
#' cfta = "-", # Ceftazidime
|
||||
#' cfur = "-", # Cefuroxime
|
||||
#' stringsAsFactors = FALSE)
|
||||
#' a
|
||||
#'
|
||||
#' b <- EUCAST_rules(a)
|
||||
#' b
|
||||
EUCAST_rules <- function(tbl,
|
||||
col_bactid = 'bactid',
|
||||
info = TRUE,
|
||||
amcl = 'amcl',
|
||||
amik = 'amik',
|
||||
amox = 'amox',
|
||||
ampi = 'ampi',
|
||||
azit = 'azit',
|
||||
azlo = 'azlo',
|
||||
aztr = 'aztr',
|
||||
cefa = 'cefa',
|
||||
cfep = 'cfep',
|
||||
cfot = 'cfot',
|
||||
cfox = 'cfox',
|
||||
cfra = 'cfra',
|
||||
cfta = 'cfta',
|
||||
cftr = 'cftr',
|
||||
cfur = 'cfur',
|
||||
chlo = 'chlo',
|
||||
cipr = 'cipr',
|
||||
clar = 'clar',
|
||||
clin = 'clin',
|
||||
clox = 'clox',
|
||||
coli = 'coli',
|
||||
czol = 'czol',
|
||||
dapt = 'dapt',
|
||||
doxy = 'doxy',
|
||||
erta = 'erta',
|
||||
eryt = 'eryt',
|
||||
fosf = 'fosf',
|
||||
fusi = 'fusi',
|
||||
gent = 'gent',
|
||||
imip = 'imip',
|
||||
kana = 'kana',
|
||||
levo = 'levo',
|
||||
linc = 'linc',
|
||||
line = 'line',
|
||||
mero = 'mero',
|
||||
mezl = 'mezl',
|
||||
mino = 'mino',
|
||||
moxi = 'moxi',
|
||||
nali = 'nali',
|
||||
neom = 'neom',
|
||||
neti = 'neti',
|
||||
nitr = 'nitr',
|
||||
norf = 'norf',
|
||||
novo = 'novo',
|
||||
oflo = 'oflo',
|
||||
peni = 'peni',
|
||||
pita = 'pita',
|
||||
poly = 'poly',
|
||||
pris = 'pris',
|
||||
qida = 'qida',
|
||||
rifa = 'rifa',
|
||||
roxi = 'roxi',
|
||||
siso = 'siso',
|
||||
teic = 'teic',
|
||||
tetr = 'tetr',
|
||||
tica = 'tica',
|
||||
tige = 'tige',
|
||||
tobr = 'tobr',
|
||||
trim = 'trim',
|
||||
trsu = 'trsu',
|
||||
vanc = 'vanc') {
|
||||
|
||||
EUCAST_VERSION <- "3.1"
|
||||
|
||||
if (!col_bactid %in% colnames(tbl)) {
|
||||
stop('Column ', col_bactid, ' not found.', call. = FALSE)
|
||||
}
|
||||
|
||||
# check columns
|
||||
col.list <- c(amcl, amik, amox, ampi, azit, azlo, aztr, cefa, cfra, cfep, cfot,
|
||||
cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
|
||||
czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana,
|
||||
levo, linc, line, mero, mezl, mino, moxi, nali, neom, neti, nitr,
|
||||
novo, norf, oflo, peni, pita, poly, pris, qida, rifa, roxi, siso,
|
||||
teic, tetr, tica, tige, tobr, trim, trsu, vanc)
|
||||
col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
|
||||
amcl <- col.list[amcl]
|
||||
amik <- col.list[amik]
|
||||
amox <- col.list[amox]
|
||||
ampi <- col.list[ampi]
|
||||
azit <- col.list[azit]
|
||||
azlo <- col.list[azlo]
|
||||
aztr <- col.list[aztr]
|
||||
cefa <- col.list[cefa]
|
||||
cfep <- col.list[cfep]
|
||||
cfot <- col.list[cfot]
|
||||
cfox <- col.list[cfox]
|
||||
cfra <- col.list[cfra]
|
||||
cfta <- col.list[cfta]
|
||||
cftr <- col.list[cftr]
|
||||
cfur <- col.list[cfur]
|
||||
chlo <- col.list[chlo]
|
||||
cipr <- col.list[cipr]
|
||||
clar <- col.list[clar]
|
||||
clin <- col.list[clin]
|
||||
clox <- col.list[clox]
|
||||
coli <- col.list[coli]
|
||||
czol <- col.list[czol]
|
||||
dapt <- col.list[dapt]
|
||||
doxy <- col.list[doxy]
|
||||
erta <- col.list[erta]
|
||||
eryt <- col.list[eryt]
|
||||
fosf <- col.list[fosf]
|
||||
fusi <- col.list[fusi]
|
||||
gent <- col.list[gent]
|
||||
imip <- col.list[imip]
|
||||
kana <- col.list[kana]
|
||||
levo <- col.list[levo]
|
||||
linc <- col.list[linc]
|
||||
line <- col.list[line]
|
||||
mero <- col.list[mero]
|
||||
mezl <- col.list[mezl]
|
||||
mino <- col.list[mino]
|
||||
moxi <- col.list[moxi]
|
||||
nali <- col.list[nali]
|
||||
neom <- col.list[neom]
|
||||
neti <- col.list[neti]
|
||||
nitr <- col.list[nitr]
|
||||
norf <- col.list[norf]
|
||||
novo <- col.list[novo]
|
||||
oflo <- col.list[oflo]
|
||||
peni <- col.list[peni]
|
||||
pita <- col.list[pita]
|
||||
poly <- col.list[poly]
|
||||
pris <- col.list[pris]
|
||||
qida <- col.list[qida]
|
||||
rifa <- col.list[rifa]
|
||||
roxi <- col.list[roxi]
|
||||
siso <- col.list[siso]
|
||||
teic <- col.list[teic]
|
||||
tetr <- col.list[tetr]
|
||||
tica <- col.list[tica]
|
||||
tige <- col.list[tige]
|
||||
tobr <- col.list[tobr]
|
||||
trim <- col.list[trim]
|
||||
trsu <- col.list[trsu]
|
||||
vanc <- col.list[vanc]
|
||||
|
||||
total <- 0
|
||||
total_rows <- integer(0)
|
||||
|
||||
# helper function for editing the table
|
||||
edit_rsi <- function(to, rows, cols, EUCAST_rule = "") {
|
||||
# later: use this as attribute for the edited observations
|
||||
EUCAST_rule <- trimws(paste("EUCAST rule", EUCAST_rule))
|
||||
cols <- cols[!is.na(cols)]
|
||||
if (length(rows) > 0 & length(cols) > 0) {
|
||||
tbl[rows, cols] <<- to
|
||||
total <<- total + (length(rows) * length(cols))
|
||||
total_rows <<- c(total_rows, rows)
|
||||
}
|
||||
}
|
||||
|
||||
# join to microorganisms data set
|
||||
if (!tbl %>% pull(col_bactid) %>% is.bactid()) {
|
||||
warning("Improve integrity of the `", col_bactid, "` column by transforming it with 'as.bactid'.")
|
||||
}
|
||||
tbl <- tbl %>% left_join_microorganisms(by = col_bactid, suffix = c("_tempmicroorganisms", ""))
|
||||
|
||||
# antibiotic classes
|
||||
aminoglycosides <- c(tobr, gent, kana, neom, neti, siso)
|
||||
tetracyclines <- c(doxy, mino, tetr) # since EUCAST v3.1 tige(cycline) is set apart
|
||||
polymyxins <- c(poly, coli)
|
||||
macrolides <- c(eryt, azit, roxi, clar) # since EUCAST v3.1 clinda is set apart
|
||||
glycopeptides <- c(vanc, teic)
|
||||
streptogramins <- c(qida, pris) # should officially also be quinupristin/dalfopristin
|
||||
cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
|
||||
carbapenems <- c(erta, imip, mero)
|
||||
aminopenicillins <- c(ampi, amox)
|
||||
ureidopenicillins <- c(pita, azlo, mezl)
|
||||
fluoroquinolones <- c(oflo, cipr, norf, levo, moxi)
|
||||
|
||||
if (info == TRUE) {
|
||||
cat(
|
||||
paste0(
|
||||
'\nApplying rules to ',
|
||||
tbl[!is.na(tbl$genus),] %>% nrow() %>% format(big.mark = ","),
|
||||
' rows according to "EUCAST Expert Rules Version ', EUCAST_VERSION, '"\n')
|
||||
)
|
||||
}
|
||||
|
||||
# Table 1: Intrinsic resistance in Enterobacteriaceae ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 1: Intrinsic resistance in Enterobacteriaceae\n')
|
||||
}
|
||||
# Intrisiek R for this group
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$family == 'Enterobacteriaceae'),
|
||||
cols = c(peni, glycopeptides, fusi, macrolides, linc, streptogramins, rifa, dapt, line))
|
||||
# Citrobacter
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Citrobacter (koseri|amalonaticus|sedlakii|farmeri|rodentium)'),
|
||||
cols = c(aminopenicillins, tica))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Citrobacter (freundii|braakii|murliniae|werkmanii|youngae)'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfox))
|
||||
# Enterobacter
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Enterobacter cloacae'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfox))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Enterobacter aerogenes'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfox))
|
||||
# Escherichia
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Escherichia hermanni'),
|
||||
cols = c(aminopenicillins, tica))
|
||||
# Hafnia
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Hafnia alvei'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfox))
|
||||
# Klebsiella
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Klebsiella'),
|
||||
cols = c(aminopenicillins, tica))
|
||||
# Morganella / Proteus
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Morganella morganii'),
|
||||
cols = c(aminopenicillins, amcl, czol, tetracyclines, polymyxins, nitr))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Proteus mirabilis'),
|
||||
cols = c(tetracyclines, tige, polymyxins, nitr))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Proteus penneri'),
|
||||
cols = c(aminopenicillins, czol, cfur, tetracyclines, tige, polymyxins, nitr))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Proteus vulgaris'),
|
||||
cols = c(aminopenicillins, czol, cfur, tetracyclines, tige, polymyxins, nitr))
|
||||
# Providencia
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Providencia rettgeri'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfur, tetracyclines, tige, polymyxins, nitr))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Providencia stuartii'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfur, tetracyclines, tige, polymyxins, nitr))
|
||||
# Raoultella
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Raoultella'),
|
||||
cols = c(aminopenicillins, tica))
|
||||
# Serratia
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Serratia marcescens'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfox, cfur, tetracyclines[tetracyclines != 'mino'], polymyxins, nitr))
|
||||
# Yersinia
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Yersinia enterocolitica'),
|
||||
cols = c(aminopenicillins, amcl, tica, czol, cfox))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Yersinia pseudotuberculosis'),
|
||||
cols = c(poly, coli))
|
||||
|
||||
|
||||
# Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria\n')
|
||||
}
|
||||
# Intrisiek R for this group
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus %in% c('Achromobacter',
|
||||
'Acinetobacter',
|
||||
'Alcaligenes',
|
||||
'Bordatella',
|
||||
'Burkholderia',
|
||||
'Elizabethkingia',
|
||||
'Flavobacterium',
|
||||
'Ochrobactrum',
|
||||
'Pseudomonas',
|
||||
'Stenotrophomonas')),
|
||||
cols = c(peni, cfox, cfur, glycopeptides, fusi, macrolides, linc, streptogramins, rifa, dapt, line))
|
||||
# Acinetobacter
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Acinetobacter (baumannii|pittii|nosocomialis|calcoaceticus)'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfot, cftr, aztr, erta, trim, fosf, tetracyclines[tetracyclines != 'mino']))
|
||||
# Achromobacter
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Achromobacter (xylosoxydans|xylosoxidans)'),
|
||||
cols = c(aminopenicillins, czol, cfot, cftr, erta))
|
||||
# Burkholderia
|
||||
edit_rsi(to = 'R',
|
||||
# onder 'Burkholderia cepacia complex' vallen deze species allemaal: PMID 16217180.
|
||||
rows = which(tbl$fullname %like% '^Burkholderia (cepacia|multivorans|cenocepacia|stabilis|vietnamiensis|dolosa|ambifaria|anthina|pyrrocinia|ubonensis)'),
|
||||
cols = c(aminopenicillins, amcl, tica, pita, czol, cfot, cftr, aztr, erta, cipr, chlo, aminoglycosides, trim, fosf, polymyxins))
|
||||
# Elizabethkingia
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Elizabethkingia meningoseptic(a|um)'),
|
||||
cols = c(aminopenicillins, amcl, tica, czol, cfot, cftr, cfta, cfep, aztr, erta, imip, mero, polymyxins))
|
||||
# Ochrobactrum
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Ochrobactrum anthropi'),
|
||||
cols = c(aminopenicillins, amcl, tica, pita, czol, cfot, cftr, cfta, cfep, aztr, erta))
|
||||
# Pseudomonas
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Pseudomonas aeruginosa'),
|
||||
cols = c(aminopenicillins, amcl, czol, cfot, cftr, erta, chlo, kana, neom, trim, trsu, tetracyclines, tige))
|
||||
# Stenotrophomonas
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Stenotrophomonas maltophilia'),
|
||||
cols = c(aminopenicillins, amcl, tica, pita, czol, cfot, cftr, cfta, aztr, erta, imip, mero, aminoglycosides, trim, fosf, tetr))
|
||||
|
||||
|
||||
# Table 3: Intrinsic resistance in other Gram-negative bacteria ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 3: Intrinsic resistance in other Gram-negative bacteria\n')
|
||||
}
|
||||
# Intrisiek R for this group
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus %in% c('Haemophilus',
|
||||
'Moraxella',
|
||||
'Neisseria',
|
||||
'Campylobacter')),
|
||||
cols = c(glycopeptides, linc, dapt, line))
|
||||
# Haemophilus
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Haemophilus influenzae'),
|
||||
cols = c(fusi, streptogramins))
|
||||
# Moraxella
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Moraxella catarrhalis'),
|
||||
cols = trim)
|
||||
# Neisseria
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Neisseria'),
|
||||
cols = trim)
|
||||
# Campylobacter
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Campylobacter fetus'),
|
||||
cols = c(fusi, streptogramins, trim, nali))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Campylobacter (jejuni|coli)'),
|
||||
cols = c(fusi, streptogramins, trim))
|
||||
|
||||
|
||||
# Table 4: Intrinsic resistance in Gram-positive bacteria ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 4: Intrinsic resistance in Gram-positive bacteria\n')
|
||||
}
|
||||
# Intrisiek R for this group
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$gramstain %like% 'Positi(e|)(v|f)'),
|
||||
cols = c(aztr, polymyxins, nali))
|
||||
# Staphylococcus
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Staphylococcus saprophyticus'),
|
||||
cols = c(fusi, cfta, fosf, novo))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Staphylococcus (cohnii|xylosus)'),
|
||||
cols = c(cfta, novo))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Staphylococcus capitis'),
|
||||
cols = c(cfta, fosf))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
|
||||
cols = cfta)
|
||||
# Streptococcus
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Streptococcus'),
|
||||
cols = c(fusi, cfta, aminoglycosides))
|
||||
# Enterococcus
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Enterococcus faecalis'),
|
||||
cols = c(fusi, cfta, cephalosporins[cephalosporins != cfta], aminoglycosides, macrolides, clin, qida, trim, trsu))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Enterococcus (gallinarum|casseliflavus)'),
|
||||
cols = c(fusi, cfta, cephalosporins[cephalosporins != cfta], aminoglycosides, macrolides, clin, qida, vanc, trim, trsu))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Enterococcus faecium'),
|
||||
cols = c(fusi, cfta, cephalosporins[cephalosporins != cfta], aminoglycosides, macrolides, trim, trsu))
|
||||
# Corynebacterium
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Corynebacterium'),
|
||||
cols = fosf)
|
||||
# Listeria
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Listeria monocytogenes'),
|
||||
cols = c(cfta, cephalosporins[cephalosporins != cfta]))
|
||||
# overig
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus %in% c('Leuconostoc', 'Pediococcus')),
|
||||
cols = c(vanc, teic))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Lactobacillus'),
|
||||
cols = c(vanc, teic))
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Clostridium (ramosum|innocuum)'),
|
||||
cols = vanc)
|
||||
|
||||
# Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci\n')
|
||||
}
|
||||
# rule 8.3
|
||||
if (!is.na(peni)) {
|
||||
edit_rsi(to = 'S',
|
||||
rows = which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|dysgalactiae|groep A|groep B|groep C|groep G)'
|
||||
& tbl[, peni] == 'S'),
|
||||
cols = c(aminopenicillins, cephalosporins, carbapenems))
|
||||
}
|
||||
# rule 8.6
|
||||
if (!is.na(ampi)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Enterococcus'
|
||||
& tbl[, ampi] == 'R'),
|
||||
cols = c(ureidopenicillins, carbapenems))
|
||||
}
|
||||
if (!is.na(amox)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Enterococcus'
|
||||
& tbl[, amox] == 'R'),
|
||||
cols = c(ureidopenicillins, carbapenems))
|
||||
}
|
||||
|
||||
# Table 9: Interpretive rules for B-lactam agents and Gram-negative rods ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 9: Interpretive rules for B-lactam agents and Gram-negative rods\n')
|
||||
}
|
||||
# rule 9.3
|
||||
if (!is.na(tica) & !is.na(pita)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$family == 'Enterobacteriaceae'
|
||||
& tbl[, tica] == 'R'
|
||||
& tbl[, pita] == 'S'),
|
||||
cols = pita)
|
||||
}
|
||||
|
||||
# Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria\n')
|
||||
}
|
||||
# rule 10.2
|
||||
if (!is.na(ampi)) {
|
||||
# you should know first if the are B-lactamase positive, so do not run for now
|
||||
# edit_rsi(to = 'R',
|
||||
# rows = which(tbl$fullname %like% '^Haemophilus influenza'
|
||||
# & tbl[, ampi] == 'R'),
|
||||
# cols = c(ampi, amox, amcl, pita, cfur))
|
||||
}
|
||||
|
||||
# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins\n')
|
||||
}
|
||||
# rule 11.1
|
||||
if (!is.na(eryt)) {
|
||||
if (!is.na(azit)) {
|
||||
tbl[, azit] <- tbl[, eryt]
|
||||
}
|
||||
if (!is.na(clar)) {
|
||||
tbl[, clar] <- tbl[, eryt]
|
||||
}
|
||||
}
|
||||
|
||||
# Table 12: Interpretive rules for aminoglycosides ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 12: Interpretive rules for aminoglycosides\n')
|
||||
}
|
||||
# rule 12.2
|
||||
if (!is.na(tobr)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Staphylococcus'
|
||||
& tbl[, tobr] == 'R'),
|
||||
cols = c(kana, amik))
|
||||
}
|
||||
# rule 12.3
|
||||
if (!is.na(gent)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Staphylococcus'
|
||||
& tbl[, gent] == 'R'),
|
||||
cols = aminoglycosides)
|
||||
}
|
||||
# rule 12.8
|
||||
if (!is.na(gent) & !is.na(tobr)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$family == 'Enterobacteriaceae'
|
||||
& tbl[, gent] == 'I'
|
||||
& tbl[, tobr] == 'S'),
|
||||
cols = gent)
|
||||
}
|
||||
# rule 12.9
|
||||
if (!is.na(gent) & !is.na(tobr)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$family == 'Enterobacteriaceae'
|
||||
& tbl[, tobr] == 'I'
|
||||
& tbl[, gent] == 'R'),
|
||||
cols = tobr)
|
||||
}
|
||||
|
||||
|
||||
# Table 13: Interpretive rules for quinolones ----
|
||||
if (info == TRUE) {
|
||||
cat('- Table 13: Interpretive rules for quinolones\n')
|
||||
}
|
||||
# rule 13.2
|
||||
if (!is.na(moxi)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$genus == 'Staphylococcus'
|
||||
& tbl[, moxi] == 'R'),
|
||||
cols = fluoroquinolones)
|
||||
}
|
||||
# rule 13.4
|
||||
if (!is.na(moxi)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Streptococcus pneumoniae'
|
||||
& tbl[, moxi] == 'R'),
|
||||
cols = fluoroquinolones)
|
||||
}
|
||||
# rule 13.5
|
||||
if (!is.na(cipr)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$family == 'Enterobacteriaceae'
|
||||
& tbl[, cipr] == 'R'),
|
||||
cols = fluoroquinolones)
|
||||
}
|
||||
# rule 13.8
|
||||
if (!is.na(cipr)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl$fullname %like% '^Neisseria gonorrhoeae'
|
||||
& tbl[, cipr] == 'R'),
|
||||
cols = fluoroquinolones)
|
||||
}
|
||||
|
||||
|
||||
# Other ----
|
||||
if (info == TRUE) {
|
||||
cat('- Non-EUCAST: trim = R where trsu = R and ampi = R where amcl = R\n')
|
||||
}
|
||||
if (!is.na(amcl)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl[, amcl] == 'R'),
|
||||
cols = ampi)
|
||||
}
|
||||
if (!is.na(trsu)) {
|
||||
edit_rsi(to = 'R',
|
||||
rows = which(tbl[, trsu] == 'R'),
|
||||
cols = trim)
|
||||
}
|
||||
if (info == TRUE) {
|
||||
cat('- Non-EUCAST: trsu = S where trim = S and amcl = S where ampi = S\n')
|
||||
}
|
||||
if (!is.na(amcl)) {
|
||||
edit_rsi(to = 'S',
|
||||
rows = which(tbl[, ampi] == 'S'),
|
||||
cols = amcl)
|
||||
}
|
||||
if (!is.na(trsu)) {
|
||||
edit_rsi(to = 'S',
|
||||
rows = which(tbl[, trim] == 'S'),
|
||||
cols = trsu)
|
||||
}
|
||||
# amox = ampi
|
||||
if (!is.na(ampi) & !is.na(amox)) {
|
||||
tbl[, amox] <- tbl %>% pull(ampi)
|
||||
}
|
||||
|
||||
# Remove added columns again
|
||||
microorganisms.ncol <- ncol(AMR::microorganisms) - 2
|
||||
tbl.ncol <- ncol(tbl)
|
||||
tbl <- tbl %>% select(-c((tbl.ncol - microorganisms.ncol):tbl.ncol))
|
||||
# and remove added suffices
|
||||
colnames(tbl) <- gsub("_tempmicroorganisms", "", colnames(tbl))
|
||||
|
||||
if (info == TRUE) {
|
||||
cat('Done.\n\nEUCAST Expert rules applied to',
|
||||
total_rows %>% unique() %>% length() %>% format(big.mark = ","),
|
||||
'different rows; overwritten a total of',
|
||||
total %>% format(big.mark = ","), 'test results.\n\n')
|
||||
}
|
||||
|
||||
tbl
|
||||
}
|
||||
|
||||
#' @rdname EUCAST
|
||||
#' @export
|
||||
interpretive_reading <- function(...) {
|
||||
EUCAST_rules(...)
|
||||
}
|
||||
|
||||
#' Poperties of a microorganism
|
||||
#'
|
||||
#' @param bactid ID of a microorganisme, like \code{"STAAUR} and \code{"ESCCOL}
|
||||
#' @param property One of the values \code{bactid}, \code{bactsys}, \code{family}, \code{genus}, \code{species}, \code{subspecies}, \code{fullname}, \code{type}, \code{gramstain}, \code{aerobic}
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% filter select
|
||||
#' @seealso \code{\link{microorganisms}}
|
||||
mo_property <- function(bactid, property = 'fullname') {
|
||||
|
||||
mocode <- as.character(bactid)
|
||||
|
||||
for (i in 1:length(mocode)) {
|
||||
bug <- mocode[i]
|
||||
|
||||
if (!is.na(bug)) {
|
||||
result = tryCatch({
|
||||
mocode[i] <-
|
||||
AMR::microorganisms %>%
|
||||
filter(bactid == bug) %>%
|
||||
select(property) %>%
|
||||
unlist() %>%
|
||||
as.character()
|
||||
}, error = function(error_condition) {
|
||||
warning('Code ', bug, ' not found in bacteria list.')
|
||||
}, finally = {
|
||||
if (mocode[i] == bug & !property %in% c('bactid', 'bactsys')) {
|
||||
mocode[i] <- NA
|
||||
}
|
||||
})
|
||||
}
|
||||
|
||||
}
|
||||
mocode
|
||||
}
|
||||
Executable
+427
@@ -0,0 +1,427 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Determine first (weighted) isolates
|
||||
#'
|
||||
#' Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.
|
||||
#' @param tbl a \code{data.frame} containing isolates.
|
||||
#' @param col_date column name of the result date (or date that is was received on the lab)
|
||||
#' @param col_patient_id column name of the unique IDs of the patients
|
||||
#' @param col_bactid column name of the unique IDs of the microorganisms: \code{bactid}'s. If this column has another class than \code{"bactid"}, values will be coerced using \code{\link{as.bactid}}.
|
||||
#' @param col_testcode column name of the test codes. Use \code{col_testcode = NA} to \strong{not} exclude certain test codes (like test codes for screening). In that case \code{testcodes_exclude} will be ignored. Supports tidyverse-like quotation.
|
||||
#' @param col_specimen column name of the specimen type or group
|
||||
#' @param col_icu column name of the logicals (\code{TRUE}/\code{FALSE}) whether a ward or department is an Intensive Care Unit (ICU)
|
||||
#' @param col_keyantibiotics column name of the key antibiotics to determine first \emph{weighted} isolates, see \code{\link{key_antibiotics}}. Supports tidyverse-like quotation.
|
||||
#' @param episode_days episode in days after which a genus/species combination will be determined as 'first isolate' again
|
||||
#' @param testcodes_exclude character vector with test codes that should be excluded (case-insensitive)
|
||||
#' @param icu_exclude logical whether ICU isolates should be excluded
|
||||
#' @param filter_specimen specimen group or type that should be excluded
|
||||
#' @param output_logical return output as \code{logical} (will else be the values \code{0} or \code{1})
|
||||
#' @param type type to determine weighed isolates; can be \code{"keyantibiotics"} or \code{"points"}, see Details
|
||||
#' @param ignore_I logical to determine whether antibiotic interpretations with \code{"I"} will be ignored when \code{type = "keyantibiotics"}, see Details
|
||||
#' @param points_threshold points until the comparison of key antibiotics will lead to inclusion of an isolate when \code{type = "points"}, see Details
|
||||
#' @param info print progress
|
||||
#' @param col_genus (deprecated, use \code{col_bactid} instead) column name of the genus of the microorganisms
|
||||
#' @param col_species (deprecated, use \code{col_bactid} instead) column name of the species of the microorganisms
|
||||
#' @details \strong{WHY THIS IS SO IMPORTANT} \cr
|
||||
#' To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode \href{https://www.ncbi.nlm.nih.gov/pubmed/17304462}{[1]}. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all \emph{S. aureus} isolates would be overestimated, because you included this MRSA more than once. It would be \href{https://en.wikipedia.org/wiki/Selection_bias}{selection bias}.
|
||||
#' @section Key antibiotics:
|
||||
#' There are two ways to determine whether isolates can be included as first \emph{weighted} isolates which will give generally the same results: \cr
|
||||
#'
|
||||
#' \strong{1. Using} \code{type = "keyantibiotics"} \strong{and parameter} \code{ignore_I} \cr
|
||||
#' Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With \code{ignore_I = FALSE}, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. \cr
|
||||
#'
|
||||
#' \strong{2. Using} \code{type = "points"} \strong{and parameter} \code{points_threshold} \cr
|
||||
#' A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds \code{points_threshold}, an isolate will be (re)selected as a first weighted isolate.
|
||||
#' @keywords isolate isolates first
|
||||
#' @seealso \code{\link{key_antibiotics}}
|
||||
#' @export
|
||||
#' @importFrom dplyr arrange_at lag between row_number filter mutate arrange
|
||||
#' @return A vector to add to table, see Examples.
|
||||
#' @source Methodology of this function is based on: \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
|
||||
#' @examples
|
||||
#' # septic_patients is a dataset available in the AMR package. It is true, genuine data.
|
||||
#' ?septic_patients
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' my_patients <- septic_patients %>%
|
||||
#' mutate(first_isolate = first_isolate(.,
|
||||
#' col_date = "date",
|
||||
#' col_patient_id = "patient_id",
|
||||
#' col_bactid = "bactid"))
|
||||
#'
|
||||
#' # Now let's see if first isolates matter:
|
||||
#' A <- my_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(count = n_rsi(gent), # gentamicin availability
|
||||
#' resistance = portion_IR(gent)) # gentamicin resistance
|
||||
#'
|
||||
#' B <- my_patients %>%
|
||||
#' filter(first_isolate == TRUE) %>% # the 1st isolate filter
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(count = n_rsi(gent), # gentamicin availability
|
||||
#' resistance = portion_IR(gent)) # gentamicin resistance
|
||||
#'
|
||||
#' # Have a look at A and B.
|
||||
#' # B is more reliable because every isolate is only counted once.
|
||||
#' # Gentamicin resitance in hospital D appears to be 5.4% higher than
|
||||
#' # when you (erroneously) would have used all isolates!
|
||||
#'
|
||||
#' ## OTHER EXAMPLES:
|
||||
#'
|
||||
#' \dontrun{
|
||||
#'
|
||||
#' # set key antibiotics to a new variable
|
||||
#' tbl$keyab <- key_antibiotics(tbl)
|
||||
#'
|
||||
#' tbl$first_isolate <-
|
||||
#' first_isolate(tbl)
|
||||
#'
|
||||
#' tbl$first_isolate_weighed <-
|
||||
#' first_isolate(tbl,
|
||||
#' col_keyantibiotics = 'keyab')
|
||||
#'
|
||||
#' tbl$first_blood_isolate <-
|
||||
#' first_isolate(tbl,
|
||||
#' filter_specimen = 'Blood')
|
||||
#'
|
||||
#' tbl$first_blood_isolate_weighed <-
|
||||
#' first_isolate(tbl,
|
||||
#' filter_specimen = 'Blood',
|
||||
#' col_keyantibiotics = 'keyab')
|
||||
#'
|
||||
#' tbl$first_urine_isolate <-
|
||||
#' first_isolate(tbl,
|
||||
#' filter_specimen = 'Urine')
|
||||
#'
|
||||
#' tbl$first_urine_isolate_weighed <-
|
||||
#' first_isolate(tbl,
|
||||
#' filter_specimen = 'Urine',
|
||||
#' col_keyantibiotics = 'keyab')
|
||||
#'
|
||||
#' tbl$first_resp_isolate <-
|
||||
#' first_isolate(tbl,
|
||||
#' filter_specimen = 'Respiratory')
|
||||
#'
|
||||
#' tbl$first_resp_isolate_weighed <-
|
||||
#' first_isolate(tbl,
|
||||
#' filter_specimen = 'Respiratory',
|
||||
#' col_keyantibiotics = 'keyab')
|
||||
#' }
|
||||
first_isolate <- function(tbl,
|
||||
col_date,
|
||||
col_patient_id,
|
||||
col_bactid = NA,
|
||||
col_testcode = NA,
|
||||
col_specimen = NA,
|
||||
col_icu = NA,
|
||||
col_keyantibiotics = NA,
|
||||
episode_days = 365,
|
||||
testcodes_exclude = '',
|
||||
icu_exclude = FALSE,
|
||||
filter_specimen = NA,
|
||||
output_logical = TRUE,
|
||||
type = "keyantibiotics",
|
||||
ignore_I = TRUE,
|
||||
points_threshold = 2,
|
||||
info = TRUE,
|
||||
col_genus = NA,
|
||||
col_species = NA) {
|
||||
|
||||
# bactid OR genus+species must be available
|
||||
if (is.na(col_bactid) & (is.na(col_genus) | is.na(col_species))) {
|
||||
stop('`col_bactid` or both `col_genus` and `col_species` must be available.')
|
||||
}
|
||||
|
||||
# check if columns exist
|
||||
check_columns_existance <- function(column, tblname = tbl) {
|
||||
if (NROW(tblname) <= 1 | NCOL(tblname) <= 1) {
|
||||
stop('Please check tbl for existance.')
|
||||
}
|
||||
|
||||
if (!is.na(column)) {
|
||||
if (!(column %in% colnames(tblname))) {
|
||||
stop('Column `', column, '` not found.')
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
check_columns_existance(col_date)
|
||||
check_columns_existance(col_patient_id)
|
||||
check_columns_existance(col_bactid)
|
||||
check_columns_existance(col_genus)
|
||||
check_columns_existance(col_species)
|
||||
check_columns_existance(col_testcode)
|
||||
check_columns_existance(col_icu)
|
||||
check_columns_existance(col_keyantibiotics)
|
||||
|
||||
if (!is.na(col_bactid)) {
|
||||
if (!tbl %>% pull(col_bactid) %>% is.bactid()) {
|
||||
warning("Improve integrity of the `", col_bactid, "` column by transforming it with 'as.bactid'.")
|
||||
}
|
||||
# join to microorganisms data set
|
||||
tbl <- tbl %>% left_join_microorganisms(by = col_bactid)
|
||||
col_genus <- "genus"
|
||||
col_species <- "species"
|
||||
}
|
||||
|
||||
if (is.na(col_testcode)) {
|
||||
testcodes_exclude <- NA
|
||||
}
|
||||
# remove testcodes
|
||||
if (!is.na(testcodes_exclude[1]) & testcodes_exclude[1] != '' & info == TRUE) {
|
||||
cat('[Criteria] Excluded test codes:\n', toString(testcodes_exclude), '\n')
|
||||
}
|
||||
|
||||
if (is.na(col_icu)) {
|
||||
icu_exclude <- FALSE
|
||||
} else {
|
||||
tbl <- tbl %>%
|
||||
mutate(col_icu = tbl %>% pull(col_icu) %>% as.logical())
|
||||
}
|
||||
|
||||
if (is.na(col_specimen)) {
|
||||
filter_specimen <- ''
|
||||
}
|
||||
|
||||
# filter on specimen group and keyantibiotics when they are filled in
|
||||
if (!is.na(filter_specimen) & filter_specimen != '') {
|
||||
check_columns_existance(col_specimen, tbl)
|
||||
if (info == TRUE) {
|
||||
cat('[Criteria] Excluded other than specimen group \'', filter_specimen, '\'\n', sep = '')
|
||||
}
|
||||
} else {
|
||||
filter_specimen <- ''
|
||||
}
|
||||
if (col_keyantibiotics %in% c(NA, '')) {
|
||||
col_keyantibiotics <- ''
|
||||
} else {
|
||||
tbl <- tbl %>% mutate(key_ab = tbl %>% pull(col_keyantibiotics))
|
||||
}
|
||||
|
||||
if (is.na(testcodes_exclude[1])) {
|
||||
testcodes_exclude <- ''
|
||||
}
|
||||
|
||||
# create new dataframe with original row index and right sorting
|
||||
tbl <- tbl %>%
|
||||
mutate(first_isolate_row_index = 1:nrow(tbl),
|
||||
date_lab = tbl %>% pull(col_date),
|
||||
patient_id = tbl %>% pull(col_patient_id),
|
||||
species = tbl %>% pull(col_species),
|
||||
genus = tbl %>% pull(col_genus)) %>%
|
||||
mutate(species = if_else(is.na(species) | species == "(no MO)", "", species),
|
||||
genus = if_else(is.na(genus) | genus == "(no MO)", "", genus))
|
||||
|
||||
if (filter_specimen == '') {
|
||||
|
||||
if (icu_exclude == FALSE) {
|
||||
if (info == TRUE & !is.na(col_icu)) {
|
||||
cat('[Criteria] Included isolates from ICU.\n')
|
||||
}
|
||||
tbl <- tbl %>%
|
||||
arrange_at(c(col_patient_id,
|
||||
col_genus,
|
||||
col_species,
|
||||
col_date))
|
||||
row.start <- 1
|
||||
row.end <- nrow(tbl)
|
||||
} else {
|
||||
if (info == TRUE) {
|
||||
cat('[Criteria] Excluded isolates from ICU.\n')
|
||||
}
|
||||
tbl <- tbl %>%
|
||||
arrange_at(c(col_icu,
|
||||
col_patient_id,
|
||||
col_genus,
|
||||
col_species,
|
||||
col_date))
|
||||
|
||||
suppressWarnings(
|
||||
row.start <- which(tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
|
||||
)
|
||||
suppressWarnings(
|
||||
row.end <- which(tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
|
||||
)
|
||||
}
|
||||
|
||||
} else {
|
||||
# sort on specimen and only analyse these row to save time
|
||||
if (icu_exclude == FALSE) {
|
||||
if (info == TRUE & !is.na(col_icu)) {
|
||||
cat('[Criteria] Included isolates from ICU.\n')
|
||||
}
|
||||
tbl <- tbl %>%
|
||||
arrange_at(c(col_specimen,
|
||||
col_patient_id,
|
||||
col_genus,
|
||||
col_species,
|
||||
col_date))
|
||||
suppressWarnings(
|
||||
row.start <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% min(na.rm = TRUE)
|
||||
)
|
||||
suppressWarnings(
|
||||
row.end <- which(tbl %>% pull(col_specimen) == filter_specimen) %>% max(na.rm = TRUE)
|
||||
)
|
||||
} else {
|
||||
if (info == TRUE) {
|
||||
cat('[Criteria] Excluded isolates from ICU.\n')
|
||||
}
|
||||
tbl <- tbl %>%
|
||||
arrange_at(c(col_icu,
|
||||
col_specimen,
|
||||
col_patient_id,
|
||||
col_genus,
|
||||
col_species,
|
||||
col_date))
|
||||
suppressWarnings(
|
||||
row.start <- which(tbl %>% pull(col_specimen) == filter_specimen
|
||||
& tbl %>% pull(col_icu) == FALSE) %>% min(na.rm = TRUE)
|
||||
)
|
||||
suppressWarnings(
|
||||
row.end <- which(tbl %>% pull(col_specimen) == filter_specimen
|
||||
& tbl %>% pull(col_icu) == FALSE) %>% max(na.rm = TRUE)
|
||||
)
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
if (abs(row.start) == Inf | abs(row.end) == Inf) {
|
||||
if (info == TRUE) {
|
||||
message('No isolates found.')
|
||||
}
|
||||
# NA's where genus is unavailable
|
||||
tbl <- tbl %>%
|
||||
mutate(real_first_isolate = if_else(genus == '', NA, FALSE))
|
||||
if (output_logical == FALSE) {
|
||||
tbl$real_first_isolate <- tbl %>% pull(real_first_isolate) %>% as.integer()
|
||||
}
|
||||
return(tbl %>% pull(real_first_isolate))
|
||||
}
|
||||
|
||||
# suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
|
||||
suppressWarnings(
|
||||
scope.size <- tbl %>%
|
||||
filter(
|
||||
row_number() %>% between(row.start,
|
||||
row.end),
|
||||
genus != '') %>%
|
||||
nrow()
|
||||
)
|
||||
|
||||
# Analysis of first isolate ----
|
||||
all_first <- tbl %>%
|
||||
mutate(other_pat_or_mo = if_else(patient_id == lag(patient_id)
|
||||
& genus == lag(genus)
|
||||
& species == lag(species),
|
||||
FALSE,
|
||||
TRUE),
|
||||
days_diff = 0) %>%
|
||||
mutate(days_diff = if_else(other_pat_or_mo == FALSE,
|
||||
(date_lab - lag(date_lab)) + lag(days_diff),
|
||||
0))
|
||||
|
||||
weighted.notice <- ''
|
||||
if (col_keyantibiotics != '') {
|
||||
weighted.notice <- 'weighted '
|
||||
if (info == TRUE) {
|
||||
if (type == 'keyantibiotics') {
|
||||
cat('[Criteria] Inclusion based on key antibiotics, ')
|
||||
if (ignore_I == FALSE) {
|
||||
cat('not ')
|
||||
}
|
||||
cat('ignoring I.\n')
|
||||
}
|
||||
if (type == 'points') {
|
||||
cat(paste0('[Criteria] Inclusion based on key antibiotics, using points threshold of '
|
||||
, points_threshold, '.\n'))
|
||||
}
|
||||
}
|
||||
type_param <- type
|
||||
# suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
|
||||
suppressWarnings(
|
||||
all_first <- all_first %>%
|
||||
mutate(key_ab_lag = lag(key_ab)) %>%
|
||||
mutate(key_ab_other = !key_antibiotics_equal(x = key_ab,
|
||||
y = key_ab_lag,
|
||||
type = type_param,
|
||||
ignore_I = ignore_I,
|
||||
points_threshold = points_threshold,
|
||||
info = info)) %>%
|
||||
mutate(
|
||||
real_first_isolate =
|
||||
if_else(
|
||||
between(row_number(), row.start, row.end)
|
||||
& genus != ''
|
||||
& (other_pat_or_mo
|
||||
| days_diff >= episode_days
|
||||
| key_ab_other),
|
||||
TRUE,
|
||||
FALSE))
|
||||
)
|
||||
} else {
|
||||
# suppress warnings because dplyr want us to use library(dplyr) when using filter(row_number())
|
||||
suppressWarnings(
|
||||
all_first <- all_first %>%
|
||||
mutate(
|
||||
real_first_isolate =
|
||||
if_else(
|
||||
between(row_number(), row.start, row.end)
|
||||
& genus != ''
|
||||
& (other_pat_or_mo
|
||||
| days_diff >= episode_days),
|
||||
TRUE,
|
||||
FALSE))
|
||||
)
|
||||
}
|
||||
|
||||
# first one as TRUE
|
||||
all_first[row.start, 'real_first_isolate'] <- TRUE
|
||||
# no tests that should be included, or ICU
|
||||
if (!is.na(col_testcode)) {
|
||||
all_first[which(all_first[, col_testcode] %in% tolower(testcodes_exclude)), 'real_first_isolate'] <- FALSE
|
||||
}
|
||||
if (icu_exclude == TRUE) {
|
||||
all_first[which(all_first[, col_icu] == TRUE), 'real_first_isolate'] <- FALSE
|
||||
}
|
||||
|
||||
# NA's where genus is unavailable
|
||||
all_first <- all_first %>%
|
||||
mutate(real_first_isolate = if_else(genus %in% c('', '(no MO)', NA), NA, real_first_isolate))
|
||||
|
||||
all_first <- all_first %>%
|
||||
arrange(first_isolate_row_index) %>%
|
||||
pull(real_first_isolate)
|
||||
|
||||
if (info == TRUE) {
|
||||
message(paste0('Found ',
|
||||
all_first %>% sum(na.rm = TRUE),
|
||||
' first ', weighted.notice, 'isolates (',
|
||||
(all_first %>% sum(na.rm = TRUE) / scope.size) %>% percent(),
|
||||
' of isolates in scope [where genus was not empty] and ',
|
||||
(all_first %>% sum(na.rm = TRUE) / tbl %>% nrow()) %>% percent(),
|
||||
' of total)'))
|
||||
}
|
||||
|
||||
if (output_logical == FALSE) {
|
||||
all_first <- all_first %>% as.integer()
|
||||
}
|
||||
|
||||
all_first
|
||||
|
||||
}
|
||||
@@ -0,0 +1,643 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Frequency table
|
||||
#'
|
||||
#' Create a frequency table of a vector with items or a data frame. Supports quasiquotation and markdown for reports. \code{top_freq} can be used to get the top/bottom \emph{n} items of a frequency table, with counts as names.
|
||||
#' @param x vector of any class or a \code{\link{data.frame}}, \code{\link{tibble}} or \code{\link{table}}
|
||||
#' @param ... up to nine different columns of \code{x} when \code{x} is a \code{data.frame} or \code{tibble}, to calculate frequencies from - see Examples
|
||||
#' @param sort.count sort on count, i.e. frequencies. This will be \code{TRUE} at default for everything except for factors.
|
||||
#' @param nmax number of row to print. The default, \code{15}, uses \code{\link{getOption}("max.print.freq")}. Use \code{nmax = 0}, \code{nmax = Inf}, \code{nmax = NULL} or \code{nmax = NA} to print all rows.
|
||||
#' @param na.rm a logical value indicating whether \code{NA} values should be removed from the frequency table. The header will always print the amount of \code{NA}s.
|
||||
#' @param row.names a logical value indicating whether row indices should be printed as \code{1:nrow(x)}
|
||||
#' @param markdown print table in markdown format (this forces \code{nmax = NA})
|
||||
#' @param digits how many significant digits are to be used for numeric values in the header (not for the items themselves, that depends on \code{\link{getOption}("digits")})
|
||||
#' @param sep a character string to separate the terms when selecting multiple columns
|
||||
#' @param f a frequency table
|
||||
#' @param n number of top \emph{n} items to return, use -n for the bottom \emph{n} items. It will include more than \code{n} rows if there are ties.
|
||||
#' @details Frequency tables (or frequency distributions) are summaries of the distribution of values in a sample. With the `freq` function, you can create univariate frequency tables. Multiple variables will be pasted into one variable, so it forces a univariate distribution. This package also has a vignette available to explain the use of this function further, run \code{browseVignettes("AMR")} to read it.
|
||||
#'
|
||||
#' For numeric values of any class, these additional values will all be calculated with \code{na.rm = TRUE} and shown into the header:
|
||||
#' \itemize{
|
||||
#' \item{Mean, using \code{\link[base]{mean}}}
|
||||
#' \item{Standard Deviation, using \code{\link[stats]{sd}}}
|
||||
#' \item{Coefficient of Variation (CV), the standard deviation divided by the mean}
|
||||
#' \item{Mean Absolute Deviation (MAD), using \code{\link[stats]{mad}}}
|
||||
#' \item{Tukey Five-Number Summaries (minimum, Q1, median, Q3, maximum), using \code{\link[stats]{fivenum}}}
|
||||
#' \item{Interquartile Range (IQR) calculated as \code{Q3 - Q1} using the Tukey Five-Number Summaries, i.e. \strong{not} using the \code{\link[stats]{quantile}} function}
|
||||
#' \item{Coefficient of Quartile Variation (CQV, sometimes called coefficient of dispersion), calculated as \code{(Q3 - Q1) / (Q3 + Q1)} using the Tukey Five-Number Summaries}
|
||||
#' \item{Outliers (total count and unique count), using \code{\link[grDevices]{boxplot.stats}}}
|
||||
#' }
|
||||
#'
|
||||
#' For dates and times of any class, these additional values will be calculated with \code{na.rm = TRUE} and shown into the header:
|
||||
#' \itemize{
|
||||
#' \item{Oldest, using \code{\link{min}}}
|
||||
#' \item{Newest, using \code{\link{max}}, with difference between newest and oldest}
|
||||
#' \item{Median, using \code{\link[stats]{median}}, with percentage since oldest}
|
||||
#' }
|
||||
#'
|
||||
#'
|
||||
#' The function \code{top_freq} uses \code{\link[dplyr]{top_n}} internally and will include more than \code{n} rows if there are ties.
|
||||
#' @importFrom stats fivenum sd mad
|
||||
#' @importFrom grDevices boxplot.stats
|
||||
#' @importFrom dplyr %>% select pull n_distinct group_by arrange desc mutate summarise n_distinct
|
||||
#' @importFrom utils browseVignettes installed.packages
|
||||
#' @importFrom tibble tibble
|
||||
#' @keywords summary summarise frequency freq
|
||||
#' @rdname freq
|
||||
#' @name freq
|
||||
#' @return A \code{data.frame} with an additional class \code{"frequency_tbl"}
|
||||
#' @export
|
||||
#' @examples
|
||||
#' library(dplyr)
|
||||
#'
|
||||
#' # this all gives the same result:
|
||||
#' freq(septic_patients$hospital_id)
|
||||
#' freq(septic_patients[, "hospital_id"])
|
||||
#' septic_patients$hospital_id %>% freq()
|
||||
#' septic_patients[, "hospital_id"] %>% freq()
|
||||
#' septic_patients %>% freq("hospital_id")
|
||||
#' septic_patients %>% freq(hospital_id) #<- easiest to remember when you're used to tidyverse
|
||||
#'
|
||||
#' # you could also use `select` or `pull` to get your variables
|
||||
#' septic_patients %>%
|
||||
#' filter(hospital_id == "A") %>%
|
||||
#' select(bactid) %>%
|
||||
#' freq()
|
||||
#'
|
||||
#' # multiple selected variables will be pasted together
|
||||
#' septic_patients %>%
|
||||
#' left_join_microorganisms %>%
|
||||
#' filter(hospital_id == "A") %>%
|
||||
#' freq(genus, species)
|
||||
#'
|
||||
#' # get top 10 bugs of hospital A as a vector
|
||||
#' septic_patients %>%
|
||||
#' filter(hospital_id == "A") %>%
|
||||
#' freq(bactid) %>%
|
||||
#' top_freq(10)
|
||||
#'
|
||||
#' # save frequency table to an object
|
||||
#' years <- septic_patients %>%
|
||||
#' mutate(year = format(date, "%Y")) %>%
|
||||
#' freq(year)
|
||||
#'
|
||||
#' # show only the top 5
|
||||
#' years %>% print(nmax = 5)
|
||||
#'
|
||||
#' # save to an object with formatted percentages
|
||||
#' years <- format(years)
|
||||
#'
|
||||
#' # print a histogram of numeric values
|
||||
#' septic_patients %>%
|
||||
#' freq(age) %>%
|
||||
#' hist() # prettier: ggplot(septic_patients, aes(age)) + geom_histogram()
|
||||
#'
|
||||
#' # or print all points to a regular plot
|
||||
#' septic_patients %>%
|
||||
#' freq(age) %>%
|
||||
#' plot()
|
||||
#'
|
||||
#' # transform to a data.frame or tibble
|
||||
#' septic_patients %>%
|
||||
#' freq(age) %>%
|
||||
#' as.data.frame()
|
||||
#'
|
||||
#' # or transform (back) to a vector
|
||||
#' septic_patients %>%
|
||||
#' freq(age) %>%
|
||||
#' as.vector()
|
||||
#'
|
||||
#' identical(septic_patients %>%
|
||||
#' freq(age) %>%
|
||||
#' as.vector() %>%
|
||||
#' sort(),
|
||||
#' sort(septic_patients$age)) # TRUE
|
||||
#'
|
||||
#' # it also supports `table` objects:
|
||||
#' table(septic_patients$sex,
|
||||
#' septic_patients$age) %>%
|
||||
#' freq(sep = " **sep** ")
|
||||
#'
|
||||
#' \dontrun{
|
||||
#' # send frequency table to clipboard (e.g. for pasting in Excel)
|
||||
#' septic_patients %>%
|
||||
#' freq(age) %>%
|
||||
#' format() %>% # this will format the percentages
|
||||
#' clipboard_export()
|
||||
#' }
|
||||
frequency_tbl <- function(x,
|
||||
...,
|
||||
sort.count = TRUE,
|
||||
nmax = getOption("max.print.freq"),
|
||||
na.rm = TRUE,
|
||||
row.names = TRUE,
|
||||
markdown = FALSE,
|
||||
digits = 2,
|
||||
sep = " ") {
|
||||
|
||||
mult.columns <- 0
|
||||
|
||||
if (any(class(x) == 'data.frame')) {
|
||||
x.name <- deparse(substitute(x))
|
||||
if (x.name == ".") {
|
||||
x.name <- NULL
|
||||
}
|
||||
dots <- base::eval(base::substitute(base::alist(...)))
|
||||
ndots <- length(dots)
|
||||
|
||||
if (NROW(x) == 0) {
|
||||
x <- NA
|
||||
} else if (ndots > 0 & ndots < 10) {
|
||||
cols <- as.character(dots)
|
||||
if (!all(cols %in% colnames(x))) {
|
||||
stop("one or more columns not found: `", paste(cols, collapse = "`, `"), '`', call. = FALSE)
|
||||
}
|
||||
x <- x[, cols]
|
||||
} else if (ndots >= 10) {
|
||||
stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
|
||||
} else {
|
||||
cols <- NULL
|
||||
}
|
||||
} else if (any(class(x) == 'table')) {
|
||||
if (!"tidyr" %in% rownames(installed.packages())) {
|
||||
stop('transformation from `table` to frequency table requires the tidyr package.', call. = FALSE)
|
||||
}
|
||||
x <- x %>%
|
||||
as.data.frame(stringsAsFactors = FALSE) %>%
|
||||
# paste first two columns
|
||||
tidyr::unite(col = "Pasted", 1:2, sep = sep, remove = TRUE)
|
||||
x <- rep(x %>% pull(Pasted), x %>% pull(Freq))
|
||||
x.name <- "a `table` object"
|
||||
cols <- NULL
|
||||
mult.columns <- 2
|
||||
} else {
|
||||
x.name <- NULL
|
||||
cols <- NULL
|
||||
}
|
||||
|
||||
if (!is.null(ncol(x))) {
|
||||
if (ncol(x) == 1 & any(class(x) == 'data.frame')) {
|
||||
x <- x %>% pull(1)
|
||||
} else if (ncol(x) < 10) {
|
||||
|
||||
mult.columns <- ncol(x)
|
||||
|
||||
colnames(x) <- LETTERS[1:ncol(x)]
|
||||
if (ncol(x) == 2) {
|
||||
x$total <- paste(x$A %>% as.character(),
|
||||
x$B %>% as.character(),
|
||||
sep = sep)
|
||||
} else if (ncol(x) == 3) {
|
||||
x$total <- paste(x$A %>% as.character(),
|
||||
x$B %>% as.character(),
|
||||
x$C %>% as.character(),
|
||||
sep = sep)
|
||||
} else if (ncol(x) == 4) {
|
||||
x$total <- paste(x$A %>% as.character(),
|
||||
x$B %>% as.character(),
|
||||
x$C %>% as.character(),
|
||||
x$D %>% as.character(),
|
||||
sep = sep)
|
||||
} else if (ncol(x) == 5) {
|
||||
x$total <- paste(x$A %>% as.character(),
|
||||
x$B %>% as.character(),
|
||||
x$C %>% as.character(),
|
||||
x$D %>% as.character(),
|
||||
x$E %>% as.character(),
|
||||
sep = sep)
|
||||
} else if (ncol(x) == 6) {
|
||||
x$total <- paste(x$A %>% as.character(),
|
||||
x$B %>% as.character(),
|
||||
x$C %>% as.character(),
|
||||
x$D %>% as.character(),
|
||||
x$E %>% as.character(),
|
||||
x$F %>% as.character(),
|
||||
sep = sep)
|
||||
} else if (ncol(x) == 7) {
|
||||
x$total <- paste(x$A %>% as.character(),
|
||||
x$B %>% as.character(),
|
||||
x$C %>% as.character(),
|
||||
x$D %>% as.character(),
|
||||
x$E %>% as.character(),
|
||||
x$F %>% as.character(),
|
||||
x$G %>% as.character(),
|
||||
sep = sep)
|
||||
} else if (ncol(x) == 8) {
|
||||
x$total <- paste(x$A %>% as.character(),
|
||||
x$B %>% as.character(),
|
||||
x$C %>% as.character(),
|
||||
x$D %>% as.character(),
|
||||
x$E %>% as.character(),
|
||||
x$F %>% as.character(),
|
||||
x$G %>% as.character(),
|
||||
x$H %>% as.character(),
|
||||
sep = sep)
|
||||
} else if (ncol(x) == 9) {
|
||||
x$total <- paste(x$A %>% as.character(),
|
||||
x$B %>% as.character(),
|
||||
x$C %>% as.character(),
|
||||
x$D %>% as.character(),
|
||||
x$E %>% as.character(),
|
||||
x$F %>% as.character(),
|
||||
x$G %>% as.character(),
|
||||
x$H %>% as.character(),
|
||||
x$I %>% as.character(),
|
||||
sep = sep)
|
||||
}
|
||||
|
||||
x <- x$total
|
||||
|
||||
} else {
|
||||
stop('A maximum of 9 columns can be analysed at the same time.', call. = FALSE)
|
||||
}
|
||||
}
|
||||
|
||||
if (mult.columns > 1) {
|
||||
NAs <- x[is.na(x) | x == trimws(strrep('NA ', mult.columns))]
|
||||
} else {
|
||||
NAs <- x[is.na(x)]
|
||||
}
|
||||
|
||||
if (na.rm == TRUE) {
|
||||
x_class <- class(x)
|
||||
x <- x[!x %in% NAs]
|
||||
class(x) <- x_class
|
||||
}
|
||||
|
||||
if (missing(sort.count) & 'factor' %in% class(x)) {
|
||||
# sort on factor level at default when x is a factor and sort.count is not set
|
||||
sort.count <- FALSE
|
||||
}
|
||||
|
||||
header <- character(0)
|
||||
|
||||
markdown_line <- ''
|
||||
if (markdown == TRUE) {
|
||||
markdown_line <- '\n'
|
||||
}
|
||||
x_align <- 'l'
|
||||
|
||||
if (mult.columns > 0) {
|
||||
header <- header %>% paste0(markdown_line, 'Columns: ', mult.columns)
|
||||
} else {
|
||||
header <- header %>% paste0(markdown_line, 'Class: ', class(x) %>% rev() %>% paste(collapse = " > "))
|
||||
}
|
||||
|
||||
if (is.list(x) | is.matrix(x) | is.environment(x) | is.function(x)) {
|
||||
stop('frequency tables do not support lists, matrices, environments and functions.', call. = FALSE)
|
||||
}
|
||||
|
||||
header <- header %>% paste0(markdown_line, '\nLength: ', (NAs %>% length() + x %>% length()) %>% format(),
|
||||
' (of which NA: ', NAs %>% length() %>% format(),
|
||||
' = ', (NAs %>% length() / (NAs %>% length() + x %>% length())) %>% percent(force_zero = TRUE, round = digits) %>% sub('NaN', '0', ., fixed = TRUE), ')')
|
||||
header <- header %>% paste0(markdown_line, '\nUnique: ', x %>% n_distinct() %>% format())
|
||||
|
||||
if (NROW(x) > 0 & any(class(x) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
|
||||
# right align number
|
||||
Tukey_five <- stats::fivenum(x, na.rm = TRUE)
|
||||
x_align <- 'r'
|
||||
header <- header %>% paste0('\n')
|
||||
header <- header %>% paste(markdown_line, '\nMean: ', x %>% base::mean(na.rm = TRUE) %>% format(digits = digits))
|
||||
header <- header %>% paste0(markdown_line, '\nStd. dev.: ', x %>% stats::sd(na.rm = TRUE) %>% format(digits = digits),
|
||||
' (CV: ', x %>% cv(na.rm = TRUE) %>% format(digits = digits),
|
||||
', MAD: ', x %>% stats::mad(na.rm = TRUE) %>% format(digits = digits), ')')
|
||||
header <- header %>% paste0(markdown_line, '\nFive-Num: ', Tukey_five %>% format(digits = digits) %>% trimws() %>% paste(collapse = ' | '),
|
||||
' (IQR: ', (Tukey_five[4] - Tukey_five[2]) %>% format(digits = digits),
|
||||
', CQV: ', x %>% cqv(na.rm = TRUE) %>% format(digits = digits), ')')
|
||||
outlier_length <- length(boxplot.stats(x)$out)
|
||||
header <- header %>% paste0(markdown_line, '\nOutliers: ', outlier_length)
|
||||
if (outlier_length > 0) {
|
||||
header <- header %>% paste0(' (unique: ', boxplot.stats(x)$out %>% n_distinct(), ')')
|
||||
}
|
||||
}
|
||||
if (NROW(x) > 0 & any(class(x) == "rsi")) {
|
||||
header <- header %>% paste0('\n')
|
||||
cnt_S <- sum(x == "S")
|
||||
cnt_I <- sum(x == "I")
|
||||
cnt_R <- sum(x == "R")
|
||||
header <- header %>% paste(markdown_line, '\n%IR: ',
|
||||
((cnt_I + cnt_R) / sum(!is.na(x))) %>% percent(force_zero = TRUE, round = digits))
|
||||
header <- header %>% paste0(markdown_line, '\nRatio SIR: 1.0 : ',
|
||||
(cnt_I / cnt_S) %>% format(digits = 1, nsmall = 1), " : ",
|
||||
(cnt_R / cnt_S) %>% format(digits = 1, nsmall = 1))
|
||||
}
|
||||
|
||||
formatdates <- "%e %B %Y" # = d mmmm yyyy
|
||||
if (any(class(x) == 'hms')) {
|
||||
x <- x %>% as.POSIXlt()
|
||||
formatdates <- "%H:%M:%S"
|
||||
}
|
||||
if (NROW(x) > 0 & any(class(x) %in% c('Date', 'POSIXct', 'POSIXlt'))) {
|
||||
header <- header %>% paste0('\n')
|
||||
mindate <- x %>% min(na.rm = TRUE)
|
||||
maxdate <- x %>% max(na.rm = TRUE)
|
||||
maxdate_days <- difftime(maxdate, mindate, units = 'auto') %>% as.double()
|
||||
mediandate <- x %>% median(na.rm = TRUE)
|
||||
median_days <- difftime(mediandate, mindate, units = 'auto') %>% as.double()
|
||||
|
||||
header <- header %>% paste0(markdown_line, '\nOldest: ', mindate %>% format(formatdates) %>% trimws())
|
||||
header <- header %>% paste0(markdown_line, '\nNewest: ', maxdate %>% format(formatdates) %>% trimws(),
|
||||
' (+', difftime(maxdate, mindate, units = 'auto') %>% as.double() %>% format(), ')')
|
||||
header <- header %>% paste0(markdown_line, '\nMedian: ', mediandate %>% format(formatdates) %>% trimws(),
|
||||
' (~', percent(median_days / maxdate_days, round = 0), ')')
|
||||
}
|
||||
if (any(class(x) == 'POSIXlt')) {
|
||||
x <- x %>% format(formatdates)
|
||||
}
|
||||
|
||||
nmax.set <- !missing(nmax)
|
||||
if (!nmax.set & is.null(nmax) & is.null(base::getOption("max.print.freq", default = NULL))) {
|
||||
# default for max print setting
|
||||
nmax <- 15
|
||||
} else if (is.null(nmax)) {
|
||||
nmax <- length(x)
|
||||
}
|
||||
|
||||
if (nmax %in% c(0, Inf, NA, NULL)) {
|
||||
nmax <- length(x)
|
||||
}
|
||||
|
||||
# create table with counts and percentages
|
||||
column_names <- c('Item', 'Count', 'Percent', 'Cum. Count', 'Cum. Percent', '(Factor Level)')
|
||||
column_names_df <- c('item', 'count', 'percent', 'cum_count', 'cum_percent', 'factor_level')
|
||||
|
||||
if (any(class(x) == 'factor')) {
|
||||
df <- tibble::tibble(item = x,
|
||||
fctlvl = x %>% as.integer()) %>%
|
||||
group_by(item, fctlvl)
|
||||
column_align <- c('l', 'r', 'r', 'r', 'r', 'r')
|
||||
} else {
|
||||
df <- tibble::tibble(item = x) %>%
|
||||
group_by(item)
|
||||
# strip factor lvl from col names
|
||||
column_names <- column_names[1:length(column_names) - 1]
|
||||
column_names_df <- column_names_df[1:length(column_names_df) - 1]
|
||||
column_align <- c(x_align, 'r', 'r', 'r', 'r')
|
||||
}
|
||||
df <- df %>% summarise(count = n())
|
||||
|
||||
if (df$item %>% paste(collapse = ',') %like% '\033') {
|
||||
df <- df %>%
|
||||
mutate(item = item %>%
|
||||
# remove escape char
|
||||
# see https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
|
||||
gsub('\033', ' ', ., fixed = TRUE))
|
||||
}
|
||||
|
||||
# sort according to setting
|
||||
if (sort.count == TRUE) {
|
||||
df <- df %>% arrange(desc(count), item)
|
||||
} else {
|
||||
if (any(class(x) == 'factor')) {
|
||||
df <- df %>% arrange(fctlvl, item)
|
||||
} else {
|
||||
df <- df %>% arrange(item)
|
||||
}
|
||||
}
|
||||
|
||||
df <- as.data.frame(df, stringsAsFactors = FALSE)
|
||||
|
||||
df$percent <- df$count / base::sum(df$count, na.rm = TRUE)
|
||||
df$cum_count <- base::cumsum(df$count)
|
||||
df$cum_percent <- df$cum_count / base::sum(df$count, na.rm = TRUE)
|
||||
|
||||
if (any(class(x) == 'factor')) {
|
||||
# put factor last
|
||||
df <- df %>% select(item, count, percent, cum_count, cum_percent, fctlvl)
|
||||
}
|
||||
|
||||
colnames(df) <- column_names_df
|
||||
|
||||
class(df) <- c('frequency_tbl', class(df))
|
||||
attr(df, 'package') <- 'AMR'
|
||||
|
||||
if (markdown == TRUE) {
|
||||
tbl_format <- 'markdown'
|
||||
} else {
|
||||
tbl_format <- 'pandoc'
|
||||
}
|
||||
|
||||
attr(df, 'opt') <- list(data = x.name,
|
||||
vars = cols,
|
||||
header = header,
|
||||
row_names = row.names,
|
||||
column_names = column_names,
|
||||
column_align = column_align,
|
||||
tbl_format = tbl_format,
|
||||
nmax = nmax,
|
||||
nmax.set = nmax.set)
|
||||
|
||||
df
|
||||
}
|
||||
|
||||
#' @rdname freq
|
||||
#' @export
|
||||
freq <- frequency_tbl
|
||||
|
||||
#' @rdname freq
|
||||
#' @export
|
||||
#' @importFrom dplyr top_n pull
|
||||
top_freq <- function(f, n) {
|
||||
if (!'frequency_tbl' %in% class(f)) {
|
||||
stop('top_freq can only be applied to frequency tables', call. = FALSE)
|
||||
}
|
||||
if (!is.numeric(n) | length(n) != 1L) {
|
||||
stop('For top_freq, `nmax` must be a number of length 1', call. = FALSE)
|
||||
}
|
||||
top <- f %>% top_n(n, count)
|
||||
vect <- top %>% pull(item)
|
||||
names(vect) <- top %>% pull(count)
|
||||
if (length(vect) > abs(n)) {
|
||||
message("top_freq: selecting ", length(vect), " items instead of ", abs(n), ", because of ties")
|
||||
}
|
||||
vect
|
||||
}
|
||||
|
||||
#' @rdname freq
|
||||
#' @exportMethod print.frequency_tbl
|
||||
#' @importFrom knitr kable
|
||||
#' @importFrom dplyr n_distinct
|
||||
#' @export
|
||||
print.frequency_tbl <- function(x, nmax = getOption("max.print.freq", default = 15), ...) {
|
||||
|
||||
opt <- attr(x, 'opt')
|
||||
|
||||
if (!is.null(opt$data) & !is.null(opt$vars)) {
|
||||
title <- paste0("of `", paste0(opt$vars, collapse = "` and `"), "` from ", opt$data)
|
||||
} else if (!is.null(opt$data) & is.null(opt$vars)) {
|
||||
title <- paste("of", opt$data)
|
||||
} else if (is.null(opt$data) & !is.null(opt$vars)) {
|
||||
title <- paste0("of `", paste0(opt$vars, collapse = "` and `"), "`")
|
||||
} else {
|
||||
title <- ""
|
||||
}
|
||||
|
||||
if (!missing(nmax)) {
|
||||
opt$nmax <- nmax
|
||||
opt$nmax.set <- TRUE
|
||||
}
|
||||
|
||||
cat("Frequency table", title, "\n")
|
||||
|
||||
if (!is.null(opt$header)) {
|
||||
cat(opt$header)
|
||||
}
|
||||
|
||||
if (NROW(x) == 0) {
|
||||
cat('\n\nNo observations.\n')
|
||||
return(invisible())
|
||||
}
|
||||
|
||||
if (all(x$count == 1)) {
|
||||
warning('All observations are unique.', call. = FALSE)
|
||||
}
|
||||
|
||||
# save old NA setting for kable
|
||||
opt.old <- options()$knitr.kable.NA
|
||||
options(knitr.kable.NA = "<NA>")
|
||||
|
||||
if (nrow(x) > opt$nmax & opt$tbl_format != "markdown") {
|
||||
|
||||
x.rows <- nrow(x)
|
||||
x.unprinted <- base::sum(x[(opt$nmax + 1):nrow(x), 'count'], na.rm = TRUE)
|
||||
x.printed <- base::sum(x$count) - x.unprinted
|
||||
|
||||
if (opt$nmax.set == TRUE) {
|
||||
nmax <- opt$nmax
|
||||
} else {
|
||||
nmax <- getOption("max.print.freq", default = 15)
|
||||
}
|
||||
|
||||
x <- x[1:nmax,]
|
||||
|
||||
if (opt$nmax.set == TRUE) {
|
||||
footer <- paste('[ reached `nmax = ', opt$nmax, '`', sep = '')
|
||||
} else {
|
||||
footer <- '[ reached getOption("max.print.freq")'
|
||||
}
|
||||
footer <- paste(footer,
|
||||
' -- omitted ',
|
||||
format(x.rows - opt$nmax),
|
||||
' entries, n = ',
|
||||
format(x.unprinted),
|
||||
' (',
|
||||
(x.unprinted / (x.unprinted + x.printed)) %>% percent(force_zero = TRUE),
|
||||
') ]\n', sep = '')
|
||||
} else {
|
||||
footer <- NULL
|
||||
}
|
||||
|
||||
if (any(class(x$item) %in% c('double', 'integer', 'numeric', 'raw', 'single'))) {
|
||||
x$item <- format(x$item)
|
||||
}
|
||||
x$count <- format(x$count)
|
||||
x$percent <- percent(x$percent, force_zero = TRUE)
|
||||
x$cum_count <- format(x$cum_count)
|
||||
x$cum_percent <- percent(x$cum_percent, force_zero = TRUE)
|
||||
|
||||
print(
|
||||
knitr::kable(x,
|
||||
format = opt$tbl_format,
|
||||
row.names = opt$row_names,
|
||||
col.names = opt$column_names,
|
||||
align = opt$column_align,
|
||||
padding = 1)
|
||||
)
|
||||
|
||||
if (!is.null(footer)) {
|
||||
cat(footer)
|
||||
}
|
||||
|
||||
cat('\n')
|
||||
|
||||
# reset old kable setting
|
||||
options(knitr.kable.NA = opt.old)
|
||||
return(invisible())
|
||||
|
||||
}
|
||||
|
||||
#' @noRd
|
||||
#' @exportMethod as.data.frame.frequency_tbl
|
||||
#' @export
|
||||
as.data.frame.frequency_tbl <- function(x, ...) {
|
||||
attr(x, 'package') <- NULL
|
||||
attr(x, 'opt') <- NULL
|
||||
as.data.frame.data.frame(x, ...)
|
||||
}
|
||||
|
||||
#' @noRd
|
||||
#' @exportMethod as_tibble.frequency_tbl
|
||||
#' @export
|
||||
#' @importFrom dplyr as_tibble
|
||||
as_tibble.frequency_tbl <- function(x, validate = TRUE, ..., rownames = NA) {
|
||||
attr(x, 'package') <- NULL
|
||||
attr(x, 'opt') <- NULL
|
||||
as_tibble(x = as.data.frame(x), validate = validate, ..., rownames = rownames)
|
||||
}
|
||||
|
||||
#' @noRd
|
||||
#' @exportMethod hist.frequency_tbl
|
||||
#' @export
|
||||
#' @importFrom graphics hist
|
||||
hist.frequency_tbl <- function(x, ...) {
|
||||
opt <- attr(x, 'opt')
|
||||
if (!is.null(opt$vars)) {
|
||||
title <- opt$vars
|
||||
} else {
|
||||
title <- ""
|
||||
}
|
||||
hist(as.vector(x), main = paste("Histogram of", title), xlab = title, ...)
|
||||
}
|
||||
|
||||
#' @noRd
|
||||
#' @exportMethod plot.frequency_tbl
|
||||
#' @export
|
||||
plot.frequency_tbl <- function(x, y, ...) {
|
||||
opt <- attr(x, 'opt')
|
||||
if (!is.null(opt$vars)) {
|
||||
title <- opt$vars
|
||||
} else {
|
||||
title <- ""
|
||||
}
|
||||
plot(x = x$item, y = x$count, ylab = "Count", xlab = title, ...)
|
||||
}
|
||||
|
||||
#' @noRd
|
||||
#' @exportMethod as.vector.frequency_tbl
|
||||
#' @export
|
||||
as.vector.frequency_tbl <- function(x, mode = "any") {
|
||||
as.vector(rep(x$item, x$count), mode = mode)
|
||||
}
|
||||
|
||||
#' @noRd
|
||||
#' @exportMethod format.frequency_tbl
|
||||
#' @export
|
||||
format.frequency_tbl <- function(x, digits = 1, ...) {
|
||||
opt <- attr(x, 'opt')
|
||||
if (opt$nmax.set == TRUE) {
|
||||
nmax <- opt$nmax
|
||||
} else {
|
||||
nmax <- getOption("max.print.freq", default = 15)
|
||||
}
|
||||
|
||||
x <- x[1:nmax,]
|
||||
x$percent <- percent(x$percent, round = digits, force_zero = TRUE)
|
||||
x$cum_percent <- percent(x$cum_percent, round = digits, force_zero = TRUE)
|
||||
base::format.data.frame(x, ...)
|
||||
}
|
||||
+257
@@ -0,0 +1,257 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' \emph{G}-test for Count Data
|
||||
#'
|
||||
#' \code{g.test} performs chi-squared contingency table tests and goodness-of-fit tests, just like \code{\link{chisq.test}} but is more reliable [1]. A \emph{G}-test can be used to see whether the number of observations in each category fits a theoretical expectation (called a \strong{\emph{G}-test of goodness-of-fit}), or to see whether the proportions of one variable are different for different values of the other variable (called a \strong{\emph{G}-test of independence}).
|
||||
#' @inherit stats::chisq.test params return
|
||||
#' @details If \code{x} is a matrix with one row or column, or if \code{x} is a vector and \code{y} is not given, then a \emph{goodness-of-fit test} is performed (\code{x} is treated as a one-dimensional contingency table). The entries of \code{x} must be non-negative integers. In this case, the hypothesis tested is whether the population probabilities equal those in \code{p}, or are all equal if \code{p} is not given.
|
||||
#'
|
||||
#' If \code{x} is a matrix with at least two rows and columns, it is taken as a two-dimensional contingency table: the entries of \code{x} must be non-negative integers. Otherwise, \code{x} and \code{y} must be vectors or factors of the same length; cases with missing values are removed, the objects are coerced to factors, and the contingency table is computed from these. Then Pearson's chi-squared test is performed of the null hypothesis that the joint distribution of the cell counts in a 2-dimensional contingency table is the product of the row and column marginals.
|
||||
#'
|
||||
#' The p-value is computed from the asymptotic chi-squared distribution of the test statistic.
|
||||
#'
|
||||
#' In the contingency table case simulation is done by random sampling from the set of all contingency tables with given marginals, and works only if the marginals are strictly positive. Note that this is not the usual sampling situation assumed for a chi-squared test (like the \emph{G}-test) but rather that for Fisher's exact test.
|
||||
#'
|
||||
#' In the goodness-of-fit case simulation is done by random sampling from the discrete distribution specified by \code{p}, each sample being of size \code{n = sum(x)}. This simulation is done in \R and may be slow.
|
||||
#' @section \emph{G}-test of goodness-of-fit (likelihood ratio test):
|
||||
#' Use the \emph{G}-test of goodness-of-fit when you have one nominal variable with two or more values (such as male and female, or red, pink and white flowers). You compare the observed counts of numbers of observations in each category with the expected counts, which you calculate using some kind of theoretical expectation (such as a 1:1 sex ratio or a 1:2:1 ratio in a genetic cross).
|
||||
#'
|
||||
#' If the expected number of observations in any category is too small, the \emph{G}-test may give inaccurate results, and you should use an exact test instead (\code{\link{fisher.test}}).
|
||||
#'
|
||||
#' The \emph{G}-test of goodness-of-fit is an alternative to the chi-square test of goodness-of-fit (\code{\link{chisq.test}}); each of these tests has some advantages and some disadvantages, and the results of the two tests are usually very similar.
|
||||
#'
|
||||
#' @section \emph{G}-test of independence:
|
||||
#' Use the \emph{G}-test of independence when you have two nominal variables, each with two or more possible values. You want to know whether the proportions for one variable are different among values of the other variable.
|
||||
#'
|
||||
#' It is also possible to do a \emph{G}-test of independence with more than two nominal variables. For example, Jackson et al. (2013) also had data for children under 3, so you could do an analysis of old vs. young, thigh vs. arm, and reaction vs. no reaction, all analyzed together.
|
||||
#'
|
||||
#' Fisher's exact test (\code{\link{fisher.test}}) is more accurate than the \emph{G}-test of independence when the expected numbers are small, so it is recommend to only use the \emph{G}-test if your total sample size is greater than 1000.
|
||||
#'
|
||||
#' The \emph{G}-test of independence is an alternative to the chi-square test of independence (\code{\link{chisq.test}}), and they will give approximately the same results.
|
||||
#' @section How the test works:
|
||||
#' Unlike the exact test of goodness-of-fit (\code{\link{fisher.test}}), the \emph{G}-test does not directly calculate the probability of obtaining the observed results or something more extreme. Instead, like almost all statistical tests, the \emph{G}-test has an intermediate step; it uses the data to calculate a test statistic that measures how far the observed data are from the null expectation. You then use a mathematical relationship, in this case the chi-square distribution, to estimate the probability of obtaining that value of the test statistic.
|
||||
#'
|
||||
#' The \emph{G}-test uses the log of the ratio of two likelihoods as the test statistic, which is why it is also called a likelihood ratio test or log-likelihood ratio test. The formula to calculate a \emph{G}-statistic is:
|
||||
#'
|
||||
#' \code{G <- 2 * sum(x * log(x / E))}
|
||||
#'
|
||||
#' where \code{E} are the expected values. Since this is chi-square distributed, the p value can be calculated with:
|
||||
#'
|
||||
#' \code{p <- stats::pchisq(G, df, lower.tail = FALSE)}
|
||||
#'
|
||||
#' where \code{df} are the degrees of freedom.
|
||||
#'
|
||||
#' If there are more than two categories and you want to find out which ones are significantly different from their null expectation, you can use the same method of testing each category vs. the sum of all categories, with the Bonferroni correction. You use \emph{G}-tests for each category, of course.
|
||||
#' @keywords chi
|
||||
#' @seealso \code{\link{chisq.test}}
|
||||
#' @references [1] McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland. \url{http://www.biostathandbook.com/gtestgof.html}.
|
||||
#' @source This code is almost identical to \code{\link{chisq.test}}, except that:
|
||||
#' \itemize{
|
||||
#' \item{The calculation of the statistic was changed to \code{2 * sum(x * log(x / E))}}
|
||||
#' \item{Yates' continuity correction was removed as it does not apply to a \emph{G}-test}
|
||||
#' \item{The possibility to simulate p values with \code{simulate.p.value} was removed}
|
||||
#' }
|
||||
#' @export
|
||||
#' @importFrom stats pchisq complete.cases
|
||||
#' @examples
|
||||
#' # = EXAMPLE 1 =
|
||||
#' # Shivrain et al. (2006) crossed clearfield rice (which are resistant
|
||||
#' # to the herbicide imazethapyr) with red rice (which are susceptible to
|
||||
#' # imazethapyr). They then crossed the hybrid offspring and examined the
|
||||
#' # F2 generation, where they found 772 resistant plants, 1611 moderately
|
||||
#' # resistant plants, and 737 susceptible plants. If resistance is controlled
|
||||
#' # by a single gene with two co-dominant alleles, you would expect a 1:2:1
|
||||
#' # ratio.
|
||||
#'
|
||||
#' x <- c(772, 1611, 737)
|
||||
#' E <- ratio(x, "1:2:1")
|
||||
#' E
|
||||
#' # 780 1560 780
|
||||
#'
|
||||
#' G <- g.test(x, p = c(1, 2, 1) / 4)
|
||||
#' # G$p.value = 0.12574.
|
||||
#'
|
||||
#' # There is no significant difference from a 1:2:1 ratio.
|
||||
#' # Meaning: resistance controlled by a single gene with two co-dominant
|
||||
#' # alleles, is plausible.
|
||||
#'
|
||||
#'
|
||||
#' # = EXAMPLE 2 =
|
||||
#' # Red crossbills (Loxia curvirostra) have the tip of the upper bill either
|
||||
#' # right or left of the lower bill, which helps them extract seeds from pine
|
||||
#' # cones. Some have hypothesized that frequency-dependent selection would
|
||||
#' # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992)
|
||||
#' # observed 1752 right-billed and 1895 left-billed crossbills.
|
||||
#'
|
||||
#' x <- c(1752, 1895)
|
||||
#' g.test(x)
|
||||
#' # p = 0.01787343
|
||||
#'
|
||||
#' # There is a significant difference from a 1:1 ratio.
|
||||
#' # Meaning: there are significantly more left-billed birds.
|
||||
#'
|
||||
g.test <- function(x,
|
||||
y = NULL,
|
||||
# correct = TRUE,
|
||||
p = rep(1/length(x), length(x)),
|
||||
rescale.p = FALSE) {
|
||||
DNAME <- deparse(substitute(x))
|
||||
if (is.data.frame(x))
|
||||
x <- as.matrix(x)
|
||||
if (is.matrix(x)) {
|
||||
if (min(dim(x)) == 1L)
|
||||
x <- as.vector(x)
|
||||
}
|
||||
if (!is.matrix(x) && !is.null(y)) {
|
||||
if (length(x) != length(y))
|
||||
stop("'x' and 'y' must have the same length")
|
||||
DNAME2 <- deparse(substitute(y))
|
||||
xname <- if (length(DNAME) > 1L || nchar(DNAME, "w") >
|
||||
30)
|
||||
""
|
||||
else DNAME
|
||||
yname <- if (length(DNAME2) > 1L || nchar(DNAME2, "w") >
|
||||
30)
|
||||
""
|
||||
else DNAME2
|
||||
OK <- complete.cases(x, y)
|
||||
x <- factor(x[OK])
|
||||
y <- factor(y[OK])
|
||||
if ((nlevels(x) < 2L) || (nlevels(y) < 2L))
|
||||
stop("'x' and 'y' must have at least 2 levels")
|
||||
x <- table(x, y)
|
||||
names(dimnames(x)) <- c(xname, yname)
|
||||
DNAME <- paste(paste(DNAME, collapse = "\n"), "and",
|
||||
paste(DNAME2, collapse = "\n"))
|
||||
}
|
||||
if (any(x < 0) || anyNA(x))
|
||||
stop("all entries of 'x' must be nonnegative and finite")
|
||||
if ((n <- sum(x)) == 0)
|
||||
stop("at least one entry of 'x' must be positive")
|
||||
# if (simulate.p.value) {
|
||||
# setMETH <- function() METHOD <<- paste(METHOD, "with simulated p-value\n\t (based on",
|
||||
# B, "replicates)")
|
||||
# almost.1 <- 1 - 64 * .Machine$double.eps
|
||||
# }
|
||||
if (is.matrix(x)) {
|
||||
METHOD <- "G-test of independence"
|
||||
nr <- as.integer(nrow(x))
|
||||
nc <- as.integer(ncol(x))
|
||||
if (is.na(nr) || is.na(nc) || is.na(nr * nc))
|
||||
stop("invalid nrow(x) or ncol(x)", domain = NA)
|
||||
sr <- rowSums(x)
|
||||
sc <- colSums(x)
|
||||
E <- outer(sr, sc, "*")/n
|
||||
v <- function(r, c, n) c * r * (n - r) * (n - c)/n^3
|
||||
V <- outer(sr, sc, v, n)
|
||||
dimnames(E) <- dimnames(x)
|
||||
# if (simulate.p.value && all(sr > 0) && all(sc > 0)) {
|
||||
# setMETH()
|
||||
# tmp <- .Call(chisq_sim, sr, sc, B, E, PACKAGE = "stats")
|
||||
# STATISTIC <- 2 * sum(x * log(x / E)) # sum(sort((x - E)^2/E, decreasing = TRUE)) for chisq.test
|
||||
# PARAMETER <- NA
|
||||
# PVAL <- (1 + sum(tmp >= almost.1 * STATISTIC))/(B +
|
||||
# 1)
|
||||
# }
|
||||
# else {
|
||||
# if (simulate.p.value)
|
||||
# warning("cannot compute simulated p-value with zero marginals")
|
||||
# if (correct && nrow(x) == 2L && ncol(x) == 2L) {
|
||||
# YATES <- min(0.5, abs(x - E))
|
||||
# if (YATES > 0)
|
||||
# METHOD <- paste(METHOD, "with Yates' continuity correction")
|
||||
# }
|
||||
# else YATES <- 0
|
||||
STATISTIC <- 2 * sum(x * log(x / E)) # sum((abs(x - E) - YATES)^2/E) for chisq.test
|
||||
PARAMETER <- (nr - 1L) * (nc - 1L)
|
||||
PVAL <- pchisq(STATISTIC, PARAMETER, lower.tail = FALSE)
|
||||
# }
|
||||
}
|
||||
else {
|
||||
if (length(dim(x)) > 2L)
|
||||
stop("invalid 'x'")
|
||||
if (length(x) == 1L)
|
||||
stop("'x' must at least have 2 elements")
|
||||
if (length(x) != length(p))
|
||||
stop("'x' and 'p' must have the same number of elements")
|
||||
if (any(p < 0))
|
||||
stop("probabilities must be non-negative.")
|
||||
if (abs(sum(p) - 1) > sqrt(.Machine$double.eps)) {
|
||||
if (rescale.p)
|
||||
p <- p/sum(p)
|
||||
else stop("probabilities must sum to 1.")
|
||||
}
|
||||
METHOD <- "G-test of goodness-of-fit (likelihood ratio test)"
|
||||
E <- n * p
|
||||
V <- n * p * (1 - p)
|
||||
STATISTIC <- 2 * sum(x * log(x / E)) # sum((x - E)^2/E) for chisq.test
|
||||
names(E) <- names(x)
|
||||
# if (simulate.p.value) {
|
||||
# setMETH()
|
||||
# nx <- length(x)
|
||||
# sm <- matrix(sample.int(nx, B * n, TRUE, prob = p),
|
||||
# nrow = n)
|
||||
# ss <- apply(sm, 2L, function(x, E, k) {
|
||||
# sum((table(factor(x, levels = 1L:k)) - E)^2/E)
|
||||
# }, E = E, k = nx)
|
||||
# PARAMETER <- NA
|
||||
# PVAL <- (1 + sum(ss >= almost.1 * STATISTIC))/(B +
|
||||
# 1)
|
||||
# }
|
||||
# else {
|
||||
PARAMETER <- length(x) - 1
|
||||
PVAL <- pchisq(STATISTIC, PARAMETER, lower.tail = FALSE)
|
||||
# }
|
||||
}
|
||||
names(STATISTIC) <- "X-squared"
|
||||
names(PARAMETER) <- "df"
|
||||
if (any(E < 5) && is.finite(PARAMETER))
|
||||
warning("G-statistic approximation may be incorrect")
|
||||
structure(list(statistic = STATISTIC, parameter = PARAMETER,
|
||||
p.value = PVAL, method = METHOD, data.name = DNAME,
|
||||
observed = x, expected = E, residuals = (x - E)/sqrt(E),
|
||||
stdres = (x - E)/sqrt(V)), class = "htest")
|
||||
}
|
||||
|
||||
#' Transform vector to ratio
|
||||
#' @param x vector of values
|
||||
#' @param ratio vector with ratios of \code{x} and with same length (like \code{ratio = c(1, 2, 1)}) or a text with characters \code{":"}, \code{"-"} or \code{","} (like \code{ratio = "1:2:1"} or even \code{ratio = "1:2:1.25"})
|
||||
#' @export
|
||||
#' @seealso \code{\link{g.test}}
|
||||
#' @references McDonald, J.H. 2014. \strong{Handbook of Biological Statistics (3rd ed.)}. Sparky House Publishing, Baltimore, Maryland.
|
||||
#' @importFrom dplyr %>%
|
||||
#' @inherit g.test examples
|
||||
ratio <- function(x, ratio) {
|
||||
if (!all(is.numeric(x))) {
|
||||
stop('`x` must be a vector of numeric values.')
|
||||
}
|
||||
if (length(ratio) == 1) {
|
||||
if (ratio %like% '^([0-9]+([.][0-9]+)?[-,:])+[0-9]+([.][0-9]+)?$') {
|
||||
# support for "1:2:1", "1-2-1", "1,2,1" and even "1.75:2:1.5"
|
||||
ratio <- ratio %>% base::strsplit("[-,:]") %>% base::unlist() %>% base::as.double()
|
||||
} else {
|
||||
stop('Invalid `ratio`: ', ratio, '.')
|
||||
}
|
||||
}
|
||||
if (length(x) != length(ratio)) {
|
||||
stop('`x` and `ratio` must be of same size.')
|
||||
}
|
||||
base::sum(x, na.rm = TRUE) * (ratio / base::sum(ratio, na.rm = TRUE))
|
||||
}
|
||||
|
||||
+191
@@ -0,0 +1,191 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' AMR bar plots with \code{ggplot}
|
||||
#'
|
||||
#' Use these functions to create bar plots for antimicrobial resistance analysis. All functions rely on internal \code{\link[ggplot2]{ggplot}} functions.
|
||||
#' @param data a \code{data.frame} with column(s) of class \code{"rsi"} (see \code{\link{as.rsi}})
|
||||
#' @param position position adjustment of bars, either \code{"stack"} (default) or \code{"dodge"}
|
||||
#' @param x variable to show on x axis, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
|
||||
#' @param fill variable to categorise using the plots legend, either \code{"Antibiotic"} (default) or \code{"Interpretation"} or a grouping variable
|
||||
#' @param facet variable to split plots by, either \code{"Interpretation"} (default) or \code{"Antibiotic"} or a grouping variable
|
||||
#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations into, using \code{\link{abname}}. Default behaviour is to translate to official names according to the WHO. Use \code{translate_ab = FALSE} to disable translation.
|
||||
#' @param ... other parameters passed on to \code{\link[ggplot2]{facet_wrap}}
|
||||
#' @details At default, the names of antibiotics will be shown on the plots using \code{\link{abname}}. This can be set with the option \code{get_antibiotic_names} (a logical value), so change it e.g. to \code{FALSE} with \code{options(get_antibiotic_names = FALSE)}.
|
||||
#'
|
||||
#' \strong{The functions}\cr
|
||||
#' \code{geom_rsi} will take any variable from the data that has an \code{rsi} class (created with \code{\link{as.rsi}}) using \code{\link{portion_df}} and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
|
||||
#'
|
||||
#' \code{facet_rsi} creates 2d plots (at default based on S/I/R) using \code{\link[ggplot2]{facet_wrap}}.
|
||||
#'
|
||||
#' \code{scale_y_percent} transforms the y axis to a 0 to 100\% range.
|
||||
#'
|
||||
#' \code{scale_rsi_colours} sets colours to the bars: green for S, yellow for I and red for R.
|
||||
#'
|
||||
#' \code{theme_rsi} is a \code{\link[ggplot2]{theme}} with minimal distraction.
|
||||
#'
|
||||
#' \code{ggplot_rsi} is a wrapper around all above functions that uses data as first input. This makes it possible to use this function after a pipe (\code{\%>\%}). See Examples.
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
#' @examples
|
||||
#' library(dplyr)
|
||||
#' library(ggplot2)
|
||||
#'
|
||||
#' # get antimicrobial results for drugs against a UTI:
|
||||
#' ggplot(septic_patients %>% select(amox, nitr, fosf, trim, cipr)) +
|
||||
#' geom_rsi()
|
||||
#'
|
||||
#' # prettify the plot using some additional functions:
|
||||
#' df <- septic_patients[, c("amox", "nitr", "fosf", "trim", "cipr")]
|
||||
#' ggplot(df) +
|
||||
#' geom_rsi() +
|
||||
#' facet_rsi() +
|
||||
#' scale_y_percent() +
|
||||
#' scale_rsi_colours() +
|
||||
#' theme_rsi()
|
||||
#'
|
||||
#' # or better yet, simplify this using the wrapper function - a single command:
|
||||
#' septic_patients %>%
|
||||
#' select(amox, nitr, fosf, trim, cipr) %>%
|
||||
#' ggplot_rsi()
|
||||
#' \donttest{
|
||||
#' # it also supports groups (don't forget to use the group on `x` or `facet`):
|
||||
#' septic_patients %>%
|
||||
#' select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' ggplot_rsi(x = "hospital_id",
|
||||
#' facet = "Antibiotic",
|
||||
#' nrow = 1) +
|
||||
#' labs(title = "AMR of Anti-UTI Drugs Per Hospital",
|
||||
#' x = "Hospital")
|
||||
#'
|
||||
#' # genuine analysis: check 2 most prevalent microorganisms
|
||||
#' septic_patients %>%
|
||||
#' # create new bacterial ID's, with all CoNS under the same group (Becker et al.)
|
||||
#' mutate(bactid = as.bactid(bactid, Becker = TRUE)) %>%
|
||||
#' # filter on top 2 bacterial ID's
|
||||
#' filter(bactid %in% top_freq(freq(.$bactid), 2)) %>%
|
||||
#' # determine first isolates
|
||||
#' mutate(first_isolate = first_isolate(.,
|
||||
#' col_date = "date",
|
||||
#' col_patient_id = "patient_id",
|
||||
#' col_bactid = "bactid")) %>%
|
||||
#' # filter on first isolates
|
||||
#' filter(first_isolate == TRUE) %>%
|
||||
#' # join the `microorganisms` data set
|
||||
#' left_join_microorganisms() %>%
|
||||
#' # select full name and some antiseptic drugs
|
||||
#' select(mo = fullname,
|
||||
#' cfur, gent, cipr) %>%
|
||||
#' # group by MO
|
||||
#' group_by(mo) %>%
|
||||
#' # plot the thing, putting MOs on the facet
|
||||
#' ggplot_rsi(x = "Antibiotic",
|
||||
#' facet = "mo") +
|
||||
#' labs(title = "AMR of Top Two Microorganisms In Blood Culture Isolates",
|
||||
#' subtitle = "Only First Isolates, CoNS grouped according to Becker et al.",
|
||||
#' x = "Microorganisms")
|
||||
#' }
|
||||
ggplot_rsi <- function(data,
|
||||
position = "stack",
|
||||
x = "Antibiotic",
|
||||
fill = "Interpretation",
|
||||
facet = NULL,
|
||||
translate_ab = "official",
|
||||
...) {
|
||||
|
||||
if (!"ggplot2" %in% rownames(installed.packages())) {
|
||||
stop('this function requires the ggplot2 package.', call. = FALSE)
|
||||
}
|
||||
|
||||
p <- ggplot2::ggplot(data = data) +
|
||||
geom_rsi(position = position, x = x, fill = fill, translate_ab = translate_ab) +
|
||||
scale_y_percent() +
|
||||
theme_rsi()
|
||||
|
||||
if (fill == "Interpretation") {
|
||||
# set RSI colours
|
||||
p <- p + scale_rsi_colours()
|
||||
}
|
||||
|
||||
if (!is.null(facet)) {
|
||||
p <- p + facet_rsi(facet = facet, ...)
|
||||
}
|
||||
|
||||
p
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
geom_rsi <- function(position = "stack",
|
||||
x = c("Antibiotic", "Interpretation"),
|
||||
fill = "Interpretation",
|
||||
translate_ab = "official") {
|
||||
|
||||
x <- x[1]
|
||||
if (x %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
|
||||
x <- "Antibiotic"
|
||||
} else if (x %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
|
||||
x <- "Interpretation"
|
||||
}
|
||||
|
||||
options(get_antibiotic_names = translate_ab)
|
||||
|
||||
ggplot2::layer(geom = "bar", stat = "identity", position = position,
|
||||
mapping = ggplot2::aes_string(x = x, y = "Percentage", fill = fill),
|
||||
data = AMR::portion_df, params = list())
|
||||
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
facet_rsi <- function(facet = c("Interpretation", "Antibiotic"), ...) {
|
||||
|
||||
facet <- facet[1]
|
||||
if (facet %in% tolower(c('SIR', 'RSI', 'interpretation', 'interpretations', 'result'))) {
|
||||
facet <- "Interpretation"
|
||||
} else if (facet %in% tolower(c('ab', 'antibiotic', 'abx', 'antibiotics'))) {
|
||||
facet <- "Antibiotic"
|
||||
}
|
||||
|
||||
ggplot2::facet_wrap(facets = facet, scales = "free", ...)
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
scale_y_percent <- function() {
|
||||
ggplot2::scale_y_continuous(name = "Percentage",
|
||||
breaks = seq(0, 1, 0.1),
|
||||
limits = c(0, 1),
|
||||
labels = percent(seq(0, 1, 0.1)))
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
scale_rsi_colours <- function() {
|
||||
ggplot2::scale_fill_brewer(palette = "RdYlGn")
|
||||
}
|
||||
|
||||
#' @rdname ggplot_rsi
|
||||
#' @export
|
||||
theme_rsi <- function() {
|
||||
theme_minimal() +
|
||||
theme(panel.grid.major.x = element_blank(),
|
||||
panel.grid.minor = element_blank(),
|
||||
panel.grid.major.y = element_line(colour = "grey75"))
|
||||
}
|
||||
Executable
+75
@@ -0,0 +1,75 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
globalVariables(c('abname',
|
||||
'Antibiotic',
|
||||
'Interpretation',
|
||||
'Percentage',
|
||||
'bind_rows',
|
||||
'element_blank',
|
||||
'element_line',
|
||||
'theme',
|
||||
'theme_minimal',
|
||||
'antibiotic',
|
||||
'antibiotics',
|
||||
'atc',
|
||||
'bactid',
|
||||
'C_chisq_sim',
|
||||
'cnt',
|
||||
'count',
|
||||
'counts',
|
||||
'cum_count',
|
||||
'cum_percent',
|
||||
'date_lab',
|
||||
'days_diff',
|
||||
'fctlvl',
|
||||
'first_isolate_row_index',
|
||||
'Freq',
|
||||
'fullname',
|
||||
'genus',
|
||||
'gramstain',
|
||||
'item',
|
||||
'key_ab',
|
||||
'key_ab_lag',
|
||||
'key_ab_other',
|
||||
'labs',
|
||||
'median',
|
||||
'mic',
|
||||
'MIC',
|
||||
'microorganisms',
|
||||
'mocode',
|
||||
'molis',
|
||||
'n',
|
||||
'na.omit',
|
||||
'observations',
|
||||
'official',
|
||||
'other_pat_or_mo',
|
||||
'Pasted',
|
||||
'patient_id',
|
||||
'quantile',
|
||||
'R',
|
||||
'real_first_isolate',
|
||||
'S',
|
||||
'septic_patients',
|
||||
'species',
|
||||
'umcg',
|
||||
'value',
|
||||
'values',
|
||||
'View',
|
||||
'y',
|
||||
'.'))
|
||||
Executable
+148
@@ -0,0 +1,148 @@
|
||||
#' Join a table with \code{microorganisms}
|
||||
#'
|
||||
#' Join the dataset \code{\link{microorganisms}} easily to an existing table or character vector.
|
||||
#' @rdname join
|
||||
#' @name join
|
||||
#' @aliases join inner_join
|
||||
#' @param x existing table to join, also supports character vectors
|
||||
#' @param by a variable to join by - could be a column name of \code{x} with values that exist in \code{microorganisms$bactid} (like \code{by = "bacteria_id"}), or another column in \code{\link{microorganisms}} (but then it should be named, like \code{by = c("my_genus_species" = "fullname")})
|
||||
#' @param suffix if there are non-joined duplicate variables in \code{x} and \code{y}, these suffixes will be added to the output to disambiguate them. Should be a character vector of length 2.
|
||||
#' @param ... other parameters to pass on to \code{dplyr::\link[dplyr]{join}}.
|
||||
#' @details As opposed to the \code{\link[dplyr]{join}} functions of \code{dplyr}, characters vectors are supported and at default existing columns will get a suffix \code{"2"} and the newly joined columns will not get a suffix. See \code{\link[dplyr]{join}} for more information.
|
||||
#' @export
|
||||
#' @examples
|
||||
#' left_join_microorganisms("STAAUR")
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>% left_join_microorganisms()
|
||||
#'
|
||||
#' df <- data.frame(date = seq(from = as.Date("2018-01-01"),
|
||||
#' to = as.Date("2018-01-07"),
|
||||
#' by = 1),
|
||||
#' bacteria_id = c("STAAUR", "STAAUR", "STAAUR", "STAAUR",
|
||||
#' "ESCCOL", "ESCCOL", "ESCCOL"),
|
||||
#' stringsAsFactors = FALSE)
|
||||
#' colnames(df)
|
||||
#' df2 <- left_join_microorganisms(df, "bacteria_id")
|
||||
#' colnames(df2)
|
||||
inner_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
|
||||
if (!any(class(x) %in% c("data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
joinby <- colnames(AMR::microorganisms)[1]
|
||||
names(joinby) <- by
|
||||
} else {
|
||||
joinby <- by
|
||||
}
|
||||
join <- suppressWarnings(
|
||||
dplyr::inner_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
|
||||
)
|
||||
if (nrow(join) > nrow(x)) {
|
||||
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
|
||||
}
|
||||
join
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
left_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
|
||||
if (!any(class(x) %in% c("data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
joinby <- colnames(AMR::microorganisms)[1]
|
||||
names(joinby) <- by
|
||||
} else {
|
||||
joinby <- by
|
||||
}
|
||||
join <- suppressWarnings(
|
||||
dplyr::left_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
|
||||
)
|
||||
if (nrow(join) > nrow(x)) {
|
||||
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
|
||||
}
|
||||
join
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
right_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
|
||||
if (!any(class(x) %in% c("data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
joinby <- colnames(AMR::microorganisms)[1]
|
||||
names(joinby) <- by
|
||||
} else {
|
||||
joinby <- by
|
||||
}
|
||||
join <- suppressWarnings(
|
||||
dplyr::right_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
|
||||
)
|
||||
if (nrow(join) > nrow(x)) {
|
||||
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
|
||||
}
|
||||
join
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
full_join_microorganisms <- function(x, by = 'bactid', suffix = c("2", ""), ...) {
|
||||
if (!any(class(x) %in% c("data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
joinby <- colnames(AMR::microorganisms)[1]
|
||||
names(joinby) <- by
|
||||
} else {
|
||||
joinby <- by
|
||||
}
|
||||
join <- suppressWarnings(
|
||||
dplyr::full_join(x = x, y = AMR::microorganisms, by = joinby, suffix = c("2", ""), ...)
|
||||
)
|
||||
if (nrow(join) > nrow(x)) {
|
||||
warning('the newly joined tbl contains ', nrow(join) - nrow(x), ' rows more that its original')
|
||||
}
|
||||
join
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
semi_join_microorganisms <- function(x, by = 'bactid', ...) {
|
||||
if (!any(class(x) %in% c("data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
joinby <- colnames(AMR::microorganisms)[1]
|
||||
names(joinby) <- by
|
||||
} else {
|
||||
joinby <- by
|
||||
}
|
||||
suppressWarnings(
|
||||
dplyr::semi_join(x = x, y = AMR::microorganisms, by = joinby, ...)
|
||||
)
|
||||
}
|
||||
|
||||
#' @rdname join
|
||||
#' @export
|
||||
anti_join_microorganisms <- function(x, by = 'bactid', ...) {
|
||||
if (!any(class(x) %in% c("data.frame", "matrix"))) {
|
||||
x <- data.frame(bactid = as.character(x), stringsAsFactors = FALSE)
|
||||
}
|
||||
# no name set to `by` parameter
|
||||
if (is.null(names(by))) {
|
||||
joinby <- colnames(AMR::microorganisms)[1]
|
||||
names(joinby) <- by
|
||||
} else {
|
||||
joinby <- by
|
||||
}
|
||||
suppressWarnings(
|
||||
dplyr::anti_join(x = x, y = AMR::microorganisms, by = joinby, ...)
|
||||
)
|
||||
}
|
||||
@@ -0,0 +1,255 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Key antibiotics for first \emph{weighted} isolates
|
||||
#'
|
||||
#' These function can be used to determine first isolates (see \code{\link{first_isolate}}). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first \emph{weighted} isolates.
|
||||
#' @param tbl table with antibiotics coloms, like \code{amox} and \code{amcl}.
|
||||
#' @param x,y characters to compare
|
||||
#' @inheritParams first_isolate
|
||||
#' @param universal_1,universal_2,universal_3,universal_4,universal_5,universal_6 column names of \strong{broad-spectrum} antibiotics, case-insensitive
|
||||
#' @param GramPos_1,GramPos_2,GramPos_3,GramPos_4,GramPos_5,GramPos_6 column names of antibiotics for \strong{Gram positives}, case-insensitive
|
||||
#' @param GramNeg_1,GramNeg_2,GramNeg_3,GramNeg_4,GramNeg_5,GramNeg_6 column names of antibiotics for \strong{Gram negatives}, case-insensitive
|
||||
#' @param warnings give warning about missing antibiotic columns, they will anyway be ignored
|
||||
#' @details The function \code{key_antibiotics} returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using \code{key_antibiotics_equal}, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (\code{"."}). The \code{\link{first_isolate}} function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible \emph{S. aureus} (MSSA) found within the same episode (see \code{episode} parameter of \code{\link{first_isolate}}). Without key antibiotic comparison it wouldn't.
|
||||
#'
|
||||
#' At default, the antibiotics that are used for \strong{Gram positive bacteria} are (colum names): \cr
|
||||
#' \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"vanc"}, \code{"teic"}, \code{"tetr"}, \code{"eryt"}, \code{"oxac"}, \code{"rifa"}.
|
||||
#'
|
||||
#' At default, the antibiotics that are used for \strong{Gram negative bacteria} are (colum names): \cr
|
||||
#' \code{"amox"}, \code{"amcl"}, \code{"cfur"}, \code{"pita"}, \code{"cipr"}, \code{"trsu"} (until here is universal), \code{"gent"}, \code{"tobr"}, \code{"coli"}, \code{"cfot"}, \code{"cfta"}, \code{"mero"}.
|
||||
#'
|
||||
#'
|
||||
#' The function \code{key_antibiotics_equal} checks the characters returned by \code{key_antibiotics} for equality, and returns a logical vector.
|
||||
#' @inheritSection first_isolate Key antibiotics
|
||||
#' @rdname key_antibiotics
|
||||
#' @export
|
||||
#' @importFrom dplyr %>% mutate if_else
|
||||
#' @seealso \code{\link{first_isolate}}
|
||||
#' @examples
|
||||
#' # septic_patients is a dataset available in the AMR package
|
||||
#' ?septic_patients
|
||||
#' my_patients <- septic_patients
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' # set key antibiotics to a new variable
|
||||
#' my_patients <- my_patients %>%
|
||||
#' mutate(keyab = key_antibiotics(.)) %>%
|
||||
#' mutate(
|
||||
#' # now calculate first isolates
|
||||
#' first_regular = first_isolate(., "date", "patient_id", "bactid"),
|
||||
#' # and first WEIGHTED isolates
|
||||
#' first_weighted = first_isolate(., "date", "patient_id", "bactid",
|
||||
#' col_keyantibiotics = "keyab")
|
||||
#' )
|
||||
#'
|
||||
#' # Check the difference, in this data set it results in 7% more isolates:
|
||||
#' sum(my_patients$first_regular, na.rm = TRUE)
|
||||
#' sum(my_patients$first_weighted, na.rm = TRUE)
|
||||
#'
|
||||
#'
|
||||
#' # output of the `key_antibiotics` function could be like this:
|
||||
#' strainA <- "SSSRR.S.R..S"
|
||||
#' strainB <- "SSSIRSSSRSSS"
|
||||
#'
|
||||
#' key_antibiotics_equal(strainA, strainB)
|
||||
#' # TRUE, because I is ignored (as are missing values)
|
||||
#'
|
||||
#' key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
|
||||
#' # FALSE, because I is not ignored and so the 4th value differs
|
||||
key_antibiotics <- function(tbl,
|
||||
col_bactid = "bactid",
|
||||
universal_1 = "amox",
|
||||
universal_2 = "amcl",
|
||||
universal_3 = "cfur",
|
||||
universal_4 = "pita",
|
||||
universal_5 = "cipr",
|
||||
universal_6 = "trsu",
|
||||
GramPos_1 = "vanc",
|
||||
GramPos_2 = "teic",
|
||||
GramPos_3 = "tetr",
|
||||
GramPos_4 = "eryt",
|
||||
GramPos_5 = "oxac",
|
||||
GramPos_6 = "rifa",
|
||||
GramNeg_1 = "gent",
|
||||
GramNeg_2 = "tobr",
|
||||
GramNeg_3 = "coli",
|
||||
GramNeg_4 = "cfot",
|
||||
GramNeg_5 = "cfta",
|
||||
GramNeg_6 = "mero",
|
||||
warnings = TRUE) {
|
||||
|
||||
if (!col_bactid %in% colnames(tbl)) {
|
||||
stop('Column ', col_bactid, ' not found.', call. = FALSE)
|
||||
}
|
||||
|
||||
# check columns
|
||||
col.list <- c(universal_1, universal_2, universal_3, universal_4, universal_5, universal_6,
|
||||
GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6,
|
||||
GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6)
|
||||
col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = warnings)
|
||||
universal_1 <- col.list[universal_1]
|
||||
universal_2 <- col.list[universal_2]
|
||||
universal_3 <- col.list[universal_3]
|
||||
universal_4 <- col.list[universal_4]
|
||||
universal_5 <- col.list[universal_5]
|
||||
universal_6 <- col.list[universal_6]
|
||||
GramPos_1 <- col.list[GramPos_1]
|
||||
GramPos_2 <- col.list[GramPos_2]
|
||||
GramPos_3 <- col.list[GramPos_3]
|
||||
GramPos_4 <- col.list[GramPos_4]
|
||||
GramPos_5 <- col.list[GramPos_5]
|
||||
GramPos_6 <- col.list[GramPos_6]
|
||||
GramNeg_1 <- col.list[GramNeg_1]
|
||||
GramNeg_2 <- col.list[GramNeg_2]
|
||||
GramNeg_3 <- col.list[GramNeg_3]
|
||||
GramNeg_4 <- col.list[GramNeg_4]
|
||||
GramNeg_5 <- col.list[GramNeg_5]
|
||||
GramNeg_6 <- col.list[GramNeg_6]
|
||||
|
||||
universal <- c(universal_1, universal_2, universal_3,
|
||||
universal_4, universal_5, universal_6)
|
||||
|
||||
gram_positive = c(universal,
|
||||
GramPos_1, GramPos_2, GramPos_3,
|
||||
GramPos_4, GramPos_5, GramPos_6)
|
||||
gram_positive <- gram_positive[!is.na(gram_positive)]
|
||||
|
||||
gram_negative = c(universal,
|
||||
GramNeg_1, GramNeg_2, GramNeg_3,
|
||||
GramNeg_4, GramNeg_5, GramNeg_6)
|
||||
gram_negative <- gram_negative[!is.na(gram_negative)]
|
||||
|
||||
# join microorganisms
|
||||
tbl <- tbl %>% left_join_microorganisms(col_bactid)
|
||||
|
||||
tbl$key_ab <- NA_character_
|
||||
|
||||
# Gram +
|
||||
tbl <- tbl %>% mutate(key_ab =
|
||||
if_else(gramstain %like% '^Positive ',
|
||||
apply(X = tbl[, gram_positive],
|
||||
MARGIN = 1,
|
||||
FUN = function(x) paste(x, collapse = "")),
|
||||
key_ab))
|
||||
|
||||
# Gram -
|
||||
tbl <- tbl %>% mutate(key_ab =
|
||||
if_else(gramstain %like% '^Negative ',
|
||||
apply(X = tbl[, gram_negative],
|
||||
MARGIN = 1,
|
||||
FUN = function(x) paste(x, collapse = "")),
|
||||
key_ab))
|
||||
|
||||
# format
|
||||
key_abs <- tbl %>%
|
||||
pull(key_ab) %>%
|
||||
gsub('(NA|NULL)', '.', .) %>%
|
||||
gsub('[^SIR]', '.', ., ignore.case = TRUE)
|
||||
|
||||
key_abs
|
||||
|
||||
}
|
||||
|
||||
#' @importFrom dplyr progress_estimated %>%
|
||||
#' @rdname key_antibiotics
|
||||
#' @export
|
||||
key_antibiotics_equal <- function(x,
|
||||
y,
|
||||
type = c("keyantibiotics", "points"),
|
||||
ignore_I = TRUE,
|
||||
points_threshold = 2,
|
||||
info = FALSE) {
|
||||
# x is active row, y is lag
|
||||
|
||||
type <- type[1]
|
||||
|
||||
if (length(x) != length(y)) {
|
||||
stop('Length of `x` and `y` must be equal.')
|
||||
}
|
||||
|
||||
# only show progress bar on points or when at least 5000 isolates
|
||||
info_needed <- info == TRUE & (type == "points" | length(x) > 5000)
|
||||
|
||||
result <- logical(length(x))
|
||||
|
||||
if (info_needed == TRUE) {
|
||||
p <- dplyr::progress_estimated(length(x))
|
||||
}
|
||||
|
||||
for (i in 1:length(x)) {
|
||||
|
||||
if (info_needed == TRUE) {
|
||||
p$tick()$print()
|
||||
}
|
||||
|
||||
if (is.na(x[i])) {
|
||||
x[i] <- ''
|
||||
}
|
||||
if (is.na(y[i])) {
|
||||
y[i] <- ''
|
||||
}
|
||||
|
||||
if (x[i] == y[i]) {
|
||||
|
||||
result[i] <- TRUE
|
||||
|
||||
} else if (nchar(x[i]) != nchar(y[i])) {
|
||||
|
||||
result[i] <- FALSE
|
||||
|
||||
} else {
|
||||
|
||||
x_split <- strsplit(x[i], "")[[1]]
|
||||
y_split <- strsplit(y[i], "")[[1]]
|
||||
|
||||
if (type == 'keyantibiotics') {
|
||||
|
||||
if (ignore_I == TRUE) {
|
||||
x_split[x_split == "I"] <- "."
|
||||
y_split[y_split == "I"] <- "."
|
||||
}
|
||||
|
||||
y_split[x_split == "."] <- "."
|
||||
x_split[y_split == "."] <- "."
|
||||
|
||||
result[i] <- all(x_split == y_split)
|
||||
|
||||
} else if (type == 'points') {
|
||||
# count points for every single character:
|
||||
# - no change is 0 points
|
||||
# - I <-> S|R is 0.5 point
|
||||
# - S|R <-> R|S is 1 point
|
||||
# use the levels of as.rsi (S = 1, I = 2, R = 3)
|
||||
|
||||
suppressWarnings(x_split <- x_split %>% as.rsi() %>% as.double())
|
||||
suppressWarnings(y_split <- y_split %>% as.rsi() %>% as.double())
|
||||
|
||||
points <- (x_split - y_split) %>% abs() %>% sum(na.rm = TRUE) / 2
|
||||
result[i] <- points >= points_threshold
|
||||
|
||||
} else {
|
||||
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
|
||||
}
|
||||
}
|
||||
}
|
||||
if (info_needed == TRUE) {
|
||||
cat('\n')
|
||||
}
|
||||
result
|
||||
}
|
||||
@@ -0,0 +1,40 @@
|
||||
#' Kurtosis of the sample
|
||||
#'
|
||||
#' @description Kurtosis is a measure of the "tailedness" of the probability distribution of a real-valued random variable.
|
||||
#'
|
||||
#' @param x a vector of values, a \code{matrix} or a \code{data frame}
|
||||
#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
|
||||
#' @exportMethod kurtosis
|
||||
#' @seealso \code{\link{skewness}}
|
||||
#' @rdname kurtosis
|
||||
#' @export
|
||||
kurtosis <- function(x, na.rm = FALSE) {
|
||||
UseMethod("kurtosis")
|
||||
}
|
||||
|
||||
#' @exportMethod kurtosis.default
|
||||
#' @rdname kurtosis
|
||||
#' @export
|
||||
kurtosis.default <- function (x, na.rm = FALSE) {
|
||||
x <- as.vector(x)
|
||||
if (na.rm == TRUE) {
|
||||
x <- x[!is.na(x)]
|
||||
}
|
||||
n <- length(x)
|
||||
n * base::sum((x - base::mean(x, na.rm = na.rm))^4, na.rm = na.rm) /
|
||||
(base::sum((x - base::mean(x, na.rm = na.rm))^2, na.rm = na.rm)^2)
|
||||
}
|
||||
|
||||
#' @exportMethod kurtosis.matrix
|
||||
#' @rdname kurtosis
|
||||
#' @export
|
||||
kurtosis.matrix <- function (x, na.rm = FALSE) {
|
||||
base::apply(x, 2, kurtosis.default, na.rm = na.rm)
|
||||
}
|
||||
|
||||
#' @exportMethod kurtosis.data.frame
|
||||
#' @rdname kurtosis
|
||||
#' @export
|
||||
kurtosis.data.frame <- function (x, na.rm = FALSE) {
|
||||
base::sapply(x, kurtosis.default, na.rm = na.rm)
|
||||
}
|
||||
@@ -0,0 +1,80 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Pattern Matching
|
||||
#'
|
||||
#' Convenient wrapper around \code{\link[base]{grep}} to match a pattern: \code{a \%like\% b}. It always returns a \code{logical} vector and is always case-insensitive. Also, \code{pattern} (\code{b}) can be as long as \code{x} (\code{a}) to compare items of each index in both vectors.
|
||||
#' @inheritParams base::grepl
|
||||
#' @return A \code{logical} vector
|
||||
#' @name like
|
||||
#' @rdname like
|
||||
#' @export
|
||||
#' @details Using RStudio? This function can also be inserted from the Addins menu and can have its own Keyboard Shortcut like Ctrl+Shift+L or Cmd+Shift+L (see Tools > Modify Keyboard Shortcuts...).
|
||||
#' @source Idea from the \href{https://github.com/Rdatatable/data.table/blob/master/R/like.R}{\code{like} function from the \code{data.table} package}, but made it case insensitive at default and let it support multiple patterns.
|
||||
#' @seealso \code{\link[base]{grep}}
|
||||
#' @examples
|
||||
#' # simple test
|
||||
#' a <- "This is a test"
|
||||
#' b <- "TEST"
|
||||
#' a %like% b
|
||||
#' #> TRUE
|
||||
#' b %like% a
|
||||
#' #> FALSE
|
||||
#'
|
||||
#' # also supports multiple patterns, length must be equal to x
|
||||
#' a <- c("Test case", "Something different", "Yet another thing")
|
||||
#' b <- c("case", "diff", "yet")
|
||||
#' a %like% b
|
||||
#' #> TRUE TRUE TRUE
|
||||
#'
|
||||
#' # get frequencies of bacteria whose name start with 'Ent' or 'ent'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' left_join_microorganisms() %>%
|
||||
#' filter(genus %like% '^ent') %>%
|
||||
#' freq(genus, species)
|
||||
like <- function(x, pattern) {
|
||||
if (length(pattern) > 1) {
|
||||
if (length(x) != length(pattern)) {
|
||||
pattern <- pattern[1]
|
||||
warning('only the first element of argument `pattern` used for `%like%`', call. = FALSE)
|
||||
} else {
|
||||
# x and pattern are of same length, so items with each other
|
||||
res <- vector(length = length(pattern))
|
||||
for (i in 1:length(res)) {
|
||||
if (is.factor(x[i])) {
|
||||
res[i] <- as.integer(x[i]) %in% base::grep(pattern[i], levels(x[i]), ignore.case = TRUE)
|
||||
} else {
|
||||
res[i] <- base::grepl(pattern[i], x[i], ignore.case = TRUE)
|
||||
}
|
||||
}
|
||||
return(res)
|
||||
}
|
||||
}
|
||||
|
||||
# the regular way how grepl works; just one pattern against one or more x
|
||||
if (is.factor(x)) {
|
||||
as.integer(x) %in% base::grep(pattern, levels(x), ignore.case = TRUE)
|
||||
} else {
|
||||
base::grepl(pattern, x, ignore.case = TRUE)
|
||||
}
|
||||
}
|
||||
|
||||
#' @rdname like
|
||||
#' @export
|
||||
"%like%" <- like
|
||||
@@ -0,0 +1,422 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Determine multidrug-resistant organisms (MDRO)
|
||||
#'
|
||||
#' Determine which isolates are multidrug-resistant organisms (MDRO) according to country-specific guidelines.
|
||||
#' @param tbl table with antibiotic columns, like e.g. \code{amox} and \code{amcl}
|
||||
#' @param country country code to determine guidelines. EUCAST rules will be used when left empty, see Details. Should be or a code from the \href{https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2#Officially_assigned_code_elements}{list of ISO 3166-1 alpha-2 country codes}. Case-insensitive. Currently supported are \code{de} (Germany) and \code{nl} (the Netherlands).
|
||||
#' @param col_bactid column name of the bacteria ID in \code{tbl} - values of this column should be present in \code{microorganisms$bactid}, see \code{\link{microorganisms}}
|
||||
#' @param info print progress
|
||||
#' @param amcl,amik,amox,ampi,azit,aztr,cefa,cfra,cfep,cfot,cfox,cfta,cftr,cfur,chlo,cipr,clar,clin,clox,coli,czol,dapt,doxy,erta,eryt,fosf,fusi,gent,imip,kana,levo,linc,line,mero,metr,mino,moxi,nali,neom,neti,nitr,novo,norf,oflo,peni,pita,poly,qida,rifa,roxi,siso,teic,tetr,tica,tige,tobr,trim,trsu,vanc column names of antibiotics. column names of antibiotics
|
||||
#' @param ... parameters that are passed on to methods
|
||||
#' @details When \code{country} will be left blank, guidelines will be taken from EUCAST Expert Rules Version 3.1 "Intrinsic Resistance and Exceptional Phenotypes Tables" (\url{http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf}).
|
||||
#' @return Ordered factor with levels \code{Unknown < Negative < Unconfirmed < Positive}.
|
||||
#' @rdname MDRO
|
||||
#' @export
|
||||
#' @examples
|
||||
#' library(dplyr)
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' mutate(EUCAST = MDRO(.),
|
||||
#' BRMO = MDRO(., "nl"))
|
||||
MDRO <- function(tbl,
|
||||
country = NULL,
|
||||
col_bactid = 'bactid',
|
||||
info = TRUE,
|
||||
amcl = 'amcl',
|
||||
amik = 'amik',
|
||||
amox = 'amox',
|
||||
ampi = 'ampi',
|
||||
azit = 'azit',
|
||||
aztr = 'aztr',
|
||||
cefa = 'cefa',
|
||||
cfra = 'cfra',
|
||||
cfep = 'cfep',
|
||||
cfot = 'cfot',
|
||||
cfox = 'cfox',
|
||||
cfta = 'cfta',
|
||||
cftr = 'cftr',
|
||||
cfur = 'cfur',
|
||||
chlo = 'chlo',
|
||||
cipr = 'cipr',
|
||||
clar = 'clar',
|
||||
clin = 'clin',
|
||||
clox = 'clox',
|
||||
coli = 'coli',
|
||||
czol = 'czol',
|
||||
dapt = 'dapt',
|
||||
doxy = 'doxy',
|
||||
erta = 'erta',
|
||||
eryt = 'eryt',
|
||||
fosf = 'fosf',
|
||||
fusi = 'fusi',
|
||||
gent = 'gent',
|
||||
imip = 'imip',
|
||||
kana = 'kana',
|
||||
levo = 'levo',
|
||||
linc = 'linc',
|
||||
line = 'line',
|
||||
mero = 'mero',
|
||||
metr = 'metr',
|
||||
mino = 'mino',
|
||||
moxi = 'moxi',
|
||||
nali = 'nali',
|
||||
neom = 'neom',
|
||||
neti = 'neti',
|
||||
nitr = 'nitr',
|
||||
novo = 'novo',
|
||||
norf = 'norf',
|
||||
oflo = 'oflo',
|
||||
peni = 'peni',
|
||||
pita = 'pita',
|
||||
poly = 'poly',
|
||||
qida = 'qida',
|
||||
rifa = 'rifa',
|
||||
roxi = 'roxi',
|
||||
siso = 'siso',
|
||||
teic = 'teic',
|
||||
tetr = 'tetr',
|
||||
tica = 'tica',
|
||||
tige = 'tige',
|
||||
tobr = 'tobr',
|
||||
trim = 'trim',
|
||||
trsu = 'trsu',
|
||||
vanc = 'vanc') {
|
||||
|
||||
if (!col_bactid %in% colnames(tbl)) {
|
||||
stop('Column ', col_bactid, ' not found.', call. = FALSE)
|
||||
}
|
||||
|
||||
# strip whitespaces
|
||||
if (length(country) > 1) {
|
||||
stop('`country` must be a length one character string.', call. = FALSE)
|
||||
}
|
||||
|
||||
if (is.null(country)) {
|
||||
country <- 'EUCAST'
|
||||
}
|
||||
country <- trimws(country)
|
||||
if (country != 'EUCAST' & !country %like% '^[a-z]{2}$') {
|
||||
stop('This is not a valid ISO 3166-1 alpha-2 country code: "', country, '". Please see ?MDRO.', call. = FALSE)
|
||||
}
|
||||
|
||||
# create list and make country code case-independent
|
||||
guideline <- list(country = list(code = tolower(country)))
|
||||
|
||||
if (guideline$country$code == 'eucast') {
|
||||
guideline$country$name <- '(European guidelines)'
|
||||
guideline$name <- 'EUCAST Expert Rules, "Intrinsic Resistance and Exceptional Phenotypes Tables"'
|
||||
guideline$version <- 'Version 3.1'
|
||||
guideline$source <- 'http://www.eucast.org/fileadmin/src/media/PDFs/EUCAST_files/Expert_Rules/Expert_rules_intrinsic_exceptional_V3.1.pdf'
|
||||
# support per country:
|
||||
} else if (guideline$country$code == 'de') {
|
||||
guideline$country$name <- 'Germany'
|
||||
guideline$name <- ''
|
||||
guideline$version <- ''
|
||||
guideline$source <- ''
|
||||
} else if (guideline$country$code == 'nl') {
|
||||
guideline$country$name <- 'The Netherlands'
|
||||
guideline$name <- 'WIP-Richtlijn BRMO'
|
||||
guideline$version <- 'Revision of December 2017'
|
||||
guideline$source <- 'https://www.rivm.nl/Documenten_en_publicaties/Professioneel_Praktisch/Richtlijnen/Infectieziekten/WIP_Richtlijnen/WIP_Richtlijnen/Ziekenhuizen/WIP_richtlijn_BRMO_Bijzonder_Resistente_Micro_Organismen_ZKH'
|
||||
# add here more countries like this:
|
||||
# } else if (country$code == 'AA') {
|
||||
# country$name <- 'country name'
|
||||
} else {
|
||||
stop('This country code is currently unsupported: ', guideline$country$code, call. = FALSE)
|
||||
}
|
||||
|
||||
# Console colours
|
||||
# source: http://www.tldp.org/HOWTO/Bash-Prompt-HOWTO/x329.html
|
||||
ANSI_red <- "\033[31m"
|
||||
ANSI_blue <- "\033[34m"
|
||||
ANSI_reset <- "\033[0m"
|
||||
|
||||
if (info == TRUE) {
|
||||
cat("Determining multidrug-resistant organisms (MDRO), according to:\n",
|
||||
"Guideline: ", ANSI_red, guideline$name, ", ", guideline$version, ANSI_reset, "\n",
|
||||
"Country : ", ANSI_red, guideline$country$name, ANSI_reset, "\n",
|
||||
"Source : ", ANSI_blue, guideline$source, ANSI_reset, "\n",
|
||||
"\n", sep = "")
|
||||
}
|
||||
|
||||
# check columns
|
||||
col.list <- c(amcl, amik, amox, ampi, azit, aztr, cefa, cfra, cfep, cfot,
|
||||
cfox, cfta, cftr, cfur, chlo, cipr, clar, clin, clox, coli,
|
||||
czol, dapt, doxy, erta, eryt, fosf, fusi, gent, imip, kana,
|
||||
levo, linc, line, mero, metr, mino, moxi, nali, neom, neti, nitr,
|
||||
novo, norf, oflo, peni, pita, poly, qida, rifa, roxi, siso,
|
||||
teic, tetr, tica, tige, tobr, trim, trsu, vanc)
|
||||
col.list <- check_available_columns(tbl = tbl, col.list = col.list, info = info)
|
||||
amcl <- col.list[amcl]
|
||||
amik <- col.list[amik]
|
||||
amox <- col.list[amox]
|
||||
ampi <- col.list[ampi]
|
||||
azit <- col.list[azit]
|
||||
aztr <- col.list[aztr]
|
||||
cefa <- col.list[cefa]
|
||||
cfra <- col.list[cfra]
|
||||
cfep <- col.list[cfep]
|
||||
cfot <- col.list[cfot]
|
||||
cfox <- col.list[cfox]
|
||||
cfta <- col.list[cfta]
|
||||
cftr <- col.list[cftr]
|
||||
cfur <- col.list[cfur]
|
||||
chlo <- col.list[chlo]
|
||||
cipr <- col.list[cipr]
|
||||
clar <- col.list[clar]
|
||||
clin <- col.list[clin]
|
||||
clox <- col.list[clox]
|
||||
coli <- col.list[coli]
|
||||
czol <- col.list[czol]
|
||||
dapt <- col.list[dapt]
|
||||
doxy <- col.list[doxy]
|
||||
erta <- col.list[erta]
|
||||
eryt <- col.list[eryt]
|
||||
fosf <- col.list[fosf]
|
||||
fusi <- col.list[fusi]
|
||||
gent <- col.list[gent]
|
||||
imip <- col.list[imip]
|
||||
kana <- col.list[kana]
|
||||
levo <- col.list[levo]
|
||||
linc <- col.list[linc]
|
||||
line <- col.list[line]
|
||||
mero <- col.list[mero]
|
||||
metr <- col.list[metr]
|
||||
mino <- col.list[mino]
|
||||
moxi <- col.list[moxi]
|
||||
nali <- col.list[nali]
|
||||
neom <- col.list[neom]
|
||||
neti <- col.list[neti]
|
||||
nitr <- col.list[nitr]
|
||||
novo <- col.list[novo]
|
||||
norf <- col.list[norf]
|
||||
oflo <- col.list[oflo]
|
||||
peni <- col.list[peni]
|
||||
pita <- col.list[pita]
|
||||
poly <- col.list[poly]
|
||||
qida <- col.list[qida]
|
||||
rifa <- col.list[rifa]
|
||||
roxi <- col.list[roxi]
|
||||
siso <- col.list[siso]
|
||||
teic <- col.list[teic]
|
||||
tetr <- col.list[tetr]
|
||||
tica <- col.list[tica]
|
||||
tige <- col.list[tige]
|
||||
tobr <- col.list[tobr]
|
||||
trim <- col.list[trim]
|
||||
trsu <- col.list[trsu]
|
||||
vanc <- col.list[vanc]
|
||||
|
||||
# antibiotic classes
|
||||
aminoglycosides <- c(tobr, gent, kana, neom, neti, siso)
|
||||
tetracyclines <- c(doxy, mino, tetr) # since EUCAST v3.1 tige(cycline) is set apart
|
||||
polymyxins <- c(poly, coli)
|
||||
macrolides <- c(eryt, azit, roxi, clar) # since EUCAST v3.1 clin(damycin) is set apart
|
||||
glycopeptides <- c(vanc, teic)
|
||||
streptogramins <- qida # should officially also be pristinamycin and quinupristin/dalfopristin
|
||||
cephalosporins <- c(cfep, cfot, cfox, cfra, cfta, cftr, cfur, czol)
|
||||
cephalosporins_3rd <- c(cfot, cftr, cfta)
|
||||
carbapenems <- c(erta, imip, mero)
|
||||
aminopenicillins <- c(ampi, amox)
|
||||
ureidopenicillins <- pita # should officially also be azlo and mezlo
|
||||
fluoroquinolones <- c(oflo, cipr, norf, levo, moxi)
|
||||
|
||||
# helper function for editing the table
|
||||
trans_tbl <- function(to, rows, cols) {
|
||||
cols <- cols[!is.na(cols)]
|
||||
if (length(rows) > 0 & length(cols) > 0) {
|
||||
col_filter <- which(tbl[, cols] == 'R')
|
||||
rows <- rows[rows %in% col_filter]
|
||||
tbl[rows, 'MDRO'] <<- to
|
||||
}
|
||||
}
|
||||
|
||||
# join microorganisms
|
||||
tbl <- tbl %>% left_join_microorganisms(col_bactid)
|
||||
|
||||
tbl$MDRO <- NA_integer_
|
||||
|
||||
if (guideline$country$code == 'eucast') {
|
||||
# EUCAST ------------------------------------------------------------------
|
||||
# Table 5
|
||||
trans_tbl(4,
|
||||
which(tbl$family == 'Enterobacteriaceae'
|
||||
| tbl$fullname %like% '^Pseudomonas aeruginosa'
|
||||
| tbl$genus == 'Acinetobacter'),
|
||||
coli)
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Salmonella Typhi'),
|
||||
c(carbapenems, fluoroquinolones))
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Haemophilus influenzae'),
|
||||
c(cephalosporins_3rd, carbapenems, fluoroquinolones))
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Moraxella catarrhalis'),
|
||||
c(cephalosporins_3rd, fluoroquinolones))
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Neisseria meningitidis'),
|
||||
c(cephalosporins_3rd, fluoroquinolones))
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Neisseria gonorrhoeae'),
|
||||
azit)
|
||||
# Table 6
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Staphylococcus (aureus|epidermidis|coagulase negatief|hominis|haemolyticus|intermedius|pseudointermedius)'),
|
||||
c(vanc, teic, dapt, line, qida, tige))
|
||||
trans_tbl(4,
|
||||
which(tbl$genus == 'Corynebacterium'),
|
||||
c(vanc, teic, dapt, line, qida, tige))
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Streptococcus pneumoniae'),
|
||||
c(carbapenems, vanc, teic, dapt, line, qida, tige, rifa))
|
||||
trans_tbl(4, # Sr. groups A/B/C/G
|
||||
which(tbl$fullname %like% '^Streptococcus (pyogenes|agalactiae|equisimilis|equi|zooepidemicus|dysgalactiae|anginosus)'),
|
||||
c(peni, cephalosporins, vanc, teic, dapt, line, qida, tige))
|
||||
trans_tbl(4,
|
||||
which(tbl$genus == 'Enterococcus'),
|
||||
c(dapt, line, tige, teic))
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Enterococcus faecalis'),
|
||||
c(ampi, amox))
|
||||
# Table 7
|
||||
trans_tbl(4,
|
||||
which(tbl$genus == 'Bacteroides'),
|
||||
metr)
|
||||
trans_tbl(4,
|
||||
which(tbl$fullname %like% '^Clostridium difficile'),
|
||||
c(metr, vanc))
|
||||
}
|
||||
|
||||
if (guideline$country$code == 'de') {
|
||||
# Germany -----------------------------------------------------------------
|
||||
stop("We are still working on German guidelines in this beta version.", call. = FALSE)
|
||||
}
|
||||
|
||||
if (guideline$country$code == 'nl') {
|
||||
# Netherlands -------------------------------------------------------------
|
||||
aminoglycosides <- aminoglycosides[!is.na(aminoglycosides)]
|
||||
fluoroquinolones <- fluoroquinolones[!is.na(fluoroquinolones)]
|
||||
carbapenems <- carbapenems[!is.na(carbapenems)]
|
||||
|
||||
# Table 1
|
||||
tbl[which(
|
||||
tbl$family == 'Enterobacteriaceae'
|
||||
& rowSums(tbl[, aminoglycosides] == 'R', na.rm = TRUE) >= 1
|
||||
& rowSums(tbl[, fluoroquinolones] == 'R', na.rm = TRUE) >= 1
|
||||
), 'MDRO'] <- 4
|
||||
tbl[which(
|
||||
tbl$family == 'Enterobacteriaceae'
|
||||
& rowSums(tbl[, carbapenems] == 'R', na.rm = TRUE) >= 1
|
||||
), 'MDRO'] <- 3
|
||||
# rest is negative
|
||||
tbl[which(
|
||||
tbl$family == 'Enterobacteriaceae'
|
||||
& tbl$MDRO == 1
|
||||
), 'MDRO'] <- 2
|
||||
|
||||
# Table 2
|
||||
tbl[which(
|
||||
tbl$genus == 'Acinetobacter'
|
||||
& rowSums(tbl[, carbapenems] == 'R', na.rm = TRUE) >= 1
|
||||
), 'MDRO'] <- 3
|
||||
tbl[which(
|
||||
tbl$genus == 'Acinetobacter'
|
||||
& rowSums(tbl[, aminoglycosides] == 'R', na.rm = TRUE) >= 1
|
||||
& rowSums(tbl[, fluoroquinolones] == 'R', na.rm = TRUE) >= 1
|
||||
), 'MDRO'] <- 4
|
||||
# rest of Acinetobacter is negative
|
||||
tbl[which(
|
||||
tbl$genus == 'Acinetobacter'
|
||||
& tbl$MDRO == 1
|
||||
), 'MDRO'] <- 2
|
||||
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Stenotrophomonas maltophilia'
|
||||
& tbl[, trsu] == 'R'
|
||||
), 'MDRO'] <- 4
|
||||
# rest of Stenotrophomonas is negative
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Stenotrophomonas maltophilia'
|
||||
& tbl$MDRO == 1
|
||||
), 'MDRO'] <- 2
|
||||
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Pseudomonas aeruginosa'
|
||||
& sum(rowSums(tbl[, carbapenems] == 'R', na.rm = TRUE) >= 1,
|
||||
rowSums(tbl[, aminoglycosides] == 'R', na.rm = TRUE) >= 1,
|
||||
rowSums(tbl[, fluoroquinolones] == 'R', na.rm = TRUE) >= 1,
|
||||
tbl[, cfta] == 'R',
|
||||
tbl[, pita] == 'R') >= 3
|
||||
), 'MDRO'] <- 4
|
||||
# rest of Pseudomonas is negative
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Pseudomonas aeruginosa'
|
||||
& tbl$MDRO == 1
|
||||
), 'MDRO'] <- 2
|
||||
|
||||
# Table 3
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Streptococcus pneumoniae'
|
||||
& tbl[, peni] == 'R'
|
||||
), 'MDRO'] <- 4
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Streptococcus pneumoniae'
|
||||
& tbl[, vanc] == 'R'
|
||||
), 'MDRO'] <- 4
|
||||
# rest of Streptococcus pneumoniae is negative
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Streptococcus pneumoniae'
|
||||
& tbl$MDRO == 1
|
||||
), 'MDRO'] <- 2
|
||||
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Enterococcus faecium'
|
||||
& rowSums(tbl[, c(peni, vanc)] == 'R', na.rm = TRUE) >= 1
|
||||
), 'MDRO'] <- 4
|
||||
# rest of Enterococcus faecium is negative
|
||||
tbl[which(
|
||||
tbl$fullname %like% 'Enterococcus faecium'
|
||||
& tbl$MDRO == 1
|
||||
), 'MDRO'] <- 2
|
||||
}
|
||||
|
||||
factor(x = tbl$MDRO,
|
||||
levels = c(1:4),
|
||||
labels = c('Unknown', 'Negative', 'Unconfirmed', 'Positive'),
|
||||
ordered = TRUE)
|
||||
}
|
||||
|
||||
#' @rdname MDRO
|
||||
#' @export
|
||||
BRMO <- function(tbl, country = "nl", ...) {
|
||||
MDRO(tbl = tbl, country = "nl", ...)
|
||||
}
|
||||
|
||||
#' @rdname MDRO
|
||||
#' @export
|
||||
MRGN <- function(tbl, country = "de", ...) {
|
||||
MDRO(tbl = tbl, country = "de", ...)
|
||||
}
|
||||
|
||||
#' @rdname MDRO
|
||||
#' @export
|
||||
EUCAST_exceptional_phenotypes <- function(tbl, country = "EUCAST", ...) {
|
||||
MDRO(tbl = tbl, country = "EUCAST", ...)
|
||||
}
|
||||
@@ -0,0 +1,211 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
# No export, no Rd
|
||||
addin_insert_in <- function() {
|
||||
rstudioapi::insertText(" %in% ")
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
addin_insert_like <- function() {
|
||||
rstudioapi::insertText(" %like% ")
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
#' @importFrom utils View
|
||||
addin_open_antibiotics <- function() {
|
||||
View(antibiotics)
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
#' @importFrom utils View
|
||||
addin_open_microorganisms <- function() {
|
||||
View(microorganisms)
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
#' @importFrom utils View
|
||||
addin_open_septic_patients <- function() {
|
||||
View(septic_patients)
|
||||
}
|
||||
|
||||
# No export, no Rd
|
||||
percent <- function(x, round = 1, force_zero = FALSE, ...) {
|
||||
val <- base::round(x * 100, digits = round)
|
||||
if (force_zero == TRUE & any(val == as.integer(val) & !is.na(val))) {
|
||||
val[val == as.integer(val)] <- paste0(val[val == as.integer(val)], ".", strrep(0, round))
|
||||
}
|
||||
pct <- base::paste0(val, "%")
|
||||
pct[pct == "NA%"] <- NA_character_
|
||||
pct
|
||||
}
|
||||
|
||||
check_available_columns <- function(tbl, col.list, info = TRUE) {
|
||||
# check columns
|
||||
col.list <- col.list[!is.na(col.list)]
|
||||
names(col.list) <- col.list
|
||||
col.list.bak <- col.list
|
||||
# are they available as upper case or lower case then?
|
||||
for (i in 1:length(col.list)) {
|
||||
if (toupper(col.list[i]) %in% colnames(tbl)) {
|
||||
col.list[i] <- toupper(col.list[i])
|
||||
} else if (tolower(col.list[i]) %in% colnames(tbl)) {
|
||||
col.list[i] <- tolower(col.list[i])
|
||||
} else if (!col.list[i] %in% colnames(tbl)) {
|
||||
col.list[i] <- NA
|
||||
}
|
||||
}
|
||||
if (!all(col.list %in% colnames(tbl))) {
|
||||
if (info == TRUE) {
|
||||
warning('These columns do not exist and will be ignored: ',
|
||||
col.list.bak[!(col.list %in% colnames(tbl))] %>% toString(),
|
||||
'.\nTHIS MAY STRONGLY INFLUENCE THE OUTCOME.',
|
||||
immediate. = TRUE,
|
||||
call. = FALSE)
|
||||
}
|
||||
}
|
||||
col.list
|
||||
}
|
||||
|
||||
# Coefficient of variation (CV)
|
||||
cv <- function(x, na.rm = TRUE) {
|
||||
stats::sd(x, na.rm = na.rm) / base::abs(base::mean(x, na.rm = na.rm))
|
||||
}
|
||||
|
||||
# Coefficient of dispersion, or coefficient of quartile variation (CQV).
|
||||
# (Bonett et al., 2006: Confidence interval for a coefficient of quartile variation).
|
||||
cqv <- function(x, na.rm = TRUE) {
|
||||
fives <- stats::fivenum(x, na.rm = na.rm)
|
||||
(fives[4] - fives[2]) / (fives[4] + fives[2])
|
||||
}
|
||||
|
||||
# show bytes as kB/MB/GB
|
||||
# size_humanreadable(123456) # 121 kB
|
||||
# size_humanreadable(12345678) # 11.8 MB
|
||||
size_humanreadable <- function(bytes, decimals = 1) {
|
||||
bytes <- bytes %>% as.double()
|
||||
# Adapted from:
|
||||
# http://jeffreysambells.com/2012/10/25/human-readable-filesize-php
|
||||
size <- c('B','kB','MB','GB','TB','PB','EB','ZB','YB')
|
||||
factor <- floor((nchar(bytes) - 1) / 3)
|
||||
# added slight improvement; no decimals for B and kB:
|
||||
decimals <- rep(decimals, length(bytes))
|
||||
decimals[size[factor + 1] %in% c('B', 'kB')] <- 0
|
||||
|
||||
out <- paste(sprintf(paste0("%.", decimals, "f"), bytes / (1024 ^ factor)), size[factor + 1])
|
||||
out
|
||||
}
|
||||
|
||||
# based on readr::parse_guess
|
||||
tbl_parse_guess <- function(tbl,
|
||||
date_names = 'en',
|
||||
date_format = '%Y-%m-%d',
|
||||
time_format = '%H:%M',
|
||||
decimal_mark = '.',
|
||||
tz = "UTC",
|
||||
encoding = "UTF-8",
|
||||
remove_ASCII_escape_char = FALSE,
|
||||
na = c("", "NA", "NULL")) {
|
||||
|
||||
date_format <- date_generic(date_format)
|
||||
time_format <- date_generic(time_format)
|
||||
# set col types with readr
|
||||
for (i in 1:ncol(tbl)) {
|
||||
if (!all(tbl %>% pull(i) %>% class() %in% c('list', 'matrix'))) {
|
||||
tbl[, i] <- readr::parse_guess(x = tbl %>% pull(i) %>% as.character(),
|
||||
na = na,
|
||||
locale = readr::locale(date_names = date_names,
|
||||
date_format = date_format,
|
||||
time_format = time_format,
|
||||
decimal_mark = decimal_mark,
|
||||
encoding = encoding,
|
||||
tz = tz,
|
||||
asciify = FALSE))
|
||||
}
|
||||
if (any(tbl %>% pull(i) %>% class() %in% c('factor', 'character'))) {
|
||||
# get values
|
||||
distinct_val <- tbl %>% pull(i) %>% unique() %>% sort()
|
||||
if (remove_ASCII_escape_char == TRUE) {
|
||||
# remove ASCII escape character: https://en.wikipedia.org/wiki/Escape_character#ASCII_escape_character
|
||||
tbl[, i] <- tbl %>% pull(i) %>% gsub('\033', ' ', ., fixed = TRUE)
|
||||
}
|
||||
# look for RSI, shouldn't all be "" and must be valid antibiotic interpretations
|
||||
if (!all(distinct_val[!is.na(distinct_val)] == '')
|
||||
& all(distinct_val[!is.na(distinct_val)] %in% c('', 'I', 'I;I', 'R', 'R;R', 'S', 'S;S'))) {
|
||||
tbl[, i] <- tbl %>% pull(i) %>% as.rsi()
|
||||
}
|
||||
}
|
||||
# convert to MIC class
|
||||
if (colnames(tbl)[i] %like% '_mic$') {
|
||||
tbl[, i] <- tbl %>% pull(i) %>% as.mic()
|
||||
}
|
||||
}
|
||||
tbl
|
||||
}
|
||||
|
||||
# transforms date format like "dddd d mmmm yyyy" to "%A %e %B %Y"
|
||||
date_generic <- function(format) {
|
||||
if (!grepl('%', format, fixed = TRUE)) {
|
||||
|
||||
# first months and minutes, after that everything is case INsensitive
|
||||
format <- gsub('mmmm', '%B1', format, fixed = TRUE)
|
||||
format <- gsub('mmm', '%b', format, fixed = TRUE)
|
||||
format <- gsub('mm', '%m', format, fixed = TRUE)
|
||||
format <- gsub('MM', '%M1', format, fixed = TRUE)
|
||||
format <- format %>%
|
||||
tolower() %>%
|
||||
gsub('%b1', '%B', ., fixed = TRUE) %>%
|
||||
gsub('%m1', '%M', ., fixed = TRUE)
|
||||
|
||||
# dates
|
||||
format <- gsub('dddd', '%A', format, fixed = TRUE)
|
||||
format <- gsub('ddd', '%a', format, fixed = TRUE)
|
||||
format <- gsub('dd', '%!', format, fixed = TRUE)
|
||||
format <- gsub('d', '%e', format, fixed = TRUE)
|
||||
format <- gsub('%!', '%d', format, fixed = TRUE)
|
||||
|
||||
format <- gsub('ww', '%V', format, fixed = TRUE)
|
||||
format <- gsub('w', '%V', format, fixed = TRUE)
|
||||
|
||||
format <- gsub('qq', 'Qq', format, fixed = TRUE) # so will be 'Q%%q' after this
|
||||
format <- gsub('kk', 'Kq', format, fixed = TRUE)
|
||||
format <- gsub('k', 'q', format, fixed = TRUE)
|
||||
format <- gsub('q', '%%q', format, fixed = TRUE)
|
||||
|
||||
format <- gsub('yyyy_iso', '%G', format, fixed = TRUE)
|
||||
format <- gsub('jjjj_iso', '%G', format, fixed = TRUE)
|
||||
format <- gsub('yyyy', '%Y', format, fixed = TRUE)
|
||||
format <- gsub('jjjj', '%Y', format, fixed = TRUE)
|
||||
format <- gsub('yy_iso', '%g', format, fixed = TRUE)
|
||||
format <- gsub('jj_iso', '%g', format, fixed = TRUE)
|
||||
format <- gsub('yy', '%y', format, fixed = TRUE)
|
||||
format <- gsub('jj', '%y', format, fixed = TRUE)
|
||||
|
||||
# time
|
||||
format <- gsub('hh', '%H', format, fixed = TRUE)
|
||||
format <- gsub('h', '%k', format, fixed = TRUE)
|
||||
format <- gsub('ss', '%S', format, fixed = TRUE)
|
||||
|
||||
# seconds since the Epoch, 1970-01-01 00:00:00
|
||||
format <- gsub('unix', '%s', format, fixed = TRUE)
|
||||
# Equivalent to %Y-%m-%d (the ISO 8601 date format)
|
||||
format <- gsub('iso', '%F', format, fixed = TRUE)
|
||||
|
||||
}
|
||||
format
|
||||
}
|
||||
@@ -0,0 +1,54 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Count cases with antimicrobial results
|
||||
#'
|
||||
#' This counts all cases where antimicrobial interpretations are available. Its use is equal to \code{\link{n_distinct}}.
|
||||
#' @param ab1,ab2 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
|
||||
#' @export
|
||||
#' @seealso The \code{\link{portion}} functions to calculate resistance and susceptibility.
|
||||
#' @examples
|
||||
#' library(dplyr)
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
|
||||
#' cipro_n = n_rsi(cipr),
|
||||
#' genta_p = portion_S(gent, as_percent = TRUE),
|
||||
#' genta_n = n_rsi(gent),
|
||||
#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
|
||||
#' combination_n = n_rsi(cipr, gent))
|
||||
n_rsi <- function(ab1, ab2 = NULL) {
|
||||
if (NCOL(ab1) > 1) {
|
||||
stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
|
||||
}
|
||||
if (!is.rsi(ab1)) {
|
||||
ab1 <- as.rsi(ab1)
|
||||
}
|
||||
if (!is.null(ab2)) {
|
||||
if (NCOL(ab2) > 1) {
|
||||
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
|
||||
}
|
||||
if (!is.rsi(ab2)) {
|
||||
ab2 <- as.rsi(ab2)
|
||||
}
|
||||
sum(!is.na(ab1) & !is.na(ab2))
|
||||
} else {
|
||||
sum(!is.na(ab1))
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,56 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Symbol of a p value
|
||||
#'
|
||||
#' Return the symbol related to the p value: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1. Values above \code{p = 1} will return \code{NA}.
|
||||
#' @param p p value
|
||||
#' @param emptychar text to show when \code{p > 0.1}
|
||||
#' @return Text
|
||||
#' @export
|
||||
p.symbol <- function(p, emptychar = " ") {
|
||||
setting.bak <- options()$scipen
|
||||
options(scipen = 999)
|
||||
s <- vector(mode = "character", length = length(p))
|
||||
for (i in 1:length(p)) {
|
||||
if (is.na(p[i])) {
|
||||
s[i] <- NA_character_
|
||||
next
|
||||
}
|
||||
if (p[i] > 1) {
|
||||
s[i] <- NA_character_
|
||||
next
|
||||
} else {
|
||||
p_test <- p[i]
|
||||
}
|
||||
|
||||
if (p_test > 0.1) {
|
||||
s[i] <- emptychar
|
||||
} else if (p_test > 0.05) {
|
||||
s[i] <- '.'
|
||||
} else if (p_test > 0.01) {
|
||||
s[i] <- '*'
|
||||
} else if (p_test > 0.001) {
|
||||
s[i] <- '**'
|
||||
} else if (p_test >= 0) {
|
||||
s[i] <- '***'
|
||||
}
|
||||
}
|
||||
options(scipen = setting.bak)
|
||||
s
|
||||
}
|
||||
Executable
+307
@@ -0,0 +1,307 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Calculate resistance of isolates
|
||||
#'
|
||||
#' @description These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}.
|
||||
#'
|
||||
#' \code{portion_R} and \code{portion_IR} can be used to calculate resistance, \code{portion_S} and \code{portion_SI} can be used to calculate susceptibility.\cr
|
||||
#' @param ab1 vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}} if needed
|
||||
#' @param ab2 like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
|
||||
#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.
|
||||
#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
|
||||
#' @param data a code{data.frame} containing columns with class \code{rsi} (see \code{\link{as.rsi}})
|
||||
#' @param translate_ab a column name of the \code{\link{antibiotics}} data set to translate the antibiotic abbreviations to, using \code{\link{abname}}. This can be set with \code{\link{getOption}("get_antibiotic_names")}.
|
||||
#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
|
||||
#'
|
||||
#' \code{portion_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and calculates the portions R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
|
||||
#'
|
||||
#' The old \code{\link{rsi}} function is still available for backwards compatibility but is deprecated.
|
||||
#' \if{html}{
|
||||
#' \cr\cr
|
||||
#' To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
|
||||
#' \out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
|
||||
#' To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
|
||||
#' \cr
|
||||
#' For two antibiotics:
|
||||
#' \out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
|
||||
#' \cr
|
||||
#' Theoretically for three antibiotics:
|
||||
#' \out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
|
||||
#' }
|
||||
#' @source \strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
|
||||
#'
|
||||
#' Wickham H. \strong{Tidy Data.} The Journal of Statistical Software, vol. 59, 2014. \url{http://vita.had.co.nz/papers/tidy-data.html}
|
||||
#' @seealso \code{\link{n_rsi}} to count cases with antimicrobial results.
|
||||
#' @keywords resistance susceptibility rsi_df rsi antibiotics isolate isolates
|
||||
#' @return Double or, when \code{as_percent = TRUE}, a character.
|
||||
#' @rdname portion
|
||||
#' @name portion
|
||||
#' @export
|
||||
#' @examples
|
||||
#' # septic_patients is a data set available in the AMR package. It is true, genuine data.
|
||||
#' ?septic_patients
|
||||
#'
|
||||
#' # Calculate resistance
|
||||
#' portion_R(septic_patients$amox)
|
||||
#' portion_IR(septic_patients$amox)
|
||||
#'
|
||||
#' # Or susceptibility
|
||||
#' portion_S(septic_patients$amox)
|
||||
#' portion_SI(septic_patients$amox)
|
||||
#'
|
||||
#' # Since n_rsi counts available isolates (and is used as denominator),
|
||||
#' # you can calculate back to count e.g. non-susceptible isolates:
|
||||
#' portion_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
|
||||
#'
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(p = portion_S(cipr),
|
||||
#' n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(R = portion_R(cipr, as_percent = TRUE),
|
||||
#' I = portion_I(cipr, as_percent = TRUE),
|
||||
#' S = portion_S(cipr, as_percent = TRUE),
|
||||
#' n = n_rsi(cipr), # works like n_distinct in dplyr
|
||||
#' total = n()) # NOT the amount of tested isolates!
|
||||
#'
|
||||
#' # Calculate co-resistance between amoxicillin/clav acid and gentamicin,
|
||||
#' # so we can see that combination therapy does a lot more than mono therapy:
|
||||
#' portion_S(septic_patients$amcl) # S = 67.3%
|
||||
#' n_rsi(septic_patients$amcl) # n = 1570
|
||||
#'
|
||||
#' portion_S(septic_patients$gent) # S = 74.0%
|
||||
#' n_rsi(septic_patients$gent) # n = 1842
|
||||
#'
|
||||
#' with(septic_patients,
|
||||
#' portion_S(amcl, gent)) # S = 92.1%
|
||||
#' with(septic_patients, # n = 1504
|
||||
#' n_rsi(amcl, gent))
|
||||
#'
|
||||
#' septic_patients %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
|
||||
#' cipro_n = n_rsi(cipr),
|
||||
#' genta_p = portion_S(gent, as_percent = TRUE),
|
||||
#' genta_n = n_rsi(gent),
|
||||
#' combination_p = portion_S(cipr, gent, as_percent = TRUE),
|
||||
#' combination_n = n_rsi(cipr, gent))
|
||||
#'
|
||||
#' # Get portions S/I/R immediately of all rsi columns
|
||||
#' septic_patients %>%
|
||||
#' select(amox, cipr) %>%
|
||||
#' portion_df(translate = FALSE)
|
||||
#'
|
||||
#' # It also supports grouping variables
|
||||
#' septic_patients %>%
|
||||
#' select(hospital_id, amox, cipr) %>%
|
||||
#' group_by(hospital_id) %>%
|
||||
#' portion_df(translate = FALSE)
|
||||
#'
|
||||
#'
|
||||
#' \dontrun{
|
||||
#'
|
||||
#' # calculate current empiric combination therapy of Helicobacter gastritis:
|
||||
#' my_table %>%
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' genus == "Helicobacter") %>%
|
||||
#' summarise(p = portion_S(amox, metr), # amoxicillin with metronidazole
|
||||
#' n = n_rsi(amox, metr))
|
||||
#' }
|
||||
portion_R <- function(ab1,
|
||||
ab2 = NULL,
|
||||
minimum = 30,
|
||||
as_percent = FALSE) {
|
||||
rsi_calc(type = "R",
|
||||
ab1 = ab1,
|
||||
ab2 = ab2,
|
||||
include_I = FALSE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_IR <- function(ab1,
|
||||
ab2 = NULL,
|
||||
minimum = 30,
|
||||
as_percent = FALSE) {
|
||||
rsi_calc(type = "R",
|
||||
ab1 = ab1,
|
||||
ab2 = ab2,
|
||||
include_I = TRUE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_I <- function(ab1,
|
||||
minimum = 30,
|
||||
as_percent = FALSE) {
|
||||
rsi_calc(type = "I",
|
||||
ab1 = ab1,
|
||||
ab2 = NULL,
|
||||
include_I = FALSE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_SI <- function(ab1,
|
||||
ab2 = NULL,
|
||||
minimum = 30,
|
||||
as_percent = FALSE) {
|
||||
rsi_calc(type = "S",
|
||||
ab1 = ab1,
|
||||
ab2 = ab2,
|
||||
include_I = TRUE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @export
|
||||
portion_S <- function(ab1,
|
||||
ab2 = NULL,
|
||||
minimum = 30,
|
||||
as_percent = FALSE) {
|
||||
rsi_calc(type = "S",
|
||||
ab1 = ab1,
|
||||
ab2 = ab2,
|
||||
include_I = FALSE,
|
||||
minimum = minimum,
|
||||
as_percent = as_percent)
|
||||
}
|
||||
|
||||
#' @rdname portion
|
||||
#' @importFrom dplyr bind_rows summarise_if mutate group_vars select everything
|
||||
#' @export
|
||||
portion_df <- function(data, translate_ab = getOption("get_antibiotic_names", "official")) {
|
||||
|
||||
if (as.character(translate_ab) == "TRUE") {
|
||||
translate_ab <- "official"
|
||||
}
|
||||
options(get_antibiotic_names = translate_ab)
|
||||
|
||||
resS <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = portion_S) %>%
|
||||
mutate(Interpretation = "S") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
resI <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = portion_I) %>%
|
||||
mutate(Interpretation = "I") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
resR <- summarise_if(.tbl = data,
|
||||
.predicate = is.rsi,
|
||||
.funs = portion_R) %>%
|
||||
mutate(Interpretation = "R") %>%
|
||||
select(Interpretation, everything())
|
||||
|
||||
data.groups <- group_vars(data)
|
||||
|
||||
res <- bind_rows(resS, resI, resR) %>%
|
||||
mutate(Interpretation = factor(Interpretation, levels = c("R", "I", "S"), ordered = TRUE)) %>%
|
||||
tidyr::gather(Antibiotic, Percentage, -Interpretation, -data.groups)
|
||||
|
||||
if (!translate_ab == FALSE) {
|
||||
if (!tolower(translate_ab) %in% tolower(colnames(AMR::antibiotics))) {
|
||||
stop("Parameter `translate_ab` does not occur in the `antibiotics` data set.", call. = FALSE)
|
||||
}
|
||||
res <- res %>% mutate(Antibiotic = abname(Antibiotic, from = "guess", to = translate_ab))
|
||||
}
|
||||
|
||||
res
|
||||
}
|
||||
|
||||
rsi_calc <- function(type,
|
||||
ab1,
|
||||
ab2,
|
||||
include_I,
|
||||
minimum,
|
||||
as_percent) {
|
||||
|
||||
if (NCOL(ab1) > 1) {
|
||||
stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
|
||||
}
|
||||
if (!is.logical(include_I)) {
|
||||
stop('`include_I` must be logical', call. = FALSE)
|
||||
}
|
||||
if (!is.numeric(minimum)) {
|
||||
stop('`minimum` must be numeric', call. = FALSE)
|
||||
}
|
||||
if (!is.logical(as_percent)) {
|
||||
stop('`as_percent` must be logical', call. = FALSE)
|
||||
}
|
||||
|
||||
print_warning <- FALSE
|
||||
if (!is.rsi(ab1)) {
|
||||
ab1 <- as.rsi(ab1)
|
||||
print_warning <- TRUE
|
||||
}
|
||||
if (!is.null(ab2)) {
|
||||
# ab_name <- paste(deparse(substitute(ab1)), "and", deparse(substitute(ab2)))
|
||||
if (NCOL(ab2) > 1) {
|
||||
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
|
||||
}
|
||||
if (!is.rsi(ab2)) {
|
||||
ab2 <- as.rsi(ab2)
|
||||
print_warning <- TRUE
|
||||
}
|
||||
x <- apply(X = data.frame(ab1 = as.integer(ab1),
|
||||
ab2 = as.integer(ab2)),
|
||||
MARGIN = 1,
|
||||
FUN = min)
|
||||
} else {
|
||||
x <- ab1
|
||||
# ab_name <- deparse(substitute(ab1))
|
||||
}
|
||||
|
||||
if (print_warning == TRUE) {
|
||||
warning("Increase speed by transforming to class `rsi` on beforehand: df %>% mutate_at(vars(col10:col20), as.rsi)")
|
||||
}
|
||||
|
||||
total <- length(x) - sum(is.na(x))
|
||||
if (total < minimum) {
|
||||
return(NA)
|
||||
}
|
||||
|
||||
if (type == "S") {
|
||||
found <- sum(as.integer(x) <= 1 + include_I, na.rm = TRUE)
|
||||
} else if (type == "I") {
|
||||
found <- sum(as.integer(x) == 2, na.rm = TRUE)
|
||||
} else if (type == "R") {
|
||||
found <- sum(as.integer(x) >= 3 - include_I, na.rm = TRUE)
|
||||
} else {
|
||||
stop("invalid type")
|
||||
}
|
||||
|
||||
if (as_percent == TRUE) {
|
||||
percent(found / total, force_zero = TRUE)
|
||||
} else {
|
||||
found / total
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,293 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Predict antimicrobial resistance
|
||||
#'
|
||||
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
|
||||
#' @inheritParams first_isolate
|
||||
#' @param col_ab column name of \code{tbl} with antimicrobial interpretations (\code{R}, \code{I} and \code{S})
|
||||
#' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already
|
||||
#' @param year_min lowest year to use in the prediction model, dafaults the lowest year in \code{col_date}
|
||||
#' @param year_max highest year to use in the prediction model, defaults to 15 years after today
|
||||
#' @param year_every unit of sequence between lowest year found in the data and \code{year_max}
|
||||
#' @param minimum minimal amount of available isolates per year to include. Years containing less observations will be estimated by the model.
|
||||
#' @param model the statistical model of choice. Valid values are \code{"binomial"} (or \code{"binom"} or \code{"logit"}) or \code{"loglin"} or \code{"linear"} (or \code{"lin"}).
|
||||
#' @param I_as_R treat \code{I} as \code{R}
|
||||
#' @param preserve_measurements logical to indicate whether predictions of years that are actually available in the data should be overwritten with the original data. The standard errors of those years will be \code{NA}.
|
||||
#' @param info print textual analysis with the name and \code{\link{summary}} of the model.
|
||||
#' @return \code{data.frame} with columns:
|
||||
#' \itemize{
|
||||
#' \item{\code{year}}
|
||||
#' \item{\code{value}, the same as \code{estimated} when \code{preserve_measurements = FALSE}, and a combination of \code{observed} and \code{estimated} otherwise}
|
||||
#' \item{\code{se_min}, the lower bound of the standard error with a minimum of \code{0}}
|
||||
#' \item{\code{se_max} the upper bound of the standard error with a maximum of \code{1}}
|
||||
#' \item{\code{observations}, the total number of observations, i.e. S + I + R}
|
||||
#' \item{\code{observed}, the original observed values}
|
||||
#' \item{\code{estimated}, the estimated values, calculated by the model}
|
||||
#' }
|
||||
#' @seealso The \code{\link{portion}} function to calculate resistance, \cr \code{\link{lm}} \code{\link{glm}}
|
||||
#' @rdname resistance_predict
|
||||
#' @export
|
||||
#' @importFrom stats predict glm lm
|
||||
#' @importFrom dplyr %>% pull mutate group_by_at summarise filter n_distinct arrange case_when
|
||||
# @importFrom tidyr spread
|
||||
#' @examples
|
||||
#' \dontrun{
|
||||
#' # use it with base R:
|
||||
#' resistance_predict(tbl = tbl[which(first_isolate == TRUE & genus == "Haemophilus"),],
|
||||
#' col_ab = "amcl", col_date = "date")
|
||||
#'
|
||||
#' # or use dplyr so you can actually read it:
|
||||
#' library(dplyr)
|
||||
#' tbl %>%
|
||||
#' filter(first_isolate == TRUE,
|
||||
#' genus == "Haemophilus") %>%
|
||||
#' resistance_predict(amcl, date)
|
||||
#' }
|
||||
#'
|
||||
#'
|
||||
#' # real live example:
|
||||
#' library(dplyr)
|
||||
#' septic_patients %>%
|
||||
#' # get bacteria properties like genus and species
|
||||
#' left_join_microorganisms("bactid") %>%
|
||||
#' # calculate first isolates
|
||||
#' mutate(first_isolate =
|
||||
#' first_isolate(.,
|
||||
#' "date",
|
||||
#' "patient_id",
|
||||
#' "bactid",
|
||||
#' col_specimen = NA,
|
||||
#' col_icu = NA)) %>%
|
||||
#' # filter on first E. coli isolates
|
||||
#' filter(genus == "Escherichia",
|
||||
#' species == "coli",
|
||||
#' first_isolate == TRUE) %>%
|
||||
#' # predict resistance of cefotaxime for next years
|
||||
#' resistance_predict(col_ab = "cfot",
|
||||
#' col_date = "date",
|
||||
#' year_max = 2025,
|
||||
#' preserve_measurements = TRUE,
|
||||
#' minimum = 0)
|
||||
#'
|
||||
#' # create nice plots with ggplot
|
||||
#' if (!require(ggplot2)) {
|
||||
#'
|
||||
#' data <- septic_patients %>%
|
||||
#' filter(bactid == "ESCCOL") %>%
|
||||
#' resistance_predict(col_ab = "amox",
|
||||
#' col_date = "date",
|
||||
#' info = FALSE,
|
||||
#' minimum = 15)
|
||||
#'
|
||||
#' ggplot(data,
|
||||
#' aes(x = year)) +
|
||||
#' geom_col(aes(y = value),
|
||||
#' fill = "grey75") +
|
||||
#' geom_errorbar(aes(ymin = se_min,
|
||||
#' ymax = se_max),
|
||||
#' colour = "grey50") +
|
||||
#' scale_y_continuous(limits = c(0, 1),
|
||||
#' breaks = seq(0, 1, 0.1),
|
||||
#' labels = paste0(seq(0, 100, 10), "%")) +
|
||||
#' labs(title = expression(paste("Forecast of amoxicillin resistance in ",
|
||||
#' italic("E. coli"))),
|
||||
#' y = "%IR",
|
||||
#' x = "Year") +
|
||||
#' theme_minimal(base_size = 13)
|
||||
#' }
|
||||
resistance_predict <- function(tbl,
|
||||
col_ab,
|
||||
col_date,
|
||||
year_min = NULL,
|
||||
year_max = NULL,
|
||||
year_every = 1,
|
||||
minimum = 30,
|
||||
model = 'binomial',
|
||||
I_as_R = TRUE,
|
||||
preserve_measurements = TRUE,
|
||||
info = TRUE) {
|
||||
|
||||
if (nrow(tbl) == 0) {
|
||||
stop('This table does not contain any observations.')
|
||||
}
|
||||
|
||||
if (!col_ab %in% colnames(tbl)) {
|
||||
stop('Column ', col_ab, ' not found.')
|
||||
}
|
||||
|
||||
if (!col_date %in% colnames(tbl)) {
|
||||
stop('Column ', col_date, ' not found.')
|
||||
}
|
||||
if ('grouped_df' %in% class(tbl)) {
|
||||
# no grouped tibbles please, mutate will throw errors
|
||||
tbl <- base::as.data.frame(tbl, stringsAsFactors = FALSE)
|
||||
}
|
||||
|
||||
if (I_as_R == TRUE) {
|
||||
tbl[, col_ab] <- gsub('I', 'R', tbl %>% pull(col_ab))
|
||||
}
|
||||
|
||||
if (!tbl %>% pull(col_ab) %>% is.rsi()) {
|
||||
tbl[, col_ab] <- tbl %>% pull(col_ab) %>% as.rsi()
|
||||
}
|
||||
|
||||
year <- function(x) {
|
||||
if (all(grepl('^[0-9]{4}$', x))) {
|
||||
x
|
||||
} else {
|
||||
as.integer(format(as.Date(x), '%Y'))
|
||||
}
|
||||
}
|
||||
|
||||
df <- tbl %>%
|
||||
mutate(year = tbl %>% pull(col_date) %>% year()) %>%
|
||||
group_by_at(c('year', col_ab)) %>%
|
||||
summarise(n())
|
||||
|
||||
if (df %>% pull(col_ab) %>% n_distinct(na.rm = TRUE) < 2) {
|
||||
stop("No variety in antimicrobial interpretations - all isolates are '",
|
||||
df %>% pull(col_ab) %>% unique() %>% .[!is.na(.)], "'.",
|
||||
call. = FALSE)
|
||||
}
|
||||
|
||||
colnames(df) <- c('year', 'antibiotic', 'observations')
|
||||
df <- df %>%
|
||||
filter(!is.na(antibiotic)) %>%
|
||||
tidyr::spread(antibiotic, observations, fill = 0) %>%
|
||||
mutate(total = R + S) %>%
|
||||
filter(total >= minimum)
|
||||
|
||||
if (NROW(df) == 0) {
|
||||
stop('There are no observations.')
|
||||
}
|
||||
|
||||
year_lowest <- min(df$year)
|
||||
if (is.null(year_min)) {
|
||||
year_min <- year_lowest
|
||||
} else {
|
||||
year_min <- max(year_min, year_lowest, na.rm = TRUE)
|
||||
}
|
||||
if (is.null(year_max)) {
|
||||
year_max <- year(Sys.Date()) + 15
|
||||
}
|
||||
|
||||
years_predict <- seq(from = year_min, to = year_max, by = year_every)
|
||||
|
||||
if (model %in% c('binomial', 'binom', 'logit')) {
|
||||
logitmodel <- with(df, glm(cbind(R, S) ~ year, family = binomial))
|
||||
if (info == TRUE) {
|
||||
cat('\nLogistic regression model (logit) with binomial distribution')
|
||||
cat('\n------------------------------------------------------------\n')
|
||||
print(summary(logitmodel))
|
||||
}
|
||||
|
||||
predictmodel <- predict(logitmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
} else if (model == 'loglin') {
|
||||
loglinmodel <- with(df, glm(R ~ year, family = poisson))
|
||||
if (info == TRUE) {
|
||||
cat('\nLog-linear regression model (loglin) with poisson distribution')
|
||||
cat('\n--------------------------------------------------------------\n')
|
||||
print(summary(loglinmodel))
|
||||
}
|
||||
|
||||
predictmodel <- predict(loglinmodel, newdata = with(df, list(year = years_predict)), type = "response", se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
} else if (model %in% c('lin', 'linear')) {
|
||||
linmodel <- with(df, lm((R / (R + S)) ~ year))
|
||||
if (info == TRUE) {
|
||||
cat('\nLinear regression model')
|
||||
cat('\n-----------------------\n')
|
||||
print(summary(linmodel))
|
||||
}
|
||||
|
||||
predictmodel <- predict(linmodel, newdata = with(df, list(year = years_predict)), se.fit = TRUE)
|
||||
prediction <- predictmodel$fit
|
||||
se <- predictmodel$se.fit
|
||||
|
||||
} else {
|
||||
stop('No valid model selected.')
|
||||
}
|
||||
|
||||
# prepare the output dataframe
|
||||
prediction <- data.frame(year = years_predict, value = prediction, stringsAsFactors = FALSE)
|
||||
|
||||
prediction$se_min <- prediction$value - se
|
||||
prediction$se_max <- prediction$value + se
|
||||
|
||||
if (model == 'loglin') {
|
||||
prediction$value <- prediction$value %>%
|
||||
format(scientific = FALSE) %>%
|
||||
as.integer()
|
||||
prediction$se_min <- prediction$se_min %>% as.integer()
|
||||
prediction$se_max <- prediction$se_max %>% as.integer()
|
||||
|
||||
colnames(prediction) <- c('year', 'amountR', 'se_max', 'se_min')
|
||||
} else {
|
||||
prediction$se_max[which(prediction$se_max > 1)] <- 1
|
||||
}
|
||||
prediction$se_min[which(prediction$se_min < 0)] <- 0
|
||||
prediction$observations = NA
|
||||
|
||||
total <- prediction
|
||||
|
||||
if (preserve_measurements == TRUE) {
|
||||
# replace estimated data by observed data
|
||||
if (I_as_R == TRUE) {
|
||||
if (!'I' %in% colnames(df)) {
|
||||
df$I <- 0
|
||||
}
|
||||
df$value <- df$R / rowSums(df[, c('R', 'S', 'I')])
|
||||
} else {
|
||||
df$value <- df$R / rowSums(df[, c('R', 'S')])
|
||||
}
|
||||
measurements <- data.frame(year = df$year,
|
||||
value = df$value,
|
||||
se_min = NA,
|
||||
se_max = NA,
|
||||
observations = df$total,
|
||||
stringsAsFactors = FALSE)
|
||||
colnames(measurements) <- colnames(prediction)
|
||||
|
||||
total <- rbind(measurements,
|
||||
prediction %>% filter(!year %in% df$year))
|
||||
if (model %in% c('binomial', 'binom', 'logit')) {
|
||||
total <- total %>% mutate(observed = ifelse(is.na(observations), NA, value),
|
||||
estimated = prediction$value)
|
||||
}
|
||||
}
|
||||
|
||||
if ("value" %in% colnames(total)) {
|
||||
total <- total %>%
|
||||
mutate(value = case_when(value > 1 ~ 1,
|
||||
value < 0 ~ 0,
|
||||
TRUE ~ value))
|
||||
}
|
||||
|
||||
total %>% arrange(year)
|
||||
|
||||
}
|
||||
|
||||
#' @rdname resistance_predict
|
||||
#' @export
|
||||
rsi_predict <- resistance_predict
|
||||
@@ -0,0 +1,53 @@
|
||||
# ==================================================================== #
|
||||
# TITLE #
|
||||
# Antimicrobial Resistance (AMR) Analysis #
|
||||
# #
|
||||
# AUTHORS #
|
||||
# Berends MS (m.s.berends@umcg.nl), Luz CF (c.f.luz@umcg.nl) #
|
||||
# #
|
||||
# LICENCE #
|
||||
# This program is free software; you can redistribute it and/or modify #
|
||||
# it under the terms of the GNU General Public License version 2.0, #
|
||||
# as published by the Free Software Foundation. #
|
||||
# #
|
||||
# This program is distributed in the hope that it will be useful, #
|
||||
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
|
||||
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
|
||||
# GNU General Public License for more details. #
|
||||
# ==================================================================== #
|
||||
|
||||
#' Calculate resistance of isolates
|
||||
#'
|
||||
#' This function is deprecated. Use the \code{\link{portion}} functions instead.
|
||||
#' @inheritParams portion
|
||||
#' @param interpretation antimicrobial interpretation to check for
|
||||
#' @param ... deprecated parameters to support usage on older versions
|
||||
#' @importFrom dplyr case_when
|
||||
#' @export
|
||||
rsi <- function(ab1,
|
||||
ab2 = NULL,
|
||||
interpretation = "IR",
|
||||
minimum = 30,
|
||||
as_percent = FALSE,
|
||||
...) {
|
||||
|
||||
result <- case_when(
|
||||
interpretation == "S" ~ portion_S(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
|
||||
interpretation %in% c("SI", "IS") ~ portion_SI(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "I" ~ portion_I(ab1 = ab1, minimum = minimum, as_percent = FALSE),
|
||||
interpretation %in% c("RI", "IR") ~ portion_IR(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
|
||||
interpretation == "R" ~ portion_R(ab1 = ab1, ab2 = ab2, minimum = minimum, as_percent = FALSE),
|
||||
TRUE ~ -1
|
||||
)
|
||||
if (result == -1) {
|
||||
stop("invalid interpretation")
|
||||
}
|
||||
|
||||
.Deprecated(new = paste0("portion_", interpretation))
|
||||
|
||||
if (as_percent == TRUE) {
|
||||
percent(result, force_zero = TRUE)
|
||||
} else {
|
||||
result
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,40 @@
|
||||
#' Skewness of the sample
|
||||
#'
|
||||
#' @description Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
|
||||
#'
|
||||
#' When negative: the left tail is longer; the mass of the distribution is concentrated on the right of the figure. When positive: the right tail is longer; the mass of the distribution is concentrated on the left of the figure.
|
||||
#' @param x a vector of values, a \code{matrix} or a \code{data frame}
|
||||
#' @param na.rm a logical value indicating whether \code{NA} values should be stripped before the computation proceeds.
|
||||
#' @exportMethod skewness
|
||||
#' @seealso \code{\link{kurtosis}}
|
||||
#' @rdname skewness
|
||||
#' @export
|
||||
skewness <- function(x, na.rm = FALSE) {
|
||||
UseMethod("skewness")
|
||||
}
|
||||
|
||||
#' @exportMethod skewness.default
|
||||
#' @rdname skewness
|
||||
#' @export
|
||||
skewness.default <- function (x, na.rm = FALSE) {
|
||||
x <- as.vector(x)
|
||||
if (na.rm == TRUE) {
|
||||
x <- x[!is.na(x)]
|
||||
}
|
||||
n <- length(x)
|
||||
(base::sum((x - base::mean(x))^3) / n) / (base::sum((x - base::mean(x))^2) / n)^(3/2)
|
||||
}
|
||||
|
||||
#' @exportMethod skewness.matrix
|
||||
#' @rdname skewness
|
||||
#' @export
|
||||
skewness.matrix <- function (x, na.rm = FALSE) {
|
||||
base::apply(x, 2, skewness.default, na.rm = na.rm)
|
||||
}
|
||||
|
||||
#' @exportMethod skewness.data.frame
|
||||
#' @rdname skewness
|
||||
#' @export
|
||||
skewness.data.frame <- function (x, na.rm = FALSE) {
|
||||
base::sapply(x, skewness.default, na.rm = na.rm)
|
||||
}
|
||||
@@ -0,0 +1,3 @@
|
||||
.onLoad <- function(libname, pkgname) {
|
||||
backports::import(pkgname)
|
||||
}
|
||||
@@ -0,0 +1,397 @@
|
||||
# `AMR`
|
||||
### An [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and work with antibiotic properties by using evidence-based methods.
|
||||
|
||||
This R package was created for academic research by PhD students of the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl) and the Medical Microbiology & Infection Prevention (MMBI) department of the [University Medical Center Groningen (UMCG)](https://www.umcg.nl).
|
||||
|
||||
:arrow_forward: Download it with `install.packages("AMR")` or see below for other possibilities.
|
||||
|
||||
## Authors
|
||||
<a href="https://orcid.org/0000-0001-7620-1800"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> Matthijs S. Berends<sup>1,2,a</sup>,
|
||||
<a href="https://orcid.org/0000-0001-5809-5995"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> Christian F. Luz<sup>1,a</sup>,
|
||||
Erwin E.A. Hassing<sup>2</sup>,
|
||||
<a href="https://orcid.org/0000-0003-1241-1328"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> Corinna Glasner<sup>1,b</sup>,
|
||||
<a href="https://orcid.org/0000-0003-4881-038X"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> Alex W. Friedrich<sup>1,b</sup>,
|
||||
<a href="https://orcid.org/0000-0003-1634-0010"><img src="https://cran.r-project.org/web/orcid.svg" height="16px"></a> Bhanu Sinha<sup>1,b</sup>
|
||||
|
||||
<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands - [rug.nl](http://www.rug.nl) [umcg.nl](http://www.umcg.nl)<br>
|
||||
<sup>2</sup> Certe Medical Diagnostics & Advice, Groningen, the Netherlands - [certe.nl](http://www.certe.nl)<br>
|
||||
<sup>a</sup> R package author and thesis dissertant<br>
|
||||
<sup>b</sup> Thesis advisor
|
||||
|
||||
<a href="https://www.rug.nl"><img src="man/figures/logo_rug.png" height="60px"></a>
|
||||
<a href="https://www.umcg.nl"><img src="man/figures/logo_umcg.png" height="60px"></a>
|
||||
<a href="https://www.certe.nl"><img src="man/figures/logo_certe.png" height="60px"></a>
|
||||
<a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_eh1h.png" height="60px"></a>
|
||||
<a href="http://www.eurhealth-1health.eu"><img src="man/figures/logo_interreg.png" height="60px"></a>
|
||||
|
||||
## Contents
|
||||
* [Why this package?](#why-this-package)
|
||||
* [How to get it?](#how-to-get-it)
|
||||
* [Install from CRAN](#install-from-cran)
|
||||
* [Install from GitHub](#install-from-github)
|
||||
* [How to use it?](#how-to-use-it)
|
||||
* [New classes](#new-classes)
|
||||
* [Overwrite/force resistance based on EUCAST rules](#overwriteforce-resistance-based-on-eucast-rules)
|
||||
* [Other (microbial) epidemiological functions](#other-microbial-epidemiological-functions)
|
||||
* [Frequency tables](#frequency-tables)
|
||||
* [Data sets included in package](#data-sets-included-in-package)
|
||||
* [Copyright](#copyright)
|
||||
|
||||
## Why this package?
|
||||
This R package was intended to make microbial epidemiology easier. Most functions contain extensive help pages to get started.
|
||||
|
||||
With `AMR` you can:
|
||||
* Calculate the resistance (and even co-resistance) of microbial isolates with the `portion_R`, `portion_IR`, `portion_I`, `portion_SI` and `portion_S` functions, that can also 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
|
||||
* Apply [EUCAST rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) with the `EUCAST_rules` function
|
||||
* Identify first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute) with the `first_isolate` function
|
||||
* 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. The following 12 antibiotics will be used as key antibiotics at default:
|
||||
* Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole
|
||||
* Specific for Gram-positives: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin
|
||||
* Specific for Gram-negatives: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem
|
||||
* Categorise *Staphylococci* into Coagulase Negative *Staphylococci* (CoNS) and Coagulase Positve *Staphylococci* (CoPS) according to [Karsten Becker *et al.*](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/25278577/)
|
||||
* Categorise *Streptococci* into Lancefield groups
|
||||
* Get antimicrobial ATC properties from the WHO Collaborating Centre for Drug Statistics Methodology ([WHOCC](https://www.whocc.no/atc_ddd_methodology/who_collaborating_centre/)), to be able to:
|
||||
* Translate antibiotic codes (like *AMOX*), official names (like *amoxicillin*) and even trade names (like *Amoxil* or *Trimox*) to an [ATC code](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like *J01CA04*) and vice versa with the `abname` function
|
||||
* Get the latest antibiotic properties like hierarchic groups and [defined daily dose](https://en.wikipedia.org/wiki/Defined_daily_dose) (DDD) with units and administration form from the WHOCC website with the `atc_property` function
|
||||
* Conduct descriptive statistics: calculate kurtosis, skewness and create frequency tables
|
||||
|
||||
And it contains:
|
||||
* A recent data set with ~2500 human pathogenic microorganisms, including family, genus, species, gram stain and aerobic/anaerobic
|
||||
* A recent data set with all antibiotics as defined by the [WHOCC](https://www.whocc.no/atc_ddd_methodology/who_collaborating_centre/), including ATC code, official name and DDD's
|
||||
* An example data set `septic_patients`, consisting of 2000 blood culture isolates from anonymised septic patients between 2001 and 2017.
|
||||
|
||||
With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).
|
||||
|
||||
**Read all changes and new functions in [NEWS.md](NEWS.md).**
|
||||
|
||||
## How to get it?
|
||||
This package [is published on CRAN](http://cran.r-project.org/package=AMR), the official R network.
|
||||
|
||||
### Install from CRAN
|
||||
[](http://cran.r-project.org/package=AMR) [](http://cran.r-project.org/package=AMR)
|
||||
|
||||
(Note: Downloads measured only by [cran.rstudio.com](https://cran.rstudio.com/package=AMR), this excludes e.g. the official [cran.r-project.org](https://cran.r-project.org/package=AMR))
|
||||
|
||||
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> Install using [RStudio](http://www.rstudio.com) (recommended):
|
||||
- Click on `Tools` and then `Install Packages...`
|
||||
- Type in `AMR` and press <kbd>Install</kbd>
|
||||
|
||||
- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> Install in R directly:
|
||||
- `install.packages("AMR")`
|
||||
|
||||
### Install from GitHub
|
||||
[](https://travis-ci.org/msberends/AMR)
|
||||
[](https://ci.appveyor.com/project/msberends/AMR)
|
||||
[](https://github.com/msberends/AMR/commits/master)
|
||||
[](https://codecov.io/gh/msberends/AMR)
|
||||
|
||||
```r install.packages("devtools")
|
||||
devtools::install_github("msberends/AMR")
|
||||
```
|
||||
|
||||
## How to use it?
|
||||
```r
|
||||
# Call it with:
|
||||
library(AMR)
|
||||
|
||||
# For a list of functions:
|
||||
help(package = "AMR")
|
||||
```
|
||||
|
||||
### New classes
|
||||
This package contains two new S3 classes: `mic` for MIC values (e.g. from Vitek or Phoenix) and `rsi` for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of `2` being higher than `<=1` but lower than `>=32`, and for class `rsi` factors are ordered as `S < I < R`).
|
||||
Both classes have extensions for existing generic functions like `print`, `summary` and `plot`.
|
||||
|
||||
These functions also try to coerce valid values.
|
||||
|
||||
#### RSI
|
||||
The `septic_patients` data set comes with antimicrobial results of more than 40 different drugs. For example, columns `amox` and `cipr` contain results of amoxicillin and ciprofloxacin, respectively.
|
||||
```r
|
||||
summary(septic_patients[, c("amox", "cipr")])
|
||||
# amox cipr
|
||||
# Mode :rsi Mode :rsi
|
||||
# <NA> :1002 <NA> :596
|
||||
# Sum S :336 Sum S :1108
|
||||
# Sum IR:662 Sum IR:296
|
||||
# -Sum R:659 -Sum R:227
|
||||
# -Sum I:3 -Sum I:69
|
||||
```
|
||||
|
||||
You can use the `plot` function from base R:
|
||||
```r
|
||||
plot(septic_patients$cipr)
|
||||
```
|
||||
|
||||
|
||||
|
||||
Or use the `ggplot2` and `dplyr` packages to create more appealing plots:
|
||||
```r
|
||||
library(dplyr)
|
||||
library(ggplot2)
|
||||
|
||||
septic_patients %>%
|
||||
select(amox, cipr) %>%
|
||||
ggplot_rsi()
|
||||
```
|
||||
|
||||
|
||||
|
||||
```r
|
||||
septic_patients %>%
|
||||
select(amox, cipr) %>%
|
||||
ggplot_rsi(x = "Interpretation", facet = "Antibiotic")
|
||||
```
|
||||
|
||||
|
||||
|
||||
It also supports grouping variables. Let's say we want to compare resistance of drugs against Urine Tract Infections (UTI) between hospitals A to D (variable `hospital_id`):
|
||||
|
||||
```r
|
||||
septic_patients %>%
|
||||
select(hospital_id, amox, nitr, fosf, trim, cipr) %>%
|
||||
group_by(hospital_id) %>%
|
||||
ggplot_rsi(x = "hospital_id",
|
||||
facet = "Antibiotic",
|
||||
nrow = 1) +
|
||||
labs(title = "AMR of Anti-UTI Drugs Per Hospital",
|
||||
x = "Hospital")
|
||||
```
|
||||
|
||||
|
||||
|
||||
You could use this to group on anything in your plots: Gram stain, age (group), genus, geographic location, et cetera.
|
||||
|
||||
#### MIC
|
||||
|
||||
```r
|
||||
# Transform values to new class
|
||||
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
|
||||
|
||||
summary(mic_data)
|
||||
# Mode:mic
|
||||
# <NA>:0
|
||||
# Min.:<=0.128
|
||||
# Max.:>=32
|
||||
|
||||
plot(mic_data)
|
||||
```
|
||||
|
||||
|
||||
|
||||
### Overwrite/force resistance based on EUCAST rules
|
||||
This is also called *interpretive reading*.
|
||||
```r
|
||||
before <- data.frame(bactid = c("STAAUR", # Staphylococcus aureus
|
||||
"ENCFAE", # Enterococcus faecalis
|
||||
"ESCCOL", # Escherichia coli
|
||||
"KLEPNE", # Klebsiella pneumoniae
|
||||
"PSEAER"), # Pseudomonas aeruginosa
|
||||
vanc = "-", # Vancomycin
|
||||
amox = "-", # Amoxicillin
|
||||
coli = "-", # Colistin
|
||||
cfta = "-", # Ceftazidime
|
||||
cfur = "-", # Cefuroxime
|
||||
stringsAsFactors = FALSE)
|
||||
before
|
||||
# bactid vanc amox coli cfta cfur
|
||||
# 1 STAAUR - - - - -
|
||||
# 2 ENCFAE - - - - -
|
||||
# 3 ESCCOL - - - - -
|
||||
# 4 KLEPNE - - - - -
|
||||
# 5 PSEAER - - - - -
|
||||
|
||||
# Now apply those rules; just need a column with bacteria ID's and antibiotic results:
|
||||
after <- EUCAST_rules(before)
|
||||
after
|
||||
# bactid vanc amox coli cfta cfur
|
||||
# 1 STAAUR - - R R -
|
||||
# 2 ENCFAE - - R R R
|
||||
# 3 ESCCOL R - - - -
|
||||
# 4 KLEPNE R R - - -
|
||||
# 5 PSEAER R R - - R
|
||||
```
|
||||
|
||||
Bacteria ID's can be retrieved with the `guess_bactid` function. It uses any type of info about a microorganism as input. For example, all these will return value `STAAUR`, the ID of *S. aureus*:
|
||||
```r
|
||||
guess_bactid("stau")
|
||||
guess_bactid("STAU")
|
||||
guess_bactid("staaur")
|
||||
guess_bactid("S. aureus")
|
||||
guess_bactid("S aureus")
|
||||
guess_bactid("Staphylococcus aureus")
|
||||
guess_bactid("MRSA") # Methicillin Resistant S. aureus
|
||||
guess_bactid("VISA") # Vancomycin Intermediate S. aureus
|
||||
guess_bactid("VRSA") # Vancomycin Resistant S. aureus
|
||||
```
|
||||
|
||||
### Other (microbial) epidemiological functions
|
||||
|
||||
```r
|
||||
# G-test to replace Chi squared test
|
||||
g.test(...)
|
||||
|
||||
# Determine key antibiotic based on bacteria ID
|
||||
key_antibiotics(...)
|
||||
|
||||
# Selection of first isolates of any patient
|
||||
first_isolate(...)
|
||||
|
||||
# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
|
||||
rsi(...)
|
||||
# Predict resistance levels of antibiotics
|
||||
rsi_predict(...)
|
||||
|
||||
# Get name of antibiotic by ATC code
|
||||
abname(...)
|
||||
abname("J01CR02", from = "atc", to = "umcg") # "AMCL"
|
||||
```
|
||||
|
||||
### Frequency tables
|
||||
Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: `freq` (or `frequency_tbl`). It can be used in two ways:
|
||||
```r
|
||||
# Like base R:
|
||||
freq(mydata$myvariable)
|
||||
|
||||
# And like tidyverse:
|
||||
mydata %>% freq(myvariable)
|
||||
```
|
||||
|
||||
Factors sort on item by default:
|
||||
```r
|
||||
septic_patients %>% freq(hospital_id)
|
||||
# Frequency table of `hospital_id`
|
||||
# Class: factor
|
||||
# Length: 2000 (of which NA: 0 = 0.0%)
|
||||
# Unique: 4
|
||||
#
|
||||
# Item Count Percent Cum. Count Cum. Percent (Factor Level)
|
||||
# --- ----- ------ -------- ----------- ------------- ---------------
|
||||
# 1 A 319 16.0% 319 16.0% 1
|
||||
# 2 B 661 33.1% 980 49.0% 2
|
||||
# 3 C 256 12.8% 1236 61.8% 3
|
||||
# 4 D 764 38.2% 2000 100.0% 4
|
||||
```
|
||||
|
||||
This can be changed with the `sort.count` parameter:
|
||||
```r
|
||||
septic_patients %>% freq(hospital_id, sort.count = TRUE)
|
||||
# Frequency table of `hospital_id`
|
||||
# Class: factor
|
||||
# Length: 2000 (of which NA: 0 = 0.0%)
|
||||
# Unique: 4
|
||||
#
|
||||
# Item Count Percent Cum. Count Cum. Percent (Factor Level)
|
||||
# --- ----- ------ -------- ----------- ------------- ---------------
|
||||
# 1 D 764 38.2% 764 38.2% 4
|
||||
# 2 B 661 33.1% 1425 71.2% 2
|
||||
# 3 A 319 16.0% 1744 87.2% 1
|
||||
# 4 C 256 12.8% 2000 100.0% 3
|
||||
```
|
||||
|
||||
All other types, like numbers, characters and dates, sort on count by default:
|
||||
```r
|
||||
septic_patients %>% freq(date)
|
||||
# Frequency table of `date`
|
||||
# Class: Date
|
||||
# Length: 2000 (of which NA: 0 = 0.0%)
|
||||
# Unique: 1151
|
||||
#
|
||||
# Oldest: 2 January 2002
|
||||
# Newest: 28 December 2017 (+5839)
|
||||
# Median: 7 Augustus 2009 (~48%)
|
||||
#
|
||||
# Item Count Percent Cum. Count Cum. Percent
|
||||
# --- ----------- ------ -------- ----------- -------------
|
||||
# 1 2016-05-21 10 0.5% 10 0.5%
|
||||
# 2 2004-11-15 8 0.4% 18 0.9%
|
||||
# 3 2013-07-29 8 0.4% 26 1.3%
|
||||
# 4 2017-06-12 8 0.4% 34 1.7%
|
||||
# 5 2015-11-19 7 0.4% 41 2.1%
|
||||
# 6 2005-12-22 6 0.3% 47 2.4%
|
||||
# 7 2015-10-12 6 0.3% 53 2.6%
|
||||
# 8 2002-05-16 5 0.2% 58 2.9%
|
||||
# 9 2004-02-02 5 0.2% 63 3.1%
|
||||
# 10 2004-02-18 5 0.2% 68 3.4%
|
||||
# 11 2005-08-16 5 0.2% 73 3.6%
|
||||
# 12 2005-09-01 5 0.2% 78 3.9%
|
||||
# 13 2006-06-29 5 0.2% 83 4.2%
|
||||
# 14 2007-08-10 5 0.2% 88 4.4%
|
||||
# 15 2008-08-29 5 0.2% 93 4.7%
|
||||
# [ reached getOption("max.print.freq") -- omitted 1136 entries, n = 1907 (95.3%) ]
|
||||
```
|
||||
For numeric values, some extra descriptive statistics will be calculated:
|
||||
```r
|
||||
freq(runif(n = 10, min = 1, max = 5))
|
||||
# Frequency table
|
||||
# Class: numeric
|
||||
# Length: 10 (of which NA: 0 = 0.0%)
|
||||
# Unique: 10
|
||||
#
|
||||
# Mean: 3.4
|
||||
# Std. dev.: 1.3 (CV: 0.38, MAD: 1.3)
|
||||
# Five-Num: 1.6 | 2.0 | 3.9 | 4.7 | 4.8 (IQR: 2.7, CQV: 0.4)
|
||||
# Outliers: 0
|
||||
#
|
||||
# Item Count Percent Cum. Count Cum. Percent
|
||||
# --- --------- ------ -------- ----------- -------------
|
||||
# 1 1.568997 1 10.0% 1 10.0%
|
||||
# 2 1.993575 1 10.0% 2 20.0%
|
||||
# 3 2.022348 1 10.0% 3 30.0%
|
||||
# 4 2.236038 1 10.0% 4 40.0%
|
||||
# 5 3.579828 1 10.0% 5 50.0%
|
||||
# 6 4.178081 1 10.0% 6 60.0%
|
||||
# 7 4.394818 1 10.0% 7 70.0%
|
||||
# 8 4.689871 1 10.0% 8 80.0%
|
||||
# 9 4.698626 1 10.0% 9 90.0%
|
||||
# 10 4.751488 1 10.0% 10 100.0%
|
||||
#
|
||||
# Warning message:
|
||||
# All observations are unique.
|
||||
```
|
||||
Learn more about this function with:
|
||||
```r
|
||||
?freq
|
||||
```
|
||||
|
||||
### Data sets included in package
|
||||
Datasets to work with antibiotics and bacteria properties.
|
||||
```r
|
||||
# Dataset with 2000 random blood culture isolates from anonymised
|
||||
# septic patients between 2001 and 2017 in 5 Dutch hospitals
|
||||
septic_patients # A tibble: 2,000 x 49
|
||||
|
||||
# Dataset with ATC antibiotics codes, official names, trade names
|
||||
# and DDD's (oral and parenteral)
|
||||
antibiotics # A tibble: 420 x 18
|
||||
|
||||
# Dataset with bacteria codes and properties like gram stain and
|
||||
# aerobic/anaerobic
|
||||
microorganisms # A tibble: 2,453 x 12
|
||||
```
|
||||
|
||||
## Copyright
|
||||
[](https://github.com/msberends/AMR/blob/master/LICENSE)
|
||||
|
||||
This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). 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
|
||||
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||||
# DO NOT CHANGE the "init" and "install" sections below
|
||||
|
||||
# Download script file from GitHub
|
||||
init:
|
||||
ps: |
|
||||
$ErrorActionPreference = "Stop"
|
||||
Invoke-WebRequest http://raw.github.com/krlmlr/r-appveyor/master/scripts/appveyor-tool.ps1 -OutFile "..\appveyor-tool.ps1"
|
||||
Import-Module '..\appveyor-tool.ps1'
|
||||
|
||||
install:
|
||||
ps: Bootstrap
|
||||
|
||||
cache:
|
||||
- C:\RLibrary
|
||||
|
||||
# Adapt as necessary starting from here
|
||||
|
||||
environment:
|
||||
global:
|
||||
R_ARCH: x64
|
||||
GCC_PATH: mingw_64
|
||||
WARNINGS_ARE_ERRORS: 1
|
||||
|
||||
matrix:
|
||||
- R_VERSION: release
|
||||
- R_VERSION: devel
|
||||
|
||||
notifications:
|
||||
- provider: Email
|
||||
to: m.s.berends@umcg.nl
|
||||
subject: 'AppVeyor: status changed'
|
||||
message: The status of https://ci.appveyor.com/project/msberends/amr has changed.
|
||||
on_build_success: false
|
||||
on_build_failure: false
|
||||
on_build_status_changed: true
|
||||
|
||||
build_script:
|
||||
- travis-tool.sh install_deps
|
||||
|
||||
test_script:
|
||||
- travis-tool.sh run_tests
|
||||
|
||||
on_failure:
|
||||
- 7z a failure.zip *.Rcheck\*
|
||||
- appveyor PushArtifact failure.zip
|
||||
|
||||
artifacts:
|
||||
- path: '*.Rcheck\**\*.log'
|
||||
name: Logs
|
||||
|
||||
- path: '*.Rcheck\**\*.out'
|
||||
name: Logs
|
||||
|
||||
- path: '*.Rcheck\**\*.fail'
|
||||
name: Logs
|
||||
|
||||
- path: '*.Rcheck\**\*.Rout'
|
||||
name: Logs
|
||||
|
||||
- path: '\*_*.tar.gz'
|
||||
name: Bits
|
||||
|
||||
- path: '\*_*.zip'
|
||||
name: Bits
|
||||
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9065</small>
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||||
<ul class="dropdown-menu" aria-labelledby="dropdown-how-to">
|
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<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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<li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
|
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<li><a class="dropdown-item" href="../articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
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<li><a class="dropdown-item" href="../articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
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<li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
|
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<li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
|
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<li><a class="dropdown-item" href="../reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
|
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<li><a class="dropdown-item" href="../articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
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<li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
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<li><a class="dropdown-item" href="../reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
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<li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
|
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<li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
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</nav><div class="container template-article">
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<div class="row">
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<main id="main" class="col-md-9"><div class="page-header">
|
||||
<img src="../logo.svg" class="logo" alt=""><h1>AMR for Python</h1>
|
||||
|
||||
|
||||
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class="external-link"><code>vignettes/AMR_for_Python.Rmd</code></a></small>
|
||||
<div class="d-none name"><code>AMR_for_Python.Rmd</code></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="section level2">
|
||||
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
|
||||
</h2>
|
||||
<p>The <code>AMR</code> package for R is a powerful tool for
|
||||
antimicrobial resistance (AMR) analysis. It provides extensive features
|
||||
for handling microbial and antimicrobial data. However, for those who
|
||||
work primarily in Python, we now have a more intuitive option available:
|
||||
the <a href="https://pypi.org/project/AMR/" class="external-link"><code>AMR</code> Python
|
||||
package</a>.</p>
|
||||
<p>This Python package is a wrapper around the <code>AMR</code> R
|
||||
package. It uses the <code>rpy2</code> package internally. Despite the
|
||||
need to have R installed, Python users can now easily work with AMR data
|
||||
directly through Python code.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="prerequisites">Prerequisites<a class="anchor" aria-label="anchor" href="#prerequisites"></a>
|
||||
</h2>
|
||||
<p>This package was only tested with a <a href="https://docs.python.org/3/library/venv.html" class="external-link">virtual environment
|
||||
(venv)</a>. You can set up such an environment by running:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="co"># linux and macOS:</span></span>
|
||||
<span id="cb1-2"><a href="#cb1-2" tabindex="-1"></a>python <span class="op">-</span>m venv <span class="op">/</span>path<span class="op">/</span>to<span class="op">/</span>new<span class="op">/</span>virtual<span class="op">/</span>environment</span>
|
||||
<span id="cb1-3"><a href="#cb1-3" tabindex="-1"></a></span>
|
||||
<span id="cb1-4"><a href="#cb1-4" tabindex="-1"></a><span class="co"># Windows:</span></span>
|
||||
<span id="cb1-5"><a href="#cb1-5" tabindex="-1"></a>python <span class="op">-</span>m venv C:\path\to\new\virtual\environment</span></code></pre></div>
|
||||
<p>Then you can <a href="https://docs.python.org/3/library/venv.html#how-venvs-work" class="external-link">activate
|
||||
the environment</a>, after which the venv is ready to work with.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="install-amr">Install AMR<a class="anchor" aria-label="anchor" href="#install-amr"></a>
|
||||
</h2>
|
||||
<ol style="list-style-type: decimal">
|
||||
<li>
|
||||
<p>Since the Python package is available on the official <a href="https://pypi.org/project/AMR/" class="external-link">Python Package Index</a>, you can
|
||||
just run:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb2-1"><a href="#cb2-1" tabindex="-1"></a><span class="ex">pip</span> install AMR</span></code></pre></div>
|
||||
</li>
|
||||
<li>
|
||||
<p>Make sure you have R installed. There is <strong>no need to
|
||||
install the <code>AMR</code> R package</strong>, as it will be installed
|
||||
automatically.</p>
|
||||
<p>For Linux:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="co"># Ubuntu / Debian</span></span>
|
||||
<span id="cb3-2"><a href="#cb3-2" tabindex="-1"></a><span class="fu">sudo</span> apt install r-base</span>
|
||||
<span id="cb3-3"><a href="#cb3-3" tabindex="-1"></a><span class="co"># Fedora:</span></span>
|
||||
<span id="cb3-4"><a href="#cb3-4" tabindex="-1"></a><span class="fu">sudo</span> dnf install R</span>
|
||||
<span id="cb3-5"><a href="#cb3-5" tabindex="-1"></a><span class="co"># CentOS/RHEL</span></span>
|
||||
<span id="cb3-6"><a href="#cb3-6" tabindex="-1"></a><span class="fu">sudo</span> yum install R</span></code></pre></div>
|
||||
<p>For macOS (using <a href="https://brew.sh" class="external-link">Homebrew</a>):</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="ex">brew</span> install r</span></code></pre></div>
|
||||
<p>For Windows, visit the <a href="https://cran.r-project.org" class="external-link">CRAN
|
||||
download page</a> to download and install R.</p>
|
||||
</li>
|
||||
</ol>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="examples-of-usage">Examples of Usage<a class="anchor" aria-label="anchor" href="#examples-of-usage"></a>
|
||||
</h2>
|
||||
<div class="section level3">
|
||||
<h3 id="cleaning-taxonomy">Cleaning Taxonomy<a class="anchor" aria-label="anchor" href="#cleaning-taxonomy"></a>
|
||||
</h3>
|
||||
<p>Here’s an example that demonstrates how to clean microorganism and
|
||||
drug names using the <code>AMR</code> Python package:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
|
||||
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> AMR</span>
|
||||
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a></span>
|
||||
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="co"># Sample data</span></span>
|
||||
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a>data <span class="op">=</span> {</span>
|
||||
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a> <span class="st">"MOs"</span>: [<span class="st">'E. coli'</span>, <span class="st">'ESCCOL'</span>, <span class="st">'esco'</span>, <span class="st">'Esche coli'</span>],</span>
|
||||
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a> <span class="st">"Drug"</span>: [<span class="st">'Cipro'</span>, <span class="st">'CIP'</span>, <span class="st">'J01MA02'</span>, <span class="st">'Ciproxin'</span>]</span>
|
||||
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a>}</span>
|
||||
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a>df <span class="op">=</span> pd.DataFrame(data)</span>
|
||||
<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a></span>
|
||||
<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a><span class="co"># Use AMR functions to clean microorganism and drug names</span></span>
|
||||
<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a>df[<span class="st">'MO_clean'</span>] <span class="op">=</span> AMR.mo_name(df[<span class="st">'MOs'</span>])</span>
|
||||
<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>df[<span class="st">'Drug_clean'</span>] <span class="op">=</span> AMR.ab_name(df[<span class="st">'Drug'</span>])</span>
|
||||
<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a></span>
|
||||
<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a><span class="co"># Display the results</span></span>
|
||||
<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a><span class="bu">print</span>(df)</span></code></pre></div>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th>MOs</th>
|
||||
<th>Drug</th>
|
||||
<th>MO_clean</th>
|
||||
<th>Drug_clean</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td>E. coli</td>
|
||||
<td>Cipro</td>
|
||||
<td>Escherichia coli</td>
|
||||
<td>Ciprofloxacin</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>ESCCOL</td>
|
||||
<td>CIP</td>
|
||||
<td>Escherichia coli</td>
|
||||
<td>Ciprofloxacin</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>esco</td>
|
||||
<td>J01MA02</td>
|
||||
<td>Escherichia coli</td>
|
||||
<td>Ciprofloxacin</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>Esche coli</td>
|
||||
<td>Ciproxin</td>
|
||||
<td>Escherichia coli</td>
|
||||
<td>Ciprofloxacin</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="section level4">
|
||||
<h4 id="explanation">Explanation<a class="anchor" aria-label="anchor" href="#explanation"></a>
|
||||
</h4>
|
||||
<ul>
|
||||
<li><p><strong>mo_name:</strong> This function standardises
|
||||
microorganism names. Here, different variations of <em>Escherichia
|
||||
coli</em> (such as “E. coli”, “ESCCOL”, “esco”, and “Esche coli”) are
|
||||
all converted into the correct, standardised form, “Escherichia
|
||||
coli”.</p></li>
|
||||
<li><p><strong>ab_name</strong>: Similarly, this function standardises
|
||||
antimicrobial names. The different representations of ciprofloxacin
|
||||
(e.g., “Cipro”, “CIP”, “J01MA02”, and “Ciproxin”) are all converted to
|
||||
the standard name, “Ciprofloxacin”.</p></li>
|
||||
</ul>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="calculating-amr">Calculating AMR<a class="anchor" aria-label="anchor" href="#calculating-amr"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> AMR</span>
|
||||
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
|
||||
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a></span>
|
||||
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a>df <span class="op">=</span> AMR.example_isolates</span>
|
||||
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>result <span class="op">=</span> AMR.resistance(df[<span class="st">"AMX"</span>])</span>
|
||||
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
|
||||
<pre><code>[0.59555556]</code></pre>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="generating-antibiograms">Generating Antibiograms<a class="anchor" aria-label="anchor" href="#generating-antibiograms"></a>
|
||||
</h3>
|
||||
<p>One of the core functions of the <code>AMR</code> package is
|
||||
generating an antibiogram, a table that summarises the antimicrobial
|
||||
susceptibility of bacterial isolates. Here’s how you can generate an
|
||||
antibiogram from Python:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a>result2a <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]])</span>
|
||||
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a><span class="bu">print</span>(result2a)</span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="22%">
|
||||
<col width="22%">
|
||||
<col width="22%">
|
||||
<col width="33%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th>Pathogen</th>
|
||||
<th>Amoxicillin</th>
|
||||
<th>Ciprofloxacin</th>
|
||||
<th>Piperacillin/tazobactam</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td>CoNS</td>
|
||||
<td>7% (10/142)</td>
|
||||
<td>73% (183/252)</td>
|
||||
<td>30% (10/33)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>E. coli</td>
|
||||
<td>50% (196/392)</td>
|
||||
<td>88% (399/456)</td>
|
||||
<td>94% (393/416)</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>K. pneumoniae</td>
|
||||
<td>0% (0/58)</td>
|
||||
<td>96% (53/55)</td>
|
||||
<td>89% (47/53)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>P. aeruginosa</td>
|
||||
<td>0% (0/30)</td>
|
||||
<td>100% (30/30)</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>P. mirabilis</td>
|
||||
<td>None</td>
|
||||
<td>94% (34/36)</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>S. aureus</td>
|
||||
<td>6% (8/131)</td>
|
||||
<td>90% (171/191)</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>S. epidermidis</td>
|
||||
<td>1% (1/91)</td>
|
||||
<td>64% (87/136)</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>S. hominis</td>
|
||||
<td>None</td>
|
||||
<td>80% (56/70)</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>S. pneumoniae</td>
|
||||
<td>100% (112/112)</td>
|
||||
<td>None</td>
|
||||
<td>100% (112/112)</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a>result2b <span class="op">=</span> AMR.antibiogram(df[[<span class="st">"mo"</span>, <span class="st">"AMX"</span>, <span class="st">"CIP"</span>, <span class="st">"TZP"</span>]], mo_transform <span class="op">=</span> <span class="st">"gramstain"</span>)</span>
|
||||
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a><span class="bu">print</span>(result2b)</span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="20%">
|
||||
<col width="22%">
|
||||
<col width="23%">
|
||||
<col width="33%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th>Pathogen</th>
|
||||
<th>Amoxicillin</th>
|
||||
<th>Ciprofloxacin</th>
|
||||
<th>Piperacillin/tazobactam</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td>Gram-negative</td>
|
||||
<td>36% (226/631)</td>
|
||||
<td>91% (621/684)</td>
|
||||
<td>88% (565/641)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>Gram-positive</td>
|
||||
<td>43% (305/703)</td>
|
||||
<td>77% (560/724)</td>
|
||||
<td>86% (296/345)</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>In this example, we generate an antibiogram by selecting various
|
||||
antibiotics.</p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="taxonomic-data-sets-now-in-python">Taxonomic Data Sets Now in Python!<a class="anchor" aria-label="anchor" href="#taxonomic-data-sets-now-in-python"></a>
|
||||
</h3>
|
||||
<p>As a Python user, you might like that the most important data sets of
|
||||
the <code>AMR</code> R package, <code>microorganisms</code>,
|
||||
<code>antimicrobials</code>, <code>clinical_breakpoints</code>, and
|
||||
<code>example_isolates</code>, are now available as regular Python data
|
||||
frames:</p>
|
||||
<div class="sourceCode" id="cb10"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb10-1"><a href="#cb10-1" tabindex="-1"></a>AMR.microorganisms</span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="11%">
|
||||
<col width="29%">
|
||||
<col width="8%">
|
||||
<col width="8%">
|
||||
<col width="8%">
|
||||
<col width="10%">
|
||||
<col width="13%">
|
||||
<col width="9%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th>mo</th>
|
||||
<th>fullname</th>
|
||||
<th>status</th>
|
||||
<th>kingdom</th>
|
||||
<th>gbif</th>
|
||||
<th>gbif_parent</th>
|
||||
<th>gbif_renamed_to</th>
|
||||
<th>prevalence</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td>B_GRAMN</td>
|
||||
<td>(unknown Gram-negatives)</td>
|
||||
<td>unknown</td>
|
||||
<td>Bacteria</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>B_GRAMP</td>
|
||||
<td>(unknown Gram-positives)</td>
|
||||
<td>unknown</td>
|
||||
<td>Bacteria</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>B_ANAER-NEG</td>
|
||||
<td>(unknown anaerobic Gram-negatives)</td>
|
||||
<td>unknown</td>
|
||||
<td>Bacteria</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>B_ANAER-POS</td>
|
||||
<td>(unknown anaerobic Gram-positives)</td>
|
||||
<td>unknown</td>
|
||||
<td>Bacteria</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>B_ANAER</td>
|
||||
<td>(unknown anaerobic bacteria)</td>
|
||||
<td>unknown</td>
|
||||
<td>Bacteria</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>B_ZYMMN_POMC</td>
|
||||
<td>Zymomonas pomaceae</td>
|
||||
<td>accepted</td>
|
||||
<td>Bacteria</td>
|
||||
<td>10744418</td>
|
||||
<td>3221412</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>B_ZYMPH</td>
|
||||
<td>Zymophilus</td>
|
||||
<td>synonym</td>
|
||||
<td>Bacteria</td>
|
||||
<td>None</td>
|
||||
<td>9475166</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>B_ZYMPH_PCVR</td>
|
||||
<td>Zymophilus paucivorans</td>
|
||||
<td>synonym</td>
|
||||
<td>Bacteria</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>B_ZYMPH_RFFN</td>
|
||||
<td>Zymophilus raffinosivorans</td>
|
||||
<td>synonym</td>
|
||||
<td>Bacteria</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>F_ZYZYG</td>
|
||||
<td>Zyzygomyces</td>
|
||||
<td>unknown</td>
|
||||
<td>Fungi</td>
|
||||
<td>None</td>
|
||||
<td>7581</td>
|
||||
<td>None</td>
|
||||
<td>2.0</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="sourceCode" id="cb11"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb11-1"><a href="#cb11-1" tabindex="-1"></a>AMR.antimicrobials</span></code></pre></div>
|
||||
<table style="width:100%;" class="table">
|
||||
<colgroup>
|
||||
<col width="4%">
|
||||
<col width="12%">
|
||||
<col width="20%">
|
||||
<col width="25%">
|
||||
<col width="9%">
|
||||
<col width="11%">
|
||||
<col width="7%">
|
||||
<col width="9%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th>ab</th>
|
||||
<th>cid</th>
|
||||
<th>name</th>
|
||||
<th>group</th>
|
||||
<th>oral_ddd</th>
|
||||
<th>oral_units</th>
|
||||
<th>iv_ddd</th>
|
||||
<th>iv_units</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td>AMA</td>
|
||||
<td>4649.0</td>
|
||||
<td>4-aminosalicylic acid</td>
|
||||
<td>Antimycobacterials</td>
|
||||
<td>12.00</td>
|
||||
<td>g</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>ACM</td>
|
||||
<td>6450012.0</td>
|
||||
<td>Acetylmidecamycin</td>
|
||||
<td>Macrolides/lincosamides</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>ASP</td>
|
||||
<td>49787020.0</td>
|
||||
<td>Acetylspiramycin</td>
|
||||
<td>Macrolides/lincosamides</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>ALS</td>
|
||||
<td>8954.0</td>
|
||||
<td>Aldesulfone sodium</td>
|
||||
<td>Other antibacterials</td>
|
||||
<td>0.33</td>
|
||||
<td>g</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>AMK</td>
|
||||
<td>37768.0</td>
|
||||
<td>Amikacin</td>
|
||||
<td>Aminoglycosides</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
<td>1.0</td>
|
||||
<td>g</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
<td>…</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>VIR</td>
|
||||
<td>11979535.0</td>
|
||||
<td>Virginiamycine</td>
|
||||
<td>Other antibacterials</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>VOR</td>
|
||||
<td>71616.0</td>
|
||||
<td>Voriconazole</td>
|
||||
<td>Antifungals/antimycotics</td>
|
||||
<td>0.40</td>
|
||||
<td>g</td>
|
||||
<td>0.4</td>
|
||||
<td>g</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>XBR</td>
|
||||
<td>72144.0</td>
|
||||
<td>Xibornol</td>
|
||||
<td>Other antibacterials</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>ZID</td>
|
||||
<td>77846445.0</td>
|
||||
<td>Zidebactam</td>
|
||||
<td>Other antibacterials</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>ZFD</td>
|
||||
<td>NaN</td>
|
||||
<td>Zoliflodacin</td>
|
||||
<td>None</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
<td>NaN</td>
|
||||
<td>None</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
|
||||
</h2>
|
||||
<p>With the <code>AMR</code> Python package, Python users can now
|
||||
effortlessly call R functions from the <code>AMR</code> R package. This
|
||||
eliminates the need for complex <code>rpy2</code> configurations and
|
||||
provides a clean, easy-to-use interface for antimicrobial resistance
|
||||
analysis. The examples provided above demonstrate how this can be
|
||||
applied to typical workflows, such as standardising microorganism and
|
||||
antimicrobial names or calculating resistance.</p>
|
||||
<p>By just running <code>import AMR</code>, users can seamlessly
|
||||
integrate the robust features of the R <code>AMR</code> package into
|
||||
Python workflows.</p>
|
||||
<p>Whether you’re cleaning data or analysing resistance patterns, the
|
||||
<code>AMR</code> Python package makes it easy to work with AMR data in
|
||||
Python.</p>
|
||||
</div>
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
</nav></aside>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
||||
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
|
||||
</div>
|
||||
|
||||
</footer>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,217 +0,0 @@
|
||||
# AMR for Python
|
||||
|
||||
## Introduction
|
||||
|
||||
The `AMR` package for R is a powerful tool for antimicrobial resistance
|
||||
(AMR) analysis. It provides extensive features for handling microbial
|
||||
and antimicrobial data. However, for those who work primarily in Python,
|
||||
we now have a more intuitive option available: the [`AMR` Python
|
||||
package](https://pypi.org/project/AMR/).
|
||||
|
||||
This Python package is a wrapper around the `AMR` R package. It uses the
|
||||
`rpy2` package internally. Despite the need to have R installed, Python
|
||||
users can now easily work with AMR data directly through Python code.
|
||||
|
||||
## Prerequisites
|
||||
|
||||
This package was only tested with a [virtual environment
|
||||
(venv)](https://docs.python.org/3/library/venv.html). You can set up
|
||||
such an environment by running:
|
||||
|
||||
``` python
|
||||
# linux and macOS:
|
||||
python -m venv /path/to/new/virtual/environment
|
||||
|
||||
# Windows:
|
||||
python -m venv C:\path\to\new\virtual\environment
|
||||
```
|
||||
|
||||
Then you can [activate the
|
||||
environment](https://docs.python.org/3/library/venv.html#how-venvs-work),
|
||||
after which the venv is ready to work with.
|
||||
|
||||
## Install AMR
|
||||
|
||||
1. Since the Python package is available on the official [Python
|
||||
Package Index](https://pypi.org/project/AMR/), you can just run:
|
||||
|
||||
``` bash
|
||||
pip install AMR
|
||||
```
|
||||
|
||||
2. Make sure you have R installed. There is **no need to install the
|
||||
`AMR` R package**, as it will be installed automatically.
|
||||
|
||||
For Linux:
|
||||
|
||||
``` bash
|
||||
# Ubuntu / Debian
|
||||
sudo apt install r-base
|
||||
# Fedora:
|
||||
sudo dnf install R
|
||||
# CentOS/RHEL
|
||||
sudo yum install R
|
||||
```
|
||||
|
||||
For macOS (using [Homebrew](https://brew.sh)):
|
||||
|
||||
``` bash
|
||||
brew install r
|
||||
```
|
||||
|
||||
For Windows, visit the [CRAN download
|
||||
page](https://cran.r-project.org) to download and install R.
|
||||
|
||||
## Examples of Usage
|
||||
|
||||
### Cleaning Taxonomy
|
||||
|
||||
Here’s an example that demonstrates how to clean microorganism and drug
|
||||
names using the `AMR` Python package:
|
||||
|
||||
``` python
|
||||
import pandas as pd
|
||||
import AMR
|
||||
|
||||
# Sample data
|
||||
data = {
|
||||
"MOs": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'],
|
||||
"Drug": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin']
|
||||
}
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# Use AMR functions to clean microorganism and drug names
|
||||
df['MO_clean'] = AMR.mo_name(df['MOs'])
|
||||
df['Drug_clean'] = AMR.ab_name(df['Drug'])
|
||||
|
||||
# Display the results
|
||||
print(df)
|
||||
```
|
||||
|
||||
| MOs | Drug | MO_clean | Drug_clean |
|
||||
|------------|----------|------------------|---------------|
|
||||
| E. coli | Cipro | Escherichia coli | Ciprofloxacin |
|
||||
| ESCCOL | CIP | Escherichia coli | Ciprofloxacin |
|
||||
| esco | J01MA02 | Escherichia coli | Ciprofloxacin |
|
||||
| Esche coli | Ciproxin | Escherichia coli | Ciprofloxacin |
|
||||
|
||||
#### Explanation
|
||||
|
||||
- **mo_name:** This function standardises microorganism names. Here,
|
||||
different variations of *Escherichia coli* (such as “E. coli”,
|
||||
“ESCCOL”, “esco”, and “Esche coli”) are all converted into the
|
||||
correct, standardised form, “Escherichia coli”.
|
||||
|
||||
- **ab_name**: Similarly, this function standardises antimicrobial
|
||||
names. The different representations of ciprofloxacin (e.g., “Cipro”,
|
||||
“CIP”, “J01MA02”, and “Ciproxin”) are all converted to the standard
|
||||
name, “Ciprofloxacin”.
|
||||
|
||||
### Calculating AMR
|
||||
|
||||
``` python
|
||||
import AMR
|
||||
import pandas as pd
|
||||
|
||||
df = AMR.example_isolates
|
||||
result = AMR.resistance(df["AMX"])
|
||||
print(result)
|
||||
```
|
||||
|
||||
[0.59555556]
|
||||
|
||||
### Generating Antibiograms
|
||||
|
||||
One of the core functions of the `AMR` package is generating an
|
||||
antibiogram, a table that summarises the antimicrobial susceptibility of
|
||||
bacterial isolates. Here’s how you can generate an antibiogram from
|
||||
Python:
|
||||
|
||||
``` python
|
||||
result2a = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]])
|
||||
print(result2a)
|
||||
```
|
||||
|
||||
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|
||||
|----------------|----------------|---------------|-------------------------|
|
||||
| CoNS | 7% (10/142) | 73% (183/252) | 30% (10/33) |
|
||||
| E. coli | 50% (196/392) | 88% (399/456) | 94% (393/416) |
|
||||
| K. pneumoniae | 0% (0/58) | 96% (53/55) | 89% (47/53) |
|
||||
| P. aeruginosa | 0% (0/30) | 100% (30/30) | None |
|
||||
| P. mirabilis | None | 94% (34/36) | None |
|
||||
| S. aureus | 6% (8/131) | 90% (171/191) | None |
|
||||
| S. epidermidis | 1% (1/91) | 64% (87/136) | None |
|
||||
| S. hominis | None | 80% (56/70) | None |
|
||||
| S. pneumoniae | 100% (112/112) | None | 100% (112/112) |
|
||||
|
||||
``` python
|
||||
result2b = AMR.antibiogram(df[["mo", "AMX", "CIP", "TZP"]], mo_transform = "gramstain")
|
||||
print(result2b)
|
||||
```
|
||||
|
||||
| Pathogen | Amoxicillin | Ciprofloxacin | Piperacillin/tazobactam |
|
||||
|---------------|---------------|---------------|-------------------------|
|
||||
| Gram-negative | 36% (226/631) | 91% (621/684) | 88% (565/641) |
|
||||
| Gram-positive | 43% (305/703) | 77% (560/724) | 86% (296/345) |
|
||||
|
||||
In this example, we generate an antibiogram by selecting various
|
||||
antibiotics.
|
||||
|
||||
### Taxonomic Data Sets Now in Python!
|
||||
|
||||
As a Python user, you might like that the most important data sets of
|
||||
the `AMR` R package, `microorganisms`, `antimicrobials`,
|
||||
`clinical_breakpoints`, and `example_isolates`, are now available as
|
||||
regular Python data frames:
|
||||
|
||||
``` python
|
||||
AMR.microorganisms
|
||||
```
|
||||
|
||||
| mo | fullname | status | kingdom | gbif | gbif_parent | gbif_renamed_to | prevalence |
|
||||
|----|----|----|----|----|----|----|----|
|
||||
| B_GRAMN | (unknown Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_GRAMP | (unknown Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER-NEG | (unknown anaerobic Gram-negatives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER-POS | (unknown anaerobic Gram-positives) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| B_ANAER | (unknown anaerobic bacteria) | unknown | Bacteria | None | None | None | 2.0 |
|
||||
| … | … | … | … | … | … | … | … |
|
||||
| B_ZYMMN_POMC | Zymomonas pomaceae | accepted | Bacteria | 10744418 | 3221412 | None | 2.0 |
|
||||
| B_ZYMPH | Zymophilus | synonym | Bacteria | None | 9475166 | None | 2.0 |
|
||||
| B_ZYMPH_PCVR | Zymophilus paucivorans | synonym | Bacteria | None | None | None | 2.0 |
|
||||
| B_ZYMPH_RFFN | Zymophilus raffinosivorans | synonym | Bacteria | None | None | None | 2.0 |
|
||||
| F_ZYZYG | Zyzygomyces | unknown | Fungi | None | 7581 | None | 2.0 |
|
||||
|
||||
``` python
|
||||
AMR.antimicrobials
|
||||
```
|
||||
|
||||
| ab | cid | name | group | oral_ddd | oral_units | iv_ddd | iv_units |
|
||||
|----|----|----|----|----|----|----|----|
|
||||
| AMA | 4649.0 | 4-aminosalicylic acid | Antimycobacterials | 12.00 | g | NaN | None |
|
||||
| ACM | 6450012.0 | Acetylmidecamycin | Macrolides/lincosamides | NaN | None | NaN | None |
|
||||
| ASP | 49787020.0 | Acetylspiramycin | Macrolides/lincosamides | NaN | None | NaN | None |
|
||||
| ALS | 8954.0 | Aldesulfone sodium | Other antibacterials | 0.33 | g | NaN | None |
|
||||
| AMK | 37768.0 | Amikacin | Aminoglycosides | NaN | None | 1.0 | g |
|
||||
| … | … | … | … | … | … | … | … |
|
||||
| VIR | 11979535.0 | Virginiamycine | Other antibacterials | NaN | None | NaN | None |
|
||||
| VOR | 71616.0 | Voriconazole | Antifungals/antimycotics | 0.40 | g | 0.4 | g |
|
||||
| XBR | 72144.0 | Xibornol | Other antibacterials | NaN | None | NaN | None |
|
||||
| ZID | 77846445.0 | Zidebactam | Other antibacterials | NaN | None | NaN | None |
|
||||
| ZFD | NaN | Zoliflodacin | None | NaN | None | NaN | None |
|
||||
|
||||
## Conclusion
|
||||
|
||||
With the `AMR` Python package, Python users can now effortlessly call R
|
||||
functions from the `AMR` R package. This eliminates the need for complex
|
||||
`rpy2` configurations and provides a clean, easy-to-use interface for
|
||||
antimicrobial resistance analysis. The examples provided above
|
||||
demonstrate how this can be applied to typical workflows, such as
|
||||
standardising microorganism and antimicrobial names or calculating
|
||||
resistance.
|
||||
|
||||
By just running `import AMR`, users can seamlessly integrate the robust
|
||||
features of the R `AMR` package into Python workflows.
|
||||
|
||||
Whether you’re cleaning data or analysing resistance patterns, the `AMR`
|
||||
Python package makes it easy to work with AMR data in Python.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -1,903 +0,0 @@
|
||||
# AMR with tidymodels
|
||||
|
||||
> This page was almost entirely written by our [AMR for R
|
||||
> Assistant](https://chat.amr-for-r.org), a ChatGPT manually-trained
|
||||
> model able to answer any question about the `AMR` package.
|
||||
|
||||
Antimicrobial resistance (AMR) is a global health crisis, and
|
||||
understanding resistance patterns is crucial for managing effective
|
||||
treatments. The `AMR` R package provides robust tools for analysing AMR
|
||||
data, including convenient antimicrobial selector functions like
|
||||
[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
|
||||
and
|
||||
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
|
||||
|
||||
In this post, we will explore how to use the `tidymodels` framework to
|
||||
predict resistance patterns in the `example_isolates` dataset in two
|
||||
examples.
|
||||
|
||||
This post contains the following examples:
|
||||
|
||||
1. Using Antimicrobial Selectors
|
||||
2. Predicting ESBL Presence Using Raw MICs
|
||||
3. Predicting AMR Over Time
|
||||
|
||||
## Example 1: Using Antimicrobial Selectors
|
||||
|
||||
By leveraging the power of `tidymodels` and the `AMR` package, we’ll
|
||||
build a reproducible machine learning workflow to predict the Gramstain
|
||||
of the microorganism to two important antibiotic classes:
|
||||
aminoglycosides and beta-lactams.
|
||||
|
||||
### **Objective**
|
||||
|
||||
Our goal is to build a predictive model using the `tidymodels` framework
|
||||
to determine the Gramstain of the microorganism based on microbial data.
|
||||
We will:
|
||||
|
||||
1. Preprocess data using the selector functions
|
||||
[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
|
||||
and
|
||||
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md).
|
||||
2. Define a logistic regression model for prediction.
|
||||
3. Use a structured `tidymodels` workflow to preprocess, train, and
|
||||
evaluate the model.
|
||||
|
||||
### **Data Preparation**
|
||||
|
||||
We begin by loading the required libraries and preparing the
|
||||
`example_isolates` dataset from the `AMR` package.
|
||||
|
||||
``` r
|
||||
|
||||
# Load required libraries
|
||||
library(AMR) # For AMR data analysis
|
||||
library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...)
|
||||
```
|
||||
|
||||
Prepare the data:
|
||||
|
||||
``` r
|
||||
|
||||
# Your data could look like this:
|
||||
example_isolates
|
||||
#> # A tibble: 2,000 × 46
|
||||
#> date patient age gender ward mo PEN OXA FLC AMX
|
||||
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
|
||||
#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
|
||||
#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
|
||||
#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
|
||||
#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
|
||||
#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA
|
||||
#> # ℹ 1,990 more rows
|
||||
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
|
||||
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
|
||||
#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
|
||||
#> # FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
|
||||
#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
|
||||
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
|
||||
|
||||
# Select relevant columns for prediction
|
||||
data <- example_isolates %>%
|
||||
# select AB results dynamically
|
||||
select(mo, aminoglycosides(), betalactams()) %>%
|
||||
# replace NAs with NI (not-interpretable)
|
||||
mutate(
|
||||
across(
|
||||
where(is.sir),
|
||||
~ replace_na(.x, "NI")
|
||||
),
|
||||
# make factors of SIR columns
|
||||
across(
|
||||
where(is.sir),
|
||||
as.integer
|
||||
),
|
||||
# get Gramstain of microorganisms
|
||||
mo = as.factor(mo_gramstain(mo))
|
||||
) %>%
|
||||
# drop NAs - the ones without a Gramstain (fungi, etc.)
|
||||
drop_na()
|
||||
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
|
||||
#> (amikacin), and KAN (kanamycin)
|
||||
#> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
|
||||
#> FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
|
||||
#> AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
|
||||
#> (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
|
||||
#> (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- [`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
|
||||
and
|
||||
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
|
||||
dynamically select columns for antimicrobials in these classes.
|
||||
- `drop_na()` ensures the model receives complete cases for training.
|
||||
|
||||
### **Defining the Workflow**
|
||||
|
||||
We now define the `tidymodels` workflow, which consists of three steps:
|
||||
preprocessing, model specification, and fitting.
|
||||
|
||||
#### 1. Preprocessing with a Recipe
|
||||
|
||||
We create a recipe to preprocess the data for modelling.
|
||||
|
||||
``` r
|
||||
|
||||
# Define the recipe for data preprocessing
|
||||
resistance_recipe <- recipe(mo ~ ., data = data) %>%
|
||||
step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9)
|
||||
resistance_recipe
|
||||
#>
|
||||
#> ── Recipe ──────────────────────────────────────────────────────────────────────
|
||||
#>
|
||||
#> ── Inputs
|
||||
#> Number of variables by role
|
||||
#> outcome: 1
|
||||
#> predictor: 20
|
||||
#>
|
||||
#> ── Operations
|
||||
#> • Correlation filter on: c(aminoglycosides(), betalactams())
|
||||
```
|
||||
|
||||
For a recipe that includes at least one preprocessing operation, like we
|
||||
have with `step_corr()`, the necessary parameters can be estimated from
|
||||
a training set using `prep()`:
|
||||
|
||||
``` r
|
||||
|
||||
prep(resistance_recipe)
|
||||
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
|
||||
#> (amikacin), and KAN (kanamycin)
|
||||
#> ℹ For `betalactams()` using columns PEN (benzylpenicillin), OXA (oxacillin),
|
||||
#> FLC (flucloxacillin), AMX (amoxicillin), AMC (amoxicillin/clavulanic acid),
|
||||
#> AMP (ampicillin), TZP (piperacillin/tazobactam), CZO (cefazolin), FEP
|
||||
#> (cefepime), CXM (cefuroxime), FOX (cefoxitin), CTX (cefotaxime), CAZ
|
||||
#> (ceftazidime), CRO (ceftriaxone), IPM (imipenem), and MEM (meropenem)
|
||||
#>
|
||||
#>
|
||||
#> ── Recipe ──────────────────────────────────────────────────────────────────────
|
||||
#>
|
||||
#>
|
||||
#>
|
||||
#> ── Inputs
|
||||
#>
|
||||
#> Number of variables by role
|
||||
#>
|
||||
#> outcome: 1
|
||||
#> predictor: 20
|
||||
#>
|
||||
#>
|
||||
#>
|
||||
#> ── Training information
|
||||
#>
|
||||
#> Training data contained 1968 data points and no incomplete rows.
|
||||
#>
|
||||
#>
|
||||
#>
|
||||
#> ── Operations
|
||||
#>
|
||||
#> • Correlation filter on: AMX CTX | Trained
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `recipe(mo ~ ., data = data)` will take the `mo` column as outcome and
|
||||
all other columns as predictors.
|
||||
- `step_corr()` removes predictors (i.e., antibiotic columns) that have
|
||||
a higher correlation than 90%.
|
||||
|
||||
Notice how the recipe contains just the antimicrobial selector
|
||||
functions - no need to define the columns specifically. In the
|
||||
preparation (retrieved with `prep()`) we can see that the columns or
|
||||
variables ‘AMX’ and ‘CTX’ were removed as they correlate too much with
|
||||
existing, other variables.
|
||||
|
||||
#### 2. Specifying the Model
|
||||
|
||||
We define a logistic regression model since resistance prediction is a
|
||||
binary classification task.
|
||||
|
||||
``` r
|
||||
|
||||
# Specify a logistic regression model
|
||||
logistic_model <- logistic_reg() %>%
|
||||
set_engine("glm") # Use the Generalised Linear Model engine
|
||||
logistic_model
|
||||
#> Logistic Regression Model Specification (classification)
|
||||
#>
|
||||
#> Computational engine: glm
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `logistic_reg()` sets up a logistic regression model.
|
||||
- `set_engine("glm")` specifies the use of R’s built-in GLM engine.
|
||||
|
||||
#### 3. Building the Workflow
|
||||
|
||||
We bundle the recipe and model together into a `workflow`, which
|
||||
organises the entire modelling process.
|
||||
|
||||
``` r
|
||||
|
||||
# Combine the recipe and model into a workflow
|
||||
resistance_workflow <- workflow() %>%
|
||||
add_recipe(resistance_recipe) %>% # Add the preprocessing recipe
|
||||
add_model(logistic_model) # Add the logistic regression model
|
||||
resistance_workflow
|
||||
#> ══ Workflow ════════════════════════════════════════════════════════════════════
|
||||
#> Preprocessor: Recipe
|
||||
#> Model: logistic_reg()
|
||||
#>
|
||||
#> ── Preprocessor ────────────────────────────────────────────────────────────────
|
||||
#> 1 Recipe Step
|
||||
#>
|
||||
#> • step_corr()
|
||||
#>
|
||||
#> ── Model ───────────────────────────────────────────────────────────────────────
|
||||
#> Logistic Regression Model Specification (classification)
|
||||
#>
|
||||
#> Computational engine: glm
|
||||
```
|
||||
|
||||
### **Training and Evaluating the Model**
|
||||
|
||||
To train the model, we split the data into training and testing sets.
|
||||
Then, we fit the workflow on the training set and evaluate its
|
||||
performance.
|
||||
|
||||
``` r
|
||||
|
||||
# Split data into training and testing sets
|
||||
set.seed(123) # For reproducibility
|
||||
data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing
|
||||
training_data <- training(data_split) # Training set
|
||||
testing_data <- testing(data_split) # Testing set
|
||||
|
||||
# Fit the workflow to the training data
|
||||
fitted_workflow <- resistance_workflow %>%
|
||||
fit(training_data) # Train the model
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `initial_split()` splits the data into training and testing sets.
|
||||
- `fit()` trains the workflow on the training set.
|
||||
|
||||
Notice how in `fit()`, the antimicrobial selector functions are
|
||||
internally called again. For training, these functions are called since
|
||||
they are stored in the recipe.
|
||||
|
||||
Next, we evaluate the model on the testing data.
|
||||
|
||||
``` r
|
||||
|
||||
# Make predictions on the testing set
|
||||
predictions <- fitted_workflow %>%
|
||||
predict(testing_data) # Generate predictions
|
||||
probabilities <- fitted_workflow %>%
|
||||
predict(testing_data, type = "prob") # Generate probabilities
|
||||
|
||||
predictions <- predictions %>%
|
||||
bind_cols(probabilities) %>%
|
||||
bind_cols(testing_data) # Combine with true labels
|
||||
|
||||
predictions
|
||||
#> # A tibble: 394 × 24
|
||||
#> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB
|
||||
#> <fct> <dbl> <dbl> <fct> <int> <int>
|
||||
#> 1 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5
|
||||
#> 2 Gram-positive 3.17e- 8 1.000e+ 0 Gram-p… 5 1
|
||||
#> 3 Gram-negative 9.99e- 1 1.42 e- 3 Gram-n… 5 5
|
||||
#> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5
|
||||
#> 5 Gram-negative 9.46e- 1 5.42 e- 2 Gram-n… 5 5
|
||||
#> 6 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5
|
||||
#> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5
|
||||
#> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4
|
||||
#> 9 Gram-negative 1 e+ 0 2.22 e-16 Gram-n… 1 1
|
||||
#> 10 Gram-positive 6.05e-11 1.000e+ 0 Gram-p… 4 4
|
||||
#> # ℹ 384 more rows
|
||||
#> # ℹ 18 more variables: AMK <int>, KAN <int>, PEN <int>, OXA <int>, FLC <int>,
|
||||
#> # AMX <int>, AMC <int>, AMP <int>, TZP <int>, CZO <int>, FEP <int>,
|
||||
#> # CXM <int>, FOX <int>, CTX <int>, CAZ <int>, CRO <int>, IPM <int>, MEM <int>
|
||||
|
||||
# Evaluate model performance
|
||||
metrics <- predictions %>%
|
||||
metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics
|
||||
|
||||
metrics
|
||||
#> # A tibble: 2 × 3
|
||||
#> .metric .estimator .estimate
|
||||
#> <chr> <chr> <dbl>
|
||||
#> 1 accuracy binary 0.995
|
||||
#> 2 kap binary 0.989
|
||||
|
||||
|
||||
# To assess some other model properties, you can make our own `metrics()` function
|
||||
our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value
|
||||
metrics2 <- predictions %>%
|
||||
our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function
|
||||
|
||||
metrics2
|
||||
#> # A tibble: 4 × 3
|
||||
#> .metric .estimator .estimate
|
||||
#> <chr> <chr> <dbl>
|
||||
#> 1 accuracy binary 0.995
|
||||
#> 2 kap binary 0.989
|
||||
#> 3 ppv binary 0.987
|
||||
#> 4 npv binary 1
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- [`predict()`](https://rdrr.io/r/stats/predict.html) generates
|
||||
predictions on the testing set.
|
||||
- `metrics()` computes evaluation metrics like accuracy and kappa.
|
||||
|
||||
It appears we can predict the Gram stain with a 99.5% accuracy based on
|
||||
AMR results of only aminoglycosides and beta-lactam antibiotics. The ROC
|
||||
curve looks like this:
|
||||
|
||||
``` r
|
||||
|
||||
predictions %>%
|
||||
roc_curve(mo, `.pred_Gram-negative`) %>%
|
||||
autoplot()
|
||||
```
|
||||
|
||||
|
||||
|
||||
### **Conclusion**
|
||||
|
||||
In this example, we demonstrated how to build a machine learning
|
||||
pipeline with the `tidymodels` framework and the `AMR` package. By
|
||||
combining selector functions like
|
||||
[`aminoglycosides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
|
||||
and
|
||||
[`betalactams()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
|
||||
with `tidymodels`, we efficiently prepared data, trained a model, and
|
||||
evaluated its performance.
|
||||
|
||||
This workflow is extensible to other antimicrobial classes and
|
||||
resistance patterns, empowering users to analyse AMR data systematically
|
||||
and reproducibly.
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## Example 2: Predicting ESBL Presence Using Raw MICs
|
||||
|
||||
In this second example, we demonstrate how to use `<mic>` columns
|
||||
directly in `tidymodels` workflows using AMR-specific recipe steps. This
|
||||
includes a transformation to `log2` scale using
|
||||
[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md),
|
||||
which prepares MIC values for use in classification models.
|
||||
|
||||
This approach and idea formed the basis for the publication [DOI:
|
||||
10.3389/fmicb.2025.1582703](https://doi.org/10.3389/fmicb.2025.1582703)
|
||||
to model the presence of extended-spectrum beta-lactamases (ESBL) based
|
||||
on MIC values.
|
||||
|
||||
### **Objective**
|
||||
|
||||
Our goal is to:
|
||||
|
||||
1. Use raw MIC values to predict whether a bacterial isolate produces
|
||||
ESBL.
|
||||
2. Apply AMR-aware preprocessing in a `tidymodels` recipe.
|
||||
3. Train a classification model and evaluate its predictive
|
||||
performance.
|
||||
|
||||
### **Data Preparation**
|
||||
|
||||
We use the `esbl_isolates` dataset that comes with the AMR package.
|
||||
|
||||
``` r
|
||||
|
||||
# Load required libraries
|
||||
library(AMR)
|
||||
library(tidymodels)
|
||||
|
||||
# View the esbl_isolates data set
|
||||
esbl_isolates
|
||||
#> # A tibble: 500 × 19
|
||||
#> esbl genus AMC AMP TZP CXM FOX CTX CAZ GEN TOB TMP SXT
|
||||
#> <lgl> <chr> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic> <mic>
|
||||
#> 1 FALSE Esch… 32 32 4 64 64 8.00 8.00 1 1 16.0 20
|
||||
#> 2 FALSE Esch… 32 32 4 64 64 4.00 8.00 1 1 16.0 320
|
||||
#> 3 FALSE Esch… 4 2 64 8 4 8.00 0.12 16 16 0.5 20
|
||||
#> 4 FALSE Kleb… 32 32 16 64 64 8.00 8.00 1 1 0.5 20
|
||||
#> 5 FALSE Esch… 32 32 4 4 4 0.25 2.00 1 1 16.0 320
|
||||
#> 6 FALSE Citr… 32 32 16 64 64 64.00 32.00 1 1 0.5 20
|
||||
#> 7 FALSE Morg… 32 32 4 64 64 16.00 2.00 1 1 0.5 20
|
||||
#> 8 FALSE Prot… 16 32 4 1 4 8.00 0.12 1 1 16.0 320
|
||||
#> 9 FALSE Ente… 32 32 8 64 64 32.00 4.00 1 1 0.5 20
|
||||
#> 10 FALSE Citr… 32 32 32 64 64 8.00 64.00 1 1 16.0 320
|
||||
#> # ℹ 490 more rows
|
||||
#> # ℹ 6 more variables: NIT <mic>, FOS <mic>, CIP <mic>, IPM <mic>, MEM <mic>,
|
||||
#> # COL <mic>
|
||||
|
||||
# Prepare a binary outcome and convert to ordered factor
|
||||
data <- esbl_isolates %>%
|
||||
mutate(esbl = factor(esbl, levels = c(FALSE, TRUE), ordered = TRUE))
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `esbl_isolates`: Contains MIC test results and ESBL status for each
|
||||
isolate.
|
||||
- `mutate(esbl = ...)`: Converts the target column to an ordered factor
|
||||
for classification.
|
||||
|
||||
### **Defining the Workflow**
|
||||
|
||||
#### 1. Preprocessing with a Recipe
|
||||
|
||||
We use our
|
||||
[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
|
||||
function to log2-transform MIC values, ensuring that MICs are numeric
|
||||
and properly scaled. All MIC predictors can easily and agnostically
|
||||
selected using the new
|
||||
[`all_mic_predictors()`](https://amr-for-r.org/reference/amr-tidymodels.md):
|
||||
|
||||
``` r
|
||||
|
||||
# Split into training and testing sets
|
||||
set.seed(123)
|
||||
split <- initial_split(data)
|
||||
training_data <- training(split)
|
||||
testing_data <- testing(split)
|
||||
|
||||
# Define the recipe
|
||||
mic_recipe <- recipe(esbl ~ ., data = training_data) %>%
|
||||
remove_role(genus, old_role = "predictor") %>% # Remove non-informative variable
|
||||
step_mic_log2(all_mic_predictors()) # Log2 transform all MIC predictors
|
||||
|
||||
prep(mic_recipe)
|
||||
#>
|
||||
#> ── Recipe ──────────────────────────────────────────────────────────────────────
|
||||
#>
|
||||
#> ── Inputs
|
||||
#> Number of variables by role
|
||||
#> outcome: 1
|
||||
#> predictor: 17
|
||||
#> undeclared role: 1
|
||||
#>
|
||||
#> ── Training information
|
||||
#> Training data contained 375 data points and no incomplete rows.
|
||||
#>
|
||||
#> ── Operations
|
||||
#> • Log2 transformation of MIC columns: AMC, AMP, TZP, CXM, FOX, ... | Trained
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `remove_role()`: Removes irrelevant variables like genus.
|
||||
- [`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md):
|
||||
Applies `log2(as.numeric(...))` to all MIC predictors in one go.
|
||||
- `prep()`: Finalises the recipe based on training data.
|
||||
|
||||
#### 2. Specifying the Model
|
||||
|
||||
We use a simple logistic regression to model ESBL presence, though
|
||||
recent models such as xgboost ([link to `parsnip`
|
||||
manual](https://parsnip.tidymodels.org/reference/details_boost_tree_xgboost.html))
|
||||
could be much more precise.
|
||||
|
||||
``` r
|
||||
|
||||
# Define the model
|
||||
model <- logistic_reg(mode = "classification") %>%
|
||||
set_engine("glm")
|
||||
|
||||
model
|
||||
#> Logistic Regression Model Specification (classification)
|
||||
#>
|
||||
#> Computational engine: glm
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `logistic_reg()`: Specifies a binary classification model.
|
||||
- `set_engine("glm")`: Uses the base R GLM engine.
|
||||
|
||||
#### 3. Building the Workflow
|
||||
|
||||
``` r
|
||||
|
||||
# Create workflow
|
||||
workflow_model <- workflow() %>%
|
||||
add_recipe(mic_recipe) %>%
|
||||
add_model(model)
|
||||
|
||||
workflow_model
|
||||
#> ══ Workflow ════════════════════════════════════════════════════════════════════
|
||||
#> Preprocessor: Recipe
|
||||
#> Model: logistic_reg()
|
||||
#>
|
||||
#> ── Preprocessor ────────────────────────────────────────────────────────────────
|
||||
#> 1 Recipe Step
|
||||
#>
|
||||
#> • step_mic_log2()
|
||||
#>
|
||||
#> ── Model ───────────────────────────────────────────────────────────────────────
|
||||
#> Logistic Regression Model Specification (classification)
|
||||
#>
|
||||
#> Computational engine: glm
|
||||
```
|
||||
|
||||
### **Training and Evaluating the Model**
|
||||
|
||||
``` r
|
||||
|
||||
# Fit the model
|
||||
fitted <- fit(workflow_model, training_data)
|
||||
|
||||
# Generate predictions
|
||||
predictions <- predict(fitted, testing_data) %>%
|
||||
bind_cols(predict(fitted, testing_data, type = "prob")) %>% # add probabilities
|
||||
bind_cols(testing_data)
|
||||
|
||||
# Evaluate model performance
|
||||
our_metrics <- metric_set(accuracy, recall, precision, sensitivity, specificity, ppv, npv)
|
||||
metrics <- our_metrics(predictions, truth = esbl, estimate = .pred_class)
|
||||
|
||||
metrics
|
||||
#> # A tibble: 7 × 3
|
||||
#> .metric .estimator .estimate
|
||||
#> <chr> <chr> <dbl>
|
||||
#> 1 accuracy binary 0.92
|
||||
#> 2 recall binary 0.921
|
||||
#> 3 precision binary 0.921
|
||||
#> 4 sensitivity binary 0.921
|
||||
#> 5 specificity binary 0.919
|
||||
#> 6 ppv binary 0.921
|
||||
#> 7 npv binary 0.919
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `fit()`: Trains the model on the processed training data.
|
||||
- [`predict()`](https://rdrr.io/r/stats/predict.html): Produces
|
||||
predictions for unseen test data.
|
||||
- `metric_set()`: Allows evaluating multiple classification metrics.
|
||||
This will make `our_metrics` to become a function that we can use to
|
||||
check the predictions with.
|
||||
|
||||
It appears we can predict ESBL gene presence with a positive predictive
|
||||
value (PPV) of 92.1% and a negative predictive value (NPV) of 91.9%
|
||||
using a simplistic logistic regression model.
|
||||
|
||||
### **Visualising Predictions**
|
||||
|
||||
We can visualise predictions by comparing predicted and actual ESBL
|
||||
status.
|
||||
|
||||
``` r
|
||||
|
||||
library(ggplot2)
|
||||
|
||||
ggplot(predictions, aes(x = esbl, fill = .pred_class)) +
|
||||
geom_bar(position = "stack") +
|
||||
labs(
|
||||
title = "Predicted vs Actual ESBL Status",
|
||||
x = "Actual ESBL",
|
||||
y = "Count"
|
||||
) +
|
||||
theme_minimal()
|
||||
```
|
||||
|
||||
|
||||
|
||||
And plot the certainties too - how certain were the actual predictions?
|
||||
|
||||
``` r
|
||||
|
||||
predictions %>%
|
||||
mutate(
|
||||
certainty = ifelse(.pred_class == "FALSE",
|
||||
.pred_FALSE,
|
||||
.pred_TRUE
|
||||
),
|
||||
correct = ifelse(esbl == .pred_class, "Right", "Wrong")
|
||||
) %>%
|
||||
ggplot(aes(
|
||||
x = seq_len(nrow(predictions)),
|
||||
y = certainty,
|
||||
colour = correct
|
||||
)) +
|
||||
scale_colour_manual(
|
||||
values = c(Right = "green3", Wrong = "red2"),
|
||||
name = "Correct?"
|
||||
) +
|
||||
geom_point() +
|
||||
scale_y_continuous(
|
||||
labels = function(x) paste0(x * 100, "%"),
|
||||
limits = c(0.5, 1)
|
||||
) +
|
||||
theme_minimal()
|
||||
```
|
||||
|
||||
|
||||
|
||||
### **Conclusion**
|
||||
|
||||
In this example, we showcased how the new `AMR`-specific recipe steps
|
||||
simplify working with `<mic>` columns in `tidymodels`. The
|
||||
[`step_mic_log2()`](https://amr-for-r.org/reference/amr-tidymodels.md)
|
||||
transformation converts MICs (with or without operators) to
|
||||
log2-transformed numerics, improving compatibility with classification
|
||||
models.
|
||||
|
||||
This pipeline enables realistic, reproducible, and interpretable
|
||||
modelling of antimicrobial resistance data.
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## Example 3: Predicting AMR Over Time
|
||||
|
||||
In this third example, we aim to predict antimicrobial resistance (AMR)
|
||||
trends over time using `tidymodels`. We will model resistance to three
|
||||
antibiotics (amoxicillin `AMX`, amoxicillin-clavulanic acid `AMC`, and
|
||||
ciprofloxacin `CIP`), based on historical data grouped by year and
|
||||
hospital ward.
|
||||
|
||||
### **Objective**
|
||||
|
||||
Our goal is to:
|
||||
|
||||
1. Prepare the dataset by aggregating resistance data over time.
|
||||
2. Define a regression model to predict AMR trends.
|
||||
3. Use `tidymodels` to preprocess, train, and evaluate the model.
|
||||
|
||||
### **Data Preparation**
|
||||
|
||||
We start by transforming the `example_isolates` dataset into a
|
||||
structured time-series format.
|
||||
|
||||
``` r
|
||||
|
||||
# Load required libraries
|
||||
library(AMR)
|
||||
library(tidymodels)
|
||||
|
||||
# Transform dataset
|
||||
data_time <- example_isolates %>%
|
||||
top_n_microorganisms(n = 10) %>% # Filter on the top #10 species
|
||||
mutate(
|
||||
year = as.integer(format(date, "%Y")), # Extract year from date
|
||||
gramstain = mo_gramstain(mo)
|
||||
) %>% # Get taxonomic names
|
||||
group_by(year, gramstain) %>%
|
||||
summarise(
|
||||
across(c(AMX, AMC, CIP),
|
||||
function(x) resistance(x, minimum = 0),
|
||||
.names = "res_{.col}"
|
||||
),
|
||||
.groups = "drop"
|
||||
) %>%
|
||||
filter(!is.na(res_AMX) & !is.na(res_AMC) & !is.na(res_CIP)) # Drop missing values
|
||||
#> ℹ Using column mo as input for `col_mo`.
|
||||
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
|
||||
#> category susceptible. Set the `guideline` argument or the `AMR_guideline`
|
||||
#> option to either "CLSI" or "EUCAST", see `?AMR-options`.
|
||||
#> ℹ This message will be shown once per session.
|
||||
|
||||
data_time
|
||||
#> # A tibble: 32 × 5
|
||||
#> year gramstain res_AMX res_AMC res_CIP
|
||||
#> <int> <chr> <dbl> <dbl> <dbl>
|
||||
#> 1 2002 Gram-negative 1 0.105 0.0606
|
||||
#> 2 2002 Gram-positive 0.838 0.182 0.162
|
||||
#> 3 2003 Gram-negative 1 0.0714 0
|
||||
#> 4 2003 Gram-positive 0.714 0.244 0.154
|
||||
#> 5 2004 Gram-negative 0.464 0.0938 0
|
||||
#> 6 2004 Gram-positive 0.849 0.299 0.244
|
||||
#> 7 2005 Gram-negative 0.412 0.132 0.0588
|
||||
#> 8 2005 Gram-positive 0.882 0.382 0.154
|
||||
#> 9 2006 Gram-negative 0.379 0 0.1
|
||||
#> 10 2006 Gram-positive 0.778 0.333 0.353
|
||||
#> # ℹ 22 more rows
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `mo_name(mo)`: Converts microbial codes into proper species names.
|
||||
- [`resistance()`](https://amr-for-r.org/reference/proportion.md):
|
||||
Converts AMR results into numeric values (proportion of resistant
|
||||
isolates).
|
||||
- `group_by(year, ward, species)`: Aggregates resistance rates by year
|
||||
and ward.
|
||||
|
||||
### **Defining the Workflow**
|
||||
|
||||
We now define the modelling workflow, which consists of a preprocessing
|
||||
step, a model specification, and the fitting process.
|
||||
|
||||
#### 1. Preprocessing with a Recipe
|
||||
|
||||
``` r
|
||||
|
||||
# Define the recipe
|
||||
resistance_recipe_time <- recipe(res_AMX ~ year + gramstain, data = data_time) %>%
|
||||
step_dummy(gramstain, one_hot = TRUE) %>% # Convert categorical to numerical
|
||||
step_normalize(year) %>% # Normalise year for better model performance
|
||||
step_nzv(all_predictors()) # Remove near-zero variance predictors
|
||||
|
||||
resistance_recipe_time
|
||||
#>
|
||||
#> ── Recipe ──────────────────────────────────────────────────────────────────────
|
||||
#>
|
||||
#> ── Inputs
|
||||
#> Number of variables by role
|
||||
#> outcome: 1
|
||||
#> predictor: 2
|
||||
#>
|
||||
#> ── Operations
|
||||
#> • Dummy variables from: gramstain
|
||||
#> • Centering and scaling for: year
|
||||
#> • Sparse, unbalanced variable filter on: all_predictors()
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `step_dummy()`: Encodes categorical variables (`ward`, `species`) as
|
||||
numerical indicators.
|
||||
- `step_normalize()`: Normalises the `year` variable.
|
||||
- `step_nzv()`: Removes near-zero variance predictors.
|
||||
|
||||
#### 2. Specifying the Model
|
||||
|
||||
We use a linear regression model to predict resistance trends.
|
||||
|
||||
``` r
|
||||
|
||||
# Define the linear regression model
|
||||
lm_model <- linear_reg() %>%
|
||||
set_engine("lm") # Use linear regression
|
||||
|
||||
lm_model
|
||||
#> Linear Regression Model Specification (regression)
|
||||
#>
|
||||
#> Computational engine: lm
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `linear_reg()`: Defines a linear regression model.
|
||||
- `set_engine("lm")`: Uses R’s built-in linear regression engine.
|
||||
|
||||
#### 3. Building the Workflow
|
||||
|
||||
We combine the preprocessing recipe and model into a workflow.
|
||||
|
||||
``` r
|
||||
|
||||
# Create workflow
|
||||
resistance_workflow_time <- workflow() %>%
|
||||
add_recipe(resistance_recipe_time) %>%
|
||||
add_model(lm_model)
|
||||
|
||||
resistance_workflow_time
|
||||
#> ══ Workflow ════════════════════════════════════════════════════════════════════
|
||||
#> Preprocessor: Recipe
|
||||
#> Model: linear_reg()
|
||||
#>
|
||||
#> ── Preprocessor ────────────────────────────────────────────────────────────────
|
||||
#> 3 Recipe Steps
|
||||
#>
|
||||
#> • step_dummy()
|
||||
#> • step_normalize()
|
||||
#> • step_nzv()
|
||||
#>
|
||||
#> ── Model ───────────────────────────────────────────────────────────────────────
|
||||
#> Linear Regression Model Specification (regression)
|
||||
#>
|
||||
#> Computational engine: lm
|
||||
```
|
||||
|
||||
### **Training and Evaluating the Model**
|
||||
|
||||
We split the data into training and testing sets, fit the model, and
|
||||
evaluate performance.
|
||||
|
||||
``` r
|
||||
|
||||
# Split the data
|
||||
set.seed(123)
|
||||
data_split_time <- initial_split(data_time, prop = 0.8)
|
||||
train_time <- training(data_split_time)
|
||||
test_time <- testing(data_split_time)
|
||||
|
||||
# Train the model
|
||||
fitted_workflow_time <- resistance_workflow_time %>%
|
||||
fit(train_time)
|
||||
|
||||
# Make predictions
|
||||
predictions_time <- fitted_workflow_time %>%
|
||||
predict(test_time) %>%
|
||||
bind_cols(test_time)
|
||||
|
||||
# Evaluate model
|
||||
metrics_time <- predictions_time %>%
|
||||
metrics(truth = res_AMX, estimate = .pred)
|
||||
|
||||
metrics_time
|
||||
#> # A tibble: 3 × 3
|
||||
#> .metric .estimator .estimate
|
||||
#> <chr> <chr> <dbl>
|
||||
#> 1 rmse standard 0.0774
|
||||
#> 2 rsq standard 0.711
|
||||
#> 3 mae standard 0.0704
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- `initial_split()`: Splits data into training and testing sets.
|
||||
- `fit()`: Trains the workflow.
|
||||
- [`predict()`](https://rdrr.io/r/stats/predict.html): Generates
|
||||
resistance predictions.
|
||||
- `metrics()`: Evaluates model performance.
|
||||
|
||||
### **Visualising Predictions**
|
||||
|
||||
We plot resistance trends over time for amoxicillin.
|
||||
|
||||
``` r
|
||||
|
||||
library(ggplot2)
|
||||
|
||||
# Plot actual vs predicted resistance over time
|
||||
ggplot(predictions_time, aes(x = year)) +
|
||||
geom_point(aes(y = res_AMX, color = "Actual")) +
|
||||
geom_line(aes(y = .pred, color = "Predicted")) +
|
||||
labs(
|
||||
title = "Predicted vs Actual AMX Resistance Over Time",
|
||||
x = "Year",
|
||||
y = "Resistance Proportion"
|
||||
) +
|
||||
theme_minimal()
|
||||
```
|
||||
|
||||
|
||||
|
||||
Additionally, we can visualise resistance trends in `ggplot2` and
|
||||
directly add linear models there:
|
||||
|
||||
``` r
|
||||
|
||||
ggplot(data_time, aes(x = year, y = res_AMX, color = gramstain)) +
|
||||
geom_line() +
|
||||
labs(
|
||||
title = "AMX Resistance Trends",
|
||||
x = "Year",
|
||||
y = "Resistance Proportion"
|
||||
) +
|
||||
# add a linear model directly in ggplot2:
|
||||
geom_smooth(
|
||||
method = "lm",
|
||||
formula = y ~ x,
|
||||
alpha = 0.25
|
||||
) +
|
||||
theme_minimal()
|
||||
```
|
||||
|
||||
|
||||
|
||||
### **Conclusion**
|
||||
|
||||
In this example, we demonstrated how to analyze AMR trends over time
|
||||
using `tidymodels`. By aggregating resistance rates by year and hospital
|
||||
ward, we built a predictive model to track changes in resistance to
|
||||
amoxicillin (`AMX`), amoxicillin-clavulanic acid (`AMC`), and
|
||||
ciprofloxacin (`CIP`).
|
||||
|
||||
This method can be extended to other antibiotics and resistance
|
||||
patterns, providing valuable insights into AMR dynamics in healthcare
|
||||
settings.
|
||||
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<main id="main" class="col-md-9"><div class="page-header">
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<img src="../logo.svg" class="logo" alt=""><h1>Apply EUCAST rules</h1>
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||||
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/EUCAST.Rmd" class="external-link"><code>vignettes/EUCAST.Rmd</code></a></small>
|
||||
<div class="d-none name"><code>EUCAST.Rmd</code></div>
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</div>
|
||||
|
||||
|
||||
|
||||
<div class="section level2">
|
||||
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
|
||||
</h2>
|
||||
<p>What are EUCAST rules? The European Committee on Antimicrobial
|
||||
Susceptibility Testing (EUCAST) states <a href="https://www.eucast.org/expert_rules_and_expected_phenotypes" class="external-link">on
|
||||
their website</a>:</p>
|
||||
<blockquote>
|
||||
<p><em>EUCAST expert rules (see below) are a tabulated collection of
|
||||
expert knowledge on interpretive rules, expected resistant phenotypes
|
||||
and expected susceptible phenotypes which should be applied to
|
||||
antimicrobial susceptibility testing in order to reduce testing, reduce
|
||||
errors and make appropriate recommendations for reporting particular
|
||||
resistances.</em></p>
|
||||
</blockquote>
|
||||
<p>In Europe, a lot of medical microbiological laboratories already
|
||||
apply these rules (<a href="https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008" class="external-link">Brown
|
||||
<em>et al.</em>, 2015</a>). Our package features their latest insights
|
||||
on expected resistant phenotypes (v1.2, 2023).</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="examples">Examples<a class="anchor" aria-label="anchor" href="#examples"></a>
|
||||
</h2>
|
||||
<p>These rules can be used to discard improbable bug-drug combinations
|
||||
in your data. For example, <em>Klebsiella</em> produces beta-lactamase
|
||||
that prevents ampicillin (or amoxicillin) from working against it. In
|
||||
other words, practically every strain of <em>Klebsiella</em> is
|
||||
resistant to ampicillin.</p>
|
||||
<p>Sometimes, laboratory data can still contain such strains with
|
||||
<em>Klebsiella</em> being susceptible to ampicillin. This could be
|
||||
because an antibiogram is available before an identification is
|
||||
available, and the antibiogram is then not re-interpreted based on the
|
||||
identification. The <code><a href="../reference/interpretive_rules.html">eucast_rules()</a></code> function resolves this,
|
||||
by applying the latest ‘EUCAST Expected Resistant Phenotypes’
|
||||
guideline:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">oops</span> <span class="op"><-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
|
||||
<span> mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
|
||||
<span> <span class="st">"Klebsiella pneumoniae"</span>,</span>
|
||||
<span> <span class="st">"Escherichia coli"</span></span>
|
||||
<span> <span class="op">)</span>,</span>
|
||||
<span> ampicillin <span class="op">=</span> <span class="fu"><a href="../reference/as.sir.html">as.sir</a></span><span class="op">(</span><span class="st">"S"</span><span class="op">)</span></span>
|
||||
<span><span class="op">)</span></span>
|
||||
<span><span class="va">oops</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
|
||||
<span><span class="co">#> mo ampicillin</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><sir></span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #5FD7AF;"> S </span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">2</span> Escherichia coli <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span></span>
|
||||
<span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">oops</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2 × 2</span></span></span>
|
||||
<span><span class="co">#> mo ampicillin</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><sir></span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">1</span> Klebsiella pneumoniae <span style="color: #080808; background-color: #FF5F5F;"> R </span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">2</span> Escherichia coli <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span></code></pre></div>
|
||||
<p>A more convenient function is
|
||||
<code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> that uses the same guideline,
|
||||
but allows to check for one or more specific microorganisms or
|
||||
antimicrobials:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
|
||||
<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"Escherichia coli"</span><span class="op">)</span>,</span>
|
||||
<span> <span class="st">"ampicillin"</span></span>
|
||||
<span><span class="op">)</span></span>
|
||||
<span><span class="co">#> [1] TRUE FALSE</span></span>
|
||||
<span></span>
|
||||
<span><span class="fu"><a href="../reference/mo_property.html">mo_is_intrinsic_resistant</a></span><span class="op">(</span></span>
|
||||
<span> <span class="st">"Klebsiella pneumoniae"</span>,</span>
|
||||
<span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"ampicillin"</span>, <span class="st">"kanamycin"</span><span class="op">)</span></span>
|
||||
<span><span class="op">)</span></span>
|
||||
<span><span class="co">#> [1] TRUE FALSE</span></span></code></pre></div>
|
||||
<p>EUCAST rules can not only be used for correction, they can also be
|
||||
used for filling in known resistance and susceptibility based on results
|
||||
of other antimicrobials drugs. This process is called <em>interpretive
|
||||
reading</em>, and is basically a form of imputation:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">data</span> <span class="op"><-</span> <span class="fu">tibble</span><span class="fu">::</span><span class="fu"><a href="https://tibble.tidyverse.org/reference/tibble.html" class="external-link">tibble</a></span><span class="op">(</span></span>
|
||||
<span> mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span></span>
|
||||
<span> <span class="st">"Staphylococcus aureus"</span>,</span>
|
||||
<span> <span class="st">"Enterococcus faecalis"</span>,</span>
|
||||
<span> <span class="st">"Escherichia coli"</span>,</span>
|
||||
<span> <span class="st">"Klebsiella pneumoniae"</span>,</span>
|
||||
<span> <span class="st">"Pseudomonas aeruginosa"</span></span>
|
||||
<span> <span class="op">)</span>,</span>
|
||||
<span> VAN <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Vancomycin</span></span>
|
||||
<span> AMX <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Amoxicillin</span></span>
|
||||
<span> COL <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Colistin</span></span>
|
||||
<span> CAZ <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Ceftazidime</span></span>
|
||||
<span> CXM <span class="op">=</span> <span class="st">"-"</span>, <span class="co"># Cefuroxime</span></span>
|
||||
<span> PEN <span class="op">=</span> <span class="st">"S"</span>, <span class="co"># Benzylenicillin</span></span>
|
||||
<span> FOX <span class="op">=</span> <span class="st">"S"</span> <span class="co"># Cefoxitin</span></span>
|
||||
<span><span class="op">)</span></span></code></pre></div>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">data</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left">mo</th>
|
||||
<th align="center">VAN</th>
|
||||
<th align="center">AMX</th>
|
||||
<th align="center">COL</th>
|
||||
<th align="center">CAZ</th>
|
||||
<th align="center">CXM</th>
|
||||
<th align="center">PEN</th>
|
||||
<th align="center">FOX</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">Staphylococcus aureus</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">Enterococcus faecalis</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">Escherichia coli</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">Klebsiella pneumoniae</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">Pseudomonas aeruginosa</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/interpretive_rules.html">eucast_rules</a></span><span class="op">(</span><span class="va">data</span>, overwrite <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left">mo</th>
|
||||
<th align="center">VAN</th>
|
||||
<th align="center">AMX</th>
|
||||
<th align="center">COL</th>
|
||||
<th align="center">CAZ</th>
|
||||
<th align="center">CXM</th>
|
||||
<th align="center">PEN</th>
|
||||
<th align="center">FOX</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">Staphylococcus aureus</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">Enterococcus faecalis</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">R</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">Escherichia coli</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">Klebsiella pneumoniae</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">Pseudomonas aeruginosa</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">-</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">R</td>
|
||||
<td align="center">R</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
</nav></aside>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
||||
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
|
||||
</div>
|
||||
|
||||
</footer>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,131 +0,0 @@
|
||||
# Apply EUCAST rules
|
||||
|
||||
## Introduction
|
||||
|
||||
What are EUCAST rules? The European Committee on Antimicrobial
|
||||
Susceptibility Testing (EUCAST) states [on their
|
||||
website](https://www.eucast.org/expert_rules_and_expected_phenotypes):
|
||||
|
||||
> *EUCAST expert rules (see below) are a tabulated collection of expert
|
||||
> knowledge on interpretive rules, expected resistant phenotypes and
|
||||
> expected susceptible phenotypes which should be applied to
|
||||
> antimicrobial susceptibility testing in order to reduce testing,
|
||||
> reduce errors and make appropriate recommendations for reporting
|
||||
> particular resistances.*
|
||||
|
||||
In Europe, a lot of medical microbiological laboratories already apply
|
||||
these rules ([Brown *et al.*,
|
||||
2015](https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2015.20.2.21008)).
|
||||
Our package features their latest insights on expected resistant
|
||||
phenotypes (v1.2, 2023).
|
||||
|
||||
## Examples
|
||||
|
||||
These rules can be used to discard improbable bug-drug combinations in
|
||||
your data. For example, *Klebsiella* produces beta-lactamase that
|
||||
prevents ampicillin (or amoxicillin) from working against it. In other
|
||||
words, practically every strain of *Klebsiella* is resistant to
|
||||
ampicillin.
|
||||
|
||||
Sometimes, laboratory data can still contain such strains with
|
||||
*Klebsiella* being susceptible to ampicillin. This could be because an
|
||||
antibiogram is available before an identification is available, and the
|
||||
antibiogram is then not re-interpreted based on the identification. The
|
||||
[`eucast_rules()`](https://amr-for-r.org/reference/interpretive_rules.md)
|
||||
function resolves this, by applying the latest ‘EUCAST Expected
|
||||
Resistant Phenotypes’ guideline:
|
||||
|
||||
``` r
|
||||
|
||||
oops <- tibble::tibble(
|
||||
mo = c(
|
||||
"Klebsiella pneumoniae",
|
||||
"Escherichia coli"
|
||||
),
|
||||
ampicillin = as.sir("S")
|
||||
)
|
||||
oops
|
||||
#> # A tibble: 2 × 2
|
||||
#> mo ampicillin
|
||||
#> <chr> <sir>
|
||||
#> 1 Klebsiella pneumoniae S
|
||||
#> 2 Escherichia coli S
|
||||
|
||||
eucast_rules(oops, info = FALSE, overwrite = TRUE)
|
||||
#> # A tibble: 2 × 2
|
||||
#> mo ampicillin
|
||||
#> <chr> <sir>
|
||||
#> 1 Klebsiella pneumoniae R
|
||||
#> 2 Escherichia coli S
|
||||
```
|
||||
|
||||
A more convenient function is
|
||||
[`mo_is_intrinsic_resistant()`](https://amr-for-r.org/reference/mo_property.md)
|
||||
that uses the same guideline, but allows to check for one or more
|
||||
specific microorganisms or antimicrobials:
|
||||
|
||||
``` r
|
||||
|
||||
mo_is_intrinsic_resistant(
|
||||
c("Klebsiella pneumoniae", "Escherichia coli"),
|
||||
"ampicillin"
|
||||
)
|
||||
#> [1] TRUE FALSE
|
||||
|
||||
mo_is_intrinsic_resistant(
|
||||
"Klebsiella pneumoniae",
|
||||
c("ampicillin", "kanamycin")
|
||||
)
|
||||
#> [1] TRUE FALSE
|
||||
```
|
||||
|
||||
EUCAST rules can not only be used for correction, they can also be used
|
||||
for filling in known resistance and susceptibility based on results of
|
||||
other antimicrobials drugs. This process is called *interpretive
|
||||
reading*, and is basically a form of imputation:
|
||||
|
||||
``` r
|
||||
|
||||
data <- tibble::tibble(
|
||||
mo = c(
|
||||
"Staphylococcus aureus",
|
||||
"Enterococcus faecalis",
|
||||
"Escherichia coli",
|
||||
"Klebsiella pneumoniae",
|
||||
"Pseudomonas aeruginosa"
|
||||
),
|
||||
VAN = "-", # Vancomycin
|
||||
AMX = "-", # Amoxicillin
|
||||
COL = "-", # Colistin
|
||||
CAZ = "-", # Ceftazidime
|
||||
CXM = "-", # Cefuroxime
|
||||
PEN = "S", # Benzylenicillin
|
||||
FOX = "S" # Cefoxitin
|
||||
)
|
||||
```
|
||||
|
||||
``` r
|
||||
|
||||
data
|
||||
```
|
||||
|
||||
| mo | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
|
||||
|:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||
| Staphylococcus aureus | \- | \- | \- | \- | \- | S | S |
|
||||
| Enterococcus faecalis | \- | \- | \- | \- | \- | S | S |
|
||||
| Escherichia coli | \- | \- | \- | \- | \- | S | S |
|
||||
| Klebsiella pneumoniae | \- | \- | \- | \- | \- | S | S |
|
||||
| Pseudomonas aeruginosa | \- | \- | \- | \- | \- | S | S |
|
||||
|
||||
``` r
|
||||
|
||||
eucast_rules(data, overwrite = TRUE)
|
||||
```
|
||||
|
||||
| mo | VAN | AMX | COL | CAZ | CXM | PEN | FOX |
|
||||
|:-----------------------|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
|
||||
| Staphylococcus aureus | \- | S | R | R | S | S | S |
|
||||
| Enterococcus faecalis | \- | \- | R | R | R | S | R |
|
||||
| Escherichia coli | R | \- | \- | \- | \- | R | S |
|
||||
| Klebsiella pneumoniae | R | R | \- | \- | \- | R | S |
|
||||
| Pseudomonas aeruginosa | R | R | \- | \- | R | R | R |
|
||||
@@ -1,254 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
|
||||
<head>
|
||||
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
|
||||
<meta charset="utf-8">
|
||||
<meta http-equiv="X-UA-Compatible" content="IE=edge">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
|
||||
<title>Conduct principal component analysis (PCA) for AMR • AMR (for R)</title>
|
||||
<!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png">
|
||||
<link rel="icon" type="”image/svg+xml”" href="../favicon.svg">
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<main id="main" class="col-md-9"><div class="page-header">
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<img src="../logo.svg" class="logo" alt=""><h1>Conduct principal component analysis (PCA) for AMR</h1>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/PCA.Rmd" class="external-link"><code>vignettes/PCA.Rmd</code></a></small>
|
||||
<div class="d-none name"><code>PCA.Rmd</code></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<p><strong>NOTE: This page will be updated soon, as the pca() function
|
||||
is currently being developed.</strong></p>
|
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<div class="section level2">
|
||||
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
|
||||
</h2>
|
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</div>
|
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<div class="section level2">
|
||||
<h2 id="transforming">Transforming<a class="anchor" aria-label="anchor" href="#transforming"></a>
|
||||
</h2>
|
||||
<p>For PCA, we need to transform our AMR data first. This is what the
|
||||
<code>example_isolates</code> data set in this package looks like:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
|
||||
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span></span>
|
||||
<span><span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">example_isolates</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> Rows: 2,000</span></span>
|
||||
<span><span class="co">#> Columns: 46</span></span>
|
||||
<span><span class="co">#> $ date <span style="color: #949494; font-style: italic;"><date></span> 2002-01-02<span style="color: #949494;">, </span>2002-01-03<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-07<span style="color: #949494;">, </span>2002-01-13<span style="color: #949494;">, </span>2…</span></span>
|
||||
<span><span class="co">#> $ patient <span style="color: #949494; font-style: italic;"><chr></span> "A77334"<span style="color: #949494;">, </span>"A77334"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"067927"<span style="color: #949494;">, </span>"4…</span></span>
|
||||
<span><span class="co">#> $ age <span style="color: #949494; font-style: italic;"><dbl></span> 65<span style="color: #949494;">, </span>65<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>78<span style="color: #949494;">, </span>45<span style="color: #949494;">, </span>79<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>67<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>71<span style="color: #949494;">, </span>75<span style="color: #949494;">, </span>50…</span></span>
|
||||
<span><span class="co">#> $ gender <span style="color: #949494; font-style: italic;"><chr></span> "F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"F"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M"<span style="color: #949494;">, </span>"M…</span></span>
|
||||
<span><span class="co">#> $ ward <span style="color: #949494; font-style: italic;"><chr></span> "Clinical"<span style="color: #949494;">, </span>"Clinical"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"ICU"<span style="color: #949494;">, </span>"Clinical"…</span></span>
|
||||
<span><span class="co">#> $ mo <span style="color: #949494; font-style: italic;"><mo></span> "B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_ESCHR_COLI"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">, </span>"B_STPHY_EPDR"<span style="color: #949494;">,</span>…</span></span>
|
||||
<span><span class="co">#> $ PEN <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
|
||||
<span><span class="co">#> $ OXA <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ FLC <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R…</span></span>
|
||||
<span><span class="co">#> $ AMX <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
|
||||
<span><span class="co">#> $ AMC <span style="color: #949494; font-style: italic;"><sir></span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
|
||||
<span><span class="co">#> $ AMP <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
|
||||
<span><span class="co">#> $ TZP <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ CZO <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
|
||||
<span><span class="co">#> $ FEP <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ CXM <span style="color: #949494; font-style: italic;"><sir></span> I<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
|
||||
<span><span class="co">#> $ FOX <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">,</span>…</span></span>
|
||||
<span><span class="co">#> $ CTX <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
|
||||
<span><span class="co">#> $ CAZ <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
|
||||
<span><span class="co">#> $ CRO <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
|
||||
<span><span class="co">#> $ GEN <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ TOB <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ AMK <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ KAN <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ TMP <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
|
||||
<span><span class="co">#> $ SXT <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
|
||||
<span><span class="co">#> $ NIT <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">,</span>…</span></span>
|
||||
<span><span class="co">#> $ FOS <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ LNZ <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
|
||||
<span><span class="co">#> $ CIP <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
|
||||
<span><span class="co">#> $ MFX <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ VAN <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>…</span></span>
|
||||
<span><span class="co">#> $ TEC <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span>
|
||||
<span><span class="co">#> $ TCY <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>I<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
|
||||
<span><span class="co">#> $ TGC <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ DOX <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ ERY <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
|
||||
<span><span class="co">#> $ CLI <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ AZM <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>S<span style="color: #949494;">,</span>…</span></span>
|
||||
<span><span class="co">#> $ IPM <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>S<span style="color: #949494;">, </span>S…</span></span>
|
||||
<span><span class="co">#> $ MEM <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ MTR <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ CHL <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ COL <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>…</span></span>
|
||||
<span><span class="co">#> $ MUP <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span>…</span></span>
|
||||
<span><span class="co">#> $ RIF <span style="color: #949494; font-style: italic;"><sir></span> R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span><span style="color: #BB0000;">NA</span><span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span>R<span style="color: #949494;">, </span><span style="color: #BB0000;">N</span>…</span></span></code></pre></div>
|
||||
<p>Now to transform this to a data set with only resistance percentages
|
||||
per taxonomic order and genus:</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">resistance_data</span> <span class="op"><-</span> <span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span></span>
|
||||
<span> order <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, <span class="co"># group on anything, like order</span></span>
|
||||
<span> genus <span class="op">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span></span>
|
||||
<span> <span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span> <span class="co"># and genus as we do here</span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise_all.html" class="external-link">summarise_if</a></span><span class="op">(</span><span class="va">is.sir</span>, <span class="va">resistance</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span> <span class="co"># then get resistance of all drugs</span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span></span>
|
||||
<span> <span class="va">order</span>, <span class="va">genus</span>, <span class="va">AMC</span>, <span class="va">CXM</span>, <span class="va">CTX</span>,</span>
|
||||
<span> <span class="va">CAZ</span>, <span class="va">GEN</span>, <span class="va">TOB</span>, <span class="va">TMP</span>, <span class="va">SXT</span></span>
|
||||
<span> <span class="op">)</span> <span class="co"># and select only relevant columns</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> `resistance()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
|
||||
<span><span class="co">#> category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
|
||||
<span><span class="co">#> option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span>
|
||||
<span></span>
|
||||
<span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 6 × 10</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># Groups: order [5]</span></span></span>
|
||||
<span><span class="co">#> order genus AMC CXM CTX CAZ GEN TOB TMP SXT</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><dbl></span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">1</span> (unknown order) (unknown ge… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">2</span> Actinomycetales Schaalia <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">3</span> Bacteroidales Bacteroides <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">4</span> Campylobacterales Campylobact… <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">5</span> Caryophanales Gemella <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">6</span> Caryophanales Listeria <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span> <span style="color: #BB0000;">NA</span></span></span></code></pre></div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="perform-principal-component-analysis">Perform principal component analysis<a class="anchor" aria-label="anchor" href="#perform-principal-component-analysis"></a>
|
||||
</h2>
|
||||
<p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows
|
||||
that contain numeric values in all selected variables, so we now only
|
||||
need to do:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">pca_result</span> <span class="op"><-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span><span class="op">(</span><span class="va">resistance_data</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Columns selected for PCA: <span style="color: #0000BB;">"\033[1mAMC\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCAZ\033[22m"</span>,</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"\033[1mCTX\033[22m"</span>, <span style="color: #0000BB;">"\033[1mCXM\033[22m"</span>, <span style="color: #0000BB;">"\033[1mGEN\033[22m"</span>,</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"\033[1mSXT\033[22m"</span>, <span style="color: #0000BB;">"\033[1mTMP\033[22m"</span>, and <span style="color: #0000BB;">"\033[1mTOB\033[22m"</span>. Total</span></span>
|
||||
<span><span class="co">#> observations available: 7.</span></span></code></pre></div>
|
||||
<p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary()</a></code>
|
||||
function:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> Groups (n=4, named as 'order'):</span></span>
|
||||
<span><span class="co">#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></span>
|
||||
<span><span class="co">#> Importance of components:</span></span>
|
||||
<span><span class="co">#> PC1 PC2 PC3 PC4 PC5 PC6 PC7</span></span>
|
||||
<span><span class="co">#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16</span></span>
|
||||
<span><span class="co">#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00</span></span>
|
||||
<span><span class="co">#> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00</span></span></code></pre></div>
|
||||
<pre><code><span><span class="co">#> Groups (n=4, named as 'order'):</span></span>
|
||||
<span><span class="co">#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"</span></span></code></pre>
|
||||
<p>Good news. The first two components explain a total of 93.3% of the
|
||||
variance (see the PC1 and PC2 values of the <em>Proportion of
|
||||
Variance</em>. We can create a so-called biplot with the base R
|
||||
<code><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot()</a></code> function, to see which antimicrobial resistance
|
||||
per drug explain the difference per microorganism.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="plotting-the-results">Plotting the results<a class="anchor" aria-label="anchor" href="#plotting-the-results"></a>
|
||||
</h2>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html" class="external-link">biplot</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" class="r-plt" alt="" width="750"></p>
|
||||
<p>But we can’t see the explanation of the points. Perhaps this works
|
||||
better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that
|
||||
automatically adds the right labels and even groups:</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="750"></p>
|
||||
<p>You can also print an ellipse per group, and edit the appearance:</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span><span class="op">(</span><span class="va">pca_result</span>, ellipse <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html" class="external-link">labs</a></span><span class="op">(</span>title <span class="op">=</span> <span class="st">"An AMR/PCA biplot!"</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="750"></p>
|
||||
</div>
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
</nav></aside>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
||||
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
|
||||
</div>
|
||||
|
||||
</footer>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
-170
@@ -1,170 +0,0 @@
|
||||
# Conduct principal component analysis (PCA) for AMR
|
||||
|
||||
**NOTE: This page will be updated soon, as the pca() function is
|
||||
currently being developed.**
|
||||
|
||||
## Introduction
|
||||
|
||||
## Transforming
|
||||
|
||||
For PCA, we need to transform our AMR data first. This is what the
|
||||
`example_isolates` data set in this package looks like:
|
||||
|
||||
``` r
|
||||
|
||||
library(AMR)
|
||||
library(dplyr)
|
||||
glimpse(example_isolates)
|
||||
#> Rows: 2,000
|
||||
#> Columns: 46
|
||||
#> $ date <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2…
|
||||
#> $ patient <chr> "A77334", "A77334", "067927", "067927", "067927", "067927", "4…
|
||||
#> $ age <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50…
|
||||
#> $ gender <chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M", "M", "M…
|
||||
#> $ ward <chr> "Clinical", "Clinical", "ICU", "ICU", "ICU", "ICU", "Clinical"…
|
||||
#> $ mo <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPHY_EPDR",…
|
||||
#> $ PEN <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,…
|
||||
#> $ OXA <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ FLC <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R…
|
||||
#> $ AMX <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
|
||||
#> $ AMC <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N…
|
||||
#> $ AMP <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
|
||||
#> $ TZP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ CZO <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
|
||||
#> $ FEP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ CXM <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S…
|
||||
#> $ FOX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
|
||||
#> $ CTX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
|
||||
#> $ CAZ <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, …
|
||||
#> $ CRO <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
|
||||
#> $ GEN <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ TOB <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA…
|
||||
#> $ AMK <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ KAN <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ TMP <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, …
|
||||
#> $ SXT <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N…
|
||||
#> $ NIT <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,…
|
||||
#> $ FOS <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ LNZ <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
|
||||
#> $ CIP <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S…
|
||||
#> $ MFX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ VAN <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, …
|
||||
#> $ TEC <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
|
||||
#> $ TCY <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, …
|
||||
#> $ TGC <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
|
||||
#> $ DOX <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
|
||||
#> $ ERY <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
|
||||
#> $ CLI <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA…
|
||||
#> $ AZM <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
|
||||
#> $ IPM <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
|
||||
#> $ MEM <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ MTR <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ CHL <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ COL <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, …
|
||||
#> $ MUP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
|
||||
#> $ RIF <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
|
||||
```
|
||||
|
||||
Now to transform this to a data set with only resistance percentages per
|
||||
taxonomic order and genus:
|
||||
|
||||
``` r
|
||||
|
||||
resistance_data <- example_isolates %>%
|
||||
group_by(
|
||||
order = mo_order(mo), # group on anything, like order
|
||||
genus = mo_genus(mo)
|
||||
) %>% # and genus as we do here
|
||||
summarise_if(is.sir, resistance) %>% # then get resistance of all drugs
|
||||
select(
|
||||
order, genus, AMC, CXM, CTX,
|
||||
CAZ, GEN, TOB, TMP, SXT
|
||||
) # and select only relevant columns
|
||||
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
|
||||
#> category susceptible. Set the `guideline` argument or the `AMR_guideline`
|
||||
#> option to either "CLSI" or "EUCAST", see `?AMR-options`.
|
||||
#> ℹ This message will be shown once per session.
|
||||
|
||||
head(resistance_data)
|
||||
#> # A tibble: 6 × 10
|
||||
#> # Groups: order [5]
|
||||
#> order genus AMC CXM CTX CAZ GEN TOB TMP SXT
|
||||
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
|
||||
#> 1 (unknown order) (unknown ge… NA NA NA NA NA NA NA NA
|
||||
#> 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA
|
||||
#> 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA
|
||||
#> 4 Campylobacterales Campylobact… NA NA NA NA NA NA NA NA
|
||||
#> 5 Caryophanales Gemella NA NA NA NA NA NA NA NA
|
||||
#> 6 Caryophanales Listeria NA NA NA NA NA NA NA NA
|
||||
```
|
||||
|
||||
## Perform principal component analysis
|
||||
|
||||
The new [`pca()`](https://amr-for-r.org/reference/pca.md) function will
|
||||
automatically filter on rows that contain numeric values in all selected
|
||||
variables, so we now only need to do:
|
||||
|
||||
``` r
|
||||
|
||||
pca_result <- pca(resistance_data)
|
||||
#> ℹ Columns selected for PCA: "\033[1mAMC\033[22m", "\033[1mCAZ\033[22m",
|
||||
#> "\033[1mCTX\033[22m", "\033[1mCXM\033[22m", "\033[1mGEN\033[22m",
|
||||
#> "\033[1mSXT\033[22m", "\033[1mTMP\033[22m", and "\033[1mTOB\033[22m". Total
|
||||
#> observations available: 7.
|
||||
```
|
||||
|
||||
The result can be reviewed with the good old
|
||||
[`summary()`](https://rdrr.io/r/base/summary.html) function:
|
||||
|
||||
``` r
|
||||
|
||||
summary(pca_result)
|
||||
#> Groups (n=4, named as 'order'):
|
||||
#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
|
||||
#> Importance of components:
|
||||
#> PC1 PC2 PC3 PC4 PC5 PC6 PC7
|
||||
#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16
|
||||
#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
|
||||
#> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
|
||||
```
|
||||
|
||||
#> Groups (n=4, named as 'order'):
|
||||
#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
|
||||
|
||||
Good news. The first two components explain a total of 93.3% of the
|
||||
variance (see the PC1 and PC2 values of the *Proportion of Variance*. We
|
||||
can create a so-called biplot with the base R
|
||||
[`biplot()`](https://rdrr.io/r/stats/biplot.html) function, to see which
|
||||
antimicrobial resistance per drug explain the difference per
|
||||
microorganism.
|
||||
|
||||
## Plotting the results
|
||||
|
||||
``` r
|
||||
|
||||
biplot(pca_result)
|
||||
```
|
||||
|
||||
|
||||
|
||||
But we can’t see the explanation of the points. Perhaps this works
|
||||
better with our new
|
||||
[`ggplot_pca()`](https://amr-for-r.org/reference/ggplot_pca.md)
|
||||
function, that automatically adds the right labels and even groups:
|
||||
|
||||
``` r
|
||||
|
||||
ggplot_pca(pca_result)
|
||||
```
|
||||
|
||||
|
||||
|
||||
You can also print an ellipse per group, and edit the appearance:
|
||||
|
||||
``` r
|
||||
|
||||
ggplot_pca(pca_result, ellipse = TRUE) +
|
||||
ggplot2::labs(title = "An AMR/PCA biplot!")
|
||||
```
|
||||
|
||||
|
||||
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<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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<main id="main" class="col-md-9"><div class="page-header">
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<img src="../logo.svg" class="logo" alt=""><h1>Work with WHONET data</h1>
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||||
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
|
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<div class="d-none name"><code>WHONET.Rmd</code></div>
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</div>
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|
||||
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<div class="section level3">
|
||||
<h3 id="import-of-data">Import of data<a class="anchor" aria-label="anchor" href="#import-of-data"></a>
|
||||
</h3>
|
||||
<p>This tutorial assumes you already imported the WHONET data with
|
||||
e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code>
|
||||
package</a>. In RStudio, this can be done using the menu button ‘Import
|
||||
Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
|
||||
select your exported file. Make sure date fields are imported
|
||||
correctly.</p>
|
||||
<p>An example syntax could look like this:</p>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span></span>
|
||||
<span><span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></span></code></pre></div>
|
||||
<p>This package comes with an <a href="https://amr-for-r.org/reference/WHONET.html">example data set
|
||||
<code>WHONET</code></a>. We will use it for this analysis.</p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
|
||||
</h3>
|
||||
<p>First, load the relevant packages if you did not yet did this. I use
|
||||
the tidyverse for all of my analyses. All of them. If you don’t know it
|
||||
yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
|
||||
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span></span>
|
||||
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span> <span class="co"># this package</span></span>
|
||||
<span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/cleaner/" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></span></code></pre></div>
|
||||
<p>We will have to transform some variables to simplify and automate the
|
||||
analysis:</p>
|
||||
<ul>
|
||||
<li>Microorganisms should be transformed to our own microorganism codes
|
||||
(called an <code>mo</code>) using <a href="https://amr-for-r.org/reference/catalogue_of_life">our Catalogue
|
||||
of Life reference data set</a>, which contains all ~70,000
|
||||
microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
|
||||
We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
|
||||
recognises almost all WHONET abbreviations of microorganisms.</li>
|
||||
<li>Antimicrobial results or interpretations have to be clean and valid.
|
||||
In other words, they should only contain values <code>"S"</code>,
|
||||
<code>"I"</code> or <code>"R"</code>. That is exactly where the
|
||||
<code><a href="../reference/as.sir.html">as.sir()</a></code> function is for.</li>
|
||||
</ul>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># transform variables</span></span>
|
||||
<span><span class="va">data</span> <span class="op"><-</span> <span class="va">WHONET</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="co"># get microbial ID based on given organism</span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class</span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.sir</span><span class="op">)</span></span></code></pre></div>
|
||||
<p>No errors or warnings, so all values are transformed succesfully.</p>
|
||||
<p>We also created a package dedicated to data cleaning and checking,
|
||||
called the <code>cleaner</code> package. Its <code><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq()</a></code>
|
||||
function can be used to create frequency tables.</p>
|
||||
<p>So let’s check our data, with a couple of frequency tables:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># our newly created `mo` variable, put in the mo_name() function</span></span>
|
||||
<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><strong>Frequency table</strong></p>
|
||||
<p>Class: character<br>
|
||||
Length: 500<br>
|
||||
Available: 500 (100%, NA: 0 = 0%)<br>
|
||||
Unique: 38</p>
|
||||
<p>Shortest: 11<br>
|
||||
Longest: 40</p>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="4%">
|
||||
<col width="47%">
|
||||
<col width="7%">
|
||||
<col width="10%">
|
||||
<col width="13%">
|
||||
<col width="15%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">Escherichia coli</td>
|
||||
<td align="right">245</td>
|
||||
<td align="right">49.0%</td>
|
||||
<td align="right">245</td>
|
||||
<td align="right">49.0%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">Coagulase-negative Staphylococcus (CoNS)</td>
|
||||
<td align="right">74</td>
|
||||
<td align="right">14.8%</td>
|
||||
<td align="right">319</td>
|
||||
<td align="right">63.8%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">Staphylococcus epidermidis</td>
|
||||
<td align="right">38</td>
|
||||
<td align="right">7.6%</td>
|
||||
<td align="right">357</td>
|
||||
<td align="right">71.4%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">4</td>
|
||||
<td align="left">Streptococcus pneumoniae</td>
|
||||
<td align="right">31</td>
|
||||
<td align="right">6.2%</td>
|
||||
<td align="right">388</td>
|
||||
<td align="right">77.6%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">5</td>
|
||||
<td align="left">Staphylococcus hominis</td>
|
||||
<td align="right">21</td>
|
||||
<td align="right">4.2%</td>
|
||||
<td align="right">409</td>
|
||||
<td align="right">81.8%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">6</td>
|
||||
<td align="left">Proteus mirabilis</td>
|
||||
<td align="right">9</td>
|
||||
<td align="right">1.8%</td>
|
||||
<td align="right">418</td>
|
||||
<td align="right">83.6%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">7</td>
|
||||
<td align="left">Enterococcus faecium</td>
|
||||
<td align="right">8</td>
|
||||
<td align="right">1.6%</td>
|
||||
<td align="right">426</td>
|
||||
<td align="right">85.2%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">8</td>
|
||||
<td align="left">Staphylococcus capitis urealyticus</td>
|
||||
<td align="right">8</td>
|
||||
<td align="right">1.6%</td>
|
||||
<td align="right">434</td>
|
||||
<td align="right">86.8%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">9</td>
|
||||
<td align="left">Enterobacter cloacae</td>
|
||||
<td align="right">5</td>
|
||||
<td align="right">1.0%</td>
|
||||
<td align="right">439</td>
|
||||
<td align="right">87.8%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">10</td>
|
||||
<td align="left">Enterococcus columbae</td>
|
||||
<td align="right">4</td>
|
||||
<td align="right">0.8%</td>
|
||||
<td align="right">443</td>
|
||||
<td align="right">88.6%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>(omitted 28 entries, n = 57 [11.4%])</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># our transformed antibiotic columns</span></span>
|
||||
<span><span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</span></span>
|
||||
<span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span> <span class="fu"><a href="https://msberends.github.io/cleaner/reference/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> `susceptibility()` assumes the EUCAST guideline and thus considers the 'I'</span></span>
|
||||
<span><span class="co">#> category susceptible. Set the `guideline` argument or the `AMR_guideline`</span></span>
|
||||
<span><span class="co">#> option to either "CLSI" or "EUCAST", see `?AMR-options`.</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> This message will be shown once per session.</span></span></code></pre></div>
|
||||
<p><strong>Frequency table</strong></p>
|
||||
<p>Class: factor > ordered > sir (numeric)<br>
|
||||
Length: 500<br>
|
||||
Levels: 8: S < SDD < I < R < NI < WT < NWT <
|
||||
NS<br>
|
||||
Available: 481 (96.2%, NA: 19 = 3.8%)<br>
|
||||
Unique: 3</p>
|
||||
<p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)<br>
|
||||
Drug group: Aminopenicillins<br>
|
||||
%SI: 78.59%</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left"></th>
|
||||
<th align="left">Item</th>
|
||||
<th align="right">Count</th>
|
||||
<th align="right">Percent</th>
|
||||
<th align="right">Cum. Count</th>
|
||||
<th align="right">Cum. Percent</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">1</td>
|
||||
<td align="left">S</td>
|
||||
<td align="right">356</td>
|
||||
<td align="right">74.01%</td>
|
||||
<td align="right">356</td>
|
||||
<td align="right">74.01%</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">2</td>
|
||||
<td align="left">R</td>
|
||||
<td align="right">103</td>
|
||||
<td align="right">21.41%</td>
|
||||
<td align="right">459</td>
|
||||
<td align="right">95.43%</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="left">3</td>
|
||||
<td align="left">I</td>
|
||||
<td align="right">22</td>
|
||||
<td align="right">4.57%</td>
|
||||
<td align="right">481</td>
|
||||
<td align="right">100.00%</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
|
||||
</h3>
|
||||
<p>An easy <code>ggplot</code> will already give a lot of information,
|
||||
using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a></code> function:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by</a></span><span class="op">(</span><span class="va">Country</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">Country</span>, <span class="va">AMP_ND2</span>, <span class="va">AMC_ED20</span>, <span class="va">CAZ_ED10</span>, <span class="va">CIP_ED5</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu"><a href="../reference/ggplot_sir.html">ggplot_sir</a></span><span class="op">(</span>translate_ab <span class="op">=</span> <span class="st">"ab"</span>, facet <span class="op">=</span> <span class="st">"Country"</span>, datalabels <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
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</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
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</nav></aside>
|
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</div>
|
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|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
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</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
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<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
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|
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</body>
|
||||
</html>
|
||||
@@ -1,147 +0,0 @@
|
||||
# Work with WHONET data
|
||||
|
||||
### Import of data
|
||||
|
||||
This tutorial assumes you already imported the WHONET data with e.g. the
|
||||
[`readxl` package](https://readxl.tidyverse.org/). In RStudio, this can
|
||||
be done using the menu button ‘Import Dataset’ in the tab ‘Environment’.
|
||||
Choose the option ‘From Excel’ and select your exported file. Make sure
|
||||
date fields are imported correctly.
|
||||
|
||||
An example syntax could look like this:
|
||||
|
||||
``` r
|
||||
|
||||
library(readxl)
|
||||
data <- read_excel(path = "path/to/your/file.xlsx")
|
||||
```
|
||||
|
||||
This package comes with an [example data set
|
||||
`WHONET`](https://amr-for-r.org/reference/WHONET.html). We will use it
|
||||
for this analysis.
|
||||
|
||||
### Preparation
|
||||
|
||||
First, load the relevant packages if you did not yet did this. I use the
|
||||
tidyverse for all of my analyses. All of them. If you don’t know it yet,
|
||||
I suggest you read about it on their website:
|
||||
<https://www.tidyverse.org/>.
|
||||
|
||||
``` r
|
||||
|
||||
library(dplyr) # part of tidyverse
|
||||
library(ggplot2) # part of tidyverse
|
||||
library(AMR) # this package
|
||||
library(cleaner) # to create frequency tables
|
||||
```
|
||||
|
||||
We will have to transform some variables to simplify and automate the
|
||||
analysis:
|
||||
|
||||
- Microorganisms should be transformed to our own microorganism codes
|
||||
(called an `mo`) using [our Catalogue of Life reference data
|
||||
set](https://amr-for-r.org/reference/catalogue_of_life), which
|
||||
contains all ~70,000 microorganisms from the taxonomic kingdoms
|
||||
Bacteria, Fungi and Protozoa. We do the tranformation with
|
||||
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md). This function
|
||||
also recognises almost all WHONET abbreviations of microorganisms.
|
||||
- Antimicrobial results or interpretations have to be clean and valid.
|
||||
In other words, they should only contain values `"S"`, `"I"` or `"R"`.
|
||||
That is exactly where the
|
||||
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) function is
|
||||
for.
|
||||
|
||||
``` r
|
||||
|
||||
# transform variables
|
||||
data <- WHONET %>%
|
||||
# get microbial ID based on given organism
|
||||
mutate(mo = as.mo(Organism)) %>%
|
||||
# transform everything from "AMP_ND10" to "CIP_EE" to the new `sir` class
|
||||
mutate_at(vars(AMP_ND10:CIP_EE), as.sir)
|
||||
```
|
||||
|
||||
No errors or warnings, so all values are transformed succesfully.
|
||||
|
||||
We also created a package dedicated to data cleaning and checking,
|
||||
called the `cleaner` package. Its
|
||||
[`freq()`](https://msberends.github.io/cleaner/reference/freq.html)
|
||||
function can be used to create frequency tables.
|
||||
|
||||
So let’s check our data, with a couple of frequency tables:
|
||||
|
||||
``` r
|
||||
|
||||
# our newly created `mo` variable, put in the mo_name() function
|
||||
data %>% freq(mo_name(mo), nmax = 10)
|
||||
```
|
||||
|
||||
**Frequency table**
|
||||
|
||||
Class: character
|
||||
Length: 500
|
||||
Available: 500 (100%, NA: 0 = 0%)
|
||||
Unique: 38
|
||||
|
||||
Shortest: 11
|
||||
Longest: 40
|
||||
|
||||
| | Item | Count | Percent | Cum. Count | Cum. Percent |
|
||||
|:---|:---|---:|---:|---:|---:|
|
||||
| 1 | Escherichia coli | 245 | 49.0% | 245 | 49.0% |
|
||||
| 2 | Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | 63.8% |
|
||||
| 3 | Staphylococcus epidermidis | 38 | 7.6% | 357 | 71.4% |
|
||||
| 4 | Streptococcus pneumoniae | 31 | 6.2% | 388 | 77.6% |
|
||||
| 5 | Staphylococcus hominis | 21 | 4.2% | 409 | 81.8% |
|
||||
| 6 | Proteus mirabilis | 9 | 1.8% | 418 | 83.6% |
|
||||
| 7 | Enterococcus faecium | 8 | 1.6% | 426 | 85.2% |
|
||||
| 8 | Staphylococcus capitis urealyticus | 8 | 1.6% | 434 | 86.8% |
|
||||
| 9 | Enterobacter cloacae | 5 | 1.0% | 439 | 87.8% |
|
||||
| 10 | Enterococcus columbae | 4 | 0.8% | 443 | 88.6% |
|
||||
|
||||
(omitted 28 entries, n = 57 \[11.4%\])
|
||||
|
||||
``` r
|
||||
|
||||
# our transformed antibiotic columns
|
||||
# amoxicillin/clavulanic acid (J01CR02) as an example
|
||||
data %>% freq(AMC_ND2)
|
||||
#> ℹ `susceptibility()` assumes the EUCAST guideline and thus considers the 'I'
|
||||
#> category susceptible. Set the `guideline` argument or the `AMR_guideline`
|
||||
#> option to either "CLSI" or "EUCAST", see `?AMR-options`.
|
||||
#> ℹ This message will be shown once per session.
|
||||
```
|
||||
|
||||
**Frequency table**
|
||||
|
||||
Class: factor \> ordered \> sir (numeric)
|
||||
Length: 500
|
||||
Levels: 8: S \< SDD \< I \< R \< NI \< WT \< NWT \< NS
|
||||
Available: 481 (96.2%, NA: 19 = 3.8%)
|
||||
Unique: 3
|
||||
|
||||
Drug: Amoxicillin/clavulanic acid (AMC, J01CR02/QJ01CR02)
|
||||
Drug group: Aminopenicillins
|
||||
%SI: 78.59%
|
||||
|
||||
| | Item | Count | Percent | Cum. Count | Cum. Percent |
|
||||
|:----|:-----|------:|--------:|-----------:|-------------:|
|
||||
| 1 | S | 356 | 74.01% | 356 | 74.01% |
|
||||
| 2 | R | 103 | 21.41% | 459 | 95.43% |
|
||||
| 3 | I | 22 | 4.57% | 481 | 100.00% |
|
||||
|
||||
### A first glimpse at results
|
||||
|
||||
An easy `ggplot` will already give a lot of information, using the
|
||||
included [`ggplot_sir()`](https://amr-for-r.org/reference/ggplot_sir.md)
|
||||
function:
|
||||
|
||||
``` r
|
||||
|
||||
data %>%
|
||||
group_by(Country) %>%
|
||||
select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
|
||||
ggplot_sir(translate_ab = "ab", facet = "Country", datalabels = FALSE)
|
||||
```
|
||||
|
||||
|
||||
Binary file not shown.
|
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|
||||
<img src="../logo.svg" class="logo" alt=""><h1>Estimating Empirical Coverage with WISCA</h1>
|
||||
|
||||
|
||||
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/WISCA.Rmd" class="external-link"><code>vignettes/WISCA.Rmd</code></a></small>
|
||||
<div class="d-none name"><code>WISCA.Rmd</code></div>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<div class="section level2">
|
||||
<h2 id="why-wisca">Why WISCA?<a class="anchor" aria-label="anchor" href="#why-wisca"></a>
|
||||
</h2>
|
||||
<p>When a clinician starts empirical antimicrobial therapy, the
|
||||
causative pathogen is unknown. The question they need answered is not
|
||||
<em>“what proportion of</em> E. coli <em>is susceptible to
|
||||
ciprofloxacin?“</em> but rather <em>“what is the probability that this
|
||||
regimen will adequately cover whatever pathogen turns out to be causing
|
||||
my patient’s infection?”</em></p>
|
||||
<p>The traditional cumulative antibiogram, as standardised by CLSI M39,
|
||||
cannot answer that question. It presents susceptibility percentages per
|
||||
species per antibiotic, but:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>It fragments information by organism.</strong> The clinician
|
||||
must mentally combine susceptibility rates across multiple species,
|
||||
weighting by how often each species causes the syndrome, a calculation
|
||||
nobody does at the bedside.</li>
|
||||
<li>
|
||||
<strong>It ignores pathogen incidence.</strong> A species that
|
||||
causes 2% of infections is given the same visual weight as one that
|
||||
causes 60%.</li>
|
||||
<li>
|
||||
<strong>It does not evaluate combination regimens.</strong> Much
|
||||
empirical therapy consists of two or more agents, but the traditional
|
||||
antibiogram only shows monotherapy per organism.</li>
|
||||
<li>
|
||||
<strong>It provides no measure of uncertainty.</strong> A reported
|
||||
“90% susceptible” based on 50 isolates has a 95% confidence interval of
|
||||
roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
|
||||
point estimate without context.</li>
|
||||
</ul>
|
||||
<p><strong>WISCA</strong> (Weighted-Incidence Syndromic Combination
|
||||
Antibiogram) resolves all four limitations. It estimates the probability
|
||||
that a regimen will provide adequate empirical coverage for a given
|
||||
infection syndrome, weighted by local pathogen incidence, with full
|
||||
uncertainty quantification via Bayesian inference.</p>
|
||||
<p>The concept was introduced by Hebert <em>et al.</em> (2012), who
|
||||
demonstrated that traditional antibiogram susceptibility rates could be
|
||||
misleading: ciprofloxacin appeared 84% effective against <em>E.
|
||||
coli</em> in the traditional antibiogram, but WISCA revealed only 62%
|
||||
coverage for UTI and 37% for abdominal infections, because enterococci
|
||||
(intrinsically resistant) and other species contribute substantially to
|
||||
these syndromes. Randhawa <em>et al.</em> (2014) showed that
|
||||
WISCA-guided regimen selection could improve time-to-adequate-coverage
|
||||
on the ICU by over 40%. Bielicki <em>et al.</em> (2016) introduced the
|
||||
Bayesian framework now used in this package, enabling credible intervals
|
||||
and multi-centre pooling. Cook <em>et al.</em> (2022) applied it
|
||||
globally across 52 hospitals in 23 countries.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="the-idea">The idea<a class="anchor" aria-label="anchor" href="#the-idea"></a>
|
||||
</h2>
|
||||
<p>WISCA asks:</p>
|
||||
<blockquote>
|
||||
<p>“What is the <strong>probability</strong> that this regimen
|
||||
<strong>will cover</strong> the pathogen, given the syndrome?”</p>
|
||||
</blockquote>
|
||||
<p>This means combining two quantities:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Pathogen incidence</strong> in the syndrome (how often each
|
||||
species causes it),</li>
|
||||
<li>
|
||||
<strong>Susceptibility</strong> of each pathogen to the
|
||||
regimen.</li>
|
||||
</ul>
|
||||
<p>We can write this as:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munder><mo>∑</mo><mi>i</mi></munder><mo stretchy="false" form="prefix">(</mo><msub><mtext mathvariant="normal">Incidence</mtext><mi>i</mi></msub><mo>×</mo><msub><mtext mathvariant="normal">Susceptibility</mtext><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)</annotation></semantics></math></p>
|
||||
<p>For example, suppose in your hospital:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<em>E. coli</em> causes 60% of UTIs, and 90% of <em>E. coli</em> are
|
||||
susceptible to a drug.</li>
|
||||
<li>
|
||||
<em>Klebsiella</em> causes 40% of UTIs, and 70% of
|
||||
<em>Klebsiella</em> are susceptible.</li>
|
||||
</ul>
|
||||
<p>Then:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>0.6</mn><mo>×</mo><mn>0.9</mn><mo stretchy="false" form="postfix">)</mo><mo>+</mo><mo stretchy="false" form="prefix">(</mo><mn>0.4</mn><mo>×</mo><mn>0.7</mn><mo stretchy="false" form="postfix">)</mo><mo>=</mo><mn>0.82</mn></mrow><annotation encoding="application/x-tex">\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82</annotation></semantics></math></p>
|
||||
<p>That 82% is a far more clinically meaningful number than the
|
||||
species-level “90% of <em>E. coli</em>” and “70% of <em>Klebsiella</em>”
|
||||
reported separately in a traditional antibiogram, because it directly
|
||||
answers the question the clinician actually faces.</p>
|
||||
<p>But in real data, both incidence and susceptibility are
|
||||
<strong>estimated from finite samples</strong>, so they carry
|
||||
uncertainty. A sample of 50 isolates is not a census. WISCA models this
|
||||
uncertainty <strong>probabilistically</strong>, using conjugate Bayesian
|
||||
distributions.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="the-bayesian-engine">The Bayesian engine<a class="anchor" aria-label="anchor" href="#the-bayesian-engine"></a>
|
||||
</h2>
|
||||
<div class="section level3">
|
||||
<h3 id="pathogen-incidence">Pathogen incidence<a class="anchor" aria-label="anchor" href="#pathogen-incidence"></a>
|
||||
</h3>
|
||||
<p>Let:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>K</mi><annotation encoding="application/x-tex">K</annotation></semantics></math>
|
||||
be the number of pathogens,</li>
|
||||
<li>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝛂</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{\alpha} = (1, 1, \ldots, 1)</annotation></semantics></math>
|
||||
be a
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mtext mathvariant="normal">Dirichlet</mtext><annotation encoding="application/x-tex">\text{Dirichlet}</annotation></semantics></math>
|
||||
prior (uniform, non-informative),</li>
|
||||
<li>
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐧</mi><mo>=</mo><mo stretchy="false" form="prefix">(</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{n} = (n_1, \ldots, n_K)</annotation></semantics></math>
|
||||
be the observed isolate counts per species.</li>
|
||||
</ul>
|
||||
<p>Then the posterior incidence is:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>1</mn></msub><mo>+</mo><msub><mi>n</mi><mn>1</mn></msub><mo>,</mo><mi>…</mi><mo>,</mo><msub><mi>α</mi><mi>K</mi></msub><mo>+</mo><msub><mi>n</mi><mi>K</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)</annotation></semantics></math></p>
|
||||
<p>To simulate from this, we use:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>x</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Gamma</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mi>i</mi></msub><mo>+</mo><msub><mi>n</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><mn>1</mn><mo stretchy="false" form="postfix">)</mo><mo>,</mo><mspace width="1.0em"></mspace><msub><mi>p</mi><mi>i</mi></msub><mo>=</mo><mfrac><msub><mi>x</mi><mi>i</mi></msub><mrow><munderover><mo>∑</mo><mrow><mi>j</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>x</mi><mi>j</mi></msub></mrow></mfrac></mrow><annotation encoding="application/x-tex">x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}</annotation></semantics></math></p>
|
||||
<p>The Dirichlet is the conjugate prior for multinomial data. With the
|
||||
non-informative prior
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Dirichlet</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>1</mn><mo>,</mo><mi>…</mi><mo>,</mo><mn>1</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Dirichlet}(1, 1, \ldots, 1)</annotation></semantics></math>,
|
||||
the posterior is dominated by the data once sample sizes are reasonable.
|
||||
With small samples, the posterior is appropriately more diffuse,
|
||||
reflecting genuine uncertainty, and the resulting credible intervals
|
||||
will be wider.</p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="susceptibility">Susceptibility<a class="anchor" aria-label="anchor" href="#susceptibility"></a>
|
||||
</h3>
|
||||
<p>Each pathogen-regimen pair has a prior and observed data:</p>
|
||||
<ul>
|
||||
<li>Default prior:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>0.5</mn><mo>,</mo><mn>0.5</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(0.5, 0.5)</annotation></semantics></math>
|
||||
(Jeffreys prior)</li>
|
||||
<li>Intrinsically resistant pairs:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><mn>1</mn><mo>,</mo><mn>9999</mn><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\text{Beta}(1, 9999)</annotation></semantics></math>,
|
||||
forcing near-zero susceptibility regardless of observed data (based on
|
||||
EUCAST Expected Resistant Phenotypes)</li>
|
||||
<li>Data:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
|
||||
susceptible out of
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>N</mi><annotation encoding="application/x-tex">N</annotation></semantics></math>
|
||||
tested</li>
|
||||
</ul>
|
||||
<p>The
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mi>S</mi><annotation encoding="application/x-tex">S</annotation></semantics></math>
|
||||
category could also include values SDD (susceptible, dose-dependent) and
|
||||
I (intermediate [CLSI], or susceptible, increased exposure
|
||||
[EUCAST]).</p>
|
||||
<p>Then the posterior is:</p>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>θ</mi><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><mi>S</mi><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><mi>N</mi><mo>−</mo><mi>S</mi><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)</annotation></semantics></math></p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="final-coverage-estimate">Final coverage estimate<a class="anchor" aria-label="anchor" href="#final-coverage-estimate"></a>
|
||||
</h3>
|
||||
<p>Putting it together:</p>
|
||||
<ol style="list-style-type: decimal">
|
||||
<li>Simulate pathogen incidence:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>𝐩</mi><mo>∼</mo><mtext mathvariant="normal">Dirichlet</mtext></mrow><annotation encoding="application/x-tex">\boldsymbol{p} \sim \text{Dirichlet}</annotation></semantics></math>
|
||||
</li>
|
||||
<li>Simulate susceptibility:
|
||||
<math display="inline" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>θ</mi><mi>i</mi></msub><mo>∼</mo><mtext mathvariant="normal">Beta</mtext><mo stretchy="false" form="prefix">(</mo><msub><mi>α</mi><mn>0</mn></msub><mo>+</mo><msub><mi>S</mi><mi>i</mi></msub><mo>,</mo><mspace width="0.222em"></mspace><msub><mi>β</mi><mn>0</mn></msub><mo>+</mo><msub><mi>N</mi><mi>i</mi></msub><mo>−</mo><msub><mi>S</mi><mi>i</mi></msub><mo stretchy="false" form="postfix">)</mo></mrow><annotation encoding="application/x-tex">\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)</annotation></semantics></math>
|
||||
</li>
|
||||
<li>Combine:</li>
|
||||
</ol>
|
||||
<p><math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="normal">Coverage</mtext><mo>=</mo><munderover><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></munderover><msub><mi>p</mi><mi>i</mi></msub><mo>⋅</mo><msub><mi>θ</mi><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i</annotation></semantics></math></p>
|
||||
<p>Repeat this simulation (e.g., 1000 times) and summarise:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Mean</strong> = expected coverage</li>
|
||||
<li>
|
||||
<strong>Quantiles</strong> = credible interval (95% by default)</li>
|
||||
</ul>
|
||||
<p>Because each simulation draws from the full posterior, the resulting
|
||||
distribution of coverage estimates naturally captures the joint
|
||||
uncertainty in both pathogen incidence and susceptibility. The credible
|
||||
interval tells you how confident you can be in the coverage estimate,
|
||||
something a traditional antibiogram never provides.</p>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="when-to-use-wisca-vs--traditional-antibiograms">When to use WISCA vs. traditional antibiograms<a class="anchor" aria-label="anchor" href="#when-to-use-wisca-vs--traditional-antibiograms"></a>
|
||||
</h2>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th>Goal</th>
|
||||
<th>Recommended approach</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td>Guide empirical therapy decisions</td>
|
||||
<td><strong>WISCA</strong></td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>Compare regimens for a syndrome</td>
|
||||
<td><strong>WISCA</strong></td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>Evaluate combination regimens</td>
|
||||
<td><strong>WISCA</strong></td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>Antimicrobial stewardship (A-team)</td>
|
||||
<td><strong>WISCA</strong></td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>Track resistance trends per species</td>
|
||||
<td>Traditional / Combination</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td>AMR surveillance reporting</td>
|
||||
<td>Traditional / Syndromic</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td>Understand species-level epidemiology</td>
|
||||
<td>Traditional</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>In short: if the end goal involves a <em>patient</em> who does not
|
||||
yet have a culture result, WISCA is the appropriate tool. If the end
|
||||
goal is <em>surveillance</em> of resistance at the species level, the
|
||||
traditional antibiogram remains fit for purpose.</p>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="practical-use-in-the-amr-package">Practical use in the <code>AMR</code> package<a class="anchor" aria-label="anchor" href="#practical-use-in-the-amr-package"></a>
|
||||
</h2>
|
||||
<div class="section level3">
|
||||
<h3 id="prepare-data">Prepare data<a class="anchor" aria-label="anchor" href="#prepare-data"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://amr-for-r.org">AMR</a></span><span class="op">)</span></span>
|
||||
<span><span class="va">data</span> <span class="op"><-</span> <span class="va">example_isolates</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Structure of our data</span></span>
|
||||
<span><span class="va">data</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2,000 × 46</span></span></span>
|
||||
<span><span class="co">#> date patient age gender ward mo PEN OXA FLC AMX </span></span>
|
||||
<span><span class="co">#> <span style="color: #949494; font-style: italic;"><date></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><dbl></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><mo></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span> <span style="color: #949494; font-style: italic;"><sir></span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> 2002-01-02 A77334 65 F Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> 2002-01-03 A77334 65 F Clinical <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> <span style="color: #949494;"> NA</span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> 2002-01-07 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> 2002-01-07 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> 2002-01-13 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> 2002-01-13 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> 2002-01-14 462729 78 M Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> 2002-01-14 462729 78 M Clinical <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> 2002-01-16 067927 45 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> 2002-01-17 858515 79 F ICU <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>EPDR <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #949494;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #949494;"> NA</span> </span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 1,990 more rows</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …</span></span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Add a synthetic syndrome column for demonstration</span></span>
|
||||
<span><span class="va">data</span><span class="op">$</span><span class="va">syndrome</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html" class="external-link">ifelse</a></span><span class="op">(</span><span class="va">data</span><span class="op">$</span><span class="va">mo</span> <span class="op"><a href="../reference/like.html">%like%</a></span> <span class="st">"coli"</span>, <span class="st">"UTI"</span>, <span class="st">"Non-UTI"</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Keep only 10 most common microorganisms</span></span>
|
||||
<span><span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="../reference/top_n_microorganisms.html">top_n_microorganisms</a></span><span class="op">(</span><span class="va">data</span>, n <span class="op">=</span> <span class="fl">10</span>, property <span class="op">=</span> <span class="st">"species"</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">mo</span> as input for `col_mo`.</span></span></code></pre></div>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="basic-wisca">Basic WISCA<a class="anchor" aria-label="anchor" href="#basic-wisca"></a>
|
||||
</h3>
|
||||
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"CIP"</span>, <span class="st">"GEN"</span><span class="op">)</span></span>
|
||||
<span><span class="op">)</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
<th align="left">Amoxicillin/clavulanic acid</th>
|
||||
<th align="left">Ciprofloxacin</th>
|
||||
<th align="left">Gentamicin</th>
|
||||
</tr></thead>
|
||||
<tbody><tr class="odd">
|
||||
<td align="left">76.8% (74.7-79.1%)</td>
|
||||
<td align="left">81.5% (78.9-84.1%)</td>
|
||||
<td align="left">82.9% (81-84.8%)</td>
|
||||
</tr></tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="use-combination-regimens">Use combination regimens<a class="anchor" aria-label="anchor" href="#use-combination-regimens"></a>
|
||||
</h3>
|
||||
<p>Combination regimens are specified with a <code>+</code> separator.
|
||||
WISCA evaluates whether <em>at least one</em> agent in the combination
|
||||
covers the pathogen:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span></span>
|
||||
<span><span class="op">)</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="24%">
|
||||
<col width="38%">
|
||||
<col width="36%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="left">Amoxicillin/clavulanic acid</th>
|
||||
<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
|
||||
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
|
||||
</tr></thead>
|
||||
<tbody><tr class="odd">
|
||||
<td align="left">76.8% (74.6-78.9%)</td>
|
||||
<td align="left">89.6% (88-91.1%)</td>
|
||||
<td align="left">93.7% (92.5-94.9%)</td>
|
||||
</tr></tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="stratify-by-syndrome">Stratify by syndrome<a class="anchor" aria-label="anchor" href="#stratify-by-syndrome"></a>
|
||||
</h3>
|
||||
<p>Use <code>syndromic_group</code> to produce separate WISCA estimates
|
||||
per clinical stratum. You can pass a column name or any expression:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">wisca_out</span> <span class="op"><-</span> <span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
|
||||
<span> syndromic_group <span class="op">=</span> <span class="st">"syndrome"</span></span>
|
||||
<span><span class="op">)</span></span>
|
||||
<span><span class="va">wisca_out</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="12%">
|
||||
<col width="21%">
|
||||
<col width="34%">
|
||||
<col width="31%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="left">Syndromic Group</th>
|
||||
<th align="left">Amoxicillin/clavulanic acid</th>
|
||||
<th align="left">Amoxicillin/clavulanic acid + Ciprofloxacin</th>
|
||||
<th align="left">Amoxicillin/clavulanic acid + Gentamicin</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">Non-UTI</td>
|
||||
<td align="left">72.5% (69.9-75.1%)</td>
|
||||
<td align="left">86.9% (84.8-89%)</td>
|
||||
<td align="left">91.4% (89.5-93%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">UTI</td>
|
||||
<td align="left">86% (82.5-89%)</td>
|
||||
<td align="left">94.8% (92.5-96.6%)</td>
|
||||
<td align="left">97.9% (96.3-99%)</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
<p>The <code>AMR</code> package is available in 28 languages, which can
|
||||
all be used for the <code><a href="../reference/antibiogram.html">wisca()</a></code> function too:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">data</span>,</span>
|
||||
<span> antimicrobials <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"AMC"</span>, <span class="st">"AMC + CIP"</span>, <span class="st">"AMC + GEN"</span><span class="op">)</span>,</span>
|
||||
<span> syndromic_group <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/grep.html" class="external-link">gsub</a></span><span class="op">(</span><span class="st">"UTI"</span>, <span class="st">"UCI"</span>, <span class="va">data</span><span class="op">$</span><span class="va">syndrome</span><span class="op">)</span>,</span>
|
||||
<span> language <span class="op">=</span> <span class="st">"Spanish"</span></span>
|
||||
<span><span class="op">)</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="12%">
|
||||
<col width="21%">
|
||||
<col width="34%">
|
||||
<col width="31%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="left">Grupo sindrómico</th>
|
||||
<th align="left">Amoxicilina/ácido clavulánico</th>
|
||||
<th align="left">Amoxicilina/ácido clavulánico + Ciprofloxacina</th>
|
||||
<th align="left">Amoxicilina/ácido clavulánico + Gentamicina</th>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="left">Non-UCI</td>
|
||||
<td align="left">72.6% (69.9-75.3%)</td>
|
||||
<td align="left">87% (84.9-89.1%)</td>
|
||||
<td align="left">91.4% (89.7-92.9%)</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="left">UCI</td>
|
||||
<td align="left">86% (82.7-89%)</td>
|
||||
<td align="left">94.8% (92.7-96.4%)</td>
|
||||
<td align="left">97.9% (96.5-99%)</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="interpreting-the-output">Interpreting the output<a class="anchor" aria-label="anchor" href="#interpreting-the-output"></a>
|
||||
</h3>
|
||||
<p>Each row shows the estimated empirical coverage for a regimen, with a
|
||||
95% credible interval. When comparing regimens:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Overlapping credible intervals</strong> mean there is no
|
||||
statistically significant difference in coverage. If a narrower-spectrum
|
||||
regimen overlaps with a broader one, the narrower-spectrum option can be
|
||||
preferred on stewardship grounds.</li>
|
||||
<li>
|
||||
<strong>Non-overlapping credible intervals</strong> indicate a
|
||||
clinically meaningful difference in coverage.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="plotting">Plotting<a class="anchor" aria-label="anchor" href="#plotting"></a>
|
||||
</h3>
|
||||
<p>WISCA results can be visualised in several ways. All plot functions
|
||||
work on the output of <code><a href="../reference/antibiogram.html">wisca()</a></code> (or
|
||||
<code>antibiogram(..., wisca = TRUE)</code>).</p>
|
||||
<p>Below we use the <code>wisca_out</code> object that was generated
|
||||
above.</p>
|
||||
<div class="section level4">
|
||||
<h4 id="coverage-with-credible-intervals">Coverage with credible intervals<a class="anchor" aria-label="anchor" href="#coverage-with-credible-intervals"></a>
|
||||
</h4>
|
||||
<p>The extended <code><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot()</a></code> method from the
|
||||
<code>ggplot2()</code> package produces a point-and-interval plot
|
||||
showing the coverage estimate and 95% credible interval for each
|
||||
regimen, grouped by syndromic stratum. This is the most direct way to
|
||||
compare regimens: overlapping intervals suggest clinical
|
||||
non-inferiority, non-overlapping intervals indicate a meaningful
|
||||
difference.</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu">ggplot2</span><span class="fu">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="WISCA_files/figure-html/unnamed-chunk-6-1.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
||||
<div class="section level4">
|
||||
<h4 id="susceptibility-vs--incidence-weight">Susceptibility vs. incidence weight<a class="anchor" aria-label="anchor" href="#susceptibility-vs--incidence-weight"></a>
|
||||
</h4>
|
||||
<p><code><a href="../reference/antibiogram.html">wisca_plot()</a></code> produces a scatter plot of the Monte Carlo
|
||||
simulation draws, showing each pathogen’s susceptibility (x-axis)
|
||||
against its incidence weight (y-axis) for each regimen. Each dot
|
||||
represents one of 1,000 simulated draws, so the spread reflects
|
||||
posterior uncertainty. This plot reveals <em>why</em> a regimen achieves
|
||||
its coverage: you can see which pathogens dominate the syndrome (high on
|
||||
the y-axis), how susceptible they are (position on the x-axis), and how
|
||||
uncertain both estimates are (spread of the cloud). The dashed vertical
|
||||
lines denote the point estimates, i.e., the coverage percentages. The
|
||||
ribbon behind the dashed lines denote the credible interval, which is
|
||||
95% at default.</p>
|
||||
<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="WISCA_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
||||
<div class="section level4">
|
||||
<h4 id="posterior-coverage-distributions">Posterior coverage distributions<a class="anchor" aria-label="anchor" href="#posterior-coverage-distributions"></a>
|
||||
</h4>
|
||||
<p>Setting <code>wisca_plot_type = "posterior_coverage"</code> shows the
|
||||
full posterior distribution of coverage for each regimen as a density
|
||||
curve. This is the most complete representation of what the Bayesian
|
||||
model produces: each curve shows the relative likelihood of each
|
||||
coverage value across all 1,000 simulations. Narrow, tall peaks indicate
|
||||
high certainty; wide, flat curves indicate greater uncertainty. Where
|
||||
two curves overlap, the regimens cannot be confidently
|
||||
distinguished.</p>
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca_plot</a></span><span class="op">(</span><span class="va">wisca_out</span>, wisca_plot_type <span class="op">=</span> <span class="st">"posterior_coverage"</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="WISCA_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="sensible-defaults-which-can-be-customised">Sensible defaults, which can be customised<a class="anchor" aria-label="anchor" href="#sensible-defaults-which-can-be-customised"></a>
|
||||
</h2>
|
||||
<ul>
|
||||
<li>
|
||||
<code>simulations = 1000</code>: number of Monte Carlo draws</li>
|
||||
<li>
|
||||
<code>conf_interval = 0.95</code>: coverage interval width</li>
|
||||
<li>
|
||||
<code>combine_SI = TRUE</code>: count “I” and “SDD” as
|
||||
susceptible</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="practical-considerations">Practical considerations<a class="anchor" aria-label="anchor" href="#practical-considerations"></a>
|
||||
</h2>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>First isolates only</strong>: always deduplicate using
|
||||
<code><a href="../reference/first_isolate.html">first_isolate()</a></code> before running WISCA. Repeat isolates
|
||||
introduce bias.</li>
|
||||
<li>
|
||||
<strong>Pathogen selection</strong>: consider filtering with
|
||||
<code><a href="../reference/top_n_microorganisms.html">top_n_microorganisms()</a></code>. Including rare contaminants
|
||||
(e.g. CoNS without clinical context) can distort estimates and may
|
||||
artificially lower coverage (Cook <em>et al.</em>, 2022).</li>
|
||||
<li>
|
||||
<strong>Sample size</strong>: coverage estimates become reliable
|
||||
with approximately 100+ isolates. For smaller datasets, consider pooling
|
||||
data from multiple sites, but only after verifying that pathogen
|
||||
distributions are sufficiently similar (Bielicki <em>et al.</em>,
|
||||
2016).</li>
|
||||
<li>
|
||||
<strong>Culture request bias</strong>: WISCA is only as good as the
|
||||
data it is based on. If cultures are selectively requested (e.g. only
|
||||
after treatment failure), the dataset will be biased towards resistant
|
||||
isolates. A robust culture policy is essential for reliable
|
||||
estimates.</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="limitations">Limitations<a class="anchor" aria-label="anchor" href="#limitations"></a>
|
||||
</h2>
|
||||
<ul>
|
||||
<li>It assumes your data are representative of the patient population
|
||||
you are treating</li>
|
||||
<li>No direct adjustment for patient-level covariates, although these
|
||||
can be passed onto the <code>syndromic_group</code> argument for
|
||||
stratification</li>
|
||||
<li>WISCA does not model resistance trends over time; for that, you
|
||||
might want to use <code>tidymodels</code>, for which we <a href="https://amr-for-r.org/articles/AMR_with_tidymodels.html">wrote a
|
||||
basic introduction</a>
|
||||
</li>
|
||||
</ul>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="summary">Summary<a class="anchor" aria-label="anchor" href="#summary"></a>
|
||||
</h2>
|
||||
<p>WISCA enables:</p>
|
||||
<ul>
|
||||
<li>
|
||||
<strong>Empirical regimen comparison</strong>, answering the
|
||||
clinician’s actual question</li>
|
||||
<li>
|
||||
<strong>Syndrome-specific coverage estimation</strong>, stratifiable
|
||||
by any clinical variable</li>
|
||||
<li>
|
||||
<strong>Fully probabilistic interpretation</strong>, with credible
|
||||
intervals that honestly communicate uncertainty</li>
|
||||
</ul>
|
||||
<p>It is available in the <code>AMR</code> package via either:</p>
|
||||
<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">wisca</a></span><span class="op">(</span><span class="va">...</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">...</span>, wisca <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
|
||||
</div>
|
||||
<div class="section level2">
|
||||
<h2 id="references">References<a class="anchor" aria-label="anchor" href="#references"></a>
|
||||
</h2>
|
||||
<ol style="list-style-type: decimal">
|
||||
<li>Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
|
||||
Demonstration of the weighted-incidence syndromic combination
|
||||
antibiogram: an empiric prescribing decision aid. <em>Infect Control
|
||||
Hosp Epidemiol.</em> 2012;33(4):381-388. <a href="https://doi.org/10.1086/664768" class="external-link uri">https://doi.org/10.1086/664768</a>
|
||||
</li>
|
||||
<li>Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
|
||||
Weighted-incidence syndromic combination antibiograms to guide empiric
|
||||
treatment of critical care infections: a retrospective cohort study.
|
||||
<em>Crit Care.</em> 2014;18(3):R112. <a href="https://doi.org/10.1186/cc13901" class="external-link uri">https://doi.org/10.1186/cc13901</a>
|
||||
</li>
|
||||
<li>Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
|
||||
Selecting appropriate empirical antibiotic regimens for paediatric
|
||||
bloodstream infections: application of a Bayesian decision model to
|
||||
local and pooled antimicrobial resistance surveillance data. <em>J
|
||||
Antimicrob Chemother.</em> 2016;71(3):794-802. <a href="https://doi.org/10.1093/jac/dkv397" class="external-link uri">https://doi.org/10.1093/jac/dkv397</a>
|
||||
</li>
|
||||
<li>Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
|
||||
prescribing in pediatric bloodstream infections: a potential application
|
||||
of weighted-incidence syndromic combination antibiograms (WISCA).
|
||||
<em>Expert Rev Anti Infect Ther.</em> 2022;20(3):445-456. <a href="https://doi.org/10.1080/14787210.2021.1967145" class="external-link uri">https://doi.org/10.1080/14787210.2021.1967145</a>
|
||||
</li>
|
||||
</ol>
|
||||
</div>
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
</nav></aside>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
||||
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
|
||||
</div>
|
||||
|
||||
</footer>
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -1,443 +0,0 @@
|
||||
# Estimating Empirical Coverage with WISCA
|
||||
|
||||
## Why WISCA?
|
||||
|
||||
When a clinician starts empirical antimicrobial therapy, the causative
|
||||
pathogen is unknown. The question they need answered is not *“what
|
||||
proportion of* E. coli *is susceptible to ciprofloxacin?“* but rather
|
||||
*“what is the probability that this regimen will adequately cover
|
||||
whatever pathogen turns out to be causing my patient’s infection?”*
|
||||
|
||||
The traditional cumulative antibiogram, as standardised by CLSI M39,
|
||||
cannot answer that question. It presents susceptibility percentages per
|
||||
species per antibiotic, but:
|
||||
|
||||
- **It fragments information by organism.** The clinician must mentally
|
||||
combine susceptibility rates across multiple species, weighting by how
|
||||
often each species causes the syndrome, a calculation nobody does at
|
||||
the bedside.
|
||||
- **It ignores pathogen incidence.** A species that causes 2% of
|
||||
infections is given the same visual weight as one that causes 60%.
|
||||
- **It does not evaluate combination regimens.** Much empirical therapy
|
||||
consists of two or more agents, but the traditional antibiogram only
|
||||
shows monotherapy per organism.
|
||||
- **It provides no measure of uncertainty.** A reported “90%
|
||||
susceptible” based on 50 isolates has a 95% confidence interval of
|
||||
roughly 78-97% (Clopper-Pearson), yet the antibiogram presents it as a
|
||||
point estimate without context.
|
||||
|
||||
**WISCA** (Weighted-Incidence Syndromic Combination Antibiogram)
|
||||
resolves all four limitations. It estimates the probability that a
|
||||
regimen will provide adequate empirical coverage for a given infection
|
||||
syndrome, weighted by local pathogen incidence, with full uncertainty
|
||||
quantification via Bayesian inference.
|
||||
|
||||
The concept was introduced by Hebert *et al.* (2012), who demonstrated
|
||||
that traditional antibiogram susceptibility rates could be misleading:
|
||||
ciprofloxacin appeared 84% effective against *E. coli* in the
|
||||
traditional antibiogram, but WISCA revealed only 62% coverage for UTI
|
||||
and 37% for abdominal infections, because enterococci (intrinsically
|
||||
resistant) and other species contribute substantially to these
|
||||
syndromes. Randhawa *et al.* (2014) showed that WISCA-guided regimen
|
||||
selection could improve time-to-adequate-coverage on the ICU by over
|
||||
40%. Bielicki *et al.* (2016) introduced the Bayesian framework now used
|
||||
in this package, enabling credible intervals and multi-centre pooling.
|
||||
Cook *et al.* (2022) applied it globally across 52 hospitals in 23
|
||||
countries.
|
||||
|
||||
## The idea
|
||||
|
||||
WISCA asks:
|
||||
|
||||
> “What is the **probability** that this regimen **will cover** the
|
||||
> pathogen, given the syndrome?”
|
||||
|
||||
This means combining two quantities:
|
||||
|
||||
- **Pathogen incidence** in the syndrome (how often each species causes
|
||||
it),
|
||||
- **Susceptibility** of each pathogen to the regimen.
|
||||
|
||||
We can write this as:
|
||||
|
||||
``` math
|
||||
\text{Coverage} = \sum_i (\text{Incidence}_i \times \text{Susceptibility}_i)
|
||||
```
|
||||
|
||||
For example, suppose in your hospital:
|
||||
|
||||
- *E. coli* causes 60% of UTIs, and 90% of *E. coli* are susceptible to
|
||||
a drug.
|
||||
- *Klebsiella* causes 40% of UTIs, and 70% of *Klebsiella* are
|
||||
susceptible.
|
||||
|
||||
Then:
|
||||
|
||||
``` math
|
||||
\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82
|
||||
```
|
||||
|
||||
That 82% is a far more clinically meaningful number than the
|
||||
species-level “90% of *E. coli*” and “70% of *Klebsiella*” reported
|
||||
separately in a traditional antibiogram, because it directly answers the
|
||||
question the clinician actually faces.
|
||||
|
||||
But in real data, both incidence and susceptibility are **estimated from
|
||||
finite samples**, so they carry uncertainty. A sample of 50 isolates is
|
||||
not a census. WISCA models this uncertainty **probabilistically**, using
|
||||
conjugate Bayesian distributions.
|
||||
|
||||
## The Bayesian engine
|
||||
|
||||
### Pathogen incidence
|
||||
|
||||
Let:
|
||||
|
||||
- $`K`$ be the number of pathogens,
|
||||
- $`\boldsymbol{\alpha} = (1, 1, \ldots, 1)`$ be a $`\text{Dirichlet}`$
|
||||
prior (uniform, non-informative),
|
||||
- $`\boldsymbol{n} = (n_1, \ldots, n_K)`$ be the observed isolate counts
|
||||
per species.
|
||||
|
||||
Then the posterior incidence is:
|
||||
|
||||
``` math
|
||||
\boldsymbol{p} \sim \text{Dirichlet}(\alpha_1 + n_1, \ldots, \alpha_K + n_K)
|
||||
```
|
||||
|
||||
To simulate from this, we use:
|
||||
|
||||
``` math
|
||||
x_i \sim \text{Gamma}(\alpha_i + n_i,\ 1), \quad p_i = \frac{x_i}{\sum_{j=1}^{K} x_j}
|
||||
```
|
||||
|
||||
The Dirichlet is the conjugate prior for multinomial data. With the
|
||||
non-informative prior $`\text{Dirichlet}(1, 1, \ldots, 1)`$, the
|
||||
posterior is dominated by the data once sample sizes are reasonable.
|
||||
With small samples, the posterior is appropriately more diffuse,
|
||||
reflecting genuine uncertainty, and the resulting credible intervals
|
||||
will be wider.
|
||||
|
||||
### Susceptibility
|
||||
|
||||
Each pathogen-regimen pair has a prior and observed data:
|
||||
|
||||
- Default prior: $`\text{Beta}(0.5, 0.5)`$ (Jeffreys prior)
|
||||
- Intrinsically resistant pairs: $`\text{Beta}(1, 9999)`$, forcing
|
||||
near-zero susceptibility regardless of observed data (based on EUCAST
|
||||
Expected Resistant Phenotypes)
|
||||
- Data: $`S`$ susceptible out of $`N`$ tested
|
||||
|
||||
The $`S`$ category could also include values SDD (susceptible,
|
||||
dose-dependent) and I (intermediate \[CLSI\], or susceptible, increased
|
||||
exposure \[EUCAST\]).
|
||||
|
||||
Then the posterior is:
|
||||
|
||||
``` math
|
||||
\theta \sim \text{Beta}(\alpha_0 + S,\ \beta_0 + N - S)
|
||||
```
|
||||
|
||||
### Final coverage estimate
|
||||
|
||||
Putting it together:
|
||||
|
||||
1. Simulate pathogen incidence:
|
||||
$`\boldsymbol{p} \sim \text{Dirichlet}`$
|
||||
2. Simulate susceptibility:
|
||||
$`\theta_i \sim \text{Beta}(\alpha_0 + S_i,\ \beta_0 + N_i - S_i)`$
|
||||
3. Combine:
|
||||
|
||||
``` math
|
||||
\text{Coverage} = \sum_{i=1}^{K} p_i \cdot \theta_i
|
||||
```
|
||||
|
||||
Repeat this simulation (e.g., 1000 times) and summarise:
|
||||
|
||||
- **Mean** = expected coverage
|
||||
- **Quantiles** = credible interval (95% by default)
|
||||
|
||||
Because each simulation draws from the full posterior, the resulting
|
||||
distribution of coverage estimates naturally captures the joint
|
||||
uncertainty in both pathogen incidence and susceptibility. The credible
|
||||
interval tells you how confident you can be in the coverage estimate,
|
||||
something a traditional antibiogram never provides.
|
||||
|
||||
## When to use WISCA vs. traditional antibiograms
|
||||
|
||||
| Goal | Recommended approach |
|
||||
|---------------------------------------|---------------------------|
|
||||
| Guide empirical therapy decisions | **WISCA** |
|
||||
| Compare regimens for a syndrome | **WISCA** |
|
||||
| Evaluate combination regimens | **WISCA** |
|
||||
| Antimicrobial stewardship (A-team) | **WISCA** |
|
||||
| Track resistance trends per species | Traditional / Combination |
|
||||
| AMR surveillance reporting | Traditional / Syndromic |
|
||||
| Understand species-level epidemiology | Traditional |
|
||||
|
||||
In short: if the end goal involves a *patient* who does not yet have a
|
||||
culture result, WISCA is the appropriate tool. If the end goal is
|
||||
*surveillance* of resistance at the species level, the traditional
|
||||
antibiogram remains fit for purpose.
|
||||
|
||||
## Practical use in the `AMR` package
|
||||
|
||||
### Prepare data
|
||||
|
||||
``` r
|
||||
|
||||
library(AMR)
|
||||
data <- example_isolates
|
||||
|
||||
# Structure of our data
|
||||
data
|
||||
#> # A tibble: 2,000 × 46
|
||||
#> date patient age gender ward mo PEN OXA FLC AMX
|
||||
#> <date> <chr> <dbl> <chr> <chr> <mo> <sir> <sir> <sir> <sir>
|
||||
#> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
|
||||
#> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA
|
||||
#> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
|
||||
#> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R
|
||||
#> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA
|
||||
#> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA
|
||||
#> # ℹ 1,990 more rows
|
||||
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
|
||||
#> # CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
|
||||
#> # TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
|
||||
#> # FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
|
||||
#> # TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
|
||||
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
|
||||
|
||||
# Add a synthetic syndrome column for demonstration
|
||||
data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "Non-UTI")
|
||||
|
||||
# Keep only 10 most common microorganisms
|
||||
data <- top_n_microorganisms(data, n = 10, property = "species")
|
||||
#> ℹ Using column mo as input for `col_mo`.
|
||||
```
|
||||
|
||||
### Basic WISCA
|
||||
|
||||
``` r
|
||||
|
||||
wisca(data,
|
||||
antimicrobials = c("AMC", "CIP", "GEN")
|
||||
)
|
||||
```
|
||||
|
||||
| Amoxicillin/clavulanic acid | Ciprofloxacin | Gentamicin |
|
||||
|:----------------------------|:-------------------|:-----------------|
|
||||
| 76.8% (74.7-79.1%) | 81.5% (78.9-84.1%) | 82.9% (81-84.8%) |
|
||||
|
||||
### Use combination regimens
|
||||
|
||||
Combination regimens are specified with a `+` separator. WISCA evaluates
|
||||
whether *at least one* agent in the combination covers the pathogen:
|
||||
|
||||
``` r
|
||||
|
||||
wisca(data,
|
||||
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN")
|
||||
)
|
||||
```
|
||||
|
||||
| Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|
||||
|:---|:---|:---|
|
||||
| 76.8% (74.6-78.9%) | 89.6% (88-91.1%) | 93.7% (92.5-94.9%) |
|
||||
|
||||
### Stratify by syndrome
|
||||
|
||||
Use `syndromic_group` to produce separate WISCA estimates per clinical
|
||||
stratum. You can pass a column name or any expression:
|
||||
|
||||
``` r
|
||||
|
||||
wisca_out <- wisca(data,
|
||||
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
|
||||
syndromic_group = "syndrome"
|
||||
)
|
||||
wisca_out
|
||||
```
|
||||
|
||||
| Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|
||||
|:---|:---|:---|:---|
|
||||
| Non-UTI | 72.5% (69.9-75.1%) | 86.9% (84.8-89%) | 91.4% (89.5-93%) |
|
||||
| UTI | 86% (82.5-89%) | 94.8% (92.5-96.6%) | 97.9% (96.3-99%) |
|
||||
|
||||
The `AMR` package is available in 28 languages, which can all be used
|
||||
for the [`wisca()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function too:
|
||||
|
||||
``` r
|
||||
|
||||
wisca(data,
|
||||
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
|
||||
syndromic_group = gsub("UTI", "UCI", data$syndrome),
|
||||
language = "Spanish"
|
||||
)
|
||||
```
|
||||
|
||||
| Grupo sindrómico | Amoxicilina/ácido clavulánico | Amoxicilina/ácido clavulánico + Ciprofloxacina | Amoxicilina/ácido clavulánico + Gentamicina |
|
||||
|:---|:---|:---|:---|
|
||||
| Non-UCI | 72.6% (69.9-75.3%) | 87% (84.9-89.1%) | 91.4% (89.7-92.9%) |
|
||||
| UCI | 86% (82.7-89%) | 94.8% (92.7-96.4%) | 97.9% (96.5-99%) |
|
||||
|
||||
### Interpreting the output
|
||||
|
||||
Each row shows the estimated empirical coverage for a regimen, with a
|
||||
95% credible interval. When comparing regimens:
|
||||
|
||||
- **Overlapping credible intervals** mean there is no statistically
|
||||
significant difference in coverage. If a narrower-spectrum regimen
|
||||
overlaps with a broader one, the narrower-spectrum option can be
|
||||
preferred on stewardship grounds.
|
||||
- **Non-overlapping credible intervals** indicate a clinically
|
||||
meaningful difference in coverage.
|
||||
|
||||
### Plotting
|
||||
|
||||
WISCA results can be visualised in several ways. All plot functions work
|
||||
on the output of
|
||||
[`wisca()`](https://amr-for-r.org/reference/antibiogram.md) (or
|
||||
`antibiogram(..., wisca = TRUE)`).
|
||||
|
||||
Below we use the `wisca_out` object that was generated above.
|
||||
|
||||
#### Coverage with credible intervals
|
||||
|
||||
The extended
|
||||
[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
|
||||
method from the `ggplot2()` package produces a point-and-interval plot
|
||||
showing the coverage estimate and 95% credible interval for each
|
||||
regimen, grouped by syndromic stratum. This is the most direct way to
|
||||
compare regimens: overlapping intervals suggest clinical
|
||||
non-inferiority, non-overlapping intervals indicate a meaningful
|
||||
difference.
|
||||
|
||||
``` r
|
||||
|
||||
ggplot2::autoplot(wisca_out)
|
||||
```
|
||||
|
||||
|
||||
|
||||
#### Susceptibility vs. incidence weight
|
||||
|
||||
[`wisca_plot()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
produces a scatter plot of the Monte Carlo simulation draws, showing
|
||||
each pathogen’s susceptibility (x-axis) against its incidence weight
|
||||
(y-axis) for each regimen. Each dot represents one of 1,000 simulated
|
||||
draws, so the spread reflects posterior uncertainty. This plot reveals
|
||||
*why* a regimen achieves its coverage: you can see which pathogens
|
||||
dominate the syndrome (high on the y-axis), how susceptible they are
|
||||
(position on the x-axis), and how uncertain both estimates are (spread
|
||||
of the cloud). The dashed vertical lines denote the point estimates,
|
||||
i.e., the coverage percentages. The ribbon behind the dashed lines
|
||||
denote the credible interval, which is 95% at default.
|
||||
|
||||
``` r
|
||||
|
||||
wisca_plot(wisca_out)
|
||||
```
|
||||
|
||||
|
||||
|
||||
#### Posterior coverage distributions
|
||||
|
||||
Setting `wisca_plot_type = "posterior_coverage"` shows the full
|
||||
posterior distribution of coverage for each regimen as a density curve.
|
||||
This is the most complete representation of what the Bayesian model
|
||||
produces: each curve shows the relative likelihood of each coverage
|
||||
value across all 1,000 simulations. Narrow, tall peaks indicate high
|
||||
certainty; wide, flat curves indicate greater uncertainty. Where two
|
||||
curves overlap, the regimens cannot be confidently distinguished.
|
||||
|
||||
``` r
|
||||
|
||||
wisca_plot(wisca_out, wisca_plot_type = "posterior_coverage")
|
||||
```
|
||||
|
||||
|
||||
|
||||
## Sensible defaults, which can be customised
|
||||
|
||||
- `simulations = 1000`: number of Monte Carlo draws
|
||||
- `conf_interval = 0.95`: coverage interval width
|
||||
- `combine_SI = TRUE`: count “I” and “SDD” as susceptible
|
||||
|
||||
## Practical considerations
|
||||
|
||||
- **First isolates only**: always deduplicate using
|
||||
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
|
||||
before running WISCA. Repeat isolates introduce bias.
|
||||
- **Pathogen selection**: consider filtering with
|
||||
[`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md).
|
||||
Including rare contaminants (e.g. CoNS without clinical context) can
|
||||
distort estimates and may artificially lower coverage (Cook *et al.*,
|
||||
2022).
|
||||
- **Sample size**: coverage estimates become reliable with approximately
|
||||
100+ isolates. For smaller datasets, consider pooling data from
|
||||
multiple sites, but only after verifying that pathogen distributions
|
||||
are sufficiently similar (Bielicki *et al.*, 2016).
|
||||
- **Culture request bias**: WISCA is only as good as the data it is
|
||||
based on. If cultures are selectively requested (e.g. only after
|
||||
treatment failure), the dataset will be biased towards resistant
|
||||
isolates. A robust culture policy is essential for reliable estimates.
|
||||
|
||||
## Limitations
|
||||
|
||||
- It assumes your data are representative of the patient population you
|
||||
are treating
|
||||
- No direct adjustment for patient-level covariates, although these can
|
||||
be passed onto the `syndromic_group` argument for stratification
|
||||
- WISCA does not model resistance trends over time; for that, you might
|
||||
want to use `tidymodels`, for which we [wrote a basic
|
||||
introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html)
|
||||
|
||||
## Summary
|
||||
|
||||
WISCA enables:
|
||||
|
||||
- **Empirical regimen comparison**, answering the clinician’s actual
|
||||
question
|
||||
- **Syndrome-specific coverage estimation**, stratifiable by any
|
||||
clinical variable
|
||||
- **Fully probabilistic interpretation**, with credible intervals that
|
||||
honestly communicate uncertainty
|
||||
|
||||
It is available in the `AMR` package via either:
|
||||
|
||||
``` r
|
||||
|
||||
wisca(...)
|
||||
|
||||
antibiogram(..., wisca = TRUE)
|
||||
```
|
||||
|
||||
## References
|
||||
|
||||
1. Hebert C, Ridgway J, Vekhter B, Brown EC, Weber SG, Robicsek A.
|
||||
Demonstration of the weighted-incidence syndromic combination
|
||||
antibiogram: an empiric prescribing decision aid. *Infect Control
|
||||
Hosp Epidemiol.* 2012;33(4):381-388.
|
||||
<https://doi.org/10.1086/664768>
|
||||
2. Randhawa V, Sarwar S, Walker S, Elligsen M, Palmay L, Daneman N.
|
||||
Weighted-incidence syndromic combination antibiograms to guide
|
||||
empiric treatment of critical care infections: a retrospective
|
||||
cohort study. *Crit Care.* 2014;18(3):R112.
|
||||
<https://doi.org/10.1186/cc13901>
|
||||
3. Bielicki JA, Sharland M, Johnson AP, Henderson KL, Cromwell DA.
|
||||
Selecting appropriate empirical antibiotic regimens for paediatric
|
||||
bloodstream infections: application of a Bayesian decision model to
|
||||
local and pooled antimicrobial resistance surveillance data. *J
|
||||
Antimicrob Chemother.* 2016;71(3):794-802.
|
||||
<https://doi.org/10.1093/jac/dkv397>
|
||||
4. Cook A, Sharland M, Yau Y, Bielicki J. Improving empiric antibiotic
|
||||
prescribing in pediatric bloodstream infections: a potential
|
||||
application of weighted-incidence syndromic combination antibiograms
|
||||
(WISCA). *Expert Rev Anti Infect Ther.* 2022;20(3):445-456.
|
||||
<https://doi.org/10.1080/14787210.2021.1967145>
|
||||
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|
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|
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|
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@@ -1,565 +0,0 @@
|
||||
# Download data sets for download / own use
|
||||
|
||||
All reference data (about microorganisms, antimicrobials, SIR
|
||||
interpretation, EUCAST rules, etc.) in this `AMR` package are reliable,
|
||||
up-to-date and freely available. We continually export our data sets to
|
||||
formats for use in R, MS Excel, Apache Feather, Apache Parquet, SPSS,
|
||||
and Stata. We also provide tab-separated text files that are
|
||||
machine-readable and suitable for input in any software program, such as
|
||||
laboratory information systems.
|
||||
|
||||
> If you are working in Python, be sure to use our [AMR for
|
||||
> Python](https://amr-for-r.org/articles/AMR_for_Python.html) package.
|
||||
> It allows all relevant AMR data sets to be natively available in
|
||||
> Python.
|
||||
|
||||
## `microorganisms`: Full Microbial Taxonomy
|
||||
|
||||
A data set with 96 982 rows and 28 columns, containing the following
|
||||
column names:
|
||||
*mo*, *fullname*, *status*, *domain*, *kingdom*, *phylum*, *class*,
|
||||
*order*, *family*, *genus*, *species*, *subspecies*, *rank*, *ref*,
|
||||
*oxygen_tolerance*, *morphology*, *source*, *lpsn*, *lpsn_parent*,
|
||||
*lpsn_renamed_to*, *mycobank*, *mycobank_parent*, *mycobank_renamed_to*,
|
||||
*gbif*, *gbif_parent*, *gbif_renamed_to*, *prevalence*, and *snomed*.
|
||||
|
||||
This data set is in R available as `microorganisms`, after you load the
|
||||
`AMR` package.
|
||||
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/microorganisms.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.rds)
|
||||
(2.2 MB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt)
|
||||
(23.1 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx)
|
||||
(11.4 MB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather)
|
||||
(11 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet)
|
||||
(4.6 MB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav)
|
||||
(35.2 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta)
|
||||
(96.6 MB)
|
||||
|
||||
**NOTE: The exported files for SPSS and Stata contain only the first 50
|
||||
SNOMED codes per record, as their file size would otherwise exceed 100
|
||||
MB; the file size limit of GitHub.** Their file structures and
|
||||
compression techniques are very inefficient. Advice? Use R instead. It’s
|
||||
free and much better in many ways.
|
||||
|
||||
The tab-separated text file and Microsoft Excel workbook both contain
|
||||
all SNOMED codes as comma separated values.
|
||||
|
||||
**Example content**
|
||||
|
||||
Included (sub)species per taxonomic kingdom:
|
||||
|
||||
| Kingdom | Number of (sub)species |
|
||||
|:-----------------:|:----------------------:|
|
||||
| | 20 |
|
||||
| (unknown kingdom) | 8 |
|
||||
| Animalia | 2 015 |
|
||||
| Archaea | 150 |
|
||||
| Bacillati | 24 200 |
|
||||
| Bacteria | 2 |
|
||||
|
||||
First 6 rows when filtering on genus *Escherichia*:
|
||||
|
||||
| mo | fullname | status | domain | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | morphology | source | lpsn | lpsn_parent | lpsn_renamed_to | mycobank | mycobank_parent | mycobank_renamed_to | gbif | gbif_parent | gbif_renamed_to | prevalence | snomed |
|
||||
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|
||||
| B_ESCHR | Escherichia | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | | | genus | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 515602 | 482 | | | | | CS33H | CRYWR | | 1 | 407310004, 407251000, 407281008, … |
|
||||
| B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata | | species | Leclerc, 1962 | likely facultative anaerobe | rods | LPSN | 776052 | 515602 | 777447 | | | | CS33J | CS33H | 3SVX6 | 1 | |
|
||||
| B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii | | species | Huys et al., 2003 | facultative anaerobe | rods | LPSN | 776053 | 515602 | | | | | 3BGTB | CS33H | | 1 | 419388003 |
|
||||
| B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae | | species | Burgess et al., 1973 | likely facultative anaerobe | rods | LPSN | 776056 | 515602 | 788468 | | | | CS33K | CS33H | 4X4P7 | 1 | |
|
||||
| B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | | species | Castellani et al., 1919 | facultative anaerobe | rods | LPSN | 776057 | 515602 | | | | | NT3L7 | CS33H | | 1 | 1095001000112106, 715307006, 737528008, … |
|
||||
| B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadati | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies | | | | GBIF | | 776057 | | | | | 12233256 | NT3L7 | | 1 | |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `antimicrobials`: Antibiotic and Antifungal Drugs
|
||||
|
||||
A data set with 505 rows and 14 columns, containing the following column
|
||||
names:
|
||||
*ab*, *cid*, *name*, *group*, *atc*, *atc_group1*, *atc_group2*,
|
||||
*abbreviations*, *synonyms*, *oral_ddd*, *oral_units*, *iv_ddd*,
|
||||
*iv_units*, and *loinc*.
|
||||
|
||||
This data set is in R available as `antimicrobials`, after you load the
|
||||
`AMR` package.
|
||||
|
||||
It was last updated on 23 June 2026 12:38:59 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/antimicrobials.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.rds)
|
||||
(44 kB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.txt)
|
||||
(0.1 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.xlsx)
|
||||
(79 kB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.feather)
|
||||
(0.1 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.parquet)
|
||||
(94 kB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.sav)
|
||||
(0.4 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.dta)
|
||||
(10 kB)
|
||||
|
||||
The tab-separated text, Microsoft Excel, SPSS, and Stata files all
|
||||
contain the ATC codes, common abbreviations, trade names and LOINC codes
|
||||
as comma separated values.
|
||||
|
||||
**Example content**
|
||||
|
||||
| ab | cid | name | group | atc | atc_group1 | atc_group2 | abbreviations | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
|
||||
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|
||||
| AMK | 37768 | Amikacin | Aminoglycosides | D06AX12, J01GB06, QD06AX12, … | Aminoglycoside antibacterials | Other aminoglycosides | ak, ami, amik, … | amikacillin, amikacina, amikacine, … | | | 1.0 | g | 101493-5, 11-7, 12-5, … |
|
||||
| AMX | 33613 | Amoxicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA04, QG51AA03, QJ01CA04 | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | ac, amox, amoxic, … | acuotricina, alfamox, alfida, … | 1.5 | g | 3.0 | g | 101498-4, 15-8, 16-6, … |
|
||||
| AMC | 23665637 | Amoxicillin/clavulanic acid | Aminopenicillins, Penicillins, Beta-lactams, … | J01CR02, QJ01CR02 | Beta-lactam antibacterials, penicillins | Combinations of penicillins, incl. beta-lactamase inhibitors | a/c, amcl, aml, … | amocla, amoclan, amoclav, … | 1.5 | g | 3.0 | g | |
|
||||
| AMP | 6249 | Ampicillin | Aminopenicillins, Penicillins, Beta-lactams | J01CA01, QJ01CA01, QJ51CA01, … | Beta-lactam antibacterials, penicillins | Penicillins with extended spectrum | am, amp, amp100, … | adobacillin, alpen, amblosin, … | 2.0 | g | 6.0 | g | 101477-8, 101478-6, 18864-9, … |
|
||||
| AZM | 447043 | Azithromycin | Macrolides | J01FA10, QJ01FA10, QS01AA26, … | Macrolides, lincosamides and streptogramins | Macrolides | az, azi, azit, … | aritromicina, aruzilina, azasite, … | 0.3 | g | 0.5 | g | 100043-9, 16420-2, 16421-0, … |
|
||||
| PEN | 5904 | Benzylpenicillin | Penicillins, Beta-lactams | J01CE01, QJ01CE01, QJ51CE01, … | Combinations of antibacterials | Combinations of antibacterials | bepe, pen, peni, … | bencilpenicilina, benzopenicillin, benzylpenicilline, … | | | 3.6 | g | |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `clinical_breakpoints`: Interpretation from MIC values & disk diameters to SIR
|
||||
|
||||
A data set with 45 555 rows and 14 columns, containing the following
|
||||
column names:
|
||||
*guideline*, *type*, *host*, *method*, *site*, *mo*, *rank_index*, *ab*,
|
||||
*ref_tbl*, *disk_dose*, *breakpoint_S*, *breakpoint_R*, *uti*, and
|
||||
*is_SDD*.
|
||||
|
||||
This data set is in R available as `clinical_breakpoints`, after you
|
||||
load the `AMR` package.
|
||||
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/clinical_breakpoints.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.rds)
|
||||
(92 kB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.txt)
|
||||
(4.2 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.xlsx)
|
||||
(2.7 MB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.feather)
|
||||
(2 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet)
|
||||
(0.1 MB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav)
|
||||
(7.5 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.dta)
|
||||
(12.6 MB)
|
||||
|
||||
**Example content**
|
||||
|
||||
| guideline | type | host | method | site | mo | mo_name | rank_index | ab | ab_name | ref_tbl | disk_dose | breakpoint_S | breakpoint_R | uti | is_SDD |
|
||||
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|
||||
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | 10 mcg | 26.000 | 20.000 | FALSE | FALSE |
|
||||
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | MEM | Meropenem | A. xylosoxidans | | 1.000 | 4.000 | FALSE | FALSE |
|
||||
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | 1.25/23.75 mcg | 26.000 | 26.000 | FALSE | FALSE |
|
||||
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | SXT | Trimethoprim/sulfamethoxazole | A. xylosoxidans | | 0.125 | 0.125 | FALSE | FALSE |
|
||||
| EUCAST 2026 | human | human | DISK | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | 30/6 mcg | 26.000 | 26.000 | FALSE | FALSE |
|
||||
| EUCAST 2026 | human | human | MIC | | B_ACHRMB_XYLS | Achromobacter xylosoxidans | 2 | TZP | Piperacillin/tazobactam | A. xylosoxidans | | 4.000 | 4.000 | FALSE | FALSE |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `microorganisms.groups`: Species Groups and Microbiological Complexes
|
||||
|
||||
A data set with 530 rows and 4 columns, containing the following column
|
||||
names:
|
||||
*mo_group*, *mo*, *mo_group_name*, and *mo_name*.
|
||||
|
||||
This data set is in R available as `microorganisms.groups`, after you
|
||||
load the `AMR` package.
|
||||
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/microorganisms.groups.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.rds)
|
||||
(6 kB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.txt)
|
||||
(50 kB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.xlsx)
|
||||
(19 kB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.feather)
|
||||
(19 kB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.parquet)
|
||||
(13 kB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.sav)
|
||||
(64 kB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta)
|
||||
(82 kB)
|
||||
|
||||
**Example content**
|
||||
|
||||
| mo_group | mo | mo_group_name | mo_name |
|
||||
|:--:|:--:|:--:|:--:|
|
||||
| B_ACNTB_BMNN-C | B_ACNTB_BMNN | Acinetobacter baumannii complex | Acinetobacter baumannii |
|
||||
| B_ACNTB_BMNN-C | B_ACNTB_CLCC | Acinetobacter baumannii complex | Acinetobacter calcoaceticus |
|
||||
| B_ACNTB_BMNN-C | B_ACNTB_LCTC | Acinetobacter baumannii complex | Acinetobacter dijkshoorniae |
|
||||
| B_ACNTB_BMNN-C | B_ACNTB_NSCM | Acinetobacter baumannii complex | Acinetobacter nosocomialis |
|
||||
| B_ACNTB_BMNN-C | B_ACNTB_PITT | Acinetobacter baumannii complex | Acinetobacter pittii |
|
||||
| B_ACNTB_BMNN-C | B_ACNTB_SFRT | Acinetobacter baumannii complex | Acinetobacter seifertii |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `intrinsic_resistant`: Intrinsic Bacterial Resistance
|
||||
|
||||
A data set with 294 079 rows and 2 columns, containing the following
|
||||
column names:
|
||||
*mo* and *ab*.
|
||||
|
||||
This data set is in R available as `intrinsic_resistant`, after you load
|
||||
the `AMR` package.
|
||||
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/intrinsic_resistant.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.rds)
|
||||
(0.1 MB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.txt)
|
||||
(10.9 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx)
|
||||
(3.1 MB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather)
|
||||
(2.5 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.parquet)
|
||||
(0.3 MB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.sav)
|
||||
(16 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta)
|
||||
(28.6 MB)
|
||||
|
||||
**Example content**
|
||||
|
||||
Example rows when filtering on *Enterobacter cloacae*:
|
||||
|
||||
| microorganism | antibiotic |
|
||||
|:--------------------:|:---------------------------:|
|
||||
| Enterobacter cloacae | Acetylmidecamycin |
|
||||
| Enterobacter cloacae | Acetylspiramycin |
|
||||
| Enterobacter cloacae | Amoxicillin |
|
||||
| Enterobacter cloacae | Amoxicillin/clavulanic acid |
|
||||
| Enterobacter cloacae | Ampicillin |
|
||||
| Enterobacter cloacae | Ampicillin/sulbactam |
|
||||
| Enterobacter cloacae | Avoparcin |
|
||||
| Enterobacter cloacae | Azithromycin |
|
||||
| Enterobacter cloacae | Benzylpenicillin |
|
||||
| Enterobacter cloacae | Bleomycin |
|
||||
| Enterobacter cloacae | Cadazolid |
|
||||
| Enterobacter cloacae | Cefadroxil |
|
||||
| Enterobacter cloacae | Cefalexin |
|
||||
| Enterobacter cloacae | Cefalotin |
|
||||
| Enterobacter cloacae | Cefazolin |
|
||||
| Enterobacter cloacae | Cefoxitin |
|
||||
| Enterobacter cloacae | Clarithromycin |
|
||||
| Enterobacter cloacae | Clindamycin |
|
||||
| Enterobacter cloacae | Cycloserine |
|
||||
| Enterobacter cloacae | Dalbavancin |
|
||||
| Enterobacter cloacae | Dirithromycin |
|
||||
| Enterobacter cloacae | Erythromycin |
|
||||
| Enterobacter cloacae | Flurithromycin |
|
||||
| Enterobacter cloacae | Fusidic acid |
|
||||
| Enterobacter cloacae | Gamithromycin |
|
||||
| Enterobacter cloacae | Josamycin |
|
||||
| Enterobacter cloacae | Kitasamycin |
|
||||
| Enterobacter cloacae | Lincomycin |
|
||||
| Enterobacter cloacae | Linezolid |
|
||||
| Enterobacter cloacae | Meleumycin |
|
||||
| Enterobacter cloacae | Midecamycin |
|
||||
| Enterobacter cloacae | Miocamycin |
|
||||
| Enterobacter cloacae | Nafithromycin |
|
||||
| Enterobacter cloacae | Norvancomycin |
|
||||
| Enterobacter cloacae | Oleandomycin |
|
||||
| Enterobacter cloacae | Oritavancin |
|
||||
| Enterobacter cloacae | Ostreogrycin |
|
||||
| Enterobacter cloacae | Pirlimycin |
|
||||
| Enterobacter cloacae | Primycin |
|
||||
| Enterobacter cloacae | Pristinamycin |
|
||||
| Enterobacter cloacae | Quinupristin/dalfopristin |
|
||||
| Enterobacter cloacae | Ramoplanin |
|
||||
| Enterobacter cloacae | Rifampicin |
|
||||
| Enterobacter cloacae | Rokitamycin |
|
||||
| Enterobacter cloacae | Roxithromycin |
|
||||
| Enterobacter cloacae | Solithromycin |
|
||||
| Enterobacter cloacae | Spiramycin |
|
||||
| Enterobacter cloacae | Tedizolid |
|
||||
| Enterobacter cloacae | Teicoplanin |
|
||||
| Enterobacter cloacae | Telavancin |
|
||||
| Enterobacter cloacae | Telithromycin |
|
||||
| Enterobacter cloacae | Thiacetazone |
|
||||
| Enterobacter cloacae | Tildipirosin |
|
||||
| Enterobacter cloacae | Tilmicosin |
|
||||
| Enterobacter cloacae | Troleandomycin |
|
||||
| Enterobacter cloacae | Tulathromycin |
|
||||
| Enterobacter cloacae | Tylosin |
|
||||
| Enterobacter cloacae | Tylvalosin |
|
||||
| Enterobacter cloacae | Vancomycin |
|
||||
| Enterobacter cloacae | Virginiamycine |
|
||||
| Enterobacter cloacae | Zorbamycin |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `dosage`: Dosage Guidelines from EUCAST
|
||||
|
||||
A data set with 759 rows and 9 columns, containing the following column
|
||||
names:
|
||||
*ab*, *name*, *type*, *dose*, *dose_times*, *administration*, *notes*,
|
||||
*original_txt*, and *eucast_version*.
|
||||
|
||||
This data set is in R available as `dosage`, after you load the `AMR`
|
||||
package.
|
||||
|
||||
It was last updated on 20 April 2025 10:55:31 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/dosage.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.rds)
|
||||
(4 kB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.txt)
|
||||
(66 kB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.xlsx)
|
||||
(37 kB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.feather)
|
||||
(28 kB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.parquet)
|
||||
(9 kB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.sav)
|
||||
(97 kB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/dosage.dta)
|
||||
(0.2 MB)
|
||||
|
||||
**Example content**
|
||||
|
||||
| ab | name | type | dose | dose_times | administration | notes | original_txt | eucast_version |
|
||||
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|
||||
| AMK | Amikacin | standard_dosage | 25-30 mg/kg | 1 | iv | | 25-30 mg/kg x 1 iv | 15 |
|
||||
| AMX | Amoxicillin | high_dosage | 2 g | 6 | iv | | 2 g x 6 iv | 15 |
|
||||
| AMX | Amoxicillin | standard_dosage | 1 g | 3 | iv | | 1 g x 3-4 iv | 15 |
|
||||
| AMX | Amoxicillin | high_dosage | 0.75-1 g | 3 | oral | | 0.75-1 g x 3 oral | 15 |
|
||||
| AMX | Amoxicillin | standard_dosage | 0.5 g | 3 | oral | | 0.5 g x 3 oral | 15 |
|
||||
| AMX | Amoxicillin | uncomplicated_uti | 0.5 g | 3 | oral | | 0.5 g x 3 oral | 15 |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `example_isolates`: Example Data for Practice
|
||||
|
||||
A data set with 2 000 rows and 46 columns, containing the following
|
||||
column names:
|
||||
*date*, *patient*, *age*, *gender*, *ward*, *mo*, *PEN*, *OXA*, *FLC*,
|
||||
*AMX*, *AMC*, *AMP*, *TZP*, *CZO*, *FEP*, *CXM*, *FOX*, *CTX*, *CAZ*,
|
||||
*CRO*, *GEN*, *TOB*, *AMK*, *KAN*, *TMP*, *SXT*, *NIT*, *FOS*, *LNZ*,
|
||||
*CIP*, *MFX*, *VAN*, *TEC*, *TCY*, *TGC*, *DOX*, *ERY*, *CLI*, *AZM*,
|
||||
*IPM*, *MEM*, *MTR*, *CHL*, *COL*, *MUP*, and *RIF*.
|
||||
|
||||
This data set is in R available as `example_isolates`, after you load
|
||||
the `AMR` package.
|
||||
|
||||
It was last updated on 24 June 2026 16:36:47 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/example_isolates.html).
|
||||
|
||||
**Example content**
|
||||
|
||||
| date | patient | age | gender | ward | mo | PEN | OXA | FLC | AMX | AMC | AMP | TZP | CZO | FEP | CXM | FOX | CTX | CAZ | CRO | GEN | TOB | AMK | KAN | TMP | SXT | NIT | FOS | LNZ | CIP | MFX | VAN | TEC | TCY | TGC | DOX | ERY | CLI | AZM | IPM | MEM | MTR | CHL | COL | MUP | RIF |
|
||||
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|
||||
| 2002-01-02 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R | | | | I | | | | | I | | | | | | | | | R | R | | | R | | | R | R | R | | | R | R | R | | | | | | | R |
|
||||
| 2002-01-03 | A77334 | 65 | F | Clinical | B_ESCHR_COLI | R | | | | I | | | | | I | | | | | | | | | R | R | | | R | | | R | R | R | | | R | R | R | | | | | | | R |
|
||||
| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | S | S | | | | | | S | | S | S | S | R | | R | | | | | R | | |
|
||||
| 2002-01-07 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | S | S | | | | | | S | | S | S | S | R | | R | | | | | R | | |
|
||||
| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | R | | | | | | | S | | S | S | S | R | | R | | | | | R | | |
|
||||
| 2002-01-13 | 067927 | 45 | F | ICU | B_STPHY_EPDR | R | | R | | | | | | | R | | | R | | | | | | R | | | | | | | S | | S | S | S | R | R | R | | | | | R | | |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `example_isolates_unclean`: Example Data for Practice
|
||||
|
||||
A data set with 3 000 rows and 8 columns, containing the following
|
||||
column names:
|
||||
*patient_id*, *hospital*, *date*, *bacteria*, *AMX*, *AMC*, *CIP*, and
|
||||
*GEN*.
|
||||
|
||||
This data set is in R available as `example_isolates_unclean`, after you
|
||||
load the `AMR` package.
|
||||
|
||||
It was last updated on 27 August 2022 18:49:37 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/example_isolates_unclean.html).
|
||||
|
||||
**Example content**
|
||||
|
||||
| patient_id | hospital | date | bacteria | AMX | AMC | CIP | GEN |
|
||||
|:----------:|:--------:|:----------:|:-------------:|:---:|:---:|:---:|:---:|
|
||||
| J3 | A | 2012-11-21 | E. coli | R | I | S | S |
|
||||
| R7 | A | 2018-04-03 | K. pneumoniae | R | I | S | S |
|
||||
| P3 | A | 2014-09-19 | E. coli | R | S | S | S |
|
||||
| P10 | A | 2015-12-10 | E. coli | S | I | S | S |
|
||||
| B7 | A | 2015-03-02 | E. coli | S | S | S | S |
|
||||
| W3 | A | 2018-03-31 | S. aureus | R | S | R | S |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `microorganisms.codes`: Common Laboratory Codes
|
||||
|
||||
A data set with 6 029 rows and 2 columns, containing the following
|
||||
column names:
|
||||
*code* and *mo*.
|
||||
|
||||
This data set is in R available as `microorganisms.codes`, after you
|
||||
load the `AMR` package.
|
||||
|
||||
It was last updated on 22 June 2026 23:38:13 UTC. Find more info about
|
||||
the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/microorganisms.codes.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.rds)
|
||||
(27 kB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.txt)
|
||||
(0.1 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.xlsx)
|
||||
(98 kB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.feather)
|
||||
(0.1 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.parquet)
|
||||
(68 kB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.sav)
|
||||
(0.2 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.dta)
|
||||
(0.2 MB)
|
||||
|
||||
**Example content**
|
||||
|
||||
| code | mo |
|
||||
|:----:|:------------:|
|
||||
| 1011 | B_GRAMP |
|
||||
| 1012 | B_GRAMP |
|
||||
| 1013 | B_GRAMN |
|
||||
| 1014 | B_GRAMN |
|
||||
| 1015 | F_YEAST |
|
||||
| 103 | B_ESCHR_COLI |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `antivirals`: Antiviral Drugs
|
||||
|
||||
A data set with 120 rows and 11 columns, containing the following column
|
||||
names:
|
||||
*av*, *name*, *atc*, *cid*, *atc_group*, *synonyms*, *oral_ddd*,
|
||||
*oral_units*, *iv_ddd*, *iv_units*, and *loinc*.
|
||||
|
||||
This data set is in R available as `antivirals`, after you load the
|
||||
`AMR` package.
|
||||
|
||||
It was last updated on 20 October 2023 12:51:48 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this [data set
|
||||
here](https://amr-for-r.org/reference/antimicrobials.html).
|
||||
|
||||
**Direct download links:**
|
||||
|
||||
- Download as [original R Data Structure (RDS)
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.rds)
|
||||
(6 kB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.txt)
|
||||
(17 kB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.xlsx)
|
||||
(16 kB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.feather)
|
||||
(16 kB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.parquet)
|
||||
(13 kB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.sav)
|
||||
(32 kB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/antivirals.dta)
|
||||
(78 kB)
|
||||
|
||||
The tab-separated text, Microsoft Excel, SPSS, and Stata files all
|
||||
contain the trade names and LOINC codes as comma separated values.
|
||||
|
||||
**Example content**
|
||||
|
||||
| av | name | atc | cid | atc_group | synonyms | oral_ddd | oral_units | iv_ddd | iv_units | loinc |
|
||||
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
|
||||
| ABA | Abacavir | J05AF06 | 441300 | Nucleoside and nucleotide reverse transcriptase inhibitors | abacavir sulfate, avacavir, ziagen | 0.6 | g | | | 29113-8, 30273-7, 30287-7, … |
|
||||
| ACI | Aciclovir | J05AB01 | 135398513 | Nucleosides and nucleotides excl. reverse transcriptase inhibitors | acicloftal, aciclovier, aciclovirum, … | 4.0 | g | 4 | g | |
|
||||
| ADD | Adefovir dipivoxil | J05AF08 | 60871 | Nucleoside and nucleotide reverse transcriptase inhibitors | adefovir di, adefovir di ester, adefovir dipivoxyl, … | 10.0 | mg | | | |
|
||||
| AME | Amenamevir | J05AX26 | 11397521 | Other antivirals | amenalief | 0.4 | g | | | |
|
||||
| AMP | Amprenavir | J05AE05 | 65016 | Protease inhibitors | agenerase, carbamate, prozei | 1.2 | g | | | 29114-6, 30296-8, 30297-6, … |
|
||||
| ASU | Asunaprevir | J05AP06 | 16076883 | Antivirals for treatment of HCV infections | sunvepra, sunvepratrade | 0.2 | g | | | |
|
||||
@@ -1,89 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Articles • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="../favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="../favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="../apple-touch-icon.png"><link rel="icon" sizes="any" href="../favicon.ico"><link rel="manifest" href="../site.webmanifest"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="../deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="../deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="../deps/headroom-0.11.0/headroom.min.js"></script><script src="../deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="../deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="../deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="../deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="../deps/search-1.0.0/fuse.min.js"></script><script src="../deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><script src="../extra.js"></script><meta property="og:title" content="Articles"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
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<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
|
||||
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9065</small>
|
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||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
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<span class="navbar-toggler-icon"></span>
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</button>
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<div id="navbar" class="collapse navbar-collapse ms-3">
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<ul class="navbar-nav me-auto"><li class="nav-item dropdown">
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<button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
|
||||
<ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
|
||||
<li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
|
||||
<li><a class="dropdown-item" href="../articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
|
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<li><a class="dropdown-item" href="../articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
|
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<li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
|
||||
<li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
|
||||
<li><a class="dropdown-item" href="../reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
|
||||
<li><a class="dropdown-item" href="../articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
|
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<li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
|
||||
<li><a class="dropdown-item" href="../reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
|
||||
<li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
|
||||
<li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
|
||||
</ul></li>
|
||||
<li class="nav-item"><a class="nav-link" href="../articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
|
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<li class="nav-item"><a class="nav-link" href="../reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
|
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<li class="nav-item"><a class="nav-link" href="../authors.html"><span class="fa fa-users"></span> Authors</a></li>
|
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</ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
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<input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="../search.json"></form></li>
|
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<li class="nav-item"><a class="nav-link" href="../news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
|
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<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
|
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</ul></div>
|
||||
|
||||
|
||||
</div>
|
||||
</nav><div class="container template-article-index">
|
||||
<div class="row">
|
||||
<main id="main" class="col-md-9"><div class="page-header">
|
||||
<img src="../logo.svg" class="logo" alt=""><h1>Articles</h1>
|
||||
</div>
|
||||
|
||||
<div class="section ">
|
||||
<h3>All vignettes</h3>
|
||||
<div class="section-desc"></div>
|
||||
|
||||
<dl><dt><a href="AMR_for_Python.html">AMR for Python</a></dt>
|
||||
<dd>
|
||||
</dd><dt><a href="AMR_with_tidymodels.html">AMR with tidymodels</a></dt>
|
||||
<dd>
|
||||
</dd><dt><a href="AMR.html">Conduct AMR data analysis</a></dt>
|
||||
<dd>
|
||||
</dd><dt><a href="datasets.html">Download data sets for download / own use</a></dt>
|
||||
<dd>
|
||||
</dd><dt><a href="EUCAST.html">Apply EUCAST rules</a></dt>
|
||||
<dd>
|
||||
</dd><dt><a href="PCA.html">Conduct principal component analysis (PCA) for AMR</a></dt>
|
||||
<dd>
|
||||
</dd><dt><a href="WHONET.html">Work with WHONET data</a></dt>
|
||||
<dd>
|
||||
</dd><dt><a href="WISCA.html">Estimating Empirical Coverage with WISCA</a></dt>
|
||||
<dd>
|
||||
</dd></dl></div>
|
||||
</main></div>
|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
||||
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
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</div>
|
||||
|
||||
</footer></div>
|
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||||
|
||||
|
||||
|
||||
|
||||
</body></html>
|
||||
|
||||
@@ -1,16 +0,0 @@
|
||||
# Articles
|
||||
|
||||
### All vignettes
|
||||
|
||||
- [AMR for Python](https://amr-for-r.org/articles/AMR_for_Python.md):
|
||||
- [AMR with
|
||||
tidymodels](https://amr-for-r.org/articles/AMR_with_tidymodels.md):
|
||||
- [Conduct AMR data analysis](https://amr-for-r.org/articles/AMR.md):
|
||||
- [Download data sets for download / own
|
||||
use](https://amr-for-r.org/articles/datasets.md):
|
||||
- [Apply EUCAST rules](https://amr-for-r.org/articles/EUCAST.md):
|
||||
- [Conduct principal component analysis (PCA) for
|
||||
AMR](https://amr-for-r.org/articles/PCA.md):
|
||||
- [Work with WHONET data](https://amr-for-r.org/articles/WHONET.md):
|
||||
- [Estimating Empirical Coverage with
|
||||
WISCA](https://amr-for-r.org/articles/WISCA.md):
|
||||
-215
@@ -1,215 +0,0 @@
|
||||
<!DOCTYPE html>
|
||||
<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Authors and Citation • AMR (for R)</title><!-- favicons --><link rel="icon" type="image/png" sizes="96x96" href="favicon-96x96.png"><link rel="icon" type="”image/svg+xml”" href="favicon.svg"><link rel="apple-touch-icon" sizes="180x180" href="apple-touch-icon.png"><link rel="icon" sizes="any" href="favicon.ico"><link rel="manifest" href="site.webmanifest"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.8/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.8/bootstrap.bundle.min.js"></script><link href="deps/Lato-0.4.10/font.css" rel="stylesheet"><link href="deps/Fira_Code-0.4.10/font.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/all.min.css" rel="stylesheet"><link href="deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet"><script src="deps/headroom-0.11.0/headroom.min.js"></script><script src="deps/headroom-0.11.0/jQuery.headroom.min.js"></script><script src="deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js"></script><script src="deps/clipboard.js-2.0.11/clipboard.min.js"></script><script src="deps/search-1.0.0/autocomplete.jquery.min.js"></script><script src="deps/search-1.0.0/fuse.min.js"></script><script src="deps/search-1.0.0/mark.min.js"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><script src="extra.js"></script><meta property="og:title" content="Authors and Citation"><meta property="og:image" content="https://amr-for-r.org/logo.svg"><link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.css" integrity="sha384-nB0miv6/jRmo5UMMR1wu3Gz6NLsoTkbqJghGIsx//Rlm+ZU03BU6SQNC66uf4l5+" crossorigin="anonymous"><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/katex.min.js" integrity="sha384-7zkQWkzuo3B5mTepMUcHkMB5jZaolc2xDwL6VFqjFALcbeS9Ggm/Yr2r3Dy4lfFg" crossorigin="anonymous"></script><script defer src="https://cdn.jsdelivr.net/npm/katex@0.16.11/dist/contrib/auto-render.min.js" integrity="sha384-43gviWU0YVjaDtb/GhzOouOXtZMP/7XUzwPTstBeZFe/+rCMvRwr4yROQP43s0Xk" crossorigin="anonymous" onload="renderMathInElement(document.body);"></script></head><body>
|
||||
<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
|
||||
|
||||
|
||||
<nav class="navbar navbar-expand-lg fixed-top bg-primary" data-bs-theme="dark" aria-label="Site navigation"><div class="container">
|
||||
|
||||
<a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
|
||||
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9065</small>
|
||||
|
||||
|
||||
<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
|
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<span class="navbar-toggler-icon"></span>
|
||||
</button>
|
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|
||||
<div id="navbar" class="collapse navbar-collapse ms-3">
|
||||
<ul class="navbar-nav me-auto"><li class="nav-item dropdown">
|
||||
<button class="nav-link dropdown-toggle" type="button" id="dropdown-how-to" data-bs-toggle="dropdown" aria-expanded="false" aria-haspopup="true"><span class="fa fa-question-circle"></span> How to</button>
|
||||
<ul class="dropdown-menu" aria-labelledby="dropdown-how-to"><li><a class="dropdown-item" href="articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
|
||||
<li><a class="dropdown-item" href="reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
|
||||
<li><a class="dropdown-item" href="articles/AMR_with_tidymodels.html"><span class="fa fa-square-root-variable"></span> Use AMR for Predictive Modelling (tidymodels)</a></li>
|
||||
<li><a class="dropdown-item" href="articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
|
||||
<li><a class="dropdown-item" href="reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
|
||||
<li><a class="dropdown-item" href="articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
|
||||
<li><a class="dropdown-item" href="reference/mdro.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
|
||||
<li><a class="dropdown-item" href="articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
|
||||
<li><a class="dropdown-item" href="articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply EUCAST Rules</a></li>
|
||||
<li><a class="dropdown-item" href="reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
|
||||
<li><a class="dropdown-item" href="reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
|
||||
<li><a class="dropdown-item" href="reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
|
||||
</ul></li>
|
||||
<li class="nav-item"><a class="nav-link" href="articles/AMR_for_Python.html"><span class="fa fab fa-python"></span> AMR for Python</a></li>
|
||||
<li class="nav-item"><a class="nav-link" href="reference/index.html"><span class="fa fa-book-open"></span> Manual</a></li>
|
||||
<li class="active nav-item"><a class="nav-link" href="authors.html"><span class="fa fa-users"></span> Authors</a></li>
|
||||
</ul><ul class="navbar-nav"><li class="nav-item"><form class="form-inline" role="search">
|
||||
<input class="form-control" type="search" name="search-input" id="search-input" autocomplete="off" aria-label="Search site" placeholder="Search for" data-search-index="search.json"></form></li>
|
||||
<li class="nav-item"><a class="nav-link" href="news/index.html"><span class="fa fa-newspaper"></span> Changelog</a></li>
|
||||
<li class="nav-item"><a class="external-link nav-link" href="https://github.com/msberends/AMR"><span class="fa fa-github"></span> Source Code</a></li>
|
||||
</ul></div>
|
||||
|
||||
|
||||
</div>
|
||||
</nav><div class="container template-citation-authors">
|
||||
<div class="row">
|
||||
<main id="main" class="col-md-9"><div class="page-header">
|
||||
<img src="logo.svg" class="logo" alt=""><h1>Authors and Citation</h1>
|
||||
</div>
|
||||
|
||||
<div class="section level2">
|
||||
<h2>Authors</h2>
|
||||
|
||||
<ul class="list-unstyled"><li>
|
||||
<p><strong>Matthijs S. Berends</strong>. Author, maintainer. <a href="https://orcid.org/0000-0001-7620-1800" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Dennis Souverein</strong>. Author, contributor. <a href="https://orcid.org/0000-0003-0455-0336" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Erwin E. A. Hassing</strong>. Author, contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Aislinn Cook</strong>. Contributor. <a href="https://orcid.org/0000-0002-9189-7815" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Andrew P. Norgan</strong>. Contributor. <a href="https://orcid.org/0000-0002-2955-2066" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Anita Williams</strong>. Contributor. <a href="https://orcid.org/0000-0002-5295-8451" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Annick Lenglet</strong>. Contributor. <a href="https://orcid.org/0000-0003-2013-8405" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Anthony Underwood</strong>. Contributor. <a href="https://orcid.org/0000-0002-8547-4277" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Anton Mymrikov</strong>. Contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Bart C. Meijer</strong>. Contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Christian F. Luz</strong>. Contributor. <a href="https://orcid.org/0000-0001-5809-5995" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Dmytro Mykhailenko</strong>. Contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Eric H. L. C. M. Hazenberg</strong>. Contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Gwen Knight</strong>. Contributor. <a href="https://orcid.org/0000-0002-7263-9896" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Jane Hawkey</strong>. Contributor. <a href="https://orcid.org/0000-0001-9661-5293" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Jason Stull</strong>. Contributor. <a href="https://orcid.org/0000-0002-9028-8153" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Javier Sanchez</strong>. Contributor. <a href="https://orcid.org/0000-0003-2605-8094" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Jonas Salm</strong>. Contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Judith M. Fonville</strong>. Contributor.
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Kathryn Holt</strong>. Contributor. <a href="https://orcid.org/0000-0003-3949-2471" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Larisse Bolton</strong>. Contributor. <a href="https://orcid.org/0000-0001-7879-2173" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Matthew Saab</strong>. Contributor. <a href="https://orcid.org/0009-0008-6626-7919" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Natacha Couto</strong>. Contributor. <a href="https://orcid.org/0000-0002-9152-5464" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Peter Dutey-Magni</strong>. Contributor. <a href="https://orcid.org/0000-0002-8942-9836" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Rogier P. Schade</strong>. Contributor. <a href="https://orcid.org/0000-0002-9487-4467" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Sofia Ny</strong>. Contributor. <a href="https://orcid.org/0000-0002-2017-1363" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Alex W. Friedrich</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-4881-038X" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Bhanu N. M. Sinha</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1634-0010" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Casper J. Albers</strong>. Thesis advisor. <a href="https://orcid.org/0000-0002-9213-6743" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Corinna Glasner</strong>. Thesis advisor. <a href="https://orcid.org/0000-0003-1241-1328" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
|
||||
</p>
|
||||
</li>
|
||||
</ul></div>
|
||||
|
||||
<div class="section level2">
|
||||
<h2 id="citation">Citation</h2>
|
||||
<p><small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/inst/CITATION" class="external-link"><code>inst/CITATION</code></a></small></p>
|
||||
|
||||
<p>Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2022).
|
||||
“AMR: An R Package for Working with Antimicrobial Resistance Data.”
|
||||
<em>Journal of Statistical Software</em>, <b>104</b>(3), 1–31.
|
||||
<a href="https://doi.org/10.18637/jss.v104.i03" class="external-link">doi:10.18637/jss.v104.i03</a>.
|
||||
</p>
|
||||
<pre>@Article{,
|
||||
title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
|
||||
author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
|
||||
journal = {Journal of Statistical Software},
|
||||
year = {2022},
|
||||
volume = {104},
|
||||
number = {3},
|
||||
pages = {1--31},
|
||||
doi = {10.18637/jss.v104.i03},
|
||||
}</pre>
|
||||
</div>
|
||||
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
</nav></aside></div>
|
||||
|
||||
|
||||
<footer><div class="pkgdown-footer-left">
|
||||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU GPL 2.0</a>. Developed at the <a target="_blank" href="https://www.rug.nl" class="external-link">University of Groningen</a> and <a target="_blank" href="https://www.umcg.nl" class="external-link">University Medical Center Groningen</a> in The Netherlands, in collaboration with <a href="https://amr-for-r.org/authors.html">many colleagues from around the world</a>.</p>
|
||||
</div>
|
||||
|
||||
<div class="pkgdown-footer-right">
|
||||
<p><a target="_blank" href="https://www.rug.nl" class="external-link"><img src="https://amr-for-r.org/logo_rug.svg" style="max-width: 150px;"></a><a target="_blank" href="https://www.umcg.nl" class="external-link"><img src="https://amr-for-r.org/logo_umcg.svg" style="max-width: 150px;"></a></p>
|
||||
</div>
|
||||
|
||||
</footer></div>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body></html>
|
||||
|
||||
-106
@@ -1,106 +0,0 @@
|
||||
# Authors and Citation
|
||||
|
||||
## Authors
|
||||
|
||||
- **Matthijs S. Berends**. Author, maintainer.
|
||||
[](https://orcid.org/0000-0001-7620-1800)
|
||||
|
||||
- **Dennis Souverein**. Author, contributor.
|
||||
[](https://orcid.org/0000-0003-0455-0336)
|
||||
|
||||
- **Erwin E. A. Hassing**. Author, contributor.
|
||||
|
||||
- **Aislinn Cook**. Contributor.
|
||||
[](https://orcid.org/0000-0002-9189-7815)
|
||||
|
||||
- **Andrew P. Norgan**. Contributor.
|
||||
[](https://orcid.org/0000-0002-2955-2066)
|
||||
|
||||
- **Anita Williams**. Contributor.
|
||||
[](https://orcid.org/0000-0002-5295-8451)
|
||||
|
||||
- **Annick Lenglet**. Contributor.
|
||||
[](https://orcid.org/0000-0003-2013-8405)
|
||||
|
||||
- **Anthony Underwood**. Contributor.
|
||||
[](https://orcid.org/0000-0002-8547-4277)
|
||||
|
||||
- **Anton Mymrikov**. Contributor.
|
||||
|
||||
- **Bart C. Meijer**. Contributor.
|
||||
|
||||
- **Christian F. Luz**. Contributor.
|
||||
[](https://orcid.org/0000-0001-5809-5995)
|
||||
|
||||
- **Dmytro Mykhailenko**. Contributor.
|
||||
|
||||
- **Eric H. L. C. M. Hazenberg**. Contributor.
|
||||
|
||||
- **Gwen Knight**. Contributor.
|
||||
[](https://orcid.org/0000-0002-7263-9896)
|
||||
|
||||
- **Jane Hawkey**. Contributor.
|
||||
[](https://orcid.org/0000-0001-9661-5293)
|
||||
|
||||
- **Jason Stull**. Contributor.
|
||||
[](https://orcid.org/0000-0002-9028-8153)
|
||||
|
||||
- **Javier Sanchez**. Contributor.
|
||||
[](https://orcid.org/0000-0003-2605-8094)
|
||||
|
||||
- **Jonas Salm**. Contributor.
|
||||
|
||||
- **Judith M. Fonville**. Contributor.
|
||||
|
||||
- **Kathryn Holt**. Contributor.
|
||||
[](https://orcid.org/0000-0003-3949-2471)
|
||||
|
||||
- **Larisse Bolton**. Contributor.
|
||||
[](https://orcid.org/0000-0001-7879-2173)
|
||||
|
||||
- **Matthew Saab**. Contributor.
|
||||
[](https://orcid.org/0009-0008-6626-7919)
|
||||
|
||||
- **Natacha Couto**. Contributor.
|
||||
[](https://orcid.org/0000-0002-9152-5464)
|
||||
|
||||
- **Peter Dutey-Magni**. Contributor.
|
||||
[](https://orcid.org/0000-0002-8942-9836)
|
||||
|
||||
- **Rogier P. Schade**. Contributor.
|
||||
[](https://orcid.org/0000-0002-9487-4467)
|
||||
|
||||
- **Sofia Ny**. Contributor. [](https://orcid.org/0000-0002-2017-1363)
|
||||
|
||||
- **Alex W. Friedrich**. Thesis advisor.
|
||||
[](https://orcid.org/0000-0003-4881-038X)
|
||||
|
||||
- **Bhanu N. M. Sinha**. Thesis advisor.
|
||||
[](https://orcid.org/0000-0003-1634-0010)
|
||||
|
||||
- **Casper J. Albers**. Thesis advisor.
|
||||
[](https://orcid.org/0000-0002-9213-6743)
|
||||
|
||||
- **Corinna Glasner**. Thesis advisor.
|
||||
[](https://orcid.org/0000-0003-1241-1328)
|
||||
|
||||
## Citation
|
||||
|
||||
Source:
|
||||
[`inst/CITATION`](https://github.com/msberends/AMR/blob/main/inst/CITATION)
|
||||
|
||||
Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C
|
||||
(2022). “AMR: An R Package for Working with Antimicrobial Resistance
|
||||
Data.” *Journal of Statistical Software*, **104**(3), 1–31.
|
||||
[doi:10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03).
|
||||
|
||||
@Article{,
|
||||
title = {{AMR}: An {R} Package for Working with Antimicrobial Resistance Data},
|
||||
author = {Matthijs S. Berends and Christian F. Luz and Alexander W. Friedrich and Bhanu N. M. Sinha and Casper J. Albers and Corinna Glasner},
|
||||
journal = {Journal of Statistical Software},
|
||||
year = {2022},
|
||||
volume = {104},
|
||||
number = {3},
|
||||
pages = {1--31},
|
||||
doi = {10.18637/jss.v104.i03},
|
||||
}
|
||||
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@font-face {
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|
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/* symbols2 */
|
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@font-face {
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unicode-range: U+2000-2001, U+2004-2008, U+200A, U+23B8-23BD, U+2500-259F;
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|
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/* latin-ext */
|
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@font-face {
|
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/* latin */
|
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@font-face {
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|
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}
|
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Reference in New Issue
Block a user