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<a class="navbar-brand me-2" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
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<a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9121</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9122</small>
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@ -29,7 +29,7 @@
<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="">2.1.1.9121</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9122</small>
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@ -88,7 +88,7 @@
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 19 December 2024.</p>
generated on 20 December 2024.</p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@ -144,21 +144,21 @@ make the structure of your data generally look like this:</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2024-12-19</td>
<td align="center">2024-12-20</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">2024-12-19</td>
<td align="center">2024-12-20</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">2024-12-19</td>
<td align="center">2024-12-20</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>

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<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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<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>`AMR` with `tidymodels` • AMR (for R)</title>
<title>AMR with tidymodels • AMR (for R)</title>
<!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png">
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@ -19,7 +19,7 @@
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<link href="../deps/font-awesome-6.5.2/css/v4-shims.min.css" rel="stylesheet">
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<script src="../extra.js"></script><meta property="og:title" content="`AMR` with `tidymodels`">
<script src="../extra.js"></script><meta property="og:title" content="AMR with tidymodels">
</head>
<body>
<a href="#main" class="visually-hidden-focusable">Skip to contents</a>
@ -29,7 +29,7 @@
<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="">2.1.1.9121</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9122</small>
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@ -75,7 +75,7 @@
<div class="row">
<main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>`AMR` with `tidymodels`</h1>
<img src="../logo.svg" class="logo" alt=""><h1>AMR with tidymodels</h1>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_with_tidymodels.Rmd" class="external-link"><code>vignettes/AMR_with_tidymodels.Rmd</code></a></small>
@ -84,6 +84,11 @@
<blockquote>
<p>This page was entirely written by our <a href="https://chatgpt.com/g/g-M4UNLwFi5-amr-for-r-assistant" class="external-link">AMR for R
Assistant</a>, a ChatGPT manually-trained model able to answer any
question about the AMR package.</p>
</blockquote>
<p>Antimicrobial resistance (AMR) is a global health crisis, and
understanding resistance patterns is crucial for managing effective
treatments. The <code>AMR</code> R package provides robust tools for
@ -94,16 +99,15 @@ framework to predict resistance patterns in the
<code>example_isolates</code> dataset.</p>
<p>By leveraging the power of <code>tidymodels</code> and the
<code>AMR</code> package, we’ll build a reproducible machine learning
workflow to predict resistance to two important antibiotic classes:
aminoglycosides and beta-lactams.</p>
<hr>
workflow to predict the Gramstain of the microorganism to two important
antibiotic classes: aminoglycosides and beta-lactams.</p>
<div class="section level3">
<h3 id="objective">
<strong>Objective</strong><a class="anchor" aria-label="anchor" href="#objective"></a>
</h3>
<p>Our goal is to build a predictive model using the
<code>tidymodels</code> framework to determine resistance patterns based
on microbial data. We will:</p>
<code>tidymodels</code> framework to determine the Gramstain of the
microorganism based on microbial data. We will:</p>
<ol style="list-style-type: decimal">
<li>Preprocess data using the selector functions
<code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antibiotic_class_selectors.html">betalactams()</a></code>.</li>
@ -111,7 +115,6 @@ on microbial data. We will:</p>
<li>Use a structured <code>tidymodels</code> workflow to preprocess,
train, and evaluate the model.</li>
</ol>
<hr>
</div>
<div class="section level3">
<h3 id="data-preparation">
@ -158,7 +161,7 @@ package.</p>
<span> <span class="co"># get Gramstain of microorganisms</span></span>
<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">%&gt;%</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 class="co"># %&gt;%</span></span>
<span> <span class="fu">drop_na</span><span class="op">(</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'</span></span>
<span><span class="co">#&gt; (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)</span></span>
<span><span class="co">#&gt; ℹ For betalactams() using columns 'PEN' (benzylpenicillin), 'OXA'</span></span>
@ -166,14 +169,16 @@ package.</p>
<span><span class="co">#&gt; (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP'</span></span>
<span><span class="co">#&gt; (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM'</span></span>
<span><span class="co">#&gt; (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime),</span></span>
<span><span class="co">#&gt; 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)</span></span>
<span> <span class="co"># Cefepime is not reliable</span></span>
<span> <span class="co">#select(-FEP)</span></span></code></pre></div>
<p><strong>Explanation:</strong> - <code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and
<code><a href="../reference/antibiotic_class_selectors.html">betalactams()</a></code> dynamically select columns for antibiotics in
these classes. - <code>drop_na()</code> ensures the model receives
complete cases for training.</p>
<hr>
<span><span class="co">#&gt; 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)</span></span></code></pre></div>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code><a href="../reference/antibiotic_class_selectors.html">aminoglycosides()</a></code> and <code><a href="../reference/antibiotic_class_selectors.html">betalactams()</a></code>
dynamically select columns for antibiotics in these classes.</li>
<li>
<code>drop_na()</code> ensures the model receives complete cases for
training.</li>
</ul>
</div>
<div class="section level3">
<h3 id="defining-the-workflow">
@ -184,11 +189,7 @@ three steps: preprocessing, model specification, and fitting.</p>
<div class="section level4">
<h4 id="preprocessing-with-a-recipe">1. Preprocessing with a Recipe<a class="anchor" aria-label="anchor" href="#preprocessing-with-a-recipe"></a>
</h4>
<p>We create a recipe to preprocess the data for modelling. This
includes: - Encoding resistance results (<code>S</code>, <code>I</code>,
<code>R</code>) as binary (resistant or not resistant). - Converting
microbial organism names (<code>mo</code>) into numerical features using
one-hot encoding.</p>
<p>We create a recipe to preprocess the data for modelling.</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Define the recipe for data preprocessing</span></span>
<span><span class="va">resistance_recipe</span> <span class="op">&lt;-</span> <span class="fu">recipe</span><span class="op">(</span><span class="va">mo</span> <span class="op">~</span> <span class="va">.</span>, data <span class="op">=</span> <span class="va">data</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
@ -204,11 +205,18 @@ one-hot encoding.</p>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Operations</span></span>
<span><span class="co">#&gt; <span style="color: #00BBBB;">•</span> Correlation filter on: <span style="color: #0000BB;">c(aminoglycosides(), betalactams())</span></span></span></code></pre></div>
<p><strong>Explanation:</strong> - <code>step_mutate()</code> transforms
resistance results (<code>R</code>) into binary variables (TRUE/FALSE).
- <code>step_dummy()</code> converts categorical organism
(<code>mo</code>) names into one-hot encoded numerical features, making
them compatible with the model.</p>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code>recipe(mo ~ ., data = data)</code> will take the
<code>mo</code> column as outcome and all other columns as
predictors.</li>
<li>
<code>step_corr()</code> removes predictors (i.e., antibiotic
columns) that have a higher correlation than 90%.</li>
</ul>
<p>Notice how the recipe contains just the antibiotic selector functions
- no need to define the columns specifically.</p>
</div>
<div class="section level4">
<h4 id="specifying-the-model">2. Specifying the Model<a class="anchor" aria-label="anchor" href="#specifying-the-model"></a>
@ -223,9 +231,15 @@ a binary classification task.</p>
<span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
<p><strong>Explanation:</strong> - <code>logistic_reg()</code> sets up a
logistic regression model. - <code>set_engine("glm")</code> specifies
the use of R’s built-in GLM engine.</p>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code>logistic_reg()</code> sets up a logistic regression
model.</li>
<li>
<code>set_engine("glm")</code> specifies the use of R’s built-in GLM
engine.</li>
</ul>
</div>
<div class="section level4">
<h4 id="building-the-workflow">3. Building the Workflow<a class="anchor" aria-label="anchor" href="#building-the-workflow"></a>
@ -236,22 +250,7 @@ which organizes the entire modeling process.</p>
<code class="sourceCode R"><span><span class="co"># Combine the recipe and model into a workflow</span></span>
<span><span class="va">resistance_workflow</span> <span class="op">&lt;-</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">%&gt;%</a></span></span>
<span> <span class="fu">add_recipe</span><span class="op">(</span><span class="va">resistance_recipe</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="co"># Add the preprocessing recipe</span></span>
<span> <span class="fu">add_model</span><span class="op">(</span><span class="va">logistic_model</span><span class="op">)</span> <span class="co"># Add the logistic regression model</span></span>
<span><span class="va">resistance_workflow</span></span>
<span><span class="co">#&gt; ══ Workflow ════════════════════════════════════════════════════════════════════</span></span>
<span><span class="co">#&gt; <span style="font-style: italic;">Preprocessor:</span> Recipe</span></span>
<span><span class="co">#&gt; <span style="font-style: italic;">Model:</span> logistic_reg()</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Preprocessor ────────────────────────────────────────────────────────────────</span></span>
<span><span class="co">#&gt; 1 Recipe Step</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; • step_corr()</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Model ───────────────────────────────────────────────────────────────────────</span></span>
<span><span class="co">#&gt; Logistic Regression Model Specification (classification)</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Computational engine: glm</span></span></code></pre></div>
<hr>
<span> <span class="fu">add_model</span><span class="op">(</span><span class="va">logistic_model</span><span class="op">)</span> <span class="co"># Add the logistic regression model</span></span></code></pre></div>
</div>
</div>
<div class="section level3">
@ -278,38 +277,18 @@ performance.</p>
<span><span class="co">#&gt; (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP'</span></span>
<span><span class="co">#&gt; (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM'</span></span>
<span><span class="co">#&gt; (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime),</span></span>
<span><span class="co">#&gt; 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)</span></span>
<span></span>
<span><span class="va">fitted_workflow</span></span>
<span><span class="co">#&gt; ══ Workflow [trained] ══════════════════════════════════════════════════════════</span></span>
<span><span class="co">#&gt; <span style="font-style: italic;">Preprocessor:</span> Recipe</span></span>
<span><span class="co">#&gt; <span style="font-style: italic;">Model:</span> logistic_reg()</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Preprocessor ────────────────────────────────────────────────────────────────</span></span>
<span><span class="co">#&gt; 1 Recipe Step</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; • step_corr()</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; ── Model ───────────────────────────────────────────────────────────────────────</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Call: stats::glm(formula = ..y ~ ., family = stats::binomial, data = data)</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Coefficients:</span></span>
<span><span class="co">#&gt; (Intercept) GEN TOB AMK KAN PEN </span></span>
<span><span class="co">#&gt; 101.11641 -3.69738 4.55879 1.86703 -23.37497 -0.57182 </span></span>
<span><span class="co">#&gt; OXA FLC AMC AMP TZP CZO </span></span>
<span><span class="co">#&gt; -4.68575 -11.69742 0.79748 -1.56197 0.87667 -2.28424 </span></span>
<span><span class="co">#&gt; FEP CXM FOX CAZ CRO IPM </span></span>
<span><span class="co">#&gt; -0.19847 0.02659 10.32455 10.27248 0.97321 -0.93096 </span></span>
<span><span class="co">#&gt; MEM </span></span>
<span><span class="co">#&gt; -0.88753 </span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; Degrees of Freedom: 1573 Total (i.e. Null); 1555 Residual</span></span>
<span><span class="co">#&gt; Null Deviance: 2071 </span></span>
<span><span class="co">#&gt; Residual Deviance: 74.91 AIC: 112.9</span></span></code></pre></div>
<p><strong>Explanation:</strong> - <code>initial_split()</code> splits
the data into training and testing sets. - <code>fit()</code> trains the
workflow on the training set.</p>
<span><span class="co">#&gt; 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)</span></span></code></pre></div>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code>initial_split()</code> splits the data into training and
testing sets.</li>
<li>
<code>fit()</code> trains the workflow on the training set.</li>
</ul>
<p>Notice how in <code>fit()</code>, the antibiotic selector functions
are internally called again. For training, these functions are called
since they are stored in the recipe.</p>
<p>Next, we evaluate the model on the testing data.</p>
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="co"># Make predictions on the testing set</span></span>
@ -351,17 +330,23 @@ workflow on the training set.</p>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> accuracy binary 0.995</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> kap binary 0.989</span></span></code></pre></div>
<p><strong>Explanation:</strong> - <code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> generates
predictions on the testing set. - <code>metrics()</code> computes
evaluation metrics like accuracy and AUC.</p>
<p><strong>Explanation:</strong></p>
<ul>
<li>
<code><a href="https://rdrr.io/r/stats/predict.html" class="external-link">predict()</a></code> generates predictions on the testing
set.</li>
<li>
<code>metrics()</code> computes evaluation metrics like accuracy and
kappa.</li>
</ul>
<p>It appears we can predict the Gram based on AMR results with a 0.995
accuracy. The ROC curve looks like:</p>
accuracy based on AMR results of aminoglycosides and beta-lactam
antibiotics. The ROC curve looks like this:</p>
<div class="sourceCode" id="cb7"><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">%&gt;%</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">%&gt;%</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-7-1.png" width="720"></p>
<hr>
</div>
<div class="section level3">
<h3 id="conclusion">
@ -376,7 +361,6 @@ and evaluated its performance.</p>
<p>This workflow is extensible to other antibiotic classes and
resistance patterns, empowering users to analyse AMR data systematically
and reproducibly.</p>
<hr>
</div>
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
</nav></aside>

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@ -29,7 +29,7 @@
<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="">2.1.1.9121</small>
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@ -77,7 +77,7 @@
<main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>Data sets for download / own use</h1>
<h4 data-toc-skip class="date">19 December 2024</h4>
<h4 data-toc-skip class="date">20 December 2024</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>

4
articles/index.html
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@ -7,7 +7,7 @@
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@ -52,7 +52,7 @@
<dl><dt><a href="AMR_for_Python.html">AMR for Python</a></dt>
<dd>
</dd><dt><a href="AMR_with_tidymodels.html">`AMR` with `tidymodels`</a></dt>
</dd><dt><a href="AMR_with_tidymodels.html">AMR with tidymodels</a></dt>
<dd>
</dd><dt><a href="AMR.html">How to conduct AMR data analysis</a></dt>
<dd>

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@ -29,7 +29,7 @@
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authors.html
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@ -32,7 +32,7 @@
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@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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@ -48,18 +48,18 @@
</div>
<div class="section level2">
<h2 class="pkg-version" data-toc-text="2.1.1.9121" id="amr-2119121">AMR 2.1.1.9121<a class="anchor" aria-label="anchor" href="#amr-2119121"></a></h2>
<h2 class="pkg-version" data-toc-text="2.1.1.9122" id="amr-2119122">AMR 2.1.1.9122<a class="anchor" aria-label="anchor" href="#amr-2119122"></a></h2>
<p><em>(this beta version will eventually become v3.0. We’re happy to reach a new major milestone soon, which will be all about the new One Health support! Install this beta using <a href="https://msberends.github.io/AMR/#latest-development-version">the instructions here</a>.)</em></p>
<div class="section level5">
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9121">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9121"></a></h5>
<h5 id="a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9122">A New Milestone: AMR v3.0 with One Health Support (= Human + Veterinary + Environmental)<a class="anchor" aria-label="anchor" href="#a-new-milestone-amr-v30-with-one-health-support--human--veterinary--environmental-2-1-1-9122"></a></h5>
<p>This package now supports not only tools for AMR data analysis in clinical settings, but also for veterinary and environmental microbiology. This was made possible through a collaboration with the <a href="https://www.upei.ca/avc" class="external-link">University of Prince Edward Island’s Atlantic Veterinary College</a>, Canada. To celebrate this great improvement of the package, we also updated the package logo to reflect this change.</p>
</div>
<div class="section level3">
<h3 id="breaking-2-1-1-9121">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9121"></a></h3>
<h3 id="breaking-2-1-1-9122">Breaking<a class="anchor" aria-label="anchor" href="#breaking-2-1-1-9122"></a></h3>
<ul><li>Removed all functions and references that used the deprecated <code>rsi</code> class, which were all replaced with their <code>sir</code> equivalents over a year ago</li>
</ul></div>
<div class="section level3">
<h3 id="new-2-1-1-9121">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9121"></a></h3>
<h3 id="new-2-1-1-9122">New<a class="anchor" aria-label="anchor" href="#new-2-1-1-9122"></a></h3>
<ul><li>
<strong>One Health implementation</strong>
<ul><li>Function <code><a href="../reference/as.sir.html">as.sir()</a></code> now has extensive support for veterinary breakpoints from CLSI. Use <code>breakpoint_type = "animal"</code> and set the <code>host</code> argument to a variable that contains animal species names.</li>
@ -104,7 +104,7 @@
</ul></li>
</ul></div>
<div class="section level3">
<h3 id="changed-2-1-1-9121">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9121"></a></h3>
<h3 id="changed-2-1-1-9122">Changed<a class="anchor" aria-label="anchor" href="#changed-2-1-1-9122"></a></h3>
<ul><li>SIR interpretation
<ul><li>It is now possible to use column names for argument <code>ab</code>, <code>mo</code>, and <code>uti</code>: <code>as.sir(..., ab = "column1", mo = "column2", uti = "column3")</code>. This greatly improves the flexibility for users.</li>
<li>Users can now set their own criteria (using regular expressions) as to what should be considered S, I, R, SDD, and NI.</li>
@ -164,14 +164,14 @@
</ul></li>
</ul></div>
<div class="section level3">
<h3 id="other-2-1-1-9121">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9121"></a></h3>
<h3 id="other-2-1-1-9122">Other<a class="anchor" aria-label="anchor" href="#other-2-1-1-9122"></a></h3>
<ul><li>Greatly improved <code>vctrs</code> integration, a Tidyverse package working in the background for many Tidyverse functions. For users, this means that functions such as <code>dplyr</code>’s <code><a href="https://dplyr.tidyverse.org/reference/bind_rows.html" class="external-link">bind_rows()</a></code>, <code><a href="https://dplyr.tidyverse.org/reference/rowwise.html" class="external-link">rowwise()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/c_across.html" class="external-link">c_across()</a></code> are now supported for e.g. columns of class <code>mic</code>. Despite this, this <code>AMR</code> package is still zero-dependent on any other package, including <code>dplyr</code> and <code>vctrs</code>.</li>
<li>Greatly updated and expanded documentation</li>
<li>Added Larisse Bolton, Jordan Stull, Matthew Saab, and Javier Sanchez as contributors, to thank them for their valuable input</li>
<li>Stopped support for SAS (<code>.xpt</code>) files, since their file structure and extremely inefficient and requires more disk space than GitHub allows in a single commit.</li>
</ul></div>
<div class="section level3">
<h3 id="older-versions-2-1-1-9121">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9121"></a></h3>
<h3 id="older-versions-2-1-1-9122">Older Versions<a class="anchor" aria-label="anchor" href="#older-versions-2-1-1-9122"></a></h3>
<p>This changelog only contains changes from AMR v3.0 (October 2024) and later.</p>
<ul><li>For prior v2 versions, please see <a href="https://github.com/msberends/AMR/blob/v2.1.1/NEWS.md" class="external-link">our v2 archive</a>.</li>
<li>For prior v1 versions, please see <a href="https://github.com/msberends/AMR/blob/v1.8.2/NEWS.md" class="external-link">our v1 archive</a>.</li>

2
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@ -12,7 +12,7 @@ articles:
resistance_predict: resistance_predict.html
welcome_to_AMR: welcome_to_AMR.html
WHONET: WHONET.html
last_built: 2024-12-19T19:21Z
last_built: 2024-12-20T09:59Z
urls:
reference: https://msberends.github.io/AMR/reference
article: https://msberends.github.io/AMR/articles

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@ -7,7 +7,7 @@
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@ -7,7 +7,7 @@
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@ -7,7 +7,7 @@
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@ -7,7 +7,7 @@
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@ -111,16 +111,16 @@
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="va">df</span></span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> birth_date age age_exact age_at_y2k</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1965-12-05 59 59.03825 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1980-03-01 44 44.80055 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1949-11-01 75 75.13115 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1947-02-14 77 77.84426 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1940-02-19 84 84.83060 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1988-01-10 36 36.93989 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1997-08-27 27 27.31148 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1978-01-26 46 46.89617 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1972-06-17 52 52.50546 27</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1986-08-10 38 38.35792 13</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 1 1965-12-05 59 59.04098 34</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 2 1980-03-01 44 44.80328 19</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 3 1949-11-01 75 75.13388 50</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 4 1947-02-14 77 77.84699 52</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 5 1940-02-19 84 84.83333 59</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 6 1988-01-10 36 36.94262 11</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 7 1997-08-27 27 27.31421 2</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 8 1978-01-26 46 46.89891 21</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 9 1972-06-17 52 52.50820 27</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1986-08-10 38 38.36066 13</span>
</code></pre></div>
</div>
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@ -21,7 +21,7 @@ All breakpoints used for interpretation are available in our clinical_breakpoint
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@ -786,16 +786,16 @@ A microorganism is categorised as "Resistant" when there is a high likelihood of
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 57 × 16</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> datetime index ab_given mo_given host_given ab mo </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">*</span> <span style="color: #949494; font-style: italic;">&lt;dttm&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;ab&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2024-12-19 <span style="color: #949494;">19:22:22</span> 4 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2024-12-19 <span style="color: #949494;">19:22:29</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2024-12-19 <span style="color: #949494;">19:22:29</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2024-12-19 <span style="color: #949494;">19:22:30</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2024-12-19 <span style="color: #949494;">19:22:30</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2024-12-19 <span style="color: #949494;">19:22:22</span> 3 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2024-12-19 <span style="color: #949494;">19:22:29</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2024-12-19 <span style="color: #949494;">19:22:29</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2024-12-19 <span style="color: #949494;">19:22:30</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2024-12-19 <span style="color: #949494;">19:22:30</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span> 2024-12-20 <span style="color: #949494;">10:00:38</span> 4 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span> 2024-12-20 <span style="color: #949494;">10:00:46</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span> 2024-12-20 <span style="color: #949494;">10:00:46</span> 4 genta Escheri… human GEN B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span> 2024-12-20 <span style="color: #949494;">10:00:47</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span> 2024-12-20 <span style="color: #949494;">10:00:47</span> 4 genta Escheri… cattle GEN B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span> 2024-12-20 <span style="color: #949494;">10:00:38</span> 3 AMX B_STRPT… human AMX B_STRPT_PNMN </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span> 2024-12-20 <span style="color: #949494;">10:00:46</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span> 2024-12-20 <span style="color: #949494;">10:00:46</span> 3 tobra Escheri… human TOB B_[ORD]_ENTRBCTR</span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span> 2024-12-20 <span style="color: #949494;">10:00:47</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span> 2024-12-20 <span style="color: #949494;">10:00:47</span> 3 tobra Escheri… horses TOB B_ESCHR_COLI </span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 47 more rows</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 9 more variables: host &lt;chr&gt;, method &lt;chr&gt;, input &lt;dbl&gt;, outcome &lt;sir&gt;,</span></span>
<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># notes &lt;chr&gt;, guideline &lt;chr&gt;, ref_table &lt;chr&gt;, uti &lt;lgl&gt;,</span></span>

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@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."><meta prope
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@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
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<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -183,10 +183,6 @@
<dt id="arg--">...<a class="anchor" aria-label="anchor" href="#arg--"></a></dt>
<dd><p>other arguments passed on to <code>geom_sir()</code> or, in case of <code><a href="plot.html">scale_sir_colours()</a></code>, named values to set colours. The default colours are colour-blind friendly, while maintaining the convention that e.g. 'susceptible' should be green and 'resistant' should be red. See <em>Examples</em>.</p></dd>
<dt id="arg-aesthetics">aesthetics<a class="anchor" aria-label="anchor" href="#arg-aesthetics"></a></dt>
<dd><p>aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"</p></dd>
</dl></div>
<div class="section level2">
<h2 id="details">Details<a class="anchor" aria-label="anchor" href="#details"></a></h2>

2
reference/guess_ab_col.html
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@ -7,7 +7,7 @@
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2
reference/index.html
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@ -7,7 +7,7 @@
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reference/intrinsic_resistant.html
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@ -7,7 +7,7 @@
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@ -7,7 +7,7 @@
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reference/join.html
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@ -7,7 +7,7 @@
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reference/key_antimicrobials.html
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@ -7,7 +7,7 @@
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reference/kurtosis.html
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@ -7,7 +7,7 @@
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reference/like.html
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@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/mdro.html
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@ -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="">2.1.1.9121</small>
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reference/mean_amr_distance.html
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@ -7,7 +7,7 @@
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2
reference/microorganisms.codes.html
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@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/microorganisms.groups.html
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@ -7,7 +7,7 @@
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reference/microorganisms.html
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@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/mo_matching_score.html
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@ -7,7 +7,7 @@
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2
reference/mo_property.html
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@ -7,7 +7,7 @@
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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reference/mo_source.html
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@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
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reference/pca.html
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@ -7,7 +7,7 @@
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72
reference/plot.html
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@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
<a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
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@ -167,7 +167,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
<span></span>
<span><span class="fu">scale_y_percent</span><span class="op">(</span></span>
<span> breaks <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/seq.html" class="external-link">seq</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html" class="external-link">max</a></span><span class="op">(</span><span class="va">x</span>, na.rm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span>, <span class="fl">0.1</span><span class="op">)</span>,</span>
<span> limits <span class="op">=</span> <span class="cn">NULL</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</span>, <span class="cn">NA</span><span class="op">)</span></span>
<span><span class="op">)</span></span>
<span></span>
<span><span class="fu">scale_sir_colours</span><span class="op">(</span></span>
@ -253,6 +253,50 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
<dt id="arg-breakpoint-type">breakpoint_type<a class="anchor" aria-label="anchor" href="#arg-breakpoint-type"></a></dt>
<dd><p>the type of breakpoints to use, either "ECOFF", "animal", or "human". ECOFF stands for Epidemiological Cut-Off values. The default is <code>"human"</code>, which can also be set with the package option <code><a href="AMR-options.html">AMR_breakpoint_type</a></code>. If <code>host</code> is set to values of veterinary species, this will automatically be set to <code>"animal"</code>.</p></dd>
<dt id="arg-facet">facet<a class="anchor" aria-label="anchor" href="#arg-facet"></a></dt>
<dd><p>variable to split plots by, either <code>"interpretation"</code> (default) or <code>"antibiotic"</code> or a grouping variable</p></dd>
<dt id="arg-nrow">nrow<a class="anchor" aria-label="anchor" href="#arg-nrow"></a></dt>
<dd><p>(when using <code>facet</code>) number of rows</p></dd>
<dt id="arg-breaks">breaks<a class="anchor" aria-label="anchor" href="#arg-breaks"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a> vector of positions</p></dd>
<dt id="arg-limits">limits<a class="anchor" aria-label="anchor" href="#arg-limits"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/numeric.html" class="external-link">numeric</a> vector of length two providing limits of the scale, use <code>NA</code> to refer to the existing minimum or maximum</p></dd>
<dt id="arg-aesthetics">aesthetics<a class="anchor" aria-label="anchor" href="#arg-aesthetics"></a></dt>
<dd><p>aesthetics to apply the colours to - the default is "fill" but can also be (a combination of) "alpha", "colour", "fill", "linetype", "shape" or "size"</p></dd>
<dt id="arg-position">position<a class="anchor" aria-label="anchor" href="#arg-position"></a></dt>
<dd><p>position adjustment of bars, either <code>"fill"</code>, <code>"stack"</code> or <code>"dodge"</code></p></dd>
<dt id="arg-translate-ab">translate_ab<a class="anchor" aria-label="anchor" href="#arg-translate-ab"></a></dt>
<dd><p>a column name of the <a href="antibiotics.html">antibiotics</a> data set to translate the antibiotic abbreviations to, using <code><a href="ab_property.html">ab_property()</a></code></p></dd>
<dt id="arg-minimum">minimum<a class="anchor" aria-label="anchor" href="#arg-minimum"></a></dt>
<dd><p>the minimum allowed number of available (tested) isolates. Any isolate count lower than <code>minimum</code> will return <code>NA</code> with a warning. The default number of <code>30</code> isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see <em>Source</em>.</p></dd>
<dt id="arg-combine-si">combine_SI<a class="anchor" aria-label="anchor" href="#arg-combine-si"></a></dt>
<dd><p>a <a href="https://rdrr.io/r/base/logical.html" class="external-link">logical</a> to indicate whether all values of S, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is <code>TRUE</code></p></dd>
<dt id="arg-datalabels-size">datalabels.size<a class="anchor" aria-label="anchor" href="#arg-datalabels-size"></a></dt>
<dd><p>size of the datalabels</p></dd>
<dt id="arg-datalabels-colour">datalabels.colour<a class="anchor" aria-label="anchor" href="#arg-datalabels-colour"></a></dt>
<dd><p>colour of the datalabels</p></dd>
</dl></div>
<div class="section level2">
<h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
@ -298,7 +342,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
<span class="r-plt img"><img src="plot-6.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Plotting using scale_x_mic()</span></span></span>
<span class="r-in"><span><span class="co"># Plotting using scale_x_mic() ---------------------------------------------</span></span></span>
<span class="r-in"><span><span class="co"># \donttest{</span></span></span>
<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://ggplot2.tidyverse.org" class="external-link">"ggplot2"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span> <span class="va">mic_plot</span> <span class="op">&lt;-</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://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mics <span class="op">=</span> <span class="fu"><a href="as.mic.html">as.mic</a></span><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">0.25</span>, <span class="st">"&lt;=4"</span>, <span class="fl">4</span>, <span class="fl">8</span>, <span class="fl">32</span>, <span class="st">"&gt;=32"</span><span class="op">)</span><span class="op">)</span>,</span></span>
@ -346,6 +390,28 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
<span class="r-in"><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">some_sir_values</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="op">}</span></span></span>
<span class="r-plt img"><img src="plot-14.png" alt="" width="700" height="433"></span>
<span class="r-in"><span></span></span>
<span class="r-in"><span><span class="co"># Plotting using scale_y_percent() -----------------------------------------</span></span></span>
<span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://ggplot2.tidyverse.org" class="external-link">"ggplot2"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
<span class="r-in"><span> <span class="va">p</span> <span class="op">&lt;-</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://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a></span><span class="op">(</span>mics <span class="op">=</span> <span class="fu"><a href="as.mic.html">as.mic</a></span><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">0.25</span>, <span class="st">"&lt;=4"</span>, <span class="fl">4</span>, <span class="fl">8</span>, <span class="fl">32</span>, <span class="st">"&gt;=32"</span><span class="op">)</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> counts <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">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">3</span><span class="op">)</span><span class="op">)</span>,</span></span>
<span class="r-in"><span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html" class="external-link">aes</a></span><span class="op">(</span><span class="va">mics</span>, <span class="va">counts</span> <span class="op">/</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html" class="external-link">sum</a></span><span class="op">(</span><span class="va">counts</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_bar.html" class="external-link">geom_col</a></span><span class="op">(</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p</span><span class="op">)</span></span></span>
<span class="r-in"><span> </span></span>
<span class="r-in"><span> <span class="va">p2</span> <span class="op">&lt;-</span> <span class="va">p</span> <span class="op">+</span></span></span>
<span class="r-in"><span> <span class="fu">scale_y_percent</span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span> <span class="fu">theme_sir</span><span class="op">(</span><span class="op">)</span></span></span>
<span class="r-in"><span> <span class="fu"><a href="https://rdrr.io/r/base/print.html" class="external-link">print</a></span><span class="op">(</span><span class="va">p2</span><span class="op">)</span></span></span>
<span class="r-in"><span> </span></span>
<span class="r-in"><span> <span class="va">p</span> <span class="op">+</span></span></span>
<span class="r-in"><span> <span class="fu">scale_y_percent</span><span class="op">(</span>breaks <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq</a></span><span class="op">(</span>from <span class="op">=</span> <span class="fl">0</span>, to <span class="op">=</span> <span class="fl">1</span>, by <span class="op">=</span> <span class="fl">0.1</span><span class="op">)</span>,</span></span>
<span class="r-in"><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</span>, <span class="fl">1</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span></span>
<span class="r-in"><span> <span class="fu">theme_sir</span><span class="op">(</span><span class="op">)</span></span></span>
<span class="r-in"><span><span class="op">}</span></span></span>
<span class="r-plt img"><img src="plot-15.png" alt="" width="700" height="433"></span>
<span class="r-plt img"><img src="plot-16.png" alt="" width="700" height="433"></span>
<span class="r-plt img"><img src="plot-17.png" alt="" width="700" height="433"></span>
<span class="r-in"><span><span class="co"># }</span></span></span>
</code></pre></div>
</div>

2
reference/proportion.html
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@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
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@ -7,7 +7,7 @@
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reference/resistance_predict.html
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@ -7,7 +7,7 @@
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reference/skewness.html
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@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
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reference/translate.html
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search.json
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