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<h1 data-toc-skip>How to work with WHONET data</h1>
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<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/HEAD/vignettes/WHONET.Rmd" class="external-link"><code>vignettes/WHONET.Rmd</code></a></small>
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<div class="section level3">
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<h3 id="import-of-data">Import of data<a class="anchor" aria-label="anchor" href="#import-of-data"></a>
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</h3>
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<p>This tutorial assumes you already imported the WHONET data with
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e.g. the <a href="https://readxl.tidyverse.org/" class="external-link"><code>readxl</code>
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package</a>. In RStudio, this can be done using the menu button ‘Import
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Dataset’ in the tab ‘Environment’. Choose the option ‘From Excel’ and
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select your exported file. Make sure date fields are imported
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correctly.</p>
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<p>An example syntax could look like this:</p>
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<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://readxl.tidyverse.org" class="external-link">readxl</a></span><span class="op">)</span>
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<span class="va">data</span> <span class="op"><-</span> <span class="fu"><a href="https://readxl.tidyverse.org/reference/read_excel.html" class="external-link">read_excel</a></span><span class="op">(</span>path <span class="op">=</span> <span class="st">"path/to/your/file.xlsx"</span><span class="op">)</span></code></pre></div>
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<p>This package comes with an <a href="https://msberends.github.io/AMR/reference/WHONET.html">example
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data set <code>WHONET</code></a>. We will use it for this analysis.</p>
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</div>
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<div class="section level3">
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<h3 id="preparation">Preparation<a class="anchor" aria-label="anchor" href="#preparation"></a>
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</h3>
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<p>First, load the relevant packages if you did not yet did this. I use
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the tidyverse for all of my analyses. All of them. If you don’t know it
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yet, I suggest you read about it on their website: <a href="https://www.tidyverse.org/" class="external-link uri">https://www.tidyverse.org/</a>.</p>
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<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://dplyr.tidyverse.org" class="external-link">dplyr</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
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<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://ggplot2.tidyverse.org" class="external-link">ggplot2</a></span><span class="op">)</span> <span class="co"># part of tidyverse</span>
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<span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://msberends.github.io/AMR/">AMR</a></span><span class="op">)</span> <span class="co"># this package</span>
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<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://github.com/msberends/cleaner" class="external-link">cleaner</a></span><span class="op">)</span> <span class="co"># to create frequency tables</span></code></pre></div>
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<p>We will have to transform some variables to simplify and automate the
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analysis:</p>
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<ul>
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<li>Microorganisms should be transformed to our own microorganism codes
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(called an <code>mo</code>) using <a href="https://msberends.github.io/AMR/reference/catalogue_of_life">our
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Catalogue of Life reference data set</a>, which contains all ~70,000
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microorganisms from the taxonomic kingdoms Bacteria, Fungi and Protozoa.
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We do the tranformation with <code><a href="../reference/as.mo.html">as.mo()</a></code>. This function also
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recognises almost all WHONET abbreviations of microorganisms.</li>
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<li>Antimicrobial results or interpretations have to be clean and valid.
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In other words, they should only contain values <code>"S"</code>,
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<code>"I"</code> or <code>"R"</code>. That is exactly where the
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<code><a href="../reference/as.rsi.html">as.rsi()</a></code> function is for.</li>
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</ul>
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<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span class="co"># transform variables</span>
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<span class="va">data</span> <span class="op"><-</span> <span class="va">WHONET</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span>
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<span class="co"># get microbial ID based on given organism</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span>mo <span class="op">=</span> <span class="fu"><a href="../reference/as.mo.html">as.mo</a></span><span class="op">(</span><span class="va">Organism</span><span class="op">)</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%>%</a></span>
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<span class="co"># transform everything from "AMP_ND10" to "CIP_EE" to the new `rsi` class</span>
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<span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_at</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/vars.html" class="external-link">vars</a></span><span class="op">(</span><span class="va">AMP_ND10</span><span class="op">:</span><span class="va">CIP_EE</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span></code></pre></div>
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<p>No errors or warnings, so all values are transformed succesfully.</p>
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<p>We also created a package dedicated to data cleaning and checking,
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called the <code>cleaner</code> package. Its <code><a href="https://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq()</a></code>
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function can be used to create frequency tables.</p>
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<p>So let’s check our data, with a couple of frequency tables:</p>
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<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
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<code class="sourceCode R"><span class="co"># our newly created `mo` variable, put in the mo_name() function</span>
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<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://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">mo</span><span class="op">)</span>, nmax <span class="op">=</span> <span class="fl">10</span><span class="op">)</span></code></pre></div>
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<p><strong>Frequency table</strong></p>
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<p>Class: character<br>
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Length: 500<br>
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Available: 500 (100%, NA: 0 = 0%)<br>
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Unique: 37</p>
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<p>Shortest: 11<br>
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Longest: 40</p>
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<table class="table">
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<colgroup>
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<col width="4%">
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<col width="47%">
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<col width="7%">
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<col width="10%">
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<col width="13%">
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<col width="15%">
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</colgroup>
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<thead><tr class="header">
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<th align="left"></th>
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<th align="left">Item</th>
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<th align="right">Count</th>
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<th align="right">Percent</th>
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<th align="right">Cum. Count</th>
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<th align="right">Cum. Percent</th>
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</tr></thead>
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<tbody>
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<tr class="odd">
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<td align="left">1</td>
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<td align="left">Escherichia coli</td>
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<td align="right">245</td>
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<td align="right">49.0%</td>
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<td align="right">245</td>
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<td align="right">49.0%</td>
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</tr>
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<tr class="even">
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<td align="left">2</td>
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<td align="left">Coagulase-negative Staphylococcus (CoNS)</td>
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<td align="right">74</td>
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<td align="right">14.8%</td>
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<td align="right">319</td>
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<td align="right">63.8%</td>
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</tr>
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<tr class="odd">
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<td align="left">3</td>
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<td align="left">Staphylococcus epidermidis</td>
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<td align="right">38</td>
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<td align="right">7.6%</td>
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<td align="right">357</td>
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<td align="right">71.4%</td>
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</tr>
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<tr class="even">
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<td align="left">4</td>
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<td align="left">Streptococcus pneumoniae</td>
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<td align="right">31</td>
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<td align="right">6.2%</td>
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<td align="right">388</td>
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<td align="right">77.6%</td>
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</tr>
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<tr class="odd">
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<td align="left">5</td>
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<td align="left">Staphylococcus hominis</td>
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<td align="right">21</td>
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<td align="right">4.2%</td>
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<td align="right">409</td>
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<td align="right">81.8%</td>
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</tr>
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<tr class="even">
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<td align="left">6</td>
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<td align="left">Proteus mirabilis</td>
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<td align="right">9</td>
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<td align="right">1.8%</td>
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<td align="right">418</td>
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<td align="right">83.6%</td>
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</tr>
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<tr class="odd">
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<td align="left">7</td>
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<td align="left">Enterococcus faecium</td>
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<td align="right">8</td>
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<td align="right">1.6%</td>
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<td align="right">426</td>
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<td align="right">85.2%</td>
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</tr>
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<tr class="even">
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<td align="left">8</td>
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<td align="left">Staphylococcus capitis</td>
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<td align="right">8</td>
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<td align="right">1.6%</td>
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<td align="right">434</td>
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<td align="right">86.8%</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td align="left">9</td>
|
||
<td align="left">Enterobacter cloacae</td>
|
||
<td align="right">5</td>
|
||
<td align="right">1.0%</td>
|
||
<td align="right">439</td>
|
||
<td align="right">87.8%</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td align="left">10</td>
|
||
<td align="left">Streptococcus anginosus</td>
|
||
<td align="right">5</td>
|
||
<td align="right">1.0%</td>
|
||
<td align="right">444</td>
|
||
<td align="right">88.8%</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>(omitted 27 entries, n = 56 [11.2%])</p>
|
||
<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
|
||
<code class="sourceCode R"><span class="co"># our transformed antibiotic columns</span>
|
||
<span class="co"># amoxicillin/clavulanic acid (J01CR02) as an example</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://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">AMC_ND2</span><span class="op">)</span></code></pre></div>
|
||
<p><strong>Frequency table</strong></p>
|
||
<p>Class: factor > ordered > rsi (numeric)<br>
|
||
Length: 500<br>
|
||
Levels: 3: S < I < R<br>
|
||
Available: 481 (96.2%, NA: 19 = 3.8%)<br>
|
||
Unique: 3</p>
|
||
<p>Drug: Amoxicillin/clavulanic acid (AMC, J01CR02)<br>
|
||
Drug group: Beta-lactams/penicillins<br>
|
||
%SI: 78.59%</p>
|
||
<table class="table">
|
||
<thead><tr class="header">
|
||
<th align="left"></th>
|
||
<th align="left">Item</th>
|
||
<th align="right">Count</th>
|
||
<th align="right">Percent</th>
|
||
<th align="right">Cum. Count</th>
|
||
<th align="right">Cum. Percent</th>
|
||
</tr></thead>
|
||
<tbody>
|
||
<tr class="odd">
|
||
<td align="left">1</td>
|
||
<td align="left">S</td>
|
||
<td align="right">356</td>
|
||
<td align="right">74.01%</td>
|
||
<td align="right">356</td>
|
||
<td align="right">74.01%</td>
|
||
</tr>
|
||
<tr class="even">
|
||
<td align="left">2</td>
|
||
<td align="left">R</td>
|
||
<td align="right">103</td>
|
||
<td align="right">21.41%</td>
|
||
<td align="right">459</td>
|
||
<td align="right">95.43%</td>
|
||
</tr>
|
||
<tr class="odd">
|
||
<td align="left">3</td>
|
||
<td align="left">I</td>
|
||
<td align="right">22</td>
|
||
<td align="right">4.57%</td>
|
||
<td align="right">481</td>
|
||
<td align="right">100.00%</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
<div class="section level3">
|
||
<h3 id="a-first-glimpse-at-results">A first glimpse at results<a class="anchor" aria-label="anchor" href="#a-first-glimpse-at-results"></a>
|
||
</h3>
|
||
<p>An easy <code>ggplot</code> will already give a lot of information,
|
||
using the included <code><a href="../reference/ggplot_rsi.html">ggplot_rsi()</a></code> function:</p>
|
||
<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
|
||
<code class="sourceCode R"><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://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 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 class="fu"><a href="../reference/ggplot_rsi.html">ggplot_rsi</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></code></pre></div>
|
||
<p><img src="WHONET_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
|
||
</div>
|
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<p></p>
|
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<p>Developed by Matthijs S. Berends, Christian F. Luz, Dennis Souverein,
|
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<p></p>
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<p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a>
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