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(v1.8.1.9011) update prevalence of some genera

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dr. M.S. (Matthijs) Berends
2022-06-03 12:43:25 +02:00
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docs/reference/as.rsi.html
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@ -17,7 +17,7 @@
</button>
<span class="navbar-brand">
<a class="navbar-link" href="../index.html">AMR (for R)</a>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1.9009</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.8.1.9011</span>
</span>
</div>
@ -249,12 +249,12 @@
<p>The <code>as.rsi()</code> function works in four ways:</p><ol><li><p>For <strong>cleaning raw / untransformed data</strong>. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with R/SI interpretations and MIC values such as <code>"&lt;0.25; S"</code> will be coerced to <code>"S"</code>. Combined interpretations for multiple test methods (as seen in laboratory records) such as <code>"S; S"</code> will be coerced to <code>"S"</code>, but a value like <code>"S; I"</code> will return <code>NA</code> with a warning that the input is unclear.</p></li>
<li><p>For <strong>interpreting minimum inhibitory concentration (MIC) values</strong> according to EUCAST or CLSI. You must clean your MIC values first using <code><a href="as.mic.html">as.mic()</a></code>, that also gives your columns the new data class <code><a href="as.mic.html">mic</a></code>. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the <code>mo</code> argument.</p><ul><li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><div class="sourceCode"><pre><code><span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_if</a></span><span class="op">(</span><span class="va">is.mic</span>, <span class="va">as.rsi</span><span class="op">)</span> <span class="co"># until dplyr 1.0.0</span>
<span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu">where</span><span class="op">(</span><span class="va">is.mic</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span><span class="op">)</span> <span class="co"># since dplyr 1.0.0</span></code></pre></div></li>
<li><p>For <strong>interpreting minimum inhibitory concentration (MIC) values</strong> according to EUCAST or CLSI. You must clean your MIC values first using <code><a href="as.mic.html">as.mic()</a></code>, that also gives your columns the new data class <code><a href="as.mic.html">mic</a></code>. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the <code>mo</code> argument.</p><ul><li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p>
<p></p><div class="sourceCode"><pre><code></code></pre><p></p></div></li>
<li><p>Operators like "&lt;=" will be stripped before interpretation. When using <code>conserve_capped_values = TRUE</code>, an MIC value of e.g. "&gt;2" will always return "R", even if the breakpoint according to the chosen guideline is "&gt;=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (<code>conserve_capped_values = FALSE</code>) considers "&gt;2" to be lower than "&gt;=4" and might in this case return "S" or "I".</p></li>
</ul></li>
<li><p>For <strong>interpreting disk diffusion diameters</strong> according to EUCAST or CLSI. You must clean your disk zones first using <code><a href="as.disk.html">as.disk()</a></code>, that also gives your columns the new data class <code><a href="as.disk.html">disk</a></code>. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the <code>mo</code> argument.</p><ul><li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p><div class="sourceCode"><pre><code><span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate_all.html" class="external-link">mutate_if</a></span><span class="op">(</span><span class="va">is.disk</span>, <span class="va">as.rsi</span><span class="op">)</span> <span class="co"># until dplyr 1.0.0</span>
<span class="va">your_data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html" class="external-link">mutate</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/across.html" class="external-link">across</a></span><span class="op">(</span><span class="fu">where</span><span class="op">(</span><span class="va">is.disk</span><span class="op">)</span>, <span class="va">as.rsi</span><span class="op">)</span><span class="op">)</span> <span class="co"># since dplyr 1.0.0</span></code></pre></div></li>
<li><p>For <strong>interpreting disk diffusion diameters</strong> according to EUCAST or CLSI. You must clean your disk zones first using <code><a href="as.disk.html">as.disk()</a></code>, that also gives your columns the new data class <code><a href="as.disk.html">disk</a></code>. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the <code>mo</code> argument.</p><ul><li><p>Using <code>dplyr</code>, R/SI interpretation can be done very easily with either:</p>
<p></p><div class="sourceCode"><pre><code></code></pre><p></p></div></li>
</ul></li>
<li><p>For <strong>interpreting a complete data set</strong>, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running <code>as.rsi(your_data)</code>.</p></li>
</ol></div>