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(v1.3.0.9035) mdro() for EUCAST 3.2, examples cleanup

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dr. M.S. (Matthijs) Berends
2020-09-29 23:35:46 +02:00
parent 68e6e1e329
commit 4e0374af29
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docs/reference/pca.html
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@ -50,7 +50,7 @@
<meta property="og:title" content="Principal Component Analysis (for AMR) — pca" />
<meta property="og:description" content="Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables." />
<meta property="og:image" content="https://msberends.github.io/AMR/logo.svg" />
<meta property="og:image" content="https://msberends.github.io/AMR/logo.png" />
@ -82,7 +82,7 @@
</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="Latest development version">1.3.0.9015</span>
<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9035</span>
</span>
</div>
@ -322,23 +322,26 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>maturing<
<pre class="examples"><span class='co'># `example_isolates` is a dataset available in the AMR package.</span>
<span class='co'># See ?example_isolates.</span>
<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
<span class='co'># calculate the resistance per group first</span>
<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='kw'><a href='https://dplyr.tidyverse.org'>dplyr</a></span>)
<span class='kw'>resistance_data</span> <span class='op'>&lt;-</span> <span class='kw'>example_isolates</span> <span class='op'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span>(order = <span class='fu'><a href='mo_property.html'>mo_order</a></span>(<span class='kw'>mo</span>), <span class='co'># group on anything, like order</span>
genus = <span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='kw'>mo</span>)) <span class='op'>%&gt;%</span> <span class='co'># and genus as we do here</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/summarise_all.html'>summarise_if</a></span>(<span class='kw'>is.rsi</span>, <span class='kw'>resistance</span>) <span class='co'># then get resistance of all drugs</span>
<span class='co'># now conduct PCA for certain antimicrobial agents</span>
<span class='kw'>pca_result</span> <span class='op'>&lt;-</span> <span class='kw'>resistance_data</span> <span class='op'>%&gt;%</span>
<span class='fu'>pca</span>(<span class='kw'>AMC</span>, <span class='kw'>CXM</span>, <span class='kw'>CTX</span>, <span class='kw'>CAZ</span>, <span class='kw'>GEN</span>, <span class='kw'>TOB</span>, <span class='kw'>TMP</span>, <span class='kw'>SXT</span>)
<span class='kw'>pca_result</span>
<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='kw'>pca_result</span>)
<span class='fu'><a href='https://rdrr.io/r/stats/biplot.html'>biplot</a></span>(<span class='kw'>pca_result</span>)
<span class='fu'><a href='ggplot_pca.html'>ggplot_pca</a></span>(<span class='kw'>pca_result</span>) <span class='co'># a new and convenient plot function</span>
}</pre>
<span class='co'># \donttest{</span>
<span class='co'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/library.html'>require</a></span>(<span class='st'><a href='https://dplyr.tidyverse.org'>"dplyr"</a></span>)) {
<span class='co'># calculate the resistance per group first </span>
<span class='kw'>resistance_data</span> <span class='op'>&lt;-</span> <span class='kw'>example_isolates</span> <span class='op'>%&gt;%</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span>(order = <span class='fu'><a href='mo_property.html'>mo_order</a></span>(<span class='kw'>mo</span>), <span class='co'># group on anything, like order</span>
genus = <span class='fu'><a href='mo_property.html'>mo_genus</a></span>(<span class='kw'>mo</span>)) <span class='op'>%&gt;%</span> <span class='co'># and genus as we do here</span>
<span class='fu'><a href='https://dplyr.tidyverse.org/reference/summarise_all.html'>summarise_if</a></span>(<span class='kw'>is.rsi</span>, <span class='kw'>resistance</span>) <span class='co'># then get resistance of all drugs</span>
<span class='co'># now conduct PCA for certain antimicrobial agents</span>
<span class='kw'>pca_result</span> <span class='op'>&lt;-</span> <span class='kw'>resistance_data</span> <span class='op'>%&gt;%</span>
<span class='fu'>pca</span>(<span class='kw'>AMC</span>, <span class='kw'>CXM</span>, <span class='kw'>CTX</span>, <span class='kw'>CAZ</span>, <span class='kw'>GEN</span>, <span class='kw'>TOB</span>, <span class='kw'>TMP</span>, <span class='kw'>SXT</span>)
<span class='kw'>pca_result</span>
<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='kw'>pca_result</span>)
<span class='fu'><a href='https://rdrr.io/r/stats/biplot.html'>biplot</a></span>(<span class='kw'>pca_result</span>)
<span class='fu'><a href='ggplot_pca.html'>ggplot_pca</a></span>(<span class='kw'>pca_result</span>) <span class='co'># a new and convenient plot function</span>
}
<span class='co'># }</span>
</pre>
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