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404.html
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404.html
@@ -31,7 +31,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
|
||||
3
404.md
Normal file
3
404.md
Normal file
@@ -0,0 +1,3 @@
|
||||
Content not found. Please use links in the navbar.
|
||||
|
||||
# Page not found (404)
|
||||
274
CLAUDE.html
Normal file
274
CLAUDE.html
Normal file
@@ -0,0 +1,274 @@
|
||||
<!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-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/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>
|
||||
<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="">3.0.1.9043</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="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">package_coverage</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 class="header"><th>Prefix/Name</th>
|
||||
<th>Purpose</th>
|
||||
</tr></thead><tbody><tr class="odd"><td><code>aa_*.R</code></td>
|
||||
<td>Loaded first (helpers, globals, options, package docs)</td>
|
||||
</tr><tr class="even"><td><code>zz_deprecated.R</code></td>
|
||||
<td>Deprecated function wrappers</td>
|
||||
</tr><tr class="odd"><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 class="header"><th>Class</th>
|
||||
<th>Created by</th>
|
||||
<th>Represents</th>
|
||||
</tr></thead><tbody><tr class="odd"><td><code><mo></code></td>
|
||||
<td><code><a href="reference/as.mo.html">as.mo()</a></code></td>
|
||||
<td>Microorganism code</td>
|
||||
</tr><tr class="even"><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 class="odd"><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 class="even"><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 class="odd"><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 class="even"><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 class="header"><th>File</th>
|
||||
<th>Contents</th>
|
||||
</tr></thead><tbody><tr class="odd"><td><code>microorganisms.rda</code></td>
|
||||
<td>~79,000 microbial species with full taxonomy</td>
|
||||
</tr><tr class="even"><td><code>antimicrobials.rda</code></td>
|
||||
<td>~620 antimicrobial drugs with ATC codes</td>
|
||||
</tr><tr class="odd"><td><code>antivirals.rda</code></td>
|
||||
<td>Antiviral drugs</td>
|
||||
</tr><tr class="even"><td><code>clinical_breakpoints.rda</code></td>
|
||||
<td>EUCAST + CLSI breakpoints (2011–2025)</td>
|
||||
</tr><tr class="odd"><td><code>intrinsic_resistant.rda</code></td>
|
||||
<td>Intrinsic resistance patterns</td>
|
||||
</tr><tr class="even"><td><code>example_isolates.rda</code></td>
|
||||
<td>Example AMR dataset for documentation/testing</td>
|
||||
</tr><tr class="odd"><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>Run the following from the repo root to determine the version string to use:</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="va">currenttag</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> describe <span class="at">--tags</span> <span class="at">--abbrev</span><span class="op">=</span>0 <span class="kw">|</span> <span class="fu">sed</span> <span class="st">'s/v//'</span><span class="va">)</span></span>
|
||||
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="va">currenttagfull</span><span class="op">=</span><span class="va">$(</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="cb5-3"><a href="#cb5-3" 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$'</span><span class="va">)</span></span>
|
||||
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="va">currentcommit</span><span class="op">=</span><span class="va">$(</span><span class="fu">git</span> rev-list <span class="at">--count</span> <span class="va">${currenttagfull}</span>..<span class="va">${defaultbranch})</span></span>
|
||||
<span id="cb5-5"><a href="#cb5-5" 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="cb5-6"><a href="#cb5-6" 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 General Public License version 2.0 (GPL-2)</a>.<br>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.</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>
|
||||
|
||||
224
CLAUDE.md
Normal file
224
CLAUDE.md
Normal file
@@ -0,0 +1,224 @@
|
||||
# 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
|
||||
|
||||
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$|^main$')
|
||||
currentcommit=$(git rev-list --count ${currenttagfull}..${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.
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
|
||||
250
LICENSE-text.md
Normal file
250
LICENSE-text.md
Normal file
@@ -0,0 +1,250 @@
|
||||
# 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
|
||||
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|
||||
- Modifications MUST be released under the same license when distributing the package
|
||||
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|
||||
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|
||||
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|
||||
- A copy of the license and copyright notice MUST be included with the package.
|
||||
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|
||||
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|
||||
|
||||
END OF THE SUMMARY
|
||||
|
||||
|
||||
GNU GENERAL PUBLIC LICENSE
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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|
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||||
|
||||
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|
||||
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||||
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|
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||||
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|
||||
|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
END OF TERMS AND CONDITIONS
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
@@ -91,7 +91,7 @@
|
||||
website update since they are based on randomly created values and the
|
||||
page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
|
||||
Markdown</a>. However, the methodology remains unchanged. This page was
|
||||
generated on 01 September 2025.</p>
|
||||
generated on 02 April 2026.</p>
|
||||
<div class="section level2">
|
||||
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
|
||||
</h2>
|
||||
@@ -125,7 +125,7 @@ including the requirements mentioned above.</p>
|
||||
<p>The <code>AMR</code> package enables standardised and reproducible
|
||||
AMR data analysis, with the application of evidence-based rules,
|
||||
determination of first isolates, translation of various codes for
|
||||
microorganisms and antimicrobial agents, determination of (multi-drug)
|
||||
microorganisms and antimicrobial drugs, determination of (multi-drug)
|
||||
resistant microorganisms, and calculation of antimicrobial resistance,
|
||||
prevalence and future trends.</p>
|
||||
</div>
|
||||
@@ -147,21 +147,21 @@ make the structure of your data generally look like this:</p>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="center">2025-09-01</td>
|
||||
<td align="center">2026-04-02</td>
|
||||
<td align="center">abcd</td>
|
||||
<td align="center">Escherichia coli</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">S</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="center">2025-09-01</td>
|
||||
<td align="center">2026-04-02</td>
|
||||
<td align="center">abcd</td>
|
||||
<td align="center">Escherichia coli</td>
|
||||
<td align="center">S</td>
|
||||
<td align="center">R</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="center">2025-09-01</td>
|
||||
<td align="center">2026-04-02</td>
|
||||
<td align="center">efgh</td>
|
||||
<td align="center">Escherichia coli</td>
|
||||
<td align="center">R</td>
|
||||
@@ -224,16 +224,16 @@ short, but still human readable. More importantly, <code><a href="../reference/a
|
||||
supports all kinds of input:</p>
|
||||
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> Class 'mo'</span></span>
|
||||
<span><span class="co">#> Class <span style="color: #0000BB;"><mo></span></span></span>
|
||||
<span><span class="co">#> [1] B_KLBSL_PNMN</span></span>
|
||||
<span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"K. pneumoniae"</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> Class 'mo'</span></span>
|
||||
<span><span class="co">#> Class <span style="color: #0000BB;"><mo></span></span></span>
|
||||
<span><span class="co">#> [1] B_KLBSL_PNMN</span></span>
|
||||
<span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"KLEPNE"</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> Class 'mo'</span></span>
|
||||
<span><span class="co">#> Class <span style="color: #0000BB;"><mo></span></span></span>
|
||||
<span><span class="co">#> [1] B_KLBSL_PNMN</span></span>
|
||||
<span><span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="st">"KLPN"</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> Class 'mo'</span></span>
|
||||
<span><span class="co">#> Class <span style="color: #0000BB;"><mo></span></span></span>
|
||||
<span><span class="co">#> [1] B_KLBSL_PNMN</span></span></code></pre></div>
|
||||
<p>The first character in above codes denote their taxonomic kingdom,
|
||||
such as Bacteria (B), Fungi (F), and Protozoa (P).</p>
|
||||
@@ -263,56 +263,51 @@ user input can be used:</p>
|
||||
<p>Now we can thus clean our data:</p>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">our_data</span><span class="op">$</span><span class="va">bacteria</span> <span class="op"><-</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">our_data</span><span class="op">$</span><span class="va">bacteria</span>, info <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ Retrieved values from the </span><span style="color: #0000BB; background-color: #EEEEEE;">`microorganisms.codes`</span><span style="color: #0000BB;"> data set for "ESCCOL",</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> "KLEPNE", "STAAUR", and "STRPNE".</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ Microorganism translation was uncertain for four microorganisms. Run</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> </span><span style="color: #0000BB; background-color: #EEEEEE;">`mo_uncertainties()`</span><span style="color: #0000BB;"> to review these uncertainties, or use</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> </span><span style="color: #0000BB; background-color: #EEEEEE;">`add_custom_microorganisms()`</span><span style="color: #0000BB;"> to add custom entries.</span></span></span></code></pre></div>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Retrieved values from the `microorganisms.codes` data set for <span style="color: #0000BB;">"ESCCOL"</span>,</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"KLEPNE"</span>, <span style="color: #0000BB;">"STAAUR"</span>, and <span style="color: #0000BB;">"STRPNE"</span>.</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Microorganism translation was uncertain for four microorganisms. Run</span></span>
|
||||
<span><span class="co">#> `mo_uncertainties()` to review these uncertainties, or use</span></span>
|
||||
<span><span class="co">#> `add_custom_microorganisms()` to add custom entries.</span></span></code></pre></div>
|
||||
<p>Apparently, there was some uncertainty about the translation to
|
||||
taxonomic codes. Let’s check this:</p>
|
||||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/as.mo.html">mo_uncertainties</a></span><span class="op">(</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">Matching scores are based on the resemblance between the input and the full</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> taxonomic name, and the pathogenicity in humans. See </span><span style="color: #0000BB; background-color: #EEEEEE;">`?mo_matching_score`</span><span style="color: #0000BB;">.</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">Colour keys: </span><span style="color: #080808; background-color: #FFAFAF;"> 0.000-0.549 </span><span style="color: #080808; background-color: #FFD787;"> 0.550-0.649 </span><span style="color: #080808; background-color: #FFFF87;"> 0.650-0.749 </span><span style="color: #080808; background-color: #5FD7AF;"> 0.750-1.000 </span></span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">--------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> "E. coli" -> <span style="font-weight: bold; font-style: italic;">Escherichia coli</span> (B_ESCHR_COLI, <span style="color: #080808; background-color: #FFFF87;">0.688</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Enterococcus crotali</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.650</span>), <span style="font-style: italic;">Escherichia coli coli</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (</span><span style="color: #080808; background-color: #FFD787;">0.643</span>), <span style="font-style: italic;">Escherichia coli expressing</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.611</span>), <span style="font-style: italic;">Enterobacter cowanii</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (</span><span style="color: #080808; background-color: #FFD787;">0.600</span>), <span style="font-style: italic;">Enterococcus columbae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.595</span>), <span style="font-style: italic;">Enterococcus camelliae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.591</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Enterococcus casseliflavus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.577</span>), <span style="font-style: italic;">Enterobacter cloacae cloacae</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (</span><span style="color: #080808; background-color: #FFD787;">0.571</span>), <span style="font-style: italic;">Enterobacter cloacae</span> complex<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.571</span>), and <span style="font-style: italic;">Enterobacter cloacae</span></span></span>
|
||||
<span><span class="co"><span style="font-style: italic;">#> dissolvens</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.565</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">--------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> "K. pneumoniae" -> <span style="font-weight: bold; font-style: italic;">Klebsiella pneumoniae</span> (B_KLBSL_PNMN, <span style="color: #080808; background-color: #5FD7AF;">0.786</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Klebsiella pneumoniae</span> complex<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.707</span>), <span style="font-style: italic;">Klebsiella</span></span></span>
|
||||
<span><span class="co"><span style="font-style: italic;">#> pneumoniae ozaenae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.707</span>), <span style="font-style: italic;">Klebsiella pneumoniae pneumoniae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.688</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Klebsiella pneumoniae rhinoscleromatis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.658</span>), <span style="font-style: italic;">Klebsiella pasteurii</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (</span><span style="color: #080808; background-color: #FFAFAF;">0.500</span>), <span style="font-style: italic;">Klebsiella planticola</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.500</span>), <span style="font-style: italic;">Kingella potus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.400</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Kluyveromyces pseudotropicale</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.386</span>), <span style="font-style: italic;">Kluyveromyces pseudotropicalis</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (</span><span style="color: #080808; background-color: #FFAFAF;">0.363</span>), and <span style="font-style: italic;">Kosakonia pseudosacchari</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.361</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">--------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> "S. aureus" -> <span style="font-weight: bold; font-style: italic;">Staphylococcus aureus</span> (B_STPHY_AURS, <span style="color: #080808; background-color: #FFFF87;">0.690</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Staphylococcus aureus aureus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.643</span>), <span style="font-style: italic;">Staphylococcus</span></span></span>
|
||||
<span><span class="co"><span style="font-style: italic;">#> argenteus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.625</span>), <span style="font-style: italic;">Staphylococcus aureus anaerobius</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.625</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Staphylococcus auricularis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.615</span>), <span style="font-style: italic;">Salmonella</span> Aurelianis<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.595</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Salmonella</span> Aarhus<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.588</span>), <span style="font-style: italic;">Salmonella</span> Amounderness<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Staphylococcus argensis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>), <span style="font-style: italic;">Streptococcus australis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>), and</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Salmonella choleraesuis arizonae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.562</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">--------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> "S. pneumoniae" -> <span style="font-weight: bold; font-style: italic;">Streptococcus pneumoniae</span> (B_STRPT_PNMN, <span style="color: #080808; background-color: #5FD7AF;">0.750</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Streptococcus pseudopneumoniae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.700</span>), <span style="font-style: italic;">Streptococcus</span></span></span>
|
||||
<span><span class="co"><span style="font-style: italic;">#> phocae salmonis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.552</span>), <span style="font-style: italic;">Serratia proteamaculans quinovora</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.545</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Streptococcus pseudoporcinus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.536</span>), <span style="font-style: italic;">Staphylococcus piscifermentans</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (</span><span style="color: #080808; background-color: #FFAFAF;">0.533</span>), <span style="font-style: italic;">Staphylococcus pseudintermedius</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.532</span>), <span style="font-style: italic;">Serratia</span></span></span>
|
||||
<span><span class="co"><span style="font-style: italic;">#> proteamaculans proteamaculans</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.526</span>), <span style="font-style: italic;">Streptococcus gallolyticus</span></span></span>
|
||||
<span><span class="co"><span style="font-style: italic;">#> pasteurianus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.526</span>), <span style="font-style: italic;">Salmonella</span> Portanigra<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.524</span>), and <span style="font-style: italic;">Streptococcus</span></span></span>
|
||||
<span><span class="co"><span style="font-style: italic;">#> periodonticum</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFAFAF;">0.519</span>)</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> Only the first 10 other matches of each record are shown. Run</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> </span><span style="color: #0000BB; background-color: #EEEEEE;">`print(mo_uncertainties(), n = ...)`</span><span style="color: #0000BB;"> to view more entries, or save</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> </span><span style="color: #0000BB; background-color: #EEEEEE;">`mo_uncertainties()`</span><span style="color: #0000BB;"> to an object.</span></span></span></code></pre></div>
|
||||
<span><span class="co">#> Matching scores are based on the resemblance between the input and the full</span></span>
|
||||
<span><span class="co">#> taxonomic name, and the pathogenicity in humans. See `mo_matching_score()`.</span></span>
|
||||
<span><span class="co">#> Colour keys: <span style="color: #080808; background-color: #FF5F5F;"> 0.000-0.549 </span><span style="color: #080808; background-color: #FFD787;"> 0.550-0.649 </span><span style="color: #080808; background-color: #FFFF87;"> 0.650-0.749 </span><span style="color: #080808; background-color: #5FD7AF;"> 0.750-1.000 </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">-------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"E. coli"</span> -> <span style="font-weight: bold; font-style: italic;">Escherichia coli</span> (B_ESCHR_COLI, <span style="color: #080808; background-color: #FFFF87;">0.688</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Enterococcus crotali</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.650</span>), <span style="font-style: italic;">Escherichia coli coli</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.643</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Escherichia coli expressing</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.611</span>), <span style="font-style: italic;">Enterobacter cowanii</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.600</span>), <span style="font-style: italic;">Enterococcus</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">columbae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.595</span>), <span style="font-style: italic;">Enterococcus camelliae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.591</span>), <span style="font-style: italic;">Enterococcus casseliflavus</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.577</span>), <span style="font-style: italic;">Enterobacter cloacae cloacae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.571</span>), <span style="font-style: italic;">Enterobacter cloacae</span> complex</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.571</span>), and <span style="font-style: italic;">Enterobacter cloacae dissolvens</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.565</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">-------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"K. pneumoniae"</span> -> <span style="font-weight: bold; font-style: italic;">Klebsiella pneumoniae</span> (B_KLBSL_PNMN, <span style="color: #080808; background-color: #5FD7AF;">0.786</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Klebsiella pneumoniae</span> complex<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.707</span>), <span style="font-style: italic;">Klebsiella pneumoniae</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">ozaenae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.707</span>), <span style="font-style: italic;">Klebsiella pneumoniae pneumoniae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.688</span>), <span style="font-style: italic;">Klebsiella</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">pneumoniae rhinoscleromatis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.658</span>), <span style="font-style: italic;">Klebsiella pasteurii</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.500</span>), <span style="font-style: italic;">Klebsiella</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">planticola</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.500</span>), <span style="font-style: italic;">Kingella potus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.400</span>), <span style="font-style: italic;">Kluyveromyces pseudotropicale</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FF5F5F;">0.386</span>), <span style="font-style: italic;">Kluyveromyces pseudotropicalis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.363</span>), and <span style="font-style: italic;">Kosakonia pseudosacchari</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FF5F5F;">0.361</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">-------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"S. aureus"</span> -> <span style="font-weight: bold; font-style: italic;">Staphylococcus aureus</span> (B_STPHY_AURS, <span style="color: #080808; background-color: #FFFF87;">0.690</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Staphylococcus aureus aureus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.643</span>), <span style="font-style: italic;">Staphylococcus argenteus</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.625</span>), <span style="font-style: italic;">Staphylococcus aureus anaerobius</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.625</span>), <span style="font-style: italic;">Staphylococcus auricularis</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.615</span>), <span style="font-style: italic;">Salmonella</span> Aurelianis<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.595</span>), <span style="font-style: italic;">Salmonella</span> Aarhus<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.588</span>), <span style="font-style: italic;">Salmonella</span></span></span>
|
||||
<span><span class="co">#> Amounderness<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>), <span style="font-style: italic;">Staphylococcus argensis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.587</span>), <span style="font-style: italic;">Streptococcus australis</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">(</span><span style="color: #080808; background-color: #FFD787;">0.587</span>), and <span style="font-style: italic;">Salmonella choleraesuis arizonae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.562</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #B2B2B2;">-------------------------------------------------------------------------------</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">"S. pneumoniae"</span> -> <span style="font-weight: bold; font-style: italic;">Streptococcus pneumoniae</span> (B_STRPT_PNMN, <span style="color: #080808; background-color: #5FD7AF;">0.750</span>)</span></span>
|
||||
<span><span class="co">#> Also matched: <span style="font-style: italic;">Streptococcus pseudopneumoniae</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFFF87;">0.700</span>), <span style="font-style: italic;">Streptococcus phocae</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">salmonis</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FFD787;">0.552</span>), <span style="font-style: italic;">Serratia proteamaculans quinovora</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.545</span>), <span style="font-style: italic;">Streptococcus</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">pseudoporcinus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.536</span>), <span style="font-style: italic;">Staphylococcus piscifermentans</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.533</span>), <span style="font-style: italic;">Staphylococcus</span></span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">pseudintermedius</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.532</span>), <span style="font-style: italic;">Serratia proteamaculans proteamaculans</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.526</span>),</span></span>
|
||||
<span><span class="co">#> <span style="font-style: italic;">Streptococcus gallolyticus pasteurianus</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.526</span>), <span style="font-style: italic;">Salmonella</span> Portanigra<span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.524</span>),</span></span>
|
||||
<span><span class="co">#> and <span style="font-style: italic;">Streptococcus periodonticum</span><span style="color: #0000BB;"> (</span><span style="color: #080808; background-color: #FF5F5F;">0.519</span>)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Only the first 10 other matches of each record are shown. Run ``</span></span>
|
||||
<span><span class="co">#> `print(mo_uncertainties(), n = ...)` `` to view more entries, or save</span></span>
|
||||
<span><span class="co">#> `mo_uncertainties()` to an object.</span></span></code></pre></div>
|
||||
<p>That’s all good.</p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
@@ -342,14 +337,14 @@ dplyr:</p>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 3,000 × 8</span></span></span>
|
||||
<span><span class="co">#> patient_id hospital date bacteria AMX AMC CIP GEN </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;"><date></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> J3 A 2012-11-21 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> R7 A 2018-04-03 <span style="color: #949494;">B_</span>KLBSL<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> P3 A 2014-09-19 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> P10 A 2015-12-10 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> J3 A 2012-11-21 <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: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> R7 A 2018-04-03 <span style="color: #949494;">B_</span>KLBSL<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> P3 A 2014-09-19 <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> P10 A 2015-12-10 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> B7 A 2015-03-02 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> W3 A 2018-03-31 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> J8 A 2016-06-14 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> M3 A 2015-10-25 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> W3 A 2018-03-31 <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> J8 A 2016-06-14 <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> M3 A 2015-10-25 <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> J3 A 2019-06-19 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> G6 A 2015-04-27 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 2,990 more rows</span></span></span></code></pre></div>
|
||||
@@ -399,14 +394,15 @@ the methods on the <code><a href="../reference/first_isolate.html">first_isolate
|
||||
<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">our_data</span> <span class="op"><-</span> <span class="va">our_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/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>first <span class="op">=</span> <span class="fu"><a href="../reference/first_isolate.html">first_isolate</a></span><span class="op">(</span>info <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #BB0000;">ℹ Determining first isolates using an episode length of </span><span style="color: #BB0000; font-weight: bold;">365 days</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ Using column '</span><span style="color: #0000BB; font-weight: bold;">bacteria</span><span style="color: #0000BB;">' as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">`col_mo`</span><span style="color: #0000BB;">.</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ Using column '</span><span style="color: #0000BB; font-weight: bold;">date</span><span style="color: #0000BB;">' as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">`col_date`</span><span style="color: #0000BB;">.</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ Using column '</span><span style="color: #0000BB; font-weight: bold;">patient_id</span><span style="color: #0000BB;">' as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">`col_patient_id`</span><span style="color: #0000BB;">.</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BB0000;">ℹ Basing inclusion on all antimicrobial results, using a points threshold</span></span></span>
|
||||
<span><span class="co"><span style="color: #BB0000;">#> of 2</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #080808;">=> Found </span><span style="color: #080808; font-weight: bold;">2,724 'phenotype-based' first isolates</span><span style="color: #080808;"> (90.8% of total where a</span></span></span>
|
||||
<span><span class="co"><span style="color: #080808;">#> microbial ID was available)</span></span></span></code></pre></div>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Determining first isolates using an episode length of <span style="font-weight: bold;">365 days</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">bacteria</span> as input for `col_mo`.</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Column <span style="color: #00BB00; font-weight: bold;">first</span> is SIR eligible (despite only having empty values), since it</span></span>
|
||||
<span><span class="co">#> seems to be cefozopran (ZOP)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">date</span> as input for `col_date`.</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Using column <span style="color: #00BB00; font-weight: bold;">patient_id</span> as input for `col_patient_id`.</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> Basing inclusion on all antimicrobial results, using a points threshold of 2</span></span>
|
||||
<span><span class="co">#> => Found <span style="font-weight: bold;">2,724 'phenotype-based' first isolates</span> (90.8% of total where a</span></span>
|
||||
<span><span class="co">#> microbial ID was available)</span></span></code></pre></div>
|
||||
<p>So only 91% is suitable for resistance analysis! We can now filter on
|
||||
it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter()</a></code> function, also from the
|
||||
<code>dplyr</code> package:</p>
|
||||
@@ -424,13 +420,13 @@ like:</p>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2,724 × 9</span></span></span>
|
||||
<span><span class="co">#> patient_id hospital date bacteria AMX AMC CIP GEN first</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;"><date></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 style="color: #949494; font-style: italic;"><lgl></span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> J3 A 2012-11-21 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> R7 A 2018-04-03 <span style="color: #949494;">B_</span>KLBSL<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> P3 A 2014-09-19 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> P10 A 2015-12-10 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> J3 A 2012-11-21 <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: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> R7 A 2018-04-03 <span style="color: #949494;">B_</span>KLBSL<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> P3 A 2014-09-19 <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> P10 A 2015-12-10 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> B7 A 2015-03-02 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> W3 A 2018-03-31 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> M3 A 2015-10-25 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> W3 A 2018-03-31 <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> M3 A 2015-10-25 <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> J3 A 2019-06-19 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> G6 A 2015-04-27 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> P4 A 2011-06-21 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
@@ -523,7 +519,8 @@ in:</p>
|
||||
<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">our_data_1st</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">date</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using column '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using column <span style="color: #00BB00; font-weight: bold;">GEN</span></span></span>
|
||||
<span><span class="co">#> (gentamicin)</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2,724 × 2</span></span></span>
|
||||
<span><span class="co">#> date GEN </span></span>
|
||||
<span><span class="co">#> <span style="color: #949494; font-style: italic;"><date></span> <span style="color: #949494; font-style: italic;"><sir></span></span></span>
|
||||
@@ -541,18 +538,18 @@ in:</p>
|
||||
<span></span>
|
||||
<span><span class="va">our_data_1st</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">bacteria</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`betalactams()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin) and '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (amoxicillin/clavulanic acid)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `betalactams()` using columns <span style="color: #00BB00; font-weight: bold;">AMX</span> (amoxicillin) and <span style="color: #00BB00; font-weight: bold;">AMC</span></span></span>
|
||||
<span><span class="co">#> (amoxicillin/clavulanic acid)</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2,724 × 3</span></span></span>
|
||||
<span><span class="co">#> bacteria AMX AMC </span></span>
|
||||
<span><span class="co">#> <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></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFD787;"> I </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> <span style="color: #949494;">B_</span>KLBSL<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFD787;"> I </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFD787;"> I </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> <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: #080808; background-color: #AFFFD7;"> I </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> <span style="color: #949494;">B_</span>KLBSL<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #AFFFD7;"> I </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> <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: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #AFFFD7;"> I </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> <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: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> <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: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
@@ -563,13 +560,13 @@ in:</p>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 2,724 × 5</span></span></span>
|
||||
<span><span class="co">#> bacteria AMX AMC CIP GEN </span></span>
|
||||
<span><span class="co">#> <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> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> <span style="color: #949494;">B_</span>KLBSL<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFD787;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> <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: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> <span style="color: #949494;">B_</span>KLBSL<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #AFFFD7;"> I </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
@@ -578,58 +575,59 @@ in:</p>
|
||||
<span><span class="co"># filtering using AB selectors is also possible:</span></span>
|
||||
<span><span class="va">our_data_1st</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/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/any.html" class="external-link">any</a></span><span class="op">(</span><span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using column '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using column <span style="color: #00BB00; font-weight: bold;">GEN</span></span></span>
|
||||
<span><span class="co">#> (gentamicin)</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 981 × 9</span></span></span>
|
||||
<span><span class="co">#> patient_id hospital date bacteria AMX AMC CIP GEN first</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;"><date></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 style="color: #949494; font-style: italic;"><lgl></span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> J5 A 2017-12-25 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> X1 A 2017-07-04 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> B3 A 2016-07-24 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> V7 A 2012-04-03 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> C9 A 2017-03-23 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> R1 A 2018-06-10 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> S2 A 2013-07-19 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> P5 A 2019-03-09 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> Q8 A 2019-08-10 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> K5 A 2013-03-15 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> J5 A 2017-12-25 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> X1 A 2017-07-04 <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: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> B3 A 2016-07-24 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> V7 A 2012-04-03 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> C9 A 2017-03-23 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> R1 A 2018-06-10 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> S2 A 2013-07-19 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> P5 A 2019-03-09 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> Q8 A 2019-08-10 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> K5 A 2013-03-15 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 971 more rows</span></span></span>
|
||||
<span></span>
|
||||
<span><span class="va">our_data_1st</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/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/all.html" class="external-link">all</a></span><span class="op">(</span><span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`betalactams()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin) and '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (amoxicillin/clavulanic acid)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `betalactams()` using columns <span style="color: #00BB00; font-weight: bold;">AMX</span> (amoxicillin) and <span style="color: #00BB00; font-weight: bold;">AMC</span></span></span>
|
||||
<span><span class="co">#> (amoxicillin/clavulanic acid)</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 462 × 9</span></span></span>
|
||||
<span><span class="co">#> patient_id hospital date bacteria AMX AMC CIP GEN first</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;"><date></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 style="color: #949494; font-style: italic;"><lgl></span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> M7 A 2013-07-22 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> R10 A 2013-12-20 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> R7 A 2015-10-25 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> R8 A 2019-10-25 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> B6 A 2016-11-20 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> I7 A 2015-08-19 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> N3 A 2014-12-29 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> Q2 A 2019-09-22 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> X7 A 2011-03-20 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> V1 A 2018-08-07 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> M7 A 2013-07-22 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> R10 A 2013-12-20 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> R7 A 2015-10-25 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> R8 A 2019-10-25 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> B6 A 2016-11-20 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> I7 A 2015-08-19 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> N3 A 2014-12-29 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> Q2 A 2019-09-22 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> X7 A 2011-03-20 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> V1 A 2018-08-07 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 452 more rows</span></span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># even works in base R (since R 3.0):</span></span>
|
||||
<span><span class="va">our_data_1st</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/all.html" class="external-link">all</a></span><span class="op">(</span><span class="fu"><a href="../reference/antimicrobial_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span>, <span class="op">]</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`betalactams()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin) and '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (amoxicillin/clavulanic acid)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `betalactams()` using columns <span style="color: #00BB00; font-weight: bold;">AMX</span> (amoxicillin) and <span style="color: #00BB00; font-weight: bold;">AMC</span></span></span>
|
||||
<span><span class="co">#> (amoxicillin/clavulanic acid)</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 462 × 9</span></span></span>
|
||||
<span><span class="co">#> patient_id hospital date bacteria AMX AMC CIP GEN first</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;"><date></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 style="color: #949494; font-style: italic;"><lgl></span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> M7 A 2013-07-22 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> R10 A 2013-12-20 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> R7 A 2015-10-25 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> R8 A 2019-10-25 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> B6 A 2016-11-20 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> I7 A 2015-08-19 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> N3 A 2014-12-29 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> Q2 A 2019-09-22 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> X7 A 2011-03-20 <span style="color: #949494;">B_</span>ESCHR<span style="color: #949494;">_</span>COLI <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> V1 A 2018-08-07 <span style="color: #949494;">B_</span>STPHY<span style="color: #949494;">_</span>AURS <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> M7 A 2013-07-22 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> R10 A 2013-12-20 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> R7 A 2015-10-25 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> R8 A 2019-10-25 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> B6 A 2016-11-20 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> I7 A 2015-08-19 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> N3 A 2014-12-29 <span style="color: #949494;">B_</span>STRPT<span style="color: #949494;">_</span>PNMN <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> Q2 A 2019-09-22 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> X7 A 2011-03-20 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FF5F5F;"> R </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> V1 A 2018-08-07 <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: #080808; background-color: #FF5F5F;"> R </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> TRUE </span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 452 more rows</span></span></span></code></pre></div>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
@@ -670,16 +668,16 @@ like:</p>
|
||||
<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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #B2B2B2;"> NA</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>
|
||||
@@ -697,9 +695,9 @@ previously mentioned antibiotic class selectors:</p>
|
||||
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
|
||||
<span> antibiotics <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="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`carbapenems()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem) and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span></code></pre></div>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span></span>
|
||||
<span><span class="co">#> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="12%">
|
||||
@@ -828,8 +826,8 @@ language to be Spanish using the <code>language</code> argument:</p>
|
||||
<span> antibiotics <span class="op">=</span> <span class="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>,</span>
|
||||
<span> ab_transform <span class="op">=</span> <span class="st">"name"</span>,</span>
|
||||
<span> language <span class="op">=</span> <span class="st">"es"</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span></code></pre></div>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span></span>
|
||||
<span><span class="co">#> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="15%">
|
||||
@@ -954,9 +952,9 @@ argument must be used. This can be any column in the data, or e.g. an
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/antibiogram.html">antibiogram</a></span><span class="op">(</span><span class="va">example_isolates</span>,</span>
|
||||
<span> antibiotics <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="fu"><a href="../reference/antimicrobial_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span>, <span class="fu"><a href="../reference/antimicrobial_selectors.html">carbapenems</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span>,</span>
|
||||
<span> syndromic_group <span class="op">=</span> <span class="st">"ward"</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`carbapenems()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem) and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span></code></pre></div>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span></span>
|
||||
<span><span class="co">#> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `carbapenems()` using columns <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem) and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span></span></code></pre></div>
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="10%">
|
||||
@@ -1254,7 +1252,7 @@ function on a grouped <code>tibble</code>, i.e., using
|
||||
provides an extension to that function:</p>
|
||||
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><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">combined_ab</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/unnamed-chunk-10-1.png" width="720"></p>
|
||||
<p><img src="AMR_files/figure-html/unnamed-chunk-10-1.png" class="r-plt" alt="" width="720"></p>
|
||||
<p>To calculate antimicrobial resistance in a more sensible way, also by
|
||||
correcting for too few results, we use the <code><a href="../reference/proportion.html">resistance()</a></code> and
|
||||
<code><a href="../reference/proportion.html">susceptibility()</a></code> functions.</p>
|
||||
@@ -1283,6 +1281,10 @@ I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equa
|
||||
own:</p>
|
||||
<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">)</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 class="co">#> [1] 0.4203377</span></span></code></pre></div>
|
||||
<p>Or can be used in conjunction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> and
|
||||
<code><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
|
||||
@@ -1314,15 +1316,15 @@ values for <em>Klebsiella pneumoniae</em> and ciprofloxacin:</p>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 100 × 2</span></span></span>
|
||||
<span><span class="co">#> MIC SIR </span></span>
|
||||
<span><span class="co">#> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><sir></span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> <span style="color: #555555;"><=</span>0.0001 <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> 0.016<span style="color: #BBBBBB;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> <span style="color: #555555;">>=</span>8<span style="color: #BBBBBB;">.0000</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> 0.032<span style="color: #BBBBBB;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> 0.008<span style="color: #BBBBBB;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> 64<span style="color: #BBBBBB;">.0000</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> 0.008<span style="color: #BBBBBB;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> 0.125<span style="color: #BBBBBB;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> 0.032<span style="color: #BBBBBB;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> <span style="color: #949494;"><=</span>0.0001 <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> 0.016<span style="color: #949494;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> <span style="color: #949494;">>=</span>8<span style="color: #949494;">.0000</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> 0.032<span style="color: #949494;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> 0.008<span style="color: #949494;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> 64<span style="color: #949494;">.0000</span> <span style="color: #080808; background-color: #FF5F5F;"> R </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> 0.008<span style="color: #949494;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> 0.125<span style="color: #949494;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> 0.032<span style="color: #949494;">0</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> 0.0002 <span style="color: #080808; background-color: #5FD7AF;"> S </span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 90 more rows</span></span></span></code></pre></div>
|
||||
<p>This allows direct interpretation according to EUCAST or CLSI
|
||||
@@ -1348,7 +1350,7 @@ categories.</p>
|
||||
<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">"MIC Distribution and SIR Interpretation"</span>,</span>
|
||||
<span> x <span class="op">=</span> <span class="st">"Sample Groups"</span>,</span>
|
||||
<span> y <span class="op">=</span> <span class="st">"MIC (mg/L)"</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/mic_plot-1.png" width="720"></p>
|
||||
<p><img src="AMR_files/figure-html/mic_plot-1.png" class="r-plt" alt="" width="720"></p>
|
||||
<p>This plot provides an intuitive way to assess susceptibility patterns
|
||||
across different groups while incorporating clinical breakpoints.</p>
|
||||
<p>For a more straightforward and less manual approach,
|
||||
@@ -1357,12 +1359,12 @@ extended by this package to directly plot MIC and disk diffusion
|
||||
values:</p>
|
||||
<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><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">mic_values</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/autoplot-1.png" width="720"></p>
|
||||
<p><img src="AMR_files/figure-html/autoplot-1.png" class="r-plt" alt="" width="720"></p>
|
||||
<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span></span>
|
||||
<span><span class="co"># by providing `mo` and `ab`, colours will indicate the SIR interpretation:</span></span>
|
||||
<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">mic_values</span>, mo <span class="op">=</span> <span class="st">"K. pneumoniae"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span>, guideline <span class="op">=</span> <span class="st">"EUCAST 2024"</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_files/figure-html/autoplot-2.png" width="720"></p>
|
||||
<p><img src="AMR_files/figure-html/autoplot-2.png" class="r-plt" alt="" width="720"></p>
|
||||
<hr>
|
||||
<p><em>Author: Dr. Matthijs Berends, 23rd Feb 2025</em></p>
|
||||
</div>
|
||||
|
||||
952
articles/AMR.md
Normal file
952
articles/AMR.md
Normal file
@@ -0,0 +1,952 @@
|
||||
# Conduct AMR data analysis
|
||||
|
||||
**Note:** values on this page will change with every website update
|
||||
since they are based on randomly created values and the page was written
|
||||
in [R Markdown](https://rmarkdown.rstudio.com/). However, the
|
||||
methodology remains unchanged. This page was generated on 02 April 2026.
|
||||
|
||||
## Introduction
|
||||
|
||||
Conducting AMR data analysis unfortunately requires in-depth knowledge
|
||||
from different scientific fields, which makes it hard to do right. At
|
||||
least, it requires:
|
||||
|
||||
- Good questions (always start with those!) and reliable data
|
||||
- A thorough understanding of (clinical) epidemiology, to understand the
|
||||
clinical and epidemiological relevance and possible bias of results
|
||||
- A thorough understanding of (clinical) microbiology/infectious
|
||||
diseases, to understand which microorganisms are causal to which
|
||||
infections and the implications of pharmaceutical treatment, as well
|
||||
as understanding intrinsic and acquired microbial resistance
|
||||
- Experience with data analysis with microbiological tests and their
|
||||
results, to understand the determination and limitations of MIC values
|
||||
and their interpretations to SIR values
|
||||
- Availability of the biological taxonomy of microorganisms and probably
|
||||
normalisation factors for pharmaceuticals, such as defined daily doses
|
||||
(DDD)
|
||||
- Available (inter-)national guidelines, and profound methods to apply
|
||||
them
|
||||
|
||||
Of course, we cannot instantly provide you with knowledge and
|
||||
experience. But with this `AMR` package, we aimed at providing (1) tools
|
||||
to simplify antimicrobial resistance data cleaning, transformation and
|
||||
analysis, (2) methods to easily incorporate international guidelines and
|
||||
(3) scientifically reliable reference data, including the requirements
|
||||
mentioned above.
|
||||
|
||||
The `AMR` package enables standardised and reproducible AMR data
|
||||
analysis, with the application of evidence-based rules, determination of
|
||||
first isolates, translation of various codes for microorganisms and
|
||||
antimicrobial drugs, determination of (multi-drug) resistant
|
||||
microorganisms, and calculation of antimicrobial resistance, prevalence
|
||||
and future trends.
|
||||
|
||||
## Preparation
|
||||
|
||||
For this tutorial, we will create fake demonstration data to work with.
|
||||
|
||||
You can skip to [Cleaning the data](#cleaning-the-data) if you already
|
||||
have your own data ready. If you start your analysis, try to make the
|
||||
structure of your data generally look like this:
|
||||
|
||||
| date | patient_id | mo | AMX | CIP |
|
||||
|:----------:|:----------:|:----------------:|:---:|:---:|
|
||||
| 2026-04-02 | abcd | Escherichia coli | S | S |
|
||||
| 2026-04-02 | abcd | Escherichia coli | S | R |
|
||||
| 2026-04-02 | efgh | Escherichia coli | R | S |
|
||||
|
||||
### Needed R packages
|
||||
|
||||
As with many uses in R, we need some additional packages for AMR data
|
||||
analysis. Our package works closely together with the [tidyverse
|
||||
packages](https://www.tidyverse.org)
|
||||
[`dplyr`](https://dplyr.tidyverse.org/) and
|
||||
[`ggplot2`](https://ggplot2.tidyverse.org) by RStudio. The tidyverse
|
||||
tremendously improves the way we conduct data science - it allows for a
|
||||
very natural way of writing syntaxes and creating beautiful plots in R.
|
||||
|
||||
We will also use the `cleaner` package, that can be used for cleaning
|
||||
data and creating frequency tables.
|
||||
|
||||
``` r
|
||||
library(dplyr)
|
||||
library(ggplot2)
|
||||
library(AMR)
|
||||
|
||||
# (if not yet installed, install with:)
|
||||
# install.packages(c("dplyr", "ggplot2", "AMR"))
|
||||
```
|
||||
|
||||
The `AMR` package contains a data set `example_isolates_unclean`, which
|
||||
might look data that users have extracted from their laboratory systems:
|
||||
|
||||
``` r
|
||||
example_isolates_unclean
|
||||
#> # A tibble: 3,000 × 8
|
||||
#> patient_id hospital date bacteria AMX AMC CIP GEN
|
||||
#> <chr> <chr> <date> <chr> <chr> <chr> <chr> <chr>
|
||||
#> 1 J3 A 2012-11-21 E. coli R I S S
|
||||
#> 2 R7 A 2018-04-03 K. pneumoniae R I S S
|
||||
#> 3 P3 A 2014-09-19 E. coli R S S S
|
||||
#> 4 P10 A 2015-12-10 E. coli S I S S
|
||||
#> 5 B7 A 2015-03-02 E. coli S S S S
|
||||
#> 6 W3 A 2018-03-31 S. aureus R S R S
|
||||
#> 7 J8 A 2016-06-14 E. coli R S S S
|
||||
#> 8 M3 A 2015-10-25 E. coli R S S S
|
||||
#> 9 J3 A 2019-06-19 E. coli S S S S
|
||||
#> 10 G6 A 2015-04-27 S. aureus S S S S
|
||||
#> # ℹ 2,990 more rows
|
||||
|
||||
# we will use 'our_data' as the data set name for this tutorial
|
||||
our_data <- example_isolates_unclean
|
||||
```
|
||||
|
||||
For AMR data analysis, we would like the microorganism column to contain
|
||||
valid, up-to-date taxonomy, and the antibiotic columns to be cleaned as
|
||||
SIR values as well.
|
||||
|
||||
### Taxonomy of microorganisms
|
||||
|
||||
With [`as.mo()`](https://amr-for-r.org/reference/as.mo.md), users can
|
||||
transform arbitrary microorganism names or codes to current taxonomy.
|
||||
The `AMR` package contains up-to-date taxonomic data. To be specific,
|
||||
currently included data were retrieved on 24 Jun 2024.
|
||||
|
||||
The codes of the AMR packages that come from
|
||||
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) are short, but
|
||||
still human readable. More importantly,
|
||||
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) supports all kinds
|
||||
of input:
|
||||
|
||||
``` r
|
||||
as.mo("Klebsiella pneumoniae")
|
||||
#> Class <mo>
|
||||
#> [1] B_KLBSL_PNMN
|
||||
as.mo("K. pneumoniae")
|
||||
#> Class <mo>
|
||||
#> [1] B_KLBSL_PNMN
|
||||
as.mo("KLEPNE")
|
||||
#> Class <mo>
|
||||
#> [1] B_KLBSL_PNMN
|
||||
as.mo("KLPN")
|
||||
#> Class <mo>
|
||||
#> [1] B_KLBSL_PNMN
|
||||
```
|
||||
|
||||
The first character in above codes denote their taxonomic kingdom, such
|
||||
as Bacteria (B), Fungi (F), and Protozoa (P).
|
||||
|
||||
The `AMR` package also contain functions to directly retrieve taxonomic
|
||||
properties, such as the name, genus, species, family, order, and even
|
||||
Gram-stain. They all start with `mo_` and they use
|
||||
[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) internally, so
|
||||
that still any arbitrary user input can be used:
|
||||
|
||||
``` r
|
||||
mo_family("K. pneumoniae")
|
||||
#> [1] "Enterobacteriaceae"
|
||||
mo_genus("K. pneumoniae")
|
||||
#> [1] "Klebsiella"
|
||||
mo_species("K. pneumoniae")
|
||||
#> [1] "pneumoniae"
|
||||
|
||||
mo_gramstain("Klebsiella pneumoniae")
|
||||
#> [1] "Gram-negative"
|
||||
|
||||
mo_ref("K. pneumoniae")
|
||||
#> [1] "Trevisan, 1887"
|
||||
|
||||
mo_snomed("K. pneumoniae")
|
||||
#> [[1]]
|
||||
#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009"
|
||||
#> [5] "56415008" "714315002" "713926009"
|
||||
```
|
||||
|
||||
Now we can thus clean our data:
|
||||
|
||||
``` r
|
||||
our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE)
|
||||
#> ℹ Retrieved values from the `microorganisms.codes` data set for "ESCCOL",
|
||||
#> "KLEPNE", "STAAUR", and "STRPNE".
|
||||
#> ℹ Microorganism translation was uncertain for four microorganisms. Run
|
||||
#> `mo_uncertainties()` to review these uncertainties, or use
|
||||
#> `add_custom_microorganisms()` to add custom entries.
|
||||
```
|
||||
|
||||
Apparently, there was some uncertainty about the translation to
|
||||
taxonomic codes. Let’s check this:
|
||||
|
||||
``` r
|
||||
mo_uncertainties()
|
||||
#> Matching scores are based on the resemblance between the input and the full
|
||||
#> taxonomic name, and the pathogenicity in humans. See `mo_matching_score()`.
|
||||
#> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000
|
||||
#> -------------------------------------------------------------------------------
|
||||
#> "E. coli" -> Escherichia coli (B_ESCHR_COLI, 0.688)
|
||||
#> Also matched: Enterococcus crotali (0.650), Escherichia coli coli (0.643),
|
||||
#> Escherichia coli expressing (0.611), Enterobacter cowanii (0.600), Enterococcus
|
||||
#> columbae (0.595), Enterococcus camelliae (0.591), Enterococcus casseliflavus
|
||||
#> (0.577), Enterobacter cloacae cloacae (0.571), Enterobacter cloacae complex
|
||||
#> (0.571), and Enterobacter cloacae dissolvens (0.565)
|
||||
#> -------------------------------------------------------------------------------
|
||||
#> "K. pneumoniae" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786)
|
||||
#> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella pneumoniae
|
||||
#> ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), Klebsiella
|
||||
#> pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii (0.500), Klebsiella
|
||||
#> planticola (0.500), Kingella potus (0.400), Kluyveromyces pseudotropicale
|
||||
#> (0.386), Kluyveromyces pseudotropicalis (0.363), and Kosakonia pseudosacchari
|
||||
#> (0.361)
|
||||
#> -------------------------------------------------------------------------------
|
||||
#> "S. aureus" -> Staphylococcus aureus (B_STPHY_AURS, 0.690)
|
||||
#> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus argenteus
|
||||
#> (0.625), Staphylococcus aureus anaerobius (0.625), Staphylococcus auricularis
|
||||
#> (0.615), Salmonella Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella
|
||||
#> Amounderness (0.587), Staphylococcus argensis (0.587), Streptococcus australis
|
||||
#> (0.587), and Salmonella choleraesuis arizonae (0.562)
|
||||
#> -------------------------------------------------------------------------------
|
||||
#> "S. pneumoniae" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750)
|
||||
#> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus phocae
|
||||
#> salmonis (0.552), Serratia proteamaculans quinovora (0.545), Streptococcus
|
||||
#> pseudoporcinus (0.536), Staphylococcus piscifermentans (0.533), Staphylococcus
|
||||
#> pseudintermedius (0.532), Serratia proteamaculans proteamaculans (0.526),
|
||||
#> Streptococcus gallolyticus pasteurianus (0.526), Salmonella Portanigra (0.524),
|
||||
#> and Streptococcus periodonticum (0.519)
|
||||
#> ℹ Only the first 10 other matches of each record are shown. Run ``
|
||||
#> `print(mo_uncertainties(), n = ...)` `` to view more entries, or save
|
||||
#> `mo_uncertainties()` to an object.
|
||||
```
|
||||
|
||||
That’s all good.
|
||||
|
||||
### Antibiotic results
|
||||
|
||||
The column with antibiotic test results must also be cleaned. The `AMR`
|
||||
package comes with three new data types to work with such test results:
|
||||
`mic` for minimal inhibitory concentrations (MIC), `disk` for disk
|
||||
diffusion diameters, and `sir` for SIR data that have been interpreted
|
||||
already. This package can also determine SIR values based on MIC or disk
|
||||
diffusion values, read more about that on the
|
||||
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) page.
|
||||
|
||||
For now, we will just clean the SIR columns in our data using dplyr:
|
||||
|
||||
``` r
|
||||
# method 1, be explicit about the columns:
|
||||
our_data <- our_data %>%
|
||||
mutate_at(vars(AMX:GEN), as.sir)
|
||||
|
||||
# method 2, let the AMR package determine the eligible columns
|
||||
our_data <- our_data %>%
|
||||
mutate_if(is_sir_eligible, as.sir)
|
||||
|
||||
# result:
|
||||
our_data
|
||||
#> # A tibble: 3,000 × 8
|
||||
#> patient_id hospital date bacteria AMX AMC CIP GEN
|
||||
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir>
|
||||
#> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S
|
||||
#> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S
|
||||
#> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S
|
||||
#> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S
|
||||
#> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S
|
||||
#> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S
|
||||
#> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S
|
||||
#> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S
|
||||
#> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S
|
||||
#> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S
|
||||
#> # ℹ 2,990 more rows
|
||||
```
|
||||
|
||||
This is basically it for the cleaning, time to start the data inclusion.
|
||||
|
||||
### First isolates
|
||||
|
||||
We need to know which isolates we can *actually* use for analysis
|
||||
without repetition bias.
|
||||
|
||||
To conduct an analysis of antimicrobial resistance, you must [only
|
||||
include the first isolate of every patient per
|
||||
episode](https:/pubmed.ncbi.nlm.nih.gov/17304462/) (Hindler *et al.*,
|
||||
Clin Infect Dis. 2007). 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 weeks (yes, some countries
|
||||
like the Netherlands have these blood drawing policies). The resistance
|
||||
percentage of oxacillin of all isolates would be overestimated, because
|
||||
you included this MRSA more than once. It would clearly be [selection
|
||||
bias](https://en.wikipedia.org/wiki/Selection_bias).
|
||||
|
||||
The Clinical and Laboratory Standards Institute (CLSI) appoints this as
|
||||
follows:
|
||||
|
||||
> *(…) When preparing a cumulative antibiogram to guide clinical
|
||||
> decisions about empirical antimicrobial therapy of initial infections,
|
||||
> **only the first isolate of a given species per patient, per analysis
|
||||
> period (eg, one year) should be included, irrespective of body site,
|
||||
> antimicrobial susceptibility profile, or other phenotypical
|
||||
> characteristics (eg, biotype)**. The first isolate is easily
|
||||
> identified, and cumulative antimicrobial susceptibility test data
|
||||
> prepared using the first isolate are generally comparable to
|
||||
> cumulative antimicrobial susceptibility test data calculated by other
|
||||
> methods, providing duplicate isolates are excluded.*
|
||||
> [M39-A4 Analysis and Presentation of Cumulative Antimicrobial
|
||||
> Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter
|
||||
> 6.4](https://clsi.org/standards/products/microbiology/documents/m39/)
|
||||
|
||||
This `AMR` package includes this methodology with the
|
||||
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
|
||||
function and is able to apply the four different methods as defined by
|
||||
[Hindler *et al.* in
|
||||
2007](https://academic.oup.com/cid/article/44/6/867/364325):
|
||||
phenotype-based, episode-based, patient-based, isolate-based. The right
|
||||
method depends on your goals and analysis, but the default
|
||||
phenotype-based method is in any case the method to properly correct for
|
||||
most duplicate isolates. Read more about the methods on the
|
||||
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
|
||||
page.
|
||||
|
||||
The outcome of the function can easily be added to our data:
|
||||
|
||||
``` r
|
||||
our_data <- our_data %>%
|
||||
mutate(first = first_isolate(info = TRUE))
|
||||
#> ℹ Determining first isolates using an episode length of 365 days
|
||||
#> ℹ Using column bacteria as input for `col_mo`.
|
||||
#> ℹ Column first is SIR eligible (despite only having empty values), since it
|
||||
#> seems to be cefozopran (ZOP)
|
||||
#> ℹ Using column date as input for `col_date`.
|
||||
#> ℹ Using column patient_id as input for `col_patient_id`.
|
||||
#> ℹ Basing inclusion on all antimicrobial results, using a points threshold of 2
|
||||
#> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a
|
||||
#> microbial ID was available)
|
||||
```
|
||||
|
||||
So only 91% is suitable for resistance analysis! We can now filter on it
|
||||
with the [`filter()`](https://dplyr.tidyverse.org/reference/filter.html)
|
||||
function, also from the `dplyr` package:
|
||||
|
||||
``` r
|
||||
our_data_1st <- our_data %>%
|
||||
filter(first == TRUE)
|
||||
```
|
||||
|
||||
For future use, the above two syntaxes can be shortened:
|
||||
|
||||
``` r
|
||||
our_data_1st <- our_data %>%
|
||||
filter_first_isolate()
|
||||
```
|
||||
|
||||
So we end up with 2 724 isolates for analysis. Now our data looks like:
|
||||
|
||||
``` r
|
||||
our_data_1st
|
||||
#> # A tibble: 2,724 × 9
|
||||
#> patient_id hospital date bacteria AMX AMC CIP GEN first
|
||||
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
|
||||
#> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE
|
||||
#> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE
|
||||
#> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE
|
||||
#> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE
|
||||
#> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE
|
||||
#> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE
|
||||
#> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE
|
||||
#> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE
|
||||
#> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE
|
||||
#> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE
|
||||
#> # ℹ 2,714 more rows
|
||||
```
|
||||
|
||||
Time for the analysis.
|
||||
|
||||
## Analysing the data
|
||||
|
||||
The base R [`summary()`](https://rdrr.io/r/base/summary.html) function
|
||||
gives a good first impression, as it comes with support for the new `mo`
|
||||
and `sir` classes that we now have in our data set:
|
||||
|
||||
``` r
|
||||
summary(our_data_1st)
|
||||
#> patient_id hospital date
|
||||
#> Length:2724 Length:2724 Min. :2011-01-01
|
||||
#> Class :character Class :character 1st Qu.:2013-04-07
|
||||
#> Mode :character Mode :character Median :2015-06-03
|
||||
#> Mean :2015-06-09
|
||||
#> 3rd Qu.:2017-08-11
|
||||
#> Max. :2019-12-27
|
||||
#> bacteria AMX AMC
|
||||
#> Class :mo Class:sir Class:sir
|
||||
#> <NA> :0 %S :41.6% (n=1133) %S :52.6% (n=1432)
|
||||
#> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0)
|
||||
#> #1 :B_ESCHR_COLI %I :16.4% (n=446) %I :12.2% (n=333)
|
||||
#> #2 :B_STPHY_AURS %R :42.0% (n=1145) %R :35.2% (n=959)
|
||||
#> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0)
|
||||
#> CIP GEN first
|
||||
#> Class:sir Class:sir Mode:logical
|
||||
#> %S :52.5% (n=1431) %S :61.0% (n=1661) TRUE:2724
|
||||
#> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0)
|
||||
#> %I : 6.5% (n=176) %I : 3.0% (n=82)
|
||||
#> %R :41.0% (n=1117) %R :36.0% (n=981)
|
||||
#> %NI : 0.0% (n=0) %NI : 0.0% (n=0)
|
||||
|
||||
glimpse(our_data_1st)
|
||||
#> Rows: 2,724
|
||||
#> Columns: 9
|
||||
#> $ patient_id <chr> "J3", "R7", "P3", "P10", "B7", "W3", "M3", "J3", "G6", "P4"…
|
||||
#> $ hospital <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…
|
||||
#> $ date <date> 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02…
|
||||
#> $ bacteria <mo> "B_ESCHR_COLI", "B_KLBSL_PNMN", "B_ESCHR_COLI", "B_ESCHR_COL…
|
||||
#> $ AMX <sir> R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,…
|
||||
#> $ AMC <sir> I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,…
|
||||
#> $ CIP <sir> S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…
|
||||
#> $ GEN <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,…
|
||||
#> $ first <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
|
||||
|
||||
# number of unique values per column:
|
||||
sapply(our_data_1st, n_distinct)
|
||||
#> patient_id hospital date bacteria AMX AMC CIP
|
||||
#> 260 3 1854 4 3 3 3
|
||||
#> GEN first
|
||||
#> 3 1
|
||||
```
|
||||
|
||||
### Availability of species
|
||||
|
||||
To just get an idea how the species are distributed, create a frequency
|
||||
table with [`count()`](https://amr-for-r.org/reference/count.md) based
|
||||
on the name of the microorganisms:
|
||||
|
||||
``` r
|
||||
our_data %>%
|
||||
count(mo_name(bacteria), sort = TRUE)
|
||||
#> # A tibble: 4 × 2
|
||||
#> `mo_name(bacteria)` n
|
||||
#> <chr> <int>
|
||||
#> 1 Escherichia coli 1518
|
||||
#> 2 Staphylococcus aureus 730
|
||||
#> 3 Streptococcus pneumoniae 426
|
||||
#> 4 Klebsiella pneumoniae 326
|
||||
|
||||
our_data_1st %>%
|
||||
count(mo_name(bacteria), sort = TRUE)
|
||||
#> # A tibble: 4 × 2
|
||||
#> `mo_name(bacteria)` n
|
||||
#> <chr> <int>
|
||||
#> 1 Escherichia coli 1321
|
||||
#> 2 Staphylococcus aureus 682
|
||||
#> 3 Streptococcus pneumoniae 402
|
||||
#> 4 Klebsiella pneumoniae 319
|
||||
```
|
||||
|
||||
### Select and filter with antibiotic selectors
|
||||
|
||||
Using so-called antibiotic class selectors, you can select or filter
|
||||
columns based on the antibiotic class that your antibiotic results are
|
||||
in:
|
||||
|
||||
``` r
|
||||
our_data_1st %>%
|
||||
select(date, aminoglycosides())
|
||||
#> ℹ For `aminoglycosides()` using column GEN
|
||||
#> (gentamicin)
|
||||
#> # A tibble: 2,724 × 2
|
||||
#> date GEN
|
||||
#> <date> <sir>
|
||||
#> 1 2012-11-21 S
|
||||
#> 2 2018-04-03 S
|
||||
#> 3 2014-09-19 S
|
||||
#> 4 2015-12-10 S
|
||||
#> 5 2015-03-02 S
|
||||
#> 6 2018-03-31 S
|
||||
#> 7 2015-10-25 S
|
||||
#> 8 2019-06-19 S
|
||||
#> 9 2015-04-27 S
|
||||
#> 10 2011-06-21 S
|
||||
#> # ℹ 2,714 more rows
|
||||
|
||||
our_data_1st %>%
|
||||
select(bacteria, betalactams())
|
||||
#> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC
|
||||
#> (amoxicillin/clavulanic acid)
|
||||
#> # A tibble: 2,724 × 3
|
||||
#> bacteria AMX AMC
|
||||
#> <mo> <sir> <sir>
|
||||
#> 1 B_ESCHR_COLI R I
|
||||
#> 2 B_KLBSL_PNMN R I
|
||||
#> 3 B_ESCHR_COLI R S
|
||||
#> 4 B_ESCHR_COLI S I
|
||||
#> 5 B_ESCHR_COLI S S
|
||||
#> 6 B_STPHY_AURS R S
|
||||
#> 7 B_ESCHR_COLI R S
|
||||
#> 8 B_ESCHR_COLI S S
|
||||
#> 9 B_STPHY_AURS S S
|
||||
#> 10 B_ESCHR_COLI S S
|
||||
#> # ℹ 2,714 more rows
|
||||
|
||||
our_data_1st %>%
|
||||
select(bacteria, where(is.sir))
|
||||
#> # A tibble: 2,724 × 5
|
||||
#> bacteria AMX AMC CIP GEN
|
||||
#> <mo> <sir> <sir> <sir> <sir>
|
||||
#> 1 B_ESCHR_COLI R I S S
|
||||
#> 2 B_KLBSL_PNMN R I S S
|
||||
#> 3 B_ESCHR_COLI R S S S
|
||||
#> 4 B_ESCHR_COLI S I S S
|
||||
#> 5 B_ESCHR_COLI S S S S
|
||||
#> 6 B_STPHY_AURS R S R S
|
||||
#> 7 B_ESCHR_COLI R S S S
|
||||
#> 8 B_ESCHR_COLI S S S S
|
||||
#> 9 B_STPHY_AURS S S S S
|
||||
#> 10 B_ESCHR_COLI S S S S
|
||||
#> # ℹ 2,714 more rows
|
||||
|
||||
# filtering using AB selectors is also possible:
|
||||
our_data_1st %>%
|
||||
filter(any(aminoglycosides() == "R"))
|
||||
#> ℹ For `aminoglycosides()` using column GEN
|
||||
#> (gentamicin)
|
||||
#> # A tibble: 981 × 9
|
||||
#> patient_id hospital date bacteria AMX AMC CIP GEN first
|
||||
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
|
||||
#> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE
|
||||
#> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE
|
||||
#> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE
|
||||
#> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE
|
||||
#> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE
|
||||
#> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE
|
||||
#> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE
|
||||
#> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE
|
||||
#> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE
|
||||
#> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE
|
||||
#> # ℹ 971 more rows
|
||||
|
||||
our_data_1st %>%
|
||||
filter(all(betalactams() == "R"))
|
||||
#> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC
|
||||
#> (amoxicillin/clavulanic acid)
|
||||
#> # A tibble: 462 × 9
|
||||
#> patient_id hospital date bacteria AMX AMC CIP GEN first
|
||||
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
|
||||
#> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE
|
||||
#> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE
|
||||
#> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE
|
||||
#> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE
|
||||
#> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE
|
||||
#> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE
|
||||
#> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE
|
||||
#> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE
|
||||
#> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE
|
||||
#> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE
|
||||
#> # ℹ 452 more rows
|
||||
|
||||
# even works in base R (since R 3.0):
|
||||
our_data_1st[all(betalactams() == "R"), ]
|
||||
#> ℹ For `betalactams()` using columns AMX (amoxicillin) and AMC
|
||||
#> (amoxicillin/clavulanic acid)
|
||||
#> # A tibble: 462 × 9
|
||||
#> patient_id hospital date bacteria AMX AMC CIP GEN first
|
||||
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
|
||||
#> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE
|
||||
#> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE
|
||||
#> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE
|
||||
#> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE
|
||||
#> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE
|
||||
#> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE
|
||||
#> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE
|
||||
#> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE
|
||||
#> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE
|
||||
#> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE
|
||||
#> # ℹ 452 more rows
|
||||
```
|
||||
|
||||
### Generate antibiograms
|
||||
|
||||
Since AMR v2.0 (March 2023), it is very easy to create different types
|
||||
of antibiograms, with support for 20 different languages.
|
||||
|
||||
There are four antibiogram types, as proposed by Klinker *et al.* (2021,
|
||||
[DOI
|
||||
10.1177/20499361211011373](https://doi.org/10.1177/20499361211011373)),
|
||||
and they are all supported by the new
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function:
|
||||
|
||||
1. **Traditional Antibiogram (TA)** e.g, for the susceptibility of
|
||||
*Pseudomonas aeruginosa* to piperacillin/tazobactam (TZP)
|
||||
2. **Combination Antibiogram (CA)** e.g, for the sdditional
|
||||
susceptibility of *Pseudomonas aeruginosa* to TZP + tobramycin
|
||||
versus TZP alone
|
||||
3. **Syndromic Antibiogram (SA)** e.g, for the susceptibility of
|
||||
*Pseudomonas aeruginosa* to TZP among respiratory specimens
|
||||
(obtained among ICU patients only)
|
||||
4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**
|
||||
e.g, for the susceptibility of *Pseudomonas aeruginosa* to TZP among
|
||||
respiratory specimens (obtained among ICU patients only) for male
|
||||
patients age \>=65 years with heart failure
|
||||
|
||||
In this section, we show how to use the
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function to create any of the above antibiogram types. For starters,
|
||||
this is what the included `example_isolates` data set looks like:
|
||||
|
||||
``` r
|
||||
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>, …
|
||||
```
|
||||
|
||||
#### Traditional Antibiogram
|
||||
|
||||
To create a traditional antibiogram, simply state which antibiotics
|
||||
should be used. The `antibiotics` argument in the
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function supports any (combination) of the previously mentioned
|
||||
antibiotic class selectors:
|
||||
|
||||
``` r
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c(aminoglycosides(), carbapenems()))
|
||||
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
|
||||
#> (amikacin), and KAN (kanamycin)
|
||||
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
|
||||
```
|
||||
|
||||
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|
||||
|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------|
|
||||
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
|
||||
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
|
||||
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
|
||||
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
|
||||
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
|
||||
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
|
||||
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
|
||||
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
|
||||
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
|
||||
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
|
||||
|
||||
Notice that the
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function automatically prints in the right format when using Quarto or R
|
||||
Markdown (such as this page), and even applies italics for taxonomic
|
||||
names (by using
|
||||
[`italicise_taxonomy()`](https://amr-for-r.org/reference/italicise_taxonomy.md)
|
||||
internally).
|
||||
|
||||
It also uses the language of your OS if this is either English, Arabic,
|
||||
Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek,
|
||||
Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish,
|
||||
Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish,
|
||||
Ukrainian, Urdu, or Vietnamese. In this next example, we force the
|
||||
language to be Spanish using the `language` argument:
|
||||
|
||||
``` r
|
||||
antibiogram(example_isolates,
|
||||
mo_transform = "gramstain",
|
||||
antibiotics = aminoglycosides(),
|
||||
ab_transform = "name",
|
||||
language = "es")
|
||||
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
|
||||
#> (amikacin), and KAN (kanamycin)
|
||||
```
|
||||
|
||||
| Patógeno | Amikacina | Gentamicina | Kanamicina | Tobramicina |
|
||||
|:--------------|:-------------------|:--------------------|:----------------|:-------------------|
|
||||
| Gram negativo | 98% (96-99%,N=256) | 96% (95-98%,N=684) | 0% (0-10%,N=35) | 96% (94-97%,N=686) |
|
||||
| Gram positivo | 0% (0-1%,N=436) | 63% (60-66%,N=1170) | 0% (0-1%,N=436) | 34% (31-38%,N=665) |
|
||||
|
||||
#### Combined Antibiogram
|
||||
|
||||
To create a combined antibiogram, use antibiotic codes or names with a
|
||||
plus `+` character like this:
|
||||
|
||||
``` r
|
||||
combined_ab <- antibiogram(example_isolates,
|
||||
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
ab_transform = NULL)
|
||||
combined_ab
|
||||
```
|
||||
|
||||
| Pathogen | TZP | TZP + GEN | TZP + TOB |
|
||||
|:-----------------|:---------------------|:---------------------|:---------------------|
|
||||
| CoNS | 30% (16-49%,N=33) | 97% (95-99%,N=274) | NA |
|
||||
| *E. coli* | 94% (92-96%,N=416) | 100% (98-100%,N=459) | 99% (97-100%,N=461) |
|
||||
| *K. pneumoniae* | 89% (77-96%,N=53) | 93% (83-98%,N=58) | 93% (83-98%,N=58) |
|
||||
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | 100% (88-100%,N=30) |
|
||||
| *P. mirabilis* | NA | 100% (90-100%,N=34) | 100% (90-100%,N=34) |
|
||||
| *S. aureus* | NA | 100% (98-100%,N=231) | 100% (96-100%,N=91) |
|
||||
| *S. epidermidis* | NA | 100% (97-100%,N=128) | 100% (92-100%,N=46) |
|
||||
| *S. hominis* | NA | 100% (95-100%,N=74) | 100% (93-100%,N=53) |
|
||||
| *S. pneumoniae* | 100% (97-100%,N=112) | 100% (97-100%,N=112) | 100% (97-100%,N=112) |
|
||||
|
||||
#### Syndromic Antibiogram
|
||||
|
||||
To create a syndromic antibiogram, the `syndromic_group` argument must
|
||||
be used. This can be any column in the data, or e.g. an
|
||||
[`ifelse()`](https://rdrr.io/r/base/ifelse.html) with calculations based
|
||||
on certain columns:
|
||||
|
||||
``` r
|
||||
antibiogram(example_isolates,
|
||||
antibiotics = c(aminoglycosides(), carbapenems()),
|
||||
syndromic_group = "ward")
|
||||
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
|
||||
#> (amikacin), and KAN (kanamycin)
|
||||
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
|
||||
```
|
||||
|
||||
| Syndromic Group | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|
||||
|:----------------|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------|
|
||||
| Clinical | CoNS | NA | 89% (84-93%,N=205) | 57% (39-74%,N=35) | NA | 57% (39-74%,N=35) | 26% (12-45%,N=31) |
|
||||
| ICU | CoNS | NA | 79% (68-88%,N=73) | NA | NA | NA | NA |
|
||||
| Outpatient | CoNS | NA | 84% (66-95%,N=31) | NA | NA | NA | NA |
|
||||
| Clinical | *E. coli* | 100% (97-100%,N=104) | 98% (96-99%,N=297) | 100% (99-100%,N=266) | NA | 100% (99-100%,N=276) | 98% (96-99%,N=299) |
|
||||
| ICU | *E. coli* | 100% (93-100%,N=52) | 99% (95-100%,N=137) | 100% (97-100%,N=133) | NA | 100% (97-100%,N=118) | 96% (92-99%,N=137) |
|
||||
| Clinical | *K. pneumoniae* | NA | 92% (81-98%,N=51) | 100% (92-100%,N=44) | NA | 100% (92-100%,N=46) | 92% (81-98%,N=51) |
|
||||
| Clinical | *P. mirabilis* | NA | 100% (88-100%,N=30) | NA | NA | NA | 100% (88-100%,N=30) |
|
||||
| Clinical | *S. aureus* | NA | 99% (95-100%,N=150) | NA | NA | NA | 97% (89-100%,N=63) |
|
||||
| ICU | *S. aureus* | NA | 100% (95-100%,N=66) | NA | NA | NA | NA |
|
||||
| Clinical | *S. epidermidis* | NA | 82% (72-90%,N=79) | NA | NA | NA | 55% (39-70%,N=44) |
|
||||
| ICU | *S. epidermidis* | NA | 72% (60-82%,N=75) | NA | NA | NA | 41% (26-58%,N=41) |
|
||||
| Clinical | *S. hominis* | NA | 96% (85-99%,N=45) | NA | NA | NA | 94% (79-99%,N=31) |
|
||||
| Clinical | *S. pneumoniae* | 0% (0-5%,N=78) | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) |
|
||||
| ICU | *S. pneumoniae* | 0% (0-12%,N=30) | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) |
|
||||
|
||||
#### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
|
||||
|
||||
To create a **Weighted-Incidence Syndromic Combination Antibiogram
|
||||
(WISCA)**, simply set `wisca = TRUE` in the
|
||||
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
|
||||
function, or use the dedicated
|
||||
[`wisca()`](https://amr-for-r.org/reference/antibiogram.md) function.
|
||||
Unlike traditional antibiograms, WISCA provides syndrome-based
|
||||
susceptibility estimates, weighted by pathogen incidence and
|
||||
antimicrobial susceptibility patterns.
|
||||
|
||||
``` r
|
||||
example_isolates %>%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
|
||||
minimum = 10) # Recommended threshold: ≥30
|
||||
```
|
||||
|
||||
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|
||||
|:------------------------|:-------------------------------------|:-------------------------------------|
|
||||
| 69.4% (64.3-74.3%) | 92.6% (91.1-93.9%) | 88.7% (85.8-91.2%) |
|
||||
|
||||
WISCA uses a **Bayesian decision model** to integrate data from multiple
|
||||
pathogens, improving empirical therapy guidance, especially for
|
||||
low-incidence infections. It is **pathogen-agnostic**, meaning results
|
||||
are syndrome-based rather than stratified by microorganism.
|
||||
|
||||
For reliable results, ensure your data includes **only first isolates**
|
||||
(use
|
||||
[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md))
|
||||
and consider filtering for **the top *n* species** (use
|
||||
[`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md)),
|
||||
as WISCA outcomes are most meaningful when based on robust incidence
|
||||
estimates.
|
||||
|
||||
For **patient- or syndrome-specific WISCA**, run the function on a
|
||||
grouped `tibble`, i.e., using
|
||||
[`group_by()`](https://dplyr.tidyverse.org/reference/group_by.html)
|
||||
first:
|
||||
|
||||
``` r
|
||||
example_isolates %>%
|
||||
top_n_microorganisms(n = 10) %>%
|
||||
group_by(age_group = age_groups(age, c(25, 50, 75)),
|
||||
gender) %>%
|
||||
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
|
||||
```
|
||||
|
||||
| age_group | gender | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|
||||
|:----------|:-------|:------------------------|:-------------------------------------|:-------------------------------------|
|
||||
| 0-24 | F | 56.6% (25.2-83.9%) | 73.6% (48-91.6%) | 68.6% (42.9-89.5%) |
|
||||
| 0-24 | M | 60.3% (28.4-87.1%) | 79.7% (57.6-94.2%) | 60.1% (29.5-87.7%) |
|
||||
| 25-49 | F | 66.6% (45.6-85.5%) | 91.7% (84.6-96.7%) | 83% (67.9-94%) |
|
||||
| 25-49 | M | 56.4% (29.1-81.7%) | 89.2% (80.3-95.7%) | 72.4% (49.7-90%) |
|
||||
| 50-74 | F | 67.8% (55.8-80.1%) | 95.6% (93.2-97.5%) | 88.1% (80.4-94.6%) |
|
||||
| 50-74 | M | 66.2% (54.8-75.8%) | 95.2% (92.4-97.4%) | 84.4% (74.4-92.5%) |
|
||||
| 75+ | F | 71.7% (61-81.7%) | 96.6% (94.4-98.2%) | 90.6% (84.6-95.3%) |
|
||||
| 75+ | M | 72.9% (63.8-82%) | 96.6% (94.6-98.1%) | 92.8% (87.8-96.5%) |
|
||||
|
||||
#### Plotting antibiograms
|
||||
|
||||
Antibiograms can be plotted using
|
||||
[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
|
||||
from the `ggplot2` packages, since this `AMR` package provides an
|
||||
extension to that function:
|
||||
|
||||
``` r
|
||||
autoplot(combined_ab)
|
||||
```
|
||||
|
||||
|
||||
|
||||
To calculate antimicrobial resistance in a more sensible way, also by
|
||||
correcting for too few results, we use the
|
||||
[`resistance()`](https://amr-for-r.org/reference/proportion.md) and
|
||||
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
|
||||
functions.
|
||||
|
||||
### Resistance percentages
|
||||
|
||||
The functions
|
||||
[`resistance()`](https://amr-for-r.org/reference/proportion.md) and
|
||||
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md) can
|
||||
be used to calculate antimicrobial resistance or susceptibility. For
|
||||
more specific analyses, the functions
|
||||
[`proportion_S()`](https://amr-for-r.org/reference/proportion.md),
|
||||
[`proportion_SI()`](https://amr-for-r.org/reference/proportion.md),
|
||||
[`proportion_I()`](https://amr-for-r.org/reference/proportion.md),
|
||||
[`proportion_IR()`](https://amr-for-r.org/reference/proportion.md) and
|
||||
[`proportion_R()`](https://amr-for-r.org/reference/proportion.md) can be
|
||||
used to determine the proportion of a specific antimicrobial outcome.
|
||||
|
||||
All these functions contain a `minimum` argument, denoting the minimum
|
||||
required number of test results for returning a value. These functions
|
||||
will otherwise return `NA`. The default is `minimum = 30`, following the
|
||||
[CLSI M39-A4
|
||||
guideline](https://clsi.org/standards/products/microbiology/documents/m39/)
|
||||
for applying microbial epidemiology.
|
||||
|
||||
As per the EUCAST guideline of 2019, we calculate resistance as the
|
||||
proportion of R
|
||||
([`proportion_R()`](https://amr-for-r.org/reference/proportion.md),
|
||||
equal to
|
||||
[`resistance()`](https://amr-for-r.org/reference/proportion.md)) and
|
||||
susceptibility as the proportion of S and I
|
||||
([`proportion_SI()`](https://amr-for-r.org/reference/proportion.md),
|
||||
equal to
|
||||
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)).
|
||||
These functions can be used on their own:
|
||||
|
||||
``` r
|
||||
our_data_1st %>% resistance(AMX)
|
||||
#> 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.
|
||||
#> [1] 0.4203377
|
||||
```
|
||||
|
||||
Or can be used in conjunction with
|
||||
[`group_by()`](https://dplyr.tidyverse.org/reference/group_by.html) and
|
||||
[`summarise()`](https://dplyr.tidyverse.org/reference/summarise.html),
|
||||
both from the `dplyr` package:
|
||||
|
||||
``` r
|
||||
our_data_1st %>%
|
||||
group_by(hospital) %>%
|
||||
summarise(amoxicillin = resistance(AMX))
|
||||
#> # A tibble: 3 × 2
|
||||
#> hospital amoxicillin
|
||||
#> <chr> <dbl>
|
||||
#> 1 A 0.340
|
||||
#> 2 B 0.551
|
||||
#> 3 C 0.370
|
||||
```
|
||||
|
||||
### Interpreting MIC and Disk Diffusion Values
|
||||
|
||||
Minimal inhibitory concentration (MIC) values and disk diffusion
|
||||
diameters can be interpreted into clinical breakpoints (SIR) using
|
||||
[`as.sir()`](https://amr-for-r.org/reference/as.sir.md). Here’s an
|
||||
example with randomly generated MIC values for *Klebsiella pneumoniae*
|
||||
and ciprofloxacin:
|
||||
|
||||
``` r
|
||||
set.seed(123)
|
||||
mic_values <- random_mic(100)
|
||||
sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
|
||||
|
||||
my_data <- tibble(MIC = mic_values, SIR = sir_values)
|
||||
my_data
|
||||
#> # A tibble: 100 × 2
|
||||
#> MIC SIR
|
||||
#> <mic> <sir>
|
||||
#> 1 <=0.0001 S
|
||||
#> 2 0.0160 S
|
||||
#> 3 >=8.0000 R
|
||||
#> 4 0.0320 S
|
||||
#> 5 0.0080 S
|
||||
#> 6 64.0000 R
|
||||
#> 7 0.0080 S
|
||||
#> 8 0.1250 S
|
||||
#> 9 0.0320 S
|
||||
#> 10 0.0002 S
|
||||
#> # ℹ 90 more rows
|
||||
```
|
||||
|
||||
This allows direct interpretation according to EUCAST or CLSI
|
||||
breakpoints, facilitating automated AMR data processing.
|
||||
|
||||
### Plotting MIC and SIR Interpretations
|
||||
|
||||
We can visualise MIC distributions and their SIR interpretations using
|
||||
`ggplot2`, using the new
|
||||
[`scale_y_mic()`](https://amr-for-r.org/reference/plot.md) for the
|
||||
y-axis and
|
||||
[`scale_colour_sir()`](https://amr-for-r.org/reference/plot.md) to
|
||||
colour-code SIR categories.
|
||||
|
||||
``` r
|
||||
# add a group
|
||||
my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
|
||||
|
||||
ggplot(my_data,
|
||||
aes(x = group, y = MIC, colour = SIR)) +
|
||||
geom_jitter(width = 0.2, size = 2) +
|
||||
geom_boxplot(fill = NA, colour = "grey40") +
|
||||
scale_y_mic() +
|
||||
scale_colour_sir() +
|
||||
labs(title = "MIC Distribution and SIR Interpretation",
|
||||
x = "Sample Groups",
|
||||
y = "MIC (mg/L)")
|
||||
```
|
||||
|
||||
|
||||
|
||||
This plot provides an intuitive way to assess susceptibility patterns
|
||||
across different groups while incorporating clinical breakpoints.
|
||||
|
||||
For a more straightforward and less manual approach, `ggplot2`’s
|
||||
function
|
||||
[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
|
||||
has been extended by this package to directly plot MIC and disk
|
||||
diffusion values:
|
||||
|
||||
``` r
|
||||
autoplot(mic_values)
|
||||
```
|
||||
|
||||
|
||||
|
||||
``` r
|
||||
|
||||
# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
|
||||
autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
|
||||
```
|
||||
|
||||
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
*Author: Dr. Matthijs Berends, 23rd Feb 2025*
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
|
||||
217
articles/AMR_for_Python.md
Normal file
217
articles/AMR_for_Python.md
Normal file
@@ -0,0 +1,217 @@
|
||||
# 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.
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
@@ -88,7 +88,7 @@
|
||||
|
||||
|
||||
<blockquote>
|
||||
<p>This page was entirely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
|
||||
<p>This page was almost entirely written by our <a href="https://chat.amr-for-r.org" class="external-link">AMR for R Assistant</a>, a ChatGPT
|
||||
manually-trained model able to answer any question about the
|
||||
<code>AMR</code> package.</p>
|
||||
</blockquote>
|
||||
@@ -147,16 +147,16 @@ package.</p>
|
||||
<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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #B2B2B2;"> NA</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>
|
||||
@@ -179,14 +179,13 @@ package.</p>
|
||||
<span> mo <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</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"># drop NAs - the ones without a Gramstain (fungi, etc.)</span></span>
|
||||
<span> <span class="fu">drop_na</span><span class="op">(</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`betalactams()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span></code></pre></div>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span></span>
|
||||
<span><span class="co">#> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `betalactams()` using columns <span style="color: #00BB00; font-weight: bold;">PEN</span> (benzylpenicillin), <span style="color: #00BB00; font-weight: bold;">OXA</span> (oxacillin),</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BB00; font-weight: bold;">FLC</span> (flucloxacillin), <span style="color: #00BB00; font-weight: bold;">AMX</span> (amoxicillin), <span style="color: #00BB00; font-weight: bold;">AMC</span> (amoxicillin/clavulanic acid),</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BB00; font-weight: bold;">AMP</span> (ampicillin), <span style="color: #00BB00; font-weight: bold;">TZP</span> (piperacillin/tazobactam), <span style="color: #00BB00; font-weight: bold;">CZO</span> (cefazolin), <span style="color: #00BB00; font-weight: bold;">FEP</span></span></span>
|
||||
<span><span class="co">#> (cefepime), <span style="color: #00BB00; font-weight: bold;">CXM</span> (cefuroxime), <span style="color: #00BB00; font-weight: bold;">FOX</span> (cefoxitin), <span style="color: #00BB00; font-weight: bold;">CTX</span> (cefotaxime), <span style="color: #00BB00; font-weight: bold;">CAZ</span></span></span>
|
||||
<span><span class="co">#> (ceftazidime), <span style="color: #00BB00; font-weight: bold;">CRO</span> (ceftriaxone), <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem), and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span></span></code></pre></div>
|
||||
<p><strong>Explanation:</strong></p>
|
||||
<ul>
|
||||
<li>
|
||||
@@ -227,26 +226,36 @@ we have with <code>step_corr()</code>, the necessary parameters can be
|
||||
estimated from a training set using <code>prep()</code>:</p>
|
||||
<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu">prep</span><span class="op">(</span><span class="va">resistance_recipe</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`aminoglycosides()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">' (gentamicin), '</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (tobramycin), '</span><span style="color: #0000BB; font-weight: bold;">AMK</span><span style="color: #0000BB;">' (amikacin), and '</span><span style="color: #0000BB; font-weight: bold;">KAN</span><span style="color: #0000BB;">' (kanamycin)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ For </span><span style="color: #0000BB; background-color: #EEEEEE;">`betalactams()`</span><span style="color: #0000BB;"> using columns '</span><span style="color: #0000BB; font-weight: bold;">PEN</span><span style="color: #0000BB;">' (benzylpenicillin), '</span><span style="color: #0000BB; font-weight: bold;">OXA</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (oxacillin), '</span><span style="color: #0000BB; font-weight: bold;">FLC</span><span style="color: #0000BB;">' (flucloxacillin), '</span><span style="color: #0000BB; font-weight: bold;">AMX</span><span style="color: #0000BB;">' (amoxicillin), '</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (amoxicillin/clavulanic acid), '</span><span style="color: #0000BB; font-weight: bold;">AMP</span><span style="color: #0000BB;">' (ampicillin), '</span><span style="color: #0000BB; font-weight: bold;">TZP</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (piperacillin/tazobactam), '</span><span style="color: #0000BB; font-weight: bold;">CZO</span><span style="color: #0000BB;">' (cefazolin), '</span><span style="color: #0000BB; font-weight: bold;">FEP</span><span style="color: #0000BB;">' (cefepime), '</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">'</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> (cefuroxime), '</span><span style="color: #0000BB; font-weight: bold;">FOX</span><span style="color: #0000BB;">' (cefoxitin), '</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">' (cefotaxime), '</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">' (ceftazidime),</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> '</span><span style="color: #0000BB; font-weight: bold;">CRO</span><span style="color: #0000BB;">' (ceftriaxone), '</span><span style="color: #0000BB; font-weight: bold;">IPM</span><span style="color: #0000BB;">' (imipenem), and '</span><span style="color: #0000BB; font-weight: bold;">MEM</span><span style="color: #0000BB;">' (meropenem)</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `aminoglycosides()` using columns <span style="color: #00BB00; font-weight: bold;">GEN</span> (gentamicin), <span style="color: #00BB00; font-weight: bold;">TOB</span> (tobramycin), <span style="color: #00BB00; font-weight: bold;">AMK</span></span></span>
|
||||
<span><span class="co">#> (amikacin), and <span style="color: #00BB00; font-weight: bold;">KAN</span> (kanamycin)</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">ℹ</span> For `betalactams()` using columns <span style="color: #00BB00; font-weight: bold;">PEN</span> (benzylpenicillin), <span style="color: #00BB00; font-weight: bold;">OXA</span> (oxacillin),</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BB00; font-weight: bold;">FLC</span> (flucloxacillin), <span style="color: #00BB00; font-weight: bold;">AMX</span> (amoxicillin), <span style="color: #00BB00; font-weight: bold;">AMC</span> (amoxicillin/clavulanic acid),</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BB00; font-weight: bold;">AMP</span> (ampicillin), <span style="color: #00BB00; font-weight: bold;">TZP</span> (piperacillin/tazobactam), <span style="color: #00BB00; font-weight: bold;">CZO</span> (cefazolin), <span style="color: #00BB00; font-weight: bold;">FEP</span></span></span>
|
||||
<span><span class="co">#> (cefepime), <span style="color: #00BB00; font-weight: bold;">CXM</span> (cefuroxime), <span style="color: #00BB00; font-weight: bold;">FOX</span> (cefoxitin), <span style="color: #00BB00; font-weight: bold;">CTX</span> (cefotaxime), <span style="color: #00BB00; font-weight: bold;">CAZ</span></span></span>
|
||||
<span><span class="co">#> (ceftazidime), <span style="color: #00BB00; font-weight: bold;">CRO</span> (ceftriaxone), <span style="color: #00BB00; font-weight: bold;">IPM</span> (imipenem), and <span style="color: #00BB00; font-weight: bold;">MEM</span> (meropenem)</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> ── Inputs</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> ── Inputs </span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> Number of variables by role</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> outcome: 1</span></span>
|
||||
<span><span class="co">#> predictor: 20</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> ── Training information</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> ── Training information </span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> Training data contained 1968 data points and no incomplete rows.</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> ── Operations</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> ── Operations </span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">AMX</span> <span style="color: #0000BB;">CTX</span> | <span style="font-style: italic;">Trained</span></span></span></code></pre></div>
|
||||
<p><strong>Explanation:</strong></p>
|
||||
<ul>
|
||||
@@ -413,13 +422,13 @@ ROC curve looks like this:</p>
|
||||
<code class="sourceCode R"><span><span class="va">predictions</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
<span> <span class="fu">roc_curve</span><span class="op">(</span><span class="va">mo</span>, <span class="va">`.pred_Gram-negative`</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://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" width="720"></p>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-8-1.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="conclusion">
|
||||
<strong>Conclusion</strong><a class="anchor" aria-label="anchor" href="#conclusion"></a>
|
||||
</h3>
|
||||
<p>In this post, we demonstrated how to build a machine learning
|
||||
<p>In this example, we demonstrated how to build a machine learning
|
||||
pipeline with the <code>tidymodels</code> framework and the
|
||||
<code>AMR</code> package. By combining selector functions like
|
||||
<code><a href="../reference/antimicrobial_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antimicrobial_selectors.html">betalactams()</a></code> with
|
||||
@@ -442,7 +451,7 @@ transformation to <code>log2</code> scale using
|
||||
classification models.</p>
|
||||
<p>This approach and idea formed the basis for the publication <a href="https://doi.org/10.3389/fmicb.2025.1582703" class="external-link">DOI:
|
||||
10.3389/fmicb.2025.1582703</a> to model the presence of
|
||||
extended-spectrum beta-lactamases (ESBL).</p>
|
||||
extended-spectrum beta-lactamases (ESBL) based on MIC values.</p>
|
||||
<div class="section level3">
|
||||
<h3 id="objective-1">
|
||||
<strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective-1"></a>
|
||||
@@ -473,16 +482,16 @@ package.</p>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 500 × 19</span></span></span>
|
||||
<span><span class="co">#> esbl genus AMC AMP TZP CXM FOX CTX CAZ GEN TOB TMP SXT</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494; font-style: italic;"><lgl></span> <span style="color: #949494; font-style: italic;"><chr></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span> <span style="color: #949494; font-style: italic;"><mic></span></span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> FALSE Esch… 32 32 4 64 64 8<span style="color: #BBBBBB;">.00</span> 8<span style="color: #BBBBBB;">.00</span> 1 1 16<span style="color: #BBBBBB;">.0</span> 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> FALSE Esch… 32 32 4 64 64 4<span style="color: #BBBBBB;">.00</span> 8<span style="color: #BBBBBB;">.00</span> 1 1 16<span style="color: #BBBBBB;">.0</span> 320</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> FALSE Esch… 4 2 64 8 4 8<span style="color: #BBBBBB;">.00</span> 0.12 16 16 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> FALSE Kleb… 32 32 16 64 64 8<span style="color: #BBBBBB;">.00</span> 8<span style="color: #BBBBBB;">.00</span> 1 1 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> FALSE Esch… 32 32 4 4 4 0.25 2<span style="color: #BBBBBB;">.00</span> 1 1 16<span style="color: #BBBBBB;">.0</span> 320</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> FALSE Citr… 32 32 16 64 64 64<span style="color: #BBBBBB;">.00</span> 32<span style="color: #BBBBBB;">.00</span> 1 1 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> FALSE Morg… 32 32 4 64 64 16<span style="color: #BBBBBB;">.00</span> 2<span style="color: #BBBBBB;">.00</span> 1 1 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> FALSE Prot… 16 32 4 1 4 8<span style="color: #BBBBBB;">.00</span> 0.12 1 1 16<span style="color: #BBBBBB;">.0</span> 320</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> FALSE Ente… 32 32 8 64 64 32<span style="color: #BBBBBB;">.00</span> 4<span style="color: #BBBBBB;">.00</span> 1 1 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> FALSE Citr… 32 32 32 64 64 8<span style="color: #BBBBBB;">.00</span> 64<span style="color: #BBBBBB;">.00</span> 1 1 16<span style="color: #BBBBBB;">.0</span> 320</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 1</span> FALSE Esch… 32 32 4 64 64 8<span style="color: #949494;">.00</span> 8<span style="color: #949494;">.00</span> 1 1 16<span style="color: #949494;">.0</span> 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 2</span> FALSE Esch… 32 32 4 64 64 4<span style="color: #949494;">.00</span> 8<span style="color: #949494;">.00</span> 1 1 16<span style="color: #949494;">.0</span> 320</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 3</span> FALSE Esch… 4 2 64 8 4 8<span style="color: #949494;">.00</span> 0.12 16 16 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 4</span> FALSE Kleb… 32 32 16 64 64 8<span style="color: #949494;">.00</span> 8<span style="color: #949494;">.00</span> 1 1 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 5</span> FALSE Esch… 32 32 4 4 4 0.25 2<span style="color: #949494;">.00</span> 1 1 16<span style="color: #949494;">.0</span> 320</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 6</span> FALSE Citr… 32 32 16 64 64 64<span style="color: #949494;">.00</span> 32<span style="color: #949494;">.00</span> 1 1 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 7</span> FALSE Morg… 32 32 4 64 64 16<span style="color: #949494;">.00</span> 2<span style="color: #949494;">.00</span> 1 1 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 8</span> FALSE Prot… 16 32 4 1 4 8<span style="color: #949494;">.00</span> 0.12 1 1 16<span style="color: #949494;">.0</span> 320</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;"> 9</span> FALSE Ente… 32 32 8 64 64 32<span style="color: #949494;">.00</span> 4<span style="color: #949494;">.00</span> 1 1 0.5 20</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">10</span> FALSE Citr… 32 32 32 64 64 8<span style="color: #949494;">.00</span> 64<span style="color: #949494;">.00</span> 1 1 16<span style="color: #949494;">.0</span> 320</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 490 more rows</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># ℹ 6 more variables: NIT <mic>, FOS <mic>, CIP <mic>, IPM <mic>, MEM <mic>,</span></span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># COL <mic></span></span></span>
|
||||
@@ -521,10 +530,9 @@ predictors can easily and agnostically selected using the new
|
||||
<span><span class="co"># Define the recipe</span></span>
|
||||
<span><span class="va">mic_recipe</span> <span class="op"><-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">esbl</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">training_data</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">remove_role</span><span class="op">(</span><span class="va">genus</span>, old_role <span class="op">=</span> <span class="st">"predictor"</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"># Remove non-informative variable</span></span>
|
||||
<span> <span class="fu"><a href="../reference/amr-tidymodels.html">step_mic_log2</a></span><span class="op">(</span><span class="fu"><a href="../reference/amr-tidymodels.html">all_mic_predictors</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co">#%>% # Log2 transform all MIC predictors</span></span>
|
||||
<span> <span class="co"># prep()</span></span>
|
||||
<span> <span class="fu"><a href="../reference/amr-tidymodels.html">step_mic_log2</a></span><span class="op">(</span><span class="fu"><a href="../reference/amr-tidymodels.html">all_mic_predictors</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span> <span class="co"># Log2 transform all MIC predictors</span></span>
|
||||
<span></span>
|
||||
<span><span class="va">mic_recipe</span></span>
|
||||
<span><span class="fu">prep</span><span class="op">(</span><span class="va">mic_recipe</span><span class="op">)</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">──</span> <span style="font-weight: bold;">Recipe</span> <span style="color: #00BBBB;">──────────────────────────────────────────────────────────────────────</span></span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
@@ -534,8 +542,11 @@ predictors can easily and agnostically selected using the new
|
||||
<span><span class="co">#> predictor: 17</span></span>
|
||||
<span><span class="co">#> undeclared role: 1</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> ── Training information</span></span>
|
||||
<span><span class="co">#> Training data contained 375 data points and no incomplete rows.</span></span>
|
||||
<span><span class="co">#> </span></span>
|
||||
<span><span class="co">#> ── Operations</span></span>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">•</span> Log2 transformation of MIC columns: <span style="color: #0000BB;">all_mic_predictors()</span></span></span></code></pre></div>
|
||||
<span><span class="co">#> <span style="color: #00BBBB;">•</span> Log2 transformation of MIC columns: <span style="color: #0000BB;">AMC</span>, <span style="color: #0000BB;">AMP</span>, <span style="color: #0000BB;">TZP</span>, <span style="color: #0000BB;">CXM</span>, <span style="color: #0000BB;">FOX</span>, ... | <span style="font-style: italic;">Trained</span></span></span></code></pre></div>
|
||||
<p><strong>Explanation:</strong></p>
|
||||
<ul>
|
||||
<li>
|
||||
@@ -608,20 +619,24 @@ model.</li>
|
||||
<span></span>
|
||||
<span><span class="co"># Generate predictions</span></span>
|
||||
<span><span class="va">predictions</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">fitted</span>, <span class="va">testing_data</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/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">fitted</span>, <span class="va">testing_data</span>, type <span class="op">=</span> <span class="st">"prob"</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 class="co"># add probabilities</span></span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">testing_data</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Evaluate model performance</span></span>
|
||||
<span><span class="va">our_metrics</span> <span class="op"><-</span> <span class="fu">metric_set</span><span class="op">(</span><span class="va">accuracy</span>, <span class="va">kap</span>, <span class="va">ppv</span>, <span class="va">npv</span><span class="op">)</span></span>
|
||||
<span><span class="va">our_metrics</span> <span class="op"><-</span> <span class="fu">metric_set</span><span class="op">(</span><span class="va">accuracy</span>, <span class="va">recall</span>, <span class="va">precision</span>, <span class="va">sensitivity</span>, <span class="va">specificity</span>, <span class="va">ppv</span>, <span class="va">npv</span><span class="op">)</span></span>
|
||||
<span><span class="va">metrics</span> <span class="op"><-</span> <span class="fu">our_metrics</span><span class="op">(</span><span class="va">predictions</span>, truth <span class="op">=</span> <span class="va">esbl</span>, estimate <span class="op">=</span> <span class="va">.pred_class</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="va">metrics</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 4 × 3</span></span></span>
|
||||
<span><span class="co">#> .metric .estimator .estimate</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></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">1</span> accuracy binary 0.92 </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">2</span> kap binary 0.840</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">3</span> ppv binary 0.921</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">4</span> npv binary 0.919</span></span></code></pre></div>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 7 × 3</span></span></span>
|
||||
<span><span class="co">#> .metric .estimator .estimate</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></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">1</span> accuracy binary 0.92 </span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">2</span> recall binary 0.921</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">3</span> precision binary 0.921</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">4</span> sensitivity binary 0.921</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">5</span> specificity binary 0.919</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">6</span> ppv binary 0.921</span></span>
|
||||
<span><span class="co">#> <span style="color: #BCBCBC;">7</span> npv binary 0.919</span></span></code></pre></div>
|
||||
<p><strong>Explanation:</strong></p>
|
||||
<ul>
|
||||
<li>
|
||||
@@ -632,11 +647,12 @@ data.</li>
|
||||
data.</li>
|
||||
<li>
|
||||
<code>metric_set()</code>: Allows evaluating multiple classification
|
||||
metrics.</li>
|
||||
metrics. This will make <code>our_metrics</code> to become a function
|
||||
that we can use to check the predictions with.</li>
|
||||
</ul>
|
||||
<p>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.</p>
|
||||
91.9% using a simplistic logistic regression model.</p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="visualising-predictions">
|
||||
@@ -653,7 +669,25 @@ status.</p>
|
||||
<span> x <span class="op">=</span> <span class="st">"Actual ESBL"</span>,</span>
|
||||
<span> y <span class="op">=</span> <span class="st">"Count"</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" width="720"></p>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-14-1.png" class="r-plt" alt="" width="720"></p>
|
||||
<p>And plot the certainties too - how certain were the actual
|
||||
predictions?</p>
|
||||
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="va">predictions</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/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>certainty <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">.pred_class</span> <span class="op">==</span> <span class="st">"FALSE"</span>,</span>
|
||||
<span> <span class="va">.pred_FALSE</span>,</span>
|
||||
<span> <span class="va">.pred_TRUE</span><span class="op">)</span>,</span>
|
||||
<span> correct <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">esbl</span> <span class="op">==</span> <span class="va">.pred_class</span>, <span class="st">"Right"</span>, <span class="st">"Wrong"</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="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_len</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/nrow.html" class="external-link">nrow</a></span><span class="op">(</span><span class="va">predictions</span><span class="op">)</span><span class="op">)</span>,</span>
|
||||
<span> y <span class="op">=</span> <span class="va">certainty</span>,</span>
|
||||
<span> colour <span class="op">=</span> <span class="va">correct</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_manual.html" class="external-link">scale_colour_manual</a></span><span class="op">(</span>values <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>Right <span class="op">=</span> <span class="st">"green3"</span>, Wrong <span class="op">=</span> <span class="st">"red2"</span><span class="op">)</span>,</span>
|
||||
<span> name <span class="op">=</span> <span class="st">"Correct?"</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_point.html" class="external-link">geom_point</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/scale_continuous.html" class="external-link">scale_y_continuous</a></span><span class="op">(</span>labels <span class="op">=</span> <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html" class="external-link">paste0</a></span><span class="op">(</span><span class="va">x</span> <span class="op">*</span> <span class="fl">100</span>, <span class="st">"%"</span><span class="op">)</span>,</span>
|
||||
<span> limits <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="fl">0.5</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-15-1.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="conclusion-1">
|
||||
@@ -662,8 +696,8 @@ status.</p>
|
||||
<p>In this example, we showcased how the new <code>AMR</code>-specific
|
||||
recipe steps simplify working with <code><mic></code> columns in
|
||||
<code>tidymodels</code>. The <code><a href="../reference/amr-tidymodels.html">step_mic_log2()</a></code> transformation
|
||||
converts ordered MICs to log2-transformed numerics, improving
|
||||
compatibility with classification models.</p>
|
||||
converts MICs (with or without operators) to log2-transformed numerics,
|
||||
improving compatibility with classification models.</p>
|
||||
<p>This pipeline enables realistic, reproducible, and interpretable
|
||||
modelling of antimicrobial resistance data.</p>
|
||||
<hr>
|
||||
@@ -696,7 +730,7 @@ model.</li>
|
||||
</h3>
|
||||
<p>We start by transforming the <code>example_isolates</code> dataset
|
||||
into a structured time-series format.</p>
|
||||
<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># Load required libraries</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>
|
||||
<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://tidymodels.tidymodels.org" class="external-link">tidymodels</a></span><span class="op">)</span></span>
|
||||
@@ -712,7 +746,11 @@ into a structured time-series format.</p>
|
||||
<span> .names <span class="op">=</span> <span class="st">"res_{.col}"</span><span class="op">)</span>, </span>
|
||||
<span> .groups <span class="op">=</span> <span class="st">"drop"</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/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_AMX</span><span class="op">)</span> <span class="op">&</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_AMC</span><span class="op">)</span> <span class="op">&</span> <span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">res_CIP</span><span class="op">)</span><span class="op">)</span> <span class="co"># Drop missing values</span></span>
|
||||
<span><span class="co">#> <span style="color: #0000BB;">ℹ Using column '</span><span style="color: #0000BB; font-weight: bold;">mo</span><span style="color: #0000BB;">' as input for </span><span style="color: #0000BB; background-color: #EEEEEE;">`col_mo`</span><span style="color: #0000BB;">.</span></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>
|
||||
<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="va">data_time</span></span>
|
||||
<span><span class="co">#> <span style="color: #949494;"># A tibble: 32 × 5</span></span></span>
|
||||
@@ -751,7 +789,7 @@ preprocessing step, a model specification, and the fitting process.</p>
|
||||
<div class="section level4">
|
||||
<h4 id="preprocessing-with-a-recipe-2">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe-2"></a>
|
||||
</h4>
|
||||
<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># Define the recipe</span></span>
|
||||
<span><span class="va">resistance_recipe_time</span> <span class="op"><-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">res_AMX</span> <span class="op">~</span> <span class="va">year</span> <span class="op">+</span> <span class="va">gramstain</span>, data <span class="op">=</span> <span class="va">data_time</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">step_dummy</span><span class="op">(</span><span class="va">gramstain</span>, one_hot <span class="op">=</span> <span class="cn">TRUE</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"># Convert categorical to numerical</span></span>
|
||||
@@ -787,7 +825,7 @@ variable.</li>
|
||||
<h4 id="specifying-the-model-2">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model-2"></a>
|
||||
</h4>
|
||||
<p>We use a linear regression model to predict resistance trends.</p>
|
||||
<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># Define the linear regression model</span></span>
|
||||
<span><span class="va">lm_model</span> <span class="op"><-</span> <span class="fu">linear_reg</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="fu">set_engine</span><span class="op">(</span><span class="st">"lm"</span><span class="op">)</span> <span class="co"># Use linear regression</span></span>
|
||||
@@ -809,7 +847,7 @@ engine.</li>
|
||||
<h4 id="building-the-workflow-2">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow-2"></a>
|
||||
</h4>
|
||||
<p>We combine the preprocessing recipe and model into a workflow.</p>
|
||||
<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># Create workflow</span></span>
|
||||
<span><span class="va">resistance_workflow_time</span> <span class="op"><-</span> <span class="fu">workflow</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="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe_time</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
@@ -839,7 +877,7 @@ engine.</li>
|
||||
</h3>
|
||||
<p>We split the data into training and testing sets, fit the model, and
|
||||
evaluate performance.</p>
|
||||
<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="co"># Split the data</span></span>
|
||||
<span><span class="fu"><a href="https://rdrr.io/r/base/Random.html" class="external-link">set.seed</a></span><span class="op">(</span><span class="fl">123</span><span class="op">)</span></span>
|
||||
<span><span class="va">data_split_time</span> <span class="op"><-</span> <span class="fu">initial_split</span><span class="op">(</span><span class="va">data_time</span>, prop <span class="op">=</span> <span class="fl">0.8</span><span class="op">)</span></span>
|
||||
@@ -853,7 +891,7 @@ evaluate performance.</p>
|
||||
<span><span class="co"># Make predictions</span></span>
|
||||
<span><span class="va">predictions_time</span> <span class="op"><-</span> <span class="va">fitted_workflow_time</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://rdrr.io/r/stats/predict.html" class="external-link">predict</a></span><span class="op">(</span><span class="va">test_time</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/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span> </span>
|
||||
<span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html" class="external-link">bind_cols</a></span><span class="op">(</span><span class="va">test_time</span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Evaluate model</span></span>
|
||||
<span><span class="va">metrics_time</span> <span class="op"><-</span> <span class="va">predictions_time</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span></span>
|
||||
@@ -884,7 +922,7 @@ sets.</li>
|
||||
<strong>Visualising Predictions</strong><a class="anchor" aria-label="anchor" href="#visualising-predictions-1"></a>
|
||||
</h3>
|
||||
<p>We plot resistance trends over time for amoxicillin.</p>
|
||||
<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb22"><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://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span></span>
|
||||
<span></span>
|
||||
<span><span class="co"># Plot actual vs predicted resistance over time</span></span>
|
||||
@@ -895,10 +933,10 @@ sets.</li>
|
||||
<span> x <span class="op">=</span> <span class="st">"Year"</span>,</span>
|
||||
<span> y <span class="op">=</span> <span class="st">"Resistance Proportion"</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-20-1.png" width="720"></p>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png" class="r-plt" alt="" width="720"></p>
|
||||
<p>Additionally, we can visualise resistance trends in
|
||||
<code>ggplot2</code> and directly add linear models there:</p>
|
||||
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
|
||||
<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html" class="external-link">ggplot</a></span><span class="op">(</span><span class="va">data_time</span>, <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">year</span>, y <span class="op">=</span> <span class="va">res_AMX</span>, color <span class="op">=</span> <span class="va">gramstain</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_path.html" class="external-link">geom_line</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<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">"AMX Resistance Trends"</span>,</span>
|
||||
@@ -909,7 +947,7 @@ sets.</li>
|
||||
<span> formula <span class="op">=</span> <span class="va">y</span> <span class="op">~</span> <span class="va">x</span>,</span>
|
||||
<span> alpha <span class="op">=</span> <span class="fl">0.25</span><span class="op">)</span> <span class="op">+</span></span>
|
||||
<span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggtheme.html" class="external-link">theme_minimal</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-21-1.png" width="720"></p>
|
||||
<p><img src="AMR_with_tidymodels_files/figure-html/unnamed-chunk-22-1.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
||||
<div class="section level3">
|
||||
<h3 id="conclusion-2">
|
||||
|
||||
852
articles/AMR_with_tidymodels.md
Normal file
852
articles/AMR_with_tidymodels.md
Normal file
@@ -0,0 +1,852 @@
|
||||
# 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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@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
@@ -118,7 +118,7 @@ resistant to ampicillin.</p>
|
||||
<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/eucast_rules.html">eucast_rules()</a></code> function resolves this,
|
||||
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">
|
||||
@@ -136,11 +136,11 @@ guideline:</p>
|
||||
<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/eucast_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="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: #FFAFAF;"> R </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,
|
||||
@@ -246,7 +246,7 @@ reading</em>, and is basically a form of imputation:</p>
|
||||
</tbody>
|
||||
</table>
|
||||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||||
<code class="sourceCode R"><span><span class="fu"><a href="../reference/eucast_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>
|
||||
<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>
|
||||
|
||||
126
articles/EUCAST.md
Normal file
126
articles/EUCAST.md
Normal file
@@ -0,0 +1,126 @@
|
||||
# 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 |
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
@@ -163,6 +163,10 @@ per taxonomic order and genus:</p>
|
||||
<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>
|
||||
@@ -184,8 +188,10 @@ 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: #0000BB;">ℹ Columns selected for PCA: "</span><span style="color: #0000BB; font-weight: bold;">AMC</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CAZ</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CTX</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">CXM</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">GEN</span><span style="color: #0000BB;">", "</span><span style="color: #0000BB; font-weight: bold;">SXT</span><span style="color: #0000BB;">",</span></span></span>
|
||||
<span><span class="co"><span style="color: #0000BB;">#> "</span><span style="color: #0000BB; font-weight: bold;">TMP</span><span style="color: #0000BB;">", and "</span><span style="color: #0000BB; font-weight: bold;">TOB</span><span style="color: #0000BB;">". Total observations available: 7.</span></span></span></code></pre></div>
|
||||
<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">
|
||||
@@ -210,18 +216,18 @@ per drug explain the difference per microorganism.</p>
|
||||
</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" width="750"></p>
|
||||
<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" width="750"></p>
|
||||
<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" width="750"></p>
|
||||
<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>
|
||||
|
||||
163
articles/PCA.md
Normal file
163
articles/PCA.md
Normal file
@@ -0,0 +1,163 @@
|
||||
# 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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|
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|
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@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
@@ -254,15 +254,20 @@ Longest: 40</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></code></pre></div>
|
||||
<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: 5: S < SDD < I < R < NI<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: Beta-lactams/penicillins<br>
|
||||
Drug group: Aminopenicillins<br>
|
||||
%SI: 78.59%</p>
|
||||
<table class="table">
|
||||
<thead><tr class="header">
|
||||
@@ -311,7 +316,7 @@ using the included <code><a href="../reference/ggplot_sir.html">ggplot_sir()</a>
|
||||
<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" width="720"></p>
|
||||
<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" class="r-plt" alt="" width="720"></p>
|
||||
</div>
|
||||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
||||
</nav></aside>
|
||||
|
||||
141
articles/WHONET.md
Normal file
141
articles/WHONET.md
Normal file
@@ -0,0 +1,141 @@
|
||||
# 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.
|
Before Width: | Height: | Size: 61 KiB After Width: | Height: | Size: 65 KiB |
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
@@ -253,16 +253,16 @@ I (intermediate [CLSI], or susceptible, increased exposure
|
||||
<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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #080808; background-color: #FFAFAF;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> 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: #FFAFAF;"> R </span> <span style="color: #B2B2B2;"> NA</span> <span style="color: #080808; background-color: #5FD7AF;"> S </span> <span style="color: #B2B2B2;"> NA</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>
|
||||
|
||||
252
articles/WISCA.md
Normal file
252
articles/WISCA.md
Normal file
@@ -0,0 +1,252 @@
|
||||
# Estimating Empirical Coverage with WISCA
|
||||
|
||||
> This explainer was largely 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.
|
||||
|
||||
## Introduction
|
||||
|
||||
Clinical guidelines for empirical antimicrobial therapy require
|
||||
*probabilistic reasoning*: what is the chance that a regimen will cover
|
||||
the likely infecting organisms, before culture results are available?
|
||||
|
||||
This is the purpose of **WISCA**, or **Weighted-Incidence Syndromic
|
||||
Combination Antibiogram**.
|
||||
|
||||
WISCA is a Bayesian approach that integrates:
|
||||
|
||||
- **Pathogen prevalence** (how often each species causes the syndrome),
|
||||
- **Regimen susceptibility** (how often a regimen works *if* the
|
||||
pathogen is known),
|
||||
|
||||
to estimate the **overall empirical coverage** of antimicrobial
|
||||
regimens, with quantified uncertainty.
|
||||
|
||||
This vignette explains how WISCA works, why it is useful, and how to
|
||||
apply it using the `AMR` package.
|
||||
|
||||
## Why traditional antibiograms fall short
|
||||
|
||||
A standard antibiogram gives you:
|
||||
|
||||
Species → Antibiotic → Susceptibility %
|
||||
|
||||
But clinicians don’t know the species *a priori*. They need to choose a
|
||||
regimen that covers the **likely pathogens**, without knowing which one
|
||||
is present.
|
||||
|
||||
Traditional antibiograms calculate the susceptibility % as just the
|
||||
number of resistant isolates divided by the total number of tested
|
||||
isolates. Therefore, traditional antibiograms:
|
||||
|
||||
- Fragment information by organism,
|
||||
- Do not weight by real-world prevalence,
|
||||
- Do not account for combination therapy or sample size,
|
||||
- Do not provide uncertainty.
|
||||
|
||||
## The idea of WISCA
|
||||
|
||||
WISCA asks:
|
||||
|
||||
> “What is the **probability** that this regimen **will cover** the
|
||||
> pathogen, given the syndrome?”
|
||||
|
||||
This means combining two things:
|
||||
|
||||
- **Incidence** of each pathogen in the syndrome,
|
||||
- **Susceptibility** of each pathogen to the regimen.
|
||||
|
||||
We can write this as:
|
||||
|
||||
$$\text{Coverage} = \sum\limits_{i}\left( \text{Incidence}_{i} \times \text{Susceptibility}_{i} \right)$$
|
||||
|
||||
For example, suppose:
|
||||
|
||||
- *E. coli* causes 60% of cases, and 90% of *E. coli* are susceptible to
|
||||
a drug.
|
||||
- *Klebsiella* causes 40% of cases, and 70% of *Klebsiella* are
|
||||
susceptible.
|
||||
|
||||
Then:
|
||||
|
||||
$$\text{Coverage} = (0.6 \times 0.9) + (0.4 \times 0.7) = 0.82$$
|
||||
|
||||
But in real data, incidence and susceptibility are **estimated from
|
||||
samples**, so they carry uncertainty. WISCA models this
|
||||
**probabilistically**, using conjugate Bayesian distributions.
|
||||
|
||||
## The Bayesian engine behind WISCA
|
||||
|
||||
### Pathogen incidence
|
||||
|
||||
Let:
|
||||
|
||||
- $K$ be the number of pathogens,
|
||||
- $\alpha = (1,1,\ldots,1)$ be a **Dirichlet** prior (uniform),
|
||||
- $n = \left( n_{1},\ldots,n_{K} \right)$ be the observed counts per
|
||||
species.
|
||||
|
||||
Then the posterior incidence is:
|
||||
|
||||
$$p \sim \text{Dirichlet}\left( \alpha_{1} + n_{1},\ldots,\alpha_{K} + n_{K} \right)$$
|
||||
|
||||
To simulate from this, we use:
|
||||
|
||||
$$x_{i} \sim \text{Gamma}\left( \alpha_{i} + n_{i},\ 1 \right),\quad p_{i} = \frac{x_{i}}{\sum\limits_{j = 1}^{K}x_{j}}$$
|
||||
|
||||
### Susceptibility
|
||||
|
||||
Each pathogen–regimen pair has a prior and data:
|
||||
|
||||
- Prior: $\text{Beta}\left( \alpha_{0},\beta_{0} \right)$, with default
|
||||
$\alpha_{0} = \beta_{0} = 1$
|
||||
- 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:
|
||||
|
||||
$$\theta \sim \text{Beta}\left( \alpha_{0} + S,\ \beta_{0} + N - S \right)$$
|
||||
|
||||
### Final coverage estimate
|
||||
|
||||
Putting it together:
|
||||
|
||||
1. Simulate pathogen incidence: $\mathbf{p} \sim \text{Dirichlet}$
|
||||
2. Simulate susceptibility:
|
||||
$\theta_{i} \sim \text{Beta}\left( 1 + S_{i},\ 1 + R_{i} \right)$
|
||||
3. Combine:
|
||||
|
||||
$$\text{Coverage} = \sum\limits_{i = 1}^{K}p_{i} \cdot \theta_{i}$$
|
||||
|
||||
Repeat this simulation (e.g. 1000×) and summarise:
|
||||
|
||||
- **Mean** = expected coverage
|
||||
- **Quantiles** = credible interval
|
||||
|
||||
## Practical use in the `AMR` package
|
||||
|
||||
### Prepare data and simulate synthetic syndrome
|
||||
|
||||
``` 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 fake syndrome column
|
||||
data$syndrome <- ifelse(data$mo %like% "coli", "UTI", "No UTI")
|
||||
```
|
||||
|
||||
### Basic WISCA antibiogram
|
||||
|
||||
``` r
|
||||
wisca(data,
|
||||
antimicrobials = c("AMC", "CIP", "GEN"))
|
||||
```
|
||||
|
||||
| Amoxicillin/clavulanic acid | Ciprofloxacin | Gentamicin |
|
||||
|:----------------------------|:-----------------|:-------------------|
|
||||
| 73.7% (71.7-75.8%) | 77% (74.3-79.4%) | 72.8% (70.7-74.8%) |
|
||||
|
||||
### Use combination regimens
|
||||
|
||||
``` r
|
||||
wisca(data,
|
||||
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"))
|
||||
```
|
||||
|
||||
| Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|
||||
|:----------------------------|:--------------------------------------------|:-----------------------------------------|
|
||||
| 73.8% (71.8-75.7%) | 87.5% (85.9-89%) | 89.7% (88.2-91.1%) |
|
||||
|
||||
### Stratify by syndrome
|
||||
|
||||
``` r
|
||||
wisca(data,
|
||||
antimicrobials = c("AMC", "AMC + CIP", "AMC + GEN"),
|
||||
syndromic_group = "syndrome")
|
||||
```
|
||||
|
||||
| Syndromic Group | Amoxicillin/clavulanic acid | Amoxicillin/clavulanic acid + Ciprofloxacin | Amoxicillin/clavulanic acid + Gentamicin |
|
||||
|:----------------|:----------------------------|:--------------------------------------------|:-----------------------------------------|
|
||||
| No UTI | 70.1% (67.8-72.3%) | 85.2% (83.1-87.2%) | 87.1% (85.3-88.7%) |
|
||||
| UTI | 80.9% (77.7-83.8%) | 88.2% (85.7-90.5%) | 90.9% (88.7-93%) |
|
||||
|
||||
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 |
|
||||
|:-----------------|:------------------------------|:-----------------------------------------------|:--------------------------------------------|
|
||||
| No UCI | 70% (67.8-72.4%) | 85.3% (83.3-87.2%) | 87% (85.3-88.8%) |
|
||||
| UCI | 80.9% (77.7-83.9%) | 88.2% (85.5-90.6%) | 90.9% (88.7-93%) |
|
||||
|
||||
## 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
|
||||
|
||||
## Limitations
|
||||
|
||||
- It assumes your data are representative
|
||||
- No adjustment for patient-level covariates, although these could be
|
||||
passed onto the `syndromic_group` argument
|
||||
- WISCA does not model resistance over time, you might want to use
|
||||
`tidymodels` for that, for which we [wrote a basic
|
||||
introduction](https://amr-for-r.org/articles/AMR_with_tidymodels.html)
|
||||
|
||||
## Summary
|
||||
|
||||
WISCA enables:
|
||||
|
||||
- Empirical regimen comparison,
|
||||
- Syndrome-specific coverage estimation,
|
||||
- Fully probabilistic interpretation.
|
||||
|
||||
It is available in the `AMR` package via either:
|
||||
|
||||
``` r
|
||||
wisca(...)
|
||||
|
||||
antibiogram(..., wisca = TRUE)
|
||||
```
|
||||
|
||||
## Reference
|
||||
|
||||
Bielicki, JA, et al. (2016). *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**. 71(3):794-802.
|
||||
<https://doi.org/10.1093/jac/dkv397>
|
||||
@@ -30,7 +30,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
@@ -80,7 +80,7 @@
|
||||
<main id="main" class="col-md-9"><div class="page-header">
|
||||
<img src="../logo.svg" class="logo" alt=""><h1>Download data sets for download / own use</h1>
|
||||
|
||||
<h4 data-toc-skip class="date">01 September 2025</h4>
|
||||
<h4 data-toc-skip class="date">02 April 2026</h4>
|
||||
|
||||
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
|
||||
<div class="d-none name"><code>datasets.Rmd</code></div>
|
||||
@@ -115,7 +115,7 @@ column names:<br><em>mo</em>, <em>fullname</em>, <em>status</em>, <em>kingdom</e
|
||||
<em>snomed</em>.</p>
|
||||
<p>This data set is in R available as <code>microorganisms</code>, after
|
||||
you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 18 September 2025 12:58:34 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
@@ -127,10 +127,10 @@ R Data Structure (RDS) file</a> (1.8 MB)<br>
|
||||
text file</a> (17.7 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx" class="external-link">Microsoft
|
||||
Excel workbook</a> (8.7 MB)<br>
|
||||
Excel workbook</a> (8.8 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather" class="external-link">Apache
|
||||
Feather file</a> (8.3 MB)<br>
|
||||
Feather file</a> (8.4 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet" class="external-link">Apache
|
||||
Parquet file</a> (3.8 MB)<br>
|
||||
@@ -417,32 +417,32 @@ all SNOMED codes as comma separated values.</p>
|
||||
<h2 id="antimicrobials-antibiotic-and-antifungal-drugs">
|
||||
<code>antimicrobials</code>: Antibiotic and Antifungal Drugs<a class="anchor" aria-label="anchor" href="#antimicrobials-antibiotic-and-antifungal-drugs"></a>
|
||||
</h2>
|
||||
<p>A data set with 496 rows and 14 columns, containing the following
|
||||
<p>A data set with 505 rows and 14 columns, containing the following
|
||||
column names:<br><em>ab</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc</em>,
|
||||
<em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>,
|
||||
<em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>,
|
||||
<em>iv_ddd</em>, <em>iv_units</em>, and <em>loinc</em>.</p>
|
||||
<p>This data set is in R available as <code>antimicrobials</code>, after
|
||||
you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 23 March 2026 09:38:28 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/antimicrobials.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
<ul>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.rds" class="external-link">original
|
||||
R Data Structure (RDS) file</a> (45 kB)<br>
|
||||
R Data Structure (RDS) file</a> (44 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.txt" class="external-link">tab-separated
|
||||
text file</a> (0.1 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.xlsx" class="external-link">Microsoft
|
||||
Excel workbook</a> (78 kB)<br>
|
||||
Excel workbook</a> (79 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.feather" class="external-link">Apache
|
||||
Feather file</a> (0.1 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.parquet" class="external-link">Apache
|
||||
Parquet file</a> (0.1 MB)<br>
|
||||
Parquet file</a> (94 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/antimicrobials.sav" class="external-link">IBM
|
||||
SPSS Statistics data file</a> (0.4 MB)<br>
|
||||
@@ -454,22 +454,22 @@ DTA file</a> (10 kB)</li>
|
||||
contain the ATC codes, common abbreviations, trade names and LOINC codes
|
||||
as comma separated values.</p>
|
||||
<p><strong>Example content</strong></p>
|
||||
<table style="width:100%;" class="table">
|
||||
<table class="table">
|
||||
<colgroup>
|
||||
<col width="1%">
|
||||
<col width="2%">
|
||||
<col width="7%">
|
||||
<col width="7%">
|
||||
<col width="9%">
|
||||
<col width="12%">
|
||||
<col width="16%">
|
||||
<col width="6%">
|
||||
<col width="16%">
|
||||
<col width="8%">
|
||||
<col width="11%">
|
||||
<col width="15%">
|
||||
<col width="5%">
|
||||
<col width="15%">
|
||||
<col width="2%">
|
||||
<col width="3%">
|
||||
<col width="2%">
|
||||
<col width="2%">
|
||||
<col width="9%">
|
||||
<col width="8%">
|
||||
</colgroup>
|
||||
<thead><tr class="header">
|
||||
<th align="center">ab</th>
|
||||
@@ -508,7 +508,7 @@ as comma separated values.</p>
|
||||
<td align="center">AMX</td>
|
||||
<td align="center">33613</td>
|
||||
<td align="center">Amoxicillin</td>
|
||||
<td align="center">Beta-lactams/penicillins</td>
|
||||
<td align="center">Aminopenicillins, Penicillins, Beta-lactams</td>
|
||||
<td align="center">J01CA04, QG51AA03, QJ01CA04</td>
|
||||
<td align="center">Beta-lactam antibacterials, penicillins</td>
|
||||
<td align="center">Penicillins with extended spectrum</td>
|
||||
@@ -524,7 +524,7 @@ as comma separated values.</p>
|
||||
<td align="center">AMC</td>
|
||||
<td align="center">23665637</td>
|
||||
<td align="center">Amoxicillin/clavulanic acid</td>
|
||||
<td align="center">Beta-lactams/penicillins</td>
|
||||
<td align="center">Aminopenicillins, Penicillins, Beta-lactams, …</td>
|
||||
<td align="center">J01CR02, QJ01CR02</td>
|
||||
<td align="center">Beta-lactam antibacterials, penicillins</td>
|
||||
<td align="center">Combinations of penicillins, incl. beta-lactamase
|
||||
@@ -541,7 +541,7 @@ inhibitors</td>
|
||||
<td align="center">AMP</td>
|
||||
<td align="center">6249</td>
|
||||
<td align="center">Ampicillin</td>
|
||||
<td align="center">Beta-lactams/penicillins</td>
|
||||
<td align="center">Aminopenicillins, Penicillins, Beta-lactams</td>
|
||||
<td align="center">J01CA01, QJ01CA01, QJ51CA01, …</td>
|
||||
<td align="center">Beta-lactam antibacterials, penicillins</td>
|
||||
<td align="center">Penicillins with extended spectrum</td>
|
||||
@@ -557,7 +557,7 @@ inhibitors</td>
|
||||
<td align="center">AZM</td>
|
||||
<td align="center">447043</td>
|
||||
<td align="center">Azithromycin</td>
|
||||
<td align="center">Macrolides/lincosamides</td>
|
||||
<td align="center">Macrolides</td>
|
||||
<td align="center">J01FA10, QJ01FA10, QS01AA26, …</td>
|
||||
<td align="center">Macrolides, lincosamides and streptogramins</td>
|
||||
<td align="center">Macrolides</td>
|
||||
@@ -573,7 +573,7 @@ inhibitors</td>
|
||||
<td align="center">PEN</td>
|
||||
<td align="center">5904</td>
|
||||
<td align="center">Benzylpenicillin</td>
|
||||
<td align="center">Beta-lactams/penicillins</td>
|
||||
<td align="center">Penicillins, Beta-lactams</td>
|
||||
<td align="center">J01CE01, QJ01CE01, QJ51CE01, …</td>
|
||||
<td align="center">Combinations of antibacterials</td>
|
||||
<td align="center">Combinations of antibacterials</td>
|
||||
@@ -595,38 +595,38 @@ inhibitors</td>
|
||||
<code>clinical_breakpoints</code>: Interpretation from MIC values
|
||||
& disk diameters to SIR<a class="anchor" aria-label="anchor" href="#clinical_breakpoints-interpretation-from-mic-values-disk-diameters-to-sir"></a>
|
||||
</h2>
|
||||
<p>A data set with 40 217 rows and 14 columns, containing the following
|
||||
<p>A data set with 45 730 rows and 14 columns, containing the following
|
||||
column names:<br><em>guideline</em>, <em>type</em>, <em>host</em>, <em>method</em>,
|
||||
<em>site</em>, <em>mo</em>, <em>rank_index</em>, <em>ab</em>,
|
||||
<em>ref_tbl</em>, <em>disk_dose</em>, <em>breakpoint_S</em>,
|
||||
<em>breakpoint_R</em>, <em>uti</em>, and <em>is_SDD</em>.</p>
|
||||
<p>This data set is in R available as <code>clinical_breakpoints</code>,
|
||||
after you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 2 April 2026 09:42:19 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/clinical_breakpoints.html">data
|
||||
set here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
<ul>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.rds" class="external-link">original
|
||||
R Data Structure (RDS) file</a> (88 kB)<br>
|
||||
R Data Structure (RDS) file</a> (92 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.txt" class="external-link">tab-separated
|
||||
text file</a> (3.7 MB)<br>
|
||||
text file</a> (4.2 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.xlsx" class="external-link">Microsoft
|
||||
Excel workbook</a> (2.4 MB)<br>
|
||||
Excel workbook</a> (2.7 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.feather" class="external-link">Apache
|
||||
Feather file</a> (1.8 MB)<br>
|
||||
Feather file</a> (2 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.parquet" class="external-link">Apache
|
||||
Parquet file</a> (0.1 MB)<br>
|
||||
Parquet file</a> (0.2 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.sav" class="external-link">IBM
|
||||
SPSS Statistics data file</a> (6.6 MB)<br>
|
||||
SPSS Statistics data file</a> (7.5 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/clinical_breakpoints.dta" class="external-link">Stata
|
||||
DTA file</a> (11.1 MB)</li>
|
||||
DTA file</a> (12.6 MB)</li>
|
||||
</ul>
|
||||
<p><strong>Example content</strong></p>
|
||||
<table class="table">
|
||||
@@ -668,7 +668,7 @@ DTA file</a> (11.1 MB)</li>
|
||||
</tr></thead>
|
||||
<tbody>
|
||||
<tr class="odd">
|
||||
<td align="center">EUCAST 2025</td>
|
||||
<td align="center">EUCAST 2026</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">DISK</td>
|
||||
@@ -686,7 +686,7 @@ DTA file</a> (11.1 MB)</li>
|
||||
<td align="center">FALSE</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="center">EUCAST 2025</td>
|
||||
<td align="center">EUCAST 2026</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">MIC</td>
|
||||
@@ -704,7 +704,7 @@ DTA file</a> (11.1 MB)</li>
|
||||
<td align="center">FALSE</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="center">EUCAST 2025</td>
|
||||
<td align="center">EUCAST 2026</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">DISK</td>
|
||||
@@ -722,7 +722,7 @@ DTA file</a> (11.1 MB)</li>
|
||||
<td align="center">FALSE</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="center">EUCAST 2025</td>
|
||||
<td align="center">EUCAST 2026</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">MIC</td>
|
||||
@@ -740,7 +740,7 @@ DTA file</a> (11.1 MB)</li>
|
||||
<td align="center">FALSE</td>
|
||||
</tr>
|
||||
<tr class="odd">
|
||||
<td align="center">EUCAST 2025</td>
|
||||
<td align="center">EUCAST 2026</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">DISK</td>
|
||||
@@ -758,7 +758,7 @@ DTA file</a> (11.1 MB)</li>
|
||||
<td align="center">FALSE</td>
|
||||
</tr>
|
||||
<tr class="even">
|
||||
<td align="center">EUCAST 2025</td>
|
||||
<td align="center">EUCAST 2026</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">human</td>
|
||||
<td align="center">MIC</td>
|
||||
@@ -790,7 +790,7 @@ column names:<br><em>mo_group</em>, <em>mo</em>, <em>mo_group_name</em>, and
|
||||
<p>This data set is in R available as
|
||||
<code>microorganisms.groups</code>, after you load the <code>AMR</code>
|
||||
package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 26 March 2025 16:19:17 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.groups.html">data
|
||||
set here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
@@ -880,7 +880,7 @@ Resistance<a class="anchor" aria-label="anchor" href="#intrinsic_resistant-intri
|
||||
column names:<br><em>mo</em> and <em>ab</em>.</p>
|
||||
<p>This data set is in R available as <code>intrinsic_resistant</code>,
|
||||
after you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 28 March 2025 10:17:49 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/intrinsic_resistant.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
@@ -1156,7 +1156,7 @@ column names:<br><em>ab</em>, <em>name</em>, <em>type</em>, <em>dose</em>,
|
||||
<em>original_txt</em>, and <em>eucast_version</em>.</p>
|
||||
<p>This data set is in R available as <code>dosage</code>, after you
|
||||
load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 20 April 2025 10:55:31 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/dosage.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
@@ -1294,7 +1294,7 @@ column names:<br><em>date</em>, <em>patient</em>, <em>age</em>, <em>gender</em>,
|
||||
<em>MUP</em>, and <em>RIF</em>.</p>
|
||||
<p>This data set is in R available as <code>example_isolates</code>,
|
||||
after you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 9 February 2026 12:16:36 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/example_isolates.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Example content</strong></p>
|
||||
@@ -1700,7 +1700,7 @@ column names:<br><em>patient_id</em>, <em>hospital</em>, <em>date</em>,
|
||||
<p>This data set is in R available as
|
||||
<code>example_isolates_unclean</code>, after you load the
|
||||
<code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 27 August 2022 18:49:37 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/example_isolates_unclean.html">data
|
||||
set here</a>.</p>
|
||||
<p><strong>Example content</strong></p>
|
||||
@@ -1794,17 +1794,17 @@ set here</a>.</p>
|
||||
<h2 id="microorganisms-codes-common-laboratory-codes">
|
||||
<code>microorganisms.codes</code>: Common Laboratory Codes<a class="anchor" aria-label="anchor" href="#microorganisms-codes-common-laboratory-codes"></a>
|
||||
</h2>
|
||||
<p>A data set with 6 036 rows and 2 columns, containing the following
|
||||
<p>A data set with 6 050 rows and 2 columns, containing the following
|
||||
column names:<br><em>code</em> and <em>mo</em>.</p>
|
||||
<p>This data set is in R available as <code>microorganisms.codes</code>,
|
||||
after you load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 30 March 2026 08:01:49 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/microorganisms.codes.html">data
|
||||
set here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
<ul>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.rds" class="external-link">original
|
||||
R Data Structure (RDS) file</a> (27 kB)<br>
|
||||
R Data Structure (RDS) file</a> (28 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.txt" class="external-link">tab-separated
|
||||
text file</a> (0.1 MB)<br>
|
||||
@@ -1816,7 +1816,7 @@ Excel workbook</a> (98 kB)<br>
|
||||
Feather file</a> (0.1 MB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.parquet" class="external-link">Apache
|
||||
Parquet file</a> (68 kB)<br>
|
||||
Parquet file</a> (69 kB)<br>
|
||||
</li>
|
||||
<li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.codes.sav" class="external-link">IBM
|
||||
SPSS Statistics data file</a> (0.2 MB)<br>
|
||||
@@ -1870,7 +1870,7 @@ column names:<br><em>av</em>, <em>name</em>, <em>atc</em>, <em>cid</em>,
|
||||
<em>loinc</em>.</p>
|
||||
<p>This data set is in R available as <code>antivirals</code>, after you
|
||||
load the <code>AMR</code> package.</p>
|
||||
<p>It was last updated on 1 September 2025 15:00:00 UTC. Find more info
|
||||
<p>It was last updated on 20 October 2023 12:51:48 UTC. Find more info
|
||||
about the contents, (scientific) source, and structure of this <a href="https://amr-for-r.org/reference/antimicrobials.html">data set
|
||||
here</a>.</p>
|
||||
<p><strong>Direct download links:</strong></p>
|
||||
|
||||
561
articles/datasets.md
Normal file
561
articles/datasets.md
Normal file
@@ -0,0 +1,561 @@
|
||||
# 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 78 679 rows and 26 columns, containing the following
|
||||
column names:
|
||||
*mo*, *fullname*, *status*, *kingdom*, *phylum*, *class*, *order*,
|
||||
*family*, *genus*, *species*, *subspecies*, *rank*, *ref*,
|
||||
*oxygen_tolerance*, *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 18 September 2025 12:58:34 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)
|
||||
(1.8 MB)
|
||||
- Download as [tab-separated text
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.txt)
|
||||
(17.7 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.xlsx)
|
||||
(8.8 MB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.feather)
|
||||
(8.4 MB)
|
||||
- Download as [Apache Parquet
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.parquet)
|
||||
(3.8 MB)
|
||||
- Download as [IBM SPSS Statistics data
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.sav)
|
||||
(28.4 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.dta)
|
||||
(89.5 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 |
|
||||
|:-----------------:|:----------------------:|
|
||||
| (unknown kingdom) | 1 |
|
||||
| Animalia | 1 628 |
|
||||
| Archaea | 1 419 |
|
||||
| Bacteria | 39 249 |
|
||||
| Chromista | 178 |
|
||||
| Fungi | 28 137 |
|
||||
|
||||
First 6 rows when filtering on genus *Escherichia*:
|
||||
|
||||
| mo | fullname | status | kingdom | phylum | class | order | family | genus | species | subspecies | rank | ref | oxygen_tolerance | 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 | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | | | genus | Castellani et al., 1919 | facultative anaerobe | LPSN | 515602 | 482 | | | | | | 11158430 | | 1 | 407310004, 407251000, 407281008, … |
|
||||
| B_ESCHR_ADCR | Escherichia adecarboxylata | synonym | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | adecarboxylata | | species | Leclerc, 1962 | likely facultative anaerobe | LPSN | 776052 | 515602 | 777447 | | | | | | | 1 | |
|
||||
| B_ESCHR_ALBR | Escherichia albertii | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | albertii | | species | Huys et al., 2003 | facultative anaerobe | LPSN | 776053 | 515602 | | | | | 5427575 | | | 1 | 419388003 |
|
||||
| B_ESCHR_BLTT | Escherichia blattae | synonym | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | blattae | | species | Burgess et al., 1973 | likely facultative anaerobe | LPSN | 776056 | 515602 | 788468 | | | | | | | 1 | |
|
||||
| B_ESCHR_COLI | Escherichia coli | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | | species | Castellani et al., 1919 | facultative anaerobe | LPSN | 776057 | 515602 | | | | | 11286021 | | | 1 | 1095001000112106, 715307006, 737528008, … |
|
||||
| B_ESCHR_COLI_COLI | Escherichia coli coli | accepted | Bacteria | Pseudomonadota | Gammaproteobacteria | Enterobacterales | Enterobacteriaceae | Escherichia | coli | coli | subspecies | | | GBIF | | 776057 | | | | | 12233256 | 11286021 | | 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 March 2026 09:38:28 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 45 730 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 2 April 2026 09:42:19 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)
|
||||
(0.2 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 534 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 26 March 2025 16:19:17 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)
|
||||
(20 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)
|
||||
(65 kB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/microorganisms.groups.dta)
|
||||
(83 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 271 905 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 28 March 2025 10:17:49 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)
|
||||
(10.1 MB)
|
||||
- Download as [Microsoft Excel
|
||||
workbook](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.xlsx)
|
||||
(2.9 MB)
|
||||
- Download as [Apache Feather
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.feather)
|
||||
(2.3 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)
|
||||
(14.8 MB)
|
||||
- Download as [Stata DTA
|
||||
file](https://github.com/msberends/AMR/raw/main/data-raw/datasets/intrinsic_resistant.dta)
|
||||
(22.6 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 | Pirlimycin |
|
||||
| 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 |
|
||||
|
||||
------------------------------------------------------------------------
|
||||
|
||||
## `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 9 February 2026 12:16:36 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 6 050 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 30 March 2026 08:01:49 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)
|
||||
(28 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)
|
||||
(69 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 | | | |
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
|
||||
16
articles/index.md
Normal file
16
articles/index.md
Normal file
@@ -0,0 +1,16 @@
|
||||
# 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):
|
||||
@@ -7,7 +7,7 @@
|
||||
|
||||
<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="">3.0.0.9019</small>
|
||||
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">3.0.1.9043</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">
|
||||
@@ -135,7 +135,7 @@
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Matthew Saab</strong>. Contributor.
|
||||
<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>
|
||||
@@ -147,7 +147,7 @@
|
||||
</p>
|
||||
</li>
|
||||
<li>
|
||||
<p><strong>Rogier P. Schade</strong>. Contributor.
|
||||
<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>
|
||||
|
||||
106
authors.md
Normal file
106
authors.md
Normal file
@@ -0,0 +1,106 @@
|
||||
# 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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