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<li><a class="dropdown-item" href="../articles/AMR.html"><span class="fa fa-directions"></span> Conduct AMR Analysis</a></li>
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<li><a class="dropdown-item" href="../reference/antibiogram.html"><span class="fa fa-file-prescription"></span> Generate Antibiogram (Trad./Syndromic/WISCA)</a></li>
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<li><a class="dropdown-item" href="../articles/resistance_predict.html"><span class="fa fa-dice"></span> Predict Antimicrobial Resistance</a></li>
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<li><a class="dropdown-item" href="../articles/datasets.html"><span class="fa fa-database"></span> Download Data Sets for Own Use</a></li>
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<li><a class="dropdown-item" href="../reference/AMR-options.html"><span class="fa fa-gear"></span> Set User- Or Team-specific Package Settings</a></li>
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<li><a class="dropdown-item" href="../articles/PCA.html"><span class="fa fa-compress"></span> Conduct Principal Component Analysis for AMR</a></li>
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<li><a class="dropdown-item" href="../articles/MDR.html"><span class="fa fa-skull-crossbones"></span> Determine Multi-Drug Resistance (MDR)</a></li>
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<li><a class="dropdown-item" href="../articles/WHONET.html"><span class="fa fa-globe-americas"></span> Work with WHONET Data</a></li>
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<li><a class="dropdown-item" href="../articles/EUCAST.html"><span class="fa fa-exchange-alt"></span> Apply Eucast Rules</a></li>
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<li><a class="dropdown-item" href="../reference/mo_property.html"><span class="fa fa-bug"></span> Get Taxonomy of a Microorganism</a></li>
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<li><a class="dropdown-item" href="../reference/ab_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antibiotic Drug</a></li>
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<li><a class="dropdown-item" href="../reference/av_property.html"><span class="fa fa-capsules"></span> Get Properties of an Antiviral Drug</a></li>
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<main id="main" class="col-md-9"><div class="page-header">
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<img src="../logo.svg" class="logo" alt=""><h1>AMR for Python</h1>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/AMR_for_Python.Rmd" class="external-link"><code>vignettes/AMR_for_Python.Rmd</code></a></small>
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<div class="d-none name"><code>AMR_for_Python.Rmd</code></div>
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</div>
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<div class="section level2">
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<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
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</h2>
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<p>The <code>AMR</code> package for R is an incredible tool for
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antimicrobial resistance (AMR) data analysis, providing extensive
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functionality for working with microbial and antimicrobial properties.
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But what if you’re working in Python and still want to benefit from the
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robust features of <code>AMR</code>?</p>
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<p>The best way is to access R directly from Python with the help of
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<code>rpy2</code>, a simple yet powerful Python package. You can easily
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call functions from the <code>AMR</code> package to process your own
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data in your own Python environment. This post will guide you through
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setting up <code>rpy2</code> and show you how to use R functions from
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<code>AMR</code> in Python to supercharge your antimicrobial resistance
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analysis.</p>
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</div>
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<div class="section level2">
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<h2 id="what-is-rpy2">What is <code>rpy2</code>?<a class="anchor" aria-label="anchor" href="#what-is-rpy2"></a>
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</h2>
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<p><code>rpy2</code> is a Python library that allows Python users to run
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R code within their Python scripts. Essentially, it acts as a bridge
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between the two languages, allowing you to tap into the rich ecosystem
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of R libraries (like <code>AMR</code>) while maintaining the flexibility
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of Python.</p>
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<div class="section level3">
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<h3 id="key-features-of-rpy2">Key Features of <code>rpy2</code>:<a class="anchor" aria-label="anchor" href="#key-features-of-rpy2"></a>
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</h3>
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<ul>
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<li>Seamlessly call R functions from Python.</li>
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<li>Convert R data structures into Python data structures like pandas
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DataFrames.</li>
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<li>Leverage the full power of R libraries without leaving your Python
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environment.</li>
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</ul>
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</div>
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</div>
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<div class="section level2">
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<h2 id="setting-up-rpy2">Setting Up <code>rpy2</code><a class="anchor" aria-label="anchor" href="#setting-up-rpy2"></a>
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</h2>
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<p>Before diving into the examples, you’ll need to install both R and
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<code>rpy2</code>. Here’s a step-by-step guide on setting things up.</p>
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<div class="section level3">
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<h3 id="step-1-install-r">Step 1: Install R<a class="anchor" aria-label="anchor" href="#step-1-install-r"></a>
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</h3>
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<p>Ensure that R is installed on your system. R has minimal dependencies
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and is very simple to install:</p>
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<ul>
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<li>
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<strong>Linux</strong>
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<ul>
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<li>Ubuntu / Debian:<br><code>sudo apt install r-base</code>
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</li>
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<li>Fedora:<br><code>sudo dnf install R</code>
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</li>
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<li>CentOS/RHEL:<br><code>sudo yum install R</code>
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</li>
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<li>Arch Linux:<br><code>sudo pacman -S r</code>
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</li>
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</ul>
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</li>
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<li>
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<strong>macOS</strong> (with Homebrew):<br><code>brew install r</code>
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</li>
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<li>
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<strong>Other Systems:</strong><br>
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Visit the <a href="https://cran.r-project.org" class="external-link">CRAN download
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page</a>.</li>
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</ul>
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</div>
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<div class="section level3">
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<h3 id="step-2-install-the-amr-package-in-r">Step 2: Install the <code>AMR</code> package in R<a class="anchor" aria-label="anchor" href="#step-2-install-the-amr-package-in-r"></a>
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</h3>
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<p>On Linux and macOS, open Terminal and run:</p>
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<div class="sourceCode" id="cb1"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="ex">Rscript</span> <span class="at">-e</span> <span class="st">'install.packages("AMR")'</span></span></code></pre></div>
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<p>For other systems, open your R console and install the
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<code>AMR</code> package by running:</p>
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<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"AMR"</span><span class="op">)</span></span></code></pre></div>
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<p>On any system, you can also install the latest development version of
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the <code>AMR</code> package by setting <code>repos</code> to our beta
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channel:</p>
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<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"AMR"</span>, repos <span class="op">=</span> <span class="st">"https://msberends.r-universe.dev"</span><span class="op">)</span></span></code></pre></div>
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</div>
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<div class="section level3">
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<h3 id="step-3-install-rpy2-in-python">Step 3: Install <code>rpy2</code> in Python<a class="anchor" aria-label="anchor" href="#step-3-install-rpy2-in-python"></a>
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</h3>
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<p>To install <code>rpy2</code>, simply run the following command in
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your terminal:</p>
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<div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="ex">pip</span> install rpy2</span></code></pre></div>
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</div>
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<div class="section level3">
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<h3 id="step-4-test-rpy2-installation">Step 4: Test <code>rpy2</code> Installation<a class="anchor" aria-label="anchor" href="#step-4-test-rpy2-installation"></a>
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</h3>
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<p>To ensure everything is set up correctly, you can test your
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installation by running the following Python script, which essentially
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runs R in the background:</p>
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<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> rpy2.robjects <span class="im">as</span> ro</span>
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<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a></span>
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<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="co"># Test a simple R function from Python</span></span>
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<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>ro.r(<span class="st">'1 + 1'</span>)</span></code></pre></div>
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<p>If this returns <code>2</code>, you’re good to go!</p>
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</div>
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</div>
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<div class="section level2">
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<h2 id="working-with-amr-in-python">Working with <code>AMR</code> in Python<a class="anchor" aria-label="anchor" href="#working-with-amr-in-python"></a>
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</h2>
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<p>Now that we have <code>rpy2</code> set up, let’s walk through some
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practical examples of using the <code>AMR</code> package within
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Python.</p>
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<div class="section level3">
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<h3 id="example-1-converting-taxonomic-data">Example 1: Converting Taxonomic Data<a class="anchor" aria-label="anchor" href="#example-1-converting-taxonomic-data"></a>
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</h3>
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<p>Let’s start by converting taxonomic user input to valid taxonomy
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using the <code>AMR</code> package, from within Python:</p>
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<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
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<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> rpy2.robjects <span class="im">as</span> ro</span>
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<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="im">from</span> rpy2.robjects.packages <span class="im">import</span> importr</span>
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<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="im">from</span> rpy2.robjects <span class="im">import</span> pandas2ri</span>
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<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a></span>
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<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="co"># Load the AMR package from R</span></span>
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<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a>amr <span class="op">=</span> importr(<span class="st">'AMR'</span>)</span>
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<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a></span>
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<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a><span class="co"># Example user dataset in Python</span></span>
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<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a>data <span class="op">=</span> pd.DataFrame({</span>
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<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a> <span class="st">'microorganism'</span>: [<span class="st">'E. coli'</span>, <span class="st">'S. aureus'</span>, <span class="st">'P. aeruginosa'</span>, <span class="st">'K. pneumoniae'</span>]</span>
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<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a>})</span>
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<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a></span>
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<span id="cb6-14"><a href="#cb6-14" tabindex="-1"></a><span class="co"># Apply mo_name() from the AMR package to the 'microorganism' column</span></span>
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<span id="cb6-15"><a href="#cb6-15" tabindex="-1"></a>ro.globalenv[<span class="st">'r_data'</span>] <span class="op">=</span> data</span>
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<span id="cb6-16"><a href="#cb6-16" tabindex="-1"></a>ro.r(<span class="st">'r_data$mo_name <- mo_name(r_data$microorganism)'</span>)</span>
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<span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a></span>
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<span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a><span class="co"># Retrieve and print the modified R DataFrame in Python</span></span>
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<span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'r_data'</span>)</span>
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<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a>result <span class="op">=</span> pandas2ri.rpy2py(result)</span>
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<span id="cb6-21"><a href="#cb6-21" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
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<p>In this example, a Python dataset with microorganism names like
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<em>E. coli</em> and <em>S. aureus</em> is passed to the R function
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<code><a href="../reference/mo_property.html">mo_name()</a></code>. The result is an updated <code>DataFrame</code>
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that includes the standardised microorganism names based on the
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<code><a href="../reference/mo_property.html">mo_name()</a></code> function from the <code>AMR</code> package.</p>
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</div>
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<div class="section level3">
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<h3 id="example-2-generating-an-antibiogram">Example 2: Generating an Antibiogram<a class="anchor" aria-label="anchor" href="#example-2-generating-an-antibiogram"></a>
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</h3>
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<p>One of the core functions of the <code>AMR</code> package is
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generating an antibiogram, a table that summarises the antimicrobial
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susceptibility of bacterial isolates. Here’s how you can generate an
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antibiogram from Python:</p>
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<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co"># Run an antibiogram in R from Python</span></span>
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<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>ro.r(<span class="st">'result <- antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()))'</span>)</span>
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<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a></span>
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<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co"># Retrieve the result in Python</span></span>
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<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
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<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
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<p>In this example, we generate an antibiogram by selecting
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aminoglycosides and carbapenems, two classes of antibiotics, and then
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convert the resulting R data frame into a Python-readable format.</p>
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</div>
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<div class="section level3">
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<h3 id="example-3-filtering-data-based-on-gram-negative-bacteria">Example 3: Filtering Data Based on Gram-Negative Bacteria<a class="anchor" aria-label="anchor" href="#example-3-filtering-data-based-on-gram-negative-bacteria"></a>
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</h3>
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<p>Let’s say you want to filter the dataset for Gram-negative bacteria
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and display their resistance to certain antibiotics:</p>
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<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="co"># Filter for Gram-negative bacteria with intrinsic resistance to cefotaxime</span></span>
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<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>ro.r(<span class="st">'result <- example_isolates[which(mo_is_gram_negative() & mo_is_intrinsic_resistant(ab = "cefotax")), c("bacteria", aminoglycosides(), carbapenems())]'</span>)</span>
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<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a></span>
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<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co"># Retrieve the filtered result in Python</span></span>
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<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
|
||
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
|
||
<p>This example uses the AMR functions
|
||
<code><a href="../reference/mo_property.html">mo_is_gram_negative()</a></code> and
|
||
<code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> to filter the dataset and
|
||
returns a subset of bacteria with resistance data.</p>
|
||
</div>
|
||
<div class="section level3">
|
||
<h3 id="example-4-customising-the-antibiogram">Example 4: Customising the Antibiogram<a class="anchor" aria-label="anchor" href="#example-4-customising-the-antibiogram"></a>
|
||
</h3>
|
||
<p>You can easily customise the antibiogram by passing different
|
||
antibiotics or microorganism transformations, as shown below:</p>
|
||
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co"># Customise the antibiogram with different settings</span></span>
|
||
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>ro.r(<span class="st">'result <- antibiogram(example_isolates, antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), mo_transform = "gramstain")'</span>)</span>
|
||
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a></span>
|
||
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co"># Retrieve and print the result</span></span>
|
||
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
|
||
<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
|
||
<p>Here, we use piperacillin/tazobactam (TZP) in combination with
|
||
tobramycin (TOB) and gentamicin (GEN) to see how they perform against
|
||
various Gram-negative bacteria.</p>
|
||
</div>
|
||
</div>
|
||
<div class="section level2">
|
||
<h2 id="conclusion">Conclusion<a class="anchor" aria-label="anchor" href="#conclusion"></a>
|
||
</h2>
|
||
<p>Using <code>rpy2</code>, you can easily integrate the power of R’s
|
||
<code>AMR</code> package into your Python workflows. Whether you are
|
||
generating antibiograms, analyzing resistance data, or performing
|
||
complex filtering, <code>rpy2</code> gives you the flexibility to run R
|
||
code without leaving the Python environment. This makes it a perfect
|
||
solution for teams working across both R and Python.</p>
|
||
</div>
|
||
</main><aside class="col-md-3"><nav id="toc" aria-label="Table of contents"><h2>On this page</h2>
|
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|
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|
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|
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|
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|
||
<footer><div class="pkgdown-footer-left">
|
||
<p><code>AMR</code> (for R). Free and open-source, licenced under the <a target="_blank" href="https://github.com/msberends/AMR/blob/main/LICENSE" class="external-link">GNU 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>
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