(v1.3.0.9001) website update

2025-09-07 02:09:37 +02:00 · 2020-08-10 12:46:03 +02:00
parent 0d9602a6a9
commit 7d16bec21f
79 changed files with 2221 additions and 1974 deletions
--- a/docs/articles/PCA.html
+++ b/docs/articles/PCA.html
@@ -39,7 +39,7 @@
      </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</span>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.3.0.9001</span>
      </span>
    </div>

@@ -186,7 +186,7 @@
      <h1 data-toc-skip>How to conduct principal component analysis (PCA) for AMR</h1>
                        <h4 class="author">Matthijs S. Berends</h4>
            
-            <h4 class="date">30 July 2020</h4>
+            <h4 class="date">10 August 2020</h4>
      
      <small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/master/vignettes/PCA.Rmd"><code>vignettes/PCA.Rmd</code></a></small>
      <div class="hidden name"><code>PCA.Rmd</code></div>
@@ -204,9 +204,10 @@
 <h1 class="hasAnchor">
 <a href="#transforming" class="anchor"></a>Transforming</h1>
 <p>For PCA, we need to transform our AMR data first. This is what the <code>example_isolates</code> data set in this package looks like:</p>
-<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">AMR</span>)
-<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">dplyr</span>)
-<span class="fu"><a href="https://dplyr.tidyverse.org/reference/reexports.html">glimpse</a></span>(<span class="no">example_isolates</span>)
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://msberends.github.io/AMR">AMR</a></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="fu"><a href="https://tibble.tidyverse.org/reference/glimpse.html">glimpse</a></span>(<span class="kw">example_isolates</span>)
 <span class="co"># Rows: 2,000</span>
 <span class="co"># Columns: 49</span>
 <span class="co"># $ date            &lt;date&gt; 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002…</span>
@@ -257,16 +258,18 @@
 <span class="co"># $ CHL             &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
 <span class="co"># $ COL             &lt;ord&gt; NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, …</span>
 <span class="co"># $ MUP             &lt;ord&gt; NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…</span>
-<span class="co"># $ RIF             &lt;ord&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span></pre></body></html></div>
+<span class="co"># $ RIF             &lt;ord&gt; R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R…</span>
+</pre></div>
 <p>Now to transform this to a data set with only resistance percentages per taxonomic order and genus:</p>
-<div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">resistance_data</span> <span class="kw">&lt;-</span> <span class="no">example_isolates</span> <span class="kw">%&gt;%</span>
-  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span>(<span class="kw">order</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_order</a></span>(<span class="no">mo</span>),       <span class="co"># group on anything, like order</span>
-           <span class="kw">genus</span> <span class="kw">=</span> <span class="fu"><a href="../reference/mo_property.html">mo_genus</a></span>(<span class="no">mo</span>)) <span class="kw">%&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="no">is.rsi</span>, <span class="no">resistance</span>) <span class="kw">%&gt;%</span> <span class="co"># then get resistance of all drugs</span>
-  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="no">order</span>, <span class="no">genus</span>, <span class="no">AMC</span>, <span class="no">CXM</span>, <span class="no">CTX</span>,
-         <span class="no">CAZ</span>, <span class="no">GEN</span>, <span class="no">TOB</span>, <span class="no">TMP</span>, <span class="no">SXT</span>)      <span class="co"># and select only relevant columns</span>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<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="../reference/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="../reference/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="op">%&gt;%</span> <span class="co"># then get resistance of all drugs</span>
+  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span>(<span class="kw">order</span>, <span class="kw">genus</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="co"># and select only relevant columns</span>

-<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="no">resistance_data</span>)
+<span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span>(<span class="kw">resistance_data</span>)
 <span class="co"># # A tibble: 6 x 10</span>
 <span class="co"># # Groups:   order [2]</span>
 <span class="co">#   order           genus            AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT</span>
@@ -276,35 +279,46 @@
 <span class="co"># 3 Actinomycetales Cutibacterium     NA    NA    NA    NA    NA    NA    NA    NA</span>
 <span class="co"># 4 Actinomycetales Dermabacter       NA    NA    NA    NA    NA    NA    NA    NA</span>
 <span class="co"># 5 Actinomycetales Micrococcus       NA    NA    NA    NA    NA    NA    NA    NA</span>
-<span class="co"># 6 Actinomycetales Rothia            NA    NA    NA    NA    NA    NA    NA    NA</span></pre></body></html></div>
+<span class="co"># 6 Actinomycetales Rothia            NA    NA    NA    NA    NA    NA    NA    NA</span>
+</pre></div>
 </div>
 <div id="perform-principal-component-analysis" class="section level1">
 <h1 class="hasAnchor">
 <a href="#perform-principal-component-analysis" class="anchor"></a>Perform principal component analysis</h1>
 <p>The new <code><a href="../reference/pca.html">pca()</a></code> function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:</p>
-<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">pca_result</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span>(<span class="no">resistance_data</span>)
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">pca_result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/pca.html">pca</a></span>(<span class="kw">resistance_data</span>)
 <span class="co"># NOTE: Columns selected for PCA: AMC CXM CTX CAZ GEN TOB TMP SXT.</span>
-<span class="co">#       Total observations available: 7.</span></pre></body></html></div>
+<span class="co">#       Total observations available: 7.</span>
+</pre></div>
 <p>The result can be reviewed with the good old <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> function:</p>
-<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">pca_result</span>)
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="kw">pca_result</span>)
 <span class="co"># Importance of components:</span>
 <span class="co">#                          PC1    PC2     PC3     PC4     PC5     PC6       PC7</span>
 <span class="co"># Standard deviation     2.154 1.6809 0.61305 0.33882 0.20755 0.03137 1.602e-16</span>
 <span class="co"># Proportion of Variance 0.580 0.3532 0.04698 0.01435 0.00538 0.00012 0.000e+00</span>
-<span class="co"># Cumulative Proportion  0.580 0.9332 0.98014 0.99449 0.99988 1.00000 1.000e+00</span></pre></body></html></div>
+<span class="co"># Cumulative Proportion  0.580 0.9332 0.98014 0.99449 0.99988 1.00000 1.000e+00</span>
+</pre></div>
 <p>Good news. The first two components explain a total of 93.3% of the variance (see the PC1 and PC2 values of the <em>Proportion of Variance</em>. We can create a so-called biplot with the base R <code><a href="https://rdrr.io/r/stats/biplot.html">biplot()</a></code> function, to see which antimicrobial resistance per drug explain the difference per microorganism.</p>
 </div>
 <div id="plotting-the-results" class="section level1">
 <h1 class="hasAnchor">
 <a href="#plotting-the-results" class="anchor"></a>Plotting the results</h1>
-<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span>(<span class="no">pca_result</span>)</pre></body></html></div>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/stats/biplot.html">biplot</a></span>(<span class="kw">pca_result</span>)
+</pre></div>
 <p><img src="PCA_files/figure-html/unnamed-chunk-5-1.png" width="750"></p>
 <p>But we can’t see the explanation of the points. Perhaps this works better with our new <code><a href="../reference/ggplot_pca.html">ggplot_pca()</a></code> function, that automatically adds the right labels and even groups:</p>
-<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="no">pca_result</span>)</pre></body></html></div>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="kw">pca_result</span>)
+</pre></div>
 <p><img src="PCA_files/figure-html/unnamed-chunk-6-1.png" width="750"></p>
 <p>You can also print an ellipse per group, and edit the appearance:</p>
-<div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="no">pca_result</span>, <span class="kw">ellipse</span> <span class="kw">=</span> <span class="fl">TRUE</span>) +
-  <span class="kw pkg">ggplot2</span><span class="kw ns">::</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span>(<span class="kw">title</span> <span class="kw">=</span> <span class="st">"An AMR/PCA biplot!"</span>)</pre></body></html></div>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="fu"><a href="../reference/ggplot_pca.html">ggplot_pca</a></span>(<span class="kw">pca_result</span>, ellipse = <span class="fl">TRUE</span>) <span class="op">+</span>
+  <span class="kw">ggplot2</span>::<span class="fu"><a href="https://ggplot2.tidyverse.org/reference/labs.html">labs</a></span>(title = <span class="st">"An AMR/PCA biplot!"</span>)
+</pre></div>
 <p><img src="PCA_files/figure-html/unnamed-chunk-7-1.png" width="750"></p>
 </div>
  </div>
@@ -324,7 +338,7 @@
 </div>

 <div class="pkgdown">
-  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.9000.</p>
 </div>

      </footer>