diff --git a/404.html b/404.html
index c8d2df12..337cfda4 100644
--- a/404.html
+++ b/404.html
@@ -36,7 +36,7 @@
     
     <a class="navbar-brand me-2" href="https://msberends.github.io/AMR/index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index 6d86cac2..2d55a5bc 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/AMR.html b/articles/AMR.html
index 666f711a..7c240d3c 100644
--- a/articles/AMR.html
+++ b/articles/AMR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -317,69 +317,69 @@
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">2014-01-13</td>
-<td align="center">A7</td>
-<td align="center">Hospital C</td>
-<td align="center">Staphylococcus aureus</td>
+<td align="center">2011-06-19</td>
+<td align="center">N8</td>
+<td align="center">Hospital D</td>
+<td align="center">Escherichia coli</td>
 <td align="center">R</td>
+<td align="center">R</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">F</td>
+</tr>
+<tr class="even">
+<td align="center">2013-07-08</td>
+<td align="center">U10</td>
+<td align="center">Hospital A</td>
+<td align="center">Escherichia coli</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">F</td>
+</tr>
+<tr class="odd">
+<td align="center">2015-07-14</td>
+<td align="center">N3</td>
+<td align="center">Hospital B</td>
+<td align="center">Escherichia coli</td>
+<td align="center">S</td>
 <td align="center">S</td>
 <td align="center">R</td>
 <td align="center">S</td>
 <td align="center">M</td>
 </tr>
 <tr class="even">
-<td align="center">2010-07-17</td>
-<td align="center">K10</td>
+<td align="center">2014-07-29</td>
+<td align="center">J1</td>
 <td align="center">Hospital B</td>
-<td align="center">Escherichia coli</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">S</td>
-<td align="center">M</td>
-</tr>
-<tr class="odd">
-<td align="center">2013-12-06</td>
-<td align="center">Z6</td>
-<td align="center">Hospital A</td>
 <td align="center">Streptococcus pneumoniae</td>
-<td align="center">R</td>
 <td align="center">S</td>
-<td align="center">R</td>
 <td align="center">S</td>
-<td align="center">F</td>
-</tr>
-<tr class="even">
-<td align="center">2013-09-04</td>
-<td align="center">U4</td>
-<td align="center">Hospital C</td>
-<td align="center">Klebsiella pneumoniae</td>
-<td align="center">R</td>
 <td align="center">S</td>
-<td align="center">R</td>
 <td align="center">S</td>
-<td align="center">F</td>
+<td align="center">M</td>
 </tr>
 <tr class="odd">
-<td align="center">2010-05-09</td>
-<td align="center">B3</td>
+<td align="center">2015-03-05</td>
+<td align="center">A9</td>
+<td align="center">Hospital C</td>
+<td align="center">Escherichia coli</td>
+<td align="center">S</td>
+<td align="center">I</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">M</td>
+</tr>
+<tr class="even">
+<td align="center">2017-10-20</td>
+<td align="center">P10</td>
 <td align="center">Hospital A</td>
 <td align="center">Staphylococcus aureus</td>
 <td align="center">S</td>
 <td align="center">S</td>
 <td align="center">R</td>
 <td align="center">S</td>
-<td align="center">M</td>
-</tr>
-<tr class="even">
-<td align="center">2013-12-21</td>
-<td align="center">U3</td>
-<td align="center">Hospital B</td>
-<td align="center">Escherichia coli</td>
-<td align="center">S</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">R</td>
 <td align="center">F</td>
 </tr>
 </tbody>
@@ -414,16 +414,16 @@ Longest: 1</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">M</td>
-<td align="right">10,403</td>
-<td align="right">52.02%</td>
-<td align="right">10,403</td>
-<td align="right">52.02%</td>
+<td align="right">10,328</td>
+<td align="right">51.64%</td>
+<td align="right">10,328</td>
+<td align="right">51.64%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">F</td>
-<td align="right">9,597</td>
-<td align="right">47.99%</td>
+<td align="right">9,672</td>
+<td align="right">48.36%</td>
 <td align="right">20,000</td>
 <td align="right">100.00%</td>
 </tr>
@@ -477,9 +477,9 @@ Longest: 1</p>
 <span><span class="co"># ℹ Using column 'patient_id' as input for `col_patient_id`.</span></span>
 <span><span class="co"># Basing inclusion on all antimicrobial results, using a points threshold of</span></span>
 <span><span class="co"># 2</span></span>
-<span><span class="co"># =&gt; Found 10,723 'phenotype-based' first isolates (53.6% of total where a</span></span>
+<span><span class="co"># =&gt; Found 10,619 'phenotype-based' first isolates (53.1% of total where a</span></span>
 <span><span class="co">#    microbial ID was available)</span></span></code></pre></div>
-<p>So only 53.6% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
+<p>So only 53.1% is suitable for resistance analysis! We can now filter on it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter()</a></code> function, also from the <code>dplyr</code> package:</p>
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data_1st</span> <span class="op">&lt;-</span> <span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="va">first</span> <span class="op">==</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
@@ -487,7 +487,7 @@ Longest: 1</p>
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data_1st</span> <span class="op">&lt;-</span> <span class="va">data</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
 <span>  <span class="fu"><a href="../reference/first_isolate.html">filter_first_isolate</a></span><span class="op">(</span><span class="op">)</span></span></code></pre></div>
-<p>So we end up with 10,723 isolates for analysis. Now our data looks like:</p>
+<p>So we end up with 10,619 isolates for analysis. Now our data looks like:</p>
 <div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">data_1st</span><span class="op">)</span></span></code></pre></div>
 <table style="width:100%;" class="table">
@@ -525,101 +525,101 @@ Longest: 1</p>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="left">1</td>
-<td align="center">2014-01-13</td>
-<td align="center">A7</td>
-<td align="center">Hospital C</td>
-<td align="center">B_STPHY_AURS</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">M</td>
-<td align="center">Gram-positive</td>
-<td align="center">Staphylococcus</td>
-<td align="center">aureus</td>
-<td align="center">TRUE</td>
-</tr>
-<tr class="even">
-<td align="left">3</td>
-<td align="center">2013-12-06</td>
-<td align="center">Z6</td>
-<td align="center">Hospital A</td>
-<td align="center">B_STRPT_PNMN</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">F</td>
-<td align="center">Gram-positive</td>
-<td align="center">Streptococcus</td>
-<td align="center">pneumoniae</td>
-<td align="center">TRUE</td>
-</tr>
-<tr class="odd">
 <td align="left">5</td>
-<td align="center">2010-05-09</td>
-<td align="center">B3</td>
-<td align="center">Hospital A</td>
-<td align="center">B_STPHY_AURS</td>
+<td align="center">2015-03-05</td>
+<td align="center">A9</td>
+<td align="center">Hospital C</td>
+<td align="center">B_ESCHR_COLI</td>
+<td align="center">S</td>
 <td align="center">S</td>
 <td align="center">S</td>
-<td align="center">R</td>
 <td align="center">S</td>
 <td align="center">M</td>
-<td align="center">Gram-positive</td>
-<td align="center">Staphylococcus</td>
-<td align="center">aureus</td>
+<td align="center">Gram-negative</td>
+<td align="center">Escherichia</td>
+<td align="center">coli</td>
 <td align="center">TRUE</td>
 </tr>
 <tr class="even">
 <td align="left">6</td>
-<td align="center">2013-12-21</td>
-<td align="center">U3</td>
-<td align="center">Hospital B</td>
-<td align="center">B_ESCHR_COLI</td>
+<td align="center">2017-10-20</td>
+<td align="center">P10</td>
+<td align="center">Hospital A</td>
+<td align="center">B_STPHY_AURS</td>
+<td align="center">S</td>
+<td align="center">S</td>
 <td align="center">R</td>
+<td align="center">S</td>
+<td align="center">F</td>
+<td align="center">Gram-positive</td>
+<td align="center">Staphylococcus</td>
+<td align="center">aureus</td>
+<td align="center">TRUE</td>
+</tr>
+<tr class="odd">
+<td align="left">8</td>
+<td align="center">2013-03-11</td>
+<td align="center">S7</td>
+<td align="center">Hospital C</td>
+<td align="center">B_ESCHR_COLI</td>
 <td align="center">R</td>
 <td align="center">S</td>
 <td align="center">R</td>
+<td align="center">S</td>
 <td align="center">F</td>
 <td align="center">Gram-negative</td>
 <td align="center">Escherichia</td>
 <td align="center">coli</td>
 <td align="center">TRUE</td>
 </tr>
-<tr class="odd">
-<td align="left">7</td>
-<td align="center">2011-06-10</td>
-<td align="center">D8</td>
-<td align="center">Hospital D</td>
-<td align="center">B_STRPT_PNMN</td>
-<td align="center">S</td>
-<td align="center">S</td>
-<td align="center">S</td>
-<td align="center">R</td>
-<td align="center">M</td>
-<td align="center">Gram-positive</td>
-<td align="center">Streptococcus</td>
-<td align="center">pneumoniae</td>
-<td align="center">TRUE</td>
-</tr>
 <tr class="even">
 <td align="left">9</td>
-<td align="center">2012-03-02</td>
-<td align="center">K7</td>
+<td align="center">2012-06-14</td>
+<td align="center">I1</td>
+<td align="center">Hospital C</td>
+<td align="center">B_ESCHR_COLI</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">M</td>
+<td align="center">Gram-negative</td>
+<td align="center">Escherichia</td>
+<td align="center">coli</td>
+<td align="center">TRUE</td>
+</tr>
+<tr class="odd">
+<td align="left">10</td>
+<td align="center">2010-11-23</td>
+<td align="center">V10</td>
 <td align="center">Hospital B</td>
 <td align="center">B_STRPT_PNMN</td>
 <td align="center">R</td>
 <td align="center">R</td>
 <td align="center">S</td>
 <td align="center">R</td>
-<td align="center">M</td>
+<td align="center">F</td>
 <td align="center">Gram-positive</td>
 <td align="center">Streptococcus</td>
 <td align="center">pneumoniae</td>
 <td align="center">TRUE</td>
 </tr>
+<tr class="even">
+<td align="left">13</td>
+<td align="center">2012-03-06</td>
+<td align="center">R5</td>
+<td align="center">Hospital B</td>
+<td align="center">B_ESCHR_COLI</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">F</td>
+<td align="center">Gram-negative</td>
+<td align="center">Escherichia</td>
+<td align="center">coli</td>
+<td align="center">TRUE</td>
+</tr>
 </tbody>
 </table>
 <p>Time for the analysis!</p>
@@ -641,8 +641,8 @@ Longest: 1</p>
 <code class="sourceCode R"><span><span class="va">data_1st</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://rdrr.io/pkg/cleaner/man/freq.html" class="external-link">freq</a></span><span class="op">(</span><span class="va">genus</span>, <span class="va">species</span><span class="op">)</span></span></code></pre></div>
 <p><strong>Frequency table</strong></p>
 <p>Class: character<br>
-Length: 10,723<br>
-Available: 10,723 (100.0%, NA: 0 = 0.0%)<br>
+Length: 10,619<br>
+Available: 10,619 (100.0%, NA: 0 = 0.0%)<br>
 Unique: 4</p>
 <p>Shortest: 16<br>
 Longest: 24</p>
@@ -659,33 +659,33 @@ Longest: 24</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Escherichia coli</td>
-<td align="right">4,683</td>
-<td align="right">43.67%</td>
-<td align="right">4,683</td>
-<td align="right">43.67%</td>
+<td align="right">4,632</td>
+<td align="right">43.62%</td>
+<td align="right">4,632</td>
+<td align="right">43.62%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Staphylococcus aureus</td>
-<td align="right">2,727</td>
-<td align="right">25.43%</td>
-<td align="right">7,410</td>
-<td align="right">69.10%</td>
+<td align="right">2,742</td>
+<td align="right">25.82%</td>
+<td align="right">7,374</td>
+<td align="right">69.44%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Streptococcus pneumoniae</td>
-<td align="right">2,104</td>
-<td align="right">19.62%</td>
-<td align="right">9,514</td>
-<td align="right">88.73%</td>
+<td align="right">2,062</td>
+<td align="right">19.42%</td>
+<td align="right">9,436</td>
+<td align="right">88.86%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Klebsiella pneumoniae</td>
-<td align="right">1,209</td>
-<td align="right">11.27%</td>
-<td align="right">10,723</td>
+<td align="right">1,183</td>
+<td align="right">11.14%</td>
+<td align="right">10,619</td>
 <td align="right">100.00%</td>
 </tr>
 </tbody>
@@ -704,14 +704,14 @@ Longest: 24</p>
 <col width="9%">
 <col width="9%">
 <col width="9%">
-<col width="11%">
+<col width="10%">
 <col width="3%">
 <col width="3%">
 <col width="3%">
 <col width="3%">
 <col width="6%">
 <col width="11%">
-<col width="11%">
+<col width="12%">
 <col width="9%">
 <col width="5%">
 </colgroup>
@@ -732,13 +732,13 @@ Longest: 24</p>
 </tr></thead>
 <tbody>
 <tr class="odd">
-<td align="center">2013-12-06</td>
-<td align="center">Z6</td>
-<td align="center">Hospital A</td>
+<td align="center">2010-11-23</td>
+<td align="center">V10</td>
+<td align="center">Hospital B</td>
 <td align="center">B_STRPT_PNMN</td>
 <td align="center">R</td>
 <td align="center">R</td>
-<td align="center">R</td>
+<td align="center">S</td>
 <td align="center">R</td>
 <td align="center">F</td>
 <td align="center">Gram-positive</td>
@@ -747,23 +747,8 @@ Longest: 24</p>
 <td align="center">TRUE</td>
 </tr>
 <tr class="even">
-<td align="center">2013-12-21</td>
-<td align="center">U3</td>
-<td align="center">Hospital B</td>
-<td align="center">B_ESCHR_COLI</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">R</td>
-<td align="center">F</td>
-<td align="center">Gram-negative</td>
-<td align="center">Escherichia</td>
-<td align="center">coli</td>
-<td align="center">TRUE</td>
-</tr>
-<tr class="odd">
-<td align="center">2011-06-10</td>
-<td align="center">D8</td>
+<td align="center">2011-11-01</td>
+<td align="center">B1</td>
 <td align="center">Hospital D</td>
 <td align="center">B_STRPT_PNMN</td>
 <td align="center">S</td>
@@ -776,9 +761,24 @@ Longest: 24</p>
 <td align="center">pneumoniae</td>
 <td align="center">TRUE</td>
 </tr>
+<tr class="odd">
+<td align="center">2017-01-30</td>
+<td align="center">H5</td>
+<td align="center">Hospital C</td>
+<td align="center">B_STRPT_PNMN</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">R</td>
+<td align="center">M</td>
+<td align="center">Gram-positive</td>
+<td align="center">Streptococcus</td>
+<td align="center">pneumoniae</td>
+<td align="center">TRUE</td>
+</tr>
 <tr class="even">
-<td align="center">2012-03-02</td>
-<td align="center">K7</td>
+<td align="center">2015-12-11</td>
+<td align="center">I5</td>
 <td align="center">Hospital B</td>
 <td align="center">B_STRPT_PNMN</td>
 <td align="center">R</td>
@@ -792,9 +792,24 @@ Longest: 24</p>
 <td align="center">TRUE</td>
 </tr>
 <tr class="odd">
-<td align="center">2012-12-25</td>
-<td align="center">W10</td>
+<td align="center">2017-08-04</td>
+<td align="center">O3</td>
 <td align="center">Hospital B</td>
+<td align="center">B_STPHY_AURS</td>
+<td align="center">I</td>
+<td align="center">S</td>
+<td align="center">S</td>
+<td align="center">R</td>
+<td align="center">F</td>
+<td align="center">Gram-positive</td>
+<td align="center">Staphylococcus</td>
+<td align="center">aureus</td>
+<td align="center">TRUE</td>
+</tr>
+<tr class="even">
+<td align="center">2014-10-06</td>
+<td align="center">V8</td>
+<td align="center">Hospital A</td>
 <td align="center">B_STRPT_PNMN</td>
 <td align="center">S</td>
 <td align="center">S</td>
@@ -806,21 +821,6 @@ Longest: 24</p>
 <td align="center">pneumoniae</td>
 <td align="center">TRUE</td>
 </tr>
-<tr class="even">
-<td align="center">2011-02-13</td>
-<td align="center">E5</td>
-<td align="center">Hospital A</td>
-<td align="center">B_STRPT_PNMN</td>
-<td align="center">R</td>
-<td align="center">R</td>
-<td align="center">S</td>
-<td align="center">R</td>
-<td align="center">M</td>
-<td align="center">Gram-positive</td>
-<td align="center">Streptococcus</td>
-<td align="center">pneumoniae</td>
-<td align="center">TRUE</td>
-</tr>
 </tbody>
 </table>
 <p>If you want to get a quick glance of the number of isolates in different bug/drug combinations, you can use the <code><a href="../reference/bug_drug_combinations.html">bug_drug_combinations()</a></code> function:</p>
@@ -842,50 +842,50 @@ Longest: 24</p>
 <tr class="odd">
 <td align="center">E. coli</td>
 <td align="center">AMX</td>
-<td align="center">2228</td>
-<td align="center">121</td>
-<td align="center">2334</td>
-<td align="center">4683</td>
+<td align="center">2203</td>
+<td align="center">123</td>
+<td align="center">2306</td>
+<td align="center">4632</td>
 </tr>
 <tr class="even">
 <td align="center">E. coli</td>
 <td align="center">AMC</td>
-<td align="center">3423</td>
-<td align="center">156</td>
-<td align="center">1104</td>
-<td align="center">4683</td>
+<td align="center">3385</td>
+<td align="center">145</td>
+<td align="center">1102</td>
+<td align="center">4632</td>
 </tr>
 <tr class="odd">
 <td align="center">E. coli</td>
 <td align="center">CIP</td>
-<td align="center">3439</td>
+<td align="center">3390</td>
 <td align="center">0</td>
-<td align="center">1244</td>
-<td align="center">4683</td>
+<td align="center">1242</td>
+<td align="center">4632</td>
 </tr>
 <tr class="even">
 <td align="center">E. coli</td>
 <td align="center">GEN</td>
-<td align="center">4064</td>
+<td align="center">4049</td>
 <td align="center">0</td>
-<td align="center">619</td>
-<td align="center">4683</td>
+<td align="center">583</td>
+<td align="center">4632</td>
 </tr>
 <tr class="odd">
 <td align="center">K. pneumoniae</td>
 <td align="center">AMX</td>
 <td align="center">0</td>
 <td align="center">0</td>
-<td align="center">1209</td>
-<td align="center">1209</td>
+<td align="center">1183</td>
+<td align="center">1183</td>
 </tr>
 <tr class="even">
 <td align="center">K. pneumoniae</td>
 <td align="center">AMC</td>
-<td align="center">960</td>
-<td align="center">46</td>
-<td align="center">203</td>
-<td align="center">1209</td>
+<td align="center">929</td>
+<td align="center">54</td>
+<td align="center">200</td>
+<td align="center">1183</td>
 </tr>
 </tbody>
 </table>
@@ -908,34 +908,34 @@ Longest: 24</p>
 <tr class="odd">
 <td align="center">E. coli</td>
 <td align="center">GEN</td>
-<td align="center">4064</td>
+<td align="center">4049</td>
 <td align="center">0</td>
-<td align="center">619</td>
-<td align="center">4683</td>
+<td align="center">583</td>
+<td align="center">4632</td>
 </tr>
 <tr class="even">
 <td align="center">K. pneumoniae</td>
 <td align="center">GEN</td>
-<td align="center">1075</td>
+<td align="center">1078</td>
 <td align="center">0</td>
-<td align="center">134</td>
-<td align="center">1209</td>
+<td align="center">105</td>
+<td align="center">1183</td>
 </tr>
 <tr class="odd">
 <td align="center">S. aureus</td>
 <td align="center">GEN</td>
-<td align="center">2421</td>
+<td align="center">2430</td>
 <td align="center">0</td>
-<td align="center">306</td>
-<td align="center">2727</td>
+<td align="center">312</td>
+<td align="center">2742</td>
 </tr>
 <tr class="even">
 <td align="center">S. pneumoniae</td>
 <td align="center">GEN</td>
 <td align="center">0</td>
 <td align="center">0</td>
-<td align="center">2104</td>
-<td align="center">2104</td>
+<td align="center">2062</td>
+<td align="center">2062</td>
 </tr>
 </tbody>
 </table>
@@ -949,7 +949,7 @@ Longest: 24</p>
 <p>As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (<code><a href="../reference/proportion.html">proportion_R()</a></code>, equal to <code><a href="../reference/proportion.html">resistance()</a></code>) and susceptibility as the proportion of S and I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equal to <code><a href="../reference/proportion.html">susceptibility()</a></code>). These functions can be used on their own:</p>
 <div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data_1st</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> <span class="fu"><a href="../reference/proportion.html">resistance</a></span><span class="op">(</span><span class="va">AMX</span><span class="op">)</span></span>
-<span><span class="co"># [1] 0.5465821</span></span></code></pre></div>
+<span><span class="co"># [1] 0.5432715</span></span></code></pre></div>
 <p>Or can be used in conjunction with <code><a href="https://dplyr.tidyverse.org/reference/group_by.html" class="external-link">group_by()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/summarise.html" class="external-link">summarise()</a></code>, both from the <code>dplyr</code> package:</p>
 <div class="sourceCode" id="cb27"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">data_1st</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
@@ -963,19 +963,19 @@ Longest: 24</p>
 <tbody>
 <tr class="odd">
 <td align="center">Hospital A</td>
-<td align="center">0.5332098</td>
+<td align="center">0.5382868</td>
 </tr>
 <tr class="even">
 <td align="center">Hospital B</td>
-<td align="center">0.5497618</td>
+<td align="center">0.5447543</td>
 </tr>
 <tr class="odd">
 <td align="center">Hospital C</td>
-<td align="center">0.5499058</td>
+<td align="center">0.5595602</td>
 </tr>
 <tr class="even">
 <td align="center">Hospital D</td>
-<td align="center">0.5588652</td>
+<td align="center">0.5353630</td>
 </tr>
 </tbody>
 </table>
@@ -994,23 +994,23 @@ Longest: 24</p>
 <tbody>
 <tr class="odd">
 <td align="center">Hospital A</td>
-<td align="center">0.5332098</td>
-<td align="center">3237</td>
+<td align="center">0.5382868</td>
+<td align="center">3082</td>
 </tr>
 <tr class="even">
 <td align="center">Hospital B</td>
-<td align="center">0.5497618</td>
-<td align="center">3778</td>
+<td align="center">0.5447543</td>
+<td align="center">3765</td>
 </tr>
 <tr class="odd">
 <td align="center">Hospital C</td>
-<td align="center">0.5499058</td>
-<td align="center">1593</td>
+<td align="center">0.5595602</td>
+<td align="center">1637</td>
 </tr>
 <tr class="even">
 <td align="center">Hospital D</td>
-<td align="center">0.5588652</td>
-<td align="center">2115</td>
+<td align="center">0.5353630</td>
+<td align="center">2135</td>
 </tr>
 </tbody>
 </table>
@@ -1031,27 +1031,27 @@ Longest: 24</p>
 <tbody>
 <tr class="odd">
 <td align="center">Escherichia</td>
-<td align="center">0.7642537</td>
-<td align="center">0.8678198</td>
-<td align="center">0.9750160</td>
+<td align="center">0.7620898</td>
+<td align="center">0.8741364</td>
+<td align="center">0.9771157</td>
 </tr>
 <tr class="even">
 <td align="center">Klebsiella</td>
-<td align="center">0.8320926</td>
-<td align="center">0.8891646</td>
-<td align="center">0.9743590</td>
+<td align="center">0.8309383</td>
+<td align="center">0.9112426</td>
+<td align="center">0.9805579</td>
 </tr>
 <tr class="odd">
 <td align="center">Staphylococcus</td>
-<td align="center">0.7920792</td>
-<td align="center">0.8877888</td>
-<td align="center">0.9845985</td>
+<td align="center">0.7884756</td>
+<td align="center">0.8862144</td>
+<td align="center">0.9817651</td>
 </tr>
 <tr class="even">
 <td align="center">Streptococcus</td>
-<td align="center">0.5361217</td>
+<td align="center">0.5354025</td>
 <td align="center">0.0000000</td>
-<td align="center">0.5361217</td>
+<td align="center">0.5354025</td>
 </tr>
 </tbody>
 </table>
@@ -1076,23 +1076,23 @@ Longest: 24</p>
 <tbody>
 <tr class="odd">
 <td align="left">Hospital A</td>
-<td align="right">53.3%</td>
+<td align="right">53.8%</td>
 <td align="right">26.2%</td>
 </tr>
 <tr class="even">
 <td align="left">Hospital B</td>
-<td align="right">55.0%</td>
+<td align="right">54.5%</td>
 <td align="right">27.0%</td>
 </tr>
 <tr class="odd">
 <td align="left">Hospital C</td>
-<td align="right">55.0%</td>
-<td align="right">25.5%</td>
+<td align="right">56.0%</td>
+<td align="right">27.9%</td>
 </tr>
 <tr class="even">
 <td align="left">Hospital D</td>
-<td align="right">55.9%</td>
-<td align="right">27.3%</td>
+<td align="right">53.5%</td>
+<td align="right">26.1%</td>
 </tr>
 </tbody>
 </table>
@@ -1176,18 +1176,16 @@ Longest: 24</p>
 <code class="sourceCode R"><span><span class="va">mic_values</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/random.html">random_mic</a></span><span class="op">(</span>size <span class="op">=</span> <span class="fl">100</span><span class="op">)</span></span>
 <span><span class="va">mic_values</span></span>
 <span><span class="co"># Class &lt;mic&gt;</span></span>
-<span><span class="co">#   [1] 32      64      128     128     64      8       32      0.002   0.025  </span></span>
-<span><span class="co">#  [10] 0.5     32      &lt;=0.001 2       128     0.01    1       2       0.01   </span></span>
-<span><span class="co">#  [19] 128     0.5     4       0.002   0.125   256     0.0625  4       0.002  </span></span>
-<span><span class="co">#  [28] 0.005   4       0.002   32      0.01    0.0625  0.25    2       64     </span></span>
-<span><span class="co">#  [37] 0.0625  0.5     256     4       1       0.025   128     0.0625  16     </span></span>
-<span><span class="co">#  [46] 0.5     0.005   0.01    1       2       0.01    2       8       0.125  </span></span>
-<span><span class="co">#  [55] 0.25    256     0.0625  0.025   0.002   8       &lt;=0.001 1       0.002  </span></span>
-<span><span class="co">#  [64] 256     8       4       0.005   0.025   4       0.002   &lt;=0.001 2      </span></span>
-<span><span class="co">#  [73] 256     0.005   256     2       4       16      128     256     &lt;=0.001</span></span>
-<span><span class="co">#  [82] 1       2       1       1       &lt;=0.001 0.5     4       0.25    32     </span></span>
-<span><span class="co">#  [91] 0.002   0.0625  0.01    32      0.005   0.01    2       64      64     </span></span>
-<span><span class="co"># [100] 2</span></span></code></pre></div>
+<span><span class="co">#   [1] 256    16     0.002  0.025  8      0.025  32     0.025  0.01   256   </span></span>
+<span><span class="co">#  [11] 16     0.01   32     0.025  0.01   0.0625 2      0.25   128    0.005 </span></span>
+<span><span class="co">#  [21] 0.01   16     0.125  0.25   0.01   0.001  8      0.025  8      1     </span></span>
+<span><span class="co">#  [31] 0.025  0.25   0.0625 256    0.001  0.025  0.001  8      0.01   0.025 </span></span>
+<span><span class="co">#  [41] 0.25   8      64     8      0.25   4      1      0.0625 0.0625 0.5   </span></span>
+<span><span class="co">#  [51] 2      0.002  16     0.002  0.01   16     0.25   64     8      256   </span></span>
+<span><span class="co">#  [61] 0.005  0.0625 1      64     64     0.5    128    0.002  32     0.002 </span></span>
+<span><span class="co">#  [71] 128    128    0.5    0.002  0.25   0.5    0.125  1      16     64    </span></span>
+<span><span class="co">#  [81] 128    32     0.025  128    16     32     32     32     0.002  0.125 </span></span>
+<span><span class="co">#  [91] 0.5    0.0625 0.5    128    0.125  128    8      0.25   128    0.25</span></span></code></pre></div>
 <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># base R:</span></span>
 <span><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">mic_values</span><span class="op">)</span></span></code></pre></div>
@@ -1216,10 +1214,10 @@ Longest: 24</p>
 <span><span class="co">#   coli). Run `mo_uncertainties()` to review this.</span></span>
 <span><span class="va">disk_values</span></span>
 <span><span class="co"># Class &lt;disk&gt;</span></span>
-<span><span class="co">#   [1] 28 22 30 19 23 20 17 21 22 18 18 27 18 20 24 28 30 20 29 31 22 31 23 24 17</span></span>
-<span><span class="co">#  [26] 23 18 17 25 30 28 23 30 23 30 31 18 26 26 26 27 17 30 18 27 31 17 24 20 26</span></span>
-<span><span class="co">#  [51] 30 21 26 27 20 23 28 29 17 29 27 24 22 27 28 27 22 30 18 28 25 18 25 18 31</span></span>
-<span><span class="co">#  [76] 29 25 17 31 20 20 21 24 24 30 28 30 31 20 30 27 19 24 17 22 30 31 27 28 24</span></span></code></pre></div>
+<span><span class="co">#   [1] 23 21 25 21 18 21 18 19 23 26 28 30 20 21 17 17 27 26 31 30 25 17 27 27 19</span></span>
+<span><span class="co">#  [26] 31 23 23 23 22 20 26 17 24 29 26 24 25 29 22 22 29 26 22 24 25 26 18 31 24</span></span>
+<span><span class="co">#  [51] 25 28 26 27 19 21 21 29 27 21 27 21 18 17 20 20 17 25 30 20 19 20 23 27 31</span></span>
+<span><span class="co">#  [76] 24 31 24 22 26 19 19 20 20 25 17 21 22 30 25 23 26 31 20 30 18 29 18 29 17</span></span></code></pre></div>
 <div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># base R:</span></span>
 <span><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">disk_values</span>, mo <span class="op">=</span> <span class="st">"E. coli"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span><span class="op">)</span></span></code></pre></div>
diff --git a/articles/AMR_files/figure-html/disk_plots-1.png b/articles/AMR_files/figure-html/disk_plots-1.png
index c29fbb45..13da955b 100644
Binary files a/articles/AMR_files/figure-html/disk_plots-1.png and b/articles/AMR_files/figure-html/disk_plots-1.png differ
diff --git a/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png b/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png
index 85292cff..3ab03a37 100644
Binary files a/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png and b/articles/AMR_files/figure-html/disk_plots_mo_ab-1.png differ
diff --git a/articles/AMR_files/figure-html/mic_plots-1.png b/articles/AMR_files/figure-html/mic_plots-1.png
index b5a97d72..a97e795f 100644
Binary files a/articles/AMR_files/figure-html/mic_plots-1.png and b/articles/AMR_files/figure-html/mic_plots-1.png differ
diff --git a/articles/AMR_files/figure-html/mic_plots-2.png b/articles/AMR_files/figure-html/mic_plots-2.png
index ee500bbc..2eb7add5 100644
Binary files a/articles/AMR_files/figure-html/mic_plots-2.png and b/articles/AMR_files/figure-html/mic_plots-2.png differ
diff --git a/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png b/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png
index efdebadf..d804838a 100644
Binary files a/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png and b/articles/AMR_files/figure-html/mic_plots_mo_ab-1.png differ
diff --git a/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png b/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png
index 5e887406..fcf57c45 100644
Binary files a/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png and b/articles/AMR_files/figure-html/mic_plots_mo_ab-2.png differ
diff --git a/articles/AMR_files/figure-html/plot 1-1.png b/articles/AMR_files/figure-html/plot 1-1.png
index 3a66ccbf..c6202307 100644
Binary files a/articles/AMR_files/figure-html/plot 1-1.png and b/articles/AMR_files/figure-html/plot 1-1.png differ
diff --git a/articles/AMR_files/figure-html/plot 3-1.png b/articles/AMR_files/figure-html/plot 3-1.png
index 64e71de3..58701d94 100644
Binary files a/articles/AMR_files/figure-html/plot 3-1.png and b/articles/AMR_files/figure-html/plot 3-1.png differ
diff --git a/articles/AMR_files/figure-html/plot 4-1.png b/articles/AMR_files/figure-html/plot 4-1.png
index 9537609b..0b9bd78d 100644
Binary files a/articles/AMR_files/figure-html/plot 4-1.png and b/articles/AMR_files/figure-html/plot 4-1.png differ
diff --git a/articles/AMR_files/figure-html/plot 5-1.png b/articles/AMR_files/figure-html/plot 5-1.png
index afc85baa..e33d091c 100644
Binary files a/articles/AMR_files/figure-html/plot 5-1.png and b/articles/AMR_files/figure-html/plot 5-1.png differ
diff --git a/articles/EUCAST.html b/articles/EUCAST.html
index 95697948..abd2c8d4 100644
--- a/articles/EUCAST.html
+++ b/articles/EUCAST.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/MDR.html b/articles/MDR.html
index 32c61427..dafc67cd 100644
--- a/articles/MDR.html
+++ b/articles/MDR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -313,18 +313,18 @@ Unique: 2</p>
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">my_TB_data</span><span class="op">)</span></span>
 <span><span class="co">#   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin</span></span>
-<span><span class="co"># 1          I         I            S          S            I            R</span></span>
-<span><span class="co"># 2          I         R            R          S            I            I</span></span>
-<span><span class="co"># 3          R         I            S          R            R            S</span></span>
-<span><span class="co"># 4          I         S            I          S            I            S</span></span>
-<span><span class="co"># 5          S         R            S          I            S            R</span></span>
-<span><span class="co"># 6          I         S            I          I            S            S</span></span>
+<span><span class="co"># 1          S         S            R          I            R            I</span></span>
+<span><span class="co"># 2          I         R            S          S            S            S</span></span>
+<span><span class="co"># 3          I         R            S          I            S            S</span></span>
+<span><span class="co"># 4          R         I            S          I            S            R</span></span>
+<span><span class="co"># 5          I         I            S          R            S            R</span></span>
+<span><span class="co"># 6          S         I            S          S            R            R</span></span>
 <span><span class="co">#   kanamycin</span></span>
-<span><span class="co"># 1         I</span></span>
-<span><span class="co"># 2         R</span></span>
-<span><span class="co"># 3         S</span></span>
+<span><span class="co"># 1         S</span></span>
+<span><span class="co"># 2         S</span></span>
+<span><span class="co"># 3         I</span></span>
 <span><span class="co"># 4         R</span></span>
-<span><span class="co"># 5         S</span></span>
+<span><span class="co"># 5         I</span></span>
 <span><span class="co"># 6         S</span></span></code></pre></div>
 <p>We can now add the interpretation of MDR-TB to our data set. You can use:</p>
 <div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
@@ -356,40 +356,40 @@ Unique: 5</p>
 <tr class="odd">
 <td align="left">1</td>
 <td align="left">Mono-resistant</td>
-<td align="right">3193</td>
-<td align="right">63.86%</td>
-<td align="right">3193</td>
-<td align="right">63.86%</td>
+<td align="right">3182</td>
+<td align="right">63.64%</td>
+<td align="right">3182</td>
+<td align="right">63.64%</td>
 </tr>
 <tr class="even">
 <td align="left">2</td>
 <td align="left">Negative</td>
-<td align="right">996</td>
-<td align="right">19.92%</td>
-<td align="right">4189</td>
-<td align="right">83.78%</td>
+<td align="right">993</td>
+<td align="right">19.86%</td>
+<td align="right">4175</td>
+<td align="right">83.50%</td>
 </tr>
 <tr class="odd">
 <td align="left">3</td>
 <td align="left">Multi-drug-resistant</td>
-<td align="right">450</td>
-<td align="right">9.00%</td>
-<td align="right">4639</td>
-<td align="right">92.78%</td>
+<td align="right">477</td>
+<td align="right">9.54%</td>
+<td align="right">4652</td>
+<td align="right">93.04%</td>
 </tr>
 <tr class="even">
 <td align="left">4</td>
 <td align="left">Poly-resistant</td>
-<td align="right">241</td>
-<td align="right">4.82%</td>
-<td align="right">4880</td>
-<td align="right">97.60%</td>
+<td align="right">250</td>
+<td align="right">5.00%</td>
+<td align="right">4902</td>
+<td align="right">98.04%</td>
 </tr>
 <tr class="odd">
 <td align="left">5</td>
 <td align="left">Extensively drug-resistant</td>
-<td align="right">120</td>
-<td align="right">2.40%</td>
+<td align="right">98</td>
+<td align="right">1.96%</td>
 <td align="right">5000</td>
 <td align="right">100.00%</td>
 </tr>
diff --git a/articles/PCA.html b/articles/PCA.html
index 602506f8..0ec73447 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/SPSS.html b/articles/SPSS.html
index 53c77471..5785f75b 100644
--- a/articles/SPSS.html
+++ b/articles/SPSS.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/WHONET.html b/articles/WHONET.html
index 45cdcc40..9670c1b1 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/datasets.html b/articles/datasets.html
index 366b416a..b7441198 100644
--- a/articles/datasets.html
+++ b/articles/datasets.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -184,7 +184,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 </h2>
 <p>A data set with 70,764 rows and 16 columns, containing the following column names:<br><em>mo</em>, <em>fullname</em>, <em>kingdom</em>, <em>phylum</em>, <em>class</em>, <em>order</em>, <em>family</em>, <em>genus</em>, <em>species</em>, <em>subspecies</em>, <em>rank</em>, <em>ref</em>, <em>species_id</em>, <em>source</em>, <em>prevalence</em> and <em>snomed</em>.</p>
 <p>This data set is in R available as <code>microorganisms</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 21 August 2022 14:52:57 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
+<p>It was last updated on 21 August 2022 15:23:37 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.rds" class="external-link">R file</a> (1.3 MB)<br>
@@ -405,7 +405,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 <p>A data set with 14,338 rows and 4 columns, containing the following column names:<br><em>fullname</em>, <em>fullname_new</em>, <em>ref</em> and <em>prevalence</em>.</p>
 <p><strong>Note:</strong> remember that the ‘ref’ columns contains the scientific reference to the old taxonomic entries, i.e. of column <em>‘fullname’</em>. For the scientific reference of the new names, i.e. of column <em>‘fullname_new’</em>, see the <code>microorganisms</code> data set.</p>
 <p>This data set is in R available as <code>microorganisms.old</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 21 August 2022 14:52:57 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
+<p>It was last updated on 21 August 2022 15:23:37 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/microorganisms.old.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/microorganisms.old.rds" class="external-link">R file</a> (0.2 MB)<br>
@@ -470,7 +470,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 </h2>
 <p>A data set with 464 rows and 14 columns, containing the following column names:<br><em>ab</em>, <em>cid</em>, <em>name</em>, <em>group</em>, <em>atc</em>, <em>atc_group1</em>, <em>atc_group2</em>, <em>abbreviations</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em>, <em>iv_units</em> and <em>loinc</em>.</p>
 <p>This data set is in R available as <code>antibiotics</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 21 August 2022 14:52:57 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
+<p>It was last updated on 21 August 2022 15:23:37 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antibiotics.rds" class="external-link">R file</a> (33 kB)<br>
@@ -638,7 +638,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 </h2>
 <p>A data set with 102 rows and 9 columns, containing the following column names:<br><em>atc</em>, <em>cid</em>, <em>name</em>, <em>atc_group</em>, <em>synonyms</em>, <em>oral_ddd</em>, <em>oral_units</em>, <em>iv_ddd</em> and <em>iv_units</em>.</p>
 <p>This data set is in R available as <code>antivirals</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 21 August 2022 14:52:57 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
+<p>It was last updated on 21 August 2022 15:23:37 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/antibiotics.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/antivirals.rds" class="external-link">R file</a> (5 kB)<br>
@@ -765,7 +765,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 </h2>
 <p>A data set with 20,369 rows and 11 columns, containing the following column names:<br><em>guideline</em>, <em>method</em>, <em>site</em>, <em>mo</em>, <em>rank_index</em>, <em>ab</em>, <em>ref_tbl</em>, <em>disk_dose</em>, <em>breakpoint_S</em>, <em>breakpoint_R</em> and <em>uti</em>.</p>
 <p>This data set is in R available as <code>rsi_translation</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 21 August 2022 14:52:57 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
+<p>It was last updated on 21 August 2022 15:23:37 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/rsi_translation.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/rsi_translation.rds" class="external-link">R file</a> (49 kB)<br>
@@ -919,7 +919,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 </h2>
 <p>A data set with 134,956 rows and 2 columns, containing the following column names:<br><em>mo</em> and <em>ab</em>.</p>
 <p>This data set is in R available as <code>intrinsic_resistant</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 21 August 2022 14:52:57 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
+<p>It was last updated on 21 August 2022 15:23:37 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/intrinsic_resistant.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/intrinsic_resistant.rds" class="external-link">R file</a> (78 kB)<br>
@@ -1186,7 +1186,7 @@ If you are reading this page from within R, please <a href="https://msberends.gi
 </h2>
 <p>A data set with 169 rows and 9 columns, containing the following column names:<br><em>ab</em>, <em>name</em>, <em>type</em>, <em>dose</em>, <em>dose_times</em>, <em>administration</em>, <em>notes</em>, <em>original_txt</em> and <em>eucast_version</em>.</p>
 <p>This data set is in R available as <code>dosage</code>, after you load the <code>AMR</code> package.</p>
-<p>It was last updated on 21 August 2022 14:52:57 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
+<p>It was last updated on 21 August 2022 15:23:37 UTC. Find more info about the structure of this data set <a href="https://msberends.github.io/AMR/reference/dosage.html">here</a>.</p>
 <p><strong>Direct download links:</strong></p>
 <ul>
 <li>Download as <a href="https://github.com/msberends/AMR/raw/main/data-raw/../data-raw/dosage.rds" class="external-link">R file</a> (3 kB)<br>
diff --git a/articles/index.html b/articles/index.html
index fead896b..c63d4d7f 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/articles/resistance_predict.html b/articles/resistance_predict.html
index b0443783..c51bf3e7 100644
--- a/articles/resistance_predict.html
+++ b/articles/resistance_predict.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -176,7 +176,7 @@
 <div class="section level2">
 <h2 id="needed-r-packages">Needed R packages<a class="anchor" aria-label="anchor" href="#needed-r-packages"></a>
 </h2>
-<p>As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a> by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
+<p>As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the <a href="https://www.tidyverse.org" class="external-link">tidyverse packages</a> <a href="https://dplyr.tidyverse.org/" class="external-link"><code>dplyr</code></a> and <a href="https://ggplot2.tidyverse.org" class="external-link"><code>ggplot2</code></a>. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.</p>
 <p>Our <code>AMR</code> package depends on these packages and even extends their use and functions.</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><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>
@@ -205,9 +205,8 @@
 <span id="cb2-12"><a href="#cb2-12" aria-hidden="true" tabindex="-1"></a>                     <span class="at">model =</span> <span class="st">"binomial"</span>)</span></code></pre></div>
 <p>The function will look for a date column itself if <code>col_date</code> is not set.</p>
 <p>When running any of these commands, a summary of the regression model will be printed unless using <code>resistance_predict(..., info = FALSE)</code>.</p>
-<pre><code><span><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></span></code></pre>
 <p>This text is only a printed summary - the actual result (output) of the function is a <code>data.frame</code> containing for each year: the number of observations, the actual observed resistance, the estimated resistance and the standard error below and above the estimation:</p>
-<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">predict_TZP</span></span>
 <span><span class="co">#    year      value    se_min    se_max observations   observed  estimated</span></span>
 <span><span class="co"># 1  2002 0.20000000        NA        NA           15 0.20000000 0.05616378</span></span>
@@ -242,15 +241,15 @@
 <span><span class="co"># 30 2031 0.51109592 0.3973697 0.6248221           NA         NA 0.51109592</span></span>
 <span><span class="co"># 31 2032 0.53574417 0.4169574 0.6545309           NA         NA 0.53574417</span></span></code></pre></div>
 <p>The function <code>plot</code> is available in base R, and can be extended by other packages to depend the output based on the type of input. We extended its function to cope with resistance predictions:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/plot.html">plot</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></code></pre></div>
 <p><img src="resistance_predict_files/figure-html/unnamed-chunk-4-1.png" width="720"></p>
 <p>This is the fastest way to plot the result. It automatically adds the right axes, error bars, titles, number of available observations and type of model.</p>
 <p>We also support the <code>ggplot2</code> package with our custom function <code><a href="../reference/resistance_predict.html">ggplot_rsi_predict()</a></code> to create more appealing plots:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span></span></code></pre></div>
 <p><img src="resistance_predict_files/figure-html/unnamed-chunk-5-1.png" width="720"></p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span></span>
 <span><span class="co"># choose for error bars instead of a ribbon</span></span>
 <span><span class="fu"><a href="../reference/resistance_predict.html">ggplot_rsi_predict</a></span><span class="op">(</span><span class="va">predict_TZP</span>, ribbon <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
@@ -259,7 +258,7 @@
 <h3 id="choosing-the-right-model">Choosing the right model<a class="anchor" aria-label="anchor" href="#choosing-the-right-model"></a>
 </h3>
 <p>Resistance is not easily predicted; if we look at vancomycin resistance in Gram-positive bacteria, the spread (i.e. standard error) is enormous:</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"binomial"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
@@ -305,7 +304,7 @@
 </tbody>
 </table>
 <p>For the vancomycin resistance in Gram-positive bacteria, a linear model might be more appropriate:</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">example_isolates</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html" class="external-link">filter</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_gramstain</a></span><span class="op">(</span><span class="va">mo</span>, language <span class="op">=</span> <span class="cn">NULL</span><span class="op">)</span> <span class="op">==</span> <span class="st">"Gram-positive"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span></span>
 <span>  <span class="fu"><a href="../reference/resistance_predict.html">resistance_predict</a></span><span class="op">(</span>col_ab <span class="op">=</span> <span class="st">"VAN"</span>, year_min <span class="op">=</span> <span class="fl">2010</span>, info <span class="op">=</span> <span class="cn">FALSE</span>, model <span class="op">=</span> <span class="st">"linear"</span><span class="op">)</span> <span class="op"><a href="https://magrittr.tidyverse.org/reference/pipe.html" class="external-link">%&gt;%</a></span> </span>
@@ -313,7 +312,7 @@
 <span><span class="co"># ℹ Using column 'date' as input for `col_date`.</span></span></code></pre></div>
 <p><img src="resistance_predict_files/figure-html/unnamed-chunk-7-1.png" width="720"></p>
 <p>The model itself is also available from the object, as an <code>attribute</code>:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">predict_TZP</span><span class="op">)</span><span class="op">$</span><span class="va">model</span></span>
 <span></span>
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diff --git a/articles/welcome_to_AMR.html b/articles/welcome_to_AMR.html
index 35be9864..a8387b30 100644
--- a/articles/welcome_to_AMR.html
+++ b/articles/welcome_to_AMR.html
@@ -38,7 +38,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
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diff --git a/authors.html b/authors.html
index 3a65d6b6..6a95f591 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/deps/bootstrap-5.1.3/bootstrap.min.css b/deps/bootstrap-5.1.3/bootstrap.min.css
index fc81c448..f83f95fe 100644
--- a/deps/bootstrap-5.1.3/bootstrap.min.css
+++ b/deps/bootstrap-5.1.3/bootstrap.min.css
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ease}.headroom--pinned{transform:translateY(0%)}.headroom--unpinned{transform:translateY(-100%)}.row>main,.row>aside{margin-top:56px}html,body{scroll-padding:56px}@media (min-width: 576px){#toc{position:sticky;top:56px;max-height:calc(100vh - 56px - 1rem);overflow-y:auto}}aside h2,aside .h2{margin-top:1.5rem;font-size:1.25rem}aside .roles{color:#4d5154}aside .list-unstyled li{margin-bottom:0.5rem}aside .dev-status .list-unstyled li{margin-bottom:0.1rem}@media (max-width: 575.98px){aside{margin:0.5rem;width:calc(100vw - 1rem);background-color:#f8f9fa;border-color:#dee2e6;border-radius:.25rem}aside h2:first-child,aside .h2:first-child{margin-top:1rem}}body{position:relative}#toc>.nav{margin-bottom:1rem}#toc>.nav a.nav-link{color:inherit;padding:0.25rem 0.5rem;margin-bottom:2px;border-radius:.25rem;border:0 solid #dee2e6}#toc>.nav a.nav-link:hover,#toc>.nav a.nav-link:focus{background-color:rgba(44,62,80,0.1);color:#000}#toc>.nav a.nav-link.active{background-color:#e9e9ea;color:#000}#toc>.nav .nav a.nav-link{margin-left:0.5rem}#toc>.nav .nav{display:none !important}#toc>.nav a.active+.nav{display:flex !important}footer{margin:1rem 0 1rem 0;font-size:.875em;border-top:1px solid #dee2e6;background:rgba(0,0,0,0);color:#4d5154;display:flex;column-gap:1rem}@media (max-width: 575.98px){footer{flex-direction:column}}@media (min-width: 576px){footer .pkgdown-footer-right{text-align:right}}footer div{flex:1 1 auto}html,body{height:100%}body>.container{min-height:100%;display:flex;flex-direction:column}body>.container .row{flex:1 0 auto}::selection{background-color:#d5d8dc}main img{max-width:100%;height:auto}main table{display:block;overflow:auto}body{font-display:fallback}.page-header{border-bottom:1px solid #dee2e6;padding-bottom:0.5rem;margin-bottom:0.5rem;margin-top:1.5rem}dd{margin-left:1.5rem}summary{margin-bottom:0.5rem}details{margin-bottom:1rem}.html-widget{margin-bottom:1rem}a.anchor{display:none;margin-left:5px;width:Min(0.9em, 20px);height:Min(0.9em, 20px);background-image:url(../../link.svg);background-repeat:no-repeat;background-size:Min(0.9em, 20px) Min(0.9em, 20px);background-position:center center}h2:hover .anchor,.h2:hover .anchor,h3:hover .anchor,.h3:hover .anchor,h4:hover .anchor,.h4:hover .anchor,h5:hover .anchor,.h5:hover .anchor,h6:hover .anchor,.h6:hover .anchor{display:inline-block}.orcid{color:#A6CE39;margin-right:4px}.fab{font-family:"Font Awesome 5 Brands" !important}img.logo{float:right;width:100px;margin-left:30px}.template-home img.logo{width:120px}@media (max-width: 575.98px){img.logo{width:80px}}@media (min-width: 576px){.page-header{min-height:88px}.template-home .page-header{min-height:104px}}.line-block{margin-bottom:1rem}.template-reference-index dt{font-weight:normal}.template-reference-index code{word-wrap:normal}.icon{float:right}.icon img{width:40px}a.footnote-ref{cursor:pointer}.popover{width:Min(100vw, 32rem);font-size:0.9rem;box-shadow:4px 4px 8px rgba(0,0,0,0.3)}.popover-body{padding:0.75rem}.popover-body p:last-child{margin-bottom:0}.tab-content{padding:1rem}.tabset-pills .tab-content{border:solid 1px #e5e5e5}.tab-content{display:flex}.tab-content>.tab-pane{display:block;visibility:hidden;margin-right:-100%;width:100%}.tab-content>.active{visibility:visible}div.csl-entry{clear:both}.hanging-indent div.csl-entry{margin-left:2em;text-indent:-2em}div.csl-left-margin{min-width:2em;float:left}div.csl-right-inline{margin-left:2em;padding-left:1em}div.csl-indent{margin-left:2em}pre,pre code{word-wrap:normal}code{overflow-wrap:break-word}.hasCopyButton{position:relative}.btn-copy-ex{position:absolute;right:5px;top:5px;visibility:hidden}.hasCopyButton:hover button.btn-copy-ex{visibility:visible}pre{padding:1rem 0.5rem}@media (max-width: 575.98px){div>div>pre{margin-left:calc(var(--bs-gutter-x) * -.5);margin-right:calc(var(--bs-gutter-x) * -.5);border-radius:0;padding-left:1rem;padding-right:1rem}.btn-copy-ex{right:calc(var(--bs-gutter-x) * -.5 + 5px)}}code a:any-link{color:inherit;text-decoration-color:#95a5a6}pre code{padding:0;background:transparent}pre code .error,pre code .warning{font-weight:bolder}pre .img img,pre .r-plt img{margin:5px 0;background-color:#fff}@media print{code a:link:after,code a:visited:after{content:""}}a.sourceLine:hover{text-decoration:none}mark,.mark{background:linear-gradient(-100deg, rgba(52,152,219,0.2), rgba(52,152,219,0.7) 95%, rgba(52,152,219,0.1))}.algolia-autocomplete .aa-hint{color:#212529}.algolia-autocomplete .aa-dropdown-menu{width:Max(100%, 20rem);background-color:#fff;border:1px solid #dee2e6;margin-top:2px;max-height:50vh;overflow-y:auto}.algolia-autocomplete .aa-dropdown-menu .aa-suggestion{cursor:pointer;padding:5px 4px;border-bottom:1px #ecf0f1 solid;font-size:0.9rem;color:#212529}.search-details{font-size:0.9rem;color:#2c3e50;display:inline;font-weight:bolder}.algolia-autocomplete .aa-dropdown-menu .aa-suggestion.aa-cursor{background-color:#eaecee}pre{background-color:#f1f3f5}pre code{color:#003B4F}pre code span.al{color:#AD0000}pre code span.an{color:#5E5E5E}pre code span.at{color:#657422}pre code span.bn{color:#AD0000}pre code span.cf{color:#003B4F}pre code span.ch{color:#20794D}pre code span.cn{color:#8f5902}pre code span.co{color:#5E5E5E}pre code span.cv{color:#5E5E5E;font-style:italic}pre code span.do{color:#5E5E5E;font-style:italic}pre code span.dt{color:#AD0000}pre code span.dv{color:#AD0000}pre code span.er{color:#AD0000}pre code span.fl{color:#AD0000}pre code span.fu{color:#4758AB}pre code span.im{color:#00769E}pre code span.in{color:#5E5E5E}pre code span.kw{color:#003B4F}pre code span.op{color:#5E5E5E}pre code span.ot{color:#003B4F}pre code span.pp{color:#AD0000}pre code span.sc{color:#5E5E5E}pre code span.ss{color:#20794D}pre code span.st{color:#20794D}pre code span.va{color:#111111}pre code span.vs{color:#20794D}pre code span.wa{color:#5E5E5E;font-style:italic}
+@import url("font.css");:root{--bs-blue: #2c3e50;--bs-indigo: #6610f2;--bs-purple: #6f42c1;--bs-pink: #e83e8c;--bs-red: #e74c3c;--bs-orange: #fd7e14;--bs-yellow: #f39c12;--bs-green: #18bc9c;--bs-teal: #20c997;--bs-cyan: #3498db;--bs-white: #fff;--bs-gray: #95a5a6;--bs-gray-dark: #343a40;--bs-gray-100: #f8f9fa;--bs-gray-200: #ecf0f1;--bs-gray-300: #dee2e6;--bs-gray-400: #ced4da;--bs-gray-500: #b4bcc2;--bs-gray-600: #95a5a6;--bs-gray-700: #7b8a8b;--bs-gray-800: #343a40;--bs-gray-900: #212529;--bs-default: #95a5a6;--bs-primary: #2c3e50;--bs-secondary: #95a5a6;--bs-success: #128F76;--bs-info: #3498db;--bs-warning: #f39c12;--bs-danger: #e74c3c;--bs-light: #ecf0f1;--bs-dark: #7b8a8b;--bs-default-rgb: 149,165,166;--bs-primary-rgb: 44,62,80;--bs-secondary-rgb: 149,165,166;--bs-success-rgb: 18,143,118;--bs-info-rgb: 52,152,219;--bs-warning-rgb: 243,156,18;--bs-danger-rgb: 231,76,60;--bs-light-rgb: 236,240,241;--bs-dark-rgb: 123,138,139;--bs-white-rgb: 255,255,255;--bs-black-rgb: 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diff --git a/extra.js b/extra.js
index c7e099b7..9b247271 100644
--- a/extra.js
+++ b/extra.js
@@ -28,15 +28,16 @@
 $(document).ready(function() {
 
   // replace \donttest and \dontrun texts in Examples
-  if ($(".ref-examples pre").length > 0) {
-    $(".ref-examples pre").html($(".ref-examples pre").html().replaceAll("# \\donttest{", ""));
-    $(".ref-examples pre").html($(".ref-examples pre").html().replaceAll("# \\dontrun{", ""));
-    $(".ref-examples pre").html($(".ref-examples pre").html().replaceAll("# }", ""));
+  if ($("#ref-examples ~ div pre").length > 0) {
+    $("#ref-examples ~ div pre").html($("#ref-examples ~ div pre").html().replaceAll("# \\donttest{", ""));
+    $("#ref-examples ~ div pre").html($("#ref-examples ~ div pre").html().replaceAll("# \\dontrun{", ""));
+    $("#ref-examples ~ div pre").html($("#ref-examples ~ div pre").html().replaceAll("# }", ""));
   }
   
   // remove leading newline in code examples on changelog
   if ($("body .template-news").length > 0) {
     $("body .template-news").html($("body .template-news").html().replaceAll('sourceCode R">\n<span', 'sourceCode R"><span'));
+    $("body .template-news").html($("body .template-news").html().replaceAll('sourceCode R"><span></span>\n<span', 'sourceCode R"><span'));
   }
 
   // add doctoral titles to authors
diff --git a/index.html b/index.html
index a9020b4b..6dc0e61e 100644
--- a/index.html
+++ b/index.html
@@ -44,7 +44,7 @@
     
     <a class="navbar-brand me-2" href="index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/news/index.html b/news/index.html
index ac22f0ac..162e3d76 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -132,14 +132,14 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="1.8.1.9020" id="amr-1819020">AMR 1.8.1.9020<a class="anchor" aria-label="anchor" href="#amr-1819020"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.8.1.9021" id="amr-1819021">AMR 1.8.1.9021<a class="anchor" aria-label="anchor" href="#amr-1819021"></a></h2>
 <div class="section level4">
-<h4 id="new-1-8-1-9020">New<a class="anchor" aria-label="anchor" href="#new-1-8-1-9020"></a></h4>
+<h4 id="new-1-8-1-9021">New<a class="anchor" aria-label="anchor" href="#new-1-8-1-9021"></a></h4>
 <ul><li>EUCAST 2022 and CLSI 2022 guidelines have been added for <code><a href="../reference/as.rsi.html">as.rsi()</a></code>. EUCAST 2022 is now the new default guideline for all MIC and disks diffusion interpretations.</li>
 <li>Support for the following languages: Chinese, Greek, Japanese, Polish, Turkish and Ukrainian. The <code>AMR</code> package is now available in 16 languages.</li>
 </ul></div>
 <div class="section level4">
-<h4 id="changed-1-8-1-9020">Changed<a class="anchor" aria-label="anchor" href="#changed-1-8-1-9020"></a></h4>
+<h4 id="changed-1-8-1-9021">Changed<a class="anchor" aria-label="anchor" href="#changed-1-8-1-9021"></a></h4>
 <ul><li>Fix for <code><a href="../reference/as.rsi.html">as.rsi()</a></code> on certain EUCAST breakpoints for MIC values</li>
 <li>Removed <code><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer()</a></code> for MIC values, since MIC are not integer values and running <code><a href="https://rdrr.io/r/base/table.html" class="external-link">table()</a></code> on MIC values consequently failed for not being able to retrieve the level position (as that’s how normally <code><a href="https://rdrr.io/r/base/integer.html" class="external-link">as.integer()</a></code> on <code>factor</code>s work)</li>
 <li>
@@ -157,7 +157,7 @@
 </li>
 </ul></div>
 <div class="section level4">
-<h4 id="other-1-8-1-9020">Other<a class="anchor" aria-label="anchor" href="#other-1-8-1-9020"></a></h4>
+<h4 id="other-1-8-1-9021">Other<a class="anchor" aria-label="anchor" href="#other-1-8-1-9021"></a></h4>
 <ul><li>New website to make use of the new Bootstrap 5 and pkgdown v2.0. The website now contains results for all examples and will be automatically regenerated with every change to our repository, using GitHub Actions</li>
 <li>Added Peter Dutey-Magni and Anton Mymrikov as contributors, to thank them for their valuable input</li>
 </ul></div>
diff --git a/pkgdown.yml b/pkgdown.yml
index 0fe89db7..224a584b 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -11,7 +11,7 @@ articles:
   datasets: datasets.html
   resistance_predict: resistance_predict.html
   welcome_to_AMR: welcome_to_AMR.html
-last_built: 2022-08-21T14:56Z
+last_built: 2022-08-21T15:26Z
 urls:
   reference: https://msberends.github.io/AMR/reference
   article: https://msberends.github.io/AMR/articles
diff --git a/reference/AMR-deprecated.html b/reference/AMR-deprecated.html
index fb9ccaac..123a5499 100644
--- a/reference/AMR-deprecated.html
+++ b/reference/AMR-deprecated.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/AMR.html b/reference/AMR.html
index 559a4bf3..6d448d16 100644
--- a/reference/AMR.html
+++ b/reference/AMR.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/Rplot005.png b/reference/Rplot005.png
index 78ebba4b..a0d001c4 100644
Binary files a/reference/Rplot005.png and b/reference/Rplot005.png differ
diff --git a/reference/Rplot006.png b/reference/Rplot006.png
index dfc8c0ac..fe21af95 100644
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diff --git a/reference/Rplot007.png b/reference/Rplot007.png
new file mode 100644
index 00000000..dfc8c0ac
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diff --git a/reference/WHOCC.html b/reference/WHOCC.html
index b021574c..c03e3697 100644
--- a/reference/WHOCC.html
+++ b/reference/WHOCC.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/WHONET.html b/reference/WHONET.html
index 3bd57b2c..06b6d334 100644
--- a/reference/WHONET.html
+++ b/reference/WHONET.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/ab_from_text.html b/reference/ab_from_text.html
index 36005b4c..30963a0c 100644
--- a/reference/ab_from_text.html
+++ b/reference/ab_from_text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/ab_property.html b/reference/ab_property.html
index 158fc55a..84bafb5d 100644
--- a/reference/ab_property.html
+++ b/reference/ab_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/age.html b/reference/age.html
index 3bbacf87..b95f69b9 100644
--- a/reference/age.html
+++ b/reference/age.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -195,16 +195,16 @@
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="va">df</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>    birth_date age age_exact age_at_y2k</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1947-08-14  75  75.01918         52</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1934-01-26  88  88.56712         65</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1961-06-01  61  61.22192         38</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1994-03-19  28  28.42466          5</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1992-01-08  30  30.61644          7</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1946-07-13  76  76.10685         53</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1984-02-02  38  38.54795         15</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1951-09-30  70  70.89041         48</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1980-03-07  42  42.45753         19</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1991-06-28  31  31.14795          8</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1  1955-03-05  67  67.46301         44</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 2  1969-09-23  52  52.90959         30</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 3  1952-10-15  69  69.84932         47</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 4  1940-09-07  81  81.95342         59</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 5  1956-10-12  65  65.85753         43</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 6  1997-03-26  25  25.40548          2</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7  1946-04-17  76  76.34521         53</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8  1987-04-28  35  35.31507         12</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9  1957-09-27  64  64.89863         42</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 10 1949-07-17  73  73.09589         50</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/age_groups.html b/reference/age_groups.html
index a8f5ae87..7284e7c8 100644
--- a/reference/age_groups.html
+++ b/reference/age_groups.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index 60496997..0129b1ea 100644
--- a/reference/antibiotic_class_selectors.html
+++ b/reference/antibiotic_class_selectors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/antibiotics.html b/reference/antibiotics.html
index ce3bc606..f7155ac0 100644
--- a/reference/antibiotics.html
+++ b/reference/antibiotics.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.ab.html b/reference/as.ab.html
index caaf6dae..62fe1134 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.disk.html b/reference/as.disk.html
index 4a56a026..a4c8ca26 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 51bd6f2d..5c50ce7d 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.mo.html b/reference/as.mo.html
index ee426eef..9a04ebae 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/as.rsi.html b/reference/as.rsi.html
index 0c9080af..4af82025 100644
--- a/reference/as.rsi.html
+++ b/reference/as.rsi.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/atc_online.html b/reference/atc_online.html
index 10a850f4..591c5bbd 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/availability.html b/reference/availability.html
index 8d2571a0..ac2dd5a3 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index 1791b89e..b2b6bcc8 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/catalogue_of_life.html b/reference/catalogue_of_life.html
index 7e8acb84..65f9608a 100644
--- a/reference/catalogue_of_life.html
+++ b/reference/catalogue_of_life.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -168,7 +168,7 @@ Function <code><a href="as.mo.html">as.mo()</a></code> to use the data for intel
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="co"># Get version info of included data set</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="catalogue_of_life_version.html">catalogue_of_life_version</a></span><span class="op">(</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> Included in this AMR package (v1.8.1.9020) are:</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> Included in this AMR package (v1.8.1.9021) are:</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Catalogue of Life: 2019 Annual Checklist</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   Available at: http://www.catalogueoflife.org</span>
diff --git a/reference/catalogue_of_life_version.html b/reference/catalogue_of_life_version.html
index 0f792dc3..7ce979e9 100644
--- a/reference/catalogue_of_life_version.html
+++ b/reference/catalogue_of_life_version.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/count.html b/reference/count.html
index d19c51d0..fa66ed20 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -12,7 +12,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index c27f66ee..951692be 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/dosage.html b/reference/dosage.html
index 70929112..39644c6c 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index 73d74ad2..db922d91 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -12,7 +12,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index 22686194..7bbde8c9 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 02fc170f..2163c411 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 6ce693f9..c8e9658a 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -12,7 +12,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/g.test.html b/reference/g.test.html
index 669382c8..bd06d1a9 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/get_episode.html b/reference/get_episode.html
index c9a32407..659c5188 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -182,41 +182,41 @@
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="va">example_isolates</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/sample.html" class="external-link">sample</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/seq.html" class="external-link">seq_len</a></span><span class="op">(</span><span class="fl">2000</span><span class="op">)</span>, size <span class="op">=</span> <span class="fl">200</span><span class="op">)</span>, <span class="op">]</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">get_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span>    <span class="co"># indices</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 11  6  3 62 46 35 47 39 18 32 12  8  8 26 63 47 24 52 25  8 15 26 15 30 14</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 63 31 38  8 28 54 27 16 37 17 43  6 21 58  2 24 60 63 44 38 19 28 51 62 13</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 30 27 54 12  7 40 42 66 39 39 58 41 38 45 56 18  9 13 63 16  7 58  3 18 60</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 63 10 26 27 14 39 15 42 12  2 13 19 61 13 38 64  1 21 48 36 24 38  5 60  8</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [101] 18 34 45 60 11 15  8  2 11 10 62 31 22 15 12 62 11 36 49 22  8 49 56 59  6</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [126] 10 17 54 43  1  7 53 55 64  6 37  9 38 29 52 63 62 13  5 28 56 13 54 41 18</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [151] 59 44 52 60 20 51  2  2 53 59 61 33 23 34 24 44 56 62  3 50 45 12 42 17 15</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [176] 65  1 18  4 13 32 41  4 34  2 45 64  9 36 38 17 41 35 57 18  6  4 63  2 25</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] 63 42 61 46 22 21 21 10 46 12 31 47 53 64  9 27 12  7 30 25 15 61 52 20 18</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [26] 46 53 39 38 54 61 58 12 58 39 21 27 35 66 46 59 57 13 43 30 29 60 51 25 35</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [51] 22  4 56 20  5 18 18 22  9 21 50  9 58 38 59 44 46 11 41 55 55  5 14 56 26</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [76] 53 63 33 20  5 31 32 10  1 51 46  2 28 24 40 10 13 26 55 12 49 54 16 43 26</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [101] 51 37 60 27 49 23 42 63  5 60  7 23  4 37 47 22 52 45 65 56 11 38 17 36 12</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [126]  9 47 25 14  1 56 38 66 42 53 19 51 10 34 16 11 15 62 31 40 22 63 49 32 62</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [151] 45  6 48  5 58 57  7 56 46 13 58 65 15 21  7 42  8  5  6  6 28 29  4 26 19</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [176]  3 56  3 60 61 63 63 56 65 16 21 40 57 23 16 47 58 61 21 32  9 60  3 17 62</span>
 <span class="r-in"><span><span class="fu">is_new_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, episode_days <span class="op">=</span> <span class="fl">60</span><span class="op">)</span> <span class="co"># TRUE/FALSE</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [13] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [25]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [49] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [97] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [109] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [121] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [133]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [157] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [169]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [181]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [193] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   [1] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [13] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [25] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [37] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [61]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [73] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [85] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [97] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [109]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [121] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [133] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [145]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [157] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [169] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [181]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [193] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># filter on results from the third 60-day episode only, using base R</span></span></span>
 <span class="r-in"><span><span class="va">df</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/which.html" class="external-link">which</a></span><span class="op">(</span><span class="fu">get_episode</span><span class="op">(</span><span class="va">df</span><span class="op">$</span><span class="va">date</span>, <span class="fl">60</span><span class="op">)</span> <span class="op">==</span> <span class="fl">3</span><span class="op">)</span>, <span class="op">]</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 3 × 49</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   date       hospita…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo          </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;date&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;fct&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span>        </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 2002-07-23 B         TRUE    FALSE   FALSE      51 M      F35553  B_STPHY_AURS</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-07-23 B         TRUE    FALSE   FALSE      51 M      F35553  B_STPHY_AURS</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2002-07-16 D         FALSE   FALSE   TRUE       78 M      241328  B_STPHY_CONS</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span> 2002-06-06 D         FALSE   TRUE    FALSE      20 F      24D393  B_ESCHR_COLI</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span> 2002-06-18 B         TRUE    FALSE   FALSE      30 M      012595  B_CRYNB     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span> 2002-07-23 B         TRUE    FALSE   FALSE      51 M      F35553  B_STPHY_AURS</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># … with 40 more variables: PEN &lt;rsi&gt;, OXA &lt;rsi&gt;, FLC &lt;rsi&gt;, AMX &lt;rsi&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   AMC &lt;rsi&gt;, AMP &lt;rsi&gt;, TZP &lt;rsi&gt;, CZO &lt;rsi&gt;, FEP &lt;rsi&gt;, CXM &lt;rsi&gt;,</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;">#   FOX &lt;rsi&gt;, CTX &lt;rsi&gt;, CAZ &lt;rsi&gt;, CRO &lt;rsi&gt;, GEN &lt;rsi&gt;, TOB &lt;rsi&gt;,</span></span>
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 30b8d633..dfc50c5a 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/ggplot_rsi.html b/reference/ggplot_rsi.html
index 73e65ea2..4cfa7c4f 100644
--- a/reference/ggplot_rsi.html
+++ b/reference/ggplot_rsi.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index ef811431..5deb8f56 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/index.html b/reference/index.html
index 02bf00da..00bfef05 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 4f186bbc..a39b23dc 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 27f08c85..dff12a7b 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/join.html b/reference/join.html
index 33fdbc59..8da35e89 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 5d7dddd2..dd0eb2a4 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index edb232f1..64a9f900 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -172,9 +172,9 @@
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">kurtosis</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">10000</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.97178</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 3.031812</span>
 <span class="r-in"><span><span class="fu">kurtosis</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Normal.html" class="external-link">rnorm</a></span><span class="op">(</span><span class="fl">10000</span><span class="op">)</span>, excess <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.01957164</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.03994734</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/like.html b/reference/like.html
index ad27564e..877a1598 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mdro.html b/reference/mdro.html
index 6a2fc52e..5c4cd627 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 621c41a4..f8ab97ec 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 5d52b1b3..ef4ee927 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/microorganisms.old.html b/reference/microorganisms.old.html
index 3a6c0900..01f0b228 100644
--- a/reference/microorganisms.old.html
+++ b/reference/microorganisms.old.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 5553fa89..27ca07a9 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mo_property.html b/reference/mo_property.html
index e88b6188..e0563673 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 47b912af..29ffac26 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -12,7 +12,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/pca.html b/reference/pca.html
index 0b028070..5aa21ad5 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/plot-1.png b/reference/plot-1.png
index 38a90179..d71eff77 100644
Binary files a/reference/plot-1.png and b/reference/plot-1.png differ
diff --git a/reference/plot-2.png b/reference/plot-2.png
index a577f2aa..f4737469 100644
Binary files a/reference/plot-2.png and b/reference/plot-2.png differ
diff --git a/reference/plot-4.png b/reference/plot-4.png
index 59b6c734..2a8b61d4 100644
Binary files a/reference/plot-4.png and b/reference/plot-4.png differ
diff --git a/reference/plot-5.png b/reference/plot-5.png
index bc088940..8af20cff 100644
Binary files a/reference/plot-5.png and b/reference/plot-5.png differ
diff --git a/reference/plot-6.png b/reference/plot-6.png
index 3ea1adb6..9daa6775 100644
Binary files a/reference/plot-6.png and b/reference/plot-6.png differ
diff --git a/reference/plot-7.png b/reference/plot-7.png
new file mode 100644
index 00000000..3ea1adb6
Binary files /dev/null and b/reference/plot-7.png differ
diff --git a/reference/plot.html b/reference/plot.html
index 5bdb10c8..4f80954f 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -307,6 +307,8 @@
 <span class="r-plt img"><img src="plot-4.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="fu">plot</span><span class="op">(</span><span class="va">some_disk_values</span>, mo <span class="op">=</span> <span class="st">"Escherichia coli"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span><span class="op">)</span></span></span>
 <span class="r-plt img"><img src="plot-5.png" alt="" width="700" height="433"></span>
+<span class="r-in"><span><span class="fu">plot</span><span class="op">(</span><span class="va">some_disk_values</span>, mo <span class="op">=</span> <span class="st">"Escherichia coli"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span>, language <span class="op">=</span> <span class="st">"uk"</span><span class="op">)</span></span></span>
+<span class="r-plt img"><img src="plot-6.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \donttest{</span></span></span>
 <span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">require</a></span><span class="op">(</span><span class="st"><a href="https://ggplot2.tidyverse.org" class="external-link">"ggplot2"</a></span><span class="op">)</span><span class="op">)</span> <span class="op">{</span></span></span>
@@ -314,7 +316,7 @@
 <span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">some_disk_values</span>, mo <span class="op">=</span> <span class="st">"Escherichia coli"</span>, ab <span class="op">=</span> <span class="st">"cipro"</span><span class="op">)</span></span></span>
 <span class="r-in"><span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/autoplot.html" class="external-link">autoplot</a></span><span class="op">(</span><span class="va">some_rsi_values</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
-<span class="r-plt img"><img src="plot-6.png" alt="" width="700" height="433"></span>
+<span class="r-plt img"><img src="plot-7.png" alt="" width="700" height="433"></span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/proportion.html b/reference/proportion.html
index 3db61d3a..d9cba062 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -12,7 +12,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/random.html b/reference/random.html
index 9e6bb12e..4a4bbf11 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -183,43 +183,42 @@
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;mic&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.01    0.002   4       2       0.005   0.025   &gt;=256   128     0.002  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 2       &lt;=0.001 2       64      16      &gt;=256   0.125   0.002   0.002  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 0.005   0.25    0.005   2       &gt;=256   0.01    16     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 8     64    8     0.25  8     2     0.005 16    256   32    16    0.125</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [13] 0.125 64    32    0.25  256   4     1     0.125 32    0.5   0.025 128  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [25] 0.25 </span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;disk&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 28 48 49 18 11 30 16 23 16 12 35 47 42  8 20 49 44 31 42 25 18  7 26 41 44</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 27 41 44 30  6 49 34 45 48 31 48 31 31 41 49 46 14 24 31 12 45 24 44 36 38</span>
 <span class="r-in"><span><span class="fu">random_rsi</span><span class="op">(</span><span class="fl">25</span><span class="op">)</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;rsi&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] R I I R S R S S I S I I S I R I R S I R S I S S R</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] I I R S S R S S S I I R I R I I S R R R S I S I S</span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># \donttest{</span></span></span>
 <span class="r-in"><span><span class="co"># make the random generation more realistic by setting a bug and/or drug:</span></span></span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span>                 <span class="co"># range 0.0625-64</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;mic&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.005   0.002   128     2       0.0625  1       &lt;=0.001 256     16     </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 32      &lt;=0.001 &lt;=0.001 32      128     64      64      32      0.025  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 8       2       0.002   0.5     0.125   &lt;=0.001 256    </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.125  0.25   0.125  0.002  4      2      8      32     0.0625 0.002 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 0.125  0.005  1      0.002  0.001  16     0.025  0.25   0.5    128   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 2      0.005  128    4      0.002 </span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"meropenem"</span><span class="op">)</span>    <span class="co"># range 0.0625-16</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;mic&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 4     8     2     8     8     &lt;=0.5 2     4     &lt;=0.5 4     32    4    </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [13] &lt;=0.5 32    &lt;=0.5 1     2     &lt;=0.5 &lt;=0.5 8     64    &lt;=0.5 16    64   </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [25] &lt;=0.5</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] &lt;=1  4    8    8    8    8    8    &gt;=16 2    2    4    2    &lt;=1  8    2   </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [16] &lt;=1  &gt;=16 2    2    8    4    4    8    &lt;=1  &lt;=1 </span>
 <span class="r-in"><span><span class="fu">random_mic</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Streptococcus pneumoniae"</span>, <span class="st">"meropenem"</span><span class="op">)</span> <span class="co"># range 0.0625-4</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;mic&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 2       4       0.25    1       &lt;=0.125 1       2       0.5     2      </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [10] 1       2       &lt;=0.125 0.5     1       4       4       1       &lt;=0.125</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [19] 0.5     0.5     0.5     2       2       4       2      </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 0.125  0.25   2      0.5    0.0625 8      0.25   0.25   0.125  1     </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [11] 2      0.125  0.0625 4      0.5    0.125  2      8      4      0.0625</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [21] 0.5    0.25   0.5    8      0.25  </span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span><span class="op">)</span>                  <span class="co"># range 8-50</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;disk&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 38 25 31 29  9 27  9 16 47 41 43 32 19 14 38 18 28 11  8 42 43 22 46 18 28</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 42 30 13 25 19 37 49 26 38  8 46 40 23 25 44 11 40 13 22 34 15 36 47 49 49</span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Klebsiella pneumoniae"</span>, <span class="st">"ampicillin"</span><span class="op">)</span>    <span class="co"># range 11-17</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;disk&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 12 15 14 17 11 15 17 16 15 17 14 12 15 14 13 16 12 13 12 12 12 16 12 15 14</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 15 14 17 16 15 16 15 11 16 16 15 15 13 13 15 11 15 15 15 17 13 11 15 17 11</span>
 <span class="r-in"><span><span class="fu">random_disk</span><span class="op">(</span><span class="fl">25</span>, <span class="st">"Streptococcus pneumoniae"</span>, <span class="st">"ampicillin"</span><span class="op">)</span> <span class="co"># range 12-27</span></span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> Class &lt;disk&gt;</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 21 18 23 25 23 16 15 19 19 19 25 27 27 17 21 21 23 18 22 25 27 25 18 15 15</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>  [1] 20 25 18 16 15 19 16 21 23 19 25 18 16 16 16 18 16 27 17 27 23 22 17 19 19</span>
 <span class="r-in"><span><span class="co"># }</span></span></span>
 </code></pre></div>
     </div>
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 81cadf9b..fbfb5570 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/rsi_translation.html b/reference/rsi_translation.html
index 0337e8f7..a1f8197f 100644
--- a/reference/rsi_translation.html
+++ b/reference/rsi_translation.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/reference/skewness.html b/reference/skewness.html
index bf11e924..b51dc52d 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -12,7 +12,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
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@@ -171,7 +171,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu">skewness</span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html" class="external-link">runif</a></span><span class="op">(</span><span class="fl">1000</span><span class="op">)</span><span class="op">)</span></span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] -0.01821464</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 0.06804385</span>
 </code></pre></div>
     </div>
   </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
diff --git a/reference/translate.html b/reference/translate.html
index 431452f4..60fbba38 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">AMR (for R)</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9020</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.8.1.9021</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/search.json b/search.json
index c2bee5f8..7930ec0d 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations RSI values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables.","code":"library(dplyr) library(ggplot2) library(AMR) library(cleaner)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\", \"cleaner\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"creation-of-data","dir":"Articles","previous_headings":"","what":"Creation of data","title":"How to conduct AMR data analysis","text":"create fake example data use analysis. AMR data analysis, need least: patient ID, name code microorganism, date antimicrobial results (antibiogram). also include specimen type (e.g. filter blood urine), ward type (e.g. filter ICUs). additional columns (like hospital name, patients gender even [well-defined] clinical properties) can comparative analysis, tutorial demonstrate .","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"patients","dir":"Articles","previous_headings":"Creation of data","what":"Patients","title":"How to conduct AMR data analysis","text":"start patients, need unique list patients. LETTERS object available R - ’s vector 26 characters: Z. patients object just created now vector length 260, values (patient IDs) varying A1 Z10. Now also set gender patients, putting ID gender table: first 135 patient IDs now male, 125 female.","code":"patients <- unlist(lapply(LETTERS, paste0, 1:10)) patients_table <- data.frame(patient_id = patients,                              gender = c(rep(\"M\", 135),                                         rep(\"F\", 125)))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dates","dir":"Articles","previous_headings":"Creation of data","what":"Dates","title":"How to conduct AMR data analysis","text":"Let’s pretend data consists blood cultures isolates 1 January 2010 1 January 2018. dates object now contains days date range.","code":"dates <- seq(as.Date(\"2010-01-01\"), as.Date(\"2018-01-01\"), by = \"day\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"microorganisms","dir":"Articles","previous_headings":"Creation of data > Dates","what":"Microorganisms","title":"How to conduct AMR data analysis","text":"tutorial, uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Klebsiella pneumoniae:","code":"bacteria <- c(\"Escherichia coli\", \"Staphylococcus aureus\",               \"Streptococcus pneumoniae\", \"Klebsiella pneumoniae\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"put-everything-together","dir":"Articles","previous_headings":"Creation of data","what":"Put everything together","title":"How to conduct AMR data analysis","text":"Using sample() function, can randomly select items objects defined earlier. let fake data reflect reality bit, also approximately define probabilities bacteria antibiotic results, using random_rsi() function. Using left_join() function dplyr package, can ‘map’ gender patient ID using patients_table object created earlier: resulting data set contains 20,000 blood culture isolates. head() function can preview first 6 rows data set: Now, let’s start cleaning analysis!","code":"sample_size <- 20000 data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),                    patient_id = sample(patients, size = sample_size, replace = TRUE),                    hospital = sample(c(\"Hospital A\",                                        \"Hospital B\",                                        \"Hospital C\",                                        \"Hospital D\"),                                      size = sample_size, replace = TRUE,                                      prob = c(0.30, 0.35, 0.15, 0.20)),                    bacteria = sample(bacteria, size = sample_size, replace = TRUE,                                      prob = c(0.50, 0.25, 0.15, 0.10)),                    AMX = random_rsi(sample_size, prob_RSI = c(0.35, 0.60, 0.05)),                    AMC = random_rsi(sample_size, prob_RSI = c(0.15, 0.75, 0.10)),                    CIP = random_rsi(sample_size, prob_RSI = c(0.20, 0.80, 0.00)),                    GEN = random_rsi(sample_size, prob_RSI = c(0.08, 0.92, 0.00))) data <- data %>% left_join(patients_table) head(data)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"cleaning-the-data","dir":"Articles","previous_headings":"","what":"Cleaning the data","title":"How to conduct AMR data analysis","text":"also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. example, gender variable: Frequency table Class: character Length: 20,000 Available: 20,000 (100.0%, NA: 0 = 0.0%) Unique: 2 Shortest: 1 Longest: 1 , can draw least two conclusions immediately. data scientists perspective, data looks clean: values M F. researchers perspective: slightly men. Nothing didn’t already know. data already quite clean, still need transform variables. bacteria column now consists text, want add variables based microbial IDs later . , transform column valid IDs. mutate() function dplyr package makes really easy: also want transform antibiotics, real life data don’t know really clean. .rsi() function ensures reliability reproducibility kind variables. .rsi.eligible() can check columns probably columns R/SI test results. Using mutate() across(), can apply transformation formal <rsi> class: Finally, apply EUCAST rules antimicrobial results. Europe, medical microbiological laboratories already apply rules. package features latest insights intrinsic resistance exceptional phenotypes. Moreover, eucast_rules() function can also apply additional rules, like forcing ampicillin = R amoxicillin/clavulanic acid = R. amoxicillin (column AMX) amoxicillin/clavulanic acid (column AMC) data generated randomly, rows undoubtedly contain AMX = S AMC = R, technically impossible. eucast_rules() fixes :","code":"data %>% freq(gender) data <- data %>%   mutate(bacteria = as.mo(bacteria)) is.rsi.eligible(data) # [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE colnames(data)[is.rsi.eligible(data)] # [1] \"AMX\" \"AMC\" \"CIP\" \"GEN\"  data <- data %>%   mutate(across(where(is.rsi.eligible), as.rsi)) data <- eucast_rules(data, col_mo = \"bacteria\", rules = \"all\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"adding-new-variables","dir":"Articles","previous_headings":"","what":"Adding new variables","title":"How to conduct AMR data analysis","text":"Now microbial ID, can add taxonomic properties:","code":"data <- data %>%    mutate(gramstain = mo_gramstain(bacteria),          genus = mo_genus(bacteria),          species = mo_species(bacteria))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Adding new variables","what":"First isolates","title":"How to conduct AMR data analysis","text":"also need know isolates can actually use analysis. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin  isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. method also takes account antimicrobial susceptibility test results using all_microbials(). Read methods first_isolate() page. outcome function can easily added data: 53.6% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 10,723 isolates analysis. Now data looks like: Time analysis!","code":"data <- data %>%    mutate(first = first_isolate(info = TRUE)) # Determining first isolates using an episode length of 365 days # ℹ Using column 'bacteria' as input for `col_mo`. # ℹ Using column 'date' as input for `col_date`. # ℹ Using column 'patient_id' as input for `col_patient_id`. # Basing inclusion on all antimicrobial results, using a points threshold of # 2 # => Found 10,723 'phenotype-based' first isolates (53.6% of total where a #    microbial ID was available) data_1st <- data %>%    filter(first == TRUE) data_1st <- data %>%    filter_first_isolate() head(data_1st)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"might want start getting idea data distributed. ’s important start, also decides continue analysis. Although package contains convenient function make frequency tables, exploratory data analysis (EDA) primary scope package. Use package like DataExplorer , read free online book Exploratory Data Analysis R Roger D. Peng.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dispersion-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Dispersion of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table freq() function. created genus species column earlier based microbial ID. paste(), can concatenate together. freq() function can used like base R language intended: can used like dplyr way, easier readable: Frequency table Class: character Length: 10,723 Available: 10,723 (100.0%, NA: 0 = 0.0%) Unique: 4 Shortest: 16 Longest: 24","code":"freq(paste(data_1st$genus, data_1st$species)) data_1st %>% freq(genus, species)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"overview-of-different-bugdrug-combinations","dir":"Articles","previous_headings":"Analysing the data","what":"Overview of different bug/drug combinations","title":"How to conduct AMR data analysis","text":"Using tidyverse selections, can also select filter columns based antibiotic class : want get quick glance number isolates different bug/drug combinations, can use bug_drug_combinations() function: give crude numbers data. calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"data_1st %>%    filter(any(aminoglycosides() == \"R\")) # ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) data_1st %>%    bug_drug_combinations() %>%    head() # show first 6 rows # ℹ Using column 'bacteria' as input for `col_mo`. data_1st %>%    select(bacteria, aminoglycosides()) %>%    bug_drug_combinations() # ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) # ℹ Using column 'bacteria' as input for `col_mo`."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: course convenient know number isolates responsible percentages. purpose n_rsi() can used, works exactly like n_distinct() dplyr package. counts isolates available every group (.e. values S, R): functions can also used get proportion multiple antibiotics, calculate empiric susceptibility combination therapies easily: curious resistance within certain antibiotic classes, use antibiotic class selector penicillins(), automatically include columns AMX AMC data: make transition next part, let’s see differences previously calculated combination therapies plotted:","code":"data_1st %>% resistance(AMX) # [1] 0.5465821 data_1st %>%    group_by(hospital) %>%    summarise(amoxicillin = resistance(AMX)) data_1st %>%    group_by(hospital) %>%    summarise(amoxicillin = resistance(AMX),             available = n_rsi(AMX)) data_1st %>%    group_by(genus) %>%    summarise(amoxiclav = susceptibility(AMC),             gentamicin = susceptibility(GEN),             amoxiclav_genta = susceptibility(AMC, GEN)) data_1st %>%    # group by hospital   group_by(hospital) %>%    #                / -> select all penicillins in the data for calculation   #                |              / -> use resistance() for all peni's per hospital   #                |              |           / -> print as percentages   summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%    # format the antibiotic column names, using so-called snake case,   # so 'Amoxicillin/clavulanic acid' becomes 'amoxicillin_clavulanic_acid'   rename_with(set_ab_names, penicillins()) data_1st %>%    group_by(genus) %>%    summarise(\"1. Amoxi/clav\" = susceptibility(AMC),             \"2. Gentamicin\" = susceptibility(GEN),             \"3. Amoxi/clav + genta\" = susceptibility(AMC, GEN)) %>%    # pivot_longer() from the tidyr package \"lengthens\" data:   tidyr::pivot_longer(-genus, names_to = \"antibiotic\") %>%    ggplot(aes(x = genus,              y = value,              fill = antibiotic)) +   geom_col(position = \"dodge2\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plots","dir":"Articles","previous_headings":"Analysing the data","what":"Plots","title":"How to conduct AMR data analysis","text":"show results plots, R users nowadays use ggplot2 package. package lets create plots layers. can read website. quick example look like syntaxes: AMR package contains functions extend ggplot2 package, example geom_rsi(). automatically transforms data count_df() proportion_df() show results stacked bars. simplest shortest example:  Omit translate_ab = FALSE antibiotic codes (AMX, AMC, CIP, GEN) translated official names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin). group e.g. genus column add additional functions package, can create :  simplify , also created ggplot_rsi() function, combines almost functions:","code":"ggplot(data = a_data_set,        mapping = aes(x = year,                      y = value)) +   geom_col() +   labs(title = \"A title\",        subtitle = \"A subtitle\",        x = \"My X axis\",        y = \"My Y axis\")  # or as short as: ggplot(a_data_set) +   geom_bar(aes(year)) ggplot(data_1st) +   geom_rsi(translate_ab = FALSE) # group the data on `genus` ggplot(data_1st %>% group_by(genus)) +    # create bars with genus on x axis   # it looks for variables with class `rsi`,   # of which we have 4 (earlier created with `as.rsi`)   geom_rsi(x = \"genus\") +    # split plots on antibiotic   facet_rsi(facet = \"antibiotic\") +   # set colours to the R/SI interpretations (colour-blind friendly)   scale_rsi_colours() +   # show percentages on y axis   scale_y_percent(breaks = 0:4 * 25) +   # turn 90 degrees, to make it bars instead of columns   coord_flip() +   # add labels   labs(title = \"Resistance per genus and antibiotic\",         subtitle = \"(this is fake data)\") +   # and print genus in italic to follow our convention   # (is now y axis because we turned the plot)   theme(axis.text.y = element_text(face = \"italic\")) data_1st %>%    group_by(genus) %>%   ggplot_rsi(x = \"genus\",              facet = \"antibiotic\",              breaks = 0:4 * 25,              datalabels = FALSE) +   coord_flip()"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data > Plots","what":"Plotting MIC and disk diffusion values","title":"How to conduct AMR data analysis","text":"AMR package also extends plot() ggplot2::autoplot() functions plotting minimum inhibitory concentrations (MIC, created .mic()) disk diffusion diameters (created .disk()). random_mic() random_disk() functions, can generate sampled values new data types (S3 classes) <mic> <disk>:   also specific, generating MICs likely found E. coli ciprofloxacin: plot() autoplot() function, can define microorganism antimicrobial agent way. add interpretation values according chosen guidelines (defaults latest EUCAST guideline). Default colours colour-blind friendly, maintaining convention e.g. ‘susceptible’ green ‘resistant’ red:   disk diffusion values, much difference plotting:  using ggplot2 package, now choosing latest implemented CLSI guideline (notice EUCAST-specific term “Susceptible, incr. exp.” changed “Intermediate”):","code":"mic_values <- random_mic(size = 100) mic_values # Class <mic> #   [1] 32      64      128     128     64      8       32      0.002   0.025   #  [10] 0.5     32      <=0.001 2       128     0.01    1       2       0.01    #  [19] 128     0.5     4       0.002   0.125   256     0.0625  4       0.002   #  [28] 0.005   4       0.002   32      0.01    0.0625  0.25    2       64      #  [37] 0.0625  0.5     256     4       1       0.025   128     0.0625  16      #  [46] 0.5     0.005   0.01    1       2       0.01    2       8       0.125   #  [55] 0.25    256     0.0625  0.025   0.002   8       <=0.001 1       0.002   #  [64] 256     8       4       0.005   0.025   4       0.002   <=0.001 2       #  [73] 256     0.005   256     2       4       16      128     256     <=0.001 #  [82] 1       2       1       1       <=0.001 0.5     4       0.25    32      #  [91] 0.002   0.0625  0.01    32      0.005   0.01    2       64      64      # [100] 2 # base R: plot(mic_values) # ggplot2: autoplot(mic_values) mic_values <- random_mic(size = 100, mo = \"E. coli\", ab = \"cipro\") # base R: plot(mic_values, mo = \"E. coli\", ab = \"cipro\") # ggplot2: autoplot(mic_values, mo = \"E. coli\", ab = \"cipro\") disk_values <- random_disk(size = 100, mo = \"E. coli\", ab = \"cipro\") # ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #   coli). Run `mo_uncertainties()` to review this. disk_values # Class <disk> #   [1] 28 22 30 19 23 20 17 21 22 18 18 27 18 20 24 28 30 20 29 31 22 31 23 24 17 #  [26] 23 18 17 25 30 28 23 30 23 30 31 18 26 26 26 27 17 30 18 27 31 17 24 20 26 #  [51] 30 21 26 27 20 23 28 29 17 29 27 24 22 27 28 27 22 30 18 28 25 18 25 18 31 #  [76] 29 25 17 31 20 20 21 24 24 30 28 30 31 20 30 27 19 24 17 22 30 31 27 28 24 # base R: plot(disk_values, mo = \"E. coli\", ab = \"cipro\") autoplot(disk_values,        mo = \"E. coli\",        ab = \"cipro\",        guideline = \"CLSI\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"independence-test","dir":"Articles","previous_headings":"Analysing the data","what":"Independence test","title":"How to conduct AMR data analysis","text":"next example uses example_isolates data set. data set included package contains 2,000 microbial isolates full antibiograms. reflects reality can used practice AMR data analysis. compare resistance fosfomycin (column FOS) hospital D. input fisher.test() can retrieved transformation like : can apply test now : can seen, p value 0.031, means fosfomycin resistance found isolates patients hospital D really different.","code":"# use package 'tidyr' to pivot data: library(tidyr)  check_FOS <- example_isolates %>%   filter(hospital_id %in% c(\"A\", \"D\")) %>% # filter on only hospitals A and D   select(hospital_id, FOS) %>%             # select the hospitals and fosfomycin   group_by(hospital_id) %>%                # group on the hospitals   count_df(combine_SI = TRUE) %>%          # count all isolates per group (hospital_id)   pivot_wider(names_from = hospital_id,    # transform output so A and D are columns               values_from = value) %>%        select(A, D) %>%                         # and only select these columns   as.matrix()                              # transform to a good old matrix for fisher.test()  check_FOS #       A  D # [1,] 25 77 # [2,] 24 33 # do Fisher's Exact Test fisher.test(check_FOS)                             #  #   Fisher's Exact Test for Count Data #  # data:  check_FOS # p-value = 0.03104 # alternative hypothesis: true odds ratio is not equal to 1 # 95 percent confidence interval: #  0.2111489 0.9485124 # sample estimates: # odds ratio  #  0.4488318"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.3, 2021). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame(mo = c(\"Klebsiella\",                            \"Escherichia\"),                    ampicillin = \"S\") oops #            mo ampicillin # 1  Klebsiella          S # 2 Escherichia          S  eucast_rules(oops, info = FALSE) #            mo ampicillin # 1  Klebsiella          R # 2 Escherichia          S mo_is_intrinsic_resistant(c(\"Klebsiella\", \"Escherichia\"),                           \"ampicillin\") # [1]  TRUE FALSE  mo_is_intrinsic_resistant(\"Klebsiella\",                           c(\"ampicillin\", \"kanamycin\")) # [1]  TRUE FALSE data <- data.frame(mo = c(\"Staphylococcus aureus\",                           \"Enterococcus faecalis\",                           \"Escherichia coli\",                           \"Klebsiella pneumoniae\",                           \"Pseudomonas aeruginosa\"),                    VAN = \"-\",       # Vancomycin                    AMX = \"-\",       # Amoxicillin                    COL = \"-\",       # Colistin                    CAZ = \"-\",       # Ceftazidime                    CXM = \"-\",       # Cefuroxime                    PEN = \"S\",       # Benzylenicillin                    FOX = \"S\",       # Cefoxitin                    stringsAsFactors = FALSE) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom # A set of custom MDRO rules: #   1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #   2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #   3. Otherwise: Negative #  # Unmatched rows will return NA. # Results will be of class <factor>, with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) # x #       Negative Elderly Type A Elderly Type B  #           1070            198            732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100.0%, NA: 0 = 0.0%) Unique: 5","code":"library(dplyr)   # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>%    mdro() %>%    freq() # show frequency table of the result # Warning: in `mdro()`: NA introduced for isolates where the available percentage of # antimicrobial classes was below 50% (set with `pct_required_classes`) # random_rsi() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(rifampicin = random_rsi(5000),                          isoniazid = random_rsi(5000),                          gatifloxacin = random_rsi(5000),                          ethambutol = random_rsi(5000),                          pyrazinamide = random_rsi(5000),                          moxifloxacin = random_rsi(5000),                          kanamycin = random_rsi(5000)) my_TB_data <- data.frame(RIF = random_rsi(5000),                          INH = random_rsi(5000),                          GAT = random_rsi(5000),                          ETH = random_rsi(5000),                          PZA = random_rsi(5000),                          MFX = random_rsi(5000),                          KAN = random_rsi(5000)) head(my_TB_data) #   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin # 1          I         I            S          S            I            R # 2          I         R            R          S            I            I # 3          R         I            S          R            R            S # 4          I         S            I          S            I            S # 5          S         R            S          I            S            R # 6          I         S            I          I            S            S #   kanamycin # 1         I # 2         R # 3         S # 4         R # 5         S # 6         S mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) # ℹ No column found as input for `col_mo`, assuming all rows contain #   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) # Rows: 2,000 # Columns: 49 # $ date            <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-… # $ hospital_id     <fct> D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B, … # $ ward_icu        <lgl> FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TR… # $ ward_clinical   <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FA… # $ ward_outpatient <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE… # $ age             <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71… # $ gender          <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\",… # $ patient_id      <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067… # $ mo              <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPH… # $ PEN             <rsi> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, … # $ OXA             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FLC             <rsi> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, … # $ AMX             <rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, … # $ AMC             <rsi> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, … # $ AMP             <rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, … # $ TZP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CZO             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FEP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CXM             <rsi> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R,… # $ FOX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CTX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, … # $ CAZ             <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S… # $ CRO             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, … # $ GEN             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TOB             <rsi> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA… # $ AMK             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ KAN             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TMP             <rsi> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R… # $ SXT             <rsi> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, … # $ NIT             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FOS             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ LNZ             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,… # $ CIP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, … # $ MFX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ VAN             <rsi> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S… # $ TEC             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,… # $ TCY             <rsi> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S… # $ TGC             <rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, … # $ DOX             <rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, … # $ ERY             <rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, … # $ CLI             <rsi> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, … # $ AZM             <rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, … # $ IPM             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, … # $ MEM             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ MTR             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CHL             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ COL             <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R… # $ MUP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ RIF             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,… resistance_data <- example_isolates %>%    group_by(order = mo_order(mo),       # group on anything, like order            genus = mo_genus(mo)) %>%   #  and genus as we do here   summarise_if(is.rsi, resistance) %>% # then get resistance of all drugs   select(order, genus, AMC, CXM, CTX,           CAZ, GEN, TOB, TMP, SXT)      # and select only relevant columns  head(resistance_data) # # A tibble: 6 × 10 # # Groups:   order [5] #   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA # 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA # 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA # 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA # 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA # 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) # ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #   and \"TOB\". Total observations available: 7. summary(pca_result) # Groups (n=4, named as 'order'): # [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" # Importance of components: #                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 # Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 5.121e-17 # Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 # Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 # Groups (n=4, named as 'order'): # [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss-sas-stata","dir":"Articles","previous_headings":"","what":"SPSS / SAS / Stata","title":"How to import data from SPSS / SAS / Stata","text":"SPSS (Statistical Package Social Sciences) probably well-known software package statistical analysis. SPSS easier learn R, SPSS click menu run parts analysis. user-friendliness, taught universities particularly useful students new statistics. experience, guess pretty much (bio)medical students know time graduate. SAS Stata comparable statistical packages popular big industries.","code":""},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"compared-to-r","dir":"Articles","previous_headings":"","what":"Compared to R","title":"How to import data from SPSS / SAS / Stata","text":"said, SPSS easier learn R. SPSS, SAS Stata come major downsides comparing R: R highly modular. official R network (CRAN) features 16,000 packages time writing, AMR package one . packages peer-reviewed publication. Aside official channel, also developers choose submit CRAN, rather keep public repository, like GitHub. may even lot 14,000 packages . Bottom line , can really extend ask somebody . Take example AMR package. Among things, adds reliable reference data R help data cleaning analysis. SPSS, SAS Stata never know valid MIC value Gram stain E. coli . species Klebiella resistant amoxicillin Floxapen® trade name flucloxacillin. facts properties often needed clean existing data, inconvenient software package without reliable reference data. See demonstration. R extremely flexible. write syntax , can anything want. flexibility transforming, arranging, grouping summarising data, drawing plots, endless - SPSS, SAS Stata bound algorithms format styles. may bit flexible, can probably never create specific publication-ready plot without using (paid) software. sometimes write syntaxes SPSS run complete analysis ‘automate’ work, lot less time R. notice writing syntaxes R lot nifty clever SPSS. Still, working statistical package, knowledge (statistically) willing accomplish. R can easily automated. last years, R Markdown really made interesting development. R Markdown, can easily produce reports, whether format Word, PowerPoint, website, PDF document just raw data Excel. even allows use reference file containing layout style (e.g. fonts colours) organisation. use lot generate weekly monthly reports automatically. Just write code enjoy automatically updated reports interval like. even professional environment, create Shiny apps: live manipulation data using custom made website. webdesign knowledge needed (JavaScript, CSS, HTML) almost zero. R huge community. Many R users just ask questions websites like StackOverflow.com, largest online community programmers. time writing, 460,146 R-related questions already asked platform (covers questions answers programming language). experience, questions answered within couple minutes. R understands data type, including SPSS/SAS/Stata. ’s vice versa ’m afraid. can import data source R. example SPSS, SAS Stata (link), Minitab, Epi Info EpiData (link), Excel (link), flat files like CSV, TXT TSV (link), directly databases datawarehouses anywhere world (link). can even scrape websites download tables live internet (link) get results API call transform data one command (link). best part - can export R data formats well. can import SPSS file, analysis neatly R export resulting tables Excel files sharing. R completely free open-source. strings attached. created maintained volunteers believe (data) science open publicly available everybody. SPSS, SAS Stata quite expensive. IBM SPSS Staticstics comes subscriptions nowadays, varying USD 1,300 USD 8,500 per user per year. SAS Analytics Pro costs around USD 10,000 per computer. Stata also business model subscription fees, varying USD 600 USD 2,800 per computer per year, lower prices come limitation number variables can work . still offer benefits R. working midsized small company, can save tens thousands dollars using R instead e.g. SPSS - gaining even functions flexibility. R enthousiasts can much PR want (like ), nobody officially associated affiliated R. really free. R (nowadays) preferred analysis software academic papers. present, R among world powerful statistical languages, generally popular science (Bollmann et al., 2017). reasons, number references R analysis method academic papers rising continuously even surpassed SPSS academic use (Muenchen, 2014). believe thing SPSS , always great user interface easy learn use. Back developed , little competition, let alone R. R didn’t even professional user interface last decade (called RStudio, see ). people used R nineties 2010 almost completely incomparable R used now. language restyled completely volunteers dedicated professionals field data science. SPSS great nothing else compete. now 2022, don’t see reason SPSS better use R. demonstrate first point:","code":"# not all values are valid MIC values: as.mic(0.125) # Class <mic> # [1] 0.125 as.mic(\"testvalue\") # Class <mic> # [1] <NA>  # the Gram stain is available for all bacteria: mo_gramstain(\"E. coli\") # [1] \"Gram-negative\"  # Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST: klebsiella_test <- data.frame(mo = \"klebsiella\",                                amox = \"S\",                               stringsAsFactors = FALSE) klebsiella_test # (our original data) #           mo amox # 1 klebsiella    S eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) #           mo amox # 1 klebsiella    R  # hundreds of trade names can be translated to a name, trade name or an ATC code: ab_name(\"floxapen\") # [1] \"Flucloxacillin\" ab_tradenames(\"floxapen\") # [1] \"floxacillin\"          \"floxapen\"             \"floxapen sodium salt\" # [4] \"fluclox\"              \"flucloxacilina\"       \"flucloxacillin\"       # [7] \"flucloxacilline\"      \"flucloxacillinum\"     \"fluorochloroxacillin\" ab_atc(\"floxapen\") # [1] \"J01CF05\""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"rstudio","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"RStudio","title":"How to import data from SPSS / SAS / Stata","text":"work R, probably best option use RStudio. open-source free desktop environment allows run R code, also supports project management, version management, package management convenient import menus work data sources. can also install RStudio Server private corporate server, brings nothing less complete RStudio software website (home work). import data file, just click Import Dataset Environment tab:  additional packages needed, RStudio ask installed beforehand. window opens, can define options (parameters) used import ’re ready go:  want named variables imported factors resembles SPSS , use as_factor(). difference :","code":"SPSS_data # # A tibble: 4,203 x 4 #     v001 sex       status    statusage #    <dbl> <dbl+lbl> <dbl+lbl>     <dbl> #  1 10002 1         1              76.6 #  2 10004 0         1              59.1 #  3 10005 1         1              54.5 #  4 10006 1         1              54.1 #  5 10007 1         1              57.7 #  6 10008 1         1              62.8 #  7 10010 0         1              63.7 #  8 10011 1         1              73.1 #  9 10017 1         1              56.7 # 10 10018 0         1              66.6 # # ... with 4,193 more rows  as_factor(SPSS_data) # # A tibble: 4,203 x 4 #     v001 sex    status statusage #    <dbl> <fct>  <fct>      <dbl> #  1 10002 Male   alive       76.6 #  2 10004 Female alive       59.1 #  3 10005 Male   alive       54.5 #  4 10006 Male   alive       54.1 #  5 10007 Male   alive       57.7 #  6 10008 Male   alive       62.8 #  7 10010 Female alive       63.7 #  8 10011 Male   alive       73.1 #  9 10017 Male   alive       56.7 # 10 10018 Female alive       66.6 # # ... with 4,193 more rows"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"base-r","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"Base R","title":"How to import data from SPSS / SAS / Stata","text":"import data SPSS, SAS Stata, can use great haven package : can now import files follows:","code":"# download and install the latest version: install.packages(\"haven\") # load the package you just installed: library(haven)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SPSS","title":"How to import data from SPSS / SAS / Stata","text":"read files SPSS R: forget as_factor(), mentioned . export R objects SPSS file format:","code":"# read any SPSS file based on file extension (best way): read_spss(file = \"path/to/file\")  # read .sav or .zsav file: read_sav(file = \"path/to/file\")  # read .por file: read_por(file = \"path/to/file\") # save as .sav file: write_sav(data = yourdata, path = \"path/to/file\")  # save as compressed .zsav file: write_sav(data = yourdata, path = \"path/to/file\", compress = TRUE)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"sas","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SAS","title":"How to import data from SPSS / SAS / Stata","text":"read files SAS R: export R objects SAS file format:","code":"# read .sas7bdat + .sas7bcat files: read_sas(data_file = \"path/to/file\", catalog_file = NULL)  # read SAS transport files (version 5 and version 8): read_xpt(file = \"path/to/file\") # save as regular SAS file: write_sas(data = yourdata, path = \"path/to/file\")  # the SAS transport format is an open format  # (required for submission of the data to the FDA) write_xpt(data = yourdata, path = \"path/to/file\", version = 8)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"stata","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"Stata","title":"How to import data from SPSS / SAS / Stata","text":"read files Stata R: export R objects Stata file format:","code":"# read .dta file: read_stata(file = \"/path/to/file\")  # works exactly the same: read_dta(file = \"/path/to/file\") # save as .dta file, Stata version 14: # (supports Stata v8 until v15 at the time of writing) write_dta(data = yourdata, path = \"/path/to/file\", version = 14)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .rsi() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100.0%, NA: 0 = 0.0%) Unique: 37 Shortest: 11 Longest: 40 (omitted 27 entries, n = 56 [11.20%]) Frequency table Class: factor > ordered > rsi (numeric) Length: 500 Levels: 3: S < < R Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr)   # part of tidyverse library(ggplot2) # part of tidyverse library(AMR)     # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>%   # get microbial ID based on given organism   mutate(mo = as.mo(Organism)) %>%    # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `rsi` class   mutate_at(vars(AMP_ND10:CIP_EE), as.rsi) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_rsi() function:","code":"data %>%   group_by(Country) %>%   select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%   ggplot_rsi(translate_ab = 'ab', facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-currently-accepted-names","dir":"Articles","previous_headings":"","what":"Microorganisms (currently accepted names)","title":"Data sets for download / own use","text":"data set 70,764 rows 16 columns, containing following column names:mo, fullname, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, species_id, source, prevalence snomed. data set R available microorganisms, load AMR package. last updated 21 August 2022 14:52:57 UTC. Find info structure data set . Direct download links: Download R file (1.3 MB) Download Excel file (4.8 MB) Download plain text file (13.1 MB) Download SAS file (30.7 MB) Download SPSS file (16.3 MB) Download Stata file (27.5 MB) NOTE: exported files SAS, SPSS Stata contain SNOMED codes, file size exceed 100 MB; file size limit GitHub. Advice? Use R instead.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"Microorganisms (currently accepted names)","what":"Source","title":"Data sets for download / own use","text":"full taxonomy microorganisms based authoritative comprehensive: Catalogue Life (included version: 2019) List Prokaryotic names Standing Nomenclature (LPSN, last updated: 5 October 2021) US Edition SNOMED CT 1 September 2020, retrieved Public Health Information Network Vocabulary Access Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"Microorganisms (currently accepted names)","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-previously-accepted-names","dir":"Articles","previous_headings":"","what":"Microorganisms (previously accepted names)","title":"Data sets for download / own use","text":"data set 14,338 rows 4 columns, containing following column names:fullname, fullname_new, ref prevalence. Note: remember ‘ref’ columns contains scientific reference old taxonomic entries, .e. column ‘fullname’. scientific reference new names, .e. column ‘fullname_new’, see microorganisms data set. data set R available microorganisms.old, load AMR package. last updated 21 August 2022 14:52:57 UTC. Find info structure data set . Direct download links: Download R file (0.2 MB) Download Excel file (0.5 MB) Download plain text file (1 MB) Download SAS file (2.1 MB) Download SPSS file (1.3 MB) Download Stata file (2 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"Microorganisms (previously accepted names)","what":"Source","title":"Data sets for download / own use","text":"data set contains old, previously accepted taxonomic names. data sources microorganisms data set: Catalogue Life (included version: 2019) List Prokaryotic names Standing Nomenclature (LPSN, last updated: 5 October 2021)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-1","dir":"Articles","previous_headings":"Microorganisms (previously accepted names)","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotic-agents","dir":"Articles","previous_headings":"","what":"Antibiotic agents","title":"Data sets for download / own use","text":"data set 464 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units loinc. data set R available antibiotics, load AMR package. last updated 21 August 2022 14:52:57 UTC. Find info structure data set . Direct download links: Download R file (33 kB) Download Excel file (65 kB) Download plain text file (0.1 MB) Download SAS file (1.8 MB) Download SPSS file (0.3 MB) Download Stata file (0.3 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"Antibiotic agents","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antiviral-agents","dir":"Articles","previous_headings":"","what":"Antiviral agents","title":"Data sets for download / own use","text":"data set 102 rows 9 columns, containing following column names:atc, cid, name, atc_group, synonyms, oral_ddd, oral_units, iv_ddd iv_units. data set R available antivirals, load AMR package. last updated 21 August 2022 14:52:57 UTC. Find info structure data set . Direct download links: Download R file (5 kB) Download Excel file (14 kB) Download plain text file (16 kB) Download SAS file (80 kB) Download SPSS file (68 kB) Download Stata file (67 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"Antiviral agents","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"interpretation-from-mic-values-disk-diameters-to-rsi","dir":"Articles","previous_headings":"","what":"Interpretation from MIC values / disk diameters to R/SI","title":"Data sets for download / own use","text":"data set 20,369 rows 11 columns, containing following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R uti. data set R available rsi_translation, load AMR package. last updated 21 August 2022 14:52:57 UTC. Find info structure data set . Direct download links: Download R file (49 kB) Download Excel file (0.9 MB) Download plain text file (2 MB) Download SAS file (4 MB) Download SPSS file (2.6 MB) Download Stata file (3.8 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"Interpretation from MIC values / disk diameters to R/SI","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2011-2022) EUCAST (2011-2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"Intrinsic bacterial resistance","title":"Data sets for download / own use","text":"data set 134,956 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 21 August 2022 14:52:57 UTC. Find info structure data set . Direct download links: Download R file (78 kB) Download Excel file (1.3 MB) Download plain text file (5.1 MB) Download SAS file (10.4 MB) Download SPSS file (7.4 MB) Download Stata file (10.2 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"Intrinsic bacterial resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-5","dir":"Articles","previous_headings":"Intrinsic bacterial resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"Dosage guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 169 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt eucast_version. data set R available dosage, load AMR package. last updated 21 August 2022 14:52:57 UTC. Find info structure data set . Direct download links: Download R file (3 kB) Download Excel file (14 kB) Download plain text file (15 kB) Download SAS file (52 kB) Download SPSS file (45 kB) Download Stata file (44 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"Dosage guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 Dr Hadley Wickham. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions:  fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_rsi_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\")  # or: example_isolates %>%    resistance_predict(col_ab = \"TZP\",                      model  \"binomial\")  # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>%    resistance_predict(col_ab = \"TZP\",                      model = \"binomial\") # ℹ Using column 'date' as input for `col_date`. predict_TZP #    year      value    se_min    se_max observations   observed  estimated # 1  2002 0.20000000        NA        NA           15 0.20000000 0.05616378 # 2  2003 0.06250000        NA        NA           32 0.06250000 0.06163839 # 3  2004 0.08536585        NA        NA           82 0.08536585 0.06760841 # 4  2005 0.05000000        NA        NA           60 0.05000000 0.07411100 # 5  2006 0.05084746        NA        NA           59 0.05084746 0.08118454 # 6  2007 0.12121212        NA        NA           66 0.12121212 0.08886843 # 7  2008 0.04166667        NA        NA           72 0.04166667 0.09720264 # 8  2009 0.01639344        NA        NA           61 0.01639344 0.10622731 # 9  2010 0.05660377        NA        NA           53 0.05660377 0.11598223 # 10 2011 0.18279570        NA        NA           93 0.18279570 0.12650615 # 11 2012 0.30769231        NA        NA           65 0.30769231 0.13783610 # 12 2013 0.06896552        NA        NA           58 0.06896552 0.15000651 # 13 2014 0.10000000        NA        NA           60 0.10000000 0.16304829 # 14 2015 0.23636364        NA        NA           55 0.23636364 0.17698785 # 15 2016 0.22619048        NA        NA           84 0.22619048 0.19184597 # 16 2017 0.16279070        NA        NA           86 0.16279070 0.20763675 # 17 2018 0.22436641 0.1938710 0.2548618           NA         NA 0.22436641 # 18 2019 0.24203228 0.2062911 0.2777735           NA         NA 0.24203228 # 19 2020 0.26062172 0.2191758 0.3020676           NA         NA 0.26062172 # 20 2021 0.28011130 0.2325557 0.3276669           NA         NA 0.28011130 # 21 2022 0.30046606 0.2464567 0.3544755           NA         NA 0.30046606 # 22 2023 0.32163907 0.2609011 0.3823771           NA         NA 0.32163907 # 23 2024 0.34357130 0.2759081 0.4112345           NA         NA 0.34357130 # 24 2025 0.36619175 0.2914934 0.4408901           NA         NA 0.36619175 # 25 2026 0.38941799 0.3076686 0.4711674           NA         NA 0.38941799 # 26 2027 0.41315710 0.3244399 0.5018743           NA         NA 0.41315710 # 27 2028 0.43730688 0.3418075 0.5328063           NA         NA 0.43730688 # 28 2029 0.46175755 0.3597639 0.5637512           NA         NA 0.46175755 # 29 2030 0.48639359 0.3782932 0.5944939           NA         NA 0.48639359 # 30 2031 0.51109592 0.3973697 0.6248221           NA         NA 0.51109592 # 31 2032 0.53574417 0.4169574 0.6545309           NA         NA 0.53574417 plot(predict_TZP) ggplot_rsi_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_rsi_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous:  Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate:  model also available object, attribute:","code":"example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>%    ggplot_rsi_predict() # ℹ Using column 'date' as input for `col_date`. example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>%    ggplot_rsi_predict() # ℹ Using column 'date' as input for `col_date`. model <- attributes(predict_TZP)$model  summary(model)$family #  # Family: binomial  # Link function: logit  summary(model)$coefficients #                  Estimate  Std. Error   z value     Pr(>|z|) # (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 # year           0.09883005  0.02295317  4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Christian F. Luz. Author, contributor. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Casper Albers. Thesis advisor. Peter Dutey-Magni. Contributor. Judith Fonville. Contributor. Alex Friedrich. Thesis advisor. Corinna Glasner. Thesis advisor. Eric Hazenberg. Contributor. Gwen Knight. Contributor. Annick Lenglet. Contributor. Bart Meijer. Contributor. Anton Mymrikov. Contributor. Sofia Ny. Contributor. Rogier Schade. Contributor. Bhanu Sinha. Thesis advisor. Anthony Underwood. Contributor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2021). AMR - R Package Working Antimicrobial Resistance Data. Journal Statistical Software (accepted publication), https://www.biorxiv.org/content/10.1101/810622, doi: 10.1101/810622. Berends, MS (2021). New Instrument Microbial Epidemiology: Empowering Antimicrobial Resistance Data Analysis (PhD thesis). University Groningen, doi: 10.33612/diss.177417131. Luz, CF (2021). Data Science Infection Management & Antimicrobial Stewardship (PhD thesis). University Groningen, doi: 10.33612/diss.192486375.","code":"@Article{,   title = {AMR - An R Package for Working with Antimicrobial Resistance Data},   author = {M S Berends and C F Luz and A W Friedrich and B N M Sinha and C J Albers and C Glasner},   doi = {10.1101/810622},   journal = {Journal of Statistical Software},   pages = {Accepted for publication},   year = {2021},   url = {https://www.biorxiv.org/content/10.1101/810622}, } @PhdThesis{,   title = {A New Instrument for Microbial Epidemiology: Empowering Antimicrobial Resistance Data Analysis},   author = {M S Berends},   publisher = {University of Groningen},   school = {University of Groningen},   doi = {10.33612/diss.177417131},   pages = {287},   year = {2021}, } @PhdThesis{,   title = {Data Science for Infection Management & Antimicrobial Stewardship},   author = {C F Luz},   publisher = {University of Groningen},   school = {University of Groningen},   doi = {10.33612/diss.192486375},   pages = {326},   year = {2021}, }"},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. rationale behind package scientifically described Journal Statistical Software (Volume xx, Issue xx; DOI 10.18637/jss.v000.i00 - waiting copy-editing finish). installing package, R knows ~71,000 distinct microbial species ~570 antibiotic, antimycotic antiviral drugs name code (including ATC, WHONET/EARS-Net, PubChem, LOINC SNOMED CT), knows valid R/SI MIC values. supports data format, including WHONET/EARS-Net data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. R package formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375) actively durably maintained two public healthcare organisations Netherlands.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-175-countries-translated-to-16-languages","dir":"","previous_headings":"Introduction","what":"Used in 175 countries, translated to 16 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. AMR package available  English,  Chinese,  Danish,  Dutch,  French,  German,  Greek,  Italian,  Japanese,  Polish,  Portuguese,  Russian,  Spanish,  Swedish,  Turkish,  Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"defined row filter Gram-negative bacteria intrinsic resistance cefotaxime (mo_is_gram_negative() mo_is_intrinsic_resistant()) column selection two antibiotic groups (aminoglycosides() carbapenems()), reference data microorganisms antibiotics AMR package make sure get meant: base R equivalent : base R snippet work version R since April 2013 (R-3.0).","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr)  example_isolates %>%   mutate(bacteria = mo_fullname()) %>%   filter(mo_is_gram_negative(),          mo_is_intrinsic_resistant(ab = \"cefotax\")) %>%   select(bacteria,          aminoglycosides(),          carbapenems()) example_isolates$bacteria <- mo_fullname(example_isolates$mo) example_isolates[which(mo_is_gram_negative() &                          mo_is_intrinsic_resistant(ab = \"cefotax\")),                  c(\"bacteria\", aminoglycosides(), carbapenems())]"},{"path":"https://msberends.github.io/AMR/index.html","id":"calculating-resistance-per-group","dir":"","previous_headings":"Practical examples","what":"Calculating resistance per group","title":"Antimicrobial Resistance Data Analysis","text":"","code":"library(AMR) library(dplyr)  out <- example_isolates %>%   # group by hospital code:   group_by(hospital_id) %>%   # calculate AMR using resistance(), over all aminoglycosides   # and polymyxins:   summarise(across(c(aminoglycosides(), polymyxins()),             resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species Catalogue Life List Prokaryotic names Standing Nomenclature (manual) Interpreting raw MIC disk diffusion values, based latest CLSI EUCAST guidelines (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antibiotic (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters R/SI (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed GitHub two ways: Manually, using: Automatically, using rOpenSci R-universe platform, adding R-universe address list repositories (‘repos’): , can install update AMR package like official release (e.g., using install.packages(\"AMR\") RStudio via Tools > Check Package Updates…).","code":"install.packages(\"remotes\") # if you haven't already remotes::install_github(\"msberends/AMR\") options(repos = c(getOption(\"repos\"),                   msberends = \"https://msberends.r-universe.dev\"))"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible :","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"functions -called 'Deprecated'. removed future release. Using functions give warning name function replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The AMR Package — AMR","text":"AMR free, open-source independent R package simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible antimicrobial resistance data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. installing package, R knows ~71,000 distinct microbial species ~570 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-NET, LOINC SNOMED CT), knows valid R/SI MIC values. supports data format, including WHONET/EARS-Net data. package fully independent R package works Windows, macOS Linux versions R since R-3.0.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice University Medical Center Groningen. R package actively maintained free software; can freely use distribute personal commercial (patent) purposes terms GNU General Public License version 2.0 (GPL-2), published Free Software Foundation. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species Catalogue Life List Prokaryotic names Standing Nomenclature Interpreting raw MIC disk diffusion values, based latest CLSI EUCAST guidelines Retrieving antimicrobial drug names, doses forms administration clinical health care records Determining first isolates used AMR data analysis Calculating antimicrobial resistance Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) Calculating (empirical) susceptibility mono therapy combination therapies Predicting future antimicrobial resistance using regression models Getting properties microorganism (Gram stain, species, genus family) Getting properties antibiotic (name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) Plotting antimicrobial resistance Applying EUCAST expert rules Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code Machine reading EUCAST CLSI guidelines 2011-2020 translate MIC values disk diffusion diameters R/SI Principal component analysis AMR","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"The AMR Package — AMR","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"read-more-on-our-website-","dir":"Reference","previous_headings":"","what":"Read more on Our Website!","title":"The AMR Package — AMR","text":"website https://msberends.github.io/AMR/ can find comprehensive tutorial conduct AMR data analysis, complete documentation functions example analysis using WHONET data.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"contact-us","dir":"Reference","previous_headings":"","what":"Contact Us","title":"The AMR Package — AMR","text":"suggestions, comments questions, please contact us : Matthijs S. Berends  m.s.berends [] umcg [dot] nl  University Groningen Department Medical Microbiology Infection Prevention  University Medical Center Groningen  Post Office Box 30001  9700 RB Groningen  Netherlands https://msberends.github.io/AMR/ found bug, please file new issue : https://github.com/msberends/AMR/issues","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class <ab> #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\"  ab_tradenames(\"flucloxacillin\") #> [1] \"floxacillin\"          \"floxapen\"             \"floxapen sodium salt\" #> [4] \"fluclox\"              \"flucloxacilina\"       \"flucloxacillin\"       #> [7] \"flucloxacilline\"      \"flucloxacillinum\"     \"fluorochloroxacillin\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antibiotic results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"data.frame 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 28 different antibiotics. can lookup abbreviations antibiotics data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antibiotic class, using .rsi().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"head(WHONET) #>   Identification number Specimen number Organism         Country #> 1            fe41d7bafa            1748      SPN         Belgium #> 2            91f175ec37            1767      eco The Netherlands #> 3            cc4015056e            1343      eco The Netherlands #> 4            e864b692f5            1894      MAP         Denmark #> 5            3d051fe345            1739      PVU         Belgium #> 6            c80762a08d            1846      103 The Netherlands #>                               Laboratory  Last name First name Sex Age #> 1         National Laboratory of Belgium       Abel         B.   F  68 #> 2 National Laboratory of The Netherlands  Delacroix         F.   M  89 #> 3 National Laboratory of The Netherlands   Steensen         F.   M  85 #> 4         National Laboratory of Denmark Beyersdorf         L.   M  62 #> 5         National Laboratory of Belgium     Hummel         W.   M  86 #> 6 National Laboratory of The Netherlands  Eikenboom         J.   F  53 #>   Age category Date of admission Specimen date Specimen type #> 1        55-74        2005-01-12    2005-01-30         Urine #> 2          75+        2006-07-30    2006-08-16         Urine #> 3          75+        2014-03-05    2014-03-14         Urine #> 4        55-74        2014-10-22    2014-11-01         Urine #> 5          75+        2016-10-28    2016-11-17         Urine #> 6        25-54        2017-12-21    2018-01-02         Urine #>   Specimen type (Numeric)  Reason Isolate number Organism type Serotype #> 1                       2 Unknown           1748      Bacteria          #> 2                       2 Unknown           1767      Bacteria          #> 3                       2 Unknown           1343      Bacteria          #> 4                       2 Unknown           1894      Bacteria          #> 5                       2 Unknown           1739      Bacteria          #> 6                       2 Unknown           1846      Bacteria          #>   Beta-lactamase  ESBL Carbapenemase MRSA screening test #> 1          FALSE FALSE         FALSE               FALSE #> 2          FALSE FALSE         FALSE               FALSE #> 3          FALSE FALSE         FALSE               FALSE #> 4          FALSE FALSE         FALSE               FALSE #> 5          FALSE FALSE         FALSE               FALSE #> 6          FALSE FALSE         FALSE               FALSE #>   Inducible clindamycin resistance Comment Date of data entry AMP_ND10 AMC_ED20 #> 1                            FALSE                 2005-01-30        S        S #> 2                            FALSE                 2006-08-16     <NA>        S #> 3                            FALSE                 2014-03-14        S        S #> 4                            FALSE                 2014-11-01        R     <NA> #> 5                            FALSE                 2016-11-17        R        R #> 6                            FALSE                 2018-01-02        S        S #>   TZP_ED30 FEP_ED30 CTX_ED5 FOX_ED30 CAZ_ED10 CRO_ED30 CIP_ED5 AMK_ED30 #> 1        S     <NA>    <NA>     <NA>        R     <NA>    <NA>     <NA> #> 2     <NA>     <NA>    <NA>     <NA>        R     <NA>    <NA>     <NA> #> 3        S     <NA>    <NA>     <NA>     <NA>     <NA>    <NA>     <NA> #> 4     <NA>     <NA>    <NA>     <NA>        R     <NA>    <NA>     <NA> #> 5        S     <NA>       S        R        S        S       S     <NA> #> 6        S     <NA>       S        S        S        S       S     <NA> #>   GEN_ED10 TOB_ED10 SXT_ED1.2 IPM_ND10 PEN_ND1 AMP_ND2 AMC_ND2 CHL_ND30 VAN_ED5 #> 1        R        R         S     <NA>       S       S       S     <NA>       S #> 2        S     <NA>         R     <NA>       R    <NA>       S     <NA>       S #> 3     <NA>     <NA>      <NA>     <NA>       S       S       S     <NA>    <NA> #> 4        R        R      <NA>     <NA>       R       R    <NA>     <NA>    <NA> #> 5        S        S         R        I       R       R       R     <NA>       R #> 6        S        S         S        S       R       S       S     <NA>       R #>   OXA_ED1 ERY_ED15 CLI_ED2 TCY_ED30 RIF_ED5 PEN_EE AMP_EE CRO_EE CIP_EE #> 1    <NA>        S    <NA>        S    <NA>      S      S   <NA>   <NA> #> 2    <NA>        S       S        S    <NA>      R   <NA>   <NA>   <NA> #> 3    <NA>     <NA>       S     <NA>    <NA>      S      S   <NA>   <NA> #> 4    <NA>     <NA>       S     <NA>    <NA>      R      R   <NA>   <NA> #> 5    <NA>        R    <NA>        R       R      R      R      S      S #> 6    <NA>        R    <NA>     <NA>       R      R      S      S      S"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_ab = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antibiotics data set translate antibiotic abbreviations , using ab_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial agents. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line,  # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tds\") #> [[1]] #> Class <ab> #> [1] AMX #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class <ab> #> [1] AMX CIP #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\"   #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\"                if (require(\"dplyr\")) {   tibble(clinical_text = c(\"given 400mg cipro and 500 mg amox\",                            \"started on doxy iv today\")) %>%      mutate(abx_codes = ab_from_text(clinical_text),            abx_doses = ab_from_text(clinical_text, type = \"doses\"),            abx_admin = ab_from_text(clinical_text, type = \"admin\"),            abx_coll = ab_from_text(clinical_text, collapse = \"|\"),            abx_coll_names = ab_from_text(clinical_text,                                          collapse = \"|\",                                          translate_ab = \"name\"),            abx_coll_doses = ab_from_text(clinical_text,                                          type = \"doses\",                                          collapse = \"|\"),            abx_coll_admin = ab_from_text(clinical_text,                                          type = \"admin\",                                          collapse = \"|\"))  } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #>   clinical_text          abx_c…¹ abx_d…² abx_a…³ abx_c…⁴ abx_c…⁵ abx_c…⁶ abx_c…⁷ #>   <chr>                  <list>  <list>  <list>  <chr>   <chr>   <chr>   <chr>   #> 1 given 400mg cipro and… <ab>    <dbl>   <chr>   CIP|AMX Ciprof… 400|500 NA      #> 2 started on doxy iv to… <ab>    <dbl>   <chr>   DOX     Doxycy… NA      iv      #> # … with abbreviated variable names ¹​abx_codes, ²​abx_doses, ³​abx_admin, #> #   ⁴​abx_coll, ⁵​abx_coll_names, ⁶​abx_coll_doses, ⁷​abx_coll_admin # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antibiotics data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  ab_cid(x, ...)  ab_synonyms(x, ...)  ab_tradenames(x, ...)  ab_group(x, language = get_AMR_locale(), ...)  ab_atc(x, only_first = FALSE, ...)  ab_atc_group1(x, language = get_AMR_locale(), ...)  ab_atc_group2(x, language = get_AMR_locale(), ...)  ab_loinc(x, ...)  ab_ddd(x, administration = \"oral\", ...)  ab_ddd_units(x, administration = \"oral\", ...)  ab_info(x, language = get_AMR_locale(), ...)  ab_url(x, open = FALSE, ...)  ab_property(x, property = \"name\", language = get_AMR_locale(), ...)  set_ab_names(   data,   ...,   property = \"name\",   language = get_AMR_locale(),   snake_case = NULL )"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic code .ab() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: variables select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antibiotics data set: vector_or(colnames(antibiotics), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\")       # \"Amoxicillin\" #> [1] \"Amoxicillin\" ab_atc(\"AMX\")        # \"J01CA04\" (ATC code from the WHO) #> [1] \"J01CA04\" ab_cid(\"AMX\")        # 33613 (Compound ID from PubChem) #> [1] 33613 ab_synonyms(\"AMX\")   # a list with brand names of amoxicillin #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicillin\"        #> [10] \"amoxicilline\"       \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"anemolin\"           \"aspenil\"            #> [19] \"biomox\"             \"bristamox\"          \"cemoxin\"            #> [22] \"clamoxyl\"           \"delacillin\"         \"dispermox\"          #> [25] \"efpenix\"            \"flemoxin\"           \"hiconcil\"           #> [28] \"histocillin\"        \"hydroxyampicillin\"  \"ibiamox\"            #> [31] \"imacillin\"          \"lamoxy\"             \"metafarma capsules\" #> [34] \"metifarma capsules\" \"moxacin\"            \"moxatag\"            #> [37] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [40] \"robamox\"            \"sawamox pm\"         \"tolodina\"           #> [43] \"unicillin\"          \"utimox\"             \"vetramox\"           ab_tradenames(\"AMX\") # same #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicillin\"        #> [10] \"amoxicilline\"       \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"anemolin\"           \"aspenil\"            #> [19] \"biomox\"             \"bristamox\"          \"cemoxin\"            #> [22] \"clamoxyl\"           \"delacillin\"         \"dispermox\"          #> [25] \"efpenix\"            \"flemoxin\"           \"hiconcil\"           #> [28] \"histocillin\"        \"hydroxyampicillin\"  \"ibiamox\"            #> [31] \"imacillin\"          \"lamoxy\"             \"metafarma capsules\" #> [34] \"metifarma capsules\" \"moxacin\"            \"moxatag\"            #> [37] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [40] \"robamox\"            \"sawamox pm\"         \"tolodina\"           #> [43] \"unicillin\"          \"utimox\"             \"vetramox\"           ab_group(\"AMX\")      # \"Beta-lactams/penicillins\" #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") # \"Beta-lactam antibacterials, penicillins\" #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") # \"Penicillins with extended spectrum\" #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\")        # link to the official WHO page #>                                                           Amoxicillin  #> \"https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no\"   # smart lowercase tranformation ab_name(x = c(\"AMC\", \"PLB\"))  # \"Amoxicillin/clavulanic acid\" \"Polymyxin B\" #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\"                 ab_name(x = c(\"AMC\", \"PLB\"),         tolower = TRUE)       # \"amoxicillin/clavulanic acid\" \"polymyxin B\" #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\"                  # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\")       #  1.5 #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") # \"g\" #> [1] \"g\" ab_ddd(\"AMX\", \"iv\")         #  3 #> [1] 3 ab_ddd_units(\"AMX\", \"iv\")   # \"g\" #> [1] \"g\"  ab_info(\"AMX\")       # all properties as a list #> $ab #> [1] \"AMX\" #>  #> $cid #> [1] 33613 #>  #> $name #> [1] \"Amoxicillin\" #>  #> $group #> [1] \"Beta-lactams/penicillins\" #>  #> $atc #> [1] \"J01CA04\" #>  #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #>  #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #>  #> $tradenames #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicillin\"        #> [10] \"amoxicilline\"       \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"anemolin\"           \"aspenil\"            #> [19] \"biomox\"             \"bristamox\"          \"cemoxin\"            #> [22] \"clamoxyl\"           \"delacillin\"         \"dispermox\"          #> [25] \"efpenix\"            \"flemoxin\"           \"hiconcil\"           #> [28] \"histocillin\"        \"hydroxyampicillin\"  \"ibiamox\"            #> [31] \"imacillin\"          \"lamoxy\"             \"metafarma capsules\" #> [34] \"metifarma capsules\" \"moxacin\"            \"moxatag\"            #> [37] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [40] \"robamox\"            \"sawamox pm\"         \"tolodina\"           #> [43] \"unicillin\"          \"utimox\"             \"vetramox\"           #>  #> $loinc #> [1] \"16365-9\" \"25274-2\" \"3344-9\"  \"80133-2\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\")           # ATC code of AMP (ampicillin) #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\")     # Drug group of ampicillins ATC code #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\")  # LOINC codes of ampicillin #> [1] \"21066-6\" \"3355-5\"  \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\")      # \"Ampicillin\" (using LOINC) #> [1] \"Ampicillin\" ab_name(6249)           # \"Ampicillin\" (using CID) #> [1] \"Ampicillin\" ab_name(\"J01CA01\")      # \"Ampicillin\" (using ATC) #> [1] \"Ampicillin\"  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\"  # use set_ab_names() for renaming columns colnames(example_isolates) #>  [1] \"date\"            \"hospital_id\"     \"ward_icu\"        \"ward_clinical\"   #>  [5] \"ward_outpatient\" \"age\"             \"gender\"          \"patient_id\"      #>  [9] \"mo\"              \"PEN\"             \"OXA\"             \"FLC\"             #> [13] \"AMX\"             \"AMC\"             \"AMP\"             \"TZP\"             #> [17] \"CZO\"             \"FEP\"             \"CXM\"             \"FOX\"             #> [21] \"CTX\"             \"CAZ\"             \"CRO\"             \"GEN\"             #> [25] \"TOB\"             \"AMK\"             \"KAN\"             \"TMP\"             #> [29] \"SXT\"             \"NIT\"             \"FOS\"             \"LNZ\"             #> [33] \"CIP\"             \"MFX\"             \"VAN\"             \"TEC\"             #> [37] \"TCY\"             \"TGC\"             \"DOX\"             \"ERY\"             #> [41] \"CLI\"             \"AZM\"             \"IPM\"             \"MEM\"             #> [45] \"MTR\"             \"CHL\"             \"COL\"             \"MUP\"             #> [49] \"RIF\"             colnames(set_ab_names(example_isolates)) #>  [1] \"date\"                          \"hospital_id\"                   #>  [3] \"ward_icu\"                      \"ward_clinical\"                 #>  [5] \"ward_outpatient\"               \"age\"                           #>  [7] \"gender\"                        \"patient_id\"                    #>  [9] \"mo\"                            \"benzylpenicillin\"              #> [11] \"oxacillin\"                     \"flucloxacillin\"                #> [13] \"amoxicillin\"                   \"amoxicillin_clavulanic_acid\"   #> [15] \"ampicillin\"                    \"piperacillin_tazobactam\"       #> [17] \"cefazolin\"                     \"cefepime\"                      #> [19] \"cefuroxime\"                    \"cefoxitin\"                     #> [21] \"cefotaxime\"                    \"ceftazidime\"                   #> [23] \"ceftriaxone\"                   \"gentamicin\"                    #> [25] \"tobramycin\"                    \"amikacin\"                      #> [27] \"kanamycin\"                     \"trimethoprim\"                  #> [29] \"trimethoprim_sulfamethoxazole\" \"nitrofurantoin\"                #> [31] \"fosfomycin\"                    \"linezolid\"                     #> [33] \"ciprofloxacin\"                 \"moxifloxacin\"                  #> [35] \"vancomycin\"                    \"teicoplanin\"                   #> [37] \"tetracycline\"                  \"tigecycline\"                   #> [39] \"doxycycline\"                   \"erythromycin\"                  #> [41] \"clindamycin\"                   \"azithromycin\"                  #> [43] \"imipenem\"                      \"meropenem\"                     #> [45] \"metronidazole\"                 \"chloramphenicol\"               #> [47] \"colistin\"                      \"mupirocin\"                     #> [49] \"rifampicin\"                    colnames(set_ab_names(example_isolates, NIT:VAN)) #>  [1] \"date\"            \"hospital_id\"     \"ward_icu\"        \"ward_clinical\"   #>  [5] \"ward_outpatient\" \"age\"             \"gender\"          \"patient_id\"      #>  [9] \"mo\"              \"PEN\"             \"OXA\"             \"FLC\"             #> [13] \"AMX\"             \"AMC\"             \"AMP\"             \"TZP\"             #> [17] \"CZO\"             \"FEP\"             \"CXM\"             \"FOX\"             #> [21] \"CTX\"             \"CAZ\"             \"CRO\"             \"GEN\"             #> [25] \"TOB\"             \"AMK\"             \"KAN\"             \"TMP\"             #> [29] \"SXT\"             \"nitrofurantoin\"  \"fosfomycin\"      \"linezolid\"       #> [33] \"ciprofloxacin\"   \"moxifloxacin\"    \"vancomycin\"      \"TEC\"             #> [37] \"TCY\"             \"TGC\"             \"DOX\"             \"ERY\"             #> [41] \"CLI\"             \"AZM\"             \"IPM\"             \"MEM\"             #> [45] \"MTR\"             \"CHL\"             \"COL\"             \"MUP\"             #> [49] \"RIF\"             # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     set_ab_names() %>%     head()        # this does the same:   example_isolates %>%     rename_with(set_ab_names)%>%     head()      # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\")%>%     head()       example_isolates %>%    set_ab_names(where(is.rsi)) %>%    colnames()      example_isolates %>%    set_ab_names(NIT:VAN) %>%    colnames() } #>  [1] \"date\"            \"hospital_id\"     \"ward_icu\"        \"ward_clinical\"   #>  [5] \"ward_outpatient\" \"age\"             \"gender\"          \"patient_id\"      #>  [9] \"mo\"              \"PEN\"             \"OXA\"             \"FLC\"             #> [13] \"AMX\"             \"AMC\"             \"AMP\"             \"TZP\"             #> [17] \"CZO\"             \"FEP\"             \"CXM\"             \"FOX\"             #> [21] \"CTX\"             \"CAZ\"             \"CRO\"             \"GEN\"             #> [25] \"TOB\"             \"AMK\"             \"KAN\"             \"TMP\"             #> [29] \"SXT\"             \"nitrofurantoin\"  \"fosfomycin\"      \"linezolid\"       #> [33] \"ciprofloxacin\"   \"moxifloxacin\"    \"vancomycin\"      \"TEC\"             #> [37] \"TCY\"             \"TGC\"             \"DOX\"             \"ERY\"             #> [41] \"CLI\"             \"AZM\"             \"IPM\"             \"MEM\"             #> [45] \"MTR\"             \"CHL\"             \"COL\"             \"MUP\"             #> [49] \"RIF\"             # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (defaults today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000)  # add ages df$age <- age(df$birth_date)  # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE)  # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\")  df #>    birth_date age age_exact age_at_y2k #> 1  1947-08-14  75  75.01918         52 #> 2  1934-01-26  88  88.56712         65 #> 3  1961-06-01  61  61.22192         38 #> 4  1994-03-19  28  28.42466          5 #> 5  1992-01-08  30  30.61644          7 #> 6  1946-07-13  76  76.10685         53 #> 7  1984-02-02  38  38.54795         15 #> 8  1951-09-30  70  70.89041         48 #> 9  1980-03-07  42  42.45753         19 #> 10 1991-06-28  31  31.14795          8"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x , defaults age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)  # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+  0-49 50+  50+  0-49 0-49 #> Levels: 0-49 < 50+  # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19  0-19  0-19  50+   20-49 50+   50+   20-49 20-49 #> Levels: 0-19 < 20-49 < 50+  # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+  # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+  # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+  # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\")) {   example_isolates %>%     filter_first_isolate() %>%     filter(mo == as.mo(\"E. coli\")) %>%     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_rsi(x = \"age_group\",                minimum = 0,                x.title = \"Age Group\",                title = \"Ciprofloxacin resistance per age group\") } #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this.  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antibiotic Selectors — antibiotic_class_selectors","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions allow filtering rows selecting columns based antibiotic test results specific antibiotic class group, without need define columns antibiotic abbreviations. short, column name resembles antimicrobial agent, picked functions matches pharmaceutical class: \"cefazolin\", \"CZO\" \"J01DB04\" picked cephalosporins().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"ab_class(ab_class, only_rsi_columns = FALSE, only_treatable = TRUE, ...)  ab_selector(filter, only_rsi_columns = FALSE, only_treatable = TRUE, ...)  aminoglycosides(only_rsi_columns = FALSE, only_treatable = TRUE, ...)  aminopenicillins(only_rsi_columns = FALSE, ...)  antifungals(only_rsi_columns = FALSE, ...)  antimycobacterials(only_rsi_columns = FALSE, ...)  betalactams(only_rsi_columns = FALSE, only_treatable = TRUE, ...)  carbapenems(only_rsi_columns = FALSE, only_treatable = TRUE, ...)  cephalosporins(only_rsi_columns = FALSE, ...)  cephalosporins_1st(only_rsi_columns = FALSE, ...)  cephalosporins_2nd(only_rsi_columns = FALSE, ...)  cephalosporins_3rd(only_rsi_columns = FALSE, ...)  cephalosporins_4th(only_rsi_columns = FALSE, ...)  cephalosporins_5th(only_rsi_columns = FALSE, ...)  fluoroquinolones(only_rsi_columns = FALSE, ...)  glycopeptides(only_rsi_columns = FALSE, ...)  lincosamides(only_rsi_columns = FALSE, ...)  lipoglycopeptides(only_rsi_columns = FALSE, ...)  macrolides(only_rsi_columns = FALSE, ...)  oxazolidinones(only_rsi_columns = FALSE, ...)  penicillins(only_rsi_columns = FALSE, ...)  polymyxins(only_rsi_columns = FALSE, only_treatable = TRUE, ...)  streptogramins(only_rsi_columns = FALSE, ...)  quinolones(only_rsi_columns = FALSE, ...)  tetracyclines(only_rsi_columns = FALSE, ...)  trimethoprims(only_rsi_columns = FALSE, ...)  ureidopenicillins(only_rsi_columns = FALSE, ...)  administrable_per_os(only_rsi_columns = FALSE, ...)  administrable_iv(only_rsi_columns = FALSE, ...)  not_intrinsic_resistant(   only_rsi_columns = FALSE,   col_mo = NULL,   version_expertrules = 3.3,   ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"ab_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antibiotics data set searched (case-insensitive) value. only_rsi_columns logical indicate whether columns class <rsi> must selected (defaults FALSE), see .rsi() only_treatable logical indicate whether agents laboratory tests excluded (defaults TRUE), gentamicin-high (GEH) imipenem/EDTA (IPE) ... ignored, place allow future extensions filter expression evaluated antibiotics data set, name %like% \"trim\" col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"(internally) character vector column names, additional class \"ab_selector\"","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions can used data set calls selecting columns filtering rows. heavily inspired Tidyverse selection helpers everything(), also work base R dplyr verbs. Nonetheless, convenient use dplyr functions select(), filter() summarise(), see Examples. columns data functions called searched known antibiotic names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antibiotics data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. ab_class() function can used filter/select manually defined antibiotic class. searches results antibiotics data set within columns group, atc_group1 atc_group2. ab_selector() function can used internally filter antibiotics data set results, see Examples. allows filtering (part ) certain name, /group name even minimum DDDs oral treatment. function yields highest flexibility, also least user-friendly, since requires hard-coded filter set. administrable_per_os() administrable_iv() functions also rely antibiotics data set - antibiotic columns matched DDD (defined daily dose) resp. oral IV treatment available antibiotics data set. not_intrinsic_resistant() function can used select antibiotic columns pose intrinsic resistance microorganisms data set. example, data set contains microorganism codes names E. coli K. pneumoniae contains column \"vancomycin\", column removed (rather, unselected) using function. currently applies 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"full-list-of-supported-antibiotic-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antibiotic) classes","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"aminoglycosides() can select:  amikacin (AMK), amikacin/fosfomycin (AKF), amphotericin B-high (AMH), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB) tobramycin-high (TOH) aminopenicillins() can select:  amoxicillin (AMX) ampicillin (AMP) antifungals() can select:  5-fluorocytosine (FCT), amphotericin B (AMB), anidulafungin (ANI), butoconazole (), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC) voriconazole (VOR) antimycobacterials() can select:  4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), prothionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), simvastatin/fenofibrate (SMF), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone/isoniazid (THI1), tiocarlide (TCR) viomycin (VIO) betalactams() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefaloridine (RID), cefamandole (MAN), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/enzyme inhibitor (CEI), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), cefuroxime (CXM), cefuroxime axetil (CXA), cephalexin (LEX), cephalothin (CEP), cephapirin (HAP), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), methicillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), phenethicillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) carbapenems() can select:  biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA) tebipenem (TBP) cephalosporins() can select:  cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefaloridine (RID), cefamandole (MAN), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/enzyme inhibitor (CEI), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), cefuroxime (CXM), cefuroxime axetil (CXA), cephalexin (LEX), cephalothin (CEP), cephapirin (HAP), cephradine (CED), latamoxef (LTM) loracarbef (LOR) cephalosporins_1st() can select:  cefacetrile (CAC), cefadroxil (CFR), cefaloridine (RID), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefroxadine (CRD), ceftezole (CTL), cephalexin (LEX), cephalothin (CEP), cephapirin (HAP) cephradine (CED) cephalosporins_2nd() can select:  cefaclor (CEC), cefamandole (MAN), cefmetazole (CMZ), cefonicid (CID), ceforanide (CND), cefotetan (CTT), cefotiam (CTF), cefoxitin (FOX), cefoxitin screening (FOX1), cefprozil (CPR), cefuroxime (CXM), cefuroxime axetil (CXA) loracarbef (LOR) cephalosporins_3rd() can select:  cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefetamet (CAT), cefetamet pivoxil (CPI), cefixime (CFM), cefmenoxime (CMX), cefodizime (DIZ), cefoperazone (CFP), cefoperazone/sulbactam (CSL), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotiam hexetil (CHE), cefovecin (FOV), cefpimizole (CFZ), cefpiramide (CPM), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefsulodin (CFS), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftriaxone (CRO) latamoxef (LTM) cephalosporins_4th() can select:  cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetecol (CCL), cefoselis (CSE), cefozopran (ZOP), cefpirome (CPO) cefquinome (CEQ) cephalosporins_5th() can select:  ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/enzyme inhibitor (CEI) ceftolozane/tazobactam (CZT) fluoroquinolones() can select:  besifloxacin (BES), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nifuroquine (NIF), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) glycopeptides() can select:  avoparcin (AVO), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN) vancomycin-macromethod (VAM) lincosamides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), clindamycin (CLI), gamithromycin (GAM), kitasamycin (KIT), lincomycin (LIN), meleumycin (MEL), nafithromycin (ZWK), pirlimycin (PRL), primycin (PRM), solithromycin (SOL), tildipirosin (TIP), tilmicosin (TIL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) lipoglycopeptides() can select:  dalbavancin (DAL), oritavancin (ORI) telavancin (TLV) macrolides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), azithromycin (AZM), clarithromycin (CLR), dirithromycin (DIR), erythromycin (ERY), flurithromycin (FLR1), gamithromycin (GAM), josamycin (JOS), kitasamycin (KIT), meleumycin (MEL), midecamycin (MID), miocamycin (MCM), nafithromycin (ZWK), oleandomycin (OLE), pirlimycin (PRL), primycin (PRM), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) oxazolidinones() can select:  cadazolid (CDZ), cycloserine (CYC), linezolid (LNZ), tedizolid (TZD) thiacetazone (THA) penicillins() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), carbenicillin (CRB), carindacillin (CRN), cefepime/nacubactam (FNC), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), epicillin (EPC), flucloxacillin (FLC), hetacillin (HET), lenampicillin (LEN), mecillinam (MEC), metampicillin (MTM), methicillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), phenethicillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) polymyxins() can select:  colistin (COL), polymyxin B (PLB) polymyxin B/polysorbate 80 (POP) quinolones() can select:  besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), flumequine (FLM), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pipemidic acid (PPA), piromidic acid (PIR), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rosoxacin (ROS), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) streptogramins() can select:  pristinamycin (PRI) quinupristin/dalfopristin (QDA) tetracyclines() can select:  cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), tetracycline (TCY) tigecycline (TGC) trimethoprims() can select:  brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP) trimethoprim/sulfamethoxazole (SXT) ureidopenicillins() can select:  azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP) piperacillin/tazobactam (TZP)","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. df <- example_isolates[ , c(\"hospital_id\", \"mo\",                             \"AMP\", \"AMC\", \"TZP\", \"CXM\", \"CRO\", \"GEN\",                             \"TOB\", \"COL\", \"IPM\", \"MEM\", \"TEC\", \"VAN\")]  # base R ------------------------------------------------------------------  # select columns 'IPM' (imipenem) and 'MEM' (meropenem) df[, carbapenems()] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <rsi> <rsi> #>  1 NA    NA    #>  2 NA    NA    #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 NA    NA    #>  8 NA    NA    #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' df[, c(\"mo\", aminoglycosides())] #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin) and 'TOB' #>   (tobramycin) #> # A tibble: 2,000 × 3 #>    mo           GEN   TOB   #>    <mo>         <rsi> <rsi> #>  1 B_ESCHR_COLI NA    NA    #>  2 B_ESCHR_COLI NA    NA    #>  3 B_STPHY_EPDR NA    NA    #>  4 B_STPHY_EPDR NA    NA    #>  5 B_STPHY_EPDR NA    NA    #>  6 B_STPHY_EPDR NA    NA    #>  7 B_STPHY_AURS NA    S     #>  8 B_STPHY_AURS NA    S     #>  9 B_STPHY_EPDR NA    NA    #> 10 B_STPHY_EPDR NA    NA    #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # select only antibiotic columns with DDDs for oral treatment df[, administrable_per_os()] #> ℹ For `administrable_per_os()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'CXM' (cefuroxime), 'COL' (colistin) and #>   'VAN' (vancomycin) #> # A tibble: 2,000 × 5 #>    AMP   AMC   CXM   COL   VAN   #>    <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 NA    I     I     NA    R     #>  2 NA    I     I     NA    R     #>  3 NA    NA    R     R     S     #>  4 NA    NA    R     R     S     #>  5 NA    NA    R     R     S     #>  6 NA    NA    R     R     S     #>  7 R     S     S     R     S     #>  8 R     S     S     R     S     #>  9 NA    NA    R     R     S     #> 10 NA    NA    S     R     S     #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # filter using any() or all() df[any(carbapenems() == \"R\"), ] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 14 #>    hospital…¹ mo           AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>      <mo>         <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 A          B_ENTRC_FACM NA    NA    NA    R     R     R     R     R     R     #>  2 A          B_ENTRC_FACM NA    NA    NA    R     R     R     R     R     R     #>  3 B          B_STNTR_MLTP R     R     R     R     R     R     R     R     R     #>  4 B          B_ENTRC      NA    NA    R     NA    NA    NA    NA    R     R     #>  5 B          B_ENTRC      NA    NA    R     NA    NA    NA    NA    R     R     #>  6 B          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  7 D          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  8 A          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  9 A          B_PROTS_MRBL NA    S     S     S     S     S     S     R     R     #> 10 A          B_PROTS_MRBL NA    S     S     S     S     S     S     R     R     #> # … with 45 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names subset(df, any(carbapenems() == \"R\")) #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 14 #>    hospital…¹ mo           AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>      <mo>         <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 A          B_ENTRC_FACM NA    NA    NA    R     R     R     R     R     R     #>  2 A          B_ENTRC_FACM NA    NA    NA    R     R     R     R     R     R     #>  3 B          B_STNTR_MLTP R     R     R     R     R     R     R     R     R     #>  4 B          B_ENTRC      NA    NA    R     NA    NA    NA    NA    R     R     #>  5 B          B_ENTRC      NA    NA    R     NA    NA    NA    NA    R     R     #>  6 B          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  7 D          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  8 A          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  9 A          B_PROTS_MRBL NA    S     S     S     S     S     S     R     R     #> 10 A          B_PROTS_MRBL NA    S     S     S     S     S     S     R     R     #> # … with 45 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): df[any(carbapenems()), ] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"R\", \"S\" or \"I\" #> # A tibble: 962 × 14 #>    hospital…¹ mo           AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>      <mo>         <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 D          B_ESCHR_COLI NA    I     NA    S     S     NA    S     NA    S     #>  2 D          B_ESCHR_COLI NA    I     NA    S     S     NA    S     NA    S     #>  3 B          B_PROTS_MRBL NA    I     NA    S     S     NA    NA    R     S     #>  4 B          B_PROTS_MRBL NA    I     NA    S     S     NA    NA    R     S     #>  5 B          B_SERRT_MRCS R     R     NA    R     NA    NA    NA    R     S     #>  6 B          B_SERRT_MRCS R     R     NA    R     NA    NA    NA    R     S     #>  7 B          B_SERRT_MRCS R     R     NA    R     NA    NA    NA    R     S     #>  8 D          B_KLBSL_PNMN R     I     NA    S     S     S     S     NA    S     #>  9 D          B_KLBSL_PNMN R     I     NA    S     S     S     S     NA    S     #> 10 C          B_ESCHR_COLI R     R     NA    R     S     S     NA    NA    S     #> # … with 952 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names df[all(carbapenems()), ] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"R\", \"S\" or \"I\" #> # A tibble: 756 × 14 #>    hospita…¹ mo            AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>     <mo>          <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 C         B_STRPT_GRPB  S     S     S     S     S     R     R     R     S     #>  2 B         B_STRPT_PYGN  S     S     S     S     S     R     R     R     S     #>  3 B         B_STRPT_GRPA  S     S     S     S     S     R     R     R     S     #>  4 B         B_STRPT_GRPA  S     S     S     S     S     R     R     R     S     #>  5 D         B_STRPT_GRPB  S     S     S     S     S     R     R     R     S     #>  6 B         B_ESCHR_COLI  R     R     S     S     S     S     S     NA    S     #>  7 B         B_ENTRBC_CLOC R     R     S     R     NA    S     S     NA    S     #>  8 B         B_ENTRBC_CLOC R     R     S     R     NA    S     S     NA    S     #>  9 B         B_ESCHR_COLI  NA    S     S     S     S     S     S     NA    S     #> 10 B         B_ESCHR_COLI  NA    S     S     S     S     S     S     NA    S     #> # … with 746 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  # filter with multiple antibiotic selectors using c() df[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin) and 'TOB' #>   (tobramycin) #> # A tibble: 26 × 14 #>    hospital…¹ mo           AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>      <mo>         <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 B          B_STNTR_MLTP R     R     R     R     R     R     R     R     R     #>  2 B          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  3 D          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  4 A          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  5 D          B_STNTR_MLTP R     R     R     R     R     R     R     R     R     #>  6 D          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  7 B          B_STPHY_CONS R     R     R     R     R     R     R     R     R     #>  8 A          B_STPHY_CONS R     R     R     R     R     R     R     R     R     #>  9 D          B_STPHY_CONS R     R     R     R     R     R     R     R     R     #> 10 D          B_STPHY_CONS R     R     R     R     R     R     R     R     R     #> # … with 16 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  # filter + select in one go: get penicillins in carbapenems-resistant strains df[any(carbapenems() == \"R\"), penicillins()] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For `penicillins()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid) and 'TZP' (piperacillin/tazobactam) #> # A tibble: 55 × 3 #>    AMP   AMC   TZP   #>    <rsi> <rsi> <rsi> #>  1 NA    NA    NA    #>  2 NA    NA    NA    #>  3 R     R     R     #>  4 NA    NA    R     #>  5 NA    NA    R     #>  6 NA    NA    R     #>  7 NA    NA    R     #>  8 NA    NA    R     #>  9 NA    S     S     #> 10 NA    S     S     #> # … with 45 more rows #> # ℹ Use `print(n = ...)` to see more rows  # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: df[, penicillins() & administrable_per_os()] #> ℹ For `penicillins()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid) and 'TZP' (piperacillin/tazobactam) #> ℹ For `administrable_per_os()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'CXM' (cefuroxime), 'COL' (colistin) and #>   'VAN' (vancomycin) #> # A tibble: 2,000 × 2 #>    AMP   AMC   #>    <rsi> <rsi> #>  1 NA    I     #>  2 NA    I     #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 R     S     #>  8 R     S     #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # ab_selector() applies a filter in the `antibiotics` data set and is thus very # flexible. For instance, to select antibiotic columns with an oral DDD of at # least 1 gram: df[, ab_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For `ab_selector(oral_ddd > 1 & oral_units == \"g\")` using columns 'AMP' #>   (ampicillin), 'AMC' (amoxicillin/clavulanic acid) and 'VAN' (vancomycin) #> # A tibble: 2,000 × 3 #>    AMP   AMC   VAN   #>    <rsi> <rsi> <rsi> #>  1 NA    I     R     #>  2 NA    I     R     #>  3 NA    NA    S     #>  4 NA    NA    S     #>  5 NA    NA    S     #>  6 NA    NA    S     #>  7 R     S     S     #>  8 R     S     S     #>  9 NA    NA    S     #> 10 NA    NA    S     #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # dplyr ------------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {    # get AMR for all aminoglycosides e.g., per hospital:   df %>%     group_by(hospital_id) %>%      summarise(across(aminoglycosides(), resistance))        # You can combine selectors with '&' to be more specific:   df %>%     select(penicillins() & administrable_per_os())      # get AMR for only drugs that matter - no intrinsic resistance:   df %>%     filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>%      group_by(hospital_id) %>%      summarise(across(not_intrinsic_resistant(), resistance))        # get susceptibility for antibiotics whose name contains \"trim\":   df %>%     filter(first_isolate()) %>%      group_by(hospital_id) %>%      summarise(across(ab_selector(name %like% \"trim\"), susceptibility))    # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):   df %>%      select(carbapenems())        # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':   df %>%      select(mo, aminoglycosides())       # any() and all() work in dplyr's filter() too:  df %>%      filter(any(aminoglycosides() == \"R\"),            all(cephalosporins_2nd() == \"R\"))       # also works with c():  df %>%      filter(any(c(carbapenems(), aminoglycosides()) == \"R\"))       # not setting any/all will automatically apply all():  df %>%      filter(aminoglycosides() == \"R\")        # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):   df %>%      select(mo, ab_class(\"mycobact\"))        # get bug/drug combinations for only glycopeptides in Gram-positives:   df %>%      filter(mo_is_gram_positive()) %>%      select(mo, glycopeptides()) %>%      bug_drug_combinations() %>%     format()        data.frame(some_column = \"some_value\",              J01CA01 = \"S\") %>%   # ATC code of ampicillin     select(penicillins())         # only the 'J01CA01' column will be selected             # with recent versions of dplyr this is all equal:   x <- df[carbapenems() == \"R\", ]   y <- df %>% filter(carbapenems() == \"R\")   z <- df %>% filter(if_all(carbapenems(), ~.x == \"R\"))   identical(x, y)   identical(y, z) } #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin) and 'TOB' #>   (tobramycin) #> ℹ For `penicillins()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid) and 'TZP' (piperacillin/tazobactam) #> ℹ For `administrable_per_os()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'CXM' (cefuroxime), 'COL' (colistin) and #>   'VAN' (vancomycin) #> ℹ Using column 'mo' as input for `mo_genus()` #> ℹ For `not_intrinsic_resistant()` removing columns 'TEC' (teicoplanin) and #>   'VAN' (vancomycin) #> Warning: Introducing NA: only 23 results available for COL in group: hospital_id = #> \"C\" (`minimum` = 30). #> Error in filter(., first_isolate()): Problem while computing `..1 = first_isolate()`. #> Caused by error: #> ! in first_isolate(): `col_date` must be set # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 566 Antimicrobial Drugs — antibiotics","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"Two data sets containing antibiotics/antimycotics antivirals. Use .ab() one ab_* functions retrieve values antibiotics data set. Three identifiers included data set: antibiotic ID (ab, primarily used package) defined WHONET/EARS-Net, ATC code (atc) defined , Compound ID (cid) found PubChem. properties data set derived one codes. Note drugs multiple ATC codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics  antivirals"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"object class data.frame 102 rows 9 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antibiotics-data-set-a-data-frame-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antibiotics data set: a data.frame with 464 observations and 14 variables:","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available cid Compound ID found PubChem name Official name used WHONET/EARS-Net group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antibiotic susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 170 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 145 drugs iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial agent. Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antivirals-data-set-a-data-frame-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a data.frame with 102 observations and 9 variables:","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem name Official name used WHONET/EARS-Net atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived Compound ID (cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"data sets available 'flat files' use even without R - can find files : https://github.com/msberends/AMR/raw/main/data-raw/antibiotics.txt https://github.com/msberends/AMR/raw/main/data-raw/antivirals.txt Files R format (preserved data structure) can found : https://github.com/msberends/AMR/raw/main/data/antibiotics.rda https://github.com/msberends/AMR/raw/main/data/antivirals.rda","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"","code":"head(antibiotics) #>    ab      cid                  name                    group #> 1 AMA     4649 4-aminosalicylic acid       Antimycobacterials #> 2 FCT     3366      5-fluorocytosine Antifungals/antimycotics #> 3 ACM  6450012     Acetylmidecamycin  Macrolides/lincosamides #> 4 ASP 49787020      Acetylspiramycin  Macrolides/lincosamides #> 5 ALS     8954    Aldesulfone sodium     Other antibacterials #> 6 AMK    37768              Amikacin          Aminoglycosides #>                         atc                          atc_group1 #> 1                   J04AA01 Drugs for treatment of tuberculosis #> 2                   D01AE21         Antifungals for topical use #> 3                        NA                                <NA> #> 4                        NA                                <NA> #> 5                   J04BA03        Drugs for treatment of lepra #> 6 D06AX12, J01GB06, S01AA21       Aminoglycoside antibacterials #>                            atc_group2          abbreviations #> 1 Aminosalicylic acid and derivatives                        #> 2   Other antifungals for topical use  5flc, fcu, fluo, fluy #> 3                                <NA>                        #> 4                                <NA>                        #> 5        Drugs for treatment of lepra                        #> 6               Other aminoglycosides ak, ami, amik, amk, an #>                                                                                                                                                                                                                                                                                                                                                                synonyms #> 1 aminopar, aminosalicylic, aminosalicylic acid, aminosalyl, aminox, apacil, deapasil, entepas, ferrosan, gabbropas, helipidyl, hellipidyl, neopasalate, osacyl, pamacyl, pamisyl, paramycin, parasal, parasalicil, parasalindon, pasalon, pasara, pascorbic, pasdium, paser granules, paskalium, pasmed, pasnodia, pasolac, propasa, rezipas, teebacin, wln: zr cq dvq #> 2                                                                                                                                                                                                                          alcobon, ancobon, ancotil, ancotyl, flucitosina, flucystine, flucytosin, flucytosine, flucytosinum, flucytosone, fluocytosine, fluorcytosine #> 3                                                                                                                                                                                                                                                                                                                                                                       #> 4                                                                                                                                                                                                                                                                                                                        acetylspiramycin, foromacidin b, spiramycin ii #> 5                                                                                                                                                 adesulfone sodium, aldapsone, aldesulfona sodica, aldesulfone, aldesulfone sodique, aldesulfone sodium, diamidin, diasone, diasone sodium, diazon, novotrone, sodium aldesulphone, sodium sulfoxone, sulfoxone sodium #> 6                                                                                                                                            amicacin, amikacillin, amikacin, amikacin base, amikacin dihydrate, amikacin sulfate, amikacina, amikacine, amikacinum, amikavet, amikin, amiklin, amikozit, amukin, arikace, briclin, lukadin, mikavir, pierami, potentox #>   oral_ddd oral_units iv_ddd iv_units #> 1    12.00          g     NA     <NA> #> 2       NA       <NA>     NA     <NA> #> 3       NA       <NA>     NA     <NA> #> 4       NA       <NA>     NA     <NA> #> 5     0.33          g     NA     <NA> #> 6       NA       <NA>      1        g #>                                                                                                                                loinc #> 1                                                                                                                                    #> 2                                                                                10974-4, 23805-5, 25142-1, 25143-9, 3639-2, 46218-4 #> 3                                                                                                                                    #> 4                                                                                                                                    #> 5                                                                                                                                    #> 6 13546-7, 15098-7, 17798-0, 31097-9, 31098-7, 31099-5, 3319-1, 3320-9, 3321-7, 35669-1, 50802-8, 50803-6, 56628-1, 59378-0, 80972-3 head(antivirals) #>       atc       cid               name #> 1 J05AF06    441300           Abacavir #> 2 J05AB01 135398513          Aciclovir #> 3 J05AF08     60871 Adefovir dipivoxil #> 4 J05AE05     65016         Amprenavir #> 5 J05AP06  16076883        Asunaprevir #> 6 J05AE08    148192         Atazanavir #>                                                            atc_group #> 1         Nucleoside and nucleotide reverse transcriptase inhibitors #> 2 Nucleosides and nucleotides excl. reverse transcriptase inhibitors #> 3         Nucleoside and nucleotide reverse transcriptase inhibitors #> 4                                                Protease inhibitors #> 5                         Antivirals for treatment of HCV infections #> 6                                                Protease inhibitors #>                                                                                                                                                                                                                                                                                                          synonyms #> 1                                                                                                                                                                                                                                                                              Abacavir, Abacavir sulfate, Ziagen #> 2 Acicloftal, Aciclovier, Aciclovir, Aciclovirum, Activir, AcycloFoam, Acycloguanosine, Acyclovir, Acyclovir Lauriad, ACYCLOVIR SODIUM, Avirax, Cargosil, Cyclovir, Genvir, Gerpevir, Hascovir, Herpevir, Maynar, Poviral, Sitavig, Sitavir, Vipral, Virolex, Viropump, Virorax, Zovirax, Zovirax topical, Zyclir #> 3                                                                                                                                 Adefovir di ester, Adefovir dipivoxil, Adefovir Dipivoxil, Adefovir dipivoxyl, Adefovir pivoxil, Adefovirdipivoxl, Bisadenine, BISADENINE, BisPMEA, Hepsera, Preveon, YouHeDing #> 4                                                                                                                                                                                                                                                             Agenerase, Amprenavir, Amprenavirum, Prozei, Vertex #> 5                                                                                                                                                                                                                                                                                           Asunaprevir, Sunvepra #> 6                                                                                                                                                                                                                                                      Atazanavir, Atazanavir Base, Latazanavir, Reyataz, Zrivada #>   oral_ddd oral_units iv_ddd iv_units #> 1      0.6          g     NA     <NA> #> 2      4.0          g      4        g #> 3     10.0         mg     NA     <NA> #> 4      1.2          g     NA     <NA> #> 5       NA       <NA>     NA     <NA> #> 6      0.3          g     NA     <NA>"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antibiotic code one antibiotics. data set antibiotics searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.ab(x)"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic code name can retrieved single input value. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antibiotics data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial agents. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class <ab> #> [1] ERY as.ab(\"J 01 FA 01\") #> Class <ab> #> [1] ERY as.ab(\"Erythromycin\") #> Class <ab> #> [1] ERY as.ab(\"eryt\") #> Class <ab> #> [1] ERY as.ab(\"   eryt 123\") #> Class <ab> #> [1] ERY as.ab(\"ERYT\") #> Class <ab> #> [1] ERY as.ab(\"ERY\") #> Class <ab> #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class <ab> #> [1] ERY as.ab(\"Erythrocin\")   # trade name #> Class <ab> #> [1] ERY as.ab(\"Romycin\")      # trade name #> Class <ab> #> [1] ERY  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\")     # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\")    # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\"  # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\")    # \"Erythromycin\" #> [1] \"Erythromycin\" ab_name(\"eryt\")       # \"Erythromycin\" #> [1] \"Erythromycin\" # \\donttest{ if (require(\"dplyr\")) {    # you can quickly rename <rsi> columns using dplyr >= 1.0.0:   example_isolates %>%     rename_with(as.ab, where(is.rsi))     } #> # A tibble: 2,000 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  4 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  5 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  6 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  7 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  8 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  9 2002-01-16 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 10 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #> # … with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # }"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 6 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE)  NA_disk_  is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values RSI values .rsi(). supports guidelines EUCAST CLSI. NA_disk_ missing value new <disk> class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame(microorganism = \"Escherichia coli\",                  AMP = 20,                  CIP = 14,                  GEN = 18,                  TOB = 16) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of  5 variables: #>  $ microorganism: chr \"Escherichia coli\" #>  $ AMP          : 'disk' int 20 #>  $ CIP          : 'disk' int 14 #>  $ GEN          : 'disk' int 18 #>  $ TOB          : 'disk' int 16  # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) {   df %>% mutate(across(AMP:TOB, as.disk)) } #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli  20  14  18  16 # }  # interpret disk values, see ?as.rsi as.rsi(x = as.disk(18),        mo = \"Strep pneu\",  # `mo` will be coerced with as.mo()        ab = \"ampicillin\",  # and `ab` with as.ab()        guideline = \"EUCAST\") #> => Interpreting disk diffusion zones of 'ampicillin' according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.rsi(df, uti = TRUE) #> ℹ Using column 'microorganism' as input for `col_mo`. #> => Interpreting disk diffusion zones of column 'AMP' (AMP, ampicillin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'CIP' (CIP, ciprofloxacin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (GEN, gentamicin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'TOB' (TOB, tobramycin) #>    according to EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli   S   R   S   S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE)  NA_mic_  is.mic(x)  # S3 method for mic droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed .mic logical indicate whether <mic> class kept, defaults FALSE ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts decimal numbers. Bare mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values RSI values, use .rsi() MIC values. supports guidelines EUCAST (2011-2022) CLSI (2011-2022). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers:   makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.:   following generic functions implemented MIC class: !, !=, %%, %/%, &, *, +, -, /, <, <=, ==, >, >=, ^, |, abs(), acos(), acosh(), (), (), asin(), asinh(), atan(), atanh(), ceiling(), cos(), cosh(), cospi(), cummax(), cummin(), cumprod(), cumsum(), digamma(), exp(), expm1(), floor(), gamma(), lgamma(), log(), log1p(), log2(), log10(), max(), mean(), min(), prod(), range(), round(), sign(), signif(), sin(), sinh(), sinpi(), sqrt(), sum(), tan(), tanh(), tanpi(), trigamma() trunc(). functions stats package also implemented: median(), quantile(), mad(), IQR(), fivenum(). Also, boxplot.stats() supported. Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain <mic> class. NA_mic_ missing value new <mic> class, analogous e.g. base R's NA_character_.","code":"x <- random_mic(10) x #> Class <mic> #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16  is.factor(x) #> [1] TRUE  x[1] * 2 #> [1] 32  median(x) #> [1] 26 x[x > 4] #> Class <mic> #> [1] 16    8     8     64    >=128 32    32    16  df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #>        x hospital #> 1     16        A #> 5     64        A #> 6  >=128        A #> 8     32        A #> 9     32        A #> 10    16        A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class <mic> #> [1] >=32    1       1       1       8       <=0.128 8       16      16      is.mic(mic_data) #> [1] TRUE  # this can also coerce combined MIC/RSI values: as.mic(\"<=0.002; S\") #> Class <mic> #> [1] <=0.002  # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1]  0.128  1.000  8.000 16.000 32.000 quantile(mic_data) #>     0%    25%    50%    75%   100%  #>  0.128  1.000  8.000 16.000 32.000  all(mic_data < 512) #> [1] TRUE  # interpret MIC values as.rsi(x = as.mic(2),        mo = as.mo(\"Streptococcus pneumoniae\"),        ab = \"AMX\",        guideline = \"EUCAST\") #> => Interpreting MIC values of 'AMX' (AMX, amoxicillin) according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] R as.rsi(x = as.mic(c(0.01, 2, 4, 8)),        mo = as.mo(\"Streptococcus pneumoniae\"),        ab = \"AMX\",        guideline = \"EUCAST\") #> => Interpreting MIC values of 'AMX' (AMX, amoxicillin) according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] S R R R  # plot MIC values, see ?plot plot(mic_data)  plot(mic_data, mo = \"E. coli\", ab = \"cipro\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this.  autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") #> Error in autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\"): could not find function \"autoplot\" autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch #> Error in autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\"): could not find function \"autoplot\" autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\") # Ukrainian #> Error in autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\"): could not find function \"autoplot\""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to a Microorganism Code — as.mo","title":"Transform Input to a Microorganism Code — as.mo","text":"Use function determine valid microorganism code (mo). Determination done using intelligent rules complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"as.mo(   x,   Becker = FALSE,   Lancefield = FALSE,   allow_uncertain = TRUE,   reference_df = get_mo_source(),   ignore_pattern = getOption(\"AMR_ignore_pattern\"),   language = get_AMR_locale(),   info = interactive(),   ... )  is.mo(x)  mo_failures()  mo_uncertainties()  mo_renamed()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to a Microorganism Code — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (1,2,3). excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (4). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. allow_uncertain number 0 (\"none\") 3 (\"\"), TRUE (= 2) FALSE (= 0) indicate whether input checked less probable results, see Details reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced, defaults TRUE interactive mode ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to a Microorganism Code — as.mo","text":"character  vector additional class mo","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"general-info","dir":"Reference","previous_headings":"","what":"General Info","title":"Transform Input to a Microorganism Code — as.mo","text":"microorganism (MO) code package (class: mo) human readable typically looks like examples:   Values coerced considered 'unknown' get MO code UNKNOWN. Use mo_* functions get properties based returned code, see Examples. algorithm uses data Catalogue Life (see ) one source (see microorganisms). .mo() function uses several coercion rules fast logical results. assesses input matching criteria following order: Human pathogenic prevalence: function  starts prevalent microorganisms, followed less prevalent ones; Taxonomic kingdom: function starts determining Bacteria, Fungi, Protozoa, others; Breakdown input values identify possible matches. lead effect e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first.","code":"Code               Full name   ---------------    --------------------------------------   B_KLBSL            Klebsiella   B_KLBSL_PNMN       Klebsiella pneumoniae   B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis   |   |    |    |   |   |    |    |   |   |    |    \\---> subspecies, a 4-5 letter acronym   |   |    \\----> species, a 4-5 letter acronym   |   \\----> genus, a 5-7 letter acronym   \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),                             C (Chromista), F (Fungi), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"addition, .mo() function can differentiate four levels uncertainty guess valid results: Uncertainty level 0: additional rules applied; Uncertainty level 1: allow previously accepted (now invalid) taxonomic names minor spelling errors; Uncertainty level 2: allow level 1, strip values brackets, inverse words input, strip text elements end keeping least two elements; Uncertainty level 3: allow level 1 2, strip text elements end, allow part taxonomic name. level uncertainty can set using argument allow_uncertain. default allow_uncertain = TRUE, equal uncertainty level 2. Using allow_uncertain = FALSE equal uncertainty level 0 skip rules. can also use e.g. .mo(..., allow_uncertain = 1) allow level 1 uncertainty. default setting (allow_uncertain = TRUE, level 2), examples lead valid results: \"Streptococcus group B (known S. agalactiae)\". text brackets removed warning thrown result Streptococcus group B (B_STRPT_GRPB) needs review. \"S. aureus - please mind: MRSA\". last word stripped, function try find match. , second last word stripped, etc. , warning thrown result Staphylococcus aureus (B_STPHY_AURS) needs review. \"Fluoroquinolone-resistant Neisseria gonorrhoeae\". first word stripped, function try find match. warning thrown result Neisseria gonorrhoeae (B_NESSR_GNRR) needs review. three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"intelligent rules consider prevalence microorganisms humans grouped three groups, available prevalence columns microorganisms microorganisms.old data sets. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). 2019. Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. Emergence coagulase-negative staphylococci 2020. Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC serological differentiation human groups hemolytic streptococci. 1933. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Catalogue Life: 2019 Annual Checklist, http://www.catalogueoflife.org List Prokaryotic names Standing Nomenclature (5 October 2021), doi:10.1099/ijsem.0.004332 US Edition SNOMED CT 1 September 2020, retrieved Public Health Information Network Vocabulary Access Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function, counts insertion, deletion substitution 1 needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based experience several microbiological laboratories Netherlands conjunction international reports pathogen prevalence. Group 1 (prevalent microorganisms) consists microorganisms taxonomic class Gammaproteobacteria taxonomic genus Enterococcus, Staphylococcus Streptococcus. group consequently contains common Gram-negative bacteria, Pseudomonas Legionella species within order Enterobacterales. Group 2 consists microorganisms taxonomic phylum Proteobacteria, Firmicutes, Actinobacteria Sarcomastigophora, taxonomic genus Absidia, Acremonium, Actinotignum, Alternaria, Anaerosalibacter, Apophysomyces, Arachnia, Aspergillus, Aureobacterium, Aureobasidium, Bacteroides, Basidiobolus, Beauveria, Blastocystis, Branhamella, Calymmatobacterium, Candida, Capnocytophaga, Catabacter, Chaetomium, Chryseobacterium, Chryseomonas, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Cryptococcus, Curvularia, Exophiala, Exserohilum, Flavobacterium, Fonsecaea, Fusarium, Fusobacterium, Hendersonula, Hypomyces, Koserella, Lelliottia, Leptosphaeria, Leptotrichia, Malassezia, Malbranchea, Mortierella, Mucor, Mycocentrospora, Mycoplasma, Nectria, Ochroconis, Oidiodendron, Phoma, Piedraia, Pithomyces, Pityrosporum, Prevotella, Pseudallescheria, Rhizomucor, Rhizopus, Rhodotorula, Scolecobasidium, Scopulariopsis, Scytalidium, Sporobolomyces, Stachybotrys, Stomatococcus, Treponema, Trichoderma, Trichophyton, Trichosporon, Tritirachium Ureaplasma. Group 3 consists microorganisms. characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.079\\), less prevalent microorganism humans), although latter alphabetically come first. Since AMR version 1.8.1, common microorganism abbreviations ignored determining matching score. abbreviations currently: AIEC, ATEC, BORSA, CRSM, DAEC, EAEC, EHEC, EIEC, EPEC, ETEC, GISA, MRPA, MRSA, MRSE, MSSA, MSSE, NMEC, PISP, PRSP, STEC, UPEC, VISA, VISP, VRE, VRSA VRSP.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Transform Input to a Microorganism Code — as.mo","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(\"sau\") # WHONET code #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"stau\") #> ℹ Function `as.mo()` is uncertain about \"stau\" (assuming Staphylococcus #>   aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"STAU\") #> ℹ Function `as.mo()` is uncertain about \"STAU\" (assuming Staphylococcus #>   aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"staaur\") #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"S. aureus\") #> ℹ Function `as.mo()` is uncertain about \"S. aureus\" (assuming #>   Staphylococcus aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"S aureus\") #> ℹ Function `as.mo()` is uncertain about \"S aureus\" (assuming Staphylococcus #>   aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"Staphylococcus aureus\") #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"Staphylococcus aureus (MRSA)\") #> ℹ Function `as.mo()` is uncertain about \"Staphylococcus aureus (MRSA)\" #>   (assuming Staphylococcus aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"Zthafilokkoockus oureuz\") # handles incorrect spelling #> ℹ Function `as.mo()` is uncertain about \"Zthafilokkoockus oureuz\" (assuming #>   Staphylococcus aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"MRSA\")    # Methicillin Resistant S. aureus #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"VISA\")    # Vancomycin Intermediate S. aureus #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"VRSA\")    # Vancomycin Resistant S. aureus #> Class <mo> #> [1] B_STPHY_AURS as.mo(115329001) # SNOMED CT code #> Class <mo> #> [1] B_STPHY_AURS  # Dyslexia is no problem - these all work: as.mo(\"Ureaplasma urealyticum\") #> Class <mo> #> [1] B_URPLS_URLY as.mo(\"Ureaplasma urealyticus\") #> ℹ Function `as.mo()` is uncertain about \"Ureaplasma urealyticus\" (assuming #>   Ureaplasma urealyticum). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_URPLS_URLY as.mo(\"Ureaplasmium urealytica\") #> ℹ Function `as.mo()` is uncertain about \"Ureaplasmium urealytica\" (assuming #>   Ureaplasma urealyticum). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_URPLS_URLY as.mo(\"Ureaplazma urealitycium\") #> ℹ Function `as.mo()` is uncertain about \"Ureaplazma urealitycium\" (assuming #>   Ureaplasma urealyticum). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_URPLS_URLY  as.mo(\"Streptococcus group A\") #> Class <mo> #> [1] B_STRPT_GRPA as.mo(\"GAS\") # Group A Streptococci #> Class <mo> #> [1] B_STRPT_GRPA as.mo(\"GBS\") # Group B Streptococci #> Class <mo> #> [1] B_STRPT_GRPB  as.mo(\"S. epidermidis\")                 # will remain species: B_STPHY_EPDR #> Class <mo> #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE)  # will not remain species: B_STPHY_CONS #> Class <mo> #> [1] B_STPHY_CONS  as.mo(\"S. pyogenes\")                    # will remain species: B_STRPT_PYGN #> Class <mo> #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class <mo> #> [1] B_STRPT_GRPA  # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\")                           # returns \"Escherichia\" #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Escherichia\" mo_gramstain(\"E. coli\")                       # returns \"Gram negative\" #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"E. coli\", \"vanco\") # returns TRUE #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #>   'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #>   will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing R/SI values. transforms input new class rsi, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"","code":"as.rsi(x, ...)  NA_rsi_  is.rsi(x)  is.rsi.eligible(x, threshold = 0.05)  # S3 method for mic as.rsi(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = \"EUCAST\",   uti = FALSE,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::rsi_translation,   ... )  # S3 method for disk as.rsi(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = \"EUCAST\",   uti = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::rsi_translation,   ... )  # S3 method for data.frame as.rsi(   x,   ...,   col_mo = NULL,   guideline = \"EUCAST\",   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::rsi_translation )"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"object class rsi (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .rsi() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline rsi_translation data set), supports EUCAST (2011-2022) CLSI (2011-2022), see Details uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .rsi() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults rsi_translation data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure rsi_translation data set (column names column types). Please note guideline argument ignored reference_data manually set. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"Ordered factor new class <rsi>","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"NA_rsi_ missing value new <rsi> class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":".rsi() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values R/SI interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, R/SI interpretation can done easily either:   Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, R/SI interpretation can done easily either:   interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .rsi(your_data).","code":"your_data %>% mutate_if(is.mic, as.rsi)             # until dplyr 1.0.0 your_data %>% mutate(across(where(is.mic), as.rsi)) # since dplyr 1.0.0 your_data %>% mutate_if(is.disk, as.rsi)             # until dplyr 1.0.0 your_data %>% mutate(across(where(is.disk), as.rsi)) # since dplyr 1.0.0"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2011-2022) CLSI (2011-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"using .rsi(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"repository package contains machine-readable version guidelines. CSV file consisting 20,369 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial agent microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"function .rsi() detects input contains class <rsi>. input data.frame, iterates columns returns logical vector. function .rsi.eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"","code":"example_isolates #> # A tibble: 2,000 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  4 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  5 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  6 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  7 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  8 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  9 2002-01-16 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 10 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #> # … with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names summary(example_isolates) # see all R/SI results at a glance #>       date            hospital_id  ward_icu       ward_clinical   #>  Min.   :2002-01-02   A:321       Mode :logical   Mode :logical   #>  1st Qu.:2005-07-31   B:663       FALSE:1354      FALSE:536       #>  Median :2009-07-31   C:254       TRUE :646       TRUE :1464      #>  Mean   :2009-11-20   D:762                                       #>  3rd Qu.:2014-05-30                                               #>  Max.   :2017-12-28                                               #>  ward_outpatient      age           gender           patient_id        #>  Mode :logical   Min.   : 0.00   Length:2000        Length:2000        #>  FALSE:1880      1st Qu.:63.00   Class :character   Class :character   #>  TRUE :120       Median :74.00   Mode  :character   Mode  :character   #>                  Mean   :70.69                                         #>                  3rd Qu.:82.00                                         #>                  Max.   :97.00                                         #>       mo                  PEN                    OXA                #>  Class :mo             Class:rsi              Class:rsi             #>  <NA>  :0              %R   :73.7% (n=1201)   %R   :31.2% (n=114)   #>  Unique:90             %SI  :26.3% (n=428)    %SI  :68.8% (n=251)   #>  #1    :B_ESCHR_COLI   - %S :25.6% (n=417)    - %S :68.8% (n=251)   #>  #2    :B_STPHY_CONS   - %I : 0.7% (n=11)     - %I : 0.0% (n=0)     #>  #3    :B_STPHY_AURS                                                #>     FLC                   AMX                   AMC                 #>  Class:rsi             Class:rsi             Class:rsi              #>  %R   :29.5% (n=278)   %R   :59.6% (n=804)   %R   :23.7% (n=446)    #>  %SI  :70.5% (n=665)   %SI  :40.4% (n=546)   %SI  :76.3% (n=1433)   #>  - %S :70.5% (n=665)   - %S :40.2% (n=543)   - %S :71.4% (n=1342)   #>  - %I : 0.0% (n=0)     - %I : 0.2% (n=3)     - %I : 4.8% (n=91)     #>                                                                     #>     AMP                   TZP                   CZO                #>  Class:rsi             Class:rsi             Class:rsi             #>  %R   :59.6% (n=804)   %R   :12.6% (n=126)   %R   :44.6% (n=199)   #>  %SI  :40.4% (n=546)   %SI  :87.4% (n=875)   %SI  :55.4% (n=247)   #>  - %S :40.2% (n=543)   - %S :86.1% (n=862)   - %S :54.9% (n=245)   #>  - %I : 0.2% (n=3)     - %I : 1.3% (n=13)    - %I : 0.4% (n=2)     #>                                                                    #>     FEP                   CXM                    FOX                #>  Class:rsi             Class:rsi              Class:rsi             #>  %R   :14.2% (n=103)   %R   :26.3% (n=470)    %R   :27.4% (n=224)   #>  %SI  :85.8% (n=621)   %SI  :73.7% (n=1319)   %SI  :72.6% (n=594)   #>  - %S :85.6% (n=620)   - %S :72.5% (n=1297)   - %S :71.6% (n=586)   #>  - %I : 0.1% (n=1)     - %I : 1.2% (n=22)     - %I : 1.0% (n=8)     #>                                                                     #>     CTX                   CAZ                    CRO                #>  Class:rsi             Class:rsi              Class:rsi             #>  %R   :15.5% (n=146)   %R   :66.5% (n=1204)   %R   :15.5% (n=146)   #>  %SI  :84.5% (n=797)   %SI  :33.5% (n=607)    %SI  :84.5% (n=797)   #>  - %S :84.4% (n=796)   - %S :33.5% (n=607)    - %S :84.4% (n=796)   #>  - %I : 0.1% (n=1)     - %I : 0.0% (n=0)      - %I : 0.1% (n=1)     #>                                                                     #>     GEN                    TOB                   AMK                #>  Class:rsi              Class:rsi             Class:rsi             #>  %R   :24.6% (n=456)    %R   :34.4% (n=465)   %R   :63.7% (n=441)   #>  %SI  :75.4% (n=1399)   %SI  :65.6% (n=886)   %SI  :36.3% (n=251)   #>  - %S :74.0% (n=1372)   - %S :65.1% (n=879)   - %S :36.3% (n=251)   #>  - %I : 1.5% (n=27)     - %I : 0.5% (n=7)     - %I : 0.0% (n=0)     #>                                                                     #>     KAN                    TMP                   SXT                 #>  Class:rsi              Class:rsi             Class:rsi              #>  %R   :100.0% (n=471)   %R   :38.1% (n=571)   %R   :20.5% (n=361)    #>  %SI  : 0.0% (n=0)      %SI  :61.9% (n=928)   %SI  :79.5% (n=1398)   #>  - %S : 0.0% (n=0)      - %S :61.2% (n=918)   - %S :79.1% (n=1392)   #>  - %I : 0.0% (n=0)      - %I : 0.7% (n=10)    - %I : 0.3% (n=6)      #>                                                                      #>     NIT                   FOS                   LNZ                #>  Class:rsi             Class:rsi             Class:rsi             #>  %R   :17.1% (n=127)   %R   :42.2% (n=148)   %R   :69.3% (n=709)   #>  %SI  :82.9% (n=616)   %SI  :57.8% (n=203)   %SI  :30.7% (n=314)   #>  - %S :76.0% (n=565)   - %S :57.8% (n=203)   - %S :30.7% (n=314)   #>  - %I : 6.9% (n=51)    - %I : 0.0% (n=0)     - %I : 0.0% (n=0)     #>                                                                    #>     CIP                    MFX                   VAN                 #>  Class:rsi              Class:rsi             Class:rsi              #>  %R   :16.2% (n=228)    %R   :33.6% (n=71)    %R   :38.3% (n=712)    #>  %SI  :83.8% (n=1181)   %SI  :66.4% (n=140)   %SI  :61.7% (n=1149)   #>  - %S :78.9% (n=1112)   - %S :64.5% (n=136)   - %S :61.7% (n=1149)   #>  - %I : 4.9% (n=69)     - %I : 1.9% (n=4)     - %I : 0.0% (n=0)      #>                                                                      #>     TEC                   TCY                   TGC                #>  Class:rsi             Class:rsi             Class:rsi             #>  %R   :75.7% (n=739)   %R   :29.8% (n=357)   %R   :12.7% (n=101)   #>  %SI  :24.3% (n=237)   %SI  :70.3% (n=843)   %SI  :87.3% (n=697)   #>  - %S :24.3% (n=237)   - %S :68.3% (n=820)   - %S :87.3% (n=697)   #>  - %I : 0.0% (n=0)     - %I : 1.9% (n=23)    - %I : 0.0% (n=0)     #>                                                                    #>     DOX                   ERY                    CLI                #>  Class:rsi             Class:rsi              Class:rsi             #>  %R   :27.7% (n=315)   %R   :57.2% (n=1084)   %R   :61.2% (n=930)   #>  %SI  :72.3% (n=821)   %SI  :42.8% (n=810)    %SI  :38.8% (n=590)   #>  - %S :71.7% (n=814)   - %S :42.3% (n=801)    - %S :38.6% (n=586)   #>  - %I : 0.6% (n=7)     - %I : 0.5% (n=9)      - %I : 0.3% (n=4)     #>                                                                     #>     AZM                    IPM                   MEM                #>  Class:rsi              Class:rsi             Class:rsi             #>  %R   :57.2% (n=1084)   %R   : 6.2% (n=55)    %R   : 5.9% (n=49)    #>  %SI  :42.8% (n=810)    %SI  :93.8% (n=834)   %SI  :94.1% (n=780)   #>  - %S :42.3% (n=801)    - %S :92.7% (n=824)   - %S :94.1% (n=780)   #>  - %I : 0.5% (n=9)      - %I : 1.1% (n=10)    - %I : 0.0% (n=0)     #>                                                                     #>     MTR                  CHL                   COL                 #>  Class:rsi            Class:rsi             Class:rsi              #>  %R   :14.7% (n=5)    %R   :21.4% (n=33)    %R   :81.2% (n=1331)   #>  %SI  :85.3% (n=29)   %SI  :78.6% (n=121)   %SI  :18.8% (n=309)    #>  - %S :85.3% (n=29)   - %S :78.6% (n=121)   - %S :18.8% (n=309)    #>  - %I : 0.0% (n=0)    - %I : 0.0% (n=0)     - %I : 0.0% (n=0)      #>                                                                    #>     MUP                   RIF                #>  Class:rsi             Class:rsi             #>  %R   : 5.9% (n=16)    %R   :69.6% (n=698)   #>  %SI  :94.1% (n=254)   %SI  :30.4% (n=305)   #>  - %S :93.0% (n=251)   - %S :30.2% (n=303)   #>  - %I : 1.1% (n=3)     - %I : 0.2% (n=2)     #>                                               # For INTERPRETING disk diffusion and MIC values -----------------------         # a whole data set, even with combined MIC values and disk zones df <- data.frame(microorganism = \"Escherichia coli\",                  AMP = as.mic(8),                  CIP = as.mic(0.256),                  GEN = as.disk(18),                  TOB = as.disk(16),                  NIT = as.mic(32),                  ERY = \"R\") as.rsi(df) #> ℹ Using column 'microorganism' as input for `col_mo`. #> => Interpreting MIC values of column 'AMP' (AMP, ampicillin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting MIC values of column 'CIP' (CIP, ciprofloxacin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (GEN, gentamicin) #>    according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of column 'TOB' (TOB, tobramycin) #>    according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of column 'NIT' (NIT, nitrofurantoin) according #>    to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of nitrofurantoin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Assigning class <rsi> to already clean column 'ERY' (erythromycin)... #>  OK. #>      microorganism AMP CIP GEN TOB  NIT ERY #> 1 Escherichia coli   S   I   S   S <NA>   R  # for single values as.rsi(x = as.mic(2),        mo = as.mo(\"S. pneumoniae\"),        ab = \"AMP\",        guideline = \"EUCAST\") #> ℹ Function `as.mo()` is uncertain about \"S. pneumoniae\" (assuming #>   Streptococcus pneumoniae). Run `mo_uncertainties()` to review this. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] R  as.rsi(x = as.disk(18),        mo = \"Strep pneu\",  # `mo` will be coerced with as.mo()        ab = \"ampicillin\",  # and `ab` with as.ab()        guideline = \"EUCAST\") #> => Interpreting disk diffusion zones of 'ampicillin' according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] R  # \\donttest{ # the dplyr way if (require(\"dplyr\")) {   df %>% mutate_if(is.mic, as.rsi)   df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi)   df %>% mutate(across(where(is.mic), as.rsi))   df %>% mutate_at(vars(AMP:TOB), as.rsi)   df %>% mutate(across(AMP:TOB, as.rsi))     df %>%     mutate_at(vars(AMP:TOB), as.rsi, mo = .$microorganism)        # to include information about urinary tract infections (UTI)   data.frame(mo = \"E. coli\",              NIT = c(\"<= 2\", 32),              from_the_bladder = c(TRUE, FALSE)) %>%     as.rsi(uti = \"from_the_bladder\")        data.frame(mo = \"E. coli\",              NIT = c(\"<= 2\", 32),              specimen = c(\"urine\", \"blood\")) %>%     as.rsi() # automatically determines urine isolates      df %>%     mutate_at(vars(AMP:NIT), as.rsi, mo = \"E. coli\", uti = TRUE)   } #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'NIT' (NIT, nitrofurantoin) based on column #>    'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of nitrofurantoin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'NIT' (NIT, nitrofurantoin) based on column #>    'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of nitrofurantoin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'NIT' (NIT, nitrofurantoin) based on column #>    'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of nitrofurantoin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) according #>    to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) according #>    to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> ℹ Using column 'mo' as input for `col_mo`. #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> => Interpreting MIC values of column 'NIT' (NIT, nitrofurantoin) according #>    to EUCAST 2022... #>  OK. #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use `as.rsi(uti = FALSE)` to prevent this. #> ℹ Using column 'mo' as input for `col_mo`. #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> => Interpreting MIC values of column 'NIT' (NIT, nitrofurantoin) according #>    to EUCAST 2022... #>  OK. #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'NIT' (NIT, nitrofurantoin) according to #>    EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB NIT ERY #> 1 Escherichia coli   S   I   S   S   S   R  # For CLEANING existing R/SI values ------------------------------------  as.rsi(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in `as.rsi()`: 3 results truncated (50%) that were invalid antimicrobial #> interpretations: \"A\", \"B\" and \"C\" #> Class <rsi> #> [1] S    I    R    <NA> <NA> <NA> as.rsi(\"<= 0.002; S\") # will return \"S\" #> Class <rsi> #> [1] S rsi_data <- as.rsi(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.rsi(rsi_data) #> [1] TRUE plot(rsi_data)    # for percentages  barplot(rsi_data) # for frequencies   # the dplyr way if (require(\"dplyr\")) {   example_isolates %>%     mutate_at(vars(PEN:RIF), as.rsi)   # same:      example_isolates %>%     as.rsi(PEN:RIF)     # fastest way to transform all columns with already valid AMR results to class `rsi`:   example_isolates %>%     mutate_if(is.rsi.eligible, as.rsi)        # since dplyr 1.0.0, this can also be:    # example_isolates %>%   #   mutate(across(where(is.rsi.eligible), as.rsi)) } #> # A tibble: 2,000 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  4 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  5 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  6 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  7 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  8 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  9 2002-01-16 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 10 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #> # … with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property(   atc_code,   property,   administration = \"O\",   url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\",   url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" )  atc_online_groups(atc_code, ...)  atc_online_ddd(atc_code, ...)  atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) {    # oral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"O\")   atc_online_ddd(ab_atc(\"amox\"))    # parenteral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"P\")    atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\"         #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\"         #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\"      # }"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #>                 count available visual_availabilty resistant  visual_resistance #> date             2000    100.0% |################|                              #> hospital_id      2000    100.0% |################|                              #> ward_icu         2000    100.0% |################|                              #> ward_clinical    2000    100.0% |################|                              #> ward_outpatient  2000    100.0% |################|                              #> age              2000    100.0% |################|                              #> gender           2000    100.0% |################|                              #> patient_id       2000    100.0% |################|                              #> mo               2000    100.0% |################|                              #> PEN              1629     81.5% |############----|     73.7% |###########-----| #> OXA               365     18.3% |##--------------|     31.2% |#####-----------| #> FLC               943     47.2% |#######---------|     29.5% |####------------| #> AMX              1350     67.5% |##########------|     59.6% |#########-------| #> AMC              1879     94.0% |###############-|     23.7% |###-------------| #> AMP              1350     67.5% |##########------|     59.6% |#########-------| #> TZP              1001     50.0% |########--------|     12.6% |##--------------| #> CZO               446     22.3% |###-------------|     44.6% |#######---------| #> FEP               724     36.2% |#####-----------|     14.2% |##--------------| #> CXM              1789     89.5% |##############--|     26.3% |####------------| #> FOX               818     40.9% |######----------|     27.4% |####------------| #> CTX               943     47.2% |#######---------|     15.5% |##--------------| #> CAZ              1811     90.6% |##############--|     66.5% |##########------| #> CRO               943     47.2% |#######---------|     15.5% |##--------------| #> GEN              1855     92.8% |##############--|     24.6% |###-------------| #> TOB              1351     67.6% |##########------|     34.4% |#####-----------| #> AMK               692     34.6% |#####-----------|     63.7% |##########------| #> KAN               471     23.6% |###-------------|    100.0% |################| #> TMP              1499     75.0% |############----|     38.1% |######----------| #> SXT              1759     88.0% |##############--|     20.5% |###-------------| #> NIT               743     37.2% |#####-----------|     17.1% |##--------------| #> FOS               351     17.6% |##--------------|     42.2% |######----------| #> LNZ              1023     51.2% |########--------|     69.3% |###########-----| #> CIP              1409     70.5% |###########-----|     16.2% |##--------------| #> MFX               211     10.6% |#---------------|     33.6% |#####-----------| #> VAN              1861     93.1% |##############--|     38.3% |######----------| #> TEC               976     48.8% |#######---------|     75.7% |############----| #> TCY              1200     60.0% |#########-------|     29.8% |####------------| #> TGC               798     39.9% |######----------|     12.7% |##--------------| #> DOX              1136     56.8% |#########-------|     27.7% |####------------| #> ERY              1894     94.7% |###############-|     57.2% |#########-------| #> CLI              1520     76.0% |############----|     61.2% |#########-------| #> AZM              1894     94.7% |###############-|     57.2% |#########-------| #> IPM               889     44.5% |#######---------|      6.2% |----------------| #> MEM               829     41.5% |######----------|      5.9% |----------------| #> MTR                34      1.7% |----------------|     14.7% |##--------------| #> CHL               154      7.7% |#---------------|     21.4% |###-------------| #> COL              1640     82.0% |#############---|     81.2% |############----| #> MUP               270     13.5% |##--------------|      5.9% |----------------| #> RIF              1003     50.2% |########--------|     69.6% |###########-----| # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     select_if(is.rsi) %>%     availability() } #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #>     count available       visual_availabilty resistant        visual_resistance #> PEN   467    100.0% |######################|    100.0% |######################| #> OXA     0      0.0% |----------------------|                                    #> FLC     0      0.0% |----------------------|                                    #> AMX   392     83.9% |##################----|     50.0% |###########-----------| #> AMC   467    100.0% |######################|     13.1% |##--------------------| #> AMP   392     83.9% |##################----|     50.0% |###########-----------| #> TZP   416     89.1% |###################---|      5.5% |#---------------------| #> CZO    82     17.6% |###-------------------|      2.4% |----------------------| #> FEP   317     67.9% |##############--------|      2.8% |----------------------| #> CXM   465     99.6% |######################|      5.4% |#---------------------| #> FOX   377     80.7% |#################-----|      6.9% |#---------------------| #> CTX   459     98.3% |#####################-|      2.4% |----------------------| #> CAZ   460     98.5% |#####################-|      2.4% |----------------------| #> CRO   459     98.3% |#####################-|      2.4% |----------------------| #> GEN   460     98.5% |#####################-|      2.0% |----------------------| #> TOB   462     98.9% |#####################-|      2.6% |----------------------| #> AMK   171     36.6% |########--------------|      0.0% |----------------------| #> KAN     0      0.0% |----------------------|                                    #> TMP   396     84.8% |##################----|     39.1% |########--------------| #> SXT   465     99.6% |######################|     31.6% |######----------------| #> NIT   458     98.1% |#####################-|      2.8% |----------------------| #> FOS    61     13.1% |##--------------------|      0.0% |----------------------| #> LNZ   467    100.0% |######################|    100.0% |######################| #> CIP   456     97.6% |#####################-|     12.5% |##--------------------| #> MFX    57     12.2% |##--------------------|    100.0% |######################| #> VAN   467    100.0% |######################|    100.0% |######################| #> TEC   467    100.0% |######################|    100.0% |######################| #> TCY     3      0.6% |----------------------|     66.7% |##############--------| #> TGC    68     14.6% |###-------------------|      0.0% |----------------------| #> DOX     0      0.0% |----------------------|                                    #> ERY   467    100.0% |######################|    100.0% |######################| #> CLI   467    100.0% |######################|    100.0% |######################| #> AZM   467    100.0% |######################|    100.0% |######################| #> IPM   422     90.4% |###################---|      0.0% |----------------------| #> MEM   418     89.5% |###################---|      0.0% |----------------------| #> MTR     2      0.4% |----------------------|      0.0% |----------------------| #> CHL     0      0.0% |----------------------|                                    #> COL   240     51.4% |###########-----------|      0.0% |----------------------| #> MUP     0      0.0% |----------------------|                                    #> RIF   467    100.0% |######################|    100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)  # S3 method for bug_drug_combinations format(   x,   translate_ab = \"name (ab, atc)\",   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   combine_IR = FALSE,   add_ab_group = TRUE,   remove_intrinsic_resistant = FALSE,   decimal.mark = getOption(\"OutDec\"),   big.mark = ifelse(decimal.mark == \",\", \".\", \",\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant). used argument combine_IR, now follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . Default TRUE. combine_IR logical indicate whether values R summed add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric     decimal point. big.mark character; empty used mark every     big.interval decimals (hence big)     decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_IR = FALSE (default) test R vs. S+combine_IR = TRUE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) #> ℹ Using column 'mo' as input for `col_mo`. head(x) #>                  mo  ab  S I R total #> 1 (unknown species) PEN 14 0 1    15 #> 2 (unknown species) OXA  0 0 1     1 #> 3 (unknown species) FLC  0 0 0     0 #> 4 (unknown species) AMX 15 0 1    16 #> 5 (unknown species) AMC 15 0 0    15 #> 6 (unknown species) AMP 15 0 1    16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #>                        Group                                    Drug #> 1            Aminoglycosides                      Amikacin (J01GB06) #> 2                                               Gentamicin (J01GB03) #> 3                                                Kanamycin (J01GB04) #> 4                                               Tobramycin (J01GB01) #> 5                Amphenicols               Chloramphenicol (J01BA01) #> 6         Antimycobacterials                    Rifampicin (J04AB02) #> 7   Beta-lactams/penicillins                   Amoxicillin (J01CA04) #> 8                              Amoxicillin/clavulanic acid (J01CR02) #> 9                                               Ampicillin (J01CA01) #> 10                                        Benzylpenicillin (J01CE01) #> 11                                          Flucloxacillin (J01CF05) #> 12                                               Oxacillin (J01CF04) #> 13                                 Piperacillin/tazobactam (J01CR05) #> 14               Carbapenems                      Imipenem (J01DH51) #> 15                                               Meropenem (J01DH02) #> 16 Cephalosporins (1st gen.)                     Cefazolin (J01DB04) #> 17 Cephalosporins (2nd gen.)                     Cefoxitin (J01DC01) #> 18                                              Cefuroxime (J01DC02) #> 19 Cephalosporins (3rd gen.)                    Cefotaxime (J01DD01) #> 20                                             Ceftazidime (J01DD02) #> 21                                             Ceftriaxone (J01DD04) #> 22 Cephalosporins (4th gen.)                      Cefepime (J01DE01) #> 23             Glycopeptides                   Teicoplanin (J01XA02) #> 24                                              Vancomycin (J01XA01) #> 25   Macrolides/lincosamides                  Azithromycin (J01FA10) #> 26                                             Clindamycin (J01FF01) #> 27                                            Erythromycin (J01FA01) #> 28      Other antibacterials                    Fosfomycin (J01XX01) #> 29                                               Mupirocin (D06AX09) #> 30                                          Nitrofurantoin (J01XE01) #> 31            Oxazolidinones                     Linezolid (J01XX08) #> 32                Polymyxins                      Colistin (J01XB01) #> 33                Quinolones                 Ciprofloxacin (J01MA02) #> 34                                            Moxifloxacin (J01MA14) #> 35             Tetracyclines                   Doxycycline (J01AA02) #> 36                                            Tetracycline (J01AA07) #> 37                                             Tigecycline (J01AA12) #> 38             Trimethoprims                  Trimethoprim (J01EA01) #> 39                           Trimethoprim/sulfamethoxazole (J01EE01) #>                CoNS          E. coli    E. faecalis  K. pneumoniae #> 1    100.0% (43/43)     0.0% (0/171) 100.0% (39/39)                #> 2    13.6% (42/309)     2.0% (9/460) 100.0% (39/39)   10.3% (6/58) #> 3    100.0% (43/43)                  100.0% (39/39)                #> 4     78.2% (43/55)    2.6% (12/462) 100.0% (39/39)   10.3% (6/58) #> 5                                                                  #> 6                   100.0% (467/467)                100.0% (58/58) #> 7   93.0% (132/142)  50.0% (196/392)                100.0% (58/58) #> 8   42.6% (132/310)   13.1% (61/467)                  10.3% (6/58) #> 9   93.0% (132/142)  50.0% (196/392)                100.0% (58/58) #> 10  77.6% (228/294) 100.0% (467/467)                100.0% (58/58) #> 11  42.8% (134/313)                                                #> 12    54.8% (34/62)                                                #> 13    69.7% (23/33)    5.5% (23/416)                  11.3% (6/53) #> 14    47.9% (23/48)     0.0% (0/422)    0.0% (0/38)    0.0% (0/51) #> 15    47.9% (23/48)     0.0% (0/418)                   0.0% (0/53) #> 16    47.9% (23/48)      2.4% (2/82) 100.0% (39/39)                #> 17    47.9% (23/48)    6.9% (26/377) 100.0% (39/39)    2.2% (1/46) #> 18  42.6% (133/312)    5.4% (25/465) 100.0% (39/39)   10.3% (6/58) #> 19    47.9% (23/48)    2.4% (11/459) 100.0% (39/39)    5.2% (3/58) #> 20 100.0% (313/313)    2.4% (11/460) 100.0% (39/39)    5.2% (3/58) #> 21    47.9% (23/48)    2.4% (11/459) 100.0% (39/39)    5.2% (3/58) #> 22    47.9% (23/48)     2.8% (9/317) 100.0% (39/39)    5.3% (2/38) #> 23                  100.0% (467/467)                100.0% (58/58) #> 24     0.3% (1/304) 100.0% (467/467)    0.0% (0/39) 100.0% (58/58) #> 25  44.1% (138/313) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58) #> 26   33.5% (59/176) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58) #> 27  44.1% (138/313) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58) #> 28                       0.0% (0/61)                               #> 29                                                                 #> 30                     2.8% (13/458)                 19.0% (11/58) #> 31                  100.0% (467/467)                100.0% (58/58) #> 32 100.0% (313/313)     0.0% (0/240) 100.0% (39/39)    5.9% (2/34) #> 33   27.4% (69/252)   12.5% (57/456)                   3.6% (2/55) #> 34                    100.0% (57/57)                               #> 35   22.2% (67/302)                                                #> 36   22.5% (59/262)                                                #> 37     0.0% (0/195)      0.0% (0/68)                               #> 38  41.4% (126/304)  39.1% (155/396) 100.0% (39/39)   18.4% (9/49) #> 39   12.2% (30/246)  31.6% (147/465) 100.0% (39/39)   10.3% (6/58) #>     P. aeruginosa   P. mirabilis        S. aureus   S. epidermidis #> 1                                                   100.0% (44/44) #> 2     0.0% (0/30)    5.9% (2/34)     0.9% (2/233)   21.5% (35/163) #> 3  100.0% (30/30)                                   100.0% (44/44) #> 4     0.0% (0/30)    5.9% (2/34)      2.3% (2/86)    49.4% (44/89) #> 5  100.0% (30/30)                     0.0% (0/46)      3.1% (1/32) #> 6  100.0% (30/30) 100.0% (36/36)     0.0% (0/108)      2.7% (2/73) #> 7  100.0% (30/30)                 93.9% (123/131)    98.9% (90/91) #> 8  100.0% (30/30)    2.8% (1/36)     0.4% (1/235)   54.5% (90/165) #> 9  100.0% (30/30)                 93.9% (123/131)    98.9% (90/91) #> 10 100.0% (30/30) 100.0% (36/36)  80.9% (123/152)   89.4% (93/104) #> 11                                   0.4% (1/235)   55.7% (97/174) #> 12                                    0.0% (0/99)    50.0% (37/74) #> 13                                                                 #> 14                   6.3% (2/32)                                   #> 15                                                                 #> 16 100.0% (30/30)                                                  #> 17 100.0% (30/30)                                                  #> 18 100.0% (30/30)    0.0% (0/36)     0.4% (1/235)   56.1% (97/173) #> 19 100.0% (30/30)    0.0% (0/36)                                   #> 20    3.3% (1/30)    0.0% (0/36) 100.0% (235/235) 100.0% (174/174) #> 21 100.0% (30/30)    0.0% (0/36)                                   #> 22                                                                 #> 23 100.0% (30/30) 100.0% (36/36)      0.0% (0/80)    64.1% (25/39) #> 24 100.0% (30/30) 100.0% (36/36)     0.0% (0/232)     0.0% (0/171) #> 25 100.0% (30/30) 100.0% (36/36)    8.9% (21/235)   53.8% (93/173) #> 26 100.0% (30/30) 100.0% (36/36)     6.0% (9/151)   37.5% (42/112) #> 27 100.0% (30/30) 100.0% (36/36)    8.9% (21/235)   53.8% (93/173) #> 28                                    0.0% (0/80)    21.9% (14/64) #> 29                                    0.0% (0/84)      8.7% (6/69) #> 30                100.0% (36/36)                                   #> 31 100.0% (30/30) 100.0% (36/36)      0.0% (0/82)      0.0% (0/68) #> 32                100.0% (36/36) 100.0% (235/235) 100.0% (174/174) #> 33    0.0% (0/30)    5.6% (2/36)   10.5% (20/191)   36.0% (49/136) #> 34                                    2.2% (1/46)      0.0% (0/31) #> 35 100.0% (30/30) 100.0% (36/36)     3.0% (7/231)   32.4% (55/170) #> 36 100.0% (30/30) 100.0% (36/36)     3.2% (7/217)   32.9% (55/167) #> 37 100.0% (30/30) 100.0% (36/36)     0.0% (0/209)     0.0% (0/109) #> 38 100.0% (30/30)  36.4% (12/33)    8.4% (13/155)   58.5% (69/118) #> 39 100.0% (30/30)   25.0% (9/36)     3.9% (9/231)   17.5% (24/137) #>        S. hominis    S. pneumoniae #> 1                 100.0% (117/117) #> 2     7.5% (6/80) 100.0% (117/117) #> 3                 100.0% (117/117) #> 4    14.5% (9/62) 100.0% (117/117) #> 5     6.5% (2/31)                  #> 6     0.0% (0/61)                  #> 7                     0.0% (0/112) #> 8   35.0% (28/80)     0.0% (0/112) #> 9                     0.0% (0/112) #> 10                    0.0% (0/117) #> 11  34.6% (28/81)                  #> 12  39.7% (23/58)                  #> 13                    0.0% (0/112) #> 14                                 #> 15                                 #> 16                                 #> 17                                 #> 18  33.3% (27/81)      0.0% (0/61) #> 19                                 #> 20 100.0% (81/81) 100.0% (117/117) #> 21                                 #> 22                                 #> 23    6.8% (4/59)                  #> 24    0.0% (0/81)      0.0% (0/94) #> 25  40.7% (33/81)     7.8% (9/116) #> 26  29.4% (20/68)      6.5% (4/62) #> 27  40.7% (33/81)     7.8% (9/116) #> 28 100.0% (59/59)                  #> 29    1.6% (1/61)                  #> 30                                 #> 31    0.0% (0/60)                  #> 32 100.0% (81/81) 100.0% (117/117) #> 33  20.0% (14/70)                  #> 34    0.0% (0/32)                  #> 35  55.0% (44/80)     4.5% (5/112) #> 36  54.3% (44/81)     4.7% (5/106) #> 37    0.0% (0/36)                  #> 38  57.9% (22/38)    17.9% (17/95) #> 39  24.7% (20/81)     7.3% (8/109)  # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates,                        FUN = mo_gramstain) #> ℹ Using column 'mo' as input for `col_mo`. #>               mo  ab    S  I    R total #> 1  Gram-negative PEN    8  0  717   725 #> 2  Gram-negative OXA    6  0    0     6 #> 3  Gram-negative FLC    6  0    0     6 #> 4  Gram-negative AMX  226  0  405   631 #> 5  Gram-negative AMC  463 89  174   726 #> 6  Gram-negative AMP  226  0  405   631 #> 7  Gram-negative TZP  554 11   76   641 #> 8  Gram-negative CZO   94  2  110   206 #> 9  Gram-negative FEP  470  1   14   485 #> 10 Gram-negative CXM  539 22  142   703 #> 11 Gram-negative FOX  435  8  135   578 #> 12 Gram-negative CTX  578  1   57   636 #> 13 Gram-negative CAZ  607  0   27   634 #> 14 Gram-negative CRO  578  1   57   636 #> 15 Gram-negative GEN  651  8   25   684 #> 16 Gram-negative TOB  651  7   28   686 #> 17 Gram-negative AMK  251  0    5   256 #> 18 Gram-negative KAN    0  0   35    35 #> 19 Gram-negative TMP  364  1  223   588 #> 20 Gram-negative SXT  506  0  197   703 #> 21 Gram-negative NIT  491 51  122   664 #> 22 Gram-negative FOS   71  0    7    78 #> 23 Gram-negative LNZ    0  0  707   707 #> 24 Gram-negative CIP  610 11   63   684 #> 25 Gram-negative MFX    0  0   63    63 #> 26 Gram-negative VAN    0  0  707   707 #> 27 Gram-negative TEC    0  0  707   707 #> 28 Gram-negative TCY   15  1  110   126 #> 29 Gram-negative TGC   87  0  101   188 #> 30 Gram-negative DOX   10  0  108   118 #> 31 Gram-negative ERY    1  2  696   699 #> 32 Gram-negative CLI   18  1  709   728 #> 33 Gram-negative AZM    1  2  696   699 #> 34 Gram-negative IPM  616 10    5   631 #> 35 Gram-negative MEM  624  0    2   626 #> 36 Gram-negative MTR   21  0    2    23 #> 37 Gram-negative CHL    1  0   30    31 #> 38 Gram-negative COL  309  0   78   387 #> 39 Gram-negative MUP    0  0    0     0 #> 40 Gram-negative RIF    1  0  695   696 #> 41 Gram-positive PEN  395 11  483   889 #> 42 Gram-positive OXA  245  0  113   358 #> 43 Gram-positive FLC  659  0  278   937 #> 44 Gram-positive AMX  302  3  398   703 #> 45 Gram-positive AMC  864  2  272  1138 #> 46 Gram-positive AMP  302  3  398   703 #> 47 Gram-positive TZP  294  2   49   345 #> 48 Gram-positive CZO  151  0   89   240 #> 49 Gram-positive FEP  150  0   89   239 #> 50 Gram-positive CXM  756  0  328  1084 #> 51 Gram-positive FOX  151  0   89   240 #> 52 Gram-positive CTX  218  0   89   307 #> 53 Gram-positive CAZ    0  0 1177  1177 #> 54 Gram-positive CRO  218  0   89   307 #> 55 Gram-positive GEN  721 19  430  1170 #> 56 Gram-positive TOB  228  0  437   665 #> 57 Gram-positive AMK    0  0  436   436 #> 58 Gram-positive KAN    0  0  436   436 #> 59 Gram-positive TMP  553  9  343   905 #> 60 Gram-positive SXT  883  6  163  1052 #> 61 Gram-positive NIT   73  0    5    78 #> 62 Gram-positive FOS  132  0  141   273 #> 63 Gram-positive LNZ  312  0    2   314 #> 64 Gram-positive CIP  502 58  164   724 #> 65 Gram-positive MFX  136  4    8   148 #> 66 Gram-positive VAN 1141  0    5  1146 #> 67 Gram-positive TEC  237  0   32   269 #> 68 Gram-positive TCY  800 22  245  1067 #> 69 Gram-positive TGC  610  0    0   610 #> 70 Gram-positive DOX  802  7  206  1015 #> 71 Gram-positive ERY  797  7  385  1189 #> 72 Gram-positive CLI  556  3  220   779 #> 73 Gram-positive AZM  797  7  385  1189 #> 74 Gram-positive IPM  207  0   50   257 #> 75 Gram-positive MEM  156  0   47   203 #> 76 Gram-positive MTR    1  0    2     3 #> 77 Gram-positive CHL  120  0    3   123 #> 78 Gram-positive COL    0  0 1237  1237 #> 79 Gram-positive MUP  251  3   16   270 #> 80 Gram-positive RIF  301  2    3   306 #> Use 'format()' on this result to get a publishable/printable format.                             bug_drug_combinations(example_isolates,                       FUN = function(x) ifelse(x == as.mo(\"E. coli\"),                                                \"E. coli\",                                                \"Others\")) #> ℹ Using column 'mo' as input for `col_mo`. #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #>         mo  ab    S  I    R total #> 1  E. coli PEN    0  0  467   467 #> 2  E. coli OXA    0  0    0     0 #> 3  E. coli FLC    0  0    0     0 #> 4  E. coli AMX  196  0  196   392 #> 5  E. coli AMC  332 74   61   467 #> 6  E. coli AMP  196  0  196   392 #> 7  E. coli TZP  388  5   23   416 #> 8  E. coli CZO   79  1    2    82 #> 9  E. coli FEP  308  0    9   317 #> 10 E. coli CXM  425 15   25   465 #> 11 E. coli FOX  347  4   26   377 #> 12 E. coli CTX  448  0   11   459 #> 13 E. coli CAZ  449  0   11   460 #> 14 E. coli CRO  448  0   11   459 #> 15 E. coli GEN  451  0    9   460 #> 16 E. coli TOB  450  0   12   462 #> 17 E. coli AMK  171  0    0   171 #> 18 E. coli KAN    0  0    0     0 #> 19 E. coli TMP  241  0  155   396 #> 20 E. coli SXT  318  0  147   465 #> 21 E. coli NIT  429 16   13   458 #> 22 E. coli FOS   61  0    0    61 #> 23 E. coli LNZ    0  0  467   467 #> 24 E. coli CIP  398  1   57   456 #> 25 E. coli MFX    0  0   57    57 #> 26 E. coli VAN    0  0  467   467 #> 27 E. coli TEC    0  0  467   467 #> 28 E. coli TCY    1  0    2     3 #> 29 E. coli TGC   68  0    0    68 #> 30 E. coli DOX    0  0    0     0 #> 31 E. coli ERY    0  0  467   467 #> 32 E. coli CLI    0  0  467   467 #> 33 E. coli AZM    0  0  467   467 #> 34 E. coli IPM  422  0    0   422 #> 35 E. coli MEM  418  0    0   418 #> 36 E. coli MTR    2  0    0     2 #> 37 E. coli CHL    0  0    0     0 #> 38 E. coli COL  240  0    0   240 #> 39 E. coli MUP    0  0    0     0 #> 40 E. coli RIF    0  0  467   467 #> 41  Others PEN  417 11  734  1162 #> 42  Others OXA  251  0  114   365 #> 43  Others FLC  665  0  278   943 #> 44  Others AMX  347  3  608   958 #> 45  Others AMC 1010 17  385  1412 #> 46  Others AMP  347  3  608   958 #> 47  Others TZP  474  8  103   585 #> 48  Others CZO  166  1  197   364 #> 49  Others FEP  312  1   94   407 #> 50  Others CXM  872  7  445  1324 #> 51  Others FOX  239  4  198   441 #> 52  Others CTX  348  1  135   484 #> 53  Others CAZ  158  0 1193  1351 #> 54  Others CRO  348  1  135   484 #> 55  Others GEN  921 27  447  1395 #> 56  Others TOB  429  7  453   889 #> 57  Others AMK   80  0  441   521 #> 58  Others KAN    0  0  471   471 #> 59  Others TMP  677 10  416  1103 #> 60  Others SXT 1074  6  214  1294 #> 61  Others NIT  136 35  114   285 #> 62  Others FOS  142  0  148   290 #> 63  Others LNZ  314  0  242   556 #> 64  Others CIP  714 68  171   953 #> 65  Others MFX  136  4   14   154 #> 66  Others VAN 1149  0  245  1394 #> 67  Others TEC  237  0  272   509 #> 68  Others TCY  819 23  355  1197 #> 69  Others TGC  629  0  101   730 #> 70  Others DOX  814  7  315  1136 #> 71  Others ERY  801  9  617  1427 #> 72  Others CLI  586  4  463  1053 #> 73  Others AZM  801  9  617  1427 #> 74  Others IPM  402 10   55   467 #> 75  Others MEM  362  0   49   411 #> 76  Others MTR   27  0    5    32 #> 77  Others CHL  121  0   33   154 #> 78  Others COL   69  0 1331  1400 #> 79  Others MUP  251  3   16   270 #> 80  Others RIF  303  2  231   536 #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life.html","id":null,"dir":"Reference","previous_headings":"","what":"The Catalogue of Life — catalogue_of_life","title":"The Catalogue of Life — catalogue_of_life","text":"package contains complete taxonomic tree (last updated: 5 October 2021) almost microorganisms authoritative comprehensive Catalogue Life (CoL), supplemented data List Prokaryotic names Standing Nomenclature (LPSN).","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"The Catalogue of Life — catalogue_of_life","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"The Catalogue of Life — catalogue_of_life","text":"Included : ~58,000 (sub)species kingdoms Archaea, Bacteria, Chromista Protozoa ~5,000 (sub)species orders kingdom Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales Tremellales, well ~4,600 fungal (sub)species. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package including everything tremendously slow algorithms . including aforementioned taxonomic orders, relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histplasma, Pneumocystis, Saccharomyces Trichophyton). ~2,200 (sub)species ~50 relevant genera kingdom Animalia (Strongyloides Taenia) ~14,000 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies responsible author(s) year scientific publication Catalogue Life (http://www.catalogueoflife.org) comprehensive authoritative global index species currently available. holds essential information names, relationships distributions 1.9 million species. Catalogue Life used support major biodiversity conservation information services Global Biodiversity Information Facility (GBIF), Encyclopedia Life (EoL) International Union Conservation Nature Red List. recognised Convention Biological Diversity significant component Global Taxonomy Initiative contribution Target 1 Global Strategy Plant Conservation. syntax used transform original data cleansed R format, can found : https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Catalogue of Life — catalogue_of_life","text":"","code":"# Get version info of included data set catalogue_of_life_version() #> Included in this AMR package (v1.8.1.9020) are: #>  #> Catalogue of Life: 2019 Annual Checklist #>   Available at: http://www.catalogueoflife.org #>   Number of included microbial species: 49,029 #> List of Prokaryotic names with Standing in Nomenclature (5 October 2021) #>   Available at: https://lpsn.dsmz.de #>   Number of included bacterial species: 21,701 #>  #> => Total number of species included:  70,764 #> => Total number of synonyms included: 14,338 #>  #> See for more info `?microorganisms` and `?catalogue_of_life`.   # Get a note when a species was renamed mo_shortname(\"Chlamydophila psittaci\") #> ℹ Chlamydophila psittaci (Everett et al. , 1999) was renamed back to #>   Chlamydia psittaci (Page, 1968) [B_CHLMY_PSTT] #> [1] \"C. psittaci\"  # Get any property from the entire taxonomic tree for all included species mo_class(\"E. coli\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Gammaproteobacteria\"  mo_family(\"E. coli\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Enterobacteriaceae\"  mo_gramstain(\"E. coli\") # based on kingdom and phylum, see ?mo_gramstain #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Gram-negative\"  mo_ref(\"E. coli\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Castellani et al., 1919\"  # Do not get mistaken - this package is about microorganisms mo_kingdom(\"C. elegans\") #> ℹ Function `as.mo()` is uncertain about \"C. elegans\" (assuming Cladosporium #>   elegans). Run `mo_uncertainties()` to review this. #> [1] \"Fungi\" mo_name(\"C. elegans\") #> ℹ Function `as.mo()` is uncertain about \"C. elegans\" (assuming Cladosporium #>   elegans). Run `mo_uncertainties()` to review this. #> [1] \"Cladosporium elegans\""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":null,"dir":"Reference","previous_headings":"","what":"Version info of included Catalogue of Life — catalogue_of_life_version","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"function returns information included data Catalogue Life.","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"","code":"catalogue_of_life_version()"},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"list, prints pretty format","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"LPSN, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE)  count_susceptible(..., only_all_tested = FALSE)  count_R(..., only_all_tested = FALSE)  count_IR(..., only_all_tested = FALSE)  count_I(..., only_all_tested = FALSE)  count_SI(..., only_all_tested = FALSE)  count_S(..., only_all_tested = FALSE)  count_all(..., only_all_tested = FALSE)  n_rsi(..., only_all_tested = FALSE)  count_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   combine_SI = TRUE,   combine_IR = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .rsi() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class rsi (see .rsi()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant). used argument combine_IR, now follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . Default TRUE. combine_IR logical indicate whether values R must merged one, output consists S vs. +R (susceptible vs. non-susceptible). outdated, see argument combine_SI.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_rsi() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data rsi class (created .rsi()) counts number S's, 's R's. also supports grouped variables. function rsi_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX)   # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX)         # counts \"S\", \"I\" and \"R\" #> [1] 1350  # be more specific count_S(example_isolates$AMX) #> Using `count_S()` is discouraged; use `count_susceptible()` instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using `count_IR()` is discouraged; use `count_resistant()` instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804  # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_rsi(example_isolates$AMX) #> [1] 1350  # n_rsi() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX) #> [1] 546  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(hospital_id) %>%     summarise(R  = count_R(CIP),               I  = count_I(CIP),               S  = count_S(CIP),               n1 = count_all(CIP),  # the actual total; sum of all three               n2 = n_rsi(CIP),      # same - analogous to n_distinct               total = n())          # NOT the number of tested isolates!                  # Number of available isolates for a whole antibiotic class   # (i.e., in this data set columns GEN, TOB, AMK, KAN)   example_isolates %>%     group_by(hospital_id) %>%     summarise(across(aminoglycosides(), n_rsi))     # Count co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy.   # Please mind that `susceptibility()` calculates percentages right away instead.   example_isolates %>% count_susceptible(AMC) # 1433   example_isolates %>% count_all(AMC)         # 1879     example_isolates %>% count_susceptible(GEN) # 1399   example_isolates %>% count_all(GEN)         # 1855     example_isolates %>% count_susceptible(AMC, GEN) # 1764   example_isolates %>% count_all(AMC, GEN)         # 1936     # Get number of S+I vs. R immediately of selected columns   example_isolates %>%     select(AMX, CIP) %>%     count_df(translate = FALSE)     # It also supports grouping variables   example_isolates %>%     select(hospital_id, AMX, CIP) %>%     group_by(hospital_id) %>%     count_df(translate = FALSE) } #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #>    hospital_id antibiotic interpretation value #> 1            A        AMX             SI    70 #> 2            A        AMX              R   108 #> 3            A        CIP             SI   181 #> 4            A        CIP              R    26 #> 5            B        AMX             SI   170 #> 6            B        AMX              R   271 #> 7            B        CIP             SI   414 #> 8            B        CIP              R    85 #> 9            C        AMX             SI    65 #> 10           C        AMX              R   110 #> 11           C        CIP             SI   161 #> 12           C        CIP              R    41 #> 13           D        AMX             SI   241 #> 14           D        AMX              R   315 #> 15           D        CIP             SI   425 #> 16           D        CIP              R    76 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work:   rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\",                          TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #>  #>   1. If TZP is \"S\" then set to  S : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If TZP is \"R\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"),                  TZP = as.rsi(\"R\"),                  ampi = as.rsi(\"S\"),                  cipro = as.rsi(\"S\")) df #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    S     S  eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    R     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception variables used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"species_id\", \"source\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\",                          TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\")  eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses agents matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(5-fluorocytosine, amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, morinamide, p-aminosalicylic acid, pretomanid, prothionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, methicillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, phenethicillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefaloridine, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, cephalexin, cephalothin, cephapirin cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, methicillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, phenethicillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\") x #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP)  # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates,              rules = \"custom\",              custom_rules = x,              info = FALSE,              verbose = TRUE) #>    row col           mo_fullname old new #> 1   33 AMP Klebsiella pneumoniae   R   I #> 2   33 AMX Klebsiella pneumoniae   R   I #> 3   34 AMP Klebsiella pneumoniae   R   I #> 4   34 AMX Klebsiella pneumoniae   R   I #> 5  531 AMP Klebsiella pneumoniae   R   I #> 6  531 AMX Klebsiella pneumoniae   R   I #> 7 1485 AMP    Klebsiella oxytoca   R   I #> 8 1485 AMX    Klebsiella oxytoca   R   I #>                                                                               rule #> 1 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 2 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 3 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 4 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 5 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 6 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 7 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 8 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #>            rule_group            rule_name #> 1 Custom EUCAST rules Custom EUCAST rule 2 #> 2 Custom EUCAST rules Custom EUCAST rule 2 #> 3 Custom EUCAST rules Custom EUCAST rule 2 #> 4 Custom EUCAST rules Custom EUCAST rule 2 #> 5 Custom EUCAST rules Custom EUCAST rule 2 #> 6 Custom EUCAST rules Custom EUCAST rule 2 #> 7 Custom EUCAST rules Custom EUCAST rule 2 #> 8 Custom EUCAST rules Custom EUCAST rule 2 #>                               rule_source #> 1 Object 'x' consisting of 2 custom rules #> 2 Object 'x' consisting of 2 custom rules #> 3 Object 'x' consisting of 2 custom rules #> 4 Object 'x' consisting of 2 custom rules #> 5 Object 'x' consisting of 2 custom rules #> 6 Object 'x' consisting of 2 custom rules #> 7 Object 'x' consisting of 2 custom rules #> 8 Object 'x' consisting of 2 custom rules               # combine rule sets x2 <- c(x,         custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\")) x2 #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   3. If TZP is \"R\" then set to  R : #>      biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #>      imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #>      meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #>      razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data.frame 169 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial agent used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"'EUCAST Clinical Breakpoint Tables' v11.0 (2021) based dosages data set.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"head(dosage) #>    ab        name              type        dose dose_times administration notes #> 1 AMK    Amikacin   standard_dosage 25-30 mg/kg          1             iv       #> 2 AMX Amoxicillin       high_dosage         2 g          6             iv       #> 3 AMX Amoxicillin   standard_dosage         1 g          3             iv       #> 4 AMX Amoxicillin       high_dosage    0.75-1 g          3           oral       #> 5 AMX Amoxicillin   standard_dosage       0.5 g          3           oral       #> 6 AMX Amoxicillin uncomplicated_uti       0.5 g          3           oral       #>         original_txt eucast_version #> 1 25-30 mg/kg x 1 iv             11 #> 2         2 g x 6 iv             11 #> 3       1 g x 3-4 iv             11 #> 4  0.75-1 g x 3 oral             11 #> 5     0.5 g x 3 oral             11 #> 6     0.5 g x 3 oral             11"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules(   x,   col_mo = NULL,   info = interactive(),   rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")),   verbose = FALSE,   version_breakpoints = 11,   version_expertrules = 3.3,   ampc_cephalosporin_resistance = NA,   only_rsi_columns = FALSE,   custom_rules = NULL,   ... )  eucast_dosage(ab, administration = \"iv\", version_breakpoints = 11)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value, e.g. using options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three agents. value NA (default) argument remove results three agents, e.g. value \"R\" make results agents resistant. Use NULL FALSE alter results three agents AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\".  EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_rsi_columns logical indicate whether antibiotic columns must detected transformed class <rsi> (see .rsi()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values RSI values. Use .rsi() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.  Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. file used input AMR package contains taxonomy updated 5 October 2021.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\")  eucast_rules(example_isolates, rules = \"custom\", custom_rules = x, info = FALSE) #> # A tibble: 2,000 x 49 #>    date       hospit~1 ward_~2 ward_~3 ward_~4   age gender patie~5 mo           #>  * <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  4 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  5 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  6 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  7 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  8 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  9 2002-01-16 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 10 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #> # ... with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, ... #> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA12), aztreonam (ATM, J01MA24), bacampicillin (BAM, J01MA07), bekanamycin (BEK, J01MA14), benzathine benzylpenicillin (BNB, D10AF05), benzathine phenoxymethylpenicillin (BNP, J01MA06), benzylpenicillin (PEN, J01MA01), besifloxacin (BES, J01MA18), biapenem (BIA, J01MA03), carbenicillin (CRB, J01MA17), carindacillin (CRN, J01MA10), cefacetrile (CAC, J01MA21), cefaclor (CEC, J01MA09), cefadroxil (CFR, J01MA05), cefaloridine (RID, P01AA05), cefamandole (MAN, J01MA22), cefatrizine (CTZ, J01MA13), cefazedone (CZD, J01CA01), cefazolin (CZO, J01CA04), cefcapene (CCP, J01CA12), cefdinir (CDR, J01CR05), cefditoren (DIT, J01CA13), cefepime (FEP, J01AA02), cefetamet (CAT, J01FA10), cefixime (CFM, J01FA09), cefmenoxime (CMX, J01CR02), cefmetazole (CMZ, J01AA08), cefodizime (DIZ, J01FA06), cefonicid (CID, J01CF04), cefoperazone (CFP, J01CF05), cefoperazone/sulbactam (CSL, J01CR01), ceforanide (CND, J01CA19), cefotaxime (CTX, J01CE04), cefotetan (CTT, J01CA09), cefotiam (CTF, J01DF01), cefoxitin (FOX, J01CA06), cefozopran (ZOP, J01CE08), cefpiramide (CPM, J01CE10), cefpirome (CPO, J01CE01), cefpodoxime (CPD, J01CA03), cefprozil (CPR, J01CA05), cefroxadine (CRD, J01CE07), cefsulodin (CFS, J01CF02), ceftaroline (CPT, J01CF01), ceftazidime (CAZ, J01CA07), ceftazidime/clavulanic acid (CCV, J01CA18), cefteram (CEM, J01CA11), ceftezole (CTL, J01CA14), ceftibuten (CTB, J01CF03), ceftizoxime (CZX, J01CA10), ceftobiprole medocaril (CFM1, J01CF06), ceftolozane/enzyme inhibitor (CEI, J01CE06), ceftriaxone (CRO, J01CE05), cefuroxime (CXM, J01CE02), cephalexin (LEX, J01CA02), cephalothin (CEP, J01CA08), cephapirin (HAP, J01CE09), cephradine (CED, J01CE03), chloramphenicol (CHL, J01CG01), ciprofloxacin (CIP, J01CA16), clarithromycin (CLR, J01CR04), clindamycin (CLI, J01CA15), clometocillin (CLM, J01CG02), cloxacillin (CLO, J01CA17), colistin (COL, J01CR03), cycloserine (CYC, J01DB10), dalbavancin (DAL, J01DC04), daptomycin (DAP, J01DB05), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DC03), dicloxacillin (DIC, J01DB07), dirithromycin (DIR, J01DB06), doripenem (DOR, J01DB04), doxycycline (DOX, J01DD17), enoxacin (ENX, J01DD15), epicillin (EPC, J01DD16), ertapenem (ETP, J01DE01), erythromycin (ERY, J01DD10), fleroxacin (FLE, J01DD08), flucloxacillin (FLC, J01DD05), flurithromycin (FLR1, J01DC09), fosfomycin (FOS, J01DD09), framycetin (FRM, J01DC06), fusidic acid (FUS, J01DD12), garenoxacin (GRN, J01DD62), gatifloxacin (GAT, J01DC11), gemifloxacin (GEM, J01DD01), gentamicin (GEN, J01DC05), grepafloxacin (GRX, J01DC07), hetacillin (HET, J01DC01), imipenem (IPM, J01DE03), isepamicin (ISE, J01DD11), josamycin (JOS, J01DE02), kanamycin (KAN, J01DD13), latamoxef (LTM, J01DC10), levofloxacin (LVX, J01DB11), levonadifloxacin (LND, J01DD03), lincomycin (LIN, J01DI02), linezolid (LNZ, J01DD02), lomefloxacin (LOM, J01DD52), loracarbef (LOR, J01DD18), mecillinam (MEC, J01DB12), meropenem (MEM, J01DD14), meropenem/vaborbactam (MEV, J01DD07), metampicillin (MTM, J01DI01), methicillin (MET, J01DI54), mezlocillin (MEZ, J01DD04), micronomicin (MCR, J01DC02), midecamycin (MID, J01DB01), minocycline (MNO, J01DB03), miocamycin (MCM, J01DB08), moxifloxacin (MFX, J01DB09), nadifloxacin (NAD, J01DD06), nafcillin (NAF, J01DC08), nalidixic acid (NAL, J01DH05), neomycin (NEO, J01DH04), netilmicin (NET, J01DH03), nitrofurantoin (NIT, J01DH51), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01XA02), oritavancin (ORI, J01XA01), oxacillin (OXA, J01XC01), pazufloxacin (PAZ, J01FA13), pefloxacin (PEF, J01FA01), penamecillin (PNM, J01FA14), phenethicillin (PHE, J01FA07), phenoxymethylpenicillin (PHN, J01FA03), piperacillin (PIP, J01FA11), piperacillin/tazobactam (TZP, J01FA05), pivampicillin (PVM, J01FA12), pivmecillinam (PME, J01FA16), plazomicin (PLZ, J01FA02), polymyxin B (PLB, J01FA15), pristinamycin (PRI, J01FA08), procaine benzylpenicillin (PRB, J01FF02), propicillin (PRP, J01FG01), prulifloxacin (PRU, J01FG02), quinupristin/dalfopristin (QDA, J04AB02), ribostamycin (RST, J01XX09), rifampicin (RIF, J01XX08), rokitamycin (ROK, J01AA07), roxithromycin (RXT, J01XB01), rufloxacin (RFL, J01XB02), sisomicin (SIS, J01XE01), sitafloxacin (SIT, J01AA12), solithromycin (SOL, J01EA01), sparfloxacin (SPX, J01XX01), spiramycin (SPI, J01BA01), streptoduocin (STR, J01GB06), streptomycin (STR1, J01GB12), sulbactam (SUL, J01GB13), sulbenicillin (SBC, J01GB09), sulfadiazine (SDI, D09AA01), sulfadiazine/trimethoprim (SLT1, J01GB03), sulfadimethoxine (SUD, J01GB11), sulfadimidine (SDM, J01GB04), sulfadimidine/trimethoprim (SLT2, S01AA22), sulfafurazole (SLF, J01GB05), sulfaisodimidine (SLF1, J01GB07), sulfalene (SLF2, J01GB14), sulfamazone (SZO, J01GB10), sulfamerazine (SLF3, J01GB08), sulfamerazine/trimethoprim (SLT3, J01GA02), sulfamethizole (SLF4, J01GA01), sulfamethoxazole (SMX, J01GB01), sulfamethoxypyridazine (SLF5, J01EE01), sulfametomidine (SLF6, J01MB02), sulfametoxydiazine (SLF7, J01FF01), sulfametrole/trimethoprim (SLT4, J01XA04), sulfamoxole (SLF8, J01XA05), sulfamoxole/trimethoprim (SLT5, J01XA03), sulfanilamide (SLF9, J04AB01), sulfaperin (SLF10, J01XX11), sulfaphenazole (SLF11, J01EC02), sulfapyridine (SLF12, J01ED01), sulfathiazole (SUT, J01EB03), sulfathiourea (SLF13, J01EB05), sultamicillin (SLT6, J01EB01), talampicillin (TAL, J01ED02), tazobactam (TAZ, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame(mo = c(\"Staphylococcus aureus\",                        \"Enterococcus faecalis\",                        \"Escherichia coli\",                        \"Klebsiella pneumoniae\",                        \"Pseudomonas aeruginosa\"),                 VAN = \"-\",       # Vancomycin                 AMX = \"-\",       # Amoxicillin                 COL = \"-\",       # Colistin                 CAZ = \"-\",       # Ceftazidime                 CXM = \"-\",       # Cefuroxime                 PEN = \"S\",       # Benzylpenicillin                 FOX = \"S\",       # Cefoxitin                 stringsAsFactors = FALSE)  head(a) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   -   -   -   S   S #> 2  Enterococcus faecalis   -   -   -   -   -   S   S #> 3       Escherichia coli   -   -   -   -   -   S   S #> 4  Klebsiella pneumoniae   -   -   -   -   -   S   S #> 5 Pseudomonas aeruginosa   -   -   -   -   -   S   S   # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in `eucast_rules()`: not all columns with antimicrobial results are of #> class <rsi> . Transform them on beforehand, with e.g.: #>   - a %>% as.rsi(CXM:AMX) #>   - a %>% mutate_if(is.rsi.eligible, as.rsi) #>   - a %>% mutate(across(where(is.rsi.eligible), as.rsi))  head(b) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   S   R   R   S   S   S #> 2  Enterococcus faecalis   -   -   R   R   R   S   R #> 3       Escherichia coli   R   -   -   -   -   R   S #> 4  Klebsiella pneumoniae   R   R   -   -   -   R   S #> 5 Pseudomonas aeruginosa   R   R   -   -   R   R   R   # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in `eucast_rules()`: not all columns with antimicrobial results are of #> class <rsi> . Transform them on beforehand, with e.g.: #>   - a %>% as.rsi(CXM:AMX) #>   - a %>% mutate_if(is.rsi.eligible, as.rsi) #>   - a %>% mutate(across(where(is.rsi.eligible), as.rsi)) head(c) #>   row col           mo_fullname old new rule   rule_group #> 1   1 AMX Staphylococcus aureus   -   S       Breakpoints #> 2   1 CXM Staphylococcus aureus   -   S       Breakpoints #> 3   1 CAZ Staphylococcus aureus   -   R      Expert Rules #> 4   1 COL Staphylococcus aureus   -   R      Expert Rules #> 5   2 CAZ Enterococcus faecalis   -   R      Expert Rules #> 6   2 COL Enterococcus faecalis   -   R      Expert Rules #>                                                 rule_name #> 1                                          Staphylococcus #> 2                                          Staphylococcus #> 3 Table 4: Intrinsic resistance in gram-positive bacteria #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #>                                                                                 rule_source #> 1                                           'EUCAST Clinical Breakpoint Tables' v11.0, 2021 #> 2                                           'EUCAST Clinical Breakpoint Tables' v11.0, 2021 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # }  # Dosage guidelines:  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v11.0 (2021). This note will be shown once per session. #>    ab          name  standard_dosage  high_dosage #> 1 TOB    Tobramycin 6-7 mg/kg x 1 iv         <NA> #> 2 GEN    Gentamicin 6-7 mg/kg x 1 iv         <NA> #> 3 CIP Ciprofloxacin     0.4 g x 2 iv 0.4 g x 3 iv  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v10.0 (2020). This note will be shown once per session. #>    ab          name  standard_dosage  high_dosage #> 1 TOB    Tobramycin 6-7 mg/kg x 1 iv         <NA> #> 2 GEN    Gentamicin 6-7 mg/kg x 1 iv         <NA> #> 3 CIP Ciprofloxacin     0.4 g x 2 iv 0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set reflects reality can used practice AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data.frame 2,000 observations 49 variables: date date receipt laboratory hospital_id ID hospital, D ward_iculogical determine ward intensive care unit ward_clinicallogical determine ward regular clinical ward ward_outpatientlogical determine ward outpatient clinic age age patient gender gender patient patient_id ID patient mo ID microorganism created .mo(), see also microorganisms PEN:RIF 40 different antibiotics class rsi (see .rsi()); column names occur antibiotics data set can translated ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"head(example_isolates) #> # A tibble: 6 × 49 #>   date       hospita…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>   <date>     <fct>     <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #> 1 2002-01-02 D         FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #> 2 2002-01-03 D         FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #> 3 2002-01-07 B         TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 4 2002-01-07 B         TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 5 2002-01-13 B         TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 6 2002-01-13 B         TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> # … with 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, AMX <rsi>, #> #   AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, CXM <rsi>, #> #   FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, TOB <rsi>, #> #   AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, FOS <rsi>, #> #   LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, TCY <rsi>, #> #   TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, IPM <rsi>, #> #   MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, RIF <rsi>, and … #> # ℹ Use `colnames()` to see all variable names"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3,000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data.frame 3,000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antibiotics transformed .rsi()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with Unclean Data — example_isolates_unclean","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"head(example_isolates_unclean) #>   patient_id hospital       date      bacteria AMX AMC CIP GEN #> 1         J3        A 2012-11-21       E. coli   R   I   S   S #> 2         R7        A 2018-04-03 K. pneumoniae   R   I   S   S #> 3         P3        A 2014-09-19       E. coli   R   S   S   S #> 4        P10        A 2015-12-10       E. coli   S   I   S   S #> 5         B7        A 2015-03-02       E. coli   S   S   S   S #> 6         W3        A 2018-03-31     S. aureus   R   S   R   S"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   col_testcode = NULL,   col_specimen = NULL,   col_icu = NULL,   col_keyantimicrobials = NULL,   episode_days = 365,   testcodes_exclude = NULL,   icu_exclude = FALSE,   specimen_group = NULL,   type = \"points\",   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ignore_I = TRUE,   points_threshold = 2,   info = interactive(),   include_unknown = FALSE,   include_untested_rsi = TRUE,   ... )  filter_first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   episode_days = 365,   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology function strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab), defaults first column date class col_patient_id column name unique IDs patients, defaults first column starts 'patient' 'patid' (case insensitive) col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU) col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). Defaults first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed, defaults TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_rsi logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_rsi = FALSE always return FALSE rows. checks data set columns class <rsi> consequently requires transforming columns antibiotic results using .rsi() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According Hindler et al. (2007, doi:10.1086/511864 ), different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. Although often inappropriate, method makes sure duplicate isolates selected patient. large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial agents available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial agents, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  example_isolates[first_isolate(), ] #> # A tibble: 1,379 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  3 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  4 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #>  5 2002-01-17 D        FALSE   TRUE    FALSE      67 M      495616  B_STPHY_EPDR #>  6 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI #>  7 2002-01-21 D        FALSE   TRUE    FALSE      75 F      462081  B_CTRBC_FRND #>  8 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL #>  9 2002-02-03 B        TRUE    FALSE   FALSE      76 M      481442  B_STPHY_CONS #> 10 2002-02-05 A        FALSE   TRUE    FALSE      50 M      023456  B_STPHY_HMNS #> # … with 1,369 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> ℹ Using column 'mo' as input for `mo_is_gram_negative()` #> # A tibble: 437 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI #>  3 2002-01-21 D        FALSE   TRUE    FALSE      75 F      462081  B_CTRBC_FRND #>  4 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL #>  5 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS #>  6 2002-02-27 D        FALSE   TRUE    FALSE      85 F      066895  B_KLBSL_PNMN #>  7 2002-03-08 C        FALSE   TRUE    FALSE      69 M      4FC193  B_ESCHR_COLI #>  8 2002-03-16 C        FALSE   TRUE    FALSE      69 M      4FC193  B_PSDMN_AERG #>  9 2002-04-01 B        TRUE    FALSE   FALSE      46 F      496896  B_ESCHR_COLI #> 10 2002-04-23 B        TRUE    FALSE   FALSE      69 F      EE2510  B_ESCHR_COLI #> # … with 427 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate())     # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE)       # flag the first isolates per group:  example_isolates %>%    group_by(hospital_id) %>%    mutate(first = first_isolate()) %>%    select(hospital_id, date, patient_id, mo, first)      # now let's see if first isolates matter:   A <- example_isolates %>%     group_by(hospital_id) %>%     summarise(count = n_rsi(GEN),            # gentamicin availability               resistance = resistance(GEN))  # gentamicin resistance     B <- example_isolates %>%     filter_first_isolate() %>%               # the 1st isolate filter     group_by(hospital_id) %>%     summarise(count = n_rsi(GEN),            # gentamicin availability               resistance = resistance(GEN))  # gentamicin resistance     # Have a look at A and B.   A   B      # B is more reliable because every isolate is counted only once.   # Gentamicin resistance in hospital D appears to be 4.2% higher than   # when you (erroneously) would have used all isolates for analysis. } #> # A tibble: 4 × 3 #>   hospital_id count resistance #>   <fct>       <int>      <dbl> #> 1 A             216      0.204 #> 2 B             407      0.270 #> 3 C             202      0.218 #> 4 D             457      0.293 # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also     factors. y numeric vector; ignored x matrix.      x factor, y factor length. p vector probabilities length x.     error given entry p negative. rescale.p logical scalar; TRUE p rescaled     (necessary) sum 1.  rescale.p FALSE,     p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following   components: statistic value chi-squared test statistic. parameter degrees freedom approximate     chi-squared distribution test statistic, NA     p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test     performed, whether Monte Carlo simulation continuity     correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals,     (observed - expected) / sqrt(expected). stdres standardized residuals,     (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007,     section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers.  Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed .  Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R :   df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio.  x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 4.1471, p-value = 0.1257 #>   # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible.   # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills.  x <- c(1752, 1895) g.test(x) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 5.6085, p-value = 0.01787 #>   # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine (New) Episodes for Patients — get_episode","title":"Determine (New) Episodes for Patients — get_episode","text":"functions determine items vector can considered (start ) new episode, based argument episode_days. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns values TRUE/FALSE indicate whether item vector start new episode.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"get_episode(x, episode_days, ...)  is_new_episode(x, episode_days, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine (New) Episodes for Patients — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days required episode length days, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine (New) Episodes for Patients — get_episode","text":"get_episode(): double vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine (New) Episodes for Patients — get_episode","text":"Dates first sorted old new. oldest date mark start first episode. date, next date marked least episode_days days later start first episode. second marked date , next date marked least episode_days days later start second episode start third episode, . vector returned, original order restored. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 200), ]  get_episode(df$date, episode_days = 60)    # indices #>   [1] 11  6  3 62 46 35 47 39 18 32 12  8  8 26 63 47 24 52 25  8 15 26 15 30 14 #>  [26] 63 31 38  8 28 54 27 16 37 17 43  6 21 58  2 24 60 63 44 38 19 28 51 62 13 #>  [51] 30 27 54 12  7 40 42 66 39 39 58 41 38 45 56 18  9 13 63 16  7 58  3 18 60 #>  [76] 63 10 26 27 14 39 15 42 12  2 13 19 61 13 38 64  1 21 48 36 24 38  5 60  8 #> [101] 18 34 45 60 11 15  8  2 11 10 62 31 22 15 12 62 11 36 49 22  8 49 56 59  6 #> [126] 10 17 54 43  1  7 53 55 64  6 37  9 38 29 52 63 62 13  5 28 56 13 54 41 18 #> [151] 59 44 52 60 20 51  2  2 53 59 61 33 23 34 24 44 56 62  3 50 45 12 42 17 15 #> [176] 65  1 18  4 13 32 41  4 34  2 45 64  9 36 38 17 41 35 57 18  6  4 63  2 25 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE #>  [13] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE #>  [25]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE  TRUE FALSE FALSE #>  [37] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE #>  [49] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE #>  [61]  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE #>  [73] FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE #>  [85] FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE #>  [97] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE #> [109] FALSE  TRUE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE #> [121] FALSE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE #> [133]  TRUE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE #> [145] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE  TRUE #> [157] FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE #> [169]  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE FALSE #> [181]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [193] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE  # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 3 × 49 #>   date       hospita…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>   <date>     <fct>     <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #> 1 2002-07-23 B         TRUE    FALSE   FALSE      51 M      F35553  B_STPHY_AURS #> 2 2002-07-23 B         TRUE    FALSE   FALSE      51 M      F35553  B_STPHY_AURS #> 3 2002-07-16 D         FALSE   FALSE   TRUE       78 M      241328  B_STPHY_CONS #> # … with 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, AMX <rsi>, #> #   AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, CXM <rsi>, #> #   FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, TOB <rsi>, #> #   AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, FOS <rsi>, #> #   LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, TCY <rsi>, #> #   TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, IPM <rsi>, #> #   MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, RIF <rsi>, and … #> # ℹ Use `colnames()` to see all variable names  # the functions also work for less than a day, e.g. to include one per hour: get_episode(c(Sys.time(),               Sys.time() + 60 * 60),             episode_days = 1/24) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(x = c(\"A\", \"B\", \"C\"),                                size = 2000,                               replace = TRUE)) %>%      group_by(condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient_id, date, condition, new_episode)        df %>%     group_by(hospital_id, patient_id) %>%     transmute(date,                patient_id,               new_index = get_episode(date, 60),               new_logical = is_new_episode(date, 60))      df %>%     group_by(hospital_id) %>%      summarise(n_patients = n_distinct(patient_id),               n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),               n_episodes_60  = sum(is_new_episode(date, episode_days = 60)),               n_episodes_30  = sum(is_new_episode(date, episode_days = 30)))             # grouping on patients and microorganisms leads to the same   # results as first_isolate() when using 'episode-based':   x <- df %>%     filter_first_isolate(include_unknown = TRUE,                          method = \"episode-based\")        y <- df %>%     group_by(patient_id, mo) %>%     filter(is_new_episode(date, 365)) %>%     ungroup()    identical(x, y)      # but is_new_episode() has a lot more flexibility than first_isolate(),   # since you can now group on anything that seems relevant:   df %>%     group_by(patient_id, mo, hospital_id, ward_icu) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> Error in mutate(., condition = sample(x = c(\"A\", \"B\", \"C\"), size = 2000,     replace = TRUE)): Problem while computing `condition = sample(x = c(\"A\", \"B\", \"C\"), size = #> 2000, replace = TRUE)`. #> ✖ `condition` must be size 200 or 1, not 2000. # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(   x,   choices = 1:2,   scale = 1,   pc.biplot = TRUE,   labels = NULL,   labels_textsize = 3,   labels_text_placement = 1.5,   groups = NULL,   ellipse = TRUE,   ellipse_prob = 0.68,   ellipse_size = 0.5,   ellipse_alpha = 0.5,   points_size = 2,   points_alpha = 0.25,   arrows = TRUE,   arrows_colour = \"darkblue\",   arrows_size = 0.5,   arrows_textsize = 3,   arrows_textangled = TRUE,   arrows_alpha = 0.75,   base_textsize = 10,   ... )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default     biplot strict sense. scale variables scaled lambda ^ scale     observations scaled lambda ^ (1-scale)     lambda singular values computed     princomp. Normally 0 <= scale <= 1, warning     issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component     biplot\", lambda = 1 observations scaled sqrt(n)     variables scaled sqrt(n).  inner products     variables approximate covariances distances observations     approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first    resistance_data <- example_isolates %>%      group_by(order = mo_order(mo),       # group on anything, like order              genus = mo_genus(mo)) %>%   #   and genus as we do here;     filter(n() >= 30) %>%                # filter on only 30 results per group     summarise_if(is.rsi, resistance)     # then get resistance of all drugs        # now conduct PCA for certain antimicrobial agents   pca_result <- resistance_data %>%              pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)         summary(pca_result)      # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   ggplot_pca(pca_result)      if (require(\"ggplot2\")) {     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #>   and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>  #> Loading required package: ggplot2   # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_rsi","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"","code":"ggplot_rsi(   data,   position = NULL,   x = \"antibiotic\",   fill = \"interpretation\",   facet = NULL,   breaks = seq(0, 1, 0.1),   limits = NULL,   translate_ab = \"name\",   combine_SI = TRUE,   combine_IR = FALSE,   minimum = 30,   language = get_AMR_locale(),   nrow = NULL,   colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R =     \"#ED553B\"),   datalabels = TRUE,   datalabels.size = 2.5,   datalabels.colour = \"grey15\",   title = NULL,   subtitle = NULL,   caption = NULL,   x.title = \"Antimicrobial\",   y.title = \"Proportion\",   ... )  geom_rsi(   position = NULL,   x = c(\"antibiotic\", \"interpretation\"),   fill = \"interpretation\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   combine_IR = FALSE,   ... )  facet_rsi(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL)  scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)  scale_rsi_colours(..., aesthetics = \"fill\")  theme_rsi()  labels_rsi_count(   position = NULL,   x = \"antibiotic\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   combine_IR = FALSE,   datalabels.size = 3,   datalabels.colour = \"grey15\" )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"data data.frame column(s) class rsi (see .rsi()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant). used argument combine_IR, now follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . Default TRUE. combine_IR logical indicate whether values R must merged one, output consists S vs. +R (susceptible vs. non-susceptible). outdated, see argument combine_SI. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_rsi_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_rsi() , case scale_rsi_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples. aesthetics aesthetics apply colours , defaults \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\"","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"default, names antibiotics shown plots using ab_name(). can set translate_ab argument. See count_df().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"the-functions","dir":"Reference","previous_headings":"","what":"The Functions","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"geom_rsi() take variable data rsi class (created .rsi()) using rsi_df() plot bars percentage R, S. default behaviour bars stacked different antibiotics x axis. facet_rsi() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_rsi_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_rsi() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_rsi_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_rsi() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"","code":"# \\donttest{ if (require(\"ggplot2\") & require(\"dplyr\")) {     # get antimicrobial results for drugs against a UTI:   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +     geom_rsi()     # prettify the plot using some additional functions:   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)   ggplot(df) +     geom_rsi() +     scale_y_percent() +     scale_rsi_colours() +     labels_rsi_count() +     theme_rsi()     # or better yet, simplify this using the wrapper function - a single command:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_rsi()     # get only proportions and no counts:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_rsi(datalabels = FALSE)     # add other ggplot2 arguments as you like:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_rsi(width = 0.5,                colour = \"black\",                size = 1,                linetype = 2,                alpha = 0.25)    # you can alter the colours with colour names:   example_isolates %>%     select(AMX) %>%     ggplot_rsi(colours = c(SI = \"yellow\"))    # but you can also use the built-in colour-blind friendly colours for   # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red:   data.frame(x = c(\"Value1\", \"Value2\", \"Value3\"),              y = c(1, 2, 3),              z = c(\"Value4\", \"Value5\", \"Value6\")) %>%     ggplot() +     geom_col(aes(x = x, y = y, fill = z)) +     scale_rsi_colours(Value4 = \"S\", Value5 = \"I\", Value6 = \"R\")      # resistance of ciprofloxacine per age group   example_isolates %>%     mutate(first_isolate = first_isolate()) %>%     filter(first_isolate == TRUE,            mo == as.mo(\"E. coli\")) %>%     # age_groups() is also a function in this AMR package:     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_rsi(x = \"age_group\")      # a shorter version which also adjusts data label colours:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_rsi(colours = FALSE)     # it also supports groups (don't forget to use the group var on `x` or `facet`):   example_isolates %>%     filter(mo_is_gram_negative()) %>%      # select only UTI-specific drugs     select(hospital_id, AMX, NIT, FOS, TMP, CIP) %>%     group_by(hospital_id) %>%     ggplot_rsi(x = \"hospital_id\",                facet = \"antibiotic\",                nrow = 1,                title = \"AMR of Anti-UTI Drugs Per Hospital\",                x.title = \"Hospital\",                datalabels = FALSE) } #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> ℹ Using column 'mo' as input for `mo_is_gram_negative()` #> Warning: Removed 6 rows containing missing values (position_stack).  # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antibiotics data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col(   x = NULL,   search_string = NULL,   verbose = FALSE,   only_rsi_columns = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_rsi_columns logical indicate whether antibiotic columns must detected transformed class <rsi> (see .rsi()) beforehand (defaults FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antibiotics data set column containing name code antibiotic. Longer columns names take precedence shorter column names.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame(amox = \"S\",                  tetr = \"R\")  guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" # [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\" # [1] \"tetr\"  guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #>  OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline). # [1] \"tetr\"  # WHONET codes df <- data.frame(AMP_ND10 = \"R\",                  AMC_ED20 = \"S\") guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" # [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" # [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\" # [1] \"AMC_ED20\"  # Longer names take precendence: df <- data.frame(AMP_ED2 = \"S\",                  AMP_ED20 = \"S\") guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ED2\" # [1] \"AMP_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data.frame 134,956 observations 2 variables: mo Microorganism ID ab Antibiotic ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"repository AMR package contains file comprising data set full taxonomic antibiotic names: https://github.com/msberends/AMR/blob/main/data-raw/intrinsic_resistant.txt. file allows machine reading EUCAST guidelines intrinsic resistance, almost impossible Excel PDF files distributed EUCAST. file updated automatically. data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"head(intrinsic_resistant) #>        mo  ab #> 1 B_GRAMP ATM #> 2 B_GRAMP COL #> 3 B_GRAMP NAL #> 4 B_GRAMP PLB #> 5 B_GRAMP TEM #> 6 B_ABTRP ATM"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\"))  italicize_taxonomy(string, type = c(\"markdown\", \"ansi\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, using ANSI colours adding \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\"  cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  semi_join_microorganisms(x, by = NULL, ...)  anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> ℹ Function `as.mo()` is uncertain about \"K. pneumoniae\" (assuming #>   Klebsiella pneumoniae). Run `mo_uncertainties()` to review this. #>             mo              fullname  kingdom         phylum #> 1 B_KLBSL_PNMN Klebsiella pneumoniae Bacteria Proteobacteria #>                 class            order             family      genus    species #> 1 Gammaproteobacteria Enterobacterales Enterobacteriaceae Klebsiella pneumoniae #>   subspecies    rank            ref species_id source prevalence #> 1            species Trevisan, 1887     777151   LPSN          1 #>                                                                          snomed #> 1 1098101000112102, 446870005, 1098201000112108, 409801009, 56415008, 714315002 left_join_microorganisms(\"B_KLBSL_PNMN\") #>             mo              fullname  kingdom         phylum #> 1 B_KLBSL_PNMN Klebsiella pneumoniae Bacteria Proteobacteria #>                 class            order             family      genus    species #> 1 Gammaproteobacteria Enterobacterales Enterobacteriaceae Klebsiella pneumoniae #>   subspecies    rank            ref species_id source prevalence #> 1            species Trevisan, 1887     777151   LPSN          1 #>                                                                          snomed #> 1 1098101000112102, 446870005, 1098201000112108, 409801009, 56415008, 714315002  # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     left_join_microorganisms() %>%      colnames()     df <- data.frame(date = seq(from = as.Date(\"2018-01-01\"),                               to = as.Date(\"2018-01-07\"),                               by = 1),                    bacteria = as.mo(c(\"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\",                                       \"E. coli\", \"E. coli\", \"E. coli\")),                    stringsAsFactors = FALSE)   colnames(df)   df_joined <- left_join_microorganisms(df, \"bacteria\")   colnames(df_joined) } #> Joining, by = \"mo\" #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli) and \"S. aureus\" (assuming Staphylococcus aureus). Run #>   `mo_uncertainties()` to review these uncertainties. #>  [1] \"date\"       \"bacteria\"   \"fullname\"   \"kingdom\"    \"phylum\"     #>  [6] \"class\"      \"order\"      \"family\"     \"genus\"      \"species\"    #> [11] \"subspecies\" \"rank\"       \"ref\"        \"species_id\" \"source\"     #> [16] \"prevalence\" \"snomed\"     # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials(   x = NULL,   col_mo = NULL,   universal = c(\"ampicillin\", \"amoxicillin/clavulanic acid\", \"cefuroxime\",     \"piperacillin/tazobactam\", \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"),   gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\", \"ceftazidime\",     \"meropenem\"),   gram_positive = c(\"vancomycin\", \"teicoplanin\", \"tetracycline\", \"erythromycin\",     \"oxacillin\", \"rifampin\"),   antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\", \"nystatin\",     \"voriconazole\"),   only_rsi_columns = FALSE,   ... )  all_antimicrobials(x = NULL, only_rsi_columns = FALSE, ...)  antimicrobials_equal(   y,   z,   type = c(\"points\", \"keyantimicrobials\"),   ignore_I = TRUE,   points_threshold = 2,   ... )"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antibiotics columns, like AMX amox. Can left blank determine automatically col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial agents, case-insensitive. Set NULL ignore. See Details default agents. gram_negative names antibiotic agents Gram-positives, case-insensitive. Set NULL ignore. See Details default agents. gram_positive names antibiotic agents Gram-negatives, case-insensitive. Set NULL ignore. See Details default agents. antifungal names antifungal agents fungi, case-insensitive. Set NULL ignore. See Details default agents. only_rsi_columns logical indicate whether columns must included transformed class <rsi> (see .rsi()) beforehand (defaults FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial agents used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial agents used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial agents used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial agents used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\"  # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values)  antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs  # \\donttest{ if (require(\"dplyr\")) {   # set key antibiotics to a new variable   my_patients <- example_isolates %>%     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`     mutate(       # now calculate first isolates       first_regular = first_isolate(col_keyantimicrobials = FALSE),       # and first WEIGHTED isolates       first_weighted = first_isolate(col_keyantimicrobials = \"keyab\")     )     # Check the difference in this data set, 'weighted' results in more isolates:   sum(my_patients$first_regular, na.rm = TRUE)   sum(my_patients$first_weighted, na.rm = TRUE) } #> [1] 1395 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for default kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for matrix kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for data.frame kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 2.97178 kurtosis(rnorm(10000), excess = TRUE) #> [1] -0.01957164"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE)  x %like% pattern  x %unlike% pattern  x %like_case% pattern  x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE  # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(     \"case\",           \"diff\",      \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE  a[1] %like% b #> [1]  TRUE FALSE FALSE a %like% b[1] #> [1]  TRUE FALSE FALSE  # get isolates whose name start with 'Ent' or 'ent' example_isolates[which(mo_name(example_isolates$mo) %like% \"^ent\"), ] #> # A tibble: 106 × 49 #>    date       hospi…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo            #>    <date>     <fct>   <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>          #>  1 2002-02-21 C       FALSE   TRUE    FALSE      69 M      4FC193  B_ENTRC_FACM  #>  2 2002-04-08 A       TRUE    TRUE    FALSE      78 M      130252  B_ENTRC_FCLS  #>  3 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  4 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  5 2003-04-20 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  6 2003-04-21 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  7 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #>  8 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC_FCLS  #>  9 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC       #> 10 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # \\donttest{ # faster way, since mo_name() is context-aware: example_isolates[which(mo_name() %like% \"^ent\"), ] #> ℹ Using column 'mo' as input for `mo_name()` #> # A tibble: 106 × 49 #>    date       hospi…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo            #>    <date>     <fct>   <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>          #>  1 2002-02-21 C       FALSE   TRUE    FALSE      69 M      4FC193  B_ENTRC_FACM  #>  2 2002-04-08 A       TRUE    TRUE    FALSE      78 M      130252  B_ENTRC_FCLS  #>  3 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  4 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  5 2003-04-20 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  6 2003-04-21 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  7 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #>  8 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC_FCLS  #>  9 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC       #> 10 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for `mo_name()` #> # A tibble: 106 × 49 #>    date       hospi…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo            #>    <date>     <fct>   <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>          #>  1 2002-02-21 C       FALSE   TRUE    FALSE      69 M      4FC193  B_ENTRC_FACM  #>  2 2002-04-08 A       TRUE    TRUE    FALSE      78 M      130252  B_ENTRC_FCLS  #>  3 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  4 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  5 2003-04-20 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  6 2003-04-21 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  7 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #>  8 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC_FCLS  #>  9 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC       #> 10 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro(   x = NULL,   guideline = \"CMI2012\",   col_mo = NULL,   info = interactive(),   pct_required_classes = 0.5,   combine_SI = TRUE,   verbose = FALSE,   only_rsi_columns = FALSE,   ... )  custom_mdro_guideline(..., as_factor = TRUE)  brmo(x = NULL, only_rsi_columns = FALSE, ...)  mrgn(x = NULL, only_rsi_columns = FALSE, ...)  mdr_tb(x = NULL, only_rsi_columns = FALSE, ...)  mdr_cmi2012(x = NULL, only_rsi_columns = FALSE, ...)  eucast_exceptional_phenotypes(x = NULL, only_rsi_columns = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antibiotics columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_rsi_columns logical indicate whether antibiotic columns must detected transformed class <rsi> (see .rsi()) beforehand (defaults FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antibiotics . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu \"WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)\" (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours.   outcome function can used guideline argument mdro() function:   Rules can also combined custom rules using c():   rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #>   1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #>   2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #>   3. Otherwise -> Negative #>  #> Unmatched rows will return NA. x <- mdro(example_isolates,           guideline = custom) table(x) #>       Negative Elderly Type A Elderly Type B #>           1070            198            732 x <- mdro(example_isolates,           guideline = c(custom,                          custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #>       Negative Elderly Type A Elderly Type B Elderly Type C  #>            961            198            732            109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA12), aztreonam (ATM, J01MA24), bacampicillin (BAM, J01MA07), bekanamycin (BEK, J01MA14), benzathine benzylpenicillin (BNB, D10AF05), benzathine phenoxymethylpenicillin (BNP, J01MA06), benzylpenicillin (PEN, J01MA01), besifloxacin (BES, J01MA18), biapenem (BIA, J01MA03), carbenicillin (CRB, J01MA17), carindacillin (CRN, J01MA10), cefacetrile (CAC, J01MA21), cefaclor (CEC, J01MA09), cefadroxil (CFR, J01MA05), cefaloridine (RID, P01AA05), cefamandole (MAN, J01MA22), cefatrizine (CTZ, J01MA13), cefazedone (CZD, J01CA01), cefazolin (CZO, J01CA04), cefcapene (CCP, J01CA12), cefdinir (CDR, J01CR05), cefditoren (DIT, J01CA13), cefepime (FEP, J01AA02), cefetamet (CAT, J01FA10), cefixime (CFM, J01FA09), cefmenoxime (CMX, J01CR02), cefmetazole (CMZ, J01AA08), cefodizime (DIZ, J01FA06), cefonicid (CID, J01CF04), cefoperazone (CFP, J01CF05), cefoperazone/sulbactam (CSL, J01CR01), ceforanide (CND, J01CA19), cefotaxime (CTX, J01CE04), cefotetan (CTT, J01CA09), cefotiam (CTF, J01DF01), cefoxitin (FOX, J01CA06), cefozopran (ZOP, J01CE08), cefpiramide (CPM, J01CE10), cefpirome (CPO, J01CE01), cefpodoxime (CPD, J01CA03), cefprozil (CPR, J01CA05), cefroxadine (CRD, J01CE07), cefsulodin (CFS, J01CF02), ceftaroline (CPT, J01CF01), ceftazidime (CAZ, J01CA07), ceftazidime/clavulanic acid (CCV, J01CA18), cefteram (CEM, J01CA11), ceftezole (CTL, J01CA14), ceftibuten (CTB, J01CF03), ceftizoxime (CZX, J01CA10), ceftobiprole medocaril (CFM1, J01CF06), ceftolozane/enzyme inhibitor (CEI, J01CE06), ceftriaxone (CRO, J01CE05), cefuroxime (CXM, J01CE02), cephalexin (LEX, J01CA02), cephalothin (CEP, J01CA08), cephapirin (HAP, J01CE09), cephradine (CED, J01CE03), chloramphenicol (CHL, J01CG01), ciprofloxacin (CIP, J01CA16), clarithromycin (CLR, J01CR04), clindamycin (CLI, J01CA15), clometocillin (CLM, J01CG02), cloxacillin (CLO, J01CA17), colistin (COL, J01CR03), cycloserine (CYC, J01DB10), dalbavancin (DAL, J01DC04), daptomycin (DAP, J01DB05), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DC03), dicloxacillin (DIC, J01DB07), dirithromycin (DIR, J01DB06), doripenem (DOR, J01DB04), doxycycline (DOX, J01DD17), enoxacin (ENX, J01DD15), epicillin (EPC, J01DD16), ertapenem (ETP, J01DE01), erythromycin (ERY, J01DD10), fleroxacin (FLE, J01DD08), flucloxacillin (FLC, J01DD05), flurithromycin (FLR1, J01DC09), fosfomycin (FOS, J01DD09), framycetin (FRM, J01DC06), fusidic acid (FUS, J01DD12), garenoxacin (GRN, J01DD62), gatifloxacin (GAT, J01DC11), gemifloxacin (GEM, J01DD01), gentamicin (GEN, J01DC05), grepafloxacin (GRX, J01DC07), hetacillin (HET, J01DC01), imipenem (IPM, J01DE03), isepamicin (ISE, J01DD11), josamycin (JOS, J01DE02), kanamycin (KAN, J01DD13), latamoxef (LTM, J01DC10), levofloxacin (LVX, J01DB11), levonadifloxacin (LND, J01DD03), lincomycin (LIN, J01DI02), linezolid (LNZ, J01DD02), lomefloxacin (LOM, J01DD52), loracarbef (LOR, J01DD18), mecillinam (MEC, J01DB12), meropenem (MEM, J01DD14), meropenem/vaborbactam (MEV, J01DD07), metampicillin (MTM, J01DI01), methicillin (MET, J01DI54), mezlocillin (MEZ, J01DD04), micronomicin (MCR, J01DC02), midecamycin (MID, J01DB01), minocycline (MNO, J01DB03), miocamycin (MCM, J01DB08), moxifloxacin (MFX, J01DB09), nadifloxacin (NAD, J01DD06), nafcillin (NAF, J01DC08), nalidixic acid (NAL, J01DH05), neomycin (NEO, J01DH04), netilmicin (NET, J01DH03), nitrofurantoin (NIT, J01DH51), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01XA02), oritavancin (ORI, J01XA01), oxacillin (OXA, J01XC01), pazufloxacin (PAZ, J01FA13), pefloxacin (PEF, J01FA01), penamecillin (PNM, J01FA14), phenethicillin (PHE, J01FA07), phenoxymethylpenicillin (PHN, J01FA03), piperacillin (PIP, J01FA11), piperacillin/tazobactam (TZP, J01FA05), pivampicillin (PVM, J01FA12), pivmecillinam (PME, J01FA16), plazomicin (PLZ, J01FA02), polymyxin B (PLB, J01FA15), pristinamycin (PRI, J01FA08), procaine benzylpenicillin (PRB, J01FF02), propicillin (PRP, J01FG01), prulifloxacin (PRU, J01FG02), quinupristin/dalfopristin (QDA, J04AB02), ribostamycin (RST, J01XX09), rifampicin (RIF, J01XX08), rokitamycin (ROK, J01AA07), roxithromycin (RXT, J01XB01), rufloxacin (RFL, J01XB02), sisomicin (SIS, J01XE01), sitafloxacin (SIT, J01AA12), solithromycin (SOL, J01EA01), sparfloxacin (SPX, J01XX01), spiramycin (SPI, J01BA01), streptoduocin (STR, J01GB06), streptomycin (STR1, J01GB12), sulbactam (SUL, J01GB13), sulbenicillin (SBC, J01GB09), sulfadiazine (SDI, D09AA01), sulfadiazine/trimethoprim (SLT1, J01GB03), sulfadimethoxine (SUD, J01GB11), sulfadimidine (SDM, J01GB04), sulfadimidine/trimethoprim (SLT2, S01AA22), sulfafurazole (SLF, J01GB05), sulfaisodimidine (SLF1, J01GB07), sulfalene (SLF2, J01GB14), sulfamazone (SZO, J01GB10), sulfamerazine (SLF3, J01GB08), sulfamerazine/trimethoprim (SLT3, J01GA02), sulfamethizole (SLF4, J01GA01), sulfamethoxazole (SMX, J01GB01), sulfamethoxypyridazine (SLF5, J01EE01), sulfametomidine (SLF6, J01MB02), sulfametoxydiazine (SLF7, J01FF01), sulfametrole/trimethoprim (SLT4, J01XA04), sulfamoxole (SLF8, J01XA05), sulfamoxole/trimethoprim (SLT5, J01XA03), sulfanilamide (SLF9, J04AB01), sulfaperin (SLF10, J01XX11), sulfaphenazole (SLF11, J01EC02), sulfapyridine (SLF12, J01ED01), sulfathiazole (SUT, J01EB03), sulfathiourea (SLF13, J01EB05), sultamicillin (SLT6, J01EB01), talampicillin (TAL, J01ED02), tazobactam (TAZ, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") #>  (16 isolates had no test results) str(out) #>  Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #>              Negative Positive, unconfirmed              Positive  #>                  1978                     0                     6   out <- mdro(example_isolates,             guideline = custom_mdro_guideline(AMX == \"R\" ~ \"Custom MDRO 1\",                                               VAN == \"R\" ~ \"Custom MDRO 2\")) table(out) #> out #>      Negative Custom MDRO 1 Custom MDRO 2  #>           870           804           326   # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     mdro() %>%     table()      # no need to define `x` when used inside dplyr verbs:   example_isolates %>%     mutate(MDRO = mdro()) %>%     pull(MDRO) %>%     table()             } #>  (16 isolates had no test results) #> Warning: in `mdro()`: NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with `pct_required_classes`) #>  (16 isolates had no test results) #> Warning: in `mdro()`: NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with `pct_required_classes`) #> . #>                         Negative       Multi-drug-resistant (MDR)  #>                             1601                              128  #> Extensively drug-resistant (XDR)          Pandrug-resistant (PDR)  #>                                0                                0  # }"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"data.frame 5,604 observations 2 variables: code Commonly used code microorganism mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"","code":"head(microorganisms.codes) #>      code      mo #> 1 _FAM_A- B_GRAMN #> 2 _FAM_A+ B_GRAMP #> 3 _FAM_AC B_GRAMP #> 4 _FAM_AE B_GRAMN #> 5 _FAM_AN B_GRAMN #> 6 _FAM_AO B_GRAMP"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 70,764 Microorganisms — microorganisms","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: 5 October 2021) six kingdoms Catalogue Life (CoL) List Prokaryotic names Standing Nomenclature (LPSN). MO codes can looked using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"data.frame 70,764 observations 16 variables: mo ID microorganism used package fullname Full name, like \"Escherichia coli\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism rank Text taxonomic rank microorganism, like \"species\" \"genus\" ref Author(s) year concerning scientific publication species_id ID species used Catalogue Life source Either \"CoL\", \"LPSN\" \"manually added\" (see Source) prevalence Prevalence microorganism, see .mo() snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, according US Edition SNOMED CT 1 September 2020 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"Catalogue Life: 2019 Annual Checklist currently implemented AMR package: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org List Prokaryotic names Standing Nomenclature (5 October 2021) currently implemented AMR package: Parte, .C., Sarda Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. Goker, M. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 Parte, .C. (2018). LPSN - List Prokaryotic names Standing Nomenclature (bacterio.net), 20 years . International Journal Systematic Evolutionary Microbiology, 68, 1825-1829; doi:10.1099/ijsem.0.002786 Parte, .C. (2014). LPSN - List Prokaryotic names Standing Nomenclature. Nucleic Acids Research, 42, Issue D1, D613-D616; doi:10.1093/nar/gkt1111 Euzeby, J.P. (1997). List Bacterial Names Standing Nomenclature: Folder Available Internet. International Journal Systematic Bacteriology, 47, 590-592; doi:10.1099/00207713-47-2-590 US Edition SNOMED CT 1 September 2020 currently implemented AMR package: Retrieved Public Health Information Network Vocabulary Access Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"Please note entries based Catalogue Life LPSN (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"convenience, entries added manually: 11 entries Streptococcus (beta-haemolytic: groups , B, C, D, F, G, H, K unspecified; : viridans, milleri) 2 entries Staphylococcus (coagulase-negative (CoNS) coagulase-positive (CoPS)) 3 entries Trichomonas (T. vaginalis, family genus) 4 entries Toxoplasma (T. gondii, order, family genus) 1 entry Candida (C.  krusei), (yet) Catalogue Life 1 entry Blastocystis (B.  hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 5 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast unknown fungus) 6 families Enterobacterales order, according Adeolu et al. (2016, PMID 27620848), (yet) Catalogue Life","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"data set available 'flat file' use even without R - can find file : https://github.com/msberends/AMR/raw/main/data-raw/microorganisms.txt. file R format (preserved data structure) can found : https://github.com/msberends/AMR/raw/main/data/microorganisms.rda.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"about-the-records-from-lpsn-see-source-","dir":"Reference","previous_headings":"","what":"About the Records from LPSN (see Source)","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"List Prokaryotic names Standing Nomenclature (LPSN) provides comprehensive information nomenclature prokaryotes. LPSN free use service founded Jean P. Euzeby 1997 later maintained Aidan C. Parte. February 2020, regularly augmented LPSN database DSMZ basis new LPSN service. new database implemented Type-Strain Genome Server augmented 2018 store kinds nomenclatural information. Data previous version LPSN Prokaryotic Nomenclature --date (PNU) service imported new system. PNU established 1993 service Leibniz Institute DSMZ, curated Norbert Weiss, Manfred Kracht Dorothea Gleim.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"","code":"head(microorganisms) #>                 mo                 fullname           kingdom           phylum #> 1         F_FUNGUS         (unknown fungus)             Fungi (unknown phylum) #> 2          B_GRAMN (unknown Gram-negatives)          Bacteria (unknown phylum) #> 3          B_GRAMP (unknown Gram-positives)          Bacteria (unknown phylum) #> 4          UNKNOWN           (unknown name) (unknown kingdom) (unknown phylum) #> 5          F_YEAST          (unknown yeast)             Fungi (unknown phylum) #> 6 B_[FAM]_ABDTBCTR       Abditibacteriaceae          Bacteria Abditibacteriota #>             class             order             family                    genus #> 1 (unknown class)   (unknown order)   (unknown family)          (unknown genus) #> 2 (unknown class)   (unknown order)   (unknown family) (unknown Gram-negatives) #> 3 (unknown class)   (unknown order)   (unknown family) (unknown Gram-positives) #> 4 (unknown class)   (unknown order)   (unknown family)          (unknown genus) #> 5 (unknown class)   (unknown order)   (unknown family)          (unknown genus) #> 6  Abditibacteria Abditibacteriales Abditibacteriaceae                          #>             species           subspecies           rank  ref species_id #> 1 (unknown species) (unknown subspecies)        species <NA>         NA #> 2 (unknown species) (unknown subspecies)        species <NA>         NA #> 3 (unknown species) (unknown subspecies)        species <NA>         NA #> 4 (unknown species) (unknown subspecies) (unknown rank) <NA>         NA #> 5 (unknown species) (unknown subspecies)        species <NA>         NA #> 6                                                family <NA>         NA #>           source prevalence snomed #> 1 manually added          3        #> 2 manually added          3        #> 3 manually added          3        #> 4 manually added          3        #> 5 manually added          3        #> 6            CoL          2"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"data set containing old (previously valid accepted) taxonomic names according Catalogue Life. data set used internally .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"","code":"microorganisms.old"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"data.frame 14,338 observations 4 variables: fullname Old full taxonomic name microorganism fullname_new New full taxonomic name microorganism ref Author(s) year concerning scientific publication prevalence Prevalence microorganism, see .mo()","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"Catalogue Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version catalogue_of_life_version()). Parte, .C. (2018). LPSN - List Prokaryotic names Standing Nomenclature (bacterio.net), 20 years . International Journal Systematic Evolutionary Microbiology, 68, 1825-1829; doi:10.1099/ijsem.0.002786","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"","code":"head(microorganisms.old) #>                     fullname                 fullname_new #> 1    Abaphospora petrucciana          Nectria petrucciana #> 2      Abathostoma stercoris    Echinocyclidium stercoris #> 3  Abbottina tannerbankensis       Nonion tannerbankensis #> 4 Abditodentrix rhomboidalis  Tortoplectella rhomboidalis #> 5       Abiotrophia adiacens      Granulicatella adiacens #> 6  Abiotrophia balaenopterae Granulicatella balaenopterae #>                       ref prevalence #> 1          Kirschst, 1939          2 #> 2 Berger in Corliss, 1979          3 #> 3         McCulloch, 1977          3 #> 4           Millett, 1899          3 #> 5   Kawamura et al., 1995          2 #> 6     Lawson et al., 1999          2"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function, counts insertion, deletion substitution 1 needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based experience several microbiological laboratories Netherlands conjunction international reports pathogen prevalence. Group 1 (prevalent microorganisms) consists microorganisms taxonomic class Gammaproteobacteria taxonomic genus Enterococcus, Staphylococcus Streptococcus. group consequently contains common Gram-negative bacteria, Pseudomonas Legionella species within order Enterobacterales. Group 2 consists microorganisms taxonomic phylum Proteobacteria, Firmicutes, Actinobacteria Sarcomastigophora, taxonomic genus Absidia, Acremonium, Actinotignum, Alternaria, Anaerosalibacter, Apophysomyces, Arachnia, Aspergillus, Aureobacterium, Aureobasidium, Bacteroides, Basidiobolus, Beauveria, Blastocystis, Branhamella, Calymmatobacterium, Candida, Capnocytophaga, Catabacter, Chaetomium, Chryseobacterium, Chryseomonas, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Cryptococcus, Curvularia, Exophiala, Exserohilum, Flavobacterium, Fonsecaea, Fusarium, Fusobacterium, Hendersonula, Hypomyces, Koserella, Lelliottia, Leptosphaeria, Leptotrichia, Malassezia, Malbranchea, Mortierella, Mucor, Mycocentrospora, Mycoplasma, Nectria, Ochroconis, Oidiodendron, Phoma, Piedraia, Pithomyces, Pityrosporum, Prevotella, Pseudallescheria, Rhizomucor, Rhizopus, Rhodotorula, Scolecobasidium, Scopulariopsis, Scytalidium, Sporobolomyces, Stachybotrys, Stomatococcus, Treponema, Trichoderma, Trichophyton, Trichosporon, Tritirachium Ureaplasma. Group 3 consists microorganisms. characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.079\\), less prevalent microorganism humans), although latter alphabetically come first. Since AMR version 1.8.1, common microorganism abbreviations ignored determining matching score. abbreviations currently: AIEC, ATEC, BORSA, CRSM, DAEC, EAEC, EHEC, EIEC, EPEC, ETEC, GISA, MRPA, MRSA, MRSE, MSSA, MSSE, NMEC, PISP, PRSP, STEC, UPEC, VISA, VISP, VRE, VRSA VRSP.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"Dr Matthijs Berends","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"as.mo(\"E. coli\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on pathogenicity in humans and the resemblance #> between the input and the full taxonomic name. See `?mo_matching_score`. #>  #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Entamoeba coli (0.079) #>   mo_matching_score(x = \"E. coli\",                   n = c(\"Escherichia coli\", \"Entamoeba coli\")) #> [1] 0.68750000 0.07936508"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(x, language = get_AMR_locale(), ...)  mo_fullname(x, language = get_AMR_locale(), ...)  mo_shortname(x, language = get_AMR_locale(), ...)  mo_subspecies(x, language = get_AMR_locale(), ...)  mo_species(x, language = get_AMR_locale(), ...)  mo_genus(x, language = get_AMR_locale(), ...)  mo_family(x, language = get_AMR_locale(), ...)  mo_order(x, language = get_AMR_locale(), ...)  mo_class(x, language = get_AMR_locale(), ...)  mo_phylum(x, language = get_AMR_locale(), ...)  mo_kingdom(x, language = get_AMR_locale(), ...)  mo_domain(x, language = get_AMR_locale(), ...)  mo_type(x, language = get_AMR_locale(), ...)  mo_gramstain(x, language = get_AMR_locale(), ...)  mo_is_gram_negative(x, language = get_AMR_locale(), ...)  mo_is_gram_positive(x, language = get_AMR_locale(), ...)  mo_is_yeast(x, language = get_AMR_locale(), ...)  mo_is_intrinsic_resistant(x, ab, language = get_AMR_locale(), ...)  mo_snomed(x, language = get_AMR_locale(), ...)  mo_ref(x, language = get_AMR_locale(), ...)  mo_authors(x, language = get_AMR_locale(), ...)  mo_year(x, language = get_AMR_locale(), ...)  mo_lpsn(x, language = get_AMR_locale(), ...)  mo_rank(x, language = get_AMR_locale(), ...)  mo_taxonomy(x, language = get_AMR_locale(), ...)  mo_synonyms(x, language = get_AMR_locale(), ...)  mo_info(x, language = get_AMR_locale(), ...)  mo_url(x, open = FALSE, language = get_AMR_locale(), ...)  mo_property(x, property = \"fullname\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language returned text, defaults system language (see get_AMR_locale()) can overwritten setting option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Also used translate text like \"growth\". Use language = NULL language = \"\" prevent translation. ... arguments passed .mo(), 'allow_uncertain' 'ignore_pattern' ab (vector ) text can coerced valid antibiotic code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"species_id\", \"source\", \"prevalence\" \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() list case mo_taxonomy() mo_info() named character case mo_url() numeric case mo_snomed() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions return recently known taxonomic property according Catalogue Life, except mo_ref(), mo_authors() mo_year(). Please refer example, knowing Escherichia blattae renamed Shimwellia blattae 2010: mo_name(\"Escherichia blattae\") return \"Shimwellia blattae\" (message renaming) mo_ref(\"Escherichia blattae\") return \"Burgess et al., 1973\" (message renaming) mo_ref(\"Shimwellia blattae\") return \"Priest et al., 2010\" (without message) short name - mo_shortname() - almost always returns first character genus full species, like \"E. coli\". Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. words, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Gram stain - mo_gramstain() - determined based taxonomic kingdom phylum. According Cavalier-Smith (2002, PMID 11837318), defined subkingdoms Negibacteria Posibacteria, phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes Tenericutes. bacteria considered Gram-positive, except members class Negativicutes Gram-negative. Members bacterial phyla considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (except input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts - mo_is_yeast() - based taxonomic kingdom class. Budding yeasts fungi phylum Ascomycetes, class Saccharomycetes (also called Hemiascomycetes). True yeasts aggregated underlying order Saccharomycetales. Thus, microorganisms fungi member taxonomic class Saccharomycetes, function return TRUE. returns FALSE otherwise (except input NA MO code UNKNOWN). Intrinsic resistance - mo_is_intrinsic_resistant() - determined based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() functions can vectorised arguments x (input microorganisms) ab (input antibiotics). output translated possible. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes - mo_snomed() - US Edition SNOMED CT 1 September 2020. See Source microorganisms data set info.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function, counts insertion, deletion substitution 1 needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based experience several microbiological laboratories Netherlands conjunction international reports pathogen prevalence. Group 1 (prevalent microorganisms) consists microorganisms taxonomic class Gammaproteobacteria taxonomic genus Enterococcus, Staphylococcus Streptococcus. group consequently contains common Gram-negative bacteria, Pseudomonas Legionella species within order Enterobacterales. Group 2 consists microorganisms taxonomic phylum Proteobacteria, Firmicutes, Actinobacteria Sarcomastigophora, taxonomic genus Absidia, Acremonium, Actinotignum, Alternaria, Anaerosalibacter, Apophysomyces, Arachnia, Aspergillus, Aureobacterium, Aureobasidium, Bacteroides, Basidiobolus, Beauveria, Blastocystis, Branhamella, Calymmatobacterium, Candida, Capnocytophaga, Catabacter, Chaetomium, Chryseobacterium, Chryseomonas, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Cryptococcus, Curvularia, Exophiala, Exserohilum, Flavobacterium, Fonsecaea, Fusarium, Fusobacterium, Hendersonula, Hypomyces, Koserella, Lelliottia, Leptosphaeria, Leptotrichia, Malassezia, Malbranchea, Mortierella, Mucor, Mycocentrospora, Mycoplasma, Nectria, Ochroconis, Oidiodendron, Phoma, Piedraia, Pithomyces, Pityrosporum, Prevotella, Pseudallescheria, Rhizomucor, Rhizopus, Rhodotorula, Scolecobasidium, Scopulariopsis, Scytalidium, Sporobolomyces, Stachybotrys, Stomatococcus, Treponema, Trichoderma, Trichophyton, Trichosporon, Tritirachium Ureaplasma. Group 3 consists microorganisms. characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.079\\), less prevalent microorganism humans), although latter alphabetically come first. Since AMR version 1.8.1, common microorganism abbreviations ignored determining matching score. abbreviations currently: AIEC, ATEC, BORSA, CRSM, DAEC, EAEC, EHEC, EIEC, EPEC, ETEC, GISA, MRPA, MRSA, MRSE, MSSA, MSSE, NMEC, PISP, PRSP, STEC, UPEC, VISA, VISP, VRE, VRSA VRSP.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Get Properties of a Microorganism — mo_property","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). 2019. Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. Emergence coagulase-negative staphylococci 2020. Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC serological differentiation human groups hemolytic streptococci. 1933. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Catalogue Life: 2019 Annual Checklist, http://www.catalogueoflife.org List Prokaryotic names Standing Nomenclature (5 October 2021), doi:10.1099/ijsem.0.004332 US Edition SNOMED CT 1 September 2020, retrieved Public Health Information Network Vocabulary Access Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree ----------------------------------------------------------- mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Proteobacteria\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\"  # colloquial properties ---------------------------------------------------- mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\"  # other properties --------------------------------------------------------- mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [1] 1.098101e+15 4.468700e+08 1.098201e+15 4.098010e+08 5.641501e+07 #> [6] 7.143150e+08 mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #>                                Klebsiella pneumoniae  #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\"  mo_synonyms(\"Klebsiella pneumoniae\") #> NULL  # scientific reference ----------------------------------------------------- mo_ref(\"Klebsiella pneumoniae\") #> [1] \"Trevisan, 1887\" mo_authors(\"Klebsiella pneumoniae\") #> [1] \"Trevisan\" mo_year(\"Klebsiella pneumoniae\") #> [1] 1887 mo_lpsn(\"Klebsiella pneumoniae\") #> [1] 777151  # abbreviations known in the field ----------------------------------------- mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\"  mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EHEC\") #> [1] \"coli\"  # known subspecies --------------------------------------------------------- mo_name(\"doylei\") #> [1] \"Campylobacter jejuni doylei\" mo_genus(\"doylei\") #> [1] \"Campylobacter\" mo_species(\"doylei\") #> [1] \"jejuni\" mo_subspecies(\"doylei\") #> [1] \"doylei\"  mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\"  # \\donttest{ # Becker classification, see ?as.mo ---------------------------------------- mo_fullname(\"S. epi\") #> ℹ Function `as.mo()` is uncertain about \"S. epi\" (assuming Staphylococcus #>   epidermidis). Run `mo_uncertainties()` to review this. #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"S. epi\", Becker = TRUE) #> ℹ Function `as.mo()` is uncertain about \"S. epi\" (assuming Staphylococcus #>   epidermidis). Run `mo_uncertainties()` to review this. #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"S. epi\") #> ℹ Function `as.mo()` is uncertain about \"S. epi\" (assuming Staphylococcus #>   epidermidis). Run `mo_uncertainties()` to review this. #> [1] \"S. epidermidis\" mo_shortname(\"S. epi\", Becker = TRUE) #> ℹ Function `as.mo()` is uncertain about \"S. epi\" (assuming Staphylococcus #>   epidermidis). Run `mo_uncertainties()` to review this. #> [1] \"CoNS\"  # Lancefield classification, see ?as.mo ------------------------------------ mo_fullname(\"S. pyo\") #> [1] \"Streptococcus pyogenes\" mo_fullname(\"S. pyo\", Lancefield = TRUE) #> [1] \"Streptococcus group A\" mo_shortname(\"S. pyo\") #> [1] \"S. pyogenes\" mo_shortname(\"S. pyo\", Lancefield = TRUE) #> [1] \"GAS\"   # language support  -------------------------------------------------------- mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") #> [1] \"Gram negativo\"  # mo_type is equal to mo_kingdom, but mo_kingdom will remain official mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\"  mo_fullname(\"S. pyogenes\",             Lancefield = TRUE,             language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\",             Lancefield = TRUE,             language = \"nl\") #> [1] \"Streptococcus groep A\"   # other --------------------------------------------------------------------  mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1]  TRUE FALSE FALSE  # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_gram_positive())        example_isolates %>%     filter(mo_is_intrinsic_resistant(ab = \"vanco\")) } #> ℹ Using column 'mo' as input for `mo_is_gram_positive()` #> ℹ Using column 'mo' as input for `mo_is_intrinsic_resistant()` #> # A tibble: 709 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI #>  4 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI #>  5 2002-01-21 D        FALSE   TRUE    FALSE      75 F      462081  B_CTRBC_FRND #>  6 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL #>  7 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL #>  8 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS #>  9 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS #> 10 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS #> # … with 699 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names   # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Proteobacteria\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>   # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Proteobacteria\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] 1.098101e+15 4.468700e+08 1.098201e+15 4.098010e+08 5.641501e+07 #> [6] 7.143150e+08 #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(   path,   destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") )  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file, default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set R option, using options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names:   save \"home//ourcodes.xlsx\". Now set source:   now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions:   edit Excel file , say, adding row 4 like :   ...new usage MO function package update data file:   delete reference data file, just use \"\", NULL FALSE input set_mo_source():   original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"|         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 |                    |                       | set_mo_source(\"home/me/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns  #>       \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class <mo> #> [1] B_ESCHR_COLI  mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\"  # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #>       Use mo_uncertainties() to review it. #> Class <mo> #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI |         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus   | Staphylococcus aureus | 5 |                    |                       | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from  #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>        \"Organisation XYZ\" and \"mo\" #> Class <mo> #> [1] B_ESCHR_COLI  mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca(   x,   ...,   retx = TRUE,   center = TRUE,   scale. = TRUE,   tol = NULL,   rank. = NULL )"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables     returned. center logical value indicating whether variables     shifted zero centered. Alternately, vector     length equal number columns x can supplied.     value passed scale. scale. logical value indicating whether variables     scaled unit variance analysis takes     place.  default FALSE consistency S,     general scaling advisable.  Alternatively, vector length     equal number columns x can supplied.      value passed scale. tol value indicating magnitude components     omitted. (Components omitted     standard deviations less equal tol times     standard deviation first component.)  default null     setting, components omitted (unless rank. specified     less min(dim(x)).).  settings tol     tol = 0 tol = sqrt(.Machine$double.eps),     omit essentially constant components. rank. optionally, number specifying maximal rank, .e.,     maximal number principal components used.  Can set     alternative addition tol, useful notably     desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first    resistance_data <- example_isolates %>%      group_by(order = mo_order(mo),       # group on anything, like order              genus = mo_genus(mo)) %>%   #   and genus as we do here;     filter(n() >= 30) %>%                # filter on only 30 results per group     summarise_if(is.rsi, resistance)     # then get resistance of all drugs        # now conduct PCA for certain antimicrobial agents   pca_result <- resistance_data %>%              pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)         pca_result   summary(pca_result)      # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   ggplot_pca(pca_result)      if (require(\"ggplot2\")) {     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #>   and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting for Classes rsi, mic and disk — plot","title":"Plotting for Classes rsi, mic and disk — plot","text":"Functions plot classes rsi, mic disk, support base R ggplot2.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting for Classes rsi, mic and disk — plot","text":"","code":"# S3 method for mic plot(   x,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   main = deparse(substitute(x)),   ylab = \"Frequency\",   xlab = \"Minimum Inhibitory Concentration (mg/L)\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic autoplot(   object,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   title = deparse(substitute(object)),   ylab = \"Frequency\",   xlab = \"Minimum Inhibitory Concentration (mg/L)\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic fortify(object, ...)  # S3 method for disk plot(   x,   main = deparse(substitute(x)),   ylab = \"Frequency\",   xlab = \"Disk diffusion diameter (mm)\",   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk autoplot(   object,   mo = NULL,   ab = NULL,   title = deparse(substitute(object)),   ylab = \"Frequency\",   xlab = \"Disk diffusion diameter (mm)\",   guideline = \"EUCAST\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk fortify(object, ...)  # S3 method for rsi plot(   x,   ylab = \"Percentage\",   xlab = \"Antimicrobial Interpretation\",   main = deparse(substitute(x)),   language = get_AMR_locale(),   ... )  # S3 method for rsi autoplot(   object,   title = deparse(substitute(object)),   xlab = \"Antimicrobial Interpretation\",   ylab = \"Frequency\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   ... )  # S3 method for rsi fortify(object, ...)"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting for Classes rsi, mic and disk — plot","text":"x, object values created .mic(), .disk() .rsi() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial code .ab() guideline interpretation guideline use, defaults latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title colours_RSI colours use filling bars, must vector three values (order R, S ). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant', defaults system language (see get_AMR_locale()) can overwritten setting option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL language = \"\" prevent translation. expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting for Classes rsi, mic and disk — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function. fortify() functions return data.frame extension usage ggplot2::ggplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plotting for Classes rsi, mic and disk — plot","text":"interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, supported guidelines used input guideline argument : \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"EUCAST 2012\", \"EUCAST 2011\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\", \"CLSI 2013\", \"CLSI 2012\" \"CLSI 2011\". Simply using \"CLSI\" \"EUCAST\" input automatically select latest version guideline.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting for Classes rsi, mic and disk — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_rsi_values <- random_rsi(50, prob_RSI = c(0.30, 0.55, 0.05))  plot(some_mic_values)  plot(some_disk_values)  plot(some_rsi_values)   # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\") #> ℹ Function `as.mo()` is uncertain about \"S. aureus\" (assuming #>   Staphylococcus aureus). Run `mo_uncertainties()` to review this.  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")   # \\donttest{ if (require(\"ggplot2\")) {   autoplot(some_mic_values)   autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")   autoplot(some_rsi_values) }  # }"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Microbial Resistance — proportion","title":"Calculate Microbial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Microbial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   minimum = 30,   as_percent = FALSE,   combine_SI = TRUE,   combine_IR = FALSE )  rsi_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   minimum = 30,   as_percent = FALSE,   combine_SI = TRUE,   combine_IR = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate Microbial Resistance — proportion","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Microbial Resistance — proportion","text":"... one vectors (columns) antibiotic interpretations. transformed internally .rsi() needed. Use multiple columns calculate (lack ) co-resistance: probability one two drugs resistant susceptible result. See Examples. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. as_percent logical indicate whether output must returned hundred fold % sign (character). value 0.123456 returned \"12.3%\". only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class rsi (see .rsi()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant). used argument combine_IR, now follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . Default TRUE. combine_IR logical indicate whether values R must merged one, output consists S vs. +R (susceptible vs. non-susceptible). outdated, see argument combine_SI.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Microbial Resistance — proportion","text":"double , as_percent = TRUE, character.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Microbial Resistance — proportion","text":"function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set. functions meant count isolates, calculate proportion resistance/susceptibility. Use count() functions count isolates. function susceptibility() essentially equal count_susceptible() / count_all(). Low counts can influence outcome - proportion functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data rsi class (created .rsi()) calculates proportions R, S. also supports grouped variables. function rsi_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Microbial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Calculate Microbial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Microbial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ resistance(example_isolates$AMX)     # determines %R #> [1] 0.5955556 susceptibility(example_isolates$AMX) # determines %S+I #> [1] 0.4044444  # be more specific proportion_S(example_isolates$AMX) #> [1] 0.4022222 proportion_SI(example_isolates$AMX) #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> [1] 0.5977778 proportion_R(example_isolates$AMX) #> [1] 0.5955556  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(hospital_id) %>%     summarise(r = resistance(CIP),               n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi     example_isolates %>%     group_by(hospital_id) %>%     summarise(R  = resistance(CIP, as_percent = TRUE),               SI = susceptibility(CIP, as_percent = TRUE),               n1 = count_all(CIP),  # the actual total; sum of all three               n2 = n_rsi(CIP),      # same - analogous to n_distinct               total = n())          # NOT the number of tested isolates!     # Calculate co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy:   example_isolates %>% susceptibility(AMC)  # %SI = 76.3%   example_isolates %>% count_all(AMC)       #   n = 1879     example_isolates %>% susceptibility(GEN)  # %SI = 75.4%   example_isolates %>% count_all(GEN)       #   n = 1855     example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%   example_isolates %>% count_all(AMC, GEN)      #   n = 1939       # See Details on how `only_all_tested` works. Example:   example_isolates %>%     summarise(numerator = count_susceptible(AMC, GEN),               denominator = count_all(AMC, GEN),               proportion = susceptibility(AMC, GEN))     example_isolates %>%     summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),               denominator = count_all(AMC, GEN, only_all_tested = TRUE),               proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))       example_isolates %>%     group_by(hospital_id) %>%     summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),               cipro_n = count_all(CIP),               genta_p = susceptibility(GEN, as_percent = TRUE),               genta_n = count_all(GEN),               combination_p = susceptibility(CIP, GEN, as_percent = TRUE),               combination_n = count_all(CIP, GEN))     # Get proportions S/I/R immediately of all rsi columns   example_isolates %>%     select(AMX, CIP) %>%     proportion_df(translate = FALSE)     # It also supports grouping variables   # (use rsi_df to also include the count)   example_isolates %>%     select(hospital_id, AMX, CIP) %>%     group_by(hospital_id) %>%     rsi_df(translate = FALSE) } #>    hospital_id antibiotic interpretation     value isolates #> 1            A        AMX             SI 0.3932584       70 #> 2            A        AMX              R 0.6067416      108 #> 3            A        CIP             SI 0.8743961      181 #> 4            A        CIP              R 0.1256039       26 #> 5            B        AMX             SI 0.3854875      170 #> 6            B        AMX              R 0.6145125      271 #> 7            B        CIP             SI 0.8296593      414 #> 8            B        CIP              R 0.1703407       85 #> 9            C        AMX             SI 0.3714286       65 #> 10           C        AMX              R 0.6285714      110 #> 11           C        CIP             SI 0.7970297      161 #> 12           C        CIP              R 0.2029703       41 #> 13           D        AMX             SI 0.4334532      241 #> 14           D        AMX              R 0.5665468      315 #> 15           D        CIP             SI 0.8483034      425 #> 16           D        CIP              R 0.1516966       76 # }"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":null,"dir":"Reference","previous_headings":"","what":"Random MIC Values/Disk Zones/RSI Generation — random","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"functions can used generating random MIC values disk diffusion diameters, AMR data analysis practice. providing microorganism antimicrobial agent, generated results reflect reality much possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"","code":"random_mic(size = NULL, mo = NULL, ab = NULL, ...)  random_disk(size = NULL, mo = NULL, ab = NULL, ...)  random_rsi(size = NULL, prob_RSI = c(0.33, 0.33, 0.33), ...)"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"size desired size returned vector. used data.frame call dplyr verb, get current (group) size left blank. mo character can coerced valid microorganism code .mo() ab character can coerced valid antimicrobial agent code .ab() ... ignored, place allow future extensions prob_RSI vector length 3: probabilities \"R\" (1st value), \"S\" (2nd value) \"\" (3rd value)","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"class <mic> random_mic() (see .mic()) class <disk> random_disk() (see .disk())","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"base R function sample() used generating values. Generated values based EUCAST 2022 guideline implemented rsi_translation data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"","code":"random_mic(25) #> Class <mic> #>  [1] 0.01    0.002   4       2       0.005   0.025   >=256   128     0.002   #> [10] 2       <=0.001 2       64      16      >=256   0.125   0.002   0.002   #> [19] 0.005   0.25    0.005   2       >=256   0.01    16      random_disk(25) #> Class <disk> #>  [1] 28 48 49 18 11 30 16 23 16 12 35 47 42  8 20 49 44 31 42 25 18  7 26 41 44 random_rsi(25) #> Class <rsi> #>  [1] R I I R S R S S I S I I S I R I R S I R S I S S R  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\")                 # range 0.0625-64 #> Class <mic> #>  [1] 0.005   0.002   128     2       0.0625  1       <=0.001 256     16      #> [10] 32      <=0.001 <=0.001 32      128     64      64      32      0.025   #> [19] 8       2       0.002   0.5     0.125   <=0.001 256     random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\")    # range 0.0625-16 #> Class <mic> #>  [1] 4     8     2     8     8     <=0.5 2     4     <=0.5 4     32    4     #> [13] <=0.5 32    <=0.5 1     2     <=0.5 <=0.5 8     64    <=0.5 16    64    #> [25] <=0.5 random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class <mic> #>  [1] 2       4       0.25    1       <=0.125 1       2       0.5     2       #> [10] 1       2       <=0.125 0.5     1       4       4       1       <=0.125 #> [19] 0.5     0.5     0.5     2       2       4       2        random_disk(25, \"Klebsiella pneumoniae\")                  # range 8-50 #> Class <disk> #>  [1] 38 25 31 29  9 27  9 16 47 41 43 32 19 14 38 18 28 11  8 42 43 22 46 18 28 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\")    # range 11-17 #> Class <disk> #>  [1] 12 15 14 17 11 15 17 16 15 17 14 12 15 14 13 16 12 13 12 12 12 16 12 15 14 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class <disk> #>  [1] 21 18 23 25 23 16 15 19 19 19 25 27 27 17 21 21 23 18 22 25 27 25 18 15 15 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  rsi_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  # S3 method for resistance_predict plot(x, main = paste(\"Resistance Prediction of\", x_name), ...)  ggplot_rsi_predict(   x,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )  # S3 method for resistance_predict autoplot(   object,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already, defaults first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model, defaults 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates,                          col_ab = \"AMX\",                         year_min = 2010,                         model = \"binomial\") #> ℹ Using column 'date' as input for `col_date`. plot(x)  # \\donttest{ if (require(\"ggplot2\")) {   ggplot_rsi_predict(x) }   # using dplyr: if (require(\"dplyr\")) {   x <- example_isolates %>%     filter_first_isolate() %>%     filter(mo_genus(mo) == \"Staphylococcus\") %>%     resistance_predict(\"PEN\", model = \"binomial\")   plot(x)    # get the model from the object   mymodel <- attributes(x)$model   summary(mymodel) } #> ℹ Using column 'date' as input for `col_date`.  #>  #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #>  #> Deviance Residuals:  #>     Min       1Q   Median       3Q      Max   #> -1.4150  -0.1855   0.0000   0.4096   0.9277   #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 47.02915   72.98739   0.644    0.519 #> year        -0.02280    0.03637  -0.627    0.531 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.9620  on 11  degrees of freedom #> Residual deviance: 5.5701  on 10  degrees of freedom #> AIC: 50.533 #>  #> Number of Fisher Scoring iterations: 4 #>   # create nice plots with ggplot2 yourself if (require(\"dplyr\") & require(\"ggplot2\")) {    data <- example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     resistance_predict(col_ab = \"AMX\",                        col_date = \"date\",                        model = \"binomial\",                        info = FALSE,                        minimum = 15)   head(data)   autoplot(data) } #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this.  # }"},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set for R/SI Interpretation — rsi_translation","title":"Data Set for R/SI Interpretation — rsi_translation","text":"Data set containing reference data interpret MIC disk diffusion R/SI values, according international guidelines. Currently implemented guidelines EUCAST (2011-2022) CLSI (2011-2022). Use .rsi() transform MICs disks measurements R/SI values.","code":""},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set for R/SI Interpretation — rsi_translation","text":"","code":"rsi_translation"},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set for R/SI Interpretation — rsi_translation","text":"data.frame 20,369 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set for R/SI Interpretation — rsi_translation","text":"repository AMR package contains file comprising exact data set: https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt. file allows machine reading EUCAST CLSI guidelines, almost impossible Excel PDF files distributed EUCAST CLSI. file updated automatically mo ab columns transformed contain full official names instead codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set for R/SI Interpretation — rsi_translation","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set for R/SI Interpretation — rsi_translation","text":"","code":"head(rsi_translation) #>     guideline method site           mo rank_index  ab     ref_tbl disk_dose #> 1 EUCAST 2022    MIC <NA> F_ASPRG_MGTS          2 AMB Aspergillus      <NA> #> 2 EUCAST 2022    MIC <NA> F_ASPRG_NIGR          2 AMB Aspergillus      <NA> #> 3 EUCAST 2022    MIC <NA>      F_CANDD          3 AMB     Candida      <NA> #> 4 EUCAST 2022    MIC <NA> F_CANDD_ALBC          2 AMB     Candida      <NA> #> 5 EUCAST 2022    MIC <NA> F_CANDD_DBLN          2 AMB     Candida      <NA> #> 6 EUCAST 2022    MIC <NA> F_CANDD_KRUS          2 AMB     Candida      <NA> #>   breakpoint_S breakpoint_R   uti #> 1            1            1 FALSE #> 2            1            1 FALSE #> 3            1            1 FALSE #> 4            1            1 FALSE #> 5            1            1 FALSE #> 6            1            1 FALSE"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE)  # S3 method for default skewness(x, na.rm = FALSE)  # S3 method for matrix skewness(x, na.rm = FALSE)  # S3 method for data.frame skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] -0.01821464"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, like mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale()  set_AMR_locale(lang)  reset_AMR_locale()  translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"lang language choose. Use one supported language names ISO-639-1 codes: \"English\" (\"en\"), \"Chinese\" (\"zh\"), \"Danish\" (\"da\"), \"Dutch\" (\"nl\"), \"French\" (\"fr\"), \"German\" (\"de\"), \"Greek\" (\"el\"), \"Italian\" (\"\"), \"Japanese\" (\"ja\"), \"Polish\" (\"pl\"), \"Portuguese\" (\"pt\"), \"Russian\" (\"ru\"), \"Spanish\" (\"es\"), \"Swedish\" (\"sv\"), \"Turkish\" (\"tr\"), \"Ukrainian\" (\"uk\"). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 16 supported languages English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish Ukrainian. languages translations available antimicrobial agents colloquial microorganism names. Please read adding updating language Wiki.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting R option AMR_locale, either using set_AMR_locale() running e.g. options(AMR_locale = \"de\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, R option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\"  # setting another language set_AMR_locale(\"Greek\") #> ℹ Using the Greek language (Ελληνικά) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Σιπροφλοξασίνη\" mo_name(\"Coagulase-negative Staphylococcus\") #> [1] \"Σταφυλόκοκκος με αρνητική πηκτικότητα (CoNS)\"  set_AMR_locale(\"Spanish\") #> ℹ Using the Spanish language (Español) for the AMR package for this #>   session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacina\" mo_name(\"Coagulase-negative Staphylococcus\") #> [1] \"Staphylococcus coagulasa negativo (SCN)\"  # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using the German language (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using the German language (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using the German language (Deutsch) for the AMR package for this session.  # reset to system default reset_AMR_locale()"},{"path":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-8-1-9020","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.8.1.9020","text":"EUCAST 2022 CLSI 2022 guidelines added .rsi(). EUCAST 2022 now new default guideline MIC disks diffusion interpretations. Support following languages: Chinese, Greek, Japanese, Polish, Turkish Ukrainian. AMR package now available 16 languages.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-8-1-9020","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.8.1.9020","text":"Fix .rsi() certain EUCAST breakpoints MIC values Removed .integer() MIC values, since MIC integer values running table() MIC values consequently failed able retrieve level position (’s normally .integer() factors work) droplevels() MIC now return common factor default lose <mic> class. Use droplevels(..., .mic = TRUE) keep <mic> class. Small fix using ab_from_text() Fixes reading text files using set_mo_source(), now also allows source file contain valid taxonomic names instead valid microorganism ID package Using random_*() function (random_mic()) now possible directly calling package without loading first: AMR::random_mic(10) Added Toxoplasma gondii (P_TXPL_GOND) microorganisms data set, together genus, family, order Changed value column prevalence microorganisms data set 3 2 genera: Acholeplasma, Alistipes, Alloprevotella, Bergeyella, Borrelia, Brachyspira, Butyricimonas, Cetobacterium, Chlamydia, Chlamydophila, Deinococcus, Dysgonomonas, Elizabethkingia, Empedobacter, Haloarcula, Halobacterium, Halococcus, Myroides, Odoribacter, Ornithobacterium, Parabacteroides, Pedobacter, Phocaeicola, Porphyromonas, Riemerella, Sphingobacterium, Streptobacillus, Tenacibaculum, Terrimonas, Victivallis, Wautersiella, Weeksella Fix using form df[carbapenems() == \"R\", ] using latest vctrs package Fix using info = FALSE mdro()","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-1-9020","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.1.9020","text":"New website make use new Bootstrap 5 pkgdown v2.0. website now contains results examples automatically regenerated every change repository, using GitHub Actions Added Peter Dutey-Magni Anton Mymrikov contributors, thank valuable input","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-181","dir":"Changelog","previous_headings":"","what":"AMR 1.8.1","title":"AMR 1.8.1","text":"CRAN release: 2022-03-24","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-8-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.8.1","text":"Fix using .rsi() values containing capped values (>=), sometimes leading NA Support antibiotic interpretations MIPS laboratory system: \"U\" S (‘susceptible urine’), \"D\" (‘susceptible dose-dependent’) Improved algorithm .mo(), especially ignoring non-taxonomic text, : informative warning messages Added 192 valid MIC Updated MIC printing tibbles Increased speed loading package","code":"mo_name(\"methicillin-resistant S. aureus (MRSA)\") #> [1] \"Staphylococcus aureus\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-1","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.1","text":"Fix unit testing R 3.3 Fix size image elements, requested CRAN","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-180","dir":"Changelog","previous_headings":"","what":"AMR 1.8.0","title":"AMR 1.8.0","text":"CRAN release: 2022-01-07","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-changes-1-8-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"AMR 1.8.0","text":"Removed p_symbol() filter_*() functions (except filter_first_isolate()), deprecated previous package version Removed key_antibiotics() key_antibiotics_equal() functions, deprecated superseded key_antimicrobials() antimicrobials_equal() Removed previously implemented ggplot2::ggplot() generics classes <mic>, <disk>, <rsi> <resistance_predict> follow ggplot2 logic. replaced ggplot2::autoplot() generics. Renamed function get_locale() get_AMR_locale() prevent conflicts packages","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-8-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.8.0","text":"Support CLSI 2021 guideline interpreting MIC/disk diffusion values, incorporated rsi_translation data set. data set now strictly follows WHONET software well. Support EUCAST Intrinsic Resistance Unusual Phenotypes v3.3 (October 2021). now default EUCAST guideline package (older guidelines still available) eucast_rules(), mo_is_intrinsic_resistant() mdro(). intrinsic_resistant data set also updated accordingly. Support antimicrobial drug (group) names colloquial microorganism names Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish Swedish Function set_ab_names() rename data set columns resemble antimicrobial drugs. allows quickly renaming columns official names, ATC codes, etc. second argument can tidyverse way selecting: Function mo_lpsn() retrieve LPSN record ID Function ab_ddd_units() get units DDDs (daily defined doses), deprecating use ab_ddd(..., units = TRUE) consistent data types function output","code":"example_isolates %>% set_ab_names(where(is.rsi)) example_isolates %>% set_ab_names(AMC:GEN, property = \"atc\")"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-8-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.8.0","text":"Updated bacterial taxonomy 5 October 2021 (according LPSN), including 11 new staphylococcal species named since 1 January last year drugs now contain multiple ATC codes (e.g., metronidazole contains 5) antibiotics$atc now list containing character vectors, atc column moved 5th position antibiotics data set ab_atc() always return character vector length 1, returns list input larger length 1 ab_info() slightly different output DDDs (daily defined doses) added updated according newly included ATC codes now also work R-3.0 R-3.1, supporting every version R since 2013 like rest package Added selectors antibiotic classes: aminopenicillins(), antifungals(), antimycobacterials(), lincosamides(), lipoglycopeptides(), polymyxins(), quinolones(), streptogramins(), trimethoprims() ureidopenicillins() Added specific selectors certain types treatment: administrable_per_os() administrable_iv(), based available Defined Daily Doses (DDDs), defined WHOCC. ideal e.g. analysing pathogens primary care IV treatment option. can combined AB selectors, e.g. select penicillins administrable per os (.e., orally): Added selector ab_selector(), accepts filter used internally antibiotics data set, yielding great flexibility drug properties, selecting antibiotic columns oral DDD least 1 gram: Added selector not_intrinsic_resistant(), keeps antibiotic columns intrinsic resistant microorganisms data set, based latest EUCAST guideline intrinsic resistance. example, data set contains microorganism codes names E. coli K. pneumoniae contains column “vancomycin”, column removed (rather, unselected) using function. Added argument only_treatable, defaults TRUE exclude drugs laboratory tests treating patients (imipenem/EDTA gentamicin-high) Fix using selectors multiple times one call (e.g., using dplyr::filter() immediately dplyr::select()) Fix using multiple columns coerced antibiotic agent Fixed using () () antibiotic selectors R Markdown file Added following antimicrobial agents now covered : aztreonam/nacubactam (ANC), cefepime/nacubactam (FNC), exebacase (EXE), ozenoxacin (OZN), zoliflodacin (ZFD), manogepix (MGX), ibrexafungerp (IBX), rezafungin (RZF). None agents ATC code yet. Fixed Gram stain (mo_gramstain()) determination taxonomic class Negativicutes within phylum Firmicutes - considered Gram-positives phylum actually Gram-negative. impacts 137 taxonomic species, genera families, Negativicoccus Veillonella. Dramatic speed improvement first_isolate() Fix prevent introducing NAs old MO codes running .mo() Added informative error messages proportion_*() count_*() functions fail printing tibble old MO code, warning thrown old codes updated using .mo() Improved automatic column selector col_* arguments left blank, e.g. first_isolate() right input types random_mic(), random_disk() random_rsi() now enforced .rsi() improved algorithm can now also correct textual input (“Susceptible”, “Resistant”) supported languages .mic() improved algorithm warnings thrown isolates count_*(), proportion_*() function (resistant() susceptible()), dplyr group shown, available Fix legends created scale_rsi_colours() using ggplot2 v3.3.4 higher (ggplot2 bug 4511, soon fixed) Fix minor translation errors Fix MIC interpretation Morganellaceae (Morganella Proteus) using EUCAST 2021 guideline Improved algorithm .mo() Improved algorithm generating random MICs random_mic() Improved plot legends MICs disk diffusion values Improved speed .ab() ab_*() functions Added fortify() extensions plotting methods NA values classes <mic>, <disk> <rsi> now exported objects package, e.g. NA_mic_ NA class mic (just like base R NA_character_ NA class character) proportion_df(), count_df() rsi_df() functions now return additional S3 class ‘rsi_df’ can extended packages mdro() function now returns NA rows test results species_id column microorganisms data set now contains LPSN record numbers. reason, column now numeric instead character, mo_url() updated reflect change. Fixed small bug functions get_episode() is_new_episode() get_episode() is_new_episode() can now cope NAs","code":"example_isolates[, penicillins() & administrable_per_os()]          # base R example_isolates %>% select(penicillins() & administrable_per_os()) # dplyr example_isolates[, ab_selector(oral_ddd > 1 & oral_units == \"g\")]          # base R example_isolates %>% select(ab_selector(oral_ddd > 1 & oral_units == \"g\")) # dplyr"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.0","text":"package now maintained two epidemiologists data scientist two different non-profit healthcare organisations.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-171","dir":"Changelog","previous_headings":"","what":"AMR 1.7.1","title":"AMR 1.7.1","text":"CRAN release: 2021-06-03","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-change-1-7-1","dir":"Changelog","previous_headings":"","what":"Breaking change","title":"AMR 1.7.1","text":"antibiotic class selectors (carbapenems(), aminoglycosides()) can now used filtering well, making accompanying filter_*() functions redundant (filter_carbapenems(), filter_aminoglycosides()). functions now deprecated removed next release. Examples selectors can used filtering:","code":"# select columns with results for carbapenems example_isolates[, carbapenems()]           # base R example_isolates %>% select(carbapenems())  # dplyr  # filter rows for resistance in any carbapenem example_isolates[any(carbapenems() == \"R\"), ]                  # base R example_isolates %>% filter(any(carbapenems() == \"R\"))         # dplyr example_isolates %>% filter(if_any(carbapenems(), ~.x == \"R\")) # dplyr (formal)  # filter rows for resistance in all carbapenems example_isolates[all(carbapenems() == \"R\"), ]           # base R example_isolates[carbapenems() == \"R\", ] example_isolates %>% filter(all(carbapenems() == \"R\"))  # dplyr example_isolates %>% filter(carbapenems() == \"R\")"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-7-1","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.7.1","text":"Support CLSI 2020 guideline interpreting MICs disk diffusion values (using .rsi()) Function custom_eucast_rules() brings support custom AMR rules eucast_rules() Function italicise_taxonomy() make taxonomic names within string italic, support markdown ANSI first_isolate() function gained argument method “phenotype-based”, “episode-based”, “patient-based”, “isolate-based”. old behaviour equal “episode-based”. new default “phenotype-based” antimicrobial test results available, “episode-based” otherwise. new default yield slightly isolates selection (good thing). Since fungal isolates can also selected, functions key_antibiotics() key_antibiotics_equal() now deprecated favour key_antimicrobials() antimicrobials_equal() functions. Also, new all_antimicrobials() function works like old key_antibiotics() function, includes column antimicrobial test results. Using key_antimicrobials() still selects six preferred antibiotics Gram-negatives, six Gram-positives, six universal antibiotics. new antifungal argument set antifungal agents (antimycotics). Using type == \"points\" first_isolate() function phenotype-based selection now consider antimicrobial drugs data set, using new all_antimicrobials() first_isolate() function can now take vector values col_keyantibiotics can episode length Inf Since phenotype-based method new default, filter_first_isolate() renders filter_first_weighted_isolate() function redundant. reason, filter_first_weighted_isolate() now deprecated. documentation first_isolate() key_antimicrobials() functions completely rewritten. Function betalactams() additional antbiotic column selector function filter_betalactams() additional antbiotic column filter. group betalactams consists carbapenems, cephalosporins penicillins. ggplot() method resistance_predict()","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-7-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.7.1","text":"bug_drug_combinations() now supports grouping using dplyr package Custom MDRO guidelines can now combined custom MDRO guidelines using c() Fix applying rules; previous versions, rows interpreted according last matched rule. Now, rows interpreted according first matched rule Fix age_groups() persons aged zero example_isolates data set now contains (fictitious) zero-year old patients Fix minor translation errors Printing microbial codes data.frame tibble now gives warning data contains old microbial codes (previous AMR package version) Now checks pattern valid regular expression Added %unlike% %unlike_case% (negations existing %like% %like_case%). greatly improves readability: Altered RStudio addin, now iterates %like% -> %unlike% -> %like_case% -> %unlike_case% keep pressing keyboard shortcut Fixed installation error R-3.0 Added info argument .mo() turn /progress bar Fixed bug col_mo functions (esp. eucast_rules() mdro()) column name microorganisms data set throw error Fix transforming numeric values RSI (.rsi()) vctrs package loaded (.e., using tidyverse) Colour fix using barplot() RSI class Added 25 common system codes bacteria microorganisms.codes data set Added 16 common system codes antimicrobial agents antibiotics data set Fix using skimr::skim() classes mo, mic disk using just released dplyr v1.0.6 Updated skimr::skim() usage MIC values also include 25th 75th percentiles Fix plotting missing MIC/disk diffusion values Updated join functions always use dplyr join functions dplyr package installed - now also preserving grouped variables Antibiotic class selectors (cephalosporins()) now maintain column order original data Fix selecting columns using fluoroquinolones() age() now vectorises x reference","code":"if (!grepl(\"EUCAST\", guideline)) ... # same: if (guideline %unlike% \"EUCAST\") ..."},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-7-1","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.7.1","text":"requested CRAN administrators: decreased package size 3 MB costs slower loading time package unit tests now processed tinytest package, instead testthat package. testthat package unfortunately requires tons dependencies also heavy usable recent R versions, disallowing developers test package R 3.* version. contrary, tinytest package lightweight dependency-free.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-160","dir":"Changelog","previous_headings":"","what":"AMR 1.6.0","title":"AMR 1.6.0","text":"CRAN release: 2021-03-14","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-6-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.6.0","text":"Support EUCAST Clinical Breakpoints v11.0 (2021), effective eucast_rules() function .rsi() interpret MIC disk diffusion values. now default guideline package. Added function eucast_dosage() get data.frame advised dosages certain bug-drug combination, based new dosage data set Added data set dosage fuel new eucast_dosage() function make data available structured way Existing data set example_isolates now reflects latest EUCAST rules Added argument only_rsi_columns functions, defaults FALSE, indicate functions must applied columns class <rsi> (.e., transformed .rsi()). increases speed since automatic determination antibiotic columns needed anymore. Affected functions : antibiotic selector functions (ab_class() wrappers, aminoglycosides(), carbapenems(), penicillins()) antibiotic filter functions (filter_ab_class() wrappers, filter_aminoglycosides(), filter_carbapenems(), filter_penicillins()) eucast_rules() mdro() (including wrappers brmo(), mrgn() eucast_exceptional_phenotypes()) guess_ab_col() Functions oxazolidinones() (antibiotic selector function) filter_oxazolidinones() (antibiotic filter function) select/filter e.g. linezolid tedizolid Support custom MDRO guidelines, using new custom_mdro_guideline() function, please see mdro() additional info ggplot() generics classes <mic> <disk> Function mo_is_yeast(), determines whether microorganism member taxonomic class Saccharomycetes taxonomic order Saccharomycetales: mo_type() function also updated reflect change: Added Pretomanid (PMD, J04AK08) antibiotics data set MIC values (see .mic()) can now used mathematical processing, usage inside functions min(), max(), range(), binary operators (+, -, etc.). allows easy distribution analysis fast filtering MIC values:","code":"library(dplyr) x <- example_isolates %>% select(date, hospital_id, oxazolidinones()) #> Selecting oxazolidinones: column 'LNZ' (linezolid)  x <- example_isolates %>% filter_oxazolidinones() #> Filtering on oxazolidinones: value in column `LNZ` (linezolid) is either \"R\", \"S\" or \"I\" mo_kingdom(c(\"Aspergillus\", \"Candida\")) #> [1] \"Fungi\" \"Fungi\"  mo_is_yeast(c(\"Aspergillus\", \"Candida\")) #> [1] FALSE  TRUE  # usage for filtering data: example_isolates[which(mo_is_yeast()), ]   # base R example_isolates %>% filter(mo_is_yeast()) # dplyr mo_type(c(\"Aspergillus\", \"Candida\")) # [1] \"Fungi\"  \"Yeasts\" mo_type(c(\"Aspergillus\", \"Candida\"), language = \"es\") # also supported: de, nl, fr, it, pt #> [1] \"Hongos\"    \"Levaduras\" x <- random_mic(10) x #> Class <mic> #>  [1] 128   0.5   2     0.125 64    0.25  >=256 8     16    4 x[x > 4] #> Class <mic> #> [1] 128   64    >=256 8     16 range(x) #> [1]   0.125 256.000 range(log2(x)) #> [1] -3  8"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-6-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.6.0","text":"Added 3,372 new species 1,523 existing species became synomyms URL bacterial species (mo_url()) now lead https://lpsn.dsmz.de Plotting MIC disk diffusion values now support interpretation colouring supply microorganism antimicrobial agent colours updated colour-blind friendly versions values R, S plot methods (also applies tibble printing) Interpretation MIC disk diffusion values R/SI now translated system language German, Dutch Spanish (see translate) Plotting now possible base R using plot() ggplot2 using ggplot() vector MIC disk diffusion values Updated SNOMED codes US Edition SNOMED CT 1 September 2020 added source help page microorganisms data set .rsi() .rsi.eligible() now return vector TRUE/FALSE input data set, iterating columns Using functions without setting data set (e.g., mo_is_gram_negative(), mo_is_gram_positive(), mo_is_intrinsic_resistant(), first_isolate(), mdro()) now work dplyrs group_by() first_isolate() can used group_by() (also using dot . input data) now returns names groups Updated data set microorganisms.codes (contains popular LIS WHONET codes microorganisms) species Mycobacterium previously incorrectly returned M. africanum WHONET code \"PNV\" now correctly interpreted PHN, antibiotic code phenoxymethylpenicillin (‘peni V’) Fix verbose output mdro(..., verbose = TRUE) German guideline (3MGRN 4MGRN) Dutch guideline (BRMO, P. aeruginosa) .rsi.eligible() now detects column name resembles antibiotic name code now returns TRUE immediately input contains values “R”, “S” “”. drastically improves speed, also lot functions rely automatic determination antibiotic columns. Functions get_episode() is_new_episode() now support less day value argument episode_days (e.g., include one patient/test per hour) Argument ampc_cephalosporin_resistance eucast_rules() now also applies value “” (“S”) Functions print() summary() Principal Components Analysis object (pca()) now print additional group info original data grouped using dplyr::group_by() Improved speed reliability guess_ab_col(). also internally improves reliability first_isolate() mdro(), might slight impact results functions. Fix mo_name() used languages English like() function (fast alias %like%) now always use Perl compatibility, improving speed many functions package (e.g., .mo() now 4 times faster) Staphylococcus cornubiensis now correctly categorised coagulase-positive random_disk() random_mic() now expanded range randomisation Support GISA (glycopeptide-intermediate S. aureus), e.g. mo_genus(\"GISA\") return \"Staphylococcus\" Added translations German Spanish 200 antimicrobial drugs Speed improvement .ab() input official name ATC code Added argument include_untested_rsi first_isolate() functions (defaults TRUE keep existing behaviour), able exclude rows R/SI values (class <rsi>, see .rsi()) empty","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-6-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.6.0","text":"Big documentation updates Loading package (.e., library(AMR)) now ~50 times faster , costs package size (increased ~3 MB)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-150","dir":"Changelog","previous_headings":"","what":"AMR 1.5.0","title":"AMR 1.5.0","text":"CRAN release: 2021-01-06","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-5-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.5.0","text":"Functions get_episode() is_new_episode() determine (patient) episodes necessarily based microorganisms. get_episode() function returns index number episode per group, is_new_episode() function returns values TRUE/FALSE indicate whether item vector start new episode. also support dplyrs grouping (.e. using group_by()): Functions mo_is_gram_negative() mo_is_gram_positive() wrappers around mo_gramstain(). always return TRUE FALSE (except input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Function mo_is_intrinsic_resistant() test intrinsic resistance, based EUCAST Intrinsic Resistance Unusual Phenotypes v3.2 2020. Functions random_mic(), random_disk() random_rsi() random value generation. functions random_mic() random_disk() take microorganism names antibiotic names input make generation realistic.","code":"library(dplyr) example_isolates %>%   group_by(patient_id, hospital_id) %>%   filter(is_new_episode(date, episode_days = 60))"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-5-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.5.0","text":"New argument ampc_cephalosporin_resistance eucast_rules() correct AmpC de-repressed cephalosporin-resistant mutants Interpretation antimicrobial resistance - .rsi(): Reference data used .rsi() can now set user, using reference_data argument. allows using interpretation guidelines. user-set data must structure rsi_translation. Better determination disk zones MIC values running .rsi() data.frame Fix using .rsi() data.frame older R versions .rsi() data.frame print message anymore values already clean R/SI values using .rsi() MICs disk diffusion intrinsic antimicrobial resistance, warning thrown remind Fix using .rsi() data.frame contains one column antibiotic interpretations functions now context-aware used inside dplyr verbs, filter(), mutate() summarise(). means data argument need set anymore. case new functions: mo_is_gram_negative() mo_is_gram_positive() mo_is_intrinsic_resistant() … existing functions: first_isolate(), key_antibiotics(), mdro(), brmo(), mrgn(), mdr_tb(), mdr_cmi2012(), eucast_exceptional_phenotypes() antibiotic selection functions (cephalosporins(), aminoglycosides()) select columns based certain antibiotic group, dependency tidyselect package removed, meaning can now also used without need package installed now also work base R function calls (rely R 3.2 later): function arguments code, now defined exact type user input (inspired typed package). user input certain function meet requirements specific argument (class length), informative error thrown. makes package robust use reproducible reliable. total, 420 arguments defined. Fix set_mo_source(), previously remember file location original file Deprecated function p_symbol() really fits scope package. removed future version. See source code preserve . Updated coagulase-negative staphylococci determination Becker et al. 2020 (PMID 32056452), meaning species S. argensis, S. caeli, S. debuckii, S. edaphicus S. pseudoxylosus now considered CoNS Fix using argument reference_df .mo() mo_*() functions contain old microbial codes (previous package versions) Fixed bug mo_uncertainties() return results based MO matching score Fixed bug .mo() return results known laboratory codes microorganisms Fixed bug .ab() sometimes fail Better tibble printing MIC values Fix plotting MIC values plot() Added plot() generic class <disk> LA-MRSA CA-MRSA now recognised abbreviation Staphylococcus aureus, meaning e.g. mo_genus(\"LA-MRSA\") return \"Staphylococcus\" mo_is_gram_positive(\"LA-MRSA\") return TRUE. Fix printing class  tibbles values NA Fix mo_shortname() input contains NA .mo() takes 30 seconds, suggestions done improve speed","code":"# to select first isolates that are Gram-negative  # and view results of cephalosporins and aminoglycosides: library(dplyr) example_isolates %>%   filter(first_isolate(), mo_is_gram_negative()) %>%    select(mo, cephalosporins(), aminoglycosides()) %>%    as_tibble() # above example in base R: example_isolates[which(first_isolate() & mo_is_gram_negative()),                  c(\"mo\", cephalosporins(), aminoglycosides())]"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-5-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.5.0","text":"messages warnings thrown package now break sentences whole words extensive unit tests Internal calls options() removed favour new internal environment pkg_env Improved internal type setting (among things: replaced sapply() calls vapply()) Added CodeFactor continuous code review package: https://www.codefactor.io/repository/github/msberends/amr/ Added Dr. Rogier Schade contributor","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-140","dir":"Changelog","previous_headings":"","what":"AMR 1.4.0","title":"AMR 1.4.0","text":"CRAN release: 2020-10-08","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-4-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.4.0","text":"Support ‘EUCAST Expert Rules’ / ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ version 3.2 May 2020. addition previously implemented version 3.1 2016, eucast_rules() function can now correct 180 different antibiotics mdro() function can determine multidrug resistance based 150 different antibiotics. previously implemented versions EUCAST rules now maintained kept available package. eucast_rules() function consequently gained arguments version_breakpoints (moment defaults v10.0, 2020) version_expertrules (moment defaults v3.2, 2020). example_isolates data set now also reflects change v3.1 v3.2. mdro() function now accepts guideline == \"EUCAST3.1\" guideline == \"EUCAST3.2\". new vignette website page info public freely available data sets, can downloaded flat files formats use R, SPSS, SAS, Stata Excel: https://msberends.github.io/AMR/articles/datasets.html Data set intrinsic_resistant. data set contains bug-drug combinations ‘bug’ intrinsic resistant ‘drug’ according latest EUCAST insights. contains just two columns: microorganism antibiotic. Curious enterococci actually intrinsic resistant vancomycin? Support veterinary ATC codes Support skimming classes <rsi>, <mic>, <disk> <mo> skimr package","code":"library(AMR) library(dplyr) intrinsic_resistant %>%   filter(antibiotic == \"Vancomycin\", microorganism %like% \"Enterococcus\") %>%    pull(microorganism) #> [1] \"Enterococcus casseliflavus\" \"Enterococcus gallinarum\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-4-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.4.0","text":"Although advertised package work R 3.0.0, still dependency R 3.6.0. fixed, meaning package now work R 3.0.0. Improvements .rsi(): Support using dplyr’s across() interpret MIC values disk zone diameters, also automatically determines column microorganism names codes. Cleaning columns data.frame now allows specify columns tidy selection, e.g. .rsi(df, col1:col9) Big speed improvement interpreting MIC values disk zone diameters. interpreting 5,000 MIC values two antibiotics (10,000 values total), benchmarks showed total run time going 80.7-85.1 seconds 1.8-2.0 seconds. Added argument ‘add_intrinsic_resistance’ (defaults FALSE), considers intrinsic resistance according EUCAST Fixed bug EUCAST rules breakpoint R interpreted “>=” “<” Added intelligent data cleaning .disk(), numbers can also extracted text decimal numbers always rounded : Improvements .mo(): completely new matching score ambiguous user input, using mo_matching_score(). user input value mean one taxonomic entry now considered ‘uncertain’. Instead warning, message thrown accompanying mo_uncertainties() changed completely; now prints possible candidates matching score. Big speed improvement already valid microorganism ID. also means significant speed improvement using mo_* functions like mo_name() microoganism IDs. Added argument ignore_pattern .mo() can also given mo_* functions like mo_name(), exclude known non-relevant input analysing. can also set option AMR_ignore_pattern. get_locale() now uses default Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(). can overwritten setting option AMR_locale. Big speed improvement eucast_rules() Overall speed improvement tweaking joining functions Function mo_shortname() now returns genus input species unknown BORSA now recognised abbreviation Staphylococcus aureus, meaning e.g. mo_genus(\"BORSA\") return “Staphylococcus” Added feature AMR 1.1.0 earlier , now without package dependencies: tibble printing support classes <rsi>, <mic>, <disk>, <ab> <mo>. using tibbles containing antimicrobial columns (class <rsi>), “S” print green, “” print yellow “R” print red. Microbial IDs (class <mo>) emphasise genus species, kingdom. Names antiviral agents data set antivirals now starting capital letter, like case antibiotics data set Updated documentation WHONET data set clarify patient names fictitious Small .ab() algorithm improvements Fix combining MIC values raw numbers, .e. c(.mic(2), 2) previously failed now returns valid MIC class ggplot_rsi() geom_rsi() gained arguments minimum language, influence internal use rsi_df() Changes antibiotics data set: Updated oral parental DDDs WHOCC Added abbreviation “piptazo” ‘Piperacillin/tazobactam’ (TZP) ‘Penicillin G’ (intravenous use) now named ‘Benzylpenicillin’ (code PEN) ‘Penicillin V’ (oral use, code PNV) removed, since actual entry ‘Phenoxymethylpenicillin’ (code PHN) already existed group name (antibiotics$group) ‘Linezolid’ (LNZ), ‘Cycloserine’ (CYC), ‘Tedizolid’ (TZD) ‘Thiacetazone’ (THA) now “Oxazolidinones” instead “antibacterials” Added support using unique() classes <rsi>, <mic>, <disk>, <ab> <mo> Added argument excess kurtosis() function (defaults FALSE), return excess kurtosis, defined kurtosis minus three.","code":"# until dplyr 1.0.0 your_data %>% mutate_if(is.mic, as.rsi) your_data %>% mutate_if(is.disk, as.rsi)  # since dplyr 1.0.0 your_data %>% mutate(across(where(is.mic), as.rsi)) your_data %>% mutate(across(where(is.disk), as.rsi)) as.disk(c(\"disk zone: 23.4 mm\", 23.4)) #> Class <disk> #> [1] 24 24"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-4-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.4.0","text":"Removed functions portion_R(), portion_S() portion_I() deprecated since version 0.9.0 (November 2019) replaced proportion_R(), proportion_S() proportion_I() Removed unnecessary references base package Added packages useful functions Suggests field DESCRIPTION file","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-130","dir":"Changelog","previous_headings":"","what":"AMR 1.3.0","title":"AMR 1.3.0","text":"CRAN release: 2020-07-31","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-3-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.3.0","text":"Function ab_from_text() retrieve antimicrobial drug names, doses forms administration clinical texts e.g. health care records, also corrects misspelling since uses .ab() internally Tidyverse selection helpers antibiotic classes, help select columns antibiotics specific antibiotic class, without need define columns antibiotic abbreviations. can used function allows selection helpers, like dplyr::select() tidyr::pivot_longer(): Added mo_domain() alias mo_kingdom() Added function filter_penicillins() filter isolates specific result column name antimicrobial ‘penicillins’ class (specific: ATC subgroup Beta-lactam antibacterials, penicillins) Added official antimicrobial names filter_ab_class() functions, filter_aminoglycosides() Added antibiotics code “FOX1” cefoxitin screening (abbreviation “cfsc”) antibiotics data set Added Monuril trade name fosfomycin Added argument conserve_capped_values .rsi() interpreting MIC values - makes sure values starting “<” (“<=”) always return “S” values starting “>” (“>=”) always return “R”. default behaviour .rsi() changed, need specifically .rsi(..., conserve_capped_values = TRUE).","code":"library(dplyr)  # Columns 'IPM' and 'MEM' are in the example_isolates data set example_isolates %>%    select(carbapenems()) #> Selecting carbapenems: `IPM` (imipenem), `MEM` (meropenem)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-3-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.3.0","text":"Big speed improvement using function microorganism codes earlier package versions (prior AMR v1.2.0), .mo(), mo_name(), first_isolate(), eucast_rules(), mdro(), etc. consequence, old microbial codes (AMR v0.5.0 lower) supported anymore. Use .mo() microorganism names codes transform current abbreviations used package. Improvements susceptibility() resistance() count_*(), proportion_*() functions: 95% speed improvement using base R functions calculation Using unexisting columns wil now return error instead dropping silently Using variables column names (well selectors like dplyr::all_of()) now works Improvements .ab(): Dramatic improvement algorithm behind .ab(), making many input errors translatable, digitalised health care records, using many vowels consonants many Added progress bar Fixed bug .ab() return error invalid input values .ab() function now throw note 1 antimicrobial drug retrieved single input value. Fixed bug eucast_rules() work tibble tibble dplyr package loaded Fixed bug CLSI 2019 guidelines (using .rsi()), also included results animals. now contains interpretation guidelines humans. *_join_microorganisms() functions bug_drug_combinations() now return original data class (e.g. tibbles data.tables) functions rsi_df(), proportion_df() count_df(): Fixed bug using grouped versions Fixed bug different antimicrobial results added rows Fixed bug calculating counts (count_df()) antibiotics data set NAs Improved auto-determination columns types <mo> <Date> Fixed bug bug_drug_combinations() one antibiotic input data Changed summary class <rsi>, highlight %SI vs. %R Improved error handling, giving useful info functions return error progress bar now show interactive mode (.e. R Markdown) Speed improvement mdro() filter_ab_class() New option arrows_textangled ggplot_pca() indicate whether text end arrows angled (defaults TRUE, previous versions) Added parenteral DDD benzylpenicillin Fixed bug .mic() handle dots without leading zero (like \"<=.25)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-3-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.3.0","text":"Moved primary location project GitLab GitHub, giving us native support automated syntax checking without dependent external services AppVeyor Travis CI.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-120","dir":"Changelog","previous_headings":"","what":"AMR 1.2.0","title":"AMR 1.2.0","text":"CRAN release: 2020-05-28","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-1-2-0","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 1.2.0","text":"Removed code dependency R packages, making package fully independent development process others. major code change, probably noticeable users. Making package independent especially tidyverse (e.g. packages dplyr tidyr) tremendously increases sustainability long term, since tidyverse functions change quite often. Good users, hard package maintainers. functions replaced versions rely base R, keeps package fully functional many years come, without requiring lot maintenance keep packages anymore. Another upside package can now used versions R since R-3.0.0 (April 2013). package used settings resources limited. Fewer dependencies newer software helpful settings. Negative effects change : Function freq() borrowed cleaner package removed. Use cleaner::freq(), run library(\"cleaner\") use freq(). Printing values class mo rsi tibble longer colour printing rsi tibble show class <ord>, <rsi> anymore. purely visual effect. functions mo_* family (like mo_name() mo_gramstain()) noticeably slower running hundreds thousands rows. developers: classes mo ab now also inherit class character, support data transformation. change invalidates code checks class length == 1.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.2.0","text":"Updated taxonomy microorganisms May 2020, using Catalogue Life (CoL), Global Biodiversity Information Facility (GBIF) List Prokaryotic names Standing Nomenclature (LPSN, hosted DSMZ since February 2020). Note: taxonomic update may always impact determination first isolates (using first_isolate()), since bacterial names might renamed genera (sub)species. expected behaviour. Removed Catalogue Life IDs (like 776351), since now work species ID (hexadecimal string) eucast_rules() function longer applies “” rules default made available package (like setting ampicillin = R ampicillin + enzyme inhibitor = R). default input value rules now c(\"breakpoints\", \"expert\") instead \"\", can changed user. return old behaviour, set options(AMR.eucast_rules = \"\"). Fixed bug checking antimicrobial results original data regarded valid R/SI values drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Added official drug names verbose output eucast_rules() Added function ab_url() return direct URL antimicrobial agent official website Improvements algorithm .ab(), e.g. .ab(\"ampi sul\") ab_name(\"ampi sul\") work Functions ab_atc() ab_group() now return NA antimicrobial agent found Small fix text input coerced valid MIC values Fix interpretation generic CLSI interpretation rules (thanks Anthony Underwood) Fix set_mo_source() make sure column mo always second column Added abbreviation “cfsc” Cefoxitin “cfav” Ceftazidime/avibactam","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-2-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.2.0","text":"Removed previously deprecated function p.symbol() - replaced p_symbol() Removed function read.4d(), useful reading data old test database.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-110","dir":"Changelog","previous_headings":"","what":"AMR 1.1.0","title":"AMR 1.1.0","text":"CRAN release: 2020-04-15","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-1-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.1.0","text":"Support easy principal component analysis AMR, using new pca() function Plotting biplots principal component analysis using new ggplot_pca() function","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.1.0","text":"Support codes ending SPE species, like \"ESCSPE\" Escherichia coli Support encoding, means language-specific character accents can used input Support arbitrary IDs used laboratory information systems Small fix preventing viruses treated bacteria Small fix preventing contamination lack growth treated valid microorganisms Support abbreviations antibiotics antimycotics used Netherlands National Institute Public Health Environment (Rijksinstituut voor Volksgezondheid en Milieu; RIVM) Added abbreviations antibiotics data set Reloaded original EUCAST master tables 2019 (2020 already available). seems reliable data used WHONET. Added generic CLSI rules R/SI interpretation using .rsi() years 2010-2019 (thanks Anthony Underwood)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-1-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.1.0","text":"Support upcoming dplyr version 1.0.0 robust assigning classes rsi mic","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-101","dir":"Changelog","previous_headings":"","what":"AMR 1.0.1","title":"AMR 1.0.1","text":"CRAN release: 2020-02-23","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.0.1","text":"Fixed important floating point error MIC comparisons EUCAST 2020 guideline Interpretation MIC values (disk zones) R/SI can now used mutate_at() dplyr package: Added antibiotic abbreviations laboratory manufacturer (GLIMS) cefuroxime, cefotaxime, ceftazidime, cefepime, cefoxitin trimethoprim/sulfamethoxazole Added uti (abbreviation urinary tract infections) argument .rsi(), interpretation MIC values disk zones can made dependent isolates specifically UTIs Info printing functions eucast_rules(), first_isolate(), mdro() resistance_predict() now default print R interactive mode (.e. RMarkdown)","code":"yourdata %>%    mutate_at(vars(antibiotic1:antibiotic25), as.rsi, mo = \"E. coli\")  yourdata %>%    mutate_at(vars(antibiotic1:antibiotic25), as.rsi, mo = .$mybacteria)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-100","dir":"Changelog","previous_headings":"","what":"AMR 1.0.0","title":"AMR 1.0.0","text":"CRAN release: 2020-02-17 software now beta considered stable. Nonetheless, package developed continually.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-0-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.0.0","text":"Support newest EUCAST Clinical Breakpoint Tables v.10.0, valid 1 January 2020. affects translation MIC disk zones using .rsi() inferred resistance susceptibility using eucast_rules(). repository package now contains clean version EUCAST CLSI guidelines 2011-2020 translate MIC disk diffusion values R/SI: https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt. allows machine reading guidelines, almost impossible Excel PDF files distributed EUCAST CLSI. file used process EUCAST Clinical Breakpoints Excel file can found . Support LOINC codes antibiotics data set. Use ab_loinc() retrieve LOINC codes, use LOINC code input ab_* function: Support SNOMED CT codes microorganisms data set. Use mo_snomed() retrieve SNOMED codes, use SNOMED code input mo_* function:","code":"ab_loinc(\"ampicillin\") #> [1] \"21066-6\" \"3355-5\"  \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_atc(\"21066-6\") #> [1] \"J01CA01\" mo_snomed(\"S. aureus\") #> [1] 115329001   3092008 113961008 mo_name(115329001) #> [1] \"Staphylococcus aureus\" mo_gramstain(115329001) #> [1] \"Gram-positive\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.0.0","text":".mo() function previously wrote package folder improve calculation speed previously calculated results. longer case, comply CRAN policies. Consequently, function clear_mo_history() removed. Bugfix WHONET microorganism codes interpreted correctly using .rsi() Support missing spaces, e.g. .mo(\"Methicillin-resistant S.aureus\") Better support determination Salmonella biovars Speed improvements, especially G. species format (G genus), like E. coli K pneumoniae Support common codes used laboratory information systems Input values .disk() limited maximum 50 millimeters Added lifecycle state every function, following lifecycle circle tidyverse .ab(): support drugs starting “co-” like co-amoxiclav, co-trimoxazole, co-trimazine co-trimazole (thanks Peter Dutey) Added synonyms colistin, imipenem piperacillin/tazobactam Moved synonyms Rifinah Rimactazid rifampicin (RIF) rifampicin/isoniazid (RFI). Please note combination rifampicin/isoniazid DDDs defined, e.g. ab_ddd(\"Rimactazid\") now return NA. Moved synonyms Bactrimel Cotrimazole sulfamethoxazole (SMX) trimethoprim/sulfamethoxazole (SXT)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-0-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.0.0","text":"Add CITATION file Full support upcoming R 4.0 Removed unnecessary AMR:: calls","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-090","dir":"Changelog","previous_headings":"","what":"AMR 0.9.0","title":"AMR 0.9.0","text":"CRAN release: 2019-11-29","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-0-9-0","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 0.9.0","text":"dependent old Enterobacteriaceae family e.g. using code: please adjust :","code":"if (mo_family(somebugs) == \"Enterobacteriaceae\") ... if (mo_order(somebugs) == \"Enterobacterales\") ..."},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-9-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.9.0","text":"Functions susceptibility() resistance() aliases proportion_SI() proportion_R(), respectively. functions added make clear “” considered susceptible resistant. Support new MDRO guideline: Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012). now new default guideline mdro() function new Verbose mode (mdro(...., verbose = TRUE)) returns informative data set reason MDRO determination given every isolate, list resistant antimicrobial agents Data set antivirals, containing entries ATC J05 group DDDs oral parenteral treatment","code":"library(dplyr) example_isolates %>%   group_by(bug = mo_name(mo)) %>%    summarise(amoxicillin = resistance(AMX),             amox_clav   = resistance(AMC)) %>%   filter(!is.na(amoxicillin) | !is.na(amox_clav))"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-0-9-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 0.9.0","text":"Now allows “ou” “au” used vice versa intelligent way coping consonants like “l” “r” Added score (certainty percentage) mo_uncertainties(), calculated using Levenshtein distance: Removed previously deprecated function .atc() - function replaced ab_atc() Renamed portion_* functions proportion_*. portion_* functions still available deprecated functions, return warning used. running .rsi() data set, now print guideline used specified user Fix Stenotrophomonas maltophilia always become ceftazidime R (following EUCAST v3.1) Fix Leuconostoc Pediococcus always become glycopeptides R non-EUCAST rules eucast_rules() now applied first last anymore. improve dependency certain antibiotics official EUCAST rules. Please see ?eucast_rules. Fix interpreting MIC values .rsi() input NA Added “imi” “imp” allowed abbreviation Imipenem (IPM) Fix automatically determining columns antibiotic results mdro() eucast_rules() Added ATC codes ceftaroline, ceftobiprole faropenem fixed two typos antibiotics data set robust way determining valid MIC values Small changed example_isolates data set better reflect reality Added microorganisms codes laboratory systems (esp. species Pseudescherichia Rodentibacter) Added Gram-stain mo_info()","code":"as.mo(c(\"Stafylococcus aureus\",         \"staphylokok aureuz\")) #> Warning:  #> Results of two values were guessed with uncertainty. Use mo_uncertainties() to review them. #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS  mo_uncertainties() #> \"Stafylococcus aureus\" -> Staphylococcus aureus (B_STPHY_AURS, score: 95.2%) #> \"staphylokok aureuz\"   -> Staphylococcus aureus (B_STPHY_AURS, score: 85.7%)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-9-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.9.0","text":"Rewrote complete documentation markdown format, able use latest version great Roxygen2, released November 2019. tremously improved documentation quality, since rewrite forced us go texts make changes needed. Change dependency clean cleaner, package renamed accordingly upon CRAN request Added Dr. Sofia Ny contributor","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-080","dir":"Changelog","previous_headings":"","what":"AMR 0.8.0","title":"AMR 0.8.0","text":"CRAN release: 2019-10-15","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-0-8-0","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 0.8.0","text":"Determination first isolates now excludes ‘unknown’ microorganisms default, .e. microbial code \"UNKNOWN\". can included new argument include_unknown: WHONET users, means records/isolates organism code \"con\" (contamination) excluded default, since .mo(\"con\") = \"UNKNOWN\". function always shows note number ‘unknown’ microorganisms included excluded. code consistency, classes ab mo now preserved subsetting assignment. sake data integrity, means invalid assignments now result NA: important, value like \"testvalue\" never understood e.g. mo_name(), although class suggest valid microbial code. Function freq() moved new package, clean (CRAN link), since creating frequency tables actually fit scope package. freq() function still works, since re-exported clean package (installed automatically upon updating AMR package). Renamed data set septic_patients example_isolates","code":"first_isolate(..., include_unknown = TRUE) # how it works in base R: x <- factor(\"A\") x[1] <- \"B\" #> Warning message: #> invalid factor level, NA generated  # how it now works similarly for classes 'mo' and 'ab': x <- as.mo(\"E. coli\") x[1] <- \"testvalue\" #> Warning message: #> invalid microorganism code, NA generated"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-8-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.8.0","text":"Function bug_drug_combinations() quickly get data.frame results bug-drug combinations data set. column containing microorganism codes guessed automatically input transformed mo_shortname() default: can format printable format, ready reporting exporting e.g. Excel base R format() function: Additional way calculate co-resistance, .e. using multiple antimicrobials input portion_* functions count_* functions. can used determine empiric susceptibility combination therapy. new argument only_all_tested (defaults FALSE) replaces old also_single_tested can used select one two methods count isolates calculate portions. difference can seen example table (also portion count help pages), %SI determined: Since major change, usage old also_single_tested throw informative error replaced only_all_tested. tibble printing support classes rsi, mic, disk, ab mo. using tibbles containing antimicrobial columns, values S print green, values print yellow values R print red. Microbial IDs (class mo) emphasise genus species, kingdom.","code":"x <- bug_drug_combinations(example_isolates) #> NOTE: Using column `mo` as input for `col_mo`. x[1:4, ] #>             mo  ab S I R total #> 1 A. baumannii AMC 0 0 3     3 #> 2 A. baumannii AMK 0 0 0     0 #> 3 A. baumannii AMP 0 0 3     3 #> 4 A. baumannii AMX 0 0 3     3 #> NOTE: Use 'format()' on this result to get a publicable/printable format.  # change the transformation with the FUN argument to anything you like: x <- bug_drug_combinations(example_isolates, FUN = mo_gramstain) #> NOTE: Using column `mo` as input for `col_mo`. x[1:4, ] #>              mo  ab   S  I   R total #> 1 Gram-negative AMC 469 89 174   732 #> 2 Gram-negative AMK 251  0   2   253 #> 3 Gram-negative AMP 227  0 405   632 #> 4 Gram-negative AMX 227  0 405   632 #> NOTE: Use 'format()' on this result to get a publicable/printable format. format(x, combine_IR = FALSE) # -------------------------------------------------------------------- #                     only_all_tested = FALSE  only_all_tested = TRUE #                     -----------------------  ----------------------- #  Drug A    Drug B   include as  include as   include as  include as #                     numerator   denominator  numerator   denominator # --------  --------  ----------  -----------  ----------  ----------- #  S or I    S or I       X            X            X            X #    R       S or I       X            X            X            X #   <NA>     S or I       X            X            -            - #  S or I      R          X            X            X            X #    R         R          -            X            -            X #   <NA>       R          -            -            -            - #  S or I     <NA>        X            X            -            - #    R        <NA>        -            -            -            - #   <NA>      <NA>        -            -            -            - # -------------------------------------------------------------------- # (run this on your own console, as this page does not support colour printing) library(dplyr) example_isolates %>%   select(mo:AMC) %>%    as_tibble()"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-8-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.8.0","text":"Self-learning algorithm - function now gains experience previously determined microorganism IDs learns (yielding 80-95% speed improvement guess first try) Big improvement misspelled input new trivial names known field now understood: meningococcus, gonococcus, pneumococcus Updated latest taxonomic data (updated August 2019, International Journal Systematic Evolutionary Microbiology Added support Viridans Group Streptococci (VGS) Milleri Group Streptococci (MGS) Added support Blastocystis Added support 5,000 new fungi Added support unknown yeasts fungi Changed microorganism IDs improve readability. example, old code B_ENTRC_FAE E. faecalis E. faecium. new code B_ENTRC_FCLS E. faecium become B_ENTRC_FACM. Also, Latin character ae now preserved start genus species abbreviation. example, old code Aerococcus urinae B_ARCCC_NAE. now B_AERCC_URIN. IMPORTANT: Old microorganism IDs still supported, support dropped future version. Use .mo() old codes transform new format. Using functions mo_* family (like mo_name() mo_gramstain()) old codes, throw warning. intelligent guessing .ab(), including bidirectional language support Added support German national guideline (3MRGN/4MRGN) mdro() function, determine multi-drug resistant organisms Fixed bug Yersinia pseudotuberculosis Added informative errors warnings Printed info now distinguishes added changes values Using Verbose mode (.e. eucast_rules(..., verbose = TRUE)) returns informative readable output Using factors input now adds missing factors levels function changes antibiotic results Improved internal auto-guessing function determining antimicrobials data set (AMR:::get_column_abx()) Removed class atc - using .atc() now deprecated favour ab_atc() return character, atc class anymore Removed deprecated functions abname(), ab_official(), atc_name(), atc_official(), atc_property(), atc_tradenames(), atc_trivial_nl() Fix speed improvement mo_shortname() Fix using mo_* functions coercion uncertainties failures available mo_uncertainties() mo_failures() anymore Deprecated country argument mdro() favour already existing guideline argument support multiple guidelines within one country name RIF now Rifampicin instead Rifampin antibiotics data set now sorted name cephalosporins now generation brackets Speed improvement guess_ab_col() now 30 times faster antibiotic abbreviations Improved filter_ab_class() reliable support 5th generation cephalosporins Function availability() now uses portion_R() instead portion_IR(), comply EUCAST insights Functions age() age_groups() now na.rm argument remove empty values Renamed function p.symbol() p_symbol() (former now deprecated removed future version) Using negative values x age_groups() now introduce NAs return error anymore Fix determining system’s language Fix key_antibiotics() foreign systems Added 80 new LIS codes microorganisms Relabeled factor levels mdr_tb() Added MIC factor levels (.mic())","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-8-0","dir":"Changelog","previous_headings":"Changed","what":"Other","title":"AMR 0.8.0","text":"Added Prof. Dr. Casper Albers doctoral advisor added Dr. Judith Fonville, Eric Hazenberg, Dr. Bart Meijer, Dr. Dennis Souverein Annick Lenglet contributors Cleaned coding style every single syntax line package help lintr package","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-071","dir":"Changelog","previous_headings":"","what":"AMR 0.7.1","title":"AMR 0.7.1","text":"CRAN release: 2019-06-23","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-7-1","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.7.1","text":"Function rsi_df() transform data.frame data set containing microbial interpretation (S, , R), antibiotic, percentage S//R number available isolates. convenient combination existing functions count_df() portion_df() immediately show resistance percentages number available isolates: Support scientifically published pathotypes E. coli date (find). Supported : AIEC (Adherent-Invasive E. coli) ATEC (Atypical Entero-pathogenic E. coli) DAEC (Diffusely Adhering E. coli) EAEC (Entero-Aggresive E. coli) EHEC (Entero-Haemorrhagic E. coli) EIEC (Entero-Invasive E. coli) EPEC (Entero-Pathogenic E. coli) ETEC (Entero-Toxigenic E. coli) NMEC (Neonatal Meningitis-causing E. coli) STEC (Shiga-toxin producing E. coli) UPEC (Uropathogenic E. coli) lead microbial ID E. coli: Function mo_info() analogy ab_info(). mo_info() prints list full taxonomy, authors, URL online database microorganism Function mo_synonyms() get previously accepted taxonomic names microorganism","code":"septic_patients %>%   select(AMX, CIP) %>%   rsi_df() #      antibiotic  interpretation      value  isolates # 1   Amoxicillin              SI  0.4442636       546 # 2   Amoxicillin               R  0.5557364       683 # 3 Ciprofloxacin              SI  0.8381831      1181 # 4 Ciprofloxacin               R  0.1618169       228 as.mo(\"UPEC\") # B_ESCHR_COL mo_name(\"UPEC\") # \"Escherichia coli\" mo_gramstain(\"EHEC\") # \"Gram-negative\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-7-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.7.1","text":"Column names output count_df() portion_df() now lowercase Fixed bug translation microorganism names Fixed bug determining taxonomic kingdoms Algorithm improvements .ab() .mo() understand even severely misspelled input Function .ab() now allows spaces coercing antibiotics names Added ggplot2 methods automatically determining scale type classes mo ab Added names object header frequency tables, even using pipes Prevented \"bacteria\" getting coerced .ab() Bacterial brand name trimethoprim (TMP) Fixed bug setting antibiotic work eucast_rules() mdro() Fixed EUCAST rule Staphylococci, amikacin resistance inferred tobramycin Removed latest_annual_release catalogue_of_life_version() function Removed antibiotic code PVM1 antibiotics data set duplicate PME Fixed bug old taxonomic names printed, using vector input .mo() Manually added Trichomonas vaginalis kingdom Protozoa, missing Catalogue Life Small improvements plot() barplot() MIC RSI classes Allow Catalogue Life IDs coerced .mo()","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-7-1","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.7.1","text":"Fixed note thrown CRAN tests","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-070","dir":"Changelog","previous_headings":"","what":"AMR 0.7.0","title":"AMR 0.7.0","text":"CRAN release: 2019-06-03","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-7-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.7.0","text":"Support translation disk diffusion MIC values RSI values (.e. antimicrobial interpretations). Supported guidelines EUCAST (2011 2019) CLSI (2011 2019). Use .rsi() MIC value (created .mic()), disk diffusion value (created new .disk()) complete date set containing columns MIC disk diffusion values. Function mo_name() alias mo_fullname() Added guidelines determine multi-drug resistance (MDR) TB (mdr_tb()) added new vignette MDR. Read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-7-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.7.0","text":"Fixed critical bug first_isolate() missing species lead incorrect FALSEs. bug present AMR v0.5.0, v0.6.0 v0.6.1. Fixed bug eucast_rules() antibiotics WHONET software recognised Column ab contains human readable EARS-Net code, used ECDC /WHONET - primary identifier used package Column atc contains ATC code, used /WHOCC Column cid contains CID code (Compound ID), used PubChem Based Compound ID, almost 5,000 official brand names added many different countries references antibiotics package now use EARS-Net codes, like AMX amoxicillin Functions atc_certe, ab_umcg atc_trivial_nl removed atc_* functions superseded ab_* functions output translated using included translation file can viewed New argument colours set bar colours New arguments title, subtitle, caption, x.title y.title set titles axis descriptions Improved intelligence looking antibiotic columns data set using guess_ab_col() Added ~5,000 old taxonomic names microorganisms.old data set, leads better results finding using .mo() function package now honours new EUCAST insight (2019) S classified susceptible, defined ‘increased exposure’ ‘intermediate’ anymore. functions like portion_df() count_df() means new argument combine_SI TRUE default. plotting function ggplot_rsi() also reflects change since uses count_df() internally. age() function gained new argument exact determine ages decimals Removed deprecated functions guess_mo(), guess_atc(), EUCAST_rules(), interpretive_reading(), rsi() speed improvement microbial IDs fixed factor level names R Markdown values unique now shows message instead warning support boxplots: Removed hardcoded EUCAST rules replaced new reference file can viewed Added ceftazidim intrinsic resistance Streptococci Changed default settings age_groups(), let groups fives tens end 100+ instead 120+ Fix freq() values NA Fix first_isolate() dates missing Improved speed guess_ab_col() Function .mo() now gently interprets number whitespace characters (like tabs) one space Function .mo() now returns UNKNOWN \"con\" (WHONET ID ‘contamination’) returns NA \"xxx\"(WHONET ID ‘growth’) Small algorithm fix .mo() Removed viruses data set microorganisms.codes cleaned Fix mo_shortname() species determined correctly","code":"septic_patients %>%    freq(age) %>%    boxplot() # grouped boxplots: septic_patients %>%    group_by(hospital_id) %>%    freq(age) %>%   boxplot()"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-7-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.7.0","text":"Support R 3.6.0 later providing support staged install","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-061","dir":"Changelog","previous_headings":"","what":"AMR 0.6.1","title":"AMR 0.6.1","text":"CRAN release: 2019-03-29","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-6-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.6.1","text":"Fixed critical bug using eucast_rules() verbose = TRUE Coercion microbial IDs now written package namespace instead user’s home folder, comply CRAN policy","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-060","dir":"Changelog","previous_headings":"","what":"AMR 0.6.0","title":"AMR 0.6.0","text":"CRAN release: 2019-03-27 New website! ’ve got new website: https://msberends.gitlab.io/AMR (built great pkgdown) Contains complete manual package functions explanation arguments Contains comprehensive tutorial conduct AMR data analysis, import data WHONET SPSS many .","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-6-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.6.0","text":"BREAKING: removed deprecated functions, arguments references ‘bactid’. Use .mo() identify MO code. Catalogue Life new taxonomic source data microorganisms, also contains ITIS data used previously. microorganisms data set now contains: ~55,000 (sub)species kingdoms Archaea, Bacteria Protozoa ~3,000 (sub)species orders kingdom Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales Schizosaccharomycetales (covering least like species Aspergillus, Candida, Pneumocystis, Saccharomyces Trichophyton) ~2,000 (sub)species ~100 relevant genera, kingdoms Animalia Plantae (like Strongyloides Taenia) ~15,000 previously accepted names included (sub)species taxonomically renamed responsible author(s) year scientific publication data updated annually - check included version new function catalogue_of_life_version(). Due change, mo codes changed (e.g. Streptococcus changed B_STRPTC B_STRPT). translation table used internally support older microorganism IDs, users notice difference. New function mo_rank() taxonomic rank (genus, species, infraspecies, etc.) New function mo_url() get direct URL species Catalogue Life Support data WHONET EARS-Net (European Antimicrobial Resistance Surveillance Network): Exported files WHONET can read used package. functions like first_isolate() eucast_rules(), arguments filled automatically. package now knows antibiotic abbrevations EARS-Net (also used WHONET) - antibiotics data set now contains column ears_net. function .mo() now knows WHONET species abbreviations , almost 2,000 microbial abbreviations added microorganisms.codes data set. New filters antimicrobial classes. Use functions filter isolates results one antibiotics specific class: antibiotics data set searched, input data checked column names value abbreviations, codes official names found antibiotics data set. example: ab_* functions deprecated replaced atc_* functions: functions use .atc() internally. old atc_property renamed atc_online_property(). done two reasons: firstly, ATC codes antibiotics (ab) can also antivirals antifungals. Secondly, input must class atc must coerable class. Properties classes start class name, analogous .mo() e.g. mo_genus. New functions set_mo_source() get_mo_source() use predefined MO codes input .mo() consequently mo_* functions Support upcoming dplyr version 0.8.0 New function guess_ab_col() find antibiotic column table New function mo_failures() review values coerced valid MO code, using .mo(). latter function now show maximum 10 uncoerced values refer mo_failures(). New function mo_uncertainties() review values coerced valid MO code using .mo(), uncertainty. New function mo_renamed() get list returned values .mo() taxonomic renaming New function age() calculate (patients) age years New function age_groups() split ages custom predefined groups (like children elderly). allows easier demographic AMR data analysis per age group. New function ggplot_rsi_predict() well base R plot() function can now used resistance prediction calculated resistance_predict(): Functions filter_first_isolate() filter_first_weighted_isolate() shorten fasten filtering data sets antimicrobial results, e.g.: equal : New function availability() check number available (non-empty) results data.frame New vignettes conduct AMR analysis, predict antimicrobial resistance, use G-test . also available (even easier readable) website: https://msberends.gitlab.io/AMR.","code":"filter_aminoglycosides() filter_carbapenems() filter_cephalosporins() filter_1st_cephalosporins() filter_2nd_cephalosporins() filter_3rd_cephalosporins() filter_4th_cephalosporins() filter_fluoroquinolones() filter_glycopeptides() filter_macrolides() filter_tetracyclines() septic_patients %>% filter_glycopeptides(result = \"R\") # Filtering on glycopeptide antibacterials: any of `vanc` or `teic` is R septic_patients %>% filter_glycopeptides(result = \"R\", scope = \"all\") # Filtering on glycopeptide antibacterials: all of `vanc` and `teic` is R ab_property -> atc_property() ab_name -> atc_name() ab_official -> atc_official() ab_trivial_nl -> atc_trivial_nl() ab_certe -> atc_certe() ab_umcg -> atc_umcg() ab_tradenames -> atc_tradenames() x <- resistance_predict(septic_patients, col_ab = \"amox\") plot(x) ggplot_rsi_predict(x) septic_patients %>% filter_first_isolate(...) # or filter_first_isolate(septic_patients, ...) septic_patients %>%   mutate(only_firsts = first_isolate(septic_patients, ...)) %>%   filter(only_firsts == TRUE) %>%   select(-only_firsts)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-6-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.6.0","text":"Updated EUCAST Clinical breakpoints version 9.0 1 January 2019, data set septic_patients now reflects changes Fixed critical bug rules depend previous applied rules applied adequately Emphasised manual penicillin meant benzylpenicillin (ATC J01CE01) New info returned running function, stating exactly changed added. Use eucast_rules(..., verbose = TRUE) get data set changed per bug drug combination. Removed data sets microorganisms.oldDT, microorganisms.prevDT, microorganisms.unprevDT microorganismsDT since longer needed contained info already available microorganisms data set Added 65 antibiotics antibiotics data set, Pharmaceuticals Community Register European Commission Removed columns atc_group1_nl atc_group2_nl antibiotics data set Functions atc_ddd() atc_groups() renamed atc_online_ddd() atc_online_groups(). old functions deprecated removed future version. Function guess_mo() now deprecated favour .mo() removed future versions Function guess_atc() now deprecated favour .atc() removed future versions Now handles incorrect spelling, like instead y f instead ph: Uncertainty algorithm now divided four levels, 0 3, default allow_uncertain = TRUE equal uncertainty level 2. Run ?.mo info levels. Using .mo(..., allow_uncertain = 3) lead unreliable results. Implemented latest publication Becker et al. (2019), categorising coagulase-negative Staphylococci microbial IDs found now saved local file ~/.Rhistory_mo. Use new function clean_mo_history() delete file, resets algorithms. Incoercible results now considered ‘unknown’, MO code UNKNOWN. foreign systems, properties translated languages already previously supported: German, Dutch, French, Italian, Spanish Portuguese. Fix vector containing empty values Finds better results input languages Better handling subspecies Better handling Salmonellae, especially ‘city like’ serovars like Salmonella London Understanding highly virulent E. coli strains like EIEC, EPEC STEC looked uncertain results default - results returned informative warning Manual (help page) now contains info algorithms Progress bar shown takes 3 seconds get results Support formatted console text Console return percentage uncoercable input Fixed bug distances dates calculated right - septic_patients data set yielded difference 0.15% isolates now use column named like “patid” patient ID (argument col_patientid), argument left blank now use column named like “key(…)ab” “key(…)antibiotics” key antibiotics (argument col_keyantibiotics()), argument left blank Removed argument output_logical, function now always return logical value Renamed argument filter_specimen specimen_group, although using filter_specimen still work note manual pages portion functions, low counts can influence outcome portion functions may camouflage , since return portion (albeit dependent minimum argument) Merged data sets microorganisms.certe microorganisms.umcg microorganisms.codes Function mo_taxonomy() now contains kingdom Reduce false positives .rsi.eligible() using new threshold argument New colours scale_rsi_colours() Summaries class mo now return top 3 unique count, e.g. using summary(mo) Small text updates summaries class rsi mic Now gives warning inputting MIC values Now accepts high low resistance: \"HIGH S\" return S Support tidyverse quasiquotation! Now can create frequency tables function outcomes: Header info now available list, header function argument header now set TRUE default, even markdown Added header info class mo show unique count families, genera species Now honours decimal.mark setting, just like format defaults getOption(\"OutDec\") new big.mark argument default \",\" decimal.mark = \".\" \".\" otherwise Fix header text observations NA New argument droplevels exclude empty factor levels input factor Factor levels header present input data (maximum 5) Fix using select() frequency tables Function scale_y_percent() now contains limits argument Automatic argument filling mdro(), key_antibiotics() eucast_rules() Updated examples resistance prediction (resistance_predict() function) Fix .mic() support values ending (several) zeroes using different lengths pattern x %like%, now return call","code":"# mo_fullname() uses as.mo() internally  mo_fullname(\"Sthafilokockus aaureuz\") #> [1] \"Staphylococcus aureus\"  mo_fullname(\"S. klossi\") #> [1] \"Staphylococcus kloosii\" # equal: as.mo(..., allow_uncertain = TRUE) as.mo(..., allow_uncertain = 2)  # also equal: as.mo(..., allow_uncertain = FALSE) as.mo(..., allow_uncertain = 0) # Determine genus of microorganisms (mo) in `septic_patients` data set: # OLD WAY septic_patients %>%   mutate(genus = mo_genus(mo)) %>%   freq(genus) # NEW WAY septic_patients %>%    freq(mo_genus(mo))  # Even supports grouping variables: septic_patients %>%   group_by(gender) %>%    freq(mo_genus(mo))"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-6-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.6.0","text":"Updated licence text emphasise GPL 2.0 R package.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-050","dir":"Changelog","previous_headings":"","what":"AMR 0.5.0","title":"AMR 0.5.0","text":"CRAN release: 2018-11-30","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-5-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.5.0","text":"Repository moved GitLab Function count_all get available isolates (like portion_* count_* functions also supports summarise group_by), old n_rsi now alias count_all Function get_locale determine language language-dependent output mo_* functions. now default value language argument, system language used default. Data sets microorganismsDT, microorganisms.prevDT, microorganisms.unprevDT microorganisms.oldDT improve speed .mo. reference , since primarily internal use .mo. Function read.4D read 4D database MMB department UMCG Functions mo_authors mo_year get specific values scientific reference taxonomic entry","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-5-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.5.0","text":"Functions MDRO, BRMO, MRGN EUCAST_exceptional_phenotypes renamed mdro, brmo, mrgn eucast_exceptional_phenotypes EUCAST_rules renamed eucast_rules, old function still exists deprecated function Big changes eucast_rules function: Now also applies rules EUCAST ‘Breakpoint tables bacteria’, version 8.1, 2018, https://www.eucast.org/clinical_breakpoints/ (see Source function) New argument rules specify rules applied (expert rules, breakpoints, others ) New argument verbose can set TRUE get specific messages columns rows affected Better error handling rules applied (.e. new values inserted) number affected values now measured per row/column combination Data set septic_patients now reflects changes Added argument pipe piperacillin (J01CA12), also mdro function Small fixes EUCAST clinical breakpoint rules Added column kingdom microorganisms data set, function mo_kingdom look values Tremendous speed improvement .mo (subsequently mo_* functions), empty values wil ignored priori Fewer 3 characters input .mo return NA Function .mo (mo_* wrappers) now supports genus abbreviations “species” attached Added argument combine_IR (TRUE/FALSE) functions portion_df count_df, indicate values R must merged one, output consists S vs. IR (susceptible vs. non-susceptible) Fix portion_*(..., as_percent = TRUE) minimal number isolates met Added argument also_single_tested portion_* count_* functions also include cases antibiotics tested least one tested antibiotics includes target antimicribial interpretation, see ?portion Using portion_* functions now throws warning total available isolate argument minimum Functions .mo, .rsi, .mic, .atc freq set package name attribute anymore Frequency tables - freq(): Support grouping variables, test : Support (un)selecting columns: Check hms::.hms Now prints markdown default non-interactive sessions longer adds factor level column sorts factors count Support class difftime New argument na, choose character print empty values New argument header turn header info (default markdown = TRUE) New argument title manually setbthe title frequency table first_isolate now tries find columns use input arguments left blank Improvements MDRO algorithm (function mdro) Data set septic_patients now data.frame, tibble anymore Removed diacritics authors (columns microorganisms$ref microorganisms.old$ref) comply CRAN policy allow ASCII characters Fix mo_property working properly Fix eucast_rules Streptococci become ceftazidime R EUCAST rule 4.5 Support named vectors class mo, useful top_freq() ggplot_rsi scale_y_percent breaks argument AI improvements .mo: \"CRS\" -> Stenotrophomonas maltophilia \"CRSM\" -> Stenotrophomonas maltophilia \"MSSA\" -> Staphylococcus aureus \"MSSE\" -> Staphylococcus epidermidis Fix join functions Speed improvement .rsi.eligible, now 15-20 times faster g.test, sum(x) 1000 expected values 5, Fisher’s Exact Test suggested ab_name try fall back .atc results found Removed addin view data sets Percentages now rounded logically (e.g. freq function)","code":"as.mo(\"E. species\")        # B_ESCHR mo_fullname(\"E. spp.\")     # \"Escherichia species\" as.mo(\"S. spp\")            # B_STPHY mo_fullname(\"S. species\")  # \"Staphylococcus species\" septic_patients %>%    group_by(hospital_id) %>%    freq(gender) septic_patients %>%    freq(hospital_id) %>%    select(-count, -cum_count) # only get item, percent, cum_percent"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-5-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.5.0","text":"New dependency package crayon, support formatted text console Dependency tidyr now mandatory (went Import field) since portion_df count_df rely Updated vignettes comply README","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-040","dir":"Changelog","previous_headings":"","what":"AMR 0.4.0","title":"AMR 0.4.0","text":"CRAN release: 2018-10-01","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-4-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.4.0","text":"data set microorganisms now contains microbial taxonomic data ITIS (kingdoms Bacteria, Fungi Protozoa), Integrated Taxonomy Information System, available via https://itis.gov. data set now contains 18,000 microorganisms known bacteria, fungi protozoa according ITIS genus, species, subspecies, family, order, class, phylum subkingdom. new data set microorganisms.old contains previously known taxonomic names kingdoms. New functions based existing function mo_property: Taxonomic names: mo_phylum, mo_class, mo_order, mo_family, mo_genus, mo_species, mo_subspecies Semantic names: mo_fullname, mo_shortname Microbial properties: mo_type, mo_gramstain Author year: mo_ref also come support German, Dutch, French, Italian, Spanish Portuguese: Furthermore, former taxonomic names give note current taxonomic name: Functions count_R, count_IR, count_I, count_SI count_S selectively count resistant susceptible isolates Extra function count_df (works like portion_df) get counts S, R data set antibiotic columns, support grouped variables Function .rsi.eligible check columns valid antimicrobial results, rsi class yet. Transform columns raw data : data %>% mutate_if(.rsi.eligible, .rsi) Functions .mo .mo replacements .bactid .bactid (since microoganisms data set contains bacteria). last two functions deprecated removed future release. .mo function determines microbial IDs using intelligent rules: great speed - quite regular Linux server 2007 takes us less 0.02 seconds transform 25,000 items: Added argument reference_df .mo, users can supply microbial IDs, name codes reference table Renamed previous references bactid mo, like: Column names inputs EUCAST_rules, first_isolate key_antibiotics Column names datasets microorganisms septic_patients old syntaxes still work version, throw warnings Function labels_rsi_count print datalabels RSI ggplot2 model Functions .atc .atc transform/look antibiotic ATC codes defined . existing function guess_atc now alias .atc. Function ab_property aliases: ab_name, ab_tradenames, ab_certe, ab_umcg ab_trivial_nl Introduction AMR vignette Removed clipboard functions violated CRAN policy Renamed septic_patients$sex septic_patients$gender","code":"mo_gramstain(\"E. coli\") # [1] \"Gram negative\" mo_gramstain(\"E. coli\", language = \"de\") # German # [1] \"Gramnegativ\" mo_gramstain(\"E. coli\", language = \"es\") # Spanish # [1] \"Gram negativo\" mo_fullname(\"S. group A\", language = \"pt\") # Portuguese # [1] \"Streptococcus grupo A\" mo_gramstain(\"Esc blattae\") # Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010) # [1] \"Gram negative\" as.mo(\"E. coli\") # [1] B_ESCHR_COL as.mo(\"MRSA\") # [1] B_STPHY_AUR as.mo(\"S group A\") # [1] B_STRPTC_GRA thousands_of_E_colis <- rep(\"E. coli\", 25000) microbenchmark::microbenchmark(as.mo(thousands_of_E_colis), unit = \"s\") # Unit: seconds #         min       median         max  neval #  0.01817717  0.01843957  0.03878077    100"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-4-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.4.0","text":"Added three antimicrobial agents antibiotics data set: Terbinafine (D01BA02), Rifaximin (A07AA11) Isoconazole (D01AC05) Added 163 trade names antibiotics data set, now contains 298 different trade names total, e.g.: first_isolate, rows ignored ’s species available Function ratio now deprecated removed future release, really scope package Fix .mic values ending zeroes real number Small fix B. fragilis found microorganisms.umcg data set Added prevalence column microorganisms data set Added arguments minimum as_percent portion_df Support quasiquotation functions series count_* portions_*, n_rsi. allows check 2 vectors columns. Edited ggplot_rsi geom_rsi can cope count_df. new fun argument value portion_df default, can set count_df. Fix ggplot_rsi ggplot2 package loaded Added datalabels function labels_rsi_count ggplot_rsi Added possibility set argument geom_rsi (ggplot_rsi) can set preferences Fix joins, predefined suffices honoured Added argument quote freq function Added generic function diff frequency tables Added longest en shortest character length frequency table (freq) header class character Support types (classes) list matrix freq lists, subsetting possible:","code":"ab_official(\"Bactroban\") # [1] \"Mupirocin\" ab_name(c(\"Bactroban\", \"Amoxil\", \"Zithromax\", \"Floxapen\")) # [1] \"Mupirocin\" \"Amoxicillin\" \"Azithromycin\" \"Flucloxacillin\" ab_atc(c(\"Bactroban\", \"Amoxil\", \"Zithromax\", \"Floxapen\")) # [1] \"R01AX06\" \"J01CA04\" \"J01FA10\" \"J01CF05\" septic_patients %>% select(amox, cipr) %>% count_IR() # which is the same as: septic_patients %>% count_IR(amox, cipr)  septic_patients %>% portion_S(amcl) septic_patients %>% portion_S(amcl, gent) septic_patients %>% portion_S(amcl, gent, pita) my_matrix = with(septic_patients, matrix(c(age, gender), ncol = 2)) freq(my_matrix) my_list = list(age = septic_patients$age, gender = septic_patients$gender) my_list %>% freq(age) my_list %>% freq(gender)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-4-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.4.0","text":"unit tests ensure better integrity functions","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-030","dir":"Changelog","previous_headings":"","what":"AMR 0.3.0","title":"AMR 0.3.0","text":"CRAN release: 2018-08-14","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-3-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.3.0","text":"New function portion_df get portions S, R data set antibiotic columns, support grouped variables Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin Gram-negative: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem New functions geom_rsi, facet_rsi, scale_y_percent, scale_rsi_colours theme_rsi septic_patients %>% select(tobr, gent) %>% ggplot_rsi show portions S, R immediately pretty plot Support grouped variables, see ?ggplot_rsi New functions .bactid .bactid transform/ look microbial ID’s. existing function guess_bactid now alias .bactid New Becker classification Staphylococcus categorise Coagulase Negative Staphylococci (CoNS) Coagulase Positve Staphylococci (CoPS) New Lancefield classification Streptococcus categorise Lancefield groups convience, new descriptive statistical functions kurtosis skewness lacking base R - generic functions support vectors, data.frames matrices Function g.test perform X2 distributed G-test, use chisq.test example: ratio(c(10, 500, 10), ratio = \"1:2:1\") return 130, 260, 130 Support Addins menu RStudio quickly insert %% %like% (give keyboard shortcuts), view datasets come package Function p.symbol transform p values related symbols: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Functions clipboard_import clipboard_export helper functions quickly copy paste /software like Excel SPSS. functions use clipr package, little altered also support headless Linux servers (can use RStudio Server) vignette explain usage Support rsi (antimicrobial resistance) use input Support table use input: freq(table(x, y)) Support existing functions hist plot use frequency table input: hist(freq(df$age)) Support .vector, .data.frame, as_tibble format Support quasiquotation: freq(mydata, mycolumn) mydata %>% freq(mycolumn) Function top_freq function return top/n items vector Header frequency tables now also show Mean Absolute Deviaton (MAD) Interquartile Range (IQR) Possibility globally set default amount items print, options(max.print.freq = n) n preset value","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-3-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.3.0","text":"Improvements forecasting resistance_predict added examples antibiotics added arguments EUCAST rules Updated version septic_patients data set better reflect reality Pretty printing tibbles removed really scope package Printing mic rsi classes now returns values - use freq check distributions Improved speed key antibiotics comparison determining first isolates Column names key_antibiotics function now generic: 6 broadspectrum ABs, 6 Gram-positive specific 6 Gram-negative specific ABs Speed improvement abname function %like% now supports multiple patterns Frequency tables now actual data.frames altered console printing make look like frequency table. , argument toConsole longer needed. Fix freq class item lost Small translational improvements septic_patients dataset column bactid now new class \"bactid\" Small improvements microorganisms dataset (especially Salmonella) column bactid now new class \"bactid\" .rsi(\"<=0.002; S\") return S .mic(\"<=0.002; S\") return <=0.002 Now possible coerce MIC values space operator value, .e. .mic(\"<= 0.002\") now works Classes rsi mic add attribute package.version anymore Added \"groups\" option atc_property(..., property). return vector ATC hierarchy defined . new function atc_groups convenient wrapper around . Build-host check atc_property requires host set url responsive Improved first_isolate algorithm exclude isolates bacteria ID genus unavailable Fix warning hybrid evaluation forced row_number (924b62) dplyr package v0.7.5 yourdata %>% select(genus, species) %>% .bactid() now also works small fixes","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-3-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.3.0","text":"Linux macOS: https://travis-ci.org/msberends/AMR Windows: https://ci.appveyor.com/project/msberends/amr Added thesis advisors DESCRIPTION file","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-020","dir":"Changelog","previous_headings":"","what":"AMR 0.2.0","title":"AMR 0.2.0","text":"CRAN release: 2018-05-03","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-2-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.2.0","text":"Full support Windows, Linux macOS Full support old R versions, R-3.0.0 (April 2013) later needed (needed packages may dependencies) Function n_rsi count cases antibiotic test results available, used conjunction dplyr::summarise, see ?rsi Function guess_bactid determine ID microorganism based genus/species known abbreviations like MRSA Function guess_atc determine ATC antibiotic based name, trade name, known abbreviations Function freq create frequency tables, additional info header Exceptional resistances defined EUCAST also supported instead countries alone Functions BRMO MRGN wrappers Dutch German guidelines, respectively New algorithm determine weighted isolates, can now \"points\" \"keyantibiotics\", see ?first_isolate New print format tibbles data.tables","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.2.0","text":"Fixed rsi class vectors contain invalid antimicrobial interpretations Renamed dataset ablist antibiotics Renamed dataset bactlist microorganisms Added common abbreviations trade names antibiotics dataset Added microorganisms microorganisms dataset Added analysis examples help page dataset septic_patients Added support character vector join functions Added warnings join results rows join Altered %like% make case insensitive arguments functions first_isolate EUCAST_rules column names now case-insensitive Functions .rsi .mic now add package name version attributes","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-2-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.2.0","text":"Expanded README.md examples Added ORCID authors DESCRIPTION file Added unit testing testthat package Added build tests Linux macOS using Travis CI (https://travis-ci.org/msberends/AMR) Added line coverage checking using CodeCov (https://codecov.io/gh/msberends/AMR/tree/main/R)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-011","dir":"Changelog","previous_headings":"","what":"AMR 0.1.1","title":"AMR 0.1.1","text":"CRAN release: 2018-03-14 EUCAST_rules applies amoxicillin even ampicillin missing Edited column names comply GLIMS, laboratory information system Added valid MIC values Renamed ‘Daily Defined Dose’ ‘Defined Daily Dose’ Added barplots rsi mic classes","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-010","dir":"Changelog","previous_headings":"","what":"AMR 0.1.0","title":"AMR 0.1.0","text":"CRAN release: 2018-02-22 First submission CRAN.","code":""}]
+[{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations RSI values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial agents, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"How to conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables.","code":"library(dplyr) library(ggplot2) library(AMR) library(cleaner)  # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\", \"cleaner\"))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"creation-of-data","dir":"Articles","previous_headings":"","what":"Creation of data","title":"How to conduct AMR data analysis","text":"create fake example data use analysis. AMR data analysis, need least: patient ID, name code microorganism, date antimicrobial results (antibiogram). also include specimen type (e.g. filter blood urine), ward type (e.g. filter ICUs). additional columns (like hospital name, patients gender even [well-defined] clinical properties) can comparative analysis, tutorial demonstrate .","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"patients","dir":"Articles","previous_headings":"Creation of data","what":"Patients","title":"How to conduct AMR data analysis","text":"start patients, need unique list patients. LETTERS object available R - ’s vector 26 characters: Z. patients object just created now vector length 260, values (patient IDs) varying A1 Z10. Now also set gender patients, putting ID gender table: first 135 patient IDs now male, 125 female.","code":"patients <- unlist(lapply(LETTERS, paste0, 1:10)) patients_table <- data.frame(patient_id = patients,                              gender = c(rep(\"M\", 135),                                         rep(\"F\", 125)))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dates","dir":"Articles","previous_headings":"Creation of data","what":"Dates","title":"How to conduct AMR data analysis","text":"Let’s pretend data consists blood cultures isolates 1 January 2010 1 January 2018. dates object now contains days date range.","code":"dates <- seq(as.Date(\"2010-01-01\"), as.Date(\"2018-01-01\"), by = \"day\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"microorganisms","dir":"Articles","previous_headings":"Creation of data > Dates","what":"Microorganisms","title":"How to conduct AMR data analysis","text":"tutorial, uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, Klebsiella pneumoniae:","code":"bacteria <- c(\"Escherichia coli\", \"Staphylococcus aureus\",               \"Streptococcus pneumoniae\", \"Klebsiella pneumoniae\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"put-everything-together","dir":"Articles","previous_headings":"Creation of data","what":"Put everything together","title":"How to conduct AMR data analysis","text":"Using sample() function, can randomly select items objects defined earlier. let fake data reflect reality bit, also approximately define probabilities bacteria antibiotic results, using random_rsi() function. Using left_join() function dplyr package, can ‘map’ gender patient ID using patients_table object created earlier: resulting data set contains 20,000 blood culture isolates. head() function can preview first 6 rows data set: Now, let’s start cleaning analysis!","code":"sample_size <- 20000 data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),                    patient_id = sample(patients, size = sample_size, replace = TRUE),                    hospital = sample(c(\"Hospital A\",                                        \"Hospital B\",                                        \"Hospital C\",                                        \"Hospital D\"),                                      size = sample_size, replace = TRUE,                                      prob = c(0.30, 0.35, 0.15, 0.20)),                    bacteria = sample(bacteria, size = sample_size, replace = TRUE,                                      prob = c(0.50, 0.25, 0.15, 0.10)),                    AMX = random_rsi(sample_size, prob_RSI = c(0.35, 0.60, 0.05)),                    AMC = random_rsi(sample_size, prob_RSI = c(0.15, 0.75, 0.10)),                    CIP = random_rsi(sample_size, prob_RSI = c(0.20, 0.80, 0.00)),                    GEN = random_rsi(sample_size, prob_RSI = c(0.08, 0.92, 0.00))) data <- data %>% left_join(patients_table) head(data)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"cleaning-the-data","dir":"Articles","previous_headings":"","what":"Cleaning the data","title":"How to conduct AMR data analysis","text":"also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. example, gender variable: Frequency table Class: character Length: 20,000 Available: 20,000 (100.0%, NA: 0 = 0.0%) Unique: 2 Shortest: 1 Longest: 1 , can draw least two conclusions immediately. data scientists perspective, data looks clean: values M F. researchers perspective: slightly men. Nothing didn’t already know. data already quite clean, still need transform variables. bacteria column now consists text, want add variables based microbial IDs later . , transform column valid IDs. mutate() function dplyr package makes really easy: also want transform antibiotics, real life data don’t know really clean. .rsi() function ensures reliability reproducibility kind variables. .rsi.eligible() can check columns probably columns R/SI test results. Using mutate() across(), can apply transformation formal <rsi> class: Finally, apply EUCAST rules antimicrobial results. Europe, medical microbiological laboratories already apply rules. package features latest insights intrinsic resistance exceptional phenotypes. Moreover, eucast_rules() function can also apply additional rules, like forcing ampicillin = R amoxicillin/clavulanic acid = R. amoxicillin (column AMX) amoxicillin/clavulanic acid (column AMC) data generated randomly, rows undoubtedly contain AMX = S AMC = R, technically impossible. eucast_rules() fixes :","code":"data %>% freq(gender) data <- data %>%   mutate(bacteria = as.mo(bacteria)) is.rsi.eligible(data) # [1] FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE colnames(data)[is.rsi.eligible(data)] # [1] \"AMX\" \"AMC\" \"CIP\" \"GEN\"  data <- data %>%   mutate(across(where(is.rsi.eligible), as.rsi)) data <- eucast_rules(data, col_mo = \"bacteria\", rules = \"all\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"adding-new-variables","dir":"Articles","previous_headings":"","what":"Adding new variables","title":"How to conduct AMR data analysis","text":"Now microbial ID, can add taxonomic properties:","code":"data <- data %>%    mutate(gramstain = mo_gramstain(bacteria),          genus = mo_genus(bacteria),          species = mo_species(bacteria))"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Adding new variables","what":"First isolates","title":"How to conduct AMR data analysis","text":"also need know isolates can actually use analysis. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin  isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. method also takes account antimicrobial susceptibility test results using all_microbials(). Read methods first_isolate() page. outcome function can easily added data: 53.1% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 10,619 isolates analysis. Now data looks like: Time analysis!","code":"data <- data %>%    mutate(first = first_isolate(info = TRUE)) # Determining first isolates using an episode length of 365 days # ℹ Using column 'bacteria' as input for `col_mo`. # ℹ Using column 'date' as input for `col_date`. # ℹ Using column 'patient_id' as input for `col_patient_id`. # Basing inclusion on all antimicrobial results, using a points threshold of # 2 # => Found 10,619 'phenotype-based' first isolates (53.1% of total where a #    microbial ID was available) data_1st <- data %>%    filter(first == TRUE) data_1st <- data %>%    filter_first_isolate() head(data_1st)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"How to conduct AMR data analysis","text":"might want start getting idea data distributed. ’s important start, also decides continue analysis. Although package contains convenient function make frequency tables, exploratory data analysis (EDA) primary scope package. Use package like DataExplorer , read free online book Exploratory Data Analysis R Roger D. Peng.","code":""},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"dispersion-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Dispersion of species","title":"How to conduct AMR data analysis","text":"just get idea species distributed, create frequency table freq() function. created genus species column earlier based microbial ID. paste(), can concatenate together. freq() function can used like base R language intended: can used like dplyr way, easier readable: Frequency table Class: character Length: 10,619 Available: 10,619 (100.0%, NA: 0 = 0.0%) Unique: 4 Shortest: 16 Longest: 24","code":"freq(paste(data_1st$genus, data_1st$species)) data_1st %>% freq(genus, species)"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"overview-of-different-bugdrug-combinations","dir":"Articles","previous_headings":"Analysing the data","what":"Overview of different bug/drug combinations","title":"How to conduct AMR data analysis","text":"Using tidyverse selections, can also select filter columns based antibiotic class : want get quick glance number isolates different bug/drug combinations, can use bug_drug_combinations() function: give crude numbers data. calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"data_1st %>%    filter(any(aminoglycosides() == \"R\")) # ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) data_1st %>%    bug_drug_combinations() %>%    head() # show first 6 rows # ℹ Using column 'bacteria' as input for `col_mo`. data_1st %>%    select(bacteria, aminoglycosides()) %>%    bug_drug_combinations() # ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) # ℹ Using column 'bacteria' as input for `col_mo`."},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"How to conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package: course convenient know number isolates responsible percentages. purpose n_rsi() can used, works exactly like n_distinct() dplyr package. counts isolates available every group (.e. values S, R): functions can also used get proportion multiple antibiotics, calculate empiric susceptibility combination therapies easily: curious resistance within certain antibiotic classes, use antibiotic class selector penicillins(), automatically include columns AMX AMC data: make transition next part, let’s see differences previously calculated combination therapies plotted:","code":"data_1st %>% resistance(AMX) # [1] 0.5432715 data_1st %>%    group_by(hospital) %>%    summarise(amoxicillin = resistance(AMX)) data_1st %>%    group_by(hospital) %>%    summarise(amoxicillin = resistance(AMX),             available = n_rsi(AMX)) data_1st %>%    group_by(genus) %>%    summarise(amoxiclav = susceptibility(AMC),             gentamicin = susceptibility(GEN),             amoxiclav_genta = susceptibility(AMC, GEN)) data_1st %>%    # group by hospital   group_by(hospital) %>%    #                / -> select all penicillins in the data for calculation   #                |              / -> use resistance() for all peni's per hospital   #                |              |           / -> print as percentages   summarise(across(penicillins(), resistance, as_percent = TRUE)) %>%    # format the antibiotic column names, using so-called snake case,   # so 'Amoxicillin/clavulanic acid' becomes 'amoxicillin_clavulanic_acid'   rename_with(set_ab_names, penicillins()) data_1st %>%    group_by(genus) %>%    summarise(\"1. Amoxi/clav\" = susceptibility(AMC),             \"2. Gentamicin\" = susceptibility(GEN),             \"3. Amoxi/clav + genta\" = susceptibility(AMC, GEN)) %>%    # pivot_longer() from the tidyr package \"lengthens\" data:   tidyr::pivot_longer(-genus, names_to = \"antibiotic\") %>%    ggplot(aes(x = genus,              y = value,              fill = antibiotic)) +   geom_col(position = \"dodge2\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plots","dir":"Articles","previous_headings":"Analysing the data","what":"Plots","title":"How to conduct AMR data analysis","text":"show results plots, R users nowadays use ggplot2 package. package lets create plots layers. can read website. quick example look like syntaxes: AMR package contains functions extend ggplot2 package, example geom_rsi(). automatically transforms data count_df() proportion_df() show results stacked bars. simplest shortest example:  Omit translate_ab = FALSE antibiotic codes (AMX, AMC, CIP, GEN) translated official names (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, gentamicin). group e.g. genus column add additional functions package, can create :  simplify , also created ggplot_rsi() function, combines almost functions:","code":"ggplot(data = a_data_set,        mapping = aes(x = year,                      y = value)) +   geom_col() +   labs(title = \"A title\",        subtitle = \"A subtitle\",        x = \"My X axis\",        y = \"My Y axis\")  # or as short as: ggplot(a_data_set) +   geom_bar(aes(year)) ggplot(data_1st) +   geom_rsi(translate_ab = FALSE) # group the data on `genus` ggplot(data_1st %>% group_by(genus)) +    # create bars with genus on x axis   # it looks for variables with class `rsi`,   # of which we have 4 (earlier created with `as.rsi`)   geom_rsi(x = \"genus\") +    # split plots on antibiotic   facet_rsi(facet = \"antibiotic\") +   # set colours to the R/SI interpretations (colour-blind friendly)   scale_rsi_colours() +   # show percentages on y axis   scale_y_percent(breaks = 0:4 * 25) +   # turn 90 degrees, to make it bars instead of columns   coord_flip() +   # add labels   labs(title = \"Resistance per genus and antibiotic\",         subtitle = \"(this is fake data)\") +   # and print genus in italic to follow our convention   # (is now y axis because we turned the plot)   theme(axis.text.y = element_text(face = \"italic\")) data_1st %>%    group_by(genus) %>%   ggplot_rsi(x = \"genus\",              facet = \"antibiotic\",              breaks = 0:4 * 25,              datalabels = FALSE) +   coord_flip()"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"plotting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data > Plots","what":"Plotting MIC and disk diffusion values","title":"How to conduct AMR data analysis","text":"AMR package also extends plot() ggplot2::autoplot() functions plotting minimum inhibitory concentrations (MIC, created .mic()) disk diffusion diameters (created .disk()). random_mic() random_disk() functions, can generate sampled values new data types (S3 classes) <mic> <disk>:   also specific, generating MICs likely found E. coli ciprofloxacin: plot() autoplot() function, can define microorganism antimicrobial agent way. add interpretation values according chosen guidelines (defaults latest EUCAST guideline). Default colours colour-blind friendly, maintaining convention e.g. ‘susceptible’ green ‘resistant’ red:   disk diffusion values, much difference plotting:  using ggplot2 package, now choosing latest implemented CLSI guideline (notice EUCAST-specific term “Susceptible, incr. exp.” changed “Intermediate”):","code":"mic_values <- random_mic(size = 100) mic_values # Class <mic> #   [1] 256    16     0.002  0.025  8      0.025  32     0.025  0.01   256    #  [11] 16     0.01   32     0.025  0.01   0.0625 2      0.25   128    0.005  #  [21] 0.01   16     0.125  0.25   0.01   0.001  8      0.025  8      1      #  [31] 0.025  0.25   0.0625 256    0.001  0.025  0.001  8      0.01   0.025  #  [41] 0.25   8      64     8      0.25   4      1      0.0625 0.0625 0.5    #  [51] 2      0.002  16     0.002  0.01   16     0.25   64     8      256    #  [61] 0.005  0.0625 1      64     64     0.5    128    0.002  32     0.002  #  [71] 128    128    0.5    0.002  0.25   0.5    0.125  1      16     64     #  [81] 128    32     0.025  128    16     32     32     32     0.002  0.125  #  [91] 0.5    0.0625 0.5    128    0.125  128    8      0.25   128    0.25 # base R: plot(mic_values) # ggplot2: autoplot(mic_values) mic_values <- random_mic(size = 100, mo = \"E. coli\", ab = \"cipro\") # base R: plot(mic_values, mo = \"E. coli\", ab = \"cipro\") # ggplot2: autoplot(mic_values, mo = \"E. coli\", ab = \"cipro\") disk_values <- random_disk(size = 100, mo = \"E. coli\", ab = \"cipro\") # ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #   coli). Run `mo_uncertainties()` to review this. disk_values # Class <disk> #   [1] 23 21 25 21 18 21 18 19 23 26 28 30 20 21 17 17 27 26 31 30 25 17 27 27 19 #  [26] 31 23 23 23 22 20 26 17 24 29 26 24 25 29 22 22 29 26 22 24 25 26 18 31 24 #  [51] 25 28 26 27 19 21 21 29 27 21 27 21 18 17 20 20 17 25 30 20 19 20 23 27 31 #  [76] 24 31 24 22 26 19 19 20 20 25 17 21 22 30 25 23 26 31 20 30 18 29 18 29 17 # base R: plot(disk_values, mo = \"E. coli\", ab = \"cipro\") autoplot(disk_values,        mo = \"E. coli\",        ab = \"cipro\",        guideline = \"CLSI\")"},{"path":"https://msberends.github.io/AMR/articles/AMR.html","id":"independence-test","dir":"Articles","previous_headings":"Analysing the data","what":"Independence test","title":"How to conduct AMR data analysis","text":"next example uses example_isolates data set. data set included package contains 2,000 microbial isolates full antibiograms. reflects reality can used practice AMR data analysis. compare resistance fosfomycin (column FOS) hospital D. input fisher.test() can retrieved transformation like : can apply test now : can seen, p value 0.031, means fosfomycin resistance found isolates patients hospital D really different.","code":"# use package 'tidyr' to pivot data: library(tidyr)  check_FOS <- example_isolates %>%   filter(hospital_id %in% c(\"A\", \"D\")) %>% # filter on only hospitals A and D   select(hospital_id, FOS) %>%             # select the hospitals and fosfomycin   group_by(hospital_id) %>%                # group on the hospitals   count_df(combine_SI = TRUE) %>%          # count all isolates per group (hospital_id)   pivot_wider(names_from = hospital_id,    # transform output so A and D are columns               values_from = value) %>%        select(A, D) %>%                         # and only select these columns   as.matrix()                              # transform to a good old matrix for fisher.test()  check_FOS #       A  D # [1,] 25 77 # [2,] 24 33 # do Fisher's Exact Test fisher.test(check_FOS)                             #  #   Fisher's Exact Test for Count Data #  # data:  check_FOS # p-value = 0.03104 # alternative hypothesis: true odds ratio is not equal to 1 # 95 percent confidence interval: #  0.2111489 0.9485124 # sample estimates: # odds ratio  #  0.4488318"},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to apply EUCAST rules","text":"EUCAST rules? European Committee Antimicrobial Susceptibility Testing (EUCAST) states website: EUCAST expert rules tabulated collection expert knowledge intrinsic resistances, exceptional resistance phenotypes interpretive rules may applied antimicrobial susceptibility testing order reduce errors make appropriate recommendations reporting particular resistances. Europe, lot medical microbiological laboratories already apply rules (Brown et al., 2015). package features latest insights intrinsic resistance unusual phenotypes (v3.3, 2021). Moreover, eucast_rules() function use purpose can also apply additional rules, like forcing ampicillin = R isolates amoxicillin/clavulanic acid = R.","code":""},{"path":"https://msberends.github.io/AMR/articles/EUCAST.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to apply EUCAST rules","text":"rules can used discard impossible bug-drug combinations data. example, Klebsiella produces beta-lactamase prevents ampicillin (amoxicillin) working . words, practically every strain Klebsiella resistant ampicillin. Sometimes, laboratory data can still contain strains ampicillin susceptible ampicillin. antibiogram available identification available, antibiogram re-interpreted based identification (namely, Klebsiella). EUCAST expert rules solve , can applied using eucast_rules(): convenient function mo_is_intrinsic_resistant() uses guideline, allows check one specific microorganisms antibiotics: EUCAST rules can used correction, can also used filling known resistance susceptibility based results antimicrobials drugs. process called interpretive reading, basically form imputation, part eucast_rules() function well:","code":"oops <- data.frame(mo = c(\"Klebsiella\",                            \"Escherichia\"),                    ampicillin = \"S\") oops #            mo ampicillin # 1  Klebsiella          S # 2 Escherichia          S  eucast_rules(oops, info = FALSE) #            mo ampicillin # 1  Klebsiella          R # 2 Escherichia          S mo_is_intrinsic_resistant(c(\"Klebsiella\", \"Escherichia\"),                           \"ampicillin\") # [1]  TRUE FALSE  mo_is_intrinsic_resistant(\"Klebsiella\",                           c(\"ampicillin\", \"kanamycin\")) # [1]  TRUE FALSE data <- data.frame(mo = c(\"Staphylococcus aureus\",                           \"Enterococcus faecalis\",                           \"Escherichia coli\",                           \"Klebsiella pneumoniae\",                           \"Pseudomonas aeruginosa\"),                    VAN = \"-\",       # Vancomycin                    AMX = \"-\",       # Amoxicillin                    COL = \"-\",       # Colistin                    CAZ = \"-\",       # Ceftazidime                    CXM = \"-\",       # Cefuroxime                    PEN = \"S\",       # Benzylenicillin                    FOX = \"S\",       # Cefoxitin                    stringsAsFactors = FALSE) data eucast_rules(data)"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"type-of-input","dir":"Articles","previous_headings":"","what":"Type of input","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function takes data set input, regular data.frame. tries automatically determine right columns info isolates, name species columns results antimicrobial agents. See help page info set right settings data command ?mdro. WHONET data (data), settings automatically set correctly.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"guidelines","dir":"Articles","previous_headings":"","what":"Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function support multiple guidelines. can select guideline guideline parameter. Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.2\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance Unusual Phenotypes” (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance Exceptional Phenotypes Tables” (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization “Companion handbook guidelines programmatic management drug-resistant tuberculosis” (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"custom-guidelines","dir":"Articles","previous_headings":"Guidelines","what":"Custom Guidelines","title":"How to determine multi-drug resistance (MDR)","text":"can also use custom guideline. Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() dplyr package, recognise input method set rules. Rules must set using R considers ‘formula notation’: row/isolate matches first rule, value first ~ (case ‘Elderly Type ’) set MDRO value. Otherwise, second rule tried . maximum number rules unlimited. can print rules set console overview. Colours help reading console supports colours. outcome function can used guideline argument mdro() function: rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom # A set of custom MDRO rules: #   1. If CIP is \"R\" and age is higher than 60 then: Elderly Type A #   2. If ERY is \"R\" and age is higher than 60 then: Elderly Type B #   3. Otherwise: Negative #  # Unmatched rows will return NA. # Results will be of class <factor>, with ordered levels: Negative < Elderly Type A < Elderly Type B x <- mdro(example_isolates, guideline = custom) table(x) # x #       Negative Elderly Type A Elderly Type B  #           1070            198            732"},{"path":"https://msberends.github.io/AMR/articles/MDR.html","id":"examples","dir":"Articles","previous_headings":"","what":"Examples","title":"How to determine multi-drug resistance (MDR)","text":"mdro() function always returns ordered factor predefined guidelines. example, output default guideline Magiorakos et al. returns factor levels ‘Negative’, ‘MDR’, ‘XDR’ ‘PDR’ order. next example uses example_isolates data set. data set included package contains full antibiograms 2,000 microbial isolates. reflects reality can used practise AMR data analysis. test MDR/XDR/PDR guideline data set, get: (16 isolates test results) Frequency table Class: factor > ordered (numeric) Length: 2,000 Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant … Available: 1,729 (86.45%, NA: 271 = 13.55%) Unique: 2 another example, create data set determine multi-drug resistant TB: column names automatically verified valid drug names codes, worked exactly way: data set now looks like : can now add interpretation MDR-TB data set. can use: shortcut mdr_tb(): Create frequency table results: Frequency table Class: factor > ordered (numeric) Length: 5,000 Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <… Available: 5,000 (100.0%, NA: 0 = 0.0%) Unique: 5","code":"library(dplyr)   # to support pipes: %>% library(cleaner) # to create frequency tables example_isolates %>%    mdro() %>%    freq() # show frequency table of the result # Warning: in `mdro()`: NA introduced for isolates where the available percentage of # antimicrobial classes was below 50% (set with `pct_required_classes`) # random_rsi() is a helper function to generate # a random vector with values S, I and R my_TB_data <- data.frame(rifampicin = random_rsi(5000),                          isoniazid = random_rsi(5000),                          gatifloxacin = random_rsi(5000),                          ethambutol = random_rsi(5000),                          pyrazinamide = random_rsi(5000),                          moxifloxacin = random_rsi(5000),                          kanamycin = random_rsi(5000)) my_TB_data <- data.frame(RIF = random_rsi(5000),                          INH = random_rsi(5000),                          GAT = random_rsi(5000),                          ETH = random_rsi(5000),                          PZA = random_rsi(5000),                          MFX = random_rsi(5000),                          KAN = random_rsi(5000)) head(my_TB_data) #   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin # 1          S         S            R          I            R            I # 2          I         R            S          S            S            S # 3          I         R            S          I            S            S # 4          R         I            S          I            S            R # 5          I         I            S          R            S            R # 6          S         I            S          S            R            R #   kanamycin # 1         S # 2         S # 3         I # 4         R # 5         I # 6         S mdro(my_TB_data, guideline = \"TB\") my_TB_data$mdr <- mdr_tb(my_TB_data) # ℹ No column found as input for `col_mo`, assuming all rows contain #   Mycobacterium tuberculosis. freq(my_TB_data$mdr)"},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"transforming","dir":"Articles","previous_headings":"","what":"Transforming","title":"How to conduct principal component analysis (PCA) for AMR","text":"PCA, need transform AMR data first. example_isolates data set package looks like: Now transform data set resistance percentages per taxonomic order genus:","code":"library(AMR) library(dplyr) glimpse(example_isolates) # Rows: 2,000 # Columns: 49 # $ date            <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-… # $ hospital_id     <fct> D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B, … # $ ward_icu        <lgl> FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TR… # $ ward_clinical   <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FA… # $ ward_outpatient <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE… # $ age             <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71… # $ gender          <chr> \"F\", \"F\", \"F\", \"F\", \"F\", \"F\", \"M\", \"M\", \"F\", \"F\", \"M\",… # $ patient_id      <chr> \"A77334\", \"A77334\", \"067927\", \"067927\", \"067927\", \"067… # $ mo              <mo> \"B_ESCHR_COLI\", \"B_ESCHR_COLI\", \"B_STPHY_EPDR\", \"B_STPH… # $ PEN             <rsi> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, … # $ OXA             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FLC             <rsi> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, … # $ AMX             <rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, … # $ AMC             <rsi> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, … # $ AMP             <rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, … # $ TZP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CZO             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FEP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CXM             <rsi> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R,… # $ FOX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CTX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, … # $ CAZ             <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S… # $ CRO             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, … # $ GEN             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TOB             <rsi> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA… # $ AMK             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ KAN             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ TMP             <rsi> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R… # $ SXT             <rsi> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, … # $ NIT             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ FOS             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ LNZ             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,… # $ CIP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, … # $ MFX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ VAN             <rsi> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S… # $ TEC             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,… # $ TCY             <rsi> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S… # $ TGC             <rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, … # $ DOX             <rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, … # $ ERY             <rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, … # $ CLI             <rsi> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, … # $ AZM             <rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, … # $ IPM             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, … # $ MEM             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ MTR             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ CHL             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ COL             <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R… # $ MUP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA… # $ RIF             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,… resistance_data <- example_isolates %>%    group_by(order = mo_order(mo),       # group on anything, like order            genus = mo_genus(mo)) %>%   #  and genus as we do here   summarise_if(is.rsi, resistance) %>% # then get resistance of all drugs   select(order, genus, AMC, CXM, CTX,           CAZ, GEN, TOB, TMP, SXT)      # and select only relevant columns  head(resistance_data) # # A tibble: 6 × 10 # # Groups:   order [5] #   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT #   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> # 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA # 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA # 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA # 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA # 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA # 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA"},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"perform-principal-component-analysis","dir":"Articles","previous_headings":"","what":"Perform principal component analysis","title":"How to conduct principal component analysis (PCA) for AMR","text":"new pca() function automatically filter rows contain numeric values selected variables, now need : result can reviewed good old summary() function: Good news. first two components explain total 93.3% variance (see PC1 PC2 values Proportion Variance. can create -called biplot base R biplot() function, see antimicrobial resistance per drug explain difference per microorganism.","code":"pca_result <- pca(resistance_data) # ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #   and \"TOB\". Total observations available: 7. summary(pca_result) # Groups (n=4, named as 'order'): # [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\" # Importance of components: #                           PC1    PC2    PC3     PC4     PC5     PC6       PC7 # Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 5.121e-17 # Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00 # Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00 # Groups (n=4, named as 'order'): # [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\""},{"path":"https://msberends.github.io/AMR/articles/PCA.html","id":"plotting-the-results","dir":"Articles","previous_headings":"","what":"Plotting the results","title":"How to conduct principal component analysis (PCA) for AMR","text":"can’t see explanation points. Perhaps works better new ggplot_pca() function, automatically adds right labels even groups:  can also print ellipse per group, edit appearance:","code":"biplot(pca_result) ggplot_pca(pca_result) ggplot_pca(pca_result, ellipse = TRUE) +   ggplot2::labs(title = \"An AMR/PCA biplot!\")"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss-sas-stata","dir":"Articles","previous_headings":"","what":"SPSS / SAS / Stata","title":"How to import data from SPSS / SAS / Stata","text":"SPSS (Statistical Package Social Sciences) probably well-known software package statistical analysis. SPSS easier learn R, SPSS click menu run parts analysis. user-friendliness, taught universities particularly useful students new statistics. experience, guess pretty much (bio)medical students know time graduate. SAS Stata comparable statistical packages popular big industries.","code":""},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"compared-to-r","dir":"Articles","previous_headings":"","what":"Compared to R","title":"How to import data from SPSS / SAS / Stata","text":"said, SPSS easier learn R. SPSS, SAS Stata come major downsides comparing R: R highly modular. official R network (CRAN) features 16,000 packages time writing, AMR package one . packages peer-reviewed publication. Aside official channel, also developers choose submit CRAN, rather keep public repository, like GitHub. may even lot 14,000 packages . Bottom line , can really extend ask somebody . Take example AMR package. Among things, adds reliable reference data R help data cleaning analysis. SPSS, SAS Stata never know valid MIC value Gram stain E. coli . species Klebiella resistant amoxicillin Floxapen® trade name flucloxacillin. facts properties often needed clean existing data, inconvenient software package without reliable reference data. See demonstration. R extremely flexible. write syntax , can anything want. flexibility transforming, arranging, grouping summarising data, drawing plots, endless - SPSS, SAS Stata bound algorithms format styles. may bit flexible, can probably never create specific publication-ready plot without using (paid) software. sometimes write syntaxes SPSS run complete analysis ‘automate’ work, lot less time R. notice writing syntaxes R lot nifty clever SPSS. Still, working statistical package, knowledge (statistically) willing accomplish. R can easily automated. last years, R Markdown really made interesting development. R Markdown, can easily produce reports, whether format Word, PowerPoint, website, PDF document just raw data Excel. even allows use reference file containing layout style (e.g. fonts colours) organisation. use lot generate weekly monthly reports automatically. Just write code enjoy automatically updated reports interval like. even professional environment, create Shiny apps: live manipulation data using custom made website. webdesign knowledge needed (JavaScript, CSS, HTML) almost zero. R huge community. Many R users just ask questions websites like StackOverflow.com, largest online community programmers. time writing, 460,146 R-related questions already asked platform (covers questions answers programming language). experience, questions answered within couple minutes. R understands data type, including SPSS/SAS/Stata. ’s vice versa ’m afraid. can import data source R. example SPSS, SAS Stata (link), Minitab, Epi Info EpiData (link), Excel (link), flat files like CSV, TXT TSV (link), directly databases datawarehouses anywhere world (link). can even scrape websites download tables live internet (link) get results API call transform data one command (link). best part - can export R data formats well. can import SPSS file, analysis neatly R export resulting tables Excel files sharing. R completely free open-source. strings attached. created maintained volunteers believe (data) science open publicly available everybody. SPSS, SAS Stata quite expensive. IBM SPSS Staticstics comes subscriptions nowadays, varying USD 1,300 USD 8,500 per user per year. SAS Analytics Pro costs around USD 10,000 per computer. Stata also business model subscription fees, varying USD 600 USD 2,800 per computer per year, lower prices come limitation number variables can work . still offer benefits R. working midsized small company, can save tens thousands dollars using R instead e.g. SPSS - gaining even functions flexibility. R enthousiasts can much PR want (like ), nobody officially associated affiliated R. really free. R (nowadays) preferred analysis software academic papers. present, R among world powerful statistical languages, generally popular science (Bollmann et al., 2017). reasons, number references R analysis method academic papers rising continuously even surpassed SPSS academic use (Muenchen, 2014). believe thing SPSS , always great user interface easy learn use. Back developed , little competition, let alone R. R didn’t even professional user interface last decade (called RStudio, see ). people used R nineties 2010 almost completely incomparable R used now. language restyled completely volunteers dedicated professionals field data science. SPSS great nothing else compete. now 2022, don’t see reason SPSS better use R. demonstrate first point:","code":"# not all values are valid MIC values: as.mic(0.125) # Class <mic> # [1] 0.125 as.mic(\"testvalue\") # Class <mic> # [1] <NA>  # the Gram stain is available for all bacteria: mo_gramstain(\"E. coli\") # [1] \"Gram-negative\"  # Klebsiella is intrinsic resistant to amoxicillin, according to EUCAST: klebsiella_test <- data.frame(mo = \"klebsiella\",                                amox = \"S\",                               stringsAsFactors = FALSE) klebsiella_test # (our original data) #           mo amox # 1 klebsiella    S eucast_rules(klebsiella_test, info = FALSE) # (the edited data by EUCAST rules) #           mo amox # 1 klebsiella    R  # hundreds of trade names can be translated to a name, trade name or an ATC code: ab_name(\"floxapen\") # [1] \"Flucloxacillin\" ab_tradenames(\"floxapen\") # [1] \"floxacillin\"          \"floxapen\"             \"floxapen sodium salt\" # [4] \"fluclox\"              \"flucloxacilina\"       \"flucloxacillin\"       # [7] \"flucloxacilline\"      \"flucloxacillinum\"     \"fluorochloroxacillin\" ab_atc(\"floxapen\") # [1] \"J01CF05\""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"rstudio","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"RStudio","title":"How to import data from SPSS / SAS / Stata","text":"work R, probably best option use RStudio. open-source free desktop environment allows run R code, also supports project management, version management, package management convenient import menus work data sources. can also install RStudio Server private corporate server, brings nothing less complete RStudio software website (home work). import data file, just click Import Dataset Environment tab:  additional packages needed, RStudio ask installed beforehand. window opens, can define options (parameters) used import ’re ready go:  want named variables imported factors resembles SPSS , use as_factor(). difference :","code":"SPSS_data # # A tibble: 4,203 x 4 #     v001 sex       status    statusage #    <dbl> <dbl+lbl> <dbl+lbl>     <dbl> #  1 10002 1         1              76.6 #  2 10004 0         1              59.1 #  3 10005 1         1              54.5 #  4 10006 1         1              54.1 #  5 10007 1         1              57.7 #  6 10008 1         1              62.8 #  7 10010 0         1              63.7 #  8 10011 1         1              73.1 #  9 10017 1         1              56.7 # 10 10018 0         1              66.6 # # ... with 4,193 more rows  as_factor(SPSS_data) # # A tibble: 4,203 x 4 #     v001 sex    status statusage #    <dbl> <fct>  <fct>      <dbl> #  1 10002 Male   alive       76.6 #  2 10004 Female alive       59.1 #  3 10005 Male   alive       54.5 #  4 10006 Male   alive       54.1 #  5 10007 Male   alive       57.7 #  6 10008 Male   alive       62.8 #  7 10010 Female alive       63.7 #  8 10011 Male   alive       73.1 #  9 10017 Male   alive       56.7 # 10 10018 Female alive       66.6 # # ... with 4,193 more rows"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"base-r","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata","what":"Base R","title":"How to import data from SPSS / SAS / Stata","text":"import data SPSS, SAS Stata, can use great haven package : can now import files follows:","code":"# download and install the latest version: install.packages(\"haven\") # load the package you just installed: library(haven)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"spss","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SPSS","title":"How to import data from SPSS / SAS / Stata","text":"read files SPSS R: forget as_factor(), mentioned . export R objects SPSS file format:","code":"# read any SPSS file based on file extension (best way): read_spss(file = \"path/to/file\")  # read .sav or .zsav file: read_sav(file = \"path/to/file\")  # read .por file: read_por(file = \"path/to/file\") # save as .sav file: write_sav(data = yourdata, path = \"path/to/file\")  # save as compressed .zsav file: write_sav(data = yourdata, path = \"path/to/file\", compress = TRUE)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"sas","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"SAS","title":"How to import data from SPSS / SAS / Stata","text":"read files SAS R: export R objects SAS file format:","code":"# read .sas7bdat + .sas7bcat files: read_sas(data_file = \"path/to/file\", catalog_file = NULL)  # read SAS transport files (version 5 and version 8): read_xpt(file = \"path/to/file\") # save as regular SAS file: write_sas(data = yourdata, path = \"path/to/file\")  # the SAS transport format is an open format  # (required for submission of the data to the FDA) write_xpt(data = yourdata, path = \"path/to/file\", version = 8)"},{"path":"https://msberends.github.io/AMR/articles/SPSS.html","id":"stata","dir":"Articles","previous_headings":"Import data from SPSS/SAS/Stata > Base R","what":"Stata","title":"How to import data from SPSS / SAS / Stata","text":"read files Stata R: export R objects Stata file format:","code":"# read .dta file: read_stata(file = \"/path/to/file\")  # works exactly the same: read_dta(file = \"/path/to/file\") # save as .dta file, Stata version 14: # (supports Stata v8 until v15 at the time of writing) write_dta(data = yourdata, path = \"/path/to/file\", version = 14)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"import-of-data","dir":"Articles","previous_headings":"","what":"Import of data","title":"How to work with WHONET data","text":"tutorial assumes already imported WHONET data e.g. readxl package. RStudio, can done using menu button ‘Import Dataset’ tab ‘Environment’. Choose option ‘Excel’ select exported file. Make sure date fields imported correctly. example syntax look like : package comes example data set WHONET. use analysis.","code":"library(readxl) data <- read_excel(path = \"path/to/your/file.xlsx\")"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"How to work with WHONET data","text":"First, load relevant packages yet . use tidyverse analyses. . don’t know yet, suggest read website: https://www.tidyverse.org/. transform variables simplify automate analysis: Microorganisms transformed microorganism codes (called mo) using Catalogue Life reference data set, contains ~70,000 microorganisms taxonomic kingdoms Bacteria, Fungi Protozoa. tranformation .mo(). function also recognises almost WHONET abbreviations microorganisms. Antimicrobial results interpretations clean valid. words, contain values \"S\", \"\" \"R\". exactly .rsi() function . errors warnings, values transformed succesfully. also created package dedicated data cleaning checking, called cleaner package. freq() function can used create frequency tables. let’s check data, couple frequency tables: Frequency table Class: character Length: 500 Available: 500 (100.0%, NA: 0 = 0.0%) Unique: 37 Shortest: 11 Longest: 40 (omitted 27 entries, n = 56 [11.20%]) Frequency table Class: factor > ordered > rsi (numeric) Length: 500 Levels: 3: S < < R Available: 481 (96.2%, NA: 19 = 3.8%) Unique: 3 Drug: Amoxicillin/clavulanic acid (AMC, J01CR02) Drug group: Beta-lactams/penicillins %SI: 78.59%","code":"library(dplyr)   # part of tidyverse library(ggplot2) # part of tidyverse library(AMR)     # this package library(cleaner) # to create frequency tables # transform variables data <- WHONET %>%   # get microbial ID based on given organism   mutate(mo = as.mo(Organism)) %>%    # transform everything from \"AMP_ND10\" to \"CIP_EE\" to the new `rsi` class   mutate_at(vars(AMP_ND10:CIP_EE), as.rsi) # our newly created `mo` variable, put in the mo_name() function data %>% freq(mo_name(mo), nmax = 10) # our transformed antibiotic columns # amoxicillin/clavulanic acid (J01CR02) as an example data %>% freq(AMC_ND2)"},{"path":"https://msberends.github.io/AMR/articles/WHONET.html","id":"a-first-glimpse-at-results","dir":"Articles","previous_headings":"","what":"A first glimpse at results","title":"How to work with WHONET data","text":"easy ggplot already give lot information, using included ggplot_rsi() function:","code":"data %>%   group_by(Country) %>%   select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%   ggplot_rsi(translate_ab = 'ab', facet = \"Country\", datalabels = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-currently-accepted-names","dir":"Articles","previous_headings":"","what":"Microorganisms (currently accepted names)","title":"Data sets for download / own use","text":"data set 70,764 rows 16 columns, containing following column names:mo, fullname, kingdom, phylum, class, order, family, genus, species, subspecies, rank, ref, species_id, source, prevalence snomed. data set R available microorganisms, load AMR package. last updated 21 August 2022 15:23:37 UTC. Find info structure data set . Direct download links: Download R file (1.3 MB) Download Excel file (4.8 MB) Download plain text file (13.1 MB) Download SAS file (30.7 MB) Download SPSS file (16.3 MB) Download Stata file (27.5 MB) NOTE: exported files SAS, SPSS Stata contain SNOMED codes, file size exceed 100 MB; file size limit GitHub. Advice? Use R instead.","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source","dir":"Articles","previous_headings":"Microorganisms (currently accepted names)","what":"Source","title":"Data sets for download / own use","text":"full taxonomy microorganisms based authoritative comprehensive: Catalogue Life (included version: 2019) List Prokaryotic names Standing Nomenclature (LPSN, last updated: 5 October 2021) US Edition SNOMED CT 1 September 2020, retrieved Public Health Information Network Vocabulary Access Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content","dir":"Articles","previous_headings":"Microorganisms (currently accepted names)","what":"Example content","title":"Data sets for download / own use","text":"Included (sub)species per taxonomic kingdom: Example rows filtering genus Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"microorganisms-previously-accepted-names","dir":"Articles","previous_headings":"","what":"Microorganisms (previously accepted names)","title":"Data sets for download / own use","text":"data set 14,338 rows 4 columns, containing following column names:fullname, fullname_new, ref prevalence. Note: remember ‘ref’ columns contains scientific reference old taxonomic entries, .e. column ‘fullname’. scientific reference new names, .e. column ‘fullname_new’, see microorganisms data set. data set R available microorganisms.old, load AMR package. last updated 21 August 2022 15:23:37 UTC. Find info structure data set . Direct download links: Download R file (0.2 MB) Download Excel file (0.5 MB) Download plain text file (1 MB) Download SAS file (2.1 MB) Download SPSS file (1.3 MB) Download Stata file (2 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-1","dir":"Articles","previous_headings":"Microorganisms (previously accepted names)","what":"Source","title":"Data sets for download / own use","text":"data set contains old, previously accepted taxonomic names. data sources microorganisms data set: Catalogue Life (included version: 2019) List Prokaryotic names Standing Nomenclature (LPSN, last updated: 5 October 2021)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-1","dir":"Articles","previous_headings":"Microorganisms (previously accepted names)","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Escherichia:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antibiotic-agents","dir":"Articles","previous_headings":"","what":"Antibiotic agents","title":"Data sets for download / own use","text":"data set 464 rows 14 columns, containing following column names:ab, cid, name, group, atc, atc_group1, atc_group2, abbreviations, synonyms, oral_ddd, oral_units, iv_ddd, iv_units loinc. data set R available antibiotics, load AMR package. last updated 21 August 2022 15:23:37 UTC. Find info structure data set . Direct download links: Download R file (33 kB) Download Excel file (65 kB) Download plain text file (0.1 MB) Download SAS file (1.8 MB) Download SPSS file (0.3 MB) Download Stata file (0.3 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-2","dir":"Articles","previous_headings":"Antibiotic agents","what":"Source","title":"Data sets for download / own use","text":"data set contains EARS-Net ATC codes gathered WHONET, compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine WHONET software 2019","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"antiviral-agents","dir":"Articles","previous_headings":"","what":"Antiviral agents","title":"Data sets for download / own use","text":"data set 102 rows 9 columns, containing following column names:atc, cid, name, atc_group, synonyms, oral_ddd, oral_units, iv_ddd iv_units. data set R available antivirals, load AMR package. last updated 21 August 2022 15:23:37 UTC. Find info structure data set . Direct download links: Download R file (5 kB) Download Excel file (14 kB) Download plain text file (16 kB) Download SAS file (80 kB) Download SPSS file (68 kB) Download Stata file (67 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-3","dir":"Articles","previous_headings":"Antiviral agents","what":"Source","title":"Data sets for download / own use","text":"data set contains ATC codes gathered compound IDs PubChem. also contains brand names (synonyms) found PubChem Defined Daily Doses (DDDs) oral parenteral administration. ATC/DDD index Collaborating Centre Drug Statistics Methodology (note: may used commercial purposes, freely available CC website personal use) PubChem US National Library Medicine","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"interpretation-from-mic-values-disk-diameters-to-rsi","dir":"Articles","previous_headings":"","what":"Interpretation from MIC values / disk diameters to R/SI","title":"Data sets for download / own use","text":"data set 20,369 rows 11 columns, containing following column names:guideline, method, site, mo, rank_index, ab, ref_tbl, disk_dose, breakpoint_S, breakpoint_R uti. data set R available rsi_translation, load AMR package. last updated 21 August 2022 15:23:37 UTC. Find info structure data set . Direct download links: Download R file (49 kB) Download Excel file (0.9 MB) Download plain text file (2 MB) Download SAS file (4 MB) Download SPSS file (2.6 MB) Download Stata file (3.8 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-4","dir":"Articles","previous_headings":"Interpretation from MIC values / disk diameters to R/SI","what":"Source","title":"Data sets for download / own use","text":"data set contains interpretation rules MIC values disk diffusion diameters. Included guidelines CLSI (2011-2022) EUCAST (2011-2022).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"intrinsic-bacterial-resistance","dir":"Articles","previous_headings":"","what":"Intrinsic bacterial resistance","title":"Data sets for download / own use","text":"data set 134,956 rows 2 columns, containing following column names:mo ab. data set R available intrinsic_resistant, load AMR package. last updated 21 August 2022 15:23:37 UTC. Find info structure data set . Direct download links: Download R file (78 kB) Download Excel file (1.3 MB) Download plain text file (5.1 MB) Download SAS file (10.4 MB) Download SPSS file (7.4 MB) Download Stata file (10.2 MB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-5","dir":"Articles","previous_headings":"Intrinsic bacterial resistance","what":"Source","title":"Data sets for download / own use","text":"data set contains defined intrinsic resistance EUCAST bug-drug combinations, based ‘EUCAST Expert Rules’ ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"example-content-5","dir":"Articles","previous_headings":"Intrinsic bacterial resistance","what":"Example content","title":"Data sets for download / own use","text":"Example rows filtering Enterobacter cloacae:","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"dosage-guidelines-from-eucast","dir":"Articles","previous_headings":"","what":"Dosage guidelines from EUCAST","title":"Data sets for download / own use","text":"data set 169 rows 9 columns, containing following column names:ab, name, type, dose, dose_times, administration, notes, original_txt eucast_version. data set R available dosage, load AMR package. last updated 21 August 2022 15:23:37 UTC. Find info structure data set . Direct download links: Download R file (3 kB) Download Excel file (14 kB) Download plain text file (15 kB) Download SAS file (52 kB) Download SPSS file (45 kB) Download Stata file (44 kB)","code":""},{"path":"https://msberends.github.io/AMR/articles/datasets.html","id":"source-6","dir":"Articles","previous_headings":"Dosage guidelines from EUCAST","what":"Source","title":"Data sets for download / own use","text":"EUCAST breakpoints used package based dosages data set. Currently included dosages data set meant : ‘EUCAST Clinical Breakpoint Tables’ v11.0 (2021).","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"needed-r-packages","dir":"Articles","previous_headings":"","what":"Needed R packages","title":"How to predict antimicrobial resistance","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. AMR package depends packages even extends use functions.","code":"library(dplyr) library(ggplot2) library(AMR)  # (if not yet installed, install with:) # install.packages(c(\"tidyverse\", \"AMR\"))"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"prediction-analysis","dir":"Articles","previous_headings":"","what":"Prediction analysis","title":"How to predict antimicrobial resistance","text":"package contains function resistance_predict(), takes input functions AMR data analysis. Based date column, calculates cases per year uses regression model predict antimicrobial resistance. basically easy : function look date column col_date set. running commands, summary regression model printed unless using resistance_predict(..., info = FALSE). text printed summary - actual result (output) function data.frame containing year: number observations, actual observed resistance, estimated resistance standard error estimation: function plot available base R, can extended packages depend output based type input. extended function cope resistance predictions:  fastest way plot result. automatically adds right axes, error bars, titles, number available observations type model. also support ggplot2 package custom function ggplot_rsi_predict() create appealing plots:","code":"# resistance prediction of piperacillin/tazobactam (TZP): resistance_predict(tbl = example_isolates, col_date = \"date\", col_ab = \"TZP\", model = \"binomial\")  # or: example_isolates %>%    resistance_predict(col_ab = \"TZP\",                      model  \"binomial\")  # to bind it to object 'predict_TZP' for example: predict_TZP <- example_isolates %>%    resistance_predict(col_ab = \"TZP\",                      model = \"binomial\") predict_TZP #    year      value    se_min    se_max observations   observed  estimated # 1  2002 0.20000000        NA        NA           15 0.20000000 0.05616378 # 2  2003 0.06250000        NA        NA           32 0.06250000 0.06163839 # 3  2004 0.08536585        NA        NA           82 0.08536585 0.06760841 # 4  2005 0.05000000        NA        NA           60 0.05000000 0.07411100 # 5  2006 0.05084746        NA        NA           59 0.05084746 0.08118454 # 6  2007 0.12121212        NA        NA           66 0.12121212 0.08886843 # 7  2008 0.04166667        NA        NA           72 0.04166667 0.09720264 # 8  2009 0.01639344        NA        NA           61 0.01639344 0.10622731 # 9  2010 0.05660377        NA        NA           53 0.05660377 0.11598223 # 10 2011 0.18279570        NA        NA           93 0.18279570 0.12650615 # 11 2012 0.30769231        NA        NA           65 0.30769231 0.13783610 # 12 2013 0.06896552        NA        NA           58 0.06896552 0.15000651 # 13 2014 0.10000000        NA        NA           60 0.10000000 0.16304829 # 14 2015 0.23636364        NA        NA           55 0.23636364 0.17698785 # 15 2016 0.22619048        NA        NA           84 0.22619048 0.19184597 # 16 2017 0.16279070        NA        NA           86 0.16279070 0.20763675 # 17 2018 0.22436641 0.1938710 0.2548618           NA         NA 0.22436641 # 18 2019 0.24203228 0.2062911 0.2777735           NA         NA 0.24203228 # 19 2020 0.26062172 0.2191758 0.3020676           NA         NA 0.26062172 # 20 2021 0.28011130 0.2325557 0.3276669           NA         NA 0.28011130 # 21 2022 0.30046606 0.2464567 0.3544755           NA         NA 0.30046606 # 22 2023 0.32163907 0.2609011 0.3823771           NA         NA 0.32163907 # 23 2024 0.34357130 0.2759081 0.4112345           NA         NA 0.34357130 # 24 2025 0.36619175 0.2914934 0.4408901           NA         NA 0.36619175 # 25 2026 0.38941799 0.3076686 0.4711674           NA         NA 0.38941799 # 26 2027 0.41315710 0.3244399 0.5018743           NA         NA 0.41315710 # 27 2028 0.43730688 0.3418075 0.5328063           NA         NA 0.43730688 # 28 2029 0.46175755 0.3597639 0.5637512           NA         NA 0.46175755 # 29 2030 0.48639359 0.3782932 0.5944939           NA         NA 0.48639359 # 30 2031 0.51109592 0.3973697 0.6248221           NA         NA 0.51109592 # 31 2032 0.53574417 0.4169574 0.6545309           NA         NA 0.53574417 plot(predict_TZP) ggplot_rsi_predict(predict_TZP) # choose for error bars instead of a ribbon ggplot_rsi_predict(predict_TZP, ribbon = FALSE)"},{"path":"https://msberends.github.io/AMR/articles/resistance_predict.html","id":"choosing-the-right-model","dir":"Articles","previous_headings":"Prediction analysis","what":"Choosing the right model","title":"How to predict antimicrobial resistance","text":"Resistance easily predicted; look vancomycin resistance Gram-positive bacteria, spread (.e. standard error) enormous:  Vancomycin resistance 100% ten years, might remain low. can define model model parameter. model chosen generalised linear regression model using binomial distribution, assuming period zero resistance followed period increasing resistance leading slowly resistance. Valid values : vancomycin resistance Gram-positive bacteria, linear model might appropriate:  model also available object, attribute:","code":"example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"binomial\") %>%    ggplot_rsi_predict() # ℹ Using column 'date' as input for `col_date`. example_isolates %>%   filter(mo_gramstain(mo, language = NULL) == \"Gram-positive\") %>%   resistance_predict(col_ab = \"VAN\", year_min = 2010, info = FALSE, model = \"linear\") %>%    ggplot_rsi_predict() # ℹ Using column 'date' as input for `col_date`. model <- attributes(predict_TZP)$model  summary(model)$family #  # Family: binomial  # Link function: logit  summary(model)$coefficients #                  Estimate  Std. Error   z value     Pr(>|z|) # (Intercept) -200.67944891 46.17315349 -4.346237 1.384932e-05 # year           0.09883005  0.02295317  4.305725 1.664395e-05"},{"path":"https://msberends.github.io/AMR/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Matthijs S. Berends. Author, maintainer. Christian F. Luz. Author, contributor. Dennis Souverein. Author, contributor. Erwin E. . Hassing. Author, contributor. Casper Albers. Thesis advisor. Peter Dutey-Magni. Contributor. Judith Fonville. Contributor. Alex Friedrich. Thesis advisor. Corinna Glasner. Thesis advisor. Eric Hazenberg. Contributor. Gwen Knight. Contributor. Annick Lenglet. Contributor. Bart Meijer. Contributor. Anton Mymrikov. Contributor. Sofia Ny. Contributor. Rogier Schade. Contributor. Bhanu Sinha. Thesis advisor. Anthony Underwood. Contributor.","code":""},{"path":"https://msberends.github.io/AMR/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Berends MS, Luz CF, Friedrich AW, Sinha BNM, Albers CJ, Glasner C (2021). AMR - R Package Working Antimicrobial Resistance Data. Journal Statistical Software (accepted publication), https://www.biorxiv.org/content/10.1101/810622, doi: 10.1101/810622. Berends, MS (2021). New Instrument Microbial Epidemiology: Empowering Antimicrobial Resistance Data Analysis (PhD thesis). University Groningen, doi: 10.33612/diss.177417131. Luz, CF (2021). Data Science Infection Management & Antimicrobial Stewardship (PhD thesis). University Groningen, doi: 10.33612/diss.192486375.","code":"@Article{,   title = {AMR - An R Package for Working with Antimicrobial Resistance Data},   author = {M S Berends and C F Luz and A W Friedrich and B N M Sinha and C J Albers and C Glasner},   doi = {10.1101/810622},   journal = {Journal of Statistical Software},   pages = {Accepted for publication},   year = {2021},   url = {https://www.biorxiv.org/content/10.1101/810622}, } @PhdThesis{,   title = {A New Instrument for Microbial Epidemiology: Empowering Antimicrobial Resistance Data Analysis},   author = {M S Berends},   publisher = {University of Groningen},   school = {University of Groningen},   doi = {10.33612/diss.177417131},   pages = {287},   year = {2021}, } @PhdThesis{,   title = {Data Science for Infection Management & Antimicrobial Stewardship},   author = {C F Luz},   publisher = {University of Groningen},   school = {University of Groningen},   doi = {10.33612/diss.192486375},   pages = {326},   year = {2021}, }"},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"introduction","dir":"","previous_headings":"","what":"Introduction","title":"Antimicrobial Resistance Data Analysis","text":"AMR package free open-source R package zero dependencies simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible AMR data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. rationale behind package scientifically described Journal Statistical Software (Volume xx, Issue xx; DOI 10.18637/jss.v000.i00 - waiting copy-editing finish). installing package, R knows ~71,000 distinct microbial species ~570 antibiotic, antimycotic antiviral drugs name code (including ATC, WHONET/EARS-Net, PubChem, LOINC SNOMED CT), knows valid R/SI MIC values. supports data format, including WHONET/EARS-Net data. package works Windows, macOS Linux versions R since R-3.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice Foundation University Medical Center Groningen. R package formed basis two PhD theses (DOI 10.33612/diss.177417131 DOI 10.33612/diss.192486375) actively durably maintained two public healthcare organisations Netherlands.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"used-in-175-countries-translated-to-16-languages","dir":"","previous_headings":"Introduction","what":"Used in 175 countries, translated to 16 languages","title":"Antimicrobial Resistance Data Analysis","text":"Since first public release early 2018, R package used almost countries world. Click map enlarge see country names. AMR package available  English,  Chinese,  Danish,  Dutch,  French,  German,  Greek,  Italian,  Japanese,  Polish,  Portuguese,  Russian,  Spanish,  Swedish,  Turkish,  Ukrainian. Antimicrobial drug (group) names colloquial microorganism names provided languages.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"filtering-and-selecting-data","dir":"","previous_headings":"Practical examples","what":"Filtering and selecting data","title":"Antimicrobial Resistance Data Analysis","text":"defined row filter Gram-negative bacteria intrinsic resistance cefotaxime (mo_is_gram_negative() mo_is_intrinsic_resistant()) column selection two antibiotic groups (aminoglycosides() carbapenems()), reference data microorganisms antibiotics AMR package make sure get meant: base R equivalent : base R snippet work version R since April 2013 (R-3.0).","code":"# AMR works great with dplyr, but it's not required or neccesary library(AMR) library(dplyr)  example_isolates %>%   mutate(bacteria = mo_fullname()) %>%   filter(mo_is_gram_negative(),          mo_is_intrinsic_resistant(ab = \"cefotax\")) %>%   select(bacteria,          aminoglycosides(),          carbapenems()) example_isolates$bacteria <- mo_fullname(example_isolates$mo) example_isolates[which(mo_is_gram_negative() &                          mo_is_intrinsic_resistant(ab = \"cefotax\")),                  c(\"bacteria\", aminoglycosides(), carbapenems())]"},{"path":"https://msberends.github.io/AMR/index.html","id":"calculating-resistance-per-group","dir":"","previous_headings":"Practical examples","what":"Calculating resistance per group","title":"Antimicrobial Resistance Data Analysis","text":"","code":"library(AMR) library(dplyr)  out <- example_isolates %>%   # group by hospital code:   group_by(hospital_id) %>%   # calculate AMR using resistance(), over all aminoglycosides   # and polymyxins:   summarise(across(c(aminoglycosides(), polymyxins()),             resistance)) out # transform the antibiotic columns to names: out %>% set_ab_names() # transform the antibiotic column to ATC codes: out %>% set_ab_names(property = \"atc\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"what-else-can-you-do-with-this-package","dir":"","previous_headings":"","what":"What else can you do with this package?","title":"Antimicrobial Resistance Data Analysis","text":"package intended comprehensive toolbox integrated AMR data analysis. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species Catalogue Life List Prokaryotic names Standing Nomenclature (manual) Interpreting raw MIC disk diffusion values, based latest CLSI EUCAST guidelines (manual) Retrieving antimicrobial drug names, doses forms administration clinical health care records (manual) Determining first isolates used AMR data analysis (manual) Calculating antimicrobial resistance (tutorial) Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial) Calculating (empirical) susceptibility mono therapy combination therapies (tutorial) Predicting future antimicrobial resistance using regression models (tutorial) Getting properties microorganism (like Gram stain, species, genus family) (manual) Getting properties antibiotic (like name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) (manual) Plotting antimicrobial resistance (tutorial) Applying EUCAST expert rules (manual) Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code (manual) Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code (manual) Machine reading EUCAST CLSI guidelines 2011-2021 translate MIC values disk diffusion diameters R/SI (link) Principal component analysis AMR (tutorial)","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-official-version","dir":"","previous_headings":"Get this package","what":"Latest official version","title":"Antimicrobial Resistance Data Analysis","text":"package available official R network (CRAN). Install package R CRAN using command: downloaded installed automatically. RStudio, click menu Tools > Install Packages… type “AMR” press Install. Note: functions website may available latest release. use functions data sets mentioned website, install latest development version.","code":"install.packages(\"AMR\")"},{"path":"https://msberends.github.io/AMR/index.html","id":"latest-development-version","dir":"","previous_headings":"Get this package","what":"Latest development version","title":"Antimicrobial Resistance Data Analysis","text":"Please read Developer Guideline . latest unpublished development version can installed GitHub two ways: Manually, using: Automatically, using rOpenSci R-universe platform, adding R-universe address list repositories (‘repos’): , can install update AMR package like official release (e.g., using install.packages(\"AMR\") RStudio via Tools > Check Package Updates…).","code":"install.packages(\"remotes\") # if you haven't already remotes::install_github(\"msberends/AMR\") options(repos = c(getOption(\"repos\"),                   msberends = \"https://msberends.r-universe.dev\"))"},{"path":"https://msberends.github.io/AMR/index.html","id":"get-started","dir":"","previous_headings":"","what":"Get started","title":"Antimicrobial Resistance Data Analysis","text":"find conduct AMR data analysis, please continue reading get started click link ‘’ menu.","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"partners","dir":"","previous_headings":"","what":"Partners","title":"Antimicrobial Resistance Data Analysis","text":"development package part , related , made possible :","code":""},{"path":"https://msberends.github.io/AMR/index.html","id":"copyright","dir":"","previous_headings":"","what":"Copyright","title":"Antimicrobial Resistance Data Analysis","text":"R package free, open-source software licensed GNU General Public License v2.0 (GPL-2). nutshell, means package: May used commercial purposes May used private purposes May used patent purposes May modified, although: Modifications must released license distributing package Changes made code must documented May distributed, although: Source code must made available package distributed copy license copyright notice must included package. Comes LIMITATION liability Comes warranty","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated Functions — AMR-deprecated","title":"Deprecated Functions — AMR-deprecated","text":"functions -called 'Deprecated'. removed future release. Using functions give warning name function replaced (one).","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":null,"dir":"Reference","previous_headings":"","what":"The AMR Package — AMR","title":"The AMR Package — AMR","text":"Welcome AMR package.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"The AMR Package — AMR","text":"AMR free, open-source independent R package simplify analysis prediction Antimicrobial Resistance (AMR) work microbial antimicrobial data properties, using evidence-based methods. aim provide standard clean reproducible antimicrobial resistance data analysis, can therefore empower epidemiological analyses continuously enable surveillance treatment evaluation setting. installing package, R knows ~71,000 distinct microbial species ~570 antibiotic, antimycotic antiviral drugs name code (including ATC, EARS-NET, LOINC SNOMED CT), knows valid R/SI MIC values. supports data format, including WHONET/EARS-Net data. package fully independent R package works Windows, macOS Linux versions R since R-3.0.0 (April 2013). designed work setting, including limited resources. created routine data analysis academic research Faculty Medical Sciences University Groningen, collaboration non-profit organisations Certe Medical Diagnostics Advice University Medical Center Groningen. R package actively maintained free software; can freely use distribute personal commercial (patent) purposes terms GNU General Public License version 2.0 (GPL-2), published Free Software Foundation. package can used : Reference taxonomy microorganisms, since package contains microbial (sub)species Catalogue Life List Prokaryotic names Standing Nomenclature Interpreting raw MIC disk diffusion values, based latest CLSI EUCAST guidelines Retrieving antimicrobial drug names, doses forms administration clinical health care records Determining first isolates used AMR data analysis Calculating antimicrobial resistance Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) Calculating (empirical) susceptibility mono therapy combination therapies Predicting future antimicrobial resistance using regression models Getting properties microorganism (Gram stain, species, genus family) Getting properties antibiotic (name, code EARS-Net/ATC/LOINC/PubChem, defined daily dose trade name) Plotting antimicrobial resistance Applying EUCAST expert rules Getting SNOMED codes microorganism, getting properties microorganism based SNOMED code Getting LOINC codes antibiotic, getting properties antibiotic based LOINC code Machine reading EUCAST CLSI guidelines 2011-2020 translate MIC values disk diffusion diameters R/SI Principal component analysis AMR","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"The AMR Package — AMR","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"read-more-on-our-website-","dir":"Reference","previous_headings":"","what":"Read more on Our Website!","title":"The AMR Package — AMR","text":"website https://msberends.github.io/AMR/ can find comprehensive tutorial conduct AMR data analysis, complete documentation functions example analysis using WHONET data.","code":""},{"path":"https://msberends.github.io/AMR/reference/AMR.html","id":"contact-us","dir":"Reference","previous_headings":"","what":"Contact Us","title":"The AMR Package — AMR","text":"suggestions, comments questions, please contact us : Matthijs S. Berends  m.s.berends [] umcg [dot] nl  University Groningen Department Medical Microbiology Infection Prevention  University Medical Center Groningen  Post Office Box 30001  9700 RB Groningen  Netherlands https://msberends.github.io/AMR/ found bug, please file new issue : https://github.com/msberends/AMR/issues","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":null,"dir":"Reference","previous_headings":"","what":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"antimicrobial drugs official names, ATC codes, ATC groups defined daily dose (DDD) included package, using Collaborating Centre Drug Statistics Methodology.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHOCC.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"WHOCC: WHO Collaborating Centre for Drug Statistics Methodology — WHOCC","text":"","code":"as.ab(\"meropenem\") #> Class <ab> #> [1] MEM ab_name(\"J01DH02\") #> [1] \"Meropenem\"  ab_tradenames(\"flucloxacillin\") #> [1] \"floxacillin\"          \"floxapen\"             \"floxapen sodium salt\" #> [4] \"fluclox\"              \"flucloxacilina\"       \"flucloxacillin\"       #> [7] \"flucloxacilline\"      \"flucloxacillinum\"     \"fluorochloroxacillin\""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 500 Isolates - WHONET Example — WHONET","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"example data set exact structure export file WHONET. files can used package, example data set shows. antibiotic results example_isolates data set. patient names created using online surname generators place practice purposes.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"WHONET"},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"data.frame 500 observations 53 variables: Identification number ID sample Specimen number ID specimen Organism Name microorganism. analysis, transform valid microbial class, using .mo(). Country Country origin Laboratory Name laboratory Last name Fictitious last name patient First name Fictitious initial patient Sex Fictitious gender patient Age Fictitious age patient Age category Age group, can also looked using age_groups() Date admissionDate hospital admission Specimen dateDate specimen received laboratory Specimen type Specimen type group Specimen type (Numeric) Translation \"Specimen type\" Reason Reason request Differential Diagnosis Isolate number ID isolate Organism type Type microorganism, can also looked using mo_type() Serotype Serotype microorganism Beta-lactamase Microorganism produces beta-lactamase? ESBL Microorganism produces extended spectrum beta-lactamase? Carbapenemase Microorganism produces carbapenemase? MRSA screening test Microorganism possible MRSA? Inducible clindamycin resistance Clindamycin can induced? Comment comments Date data entryDate data entered WHONET AMP_ND10:CIP_EE 28 different antibiotics. can lookup abbreviations antibiotics data set, use e.g. ab_name(\"AMP\") get official name immediately. analysis, transform valid antibiotic class, using .rsi().","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/WHONET.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 500 Isolates - WHONET Example — WHONET","text":"","code":"head(WHONET) #>   Identification number Specimen number Organism         Country #> 1            fe41d7bafa            1748      SPN         Belgium #> 2            91f175ec37            1767      eco The Netherlands #> 3            cc4015056e            1343      eco The Netherlands #> 4            e864b692f5            1894      MAP         Denmark #> 5            3d051fe345            1739      PVU         Belgium #> 6            c80762a08d            1846      103 The Netherlands #>                               Laboratory  Last name First name Sex Age #> 1         National Laboratory of Belgium       Abel         B.   F  68 #> 2 National Laboratory of The Netherlands  Delacroix         F.   M  89 #> 3 National Laboratory of The Netherlands   Steensen         F.   M  85 #> 4         National Laboratory of Denmark Beyersdorf         L.   M  62 #> 5         National Laboratory of Belgium     Hummel         W.   M  86 #> 6 National Laboratory of The Netherlands  Eikenboom         J.   F  53 #>   Age category Date of admission Specimen date Specimen type #> 1        55-74        2005-01-12    2005-01-30         Urine #> 2          75+        2006-07-30    2006-08-16         Urine #> 3          75+        2014-03-05    2014-03-14         Urine #> 4        55-74        2014-10-22    2014-11-01         Urine #> 5          75+        2016-10-28    2016-11-17         Urine #> 6        25-54        2017-12-21    2018-01-02         Urine #>   Specimen type (Numeric)  Reason Isolate number Organism type Serotype #> 1                       2 Unknown           1748      Bacteria          #> 2                       2 Unknown           1767      Bacteria          #> 3                       2 Unknown           1343      Bacteria          #> 4                       2 Unknown           1894      Bacteria          #> 5                       2 Unknown           1739      Bacteria          #> 6                       2 Unknown           1846      Bacteria          #>   Beta-lactamase  ESBL Carbapenemase MRSA screening test #> 1          FALSE FALSE         FALSE               FALSE #> 2          FALSE FALSE         FALSE               FALSE #> 3          FALSE FALSE         FALSE               FALSE #> 4          FALSE FALSE         FALSE               FALSE #> 5          FALSE FALSE         FALSE               FALSE #> 6          FALSE FALSE         FALSE               FALSE #>   Inducible clindamycin resistance Comment Date of data entry AMP_ND10 AMC_ED20 #> 1                            FALSE                 2005-01-30        S        S #> 2                            FALSE                 2006-08-16     <NA>        S #> 3                            FALSE                 2014-03-14        S        S #> 4                            FALSE                 2014-11-01        R     <NA> #> 5                            FALSE                 2016-11-17        R        R #> 6                            FALSE                 2018-01-02        S        S #>   TZP_ED30 FEP_ED30 CTX_ED5 FOX_ED30 CAZ_ED10 CRO_ED30 CIP_ED5 AMK_ED30 #> 1        S     <NA>    <NA>     <NA>        R     <NA>    <NA>     <NA> #> 2     <NA>     <NA>    <NA>     <NA>        R     <NA>    <NA>     <NA> #> 3        S     <NA>    <NA>     <NA>     <NA>     <NA>    <NA>     <NA> #> 4     <NA>     <NA>    <NA>     <NA>        R     <NA>    <NA>     <NA> #> 5        S     <NA>       S        R        S        S       S     <NA> #> 6        S     <NA>       S        S        S        S       S     <NA> #>   GEN_ED10 TOB_ED10 SXT_ED1.2 IPM_ND10 PEN_ND1 AMP_ND2 AMC_ND2 CHL_ND30 VAN_ED5 #> 1        R        R         S     <NA>       S       S       S     <NA>       S #> 2        S     <NA>         R     <NA>       R    <NA>       S     <NA>       S #> 3     <NA>     <NA>      <NA>     <NA>       S       S       S     <NA>    <NA> #> 4        R        R      <NA>     <NA>       R       R    <NA>     <NA>    <NA> #> 5        S        S         R        I       R       R       R     <NA>       R #> 6        S        S         S        S       R       S       S     <NA>       R #>   OXA_ED1 ERY_ED15 CLI_ED2 TCY_ED30 RIF_ED5 PEN_EE AMP_EE CRO_EE CIP_EE #> 1    <NA>        S    <NA>        S    <NA>      S      S   <NA>   <NA> #> 2    <NA>        S       S        S    <NA>      R   <NA>   <NA>   <NA> #> 3    <NA>     <NA>       S     <NA>    <NA>      S      S   <NA>   <NA> #> 4    <NA>     <NA>       S     <NA>    <NA>      R      R   <NA>   <NA> #> 5    <NA>        R    <NA>        R       R      R      R      S      S #> 6    <NA>        R    <NA>     <NA>       R      R      S      S      S"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":null,"dir":"Reference","previous_headings":"","what":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Use function e.g. clinical texts health care records. returns list antimicrobial drugs, doses forms administration found texts.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"ab_from_text(   text,   type = c(\"drug\", \"dose\", \"administration\"),   collapse = NULL,   translate_ab = FALSE,   thorough_search = NULL,   info = interactive(),   ... )"},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"text text analyse type type property search , either \"drug\", \"dose\" \"administration\", see Examples collapse character pass paste(, collapse = ...) return one character per element text, see Examples translate_ab type = \"drug\": column name antibiotics data set translate antibiotic abbreviations , using ab_property(). Defaults FALSE. Using TRUE equal using \"name\". thorough_search logical indicate whether input must extensively searched misspelling faulty input values. Setting TRUE take considerably time using FALSE. default, turn TRUE input elements contain maximum three words. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed .ab()","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"list,  character collapse NULL","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"function also internally used .ab(), although searches first drug name throw note drug names returned. Note: .ab() function may use long regular expression match brand names antimicrobial agents. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-type","dir":"Reference","previous_headings":"","what":"Argument type","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"default, function search antimicrobial drug names. text elements searched official names, ATC codes brand names. uses .ab() internally, correct misspelling. type = \"dose\" (similar, like \"dosing\", \"doses\"), text elements searched numeric values higher 100 resemble years. output numeric. supports unit (g, mg, IE, etc.) multiple values one clinical text, see Examples. type = \"administration\" (abbreviations, like \"admin\", \"adm\"), text elements searched form drug administration. supports following forms (including common abbreviations): buccal, implant, inhalation, instillation, intravenous, nasal, oral, parenteral, rectal, sublingual, transdermal vaginal. Abbreviations oral ('po', 'per os') become \"oral\", values intravenous ('iv', 'intraven') become \"iv\". supports multiple values one clinical text, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"argument-collapse","dir":"Reference","previous_headings":"","what":"Argument collapse","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"Without using collapse, function return list. can convenient use e.g. inside mutate()):df %>% mutate(abx = ab_from_text(clinical_text)) returned AB codes can transformed official names, groups, etc. ab_* functions ab_name() ab_group(), using translate_ab argument. using collapse, function return character:df %>% mutate(abx = ab_from_text(clinical_text, collapse = \"|\"))","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_from_text.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Retrieve Antimicrobial Drug Names and Doses from Clinical Text — ab_from_text","text":"","code":"# mind the bad spelling of amoxicillin in this line,  # straight from a true health care record: ab_from_text(\"28/03/2020 regular amoxicilliin 500mg po tds\") #> [[1]] #> Class <ab> #> [1] AMX #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") #> [[1]] #> Class <ab> #> [1] AMX CIP #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"dose\") #> [[1]] #> [1] 500 400 #>  ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", type = \"admin\") #> [[1]] #> [1] \"oral\" \"iv\"   #>   ab_from_text(\"500 mg amoxi po and 400mg cipro iv\", collapse = \", \") #> [1] \"AMX, CIP\" # \\donttest{ # if you want to know which antibiotic groups were administered, do e.g.: abx <- ab_from_text(\"500 mg amoxi po and 400mg cipro iv\") ab_group(abx[[1]]) #> [1] \"Beta-lactams/penicillins\" \"Quinolones\"                if (require(\"dplyr\")) {   tibble(clinical_text = c(\"given 400mg cipro and 500 mg amox\",                            \"started on doxy iv today\")) %>%      mutate(abx_codes = ab_from_text(clinical_text),            abx_doses = ab_from_text(clinical_text, type = \"doses\"),            abx_admin = ab_from_text(clinical_text, type = \"admin\"),            abx_coll = ab_from_text(clinical_text, collapse = \"|\"),            abx_coll_names = ab_from_text(clinical_text,                                          collapse = \"|\",                                          translate_ab = \"name\"),            abx_coll_doses = ab_from_text(clinical_text,                                          type = \"doses\",                                          collapse = \"|\"),            abx_coll_admin = ab_from_text(clinical_text,                                          type = \"admin\",                                          collapse = \"|\"))  } #> Loading required package: dplyr #>  #> Attaching package: ‘dplyr’ #> The following objects are masked from ‘package:stats’: #>  #>     filter, lag #> The following objects are masked from ‘package:base’: #>  #>     intersect, setdiff, setequal, union #> # A tibble: 2 × 8 #>   clinical_text          abx_c…¹ abx_d…² abx_a…³ abx_c…⁴ abx_c…⁵ abx_c…⁶ abx_c…⁷ #>   <chr>                  <list>  <list>  <list>  <chr>   <chr>   <chr>   <chr>   #> 1 given 400mg cipro and… <ab>    <dbl>   <chr>   CIP|AMX Ciprof… 400|500 NA      #> 2 started on doxy iv to… <ab>    <dbl>   <chr>   DOX     Doxycy… NA      iv      #> # … with abbreviated variable names ¹​abx_codes, ²​abx_doses, ³​abx_admin, #> #   ⁴​abx_coll, ⁵​abx_coll_names, ⁶​abx_coll_doses, ⁷​abx_coll_admin # }"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of an Antibiotic — ab_property","title":"Get Properties of an Antibiotic — ab_property","text":"Use functions return specific property antibiotic antibiotics data set. input values evaluated internally .ab().","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"ab_name(x, language = get_AMR_locale(), tolower = FALSE, ...)  ab_cid(x, ...)  ab_synonyms(x, ...)  ab_tradenames(x, ...)  ab_group(x, language = get_AMR_locale(), ...)  ab_atc(x, only_first = FALSE, ...)  ab_atc_group1(x, language = get_AMR_locale(), ...)  ab_atc_group2(x, language = get_AMR_locale(), ...)  ab_loinc(x, ...)  ab_ddd(x, administration = \"oral\", ...)  ab_ddd_units(x, administration = \"oral\", ...)  ab_info(x, language = get_AMR_locale(), ...)  ab_url(x, open = FALSE, ...)  ab_property(x, property = \"name\", language = get_AMR_locale(), ...)  set_ab_names(   data,   ...,   property = \"name\",   language = get_AMR_locale(),   snake_case = NULL )"},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of an Antibiotic — ab_property","text":"x (vector ) text can coerced valid antibiotic code .ab() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. tolower logical indicate whether first character every output transformed lower case character. lead e.g. \"polymyxin B\" \"polymyxin b\". ... case set_ab_names() data data.frame: variables select (supports tidy selection column1:column4), otherwise arguments passed .ab() only_first logical indicate whether first ATC code must returned, giving preference J0-codes (.e., antimicrobial drug group) administration way administration, either \"oral\" \"iv\" open browse URL using utils::browseURL() property one column names one antibiotics data set: vector_or(colnames(antibiotics), sort = FALSE). data data.frame columns need renamed, character vector column names snake_case logical indicate whether names -called snake case: lower case spaces/slashes replaced underscore (_)","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of an Antibiotic — ab_property","text":"integer case ab_cid() named list case ab_info() multiple ab_atc()/ab_synonyms()/ab_tradenames() double case ab_ddd() data.frame case set_ab_names() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of an Antibiotic — ab_property","text":"output translated possible. function ab_url() return direct URL official website. warning returned required ATC code available. function set_ab_names() special column renaming function data.frames. renames columns names resemble antimicrobial drugs. always makes sure new column names unique. property = \"atc\" set, preference given ATC codes J-group.","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of an Antibiotic — ab_property","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of an Antibiotic — ab_property","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/ab_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of an Antibiotic — ab_property","text":"","code":"# all properties: ab_name(\"AMX\")       # \"Amoxicillin\" #> [1] \"Amoxicillin\" ab_atc(\"AMX\")        # \"J01CA04\" (ATC code from the WHO) #> [1] \"J01CA04\" ab_cid(\"AMX\")        # 33613 (Compound ID from PubChem) #> [1] 33613 ab_synonyms(\"AMX\")   # a list with brand names of amoxicillin #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicillin\"        #> [10] \"amoxicilline\"       \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"anemolin\"           \"aspenil\"            #> [19] \"biomox\"             \"bristamox\"          \"cemoxin\"            #> [22] \"clamoxyl\"           \"delacillin\"         \"dispermox\"          #> [25] \"efpenix\"            \"flemoxin\"           \"hiconcil\"           #> [28] \"histocillin\"        \"hydroxyampicillin\"  \"ibiamox\"            #> [31] \"imacillin\"          \"lamoxy\"             \"metafarma capsules\" #> [34] \"metifarma capsules\" \"moxacin\"            \"moxatag\"            #> [37] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [40] \"robamox\"            \"sawamox pm\"         \"tolodina\"           #> [43] \"unicillin\"          \"utimox\"             \"vetramox\"           ab_tradenames(\"AMX\") # same #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicillin\"        #> [10] \"amoxicilline\"       \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"anemolin\"           \"aspenil\"            #> [19] \"biomox\"             \"bristamox\"          \"cemoxin\"            #> [22] \"clamoxyl\"           \"delacillin\"         \"dispermox\"          #> [25] \"efpenix\"            \"flemoxin\"           \"hiconcil\"           #> [28] \"histocillin\"        \"hydroxyampicillin\"  \"ibiamox\"            #> [31] \"imacillin\"          \"lamoxy\"             \"metafarma capsules\" #> [34] \"metifarma capsules\" \"moxacin\"            \"moxatag\"            #> [37] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [40] \"robamox\"            \"sawamox pm\"         \"tolodina\"           #> [43] \"unicillin\"          \"utimox\"             \"vetramox\"           ab_group(\"AMX\")      # \"Beta-lactams/penicillins\" #> [1] \"Beta-lactams/penicillins\" ab_atc_group1(\"AMX\") # \"Beta-lactam antibacterials, penicillins\" #> [1] \"Beta-lactam antibacterials, penicillins\" ab_atc_group2(\"AMX\") # \"Penicillins with extended spectrum\" #> [1] \"Penicillins with extended spectrum\" ab_url(\"AMX\")        # link to the official WHO page #>                                                           Amoxicillin  #> \"https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no\"   # smart lowercase tranformation ab_name(x = c(\"AMC\", \"PLB\"))  # \"Amoxicillin/clavulanic acid\" \"Polymyxin B\" #> [1] \"Amoxicillin/clavulanic acid\" \"Polymyxin B\"                 ab_name(x = c(\"AMC\", \"PLB\"),         tolower = TRUE)       # \"amoxicillin/clavulanic acid\" \"polymyxin B\" #> [1] \"amoxicillin/clavulanic acid\" \"polymyxin B\"                  # defined daily doses (DDD) ab_ddd(\"AMX\", \"oral\")       #  1.5 #> [1] 1.5 ab_ddd_units(\"AMX\", \"oral\") # \"g\" #> [1] \"g\" ab_ddd(\"AMX\", \"iv\")         #  3 #> [1] 3 ab_ddd_units(\"AMX\", \"iv\")   # \"g\" #> [1] \"g\"  ab_info(\"AMX\")       # all properties as a list #> $ab #> [1] \"AMX\" #>  #> $cid #> [1] 33613 #>  #> $name #> [1] \"Amoxicillin\" #>  #> $group #> [1] \"Beta-lactams/penicillins\" #>  #> $atc #> [1] \"J01CA04\" #>  #> $atc_group1 #> [1] \"Beta-lactam antibacterials, penicillins\" #>  #> $atc_group2 #> [1] \"Penicillins with extended spectrum\" #>  #> $tradenames #>  [1] \"actimoxi\"           \"amoclen\"            \"amolin\"             #>  [4] \"amopen\"             \"amopenixin\"         \"amoxibiotic\"        #>  [7] \"amoxicaps\"          \"amoxicilina\"        \"amoxicillin\"        #> [10] \"amoxicilline\"       \"amoxicillinum\"      \"amoxiden\"           #> [13] \"amoxil\"             \"amoxivet\"           \"amoxy\"              #> [16] \"amoxycillin\"        \"anemolin\"           \"aspenil\"            #> [19] \"biomox\"             \"bristamox\"          \"cemoxin\"            #> [22] \"clamoxyl\"           \"delacillin\"         \"dispermox\"          #> [25] \"efpenix\"            \"flemoxin\"           \"hiconcil\"           #> [28] \"histocillin\"        \"hydroxyampicillin\"  \"ibiamox\"            #> [31] \"imacillin\"          \"lamoxy\"             \"metafarma capsules\" #> [34] \"metifarma capsules\" \"moxacin\"            \"moxatag\"            #> [37] \"ospamox\"            \"pamoxicillin\"       \"piramox\"            #> [40] \"robamox\"            \"sawamox pm\"         \"tolodina\"           #> [43] \"unicillin\"          \"utimox\"             \"vetramox\"           #>  #> $loinc #> [1] \"16365-9\" \"25274-2\" \"3344-9\"  \"80133-2\" #>  #> $ddd #> $ddd$oral #> $ddd$oral$amount #> [1] 1.5 #>  #> $ddd$oral$units #> [1] \"g\" #>  #>  #> $ddd$iv #> $ddd$iv$amount #> [1] 3 #>  #> $ddd$iv$units #> [1] \"g\" #>  #>  #>   # all ab_* functions use as.ab() internally, so you can go from 'any' to 'any': ab_atc(\"AMP\")           # ATC code of AMP (ampicillin) #> [1] \"J01CA01\" \"S01AA19\" ab_group(\"J01CA01\")     # Drug group of ampicillins ATC code #> [1] \"Beta-lactams/penicillins\" ab_loinc(\"ampicillin\")  # LOINC codes of ampicillin #> [1] \"21066-6\" \"3355-5\"  \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\")      # \"Ampicillin\" (using LOINC) #> [1] \"Ampicillin\" ab_name(6249)           # \"Ampicillin\" (using CID) #> [1] \"Ampicillin\" ab_name(\"J01CA01\")      # \"Ampicillin\" (using ATC) #> [1] \"Ampicillin\"  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\") #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\") #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") #> [1] \"J01DD04\"  # use set_ab_names() for renaming columns colnames(example_isolates) #>  [1] \"date\"            \"hospital_id\"     \"ward_icu\"        \"ward_clinical\"   #>  [5] \"ward_outpatient\" \"age\"             \"gender\"          \"patient_id\"      #>  [9] \"mo\"              \"PEN\"             \"OXA\"             \"FLC\"             #> [13] \"AMX\"             \"AMC\"             \"AMP\"             \"TZP\"             #> [17] \"CZO\"             \"FEP\"             \"CXM\"             \"FOX\"             #> [21] \"CTX\"             \"CAZ\"             \"CRO\"             \"GEN\"             #> [25] \"TOB\"             \"AMK\"             \"KAN\"             \"TMP\"             #> [29] \"SXT\"             \"NIT\"             \"FOS\"             \"LNZ\"             #> [33] \"CIP\"             \"MFX\"             \"VAN\"             \"TEC\"             #> [37] \"TCY\"             \"TGC\"             \"DOX\"             \"ERY\"             #> [41] \"CLI\"             \"AZM\"             \"IPM\"             \"MEM\"             #> [45] \"MTR\"             \"CHL\"             \"COL\"             \"MUP\"             #> [49] \"RIF\"             colnames(set_ab_names(example_isolates)) #>  [1] \"date\"                          \"hospital_id\"                   #>  [3] \"ward_icu\"                      \"ward_clinical\"                 #>  [5] \"ward_outpatient\"               \"age\"                           #>  [7] \"gender\"                        \"patient_id\"                    #>  [9] \"mo\"                            \"benzylpenicillin\"              #> [11] \"oxacillin\"                     \"flucloxacillin\"                #> [13] \"amoxicillin\"                   \"amoxicillin_clavulanic_acid\"   #> [15] \"ampicillin\"                    \"piperacillin_tazobactam\"       #> [17] \"cefazolin\"                     \"cefepime\"                      #> [19] \"cefuroxime\"                    \"cefoxitin\"                     #> [21] \"cefotaxime\"                    \"ceftazidime\"                   #> [23] \"ceftriaxone\"                   \"gentamicin\"                    #> [25] \"tobramycin\"                    \"amikacin\"                      #> [27] \"kanamycin\"                     \"trimethoprim\"                  #> [29] \"trimethoprim_sulfamethoxazole\" \"nitrofurantoin\"                #> [31] \"fosfomycin\"                    \"linezolid\"                     #> [33] \"ciprofloxacin\"                 \"moxifloxacin\"                  #> [35] \"vancomycin\"                    \"teicoplanin\"                   #> [37] \"tetracycline\"                  \"tigecycline\"                   #> [39] \"doxycycline\"                   \"erythromycin\"                  #> [41] \"clindamycin\"                   \"azithromycin\"                  #> [43] \"imipenem\"                      \"meropenem\"                     #> [45] \"metronidazole\"                 \"chloramphenicol\"               #> [47] \"colistin\"                      \"mupirocin\"                     #> [49] \"rifampicin\"                    colnames(set_ab_names(example_isolates, NIT:VAN)) #>  [1] \"date\"            \"hospital_id\"     \"ward_icu\"        \"ward_clinical\"   #>  [5] \"ward_outpatient\" \"age\"             \"gender\"          \"patient_id\"      #>  [9] \"mo\"              \"PEN\"             \"OXA\"             \"FLC\"             #> [13] \"AMX\"             \"AMC\"             \"AMP\"             \"TZP\"             #> [17] \"CZO\"             \"FEP\"             \"CXM\"             \"FOX\"             #> [21] \"CTX\"             \"CAZ\"             \"CRO\"             \"GEN\"             #> [25] \"TOB\"             \"AMK\"             \"KAN\"             \"TMP\"             #> [29] \"SXT\"             \"nitrofurantoin\"  \"fosfomycin\"      \"linezolid\"       #> [33] \"ciprofloxacin\"   \"moxifloxacin\"    \"vancomycin\"      \"TEC\"             #> [37] \"TCY\"             \"TGC\"             \"DOX\"             \"ERY\"             #> [41] \"CLI\"             \"AZM\"             \"IPM\"             \"MEM\"             #> [45] \"MTR\"             \"CHL\"             \"COL\"             \"MUP\"             #> [49] \"RIF\"             # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     set_ab_names() %>%     head()        # this does the same:   example_isolates %>%     rename_with(set_ab_names)%>%     head()      # set_ab_names() works with any AB property:   example_isolates %>%     set_ab_names(property = \"atc\")%>%     head()       example_isolates %>%    set_ab_names(where(is.rsi)) %>%    colnames()      example_isolates %>%    set_ab_names(NIT:VAN) %>%    colnames() } #>  [1] \"date\"            \"hospital_id\"     \"ward_icu\"        \"ward_clinical\"   #>  [5] \"ward_outpatient\" \"age\"             \"gender\"          \"patient_id\"      #>  [9] \"mo\"              \"PEN\"             \"OXA\"             \"FLC\"             #> [13] \"AMX\"             \"AMC\"             \"AMP\"             \"TZP\"             #> [17] \"CZO\"             \"FEP\"             \"CXM\"             \"FOX\"             #> [21] \"CTX\"             \"CAZ\"             \"CRO\"             \"GEN\"             #> [25] \"TOB\"             \"AMK\"             \"KAN\"             \"TMP\"             #> [29] \"SXT\"             \"nitrofurantoin\"  \"fosfomycin\"      \"linezolid\"       #> [33] \"ciprofloxacin\"   \"moxifloxacin\"    \"vancomycin\"      \"TEC\"             #> [37] \"TCY\"             \"TGC\"             \"DOX\"             \"ERY\"             #> [41] \"CLI\"             \"AZM\"             \"IPM\"             \"MEM\"             #> [45] \"MTR\"             \"CHL\"             \"COL\"             \"MUP\"             #> [49] \"RIF\"             # }"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":null,"dir":"Reference","previous_headings":"","what":"Age in Years of Individuals — age","title":"Age in Years of Individuals — age","text":"Calculates age years based reference date, system date default.","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Age in Years of Individuals — age","text":"","code":"age(x, reference = Sys.Date(), exact = FALSE, na.rm = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Age in Years of Individuals — age","text":"x date(s), character (vectors) coerced .POSIXlt() reference reference date(s) (defaults today), character (vectors) coerced .POSIXlt() exact logical indicate whether age calculation exact, .e. decimals. divides number days year--date (YTD) x number days year reference (either 365 366). na.rm logical indicate whether missing values removed ... arguments passed .POSIXlt(), origin","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Age in Years of Individuals — age","text":"integer (decimals) exact = FALSE, double (decimals) otherwise","code":""},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Age in Years of Individuals — age","text":"Ages 0 returned NA warning. Ages 120 give warning. function vectorises x reference, meaning either can length 1 argument larger length.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Age in Years of Individuals — age","text":"","code":"# 10 random pre-Y2K birth dates df <- data.frame(birth_date = as.Date(\"2000-01-01\") - runif(10) * 25000)  # add ages df$age <- age(df$birth_date)  # add exact ages df$age_exact <- age(df$birth_date, exact = TRUE)  # add age at millenium switch df$age_at_y2k <- age(df$birth_date, \"2000-01-01\")  df #>    birth_date age age_exact age_at_y2k #> 1  1955-03-05  67  67.46301         44 #> 2  1969-09-23  52  52.90959         30 #> 3  1952-10-15  69  69.84932         47 #> 4  1940-09-07  81  81.95342         59 #> 5  1956-10-12  65  65.85753         43 #> 6  1997-03-26  25  25.40548          2 #> 7  1946-04-17  76  76.34521         53 #> 8  1987-04-28  35  35.31507         12 #> 9  1957-09-27  64  64.89863         42 #> 10 1949-07-17  73  73.09589         50"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Split Ages into Age Groups — age_groups","title":"Split Ages into Age Groups — age_groups","text":"Split ages age groups defined split argument. allows easier demographic (antimicrobial resistance) analysis.","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split Ages into Age Groups — age_groups","text":"","code":"age_groups(x, split_at = c(12, 25, 55, 75), na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split Ages into Age Groups — age_groups","text":"x age, e.g. calculated age() split_at values split x , defaults age groups 0-11, 12-24, 25-54, 55-74 75+. See Details. na.rm logical indicate whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split Ages into Age Groups — age_groups","text":"Ordered factor","code":""},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split Ages into Age Groups — age_groups","text":"split ages, input split_at argument can : numeric vector. value e.g. c(10, 20) split x 0-9, 10-19 20+. value 50 split x 0-49 50+. default split young children (0-11), youth (12-24), young adults (25-54), middle-aged adults (55-74) elderly (75+). character: \"children\" \"kids\", equivalent : c(0, 1, 2, 4, 6, 13, 18). split 0, 1, 2-3, 4-5, 6-12, 13-17 18+. \"elderly\" \"seniors\", equivalent : c(65, 75, 85). split 0-64, 65-74, 75-84, 85+. \"fives\", equivalent : 1:20 * 5. split 0-4, 5-9, ..., 95-99, 100+. \"tens\", equivalent : 1:10 * 10. split 0-9, 10-19, ..., 90-99, 100+.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/age_groups.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split Ages into Age Groups — age_groups","text":"","code":"ages <- c(3, 8, 16, 54, 31, 76, 101, 43, 21)  # split into 0-49 and 50+ age_groups(ages, 50) #> [1] 0-49 0-49 0-49 50+  0-49 50+  50+  0-49 0-49 #> Levels: 0-49 < 50+  # split into 0-19, 20-49 and 50+ age_groups(ages, c(20, 50)) #> [1] 0-19  0-19  0-19  50+   20-49 50+   50+   20-49 20-49 #> Levels: 0-19 < 20-49 < 50+  # split into groups of ten years age_groups(ages, 1:10 * 10) #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+ age_groups(ages, split_at = \"tens\") #> [1] 0-9   0-9   10-19 50-59 30-39 70-79 100+  40-49 20-29 #> 11 Levels: 0-9 < 10-19 < 20-29 < 30-39 < 40-49 < 50-59 < 60-69 < ... < 100+  # split into groups of five years age_groups(ages, 1:20 * 5) #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+ age_groups(ages, split_at = \"fives\") #> [1] 0-4   5-9   15-19 50-54 30-34 75-79 100+  40-44 20-24 #> 21 Levels: 0-4 < 5-9 < 10-14 < 15-19 < 20-24 < 25-29 < 30-34 < ... < 100+  # split specifically for children age_groups(ages, c(1, 2, 4, 6, 13, 18)) #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+ age_groups(ages, \"children\") #> [1] 2-3   6-12  13-17 18+   18+   18+   18+   18+   18+   #> Levels: 0 < 1 < 2-3 < 4-5 < 6-12 < 13-17 < 18+  # \\donttest{ # resistance of ciprofloxacin per age group if (require(\"dplyr\")) {   example_isolates %>%     filter_first_isolate() %>%     filter(mo == as.mo(\"E. coli\")) %>%     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_rsi(x = \"age_group\",                minimum = 0,                x.title = \"Age Group\",                title = \"Ciprofloxacin resistance per age group\") } #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this.  # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":null,"dir":"Reference","previous_headings":"","what":"Antibiotic Selectors — antibiotic_class_selectors","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions allow filtering rows selecting columns based antibiotic test results specific antibiotic class group, without need define columns antibiotic abbreviations. short, column name resembles antimicrobial agent, picked functions matches pharmaceutical class: \"cefazolin\", \"CZO\" \"J01DB04\" picked cephalosporins().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"ab_class(ab_class, only_rsi_columns = FALSE, only_treatable = TRUE, ...)  ab_selector(filter, only_rsi_columns = FALSE, only_treatable = TRUE, ...)  aminoglycosides(only_rsi_columns = FALSE, only_treatable = TRUE, ...)  aminopenicillins(only_rsi_columns = FALSE, ...)  antifungals(only_rsi_columns = FALSE, ...)  antimycobacterials(only_rsi_columns = FALSE, ...)  betalactams(only_rsi_columns = FALSE, only_treatable = TRUE, ...)  carbapenems(only_rsi_columns = FALSE, only_treatable = TRUE, ...)  cephalosporins(only_rsi_columns = FALSE, ...)  cephalosporins_1st(only_rsi_columns = FALSE, ...)  cephalosporins_2nd(only_rsi_columns = FALSE, ...)  cephalosporins_3rd(only_rsi_columns = FALSE, ...)  cephalosporins_4th(only_rsi_columns = FALSE, ...)  cephalosporins_5th(only_rsi_columns = FALSE, ...)  fluoroquinolones(only_rsi_columns = FALSE, ...)  glycopeptides(only_rsi_columns = FALSE, ...)  lincosamides(only_rsi_columns = FALSE, ...)  lipoglycopeptides(only_rsi_columns = FALSE, ...)  macrolides(only_rsi_columns = FALSE, ...)  oxazolidinones(only_rsi_columns = FALSE, ...)  penicillins(only_rsi_columns = FALSE, ...)  polymyxins(only_rsi_columns = FALSE, only_treatable = TRUE, ...)  streptogramins(only_rsi_columns = FALSE, ...)  quinolones(only_rsi_columns = FALSE, ...)  tetracyclines(only_rsi_columns = FALSE, ...)  trimethoprims(only_rsi_columns = FALSE, ...)  ureidopenicillins(only_rsi_columns = FALSE, ...)  administrable_per_os(only_rsi_columns = FALSE, ...)  administrable_iv(only_rsi_columns = FALSE, ...)  not_intrinsic_resistant(   only_rsi_columns = FALSE,   col_mo = NULL,   version_expertrules = 3.3,   ... )"},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"ab_class antimicrobial class part , \"carba\" \"carbapenems\". columns group, atc_group1 atc_group2 antibiotics data set searched (case-insensitive) value. only_rsi_columns logical indicate whether columns class <rsi> must selected (defaults FALSE), see .rsi() only_treatable logical indicate whether agents laboratory tests excluded (defaults TRUE), gentamicin-high (GEH) imipenem/EDTA (IPE) ... ignored, place allow future extensions filter expression evaluated antibiotics data set, name %like% \"trim\" col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\".","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"(internally) character vector column names, additional class \"ab_selector\"","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"functions can used data set calls selecting columns filtering rows. heavily inspired Tidyverse selection helpers everything(), also work base R dplyr verbs. Nonetheless, convenient use dplyr functions select(), filter() summarise(), see Examples. columns data functions called searched known antibiotic names, abbreviations, brand names, codes (ATC, EARS-Net, , etc.) according antibiotics data set. means selector aminoglycosides() pick column names like 'gen', 'genta', 'J01GB03', 'tobra', 'Tobracin', etc. ab_class() function can used filter/select manually defined antibiotic class. searches results antibiotics data set within columns group, atc_group1 atc_group2. ab_selector() function can used internally filter antibiotics data set results, see Examples. allows filtering (part ) certain name, /group name even minimum DDDs oral treatment. function yields highest flexibility, also least user-friendly, since requires hard-coded filter set. administrable_per_os() administrable_iv() functions also rely antibiotics data set - antibiotic columns matched DDD (defined daily dose) resp. oral IV treatment available antibiotics data set. not_intrinsic_resistant() function can used select antibiotic columns pose intrinsic resistance microorganisms data set. example, data set contains microorganism codes names E. coli K. pneumoniae contains column \"vancomycin\", column removed (rather, unselected) using function. currently applies 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021) determine intrinsic resistance, using eucast_rules() function internally. determination, function quite slow terms performance.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"full-list-of-supported-antibiotic-classes","dir":"Reference","previous_headings":"","what":"Full list of supported (antibiotic) classes","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"aminoglycosides() can select:  amikacin (AMK), amikacin/fosfomycin (AKF), amphotericin B-high (AMH), apramycin (APR), arbekacin (ARB), astromicin (AST), bekanamycin (BEK), dibekacin (DKB), framycetin (FRM), gentamicin (GEN), gentamicin-high (GEH), habekacin (HAB), hygromycin (HYG), isepamicin (ISE), kanamycin (KAN), kanamycin-high (KAH), kanamycin/cephalexin (KAC), micronomicin (MCR), neomycin (NEO), netilmicin (NET), pentisomicin (PIM), plazomicin (PLZ), propikacin (PKA), ribostamycin (RST), sisomicin (SIS), streptoduocin (STR), streptomycin (STR1), streptomycin-high (STH), tobramycin (TOB) tobramycin-high (TOH) aminopenicillins() can select:  amoxicillin (AMX) ampicillin (AMP) antifungals() can select:  5-fluorocytosine (FCT), amphotericin B (AMB), anidulafungin (ANI), butoconazole (), caspofungin (CAS), ciclopirox (CIX), clotrimazole (CTR), econazole (ECO), fluconazole (FLU), fosfluconazole (FFL), griseofulvin (GRI), hachimycin (HCH), ibrexafungerp (IBX), isavuconazole (ISV), isoconazole (ISO), itraconazole (ITR), ketoconazole (KET), manogepix (MGX), micafungin (MIF), miconazole (MCZ), nystatin (NYS), pimaricin (PMR), posaconazole (POS), rezafungin (RZF), ribociclib (RBC), sulconazole (SUC), terbinafine (TRB), terconazole (TRC) voriconazole (VOR) antimycobacterials() can select:  4-aminosalicylic acid (AMA), calcium aminosalicylate (CLA), capreomycin (CAP), clofazimine (CLF), delamanid (DLM), enviomycin (ENV), ethambutol (ETH), ethambutol/isoniazid (ETI), ethionamide (ETI1), isoniazid (INH), morinamide (MRN), p-aminosalicylic acid (PAS), pretomanid (PMD), prothionamide (PTH), pyrazinamide (PZA), rifabutin (RIB), rifampicin (RIF), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), rifapentine (RFP), simvastatin/fenofibrate (SMF), sodium aminosalicylate (SDA), streptomycin/isoniazid (STI), terizidone (TRZ), thioacetazone/isoniazid (THI1), tiocarlide (TCR) viomycin (VIO) betalactams() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), biapenem (BIA), carbenicillin (CRB), carindacillin (CRN), cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefaloridine (RID), cefamandole (MAN), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/nacubactam (FNC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/enzyme inhibitor (CEI), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), cefuroxime (CXM), cefuroxime axetil (CXA), cephalexin (LEX), cephalothin (CEP), cephapirin (HAP), cephradine (CED), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), doripenem (DOR), epicillin (EPC), ertapenem (ETP), flucloxacillin (FLC), hetacillin (HET), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), latamoxef (LTM), lenampicillin (LEN), loracarbef (LOR), mecillinam (MEC), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), metampicillin (MTM), methicillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), panipenem (PAN), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), phenethicillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), tebipenem (TBP), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) carbapenems() can select:  biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA) tebipenem (TBP) cephalosporins() can select:  cefacetrile (CAC), cefaclor (CEC), cefadroxil (CFR), cefaloridine (RID), cefamandole (MAN), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetamet (CAT), cefetamet pivoxil (CPI), cefetecol (CCL), cefetrizole (CZL), cefixime (CFM), cefmenoxime (CMX), cefmetazole (CMZ), cefodizime (DIZ), cefonicid (CID), cefoperazone (CFP), cefoperazone/sulbactam (CSL), ceforanide (CND), cefoselis (CSE), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotetan (CTT), cefotiam (CTF), cefotiam hexetil (CHE), cefovecin (FOV), cefoxitin (FOX), cefoxitin screening (FOX1), cefozopran (ZOP), cefpimizole (CFZ), cefpiramide (CPM), cefpirome (CPO), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefprozil (CPR), cefquinome (CEQ), cefroxadine (CRD), cefsulodin (CFS), cefsumide (CSU), ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftezole (CTL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/enzyme inhibitor (CEI), ceftolozane/tazobactam (CZT), ceftriaxone (CRO), cefuroxime (CXM), cefuroxime axetil (CXA), cephalexin (LEX), cephalothin (CEP), cephapirin (HAP), cephradine (CED), latamoxef (LTM) loracarbef (LOR) cephalosporins_1st() can select:  cefacetrile (CAC), cefadroxil (CFR), cefaloridine (RID), cefatrizine (CTZ), cefazedone (CZD), cefazolin (CZO), cefroxadine (CRD), ceftezole (CTL), cephalexin (LEX), cephalothin (CEP), cephapirin (HAP) cephradine (CED) cephalosporins_2nd() can select:  cefaclor (CEC), cefamandole (MAN), cefmetazole (CMZ), cefonicid (CID), ceforanide (CND), cefotetan (CTT), cefotiam (CTF), cefoxitin (FOX), cefoxitin screening (FOX1), cefprozil (CPR), cefuroxime (CXM), cefuroxime axetil (CXA) loracarbef (LOR) cephalosporins_3rd() can select:  cefcapene (CCP), cefcapene pivoxil (CCX), cefdinir (CDR), cefditoren (DIT), cefditoren pivoxil (DIX), cefetamet (CAT), cefetamet pivoxil (CPI), cefixime (CFM), cefmenoxime (CMX), cefodizime (DIZ), cefoperazone (CFP), cefoperazone/sulbactam (CSL), cefotaxime (CTX), cefotaxime/clavulanic acid (CTC), cefotaxime/sulbactam (CTS), cefotiam hexetil (CHE), cefovecin (FOV), cefpimizole (CFZ), cefpiramide (CPM), cefpodoxime (CPD), cefpodoxime proxetil (CPX), cefpodoxime/clavulanic acid (CDC), cefsulodin (CFS), ceftazidime (CAZ), ceftazidime/avibactam (CZA), ceftazidime/clavulanic acid (CCV), cefteram (CEM), cefteram pivoxil (CPL), ceftibuten (CTB), ceftiofur (TIO), ceftizoxime (CZX), ceftizoxime alapivoxil (CZP), ceftriaxone (CRO) latamoxef (LTM) cephalosporins_4th() can select:  cefepime (FEP), cefepime/clavulanic acid (CPC), cefepime/tazobactam (FPT), cefetecol (CCL), cefoselis (CSE), cefozopran (ZOP), cefpirome (CPO) cefquinome (CEQ) cephalosporins_5th() can select:  ceftaroline (CPT), ceftaroline/avibactam (CPA), ceftobiprole (BPR), ceftobiprole medocaril (CFM1), ceftolozane/enzyme inhibitor (CEI) ceftolozane/tazobactam (CZT) fluoroquinolones() can select:  besifloxacin (BES), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nifuroquine (NIF), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), pazufloxacin (PAZ), pefloxacin (PEF), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) glycopeptides() can select:  avoparcin (AVO), dalbavancin (DAL), norvancomycin (NVA), oritavancin (ORI), ramoplanin (RAM), teicoplanin (TEC), teicoplanin-macromethod (TCM), telavancin (TLV), vancomycin (VAN) vancomycin-macromethod (VAM) lincosamides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), clindamycin (CLI), gamithromycin (GAM), kitasamycin (KIT), lincomycin (LIN), meleumycin (MEL), nafithromycin (ZWK), pirlimycin (PRL), primycin (PRM), solithromycin (SOL), tildipirosin (TIP), tilmicosin (TIL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) lipoglycopeptides() can select:  dalbavancin (DAL), oritavancin (ORI) telavancin (TLV) macrolides() can select:  acetylmidecamycin (ACM), acetylspiramycin (ASP), azithromycin (AZM), clarithromycin (CLR), dirithromycin (DIR), erythromycin (ERY), flurithromycin (FLR1), gamithromycin (GAM), josamycin (JOS), kitasamycin (KIT), meleumycin (MEL), midecamycin (MID), miocamycin (MCM), nafithromycin (ZWK), oleandomycin (OLE), pirlimycin (PRL), primycin (PRM), rokitamycin (ROK), roxithromycin (RXT), solithromycin (SOL), spiramycin (SPI), telithromycin (TLT), tildipirosin (TIP), tilmicosin (TIL), troleandomycin (TRL), tulathromycin (TUL), tylosin (TYL) tylvalosin (TYL1) oxazolidinones() can select:  cadazolid (CDZ), cycloserine (CYC), linezolid (LNZ), tedizolid (TZD) thiacetazone (THA) penicillins() can select:  amoxicillin (AMX), amoxicillin/clavulanic acid (AMC), amoxicillin/sulbactam (AXS), ampicillin (AMP), ampicillin/sulbactam (SAM), apalcillin (APL), aspoxicillin (APX), avibactam (AVB), azidocillin (AZD), azlocillin (AZL), aztreonam (ATM), aztreonam/avibactam (AZA), aztreonam/nacubactam (ANC), bacampicillin (BAM), benzathine benzylpenicillin (BNB), benzathine phenoxymethylpenicillin (BNP), benzylpenicillin (PEN), carbenicillin (CRB), carindacillin (CRN), cefepime/nacubactam (FNC), ciclacillin (CIC), clometocillin (CLM), cloxacillin (CLO), dicloxacillin (DIC), epicillin (EPC), flucloxacillin (FLC), hetacillin (HET), lenampicillin (LEN), mecillinam (MEC), metampicillin (MTM), methicillin (MET), mezlocillin (MEZ), mezlocillin/sulbactam (MSU), nacubactam (NAC), nafcillin (NAF), oxacillin (OXA), penamecillin (PNM), penicillin/novobiocin (PNO), penicillin/sulbactam (PSU), phenethicillin (PHE), phenoxymethylpenicillin (PHN), piperacillin (PIP), piperacillin/sulbactam (PIS), piperacillin/tazobactam (TZP), piridicillin (PRC), pivampicillin (PVM), pivmecillinam (PME), procaine benzylpenicillin (PRB), propicillin (PRP), sarmoxicillin (SRX), sulbactam (SUL), sulbenicillin (SBC), sultamicillin (SLT6), talampicillin (TAL), tazobactam (TAZ), temocillin (TEM), ticarcillin (TIC) ticarcillin/clavulanic acid (TCC) polymyxins() can select:  colistin (COL), polymyxin B (PLB) polymyxin B/polysorbate 80 (POP) quinolones() can select:  besifloxacin (BES), cinoxacin (CIN), ciprofloxacin (CIP), clinafloxacin (CLX), danofloxacin (DAN), delafloxacin (DFX), difloxacin (DIF), enoxacin (ENX), enrofloxacin (ENR), finafloxacin (FIN), fleroxacin (FLE), flumequine (FLM), garenoxacin (GRN), gatifloxacin (GAT), gemifloxacin (GEM), grepafloxacin (GRX), levofloxacin (LVX), levonadifloxacin (LND), lomefloxacin (LOM), marbofloxacin (MAR), metioxate (MXT), miloxacin (MIL), moxifloxacin (MFX), nadifloxacin (NAD), nalidixic acid (NAL), nifuroquine (NIF), nitroxoline (NTR), norfloxacin (), ofloxacin (OFX), orbifloxacin (ORB), oxolinic acid (OXO), pazufloxacin (PAZ), pefloxacin (PEF), pipemidic acid (PPA), piromidic acid (PIR), pradofloxacin (PRA), premafloxacin (PRX), prulifloxacin (PRU), rosoxacin (ROS), rufloxacin (RFL), sarafloxacin (SAR), sitafloxacin (SIT), sparfloxacin (SPX), temafloxacin (TMX), tilbroquinol (TBQ), tioxacin (TXC), tosufloxacin (TFX) trovafloxacin (TVA) streptogramins() can select:  pristinamycin (PRI) quinupristin/dalfopristin (QDA) tetracyclines() can select:  cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), tetracycline (TCY) tigecycline (TGC) trimethoprims() can select:  brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP) trimethoprim/sulfamethoxazole (SXT) ureidopenicillins() can select:  azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP) piperacillin/tazobactam (TZP)","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Antibiotic Selectors — antibiotic_class_selectors","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates. df <- example_isolates[ , c(\"hospital_id\", \"mo\",                             \"AMP\", \"AMC\", \"TZP\", \"CXM\", \"CRO\", \"GEN\",                             \"TOB\", \"COL\", \"IPM\", \"MEM\", \"TEC\", \"VAN\")]  # base R ------------------------------------------------------------------  # select columns 'IPM' (imipenem) and 'MEM' (meropenem) df[, carbapenems()] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 2,000 × 2 #>    IPM   MEM   #>    <rsi> <rsi> #>  1 NA    NA    #>  2 NA    NA    #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 NA    NA    #>  8 NA    NA    #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB' df[, c(\"mo\", aminoglycosides())] #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin) and 'TOB' #>   (tobramycin) #> # A tibble: 2,000 × 3 #>    mo           GEN   TOB   #>    <mo>         <rsi> <rsi> #>  1 B_ESCHR_COLI NA    NA    #>  2 B_ESCHR_COLI NA    NA    #>  3 B_STPHY_EPDR NA    NA    #>  4 B_STPHY_EPDR NA    NA    #>  5 B_STPHY_EPDR NA    NA    #>  6 B_STPHY_EPDR NA    NA    #>  7 B_STPHY_AURS NA    S     #>  8 B_STPHY_AURS NA    S     #>  9 B_STPHY_EPDR NA    NA    #> 10 B_STPHY_EPDR NA    NA    #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # select only antibiotic columns with DDDs for oral treatment df[, administrable_per_os()] #> ℹ For `administrable_per_os()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'CXM' (cefuroxime), 'COL' (colistin) and #>   'VAN' (vancomycin) #> # A tibble: 2,000 × 5 #>    AMP   AMC   CXM   COL   VAN   #>    <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 NA    I     I     NA    R     #>  2 NA    I     I     NA    R     #>  3 NA    NA    R     R     S     #>  4 NA    NA    R     R     S     #>  5 NA    NA    R     R     S     #>  6 NA    NA    R     R     S     #>  7 R     S     S     R     S     #>  8 R     S     S     R     S     #>  9 NA    NA    R     R     S     #> 10 NA    NA    S     R     S     #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # filter using any() or all() df[any(carbapenems() == \"R\"), ] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 14 #>    hospital…¹ mo           AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>      <mo>         <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 A          B_ENTRC_FACM NA    NA    NA    R     R     R     R     R     R     #>  2 A          B_ENTRC_FACM NA    NA    NA    R     R     R     R     R     R     #>  3 B          B_STNTR_MLTP R     R     R     R     R     R     R     R     R     #>  4 B          B_ENTRC      NA    NA    R     NA    NA    NA    NA    R     R     #>  5 B          B_ENTRC      NA    NA    R     NA    NA    NA    NA    R     R     #>  6 B          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  7 D          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  8 A          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  9 A          B_PROTS_MRBL NA    S     S     S     S     S     S     R     R     #> 10 A          B_PROTS_MRBL NA    S     S     S     S     S     S     R     R     #> # … with 45 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names subset(df, any(carbapenems() == \"R\")) #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> # A tibble: 55 × 14 #>    hospital…¹ mo           AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>      <mo>         <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 A          B_ENTRC_FACM NA    NA    NA    R     R     R     R     R     R     #>  2 A          B_ENTRC_FACM NA    NA    NA    R     R     R     R     R     R     #>  3 B          B_STNTR_MLTP R     R     R     R     R     R     R     R     R     #>  4 B          B_ENTRC      NA    NA    R     NA    NA    NA    NA    R     R     #>  5 B          B_ENTRC      NA    NA    R     NA    NA    NA    NA    R     R     #>  6 B          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  7 D          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  8 A          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  9 A          B_PROTS_MRBL NA    S     S     S     S     S     S     R     R     #> 10 A          B_PROTS_MRBL NA    S     S     S     S     S     S     R     R     #> # … with 45 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  # filter on any or all results in the carbapenem columns (i.e., IPM, MEM): df[any(carbapenems()), ] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering any of columns 'IPM' and 'MEM' to contain value \"R\", \"S\" or \"I\" #> # A tibble: 962 × 14 #>    hospital…¹ mo           AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>      <mo>         <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 D          B_ESCHR_COLI NA    I     NA    S     S     NA    S     NA    S     #>  2 D          B_ESCHR_COLI NA    I     NA    S     S     NA    S     NA    S     #>  3 B          B_PROTS_MRBL NA    I     NA    S     S     NA    NA    R     S     #>  4 B          B_PROTS_MRBL NA    I     NA    S     S     NA    NA    R     S     #>  5 B          B_SERRT_MRCS R     R     NA    R     NA    NA    NA    R     S     #>  6 B          B_SERRT_MRCS R     R     NA    R     NA    NA    NA    R     S     #>  7 B          B_SERRT_MRCS R     R     NA    R     NA    NA    NA    R     S     #>  8 D          B_KLBSL_PNMN R     I     NA    S     S     S     S     NA    S     #>  9 D          B_KLBSL_PNMN R     I     NA    S     S     S     S     NA    S     #> 10 C          B_ESCHR_COLI R     R     NA    R     S     S     NA    NA    S     #> # … with 952 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names df[all(carbapenems()), ] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ Filtering all of columns 'IPM' and 'MEM' to contain value \"R\", \"S\" or \"I\" #> # A tibble: 756 × 14 #>    hospita…¹ mo            AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>     <mo>          <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 C         B_STRPT_GRPB  S     S     S     S     S     R     R     R     S     #>  2 B         B_STRPT_PYGN  S     S     S     S     S     R     R     R     S     #>  3 B         B_STRPT_GRPA  S     S     S     S     S     R     R     R     S     #>  4 B         B_STRPT_GRPA  S     S     S     S     S     R     R     R     S     #>  5 D         B_STRPT_GRPB  S     S     S     S     S     R     R     R     S     #>  6 B         B_ESCHR_COLI  R     R     S     S     S     S     S     NA    S     #>  7 B         B_ENTRBC_CLOC R     R     S     R     NA    S     S     NA    S     #>  8 B         B_ENTRBC_CLOC R     R     S     R     NA    S     S     NA    S     #>  9 B         B_ESCHR_COLI  NA    S     S     S     S     S     S     NA    S     #> 10 B         B_ESCHR_COLI  NA    S     S     S     S     S     S     NA    S     #> # … with 746 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  # filter with multiple antibiotic selectors using c() df[all(c(carbapenems(), aminoglycosides()) == \"R\"), ] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin) and 'TOB' #>   (tobramycin) #> # A tibble: 26 × 14 #>    hospital…¹ mo           AMP   AMC   TZP   CXM   CRO   GEN   TOB   COL   IPM   #>    <fct>      <mo>         <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> <rsi> #>  1 B          B_STNTR_MLTP R     R     R     R     R     R     R     R     R     #>  2 B          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  3 D          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  4 A          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  5 D          B_STNTR_MLTP R     R     R     R     R     R     R     R     R     #>  6 D          B_ENTRC_FACM NA    NA    R     R     R     R     R     R     R     #>  7 B          B_STPHY_CONS R     R     R     R     R     R     R     R     R     #>  8 A          B_STPHY_CONS R     R     R     R     R     R     R     R     R     #>  9 D          B_STPHY_CONS R     R     R     R     R     R     R     R     R     #> 10 D          B_STPHY_CONS R     R     R     R     R     R     R     R     R     #> # … with 16 more rows, 3 more variables: MEM <rsi>, TEC <rsi>, VAN <rsi>, and #> #   abbreviated variable name ¹​hospital_id #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  # filter + select in one go: get penicillins in carbapenems-resistant strains df[any(carbapenems() == \"R\"), penicillins()] #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) #> ℹ For `penicillins()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid) and 'TZP' (piperacillin/tazobactam) #> # A tibble: 55 × 3 #>    AMP   AMC   TZP   #>    <rsi> <rsi> <rsi> #>  1 NA    NA    NA    #>  2 NA    NA    NA    #>  3 R     R     R     #>  4 NA    NA    R     #>  5 NA    NA    R     #>  6 NA    NA    R     #>  7 NA    NA    R     #>  8 NA    NA    R     #>  9 NA    S     S     #> 10 NA    S     S     #> # … with 45 more rows #> # ℹ Use `print(n = ...)` to see more rows  # You can combine selectors with '&' to be more specific. For example, # penicillins() would select benzylpenicillin ('peni G') and # administrable_per_os() would select erythromycin. Yet, when combined these # drugs are both omitted since benzylpenicillin is not administrable per os # and erythromycin is not a penicillin: df[, penicillins() & administrable_per_os()] #> ℹ For `penicillins()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid) and 'TZP' (piperacillin/tazobactam) #> ℹ For `administrable_per_os()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'CXM' (cefuroxime), 'COL' (colistin) and #>   'VAN' (vancomycin) #> # A tibble: 2,000 × 2 #>    AMP   AMC   #>    <rsi> <rsi> #>  1 NA    I     #>  2 NA    I     #>  3 NA    NA    #>  4 NA    NA    #>  5 NA    NA    #>  6 NA    NA    #>  7 R     S     #>  8 R     S     #>  9 NA    NA    #> 10 NA    NA    #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # ab_selector() applies a filter in the `antibiotics` data set and is thus very # flexible. For instance, to select antibiotic columns with an oral DDD of at # least 1 gram: df[, ab_selector(oral_ddd > 1 & oral_units == \"g\")] #> ℹ For `ab_selector(oral_ddd > 1 & oral_units == \"g\")` using columns 'AMP' #>   (ampicillin), 'AMC' (amoxicillin/clavulanic acid) and 'VAN' (vancomycin) #> # A tibble: 2,000 × 3 #>    AMP   AMC   VAN   #>    <rsi> <rsi> <rsi> #>  1 NA    I     R     #>  2 NA    I     R     #>  3 NA    NA    S     #>  4 NA    NA    S     #>  5 NA    NA    S     #>  6 NA    NA    S     #>  7 R     S     S     #>  8 R     S     S     #>  9 NA    NA    S     #> 10 NA    NA    S     #> # … with 1,990 more rows #> # ℹ Use `print(n = ...)` to see more rows  # dplyr ------------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {    # get AMR for all aminoglycosides e.g., per hospital:   df %>%     group_by(hospital_id) %>%      summarise(across(aminoglycosides(), resistance))        # You can combine selectors with '&' to be more specific:   df %>%     select(penicillins() & administrable_per_os())      # get AMR for only drugs that matter - no intrinsic resistance:   df %>%     filter(mo_genus() %in% c(\"Escherichia\", \"Klebsiella\")) %>%      group_by(hospital_id) %>%      summarise(across(not_intrinsic_resistant(), resistance))        # get susceptibility for antibiotics whose name contains \"trim\":   df %>%     filter(first_isolate()) %>%      group_by(hospital_id) %>%      summarise(across(ab_selector(name %like% \"trim\"), susceptibility))    # this will select columns 'IPM' (imipenem) and 'MEM' (meropenem):   df %>%      select(carbapenems())        # this will select columns 'mo', 'AMK', 'GEN', 'KAN' and 'TOB':   df %>%      select(mo, aminoglycosides())       # any() and all() work in dplyr's filter() too:  df %>%      filter(any(aminoglycosides() == \"R\"),            all(cephalosporins_2nd() == \"R\"))       # also works with c():  df %>%      filter(any(c(carbapenems(), aminoglycosides()) == \"R\"))       # not setting any/all will automatically apply all():  df %>%      filter(aminoglycosides() == \"R\")        # this will select columns 'mo' and all antimycobacterial drugs ('RIF'):   df %>%      select(mo, ab_class(\"mycobact\"))        # get bug/drug combinations for only glycopeptides in Gram-positives:   df %>%      filter(mo_is_gram_positive()) %>%      select(mo, glycopeptides()) %>%      bug_drug_combinations() %>%     format()        data.frame(some_column = \"some_value\",              J01CA01 = \"S\") %>%   # ATC code of ampicillin     select(penicillins())         # only the 'J01CA01' column will be selected             # with recent versions of dplyr this is all equal:   x <- df[carbapenems() == \"R\", ]   y <- df %>% filter(carbapenems() == \"R\")   z <- df %>% filter(if_all(carbapenems(), ~.x == \"R\"))   identical(x, y)   identical(y, z) } #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin) and 'TOB' #>   (tobramycin) #> ℹ For `penicillins()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid) and 'TZP' (piperacillin/tazobactam) #> ℹ For `administrable_per_os()` using columns 'AMP' (ampicillin), 'AMC' #>   (amoxicillin/clavulanic acid), 'CXM' (cefuroxime), 'COL' (colistin) and #>   'VAN' (vancomycin) #> ℹ Using column 'mo' as input for `mo_genus()` #> ℹ For `not_intrinsic_resistant()` removing columns 'TEC' (teicoplanin) and #>   'VAN' (vancomycin) #> Warning: Introducing NA: only 23 results available for COL in group: hospital_id = #> \"C\" (`minimum` = 30). #> Error in filter(., first_isolate()): Problem while computing `..1 = first_isolate()`. #> Caused by error: #> ! in first_isolate(): `col_date` must be set # }"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Sets with 566 Antimicrobial Drugs — antibiotics","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"Two data sets containing antibiotics/antimycotics antivirals. Use .ab() one ab_* functions retrieve values antibiotics data set. Three identifiers included data set: antibiotic ID (ab, primarily used package) defined WHONET/EARS-Net, ATC code (atc) defined , Compound ID (cid) found PubChem. properties data set derived one codes. Note drugs multiple ATC codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"","code":"antibiotics  antivirals"},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"object class data.frame 102 rows 9 columns.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antibiotics-data-set-a-data-frame-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antibiotics data set: a data.frame with 464 observations and 14 variables:","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available cid Compound ID found PubChem name Official name used WHONET/EARS-Net group short concise group name, based WHONET WHOCC definitions atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC, like J01CR02 atc_group1 Official pharmacological subgroup (3rd level ATC code) defined WHOCC, like \"Macrolides, lincosamides streptogramins\" atc_group2 Official chemical subgroup (4th level ATC code) defined WHOCC, like \"Macrolides\" abbr List abbreviations used many countries, also antibiotic susceptibility testing (AST) synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment, currently available 170 drugs oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral (intravenous) treatment, currently available 145 drugs iv_units Units iv_ddd loinc LOINC codes (Logical Observation Identifiers Names Codes) associated name antimicrobial agent. Use ab_loinc() retrieve quickly, see ab_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"for-the-antivirals-data-set-a-data-frame-with-observations-and-variables-","dir":"Reference","previous_headings":"","what":"For the antivirals data set: a data.frame with 102 observations and 9 variables:","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"atc ATC codes (Anatomical Therapeutic Chemical) defined WHOCC cid Compound ID found PubChem name Official name used WHONET/EARS-Net atc_group Official pharmacological subgroup (3rd level ATC code) defined WHOCC synonyms Synonyms (often trade names) drug, found PubChem based compound ID oral_ddd Defined Daily Dose (DDD), oral treatment oral_units Units oral_ddd iv_ddd Defined Daily Dose (DDD), parenteral treatment iv_units Units iv_ddd","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology (WHOCC): https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"Properties based ATC code available ATC available. properties : atc_group1, atc_group2, oral_ddd, oral_units, iv_ddd iv_units. Synonyms (.e. trade names) derived Compound ID (cid) consequently available CID available.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"data sets available 'flat files' use even without R - can find files : https://github.com/msberends/AMR/raw/main/data-raw/antibiotics.txt https://github.com/msberends/AMR/raw/main/data-raw/antivirals.txt Files R format (preserved data structure) can found : https://github.com/msberends/AMR/raw/main/data/antibiotics.rda https://github.com/msberends/AMR/raw/main/data/antivirals.rda","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/antibiotics.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Sets with 566 Antimicrobial Drugs — antibiotics","text":"","code":"head(antibiotics) #>    ab      cid                  name                    group #> 1 AMA     4649 4-aminosalicylic acid       Antimycobacterials #> 2 FCT     3366      5-fluorocytosine Antifungals/antimycotics #> 3 ACM  6450012     Acetylmidecamycin  Macrolides/lincosamides #> 4 ASP 49787020      Acetylspiramycin  Macrolides/lincosamides #> 5 ALS     8954    Aldesulfone sodium     Other antibacterials #> 6 AMK    37768              Amikacin          Aminoglycosides #>                         atc                          atc_group1 #> 1                   J04AA01 Drugs for treatment of tuberculosis #> 2                   D01AE21         Antifungals for topical use #> 3                        NA                                <NA> #> 4                        NA                                <NA> #> 5                   J04BA03        Drugs for treatment of lepra #> 6 D06AX12, J01GB06, S01AA21       Aminoglycoside antibacterials #>                            atc_group2          abbreviations #> 1 Aminosalicylic acid and derivatives                        #> 2   Other antifungals for topical use  5flc, fcu, fluo, fluy #> 3                                <NA>                        #> 4                                <NA>                        #> 5        Drugs for treatment of lepra                        #> 6               Other aminoglycosides ak, ami, amik, amk, an #>                                                                                                                                                                                                                                                                                                                                                                synonyms #> 1 aminopar, aminosalicylic, aminosalicylic acid, aminosalyl, aminox, apacil, deapasil, entepas, ferrosan, gabbropas, helipidyl, hellipidyl, neopasalate, osacyl, pamacyl, pamisyl, paramycin, parasal, parasalicil, parasalindon, pasalon, pasara, pascorbic, pasdium, paser granules, paskalium, pasmed, pasnodia, pasolac, propasa, rezipas, teebacin, wln: zr cq dvq #> 2                                                                                                                                                                                                                          alcobon, ancobon, ancotil, ancotyl, flucitosina, flucystine, flucytosin, flucytosine, flucytosinum, flucytosone, fluocytosine, fluorcytosine #> 3                                                                                                                                                                                                                                                                                                                                                                       #> 4                                                                                                                                                                                                                                                                                                                        acetylspiramycin, foromacidin b, spiramycin ii #> 5                                                                                                                                                 adesulfone sodium, aldapsone, aldesulfona sodica, aldesulfone, aldesulfone sodique, aldesulfone sodium, diamidin, diasone, diasone sodium, diazon, novotrone, sodium aldesulphone, sodium sulfoxone, sulfoxone sodium #> 6                                                                                                                                            amicacin, amikacillin, amikacin, amikacin base, amikacin dihydrate, amikacin sulfate, amikacina, amikacine, amikacinum, amikavet, amikin, amiklin, amikozit, amukin, arikace, briclin, lukadin, mikavir, pierami, potentox #>   oral_ddd oral_units iv_ddd iv_units #> 1    12.00          g     NA     <NA> #> 2       NA       <NA>     NA     <NA> #> 3       NA       <NA>     NA     <NA> #> 4       NA       <NA>     NA     <NA> #> 5     0.33          g     NA     <NA> #> 6       NA       <NA>      1        g #>                                                                                                                                loinc #> 1                                                                                                                                    #> 2                                                                                10974-4, 23805-5, 25142-1, 25143-9, 3639-2, 46218-4 #> 3                                                                                                                                    #> 4                                                                                                                                    #> 5                                                                                                                                    #> 6 13546-7, 15098-7, 17798-0, 31097-9, 31098-7, 31099-5, 3319-1, 3320-9, 3321-7, 35669-1, 50802-8, 50803-6, 56628-1, 59378-0, 80972-3 head(antivirals) #>       atc       cid               name #> 1 J05AF06    441300           Abacavir #> 2 J05AB01 135398513          Aciclovir #> 3 J05AF08     60871 Adefovir dipivoxil #> 4 J05AE05     65016         Amprenavir #> 5 J05AP06  16076883        Asunaprevir #> 6 J05AE08    148192         Atazanavir #>                                                            atc_group #> 1         Nucleoside and nucleotide reverse transcriptase inhibitors #> 2 Nucleosides and nucleotides excl. reverse transcriptase inhibitors #> 3         Nucleoside and nucleotide reverse transcriptase inhibitors #> 4                                                Protease inhibitors #> 5                         Antivirals for treatment of HCV infections #> 6                                                Protease inhibitors #>                                                                                                                                                                                                                                                                                                          synonyms #> 1                                                                                                                                                                                                                                                                              Abacavir, Abacavir sulfate, Ziagen #> 2 Acicloftal, Aciclovier, Aciclovir, Aciclovirum, Activir, AcycloFoam, Acycloguanosine, Acyclovir, Acyclovir Lauriad, ACYCLOVIR SODIUM, Avirax, Cargosil, Cyclovir, Genvir, Gerpevir, Hascovir, Herpevir, Maynar, Poviral, Sitavig, Sitavir, Vipral, Virolex, Viropump, Virorax, Zovirax, Zovirax topical, Zyclir #> 3                                                                                                                                 Adefovir di ester, Adefovir dipivoxil, Adefovir Dipivoxil, Adefovir dipivoxyl, Adefovir pivoxil, Adefovirdipivoxl, Bisadenine, BISADENINE, BisPMEA, Hepsera, Preveon, YouHeDing #> 4                                                                                                                                                                                                                                                             Agenerase, Amprenavir, Amprenavirum, Prozei, Vertex #> 5                                                                                                                                                                                                                                                                                           Asunaprevir, Sunvepra #> 6                                                                                                                                                                                                                                                      Atazanavir, Atazanavir Base, Latazanavir, Reyataz, Zrivada #>   oral_ddd oral_units iv_ddd iv_units #> 1      0.6          g     NA     <NA> #> 2      4.0          g      4        g #> 3     10.0         mg     NA     <NA> #> 4      1.2          g     NA     <NA> #> 5       NA       <NA>     NA     <NA> #> 6      0.3          g     NA     <NA>"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to an Antibiotic ID — as.ab","title":"Transform Input to an Antibiotic ID — as.ab","text":"Use function determine antibiotic code one antibiotics. data set antibiotics searched abbreviations, official names synonyms (brand names).","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"as.ab(x, flag_multiple_results = TRUE, info = interactive(), ...)  is.ab(x)"},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to an Antibiotic ID — as.ab","text":"x character vector determine antibiotic ID flag_multiple_results logical indicate whether note printed console probably one antibiotic code name can retrieved single input value. info logical indicate whether progress bar printed, defaults TRUE interactive mode ... arguments passed internal functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to an Antibiotic ID — as.ab","text":"character  vector additional class ab","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to an Antibiotic ID — as.ab","text":"entries antibiotics data set three different identifiers: human readable EARS-Net code (column ab, used ECDC WHONET), ATC code (column atc, used ), CID code (column cid, Compound ID, used PubChem). data set contains 5,000 official brand names many different countries, found PubChem. drugs contain multiple ATC codes. properties searched user input. .ab() can correct different forms misspelling: Wrong spelling drug names (\"tobramicin\" \"gentamycin\"), corrects audible similarities f/ph, x/ks, c/z/s, t/th, etc. many vowels consonants Switching two characters (\"mreopenem\", often case clinical data, doctors typed fast) Digitalised paper records, leaving artefacts like 0/o/O (zero O's), B/8, n/r, etc. Use ab_* functions get properties based returned antibiotic ID, see Examples. Note: .ab() ab_* functions may use long regular expression match brand names antimicrobial agents. may fail systems.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to an Antibiotic ID — as.ab","text":"World Health Organization () Collaborating Centre Drug Statistics Methodology: https://www.whocc./atc_ddd_index/ European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"whocc","dir":"Reference","previous_headings":"","what":"WHOCC","title":"Transform Input to an Antibiotic ID — as.ab","text":"package contains ~550 antibiotic, antimycotic antiviral drugs Anatomical Therapeutic Chemical (ATC) codes, ATC groups Defined Daily Dose (DDD) World Health Organization Collaborating Centre Drug Statistics Methodology (WHOCC, https://www.whocc.) Pharmaceuticals Community Register European Commission (https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm). become gold standard international drug utilisation monitoring research. WHOCC located Oslo Norwegian Institute Public Health funded Norwegian government. European Commission executive European Union promotes general interest. NOTE: WHOCC copyright allow use commercial purposes, unlike info package. See https://www.whocc./copyright_disclaimer/.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to an Antibiotic ID — as.ab","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.ab.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to an Antibiotic ID — as.ab","text":"","code":"# these examples all return \"ERY\", the ID of erythromycin: as.ab(\"J01FA01\") #> Class <ab> #> [1] ERY as.ab(\"J 01 FA 01\") #> Class <ab> #> [1] ERY as.ab(\"Erythromycin\") #> Class <ab> #> [1] ERY as.ab(\"eryt\") #> Class <ab> #> [1] ERY as.ab(\"   eryt 123\") #> Class <ab> #> [1] ERY as.ab(\"ERYT\") #> Class <ab> #> [1] ERY as.ab(\"ERY\") #> Class <ab> #> [1] ERY as.ab(\"eritromicine\") # spelled wrong, yet works #> Class <ab> #> [1] ERY as.ab(\"Erythrocin\")   # trade name #> Class <ab> #> [1] ERY as.ab(\"Romycin\")      # trade name #> Class <ab> #> [1] ERY  # spelling from different languages and dyslexia are no problem ab_atc(\"ceftriaxon\") #> [1] \"J01DD04\" ab_atc(\"cephtriaxone\")     # small spelling error #> [1] \"J01DD04\" ab_atc(\"cephthriaxone\")    # or a bit more severe #> [1] \"J01DD04\" ab_atc(\"seephthriaaksone\") # and even this works #> [1] \"J01DD04\"  # use ab_* functions to get a specific properties (see ?ab_property); # they use as.ab() internally: ab_name(\"J01FA01\")    # \"Erythromycin\" #> [1] \"Erythromycin\" ab_name(\"eryt\")       # \"Erythromycin\" #> [1] \"Erythromycin\" # \\donttest{ if (require(\"dplyr\")) {    # you can quickly rename <rsi> columns using dplyr >= 1.0.0:   example_isolates %>%     rename_with(as.ab, where(is.rsi))     } #> # A tibble: 2,000 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  4 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  5 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  6 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  7 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  8 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  9 2002-01-16 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 10 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #> # … with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # }"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Disk Diffusion Diameters — as.disk","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"transforms vector new class disk, disk diffusion growth zone size (around antibiotic disk) millimetres 6 50.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"as.disk(x, na.rm = FALSE)  NA_disk_  is.disk(x)"},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"object class disk (inherits integer) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"x vector na.rm logical indicating whether missing values removed","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"integer additional class disk","code":""},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"Interpret disk values RSI values .rsi(). supports guidelines EUCAST CLSI. NA_disk_ missing value new <disk> class.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.disk.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Disk Diffusion Diameters — as.disk","text":"","code":"# transform existing disk zones to the `disk` class (using base R) df <- data.frame(microorganism = \"Escherichia coli\",                  AMP = 20,                  CIP = 14,                  GEN = 18,                  TOB = 16) df[, 2:5] <- lapply(df[, 2:5], as.disk) str(df) #> 'data.frame':\t1 obs. of  5 variables: #>  $ microorganism: chr \"Escherichia coli\" #>  $ AMP          : 'disk' int 20 #>  $ CIP          : 'disk' int 14 #>  $ GEN          : 'disk' int 18 #>  $ TOB          : 'disk' int 16  # \\donttest{ # transforming is easier with dplyr: if (require(\"dplyr\")) {   df %>% mutate(across(AMP:TOB, as.disk)) } #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli  20  14  18  16 # }  # interpret disk values, see ?as.rsi as.rsi(x = as.disk(18),        mo = \"Strep pneu\",  # `mo` will be coerced with as.mo()        ab = \"ampicillin\",  # and `ab` with as.ab()        guideline = \"EUCAST\") #> => Interpreting disk diffusion zones of 'ampicillin' according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] R  # interpret whole data set, pretend to be all from urinary tract infections: as.rsi(df, uti = TRUE) #> ℹ Using column 'microorganism' as input for `col_mo`. #> => Interpreting disk diffusion zones of column 'AMP' (AMP, ampicillin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'CIP' (CIP, ciprofloxacin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (GEN, gentamicin) #>    according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'TOB' (TOB, tobramycin) #>    according to EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB #> 1 Escherichia coli   S   R   S   S"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"transforms vectors new class mic, treats input decimal numbers, maintaining operators (\">=\") allowing valid MIC values known field (medical) microbiology.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"as.mic(x, na.rm = FALSE)  NA_mic_  is.mic(x)  # S3 method for mic droplevels(x, as.mic = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"x character numeric vector na.rm logical indicating whether missing values removed .mic logical indicate whether <mic> class kept, defaults FALSE ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"Ordered factor additional class mic, mathematical operations acts decimal numbers. Bare mind outcome mathematical operation MICs return numeric value.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"interpret MIC values RSI values, use .rsi() MIC values. supports guidelines EUCAST (2011-2022) CLSI (2011-2022). class MIC values quite special data type: formally ordered factor valid MIC values factor levels (make sure valid MIC values retained), mathematical operation acts decimal numbers:   makes possible maintain operators often come MIC values, \">=\" \"<=\", even filtering using numeric values data analysis, e.g.:   following generic functions implemented MIC class: !, !=, %%, %/%, &, *, +, -, /, <, <=, ==, >, >=, ^, |, abs(), acos(), acosh(), (), (), asin(), asinh(), atan(), atanh(), ceiling(), cos(), cosh(), cospi(), cummax(), cummin(), cumprod(), cumsum(), digamma(), exp(), expm1(), floor(), gamma(), lgamma(), log(), log1p(), log2(), log10(), max(), mean(), min(), prod(), range(), round(), sign(), signif(), sin(), sinh(), sinpi(), sqrt(), sum(), tan(), tanh(), tanpi(), trigamma() trunc(). functions stats package also implemented: median(), quantile(), mad(), IQR(), fivenum(). Also, boxplot.stats() supported. Since sd() var() non-generic functions, extended. Use mad() alternative, use e.g. sd(.numeric(x)) x vector MIC values. Using .double() .numeric() MIC values remove operators return numeric vector. use .integer() MIC values R convention factors, return index factor levels (often useless regular users). Use droplevels() drop unused levels. default, return plain factor. Use droplevels(..., .mic = TRUE) maintain <mic> class. NA_mic_ missing value new <mic> class, analogous e.g. base R's NA_character_.","code":"x <- random_mic(10) x #> Class <mic> #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16  is.factor(x) #> [1] TRUE  x[1] * 2 #> [1] 32  median(x) #> [1] 26 x[x > 4] #> Class <mic> #> [1] 16    8     8     64    >=128 32    32    16  df <- data.frame(x, hospital = \"A\") subset(df, x > 4) # or with dplyr: df %>% filter(x > 4) #>        x hospital #> 1     16        A #> 5     64        A #> 6  >=128        A #> 8     32        A #> 9     32        A #> 10    16        A"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mic.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to Minimum Inhibitory Concentrations (MIC) — as.mic","text":"","code":"mic_data <- as.mic(c(\">=32\", \"1.0\", \"1\", \"1.00\", 8, \"<=0.128\", \"8\", \"16\", \"16\")) mic_data #> Class <mic> #> [1] >=32    1       1       1       8       <=0.128 8       16      16      is.mic(mic_data) #> [1] TRUE  # this can also coerce combined MIC/RSI values: as.mic(\"<=0.002; S\") #> Class <mic> #> [1] <=0.002  # mathematical processing treats MICs as numeric values fivenum(mic_data) #> [1]  0.128  1.000  8.000 16.000 32.000 quantile(mic_data) #>     0%    25%    50%    75%   100%  #>  0.128  1.000  8.000 16.000 32.000  all(mic_data < 512) #> [1] TRUE  # interpret MIC values as.rsi(x = as.mic(2),        mo = as.mo(\"Streptococcus pneumoniae\"),        ab = \"AMX\",        guideline = \"EUCAST\") #> => Interpreting MIC values of 'AMX' (AMX, amoxicillin) according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] R as.rsi(x = as.mic(c(0.01, 2, 4, 8)),        mo = as.mo(\"Streptococcus pneumoniae\"),        ab = \"AMX\",        guideline = \"EUCAST\") #> => Interpreting MIC values of 'AMX' (AMX, amoxicillin) according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] S R R R  # plot MIC values, see ?plot plot(mic_data)  plot(mic_data, mo = \"E. coli\", ab = \"cipro\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this.  autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\") #> Error in autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\"): could not find function \"autoplot\" autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\") # Dutch #> Error in autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"nl\"): could not find function \"autoplot\" autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\") # Ukrainian #> Error in autoplot(mic_data, mo = \"E. coli\", ab = \"cipro\", language = \"uk\"): could not find function \"autoplot\""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform Input to a Microorganism Code — as.mo","title":"Transform Input to a Microorganism Code — as.mo","text":"Use function determine valid microorganism code (mo). Determination done using intelligent rules complete taxonomic kingdoms Bacteria, Chromista, Protozoa, Archaea microbial species kingdom Fungi (see Source). input can almost anything: full name (like \"Staphylococcus aureus\"), abbreviated name (\"S. aureus\"), abbreviation known field (\"MRSA\"), just genus. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"as.mo(   x,   Becker = FALSE,   Lancefield = FALSE,   allow_uncertain = TRUE,   reference_df = get_mo_source(),   ignore_pattern = getOption(\"AMR_ignore_pattern\"),   language = get_AMR_locale(),   info = interactive(),   ... )  is.mo(x)  mo_failures()  mo_uncertainties()  mo_renamed()"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform Input to a Microorganism Code — as.mo","text":"x character vector data.frame one two columns Becker logical indicate whether staphylococci categorised coagulase-negative staphylococci (\"CoNS\") coagulase-positive staphylococci (\"CoPS\") instead species, according Karsten Becker et al. (1,2,3). excludes Staphylococcus aureus default, use Becker = \"\" also categorise S. aureus \"CoPS\". Lancefield logical indicate whether beta-haemolytic Streptococcus categorised Lancefield groups instead species, according Rebecca C. Lancefield (4). streptococci categorised first group, e.g. Streptococcus dysgalactiae group C, although officially also categorised groups G L. excludes enterococci default (group D), use Lancefield = \"\" also categorise enterococci group D. allow_uncertain number 0 (\"none\") 3 (\"\"), TRUE (= 2) FALSE (= 0) indicate whether input checked less probable results, see Details reference_df data.frame used extra reference translating x valid mo. See set_mo_source() get_mo_source() automate usage codes (e.g. used analysis organisation). ignore_pattern regular expression (case-insensitive) matches x must return NA. can convenient exclude known non-relevant input can also set option AMR_ignore_pattern, e.g. options(AMR_ignore_pattern = \"(reported|contaminated flora)\"). language language translate text like \"growth\", defaults system language (see get_AMR_locale()) info logical indicate progress bar printed 25 items coerced, defaults TRUE interactive mode ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform Input to a Microorganism Code — as.mo","text":"character  vector additional class mo","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"general-info","dir":"Reference","previous_headings":"","what":"General Info","title":"Transform Input to a Microorganism Code — as.mo","text":"microorganism (MO) code package (class: mo) human readable typically looks like examples:   Values coerced considered 'unknown' get MO code UNKNOWN. Use mo_* functions get properties based returned code, see Examples. algorithm uses data Catalogue Life (see ) one source (see microorganisms). .mo() function uses several coercion rules fast logical results. assesses input matching criteria following order: Human pathogenic prevalence: function  starts prevalent microorganisms, followed less prevalent ones; Taxonomic kingdom: function starts determining Bacteria, Fungi, Protozoa, others; Breakdown input values identify possible matches. lead effect e.g. \"E. coli\" (microorganism highly prevalent humans) return microbial ID Escherichia coli Entamoeba coli (microorganism less prevalent humans), although latter alphabetically come first.","code":"Code               Full name   ---------------    --------------------------------------   B_KLBSL            Klebsiella   B_KLBSL_PNMN       Klebsiella pneumoniae   B_KLBSL_PNMN_RHNS  Klebsiella pneumoniae rhinoscleromatis   |   |    |    |   |   |    |    |   |   |    |    \\---> subspecies, a 4-5 letter acronym   |   |    \\----> species, a 4-5 letter acronym   |   \\----> genus, a 5-7 letter acronym   \\----> taxonomic kingdom: A (Archaea), AN (Animalia), B (Bacteria),                             C (Chromista), F (Fungi), P (Protozoa)"},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"coping-with-uncertain-results","dir":"Reference","previous_headings":"","what":"Coping with Uncertain Results","title":"Transform Input to a Microorganism Code — as.mo","text":"addition, .mo() function can differentiate four levels uncertainty guess valid results: Uncertainty level 0: additional rules applied; Uncertainty level 1: allow previously accepted (now invalid) taxonomic names minor spelling errors; Uncertainty level 2: allow level 1, strip values brackets, inverse words input, strip text elements end keeping least two elements; Uncertainty level 3: allow level 1 2, strip text elements end, allow part taxonomic name. level uncertainty can set using argument allow_uncertain. default allow_uncertain = TRUE, equal uncertainty level 2. Using allow_uncertain = FALSE equal uncertainty level 0 skip rules. can also use e.g. .mo(..., allow_uncertain = 1) allow level 1 uncertainty. default setting (allow_uncertain = TRUE, level 2), examples lead valid results: \"Streptococcus group B (known S. agalactiae)\". text brackets removed warning thrown result Streptococcus group B (B_STRPT_GRPB) needs review. \"S. aureus - please mind: MRSA\". last word stripped, function try find match. , second last word stripped, etc. , warning thrown result Staphylococcus aureus (B_STPHY_AURS) needs review. \"Fluoroquinolone-resistant Neisseria gonorrhoeae\". first word stripped, function try find match. warning thrown result Neisseria gonorrhoeae (B_NESSR_GNRR) needs review. three helper functions can run using .mo() function: Use mo_uncertainties() get data.frame prints pretty format taxonomic names guessed. output contains matching score matches (see Matching Score Microorganisms ). Use mo_failures() get character vector values coerced valid value. Use mo_renamed() get data.frame values coerced based old, previously accepted taxonomic names.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"microbial-prevalence-of-pathogens-in-humans","dir":"Reference","previous_headings":"","what":"Microbial Prevalence of Pathogens in Humans","title":"Transform Input to a Microorganism Code — as.mo","text":"intelligent rules consider prevalence microorganisms humans grouped three groups, available prevalence columns microorganisms microorganisms.old data sets. grouping human pathogenic prevalence explained section Matching Score Microorganisms .","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Transform Input to a Microorganism Code — as.mo","text":"Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). 2019. Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. Emergence coagulase-negative staphylococci 2020. Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC serological differentiation human groups hemolytic streptococci. 1933. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Catalogue Life: 2019 Annual Checklist, http://www.catalogueoflife.org List Prokaryotic names Standing Nomenclature (5 October 2021), doi:10.1099/ijsem.0.004332 US Edition SNOMED CT 1 September 2020, retrieved Public Health Information Network Vocabulary Access Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Transform Input to a Microorganism Code — as.mo","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function, counts insertion, deletion substitution 1 needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based experience several microbiological laboratories Netherlands conjunction international reports pathogen prevalence. Group 1 (prevalent microorganisms) consists microorganisms taxonomic class Gammaproteobacteria taxonomic genus Enterococcus, Staphylococcus Streptococcus. group consequently contains common Gram-negative bacteria, Pseudomonas Legionella species within order Enterobacterales. Group 2 consists microorganisms taxonomic phylum Proteobacteria, Firmicutes, Actinobacteria Sarcomastigophora, taxonomic genus Absidia, Acremonium, Actinotignum, Alternaria, Anaerosalibacter, Apophysomyces, Arachnia, Aspergillus, Aureobacterium, Aureobasidium, Bacteroides, Basidiobolus, Beauveria, Blastocystis, Branhamella, Calymmatobacterium, Candida, Capnocytophaga, Catabacter, Chaetomium, Chryseobacterium, Chryseomonas, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Cryptococcus, Curvularia, Exophiala, Exserohilum, Flavobacterium, Fonsecaea, Fusarium, Fusobacterium, Hendersonula, Hypomyces, Koserella, Lelliottia, Leptosphaeria, Leptotrichia, Malassezia, Malbranchea, Mortierella, Mucor, Mycocentrospora, Mycoplasma, Nectria, Ochroconis, Oidiodendron, Phoma, Piedraia, Pithomyces, Pityrosporum, Prevotella, Pseudallescheria, Rhizomucor, Rhizopus, Rhodotorula, Scolecobasidium, Scopulariopsis, Scytalidium, Sporobolomyces, Stachybotrys, Stomatococcus, Treponema, Trichoderma, Trichophyton, Trichosporon, Tritirachium Ureaplasma. Group 3 consists microorganisms. characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.079\\), less prevalent microorganism humans), although latter alphabetically come first. Since AMR version 1.8.1, common microorganism abbreviations ignored determining matching score. abbreviations currently: AIEC, ATEC, BORSA, CRSM, DAEC, EAEC, EHEC, EIEC, EPEC, ETEC, GISA, MRPA, MRSA, MRSE, MSSA, MSSE, NMEC, PISP, PRSP, STEC, UPEC, VISA, VISP, VRE, VRSA VRSP.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Transform Input to a Microorganism Code — as.mo","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Transform Input to a Microorganism Code — as.mo","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.mo.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform Input to a Microorganism Code — as.mo","text":"","code":"# \\donttest{ # These examples all return \"B_STPHY_AURS\", the ID of S. aureus: as.mo(\"sau\") # WHONET code #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"stau\") #> ℹ Function `as.mo()` is uncertain about \"stau\" (assuming Staphylococcus #>   aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"STAU\") #> ℹ Function `as.mo()` is uncertain about \"STAU\" (assuming Staphylococcus #>   aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"staaur\") #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"S. aureus\") #> ℹ Function `as.mo()` is uncertain about \"S. aureus\" (assuming #>   Staphylococcus aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"S aureus\") #> ℹ Function `as.mo()` is uncertain about \"S aureus\" (assuming Staphylococcus #>   aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"Staphylococcus aureus\") #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"Staphylococcus aureus (MRSA)\") #> ℹ Function `as.mo()` is uncertain about \"Staphylococcus aureus (MRSA)\" #>   (assuming Staphylococcus aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"Zthafilokkoockus oureuz\") # handles incorrect spelling #> ℹ Function `as.mo()` is uncertain about \"Zthafilokkoockus oureuz\" (assuming #>   Staphylococcus aureus). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"MRSA\")    # Methicillin Resistant S. aureus #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"VISA\")    # Vancomycin Intermediate S. aureus #> Class <mo> #> [1] B_STPHY_AURS as.mo(\"VRSA\")    # Vancomycin Resistant S. aureus #> Class <mo> #> [1] B_STPHY_AURS as.mo(115329001) # SNOMED CT code #> Class <mo> #> [1] B_STPHY_AURS  # Dyslexia is no problem - these all work: as.mo(\"Ureaplasma urealyticum\") #> Class <mo> #> [1] B_URPLS_URLY as.mo(\"Ureaplasma urealyticus\") #> ℹ Function `as.mo()` is uncertain about \"Ureaplasma urealyticus\" (assuming #>   Ureaplasma urealyticum). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_URPLS_URLY as.mo(\"Ureaplasmium urealytica\") #> ℹ Function `as.mo()` is uncertain about \"Ureaplasmium urealytica\" (assuming #>   Ureaplasma urealyticum). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_URPLS_URLY as.mo(\"Ureaplazma urealitycium\") #> ℹ Function `as.mo()` is uncertain about \"Ureaplazma urealitycium\" (assuming #>   Ureaplasma urealyticum). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_URPLS_URLY  as.mo(\"Streptococcus group A\") #> Class <mo> #> [1] B_STRPT_GRPA as.mo(\"GAS\") # Group A Streptococci #> Class <mo> #> [1] B_STRPT_GRPA as.mo(\"GBS\") # Group B Streptococci #> Class <mo> #> [1] B_STRPT_GRPB  as.mo(\"S. epidermidis\")                 # will remain species: B_STPHY_EPDR #> Class <mo> #> [1] B_STPHY_EPDR as.mo(\"S. epidermidis\", Becker = TRUE)  # will not remain species: B_STPHY_CONS #> Class <mo> #> [1] B_STPHY_CONS  as.mo(\"S. pyogenes\")                    # will remain species: B_STRPT_PYGN #> Class <mo> #> [1] B_STRPT_PYGN as.mo(\"S. pyogenes\", Lancefield = TRUE) # will not remain species: B_STRPT_GRPA #> Class <mo> #> [1] B_STRPT_GRPA  # All mo_* functions use as.mo() internally too (see ?mo_property): mo_genus(\"E. coli\")                           # returns \"Escherichia\" #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Escherichia\" mo_gramstain(\"E. coli\")                       # returns \"Gram negative\" #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Gram-negative\" mo_is_intrinsic_resistant(\"E. coli\", \"vanco\") # returns TRUE #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> ℹ Determining intrinsic resistance based on 'EUCAST Expert Rules' and #>   'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). This note #>   will be shown once per session. #> [1] TRUE # }"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":null,"dir":"Reference","previous_headings":"","what":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"Interpret minimum inhibitory concentration (MIC) values disk diffusion diameters according EUCAST CLSI, clean existing R/SI values. transforms input new class rsi, ordered factor levels S < < R.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"","code":"as.rsi(x, ...)  NA_rsi_  is.rsi(x)  is.rsi.eligible(x, threshold = 0.05)  # S3 method for mic as.rsi(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = \"EUCAST\",   uti = FALSE,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::rsi_translation,   ... )  # S3 method for disk as.rsi(   x,   mo = NULL,   ab = deparse(substitute(x)),   guideline = \"EUCAST\",   uti = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::rsi_translation,   ... )  # S3 method for data.frame as.rsi(   x,   ...,   col_mo = NULL,   guideline = \"EUCAST\",   uti = NULL,   conserve_capped_values = FALSE,   add_intrinsic_resistance = FALSE,   reference_data = AMR::rsi_translation )"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"object class rsi (inherits ordered, factor) length 1.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"x vector values (class mic: MIC values mg/L, class disk: disk diffusion radius millimetres) ... using data.frame: names columns apply .rsi() (supports tidy selection column1:column4). Otherwise: arguments passed methods. threshold maximum fraction invalid antimicrobial interpretations x, see Examples mo (vector ) text can coerced valid microorganism codes .mo(), can left empty determine automatically ab (vector ) text can coerced valid antimicrobial code .ab() guideline defaults EUCAST 2022 (latest implemented EUCAST guideline rsi_translation data set), supports EUCAST (2011-2022) CLSI (2011-2022), see Details uti (Urinary Tract Infection) vector logicals (TRUE FALSE) specify whether UTI specific interpretation guideline chosen. using .rsi() data.frame, can also column containing logicals left blank, data set searched column 'specimen', rows within column containing 'urin' ('urine', 'urina') regarded isolates UTI. See Examples. conserve_capped_values logical indicate MIC values starting \">\" (\">=\") must always return \"R\" , MIC values starting \"<\" (\"<=\") must always return \"S\" add_intrinsic_resistance (useful using EUCAST guideline) logical indicate whether intrinsic antibiotic resistance must also considered applicable bug-drug combinations, meaning e.g. ampicillin always return \"R\" Klebsiella species. Determination based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). reference_data data.frame used interpretation, defaults rsi_translation data set. Changing argument allows using interpretation guidelines. argument must contain data set equal structure rsi_translation data set (column names column types). Please note guideline argument ignored reference_data manually set. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"Ordered factor new class <rsi>","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"NA_rsi_ missing value new <rsi> class, analogous e.g. base R's NA_character_.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"how-it-works","dir":"Reference","previous_headings":"","what":"How it Works","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":".rsi() function works four ways: cleaning raw / untransformed data. data cleaned contain values S, R try best determine intelligence. example, mixed values R/SI interpretations MIC values \"<0.25; S\" coerced \"S\". Combined interpretations multiple test methods (seen laboratory records) \"S; S\" coerced \"S\", value like \"S; \" return NA warning input unclear. interpreting minimum inhibitory concentration (MIC) values according EUCAST CLSI. must clean MIC values first using .mic(), also gives columns new data class mic. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, R/SI interpretation can done easily either:   Operators like \"<=\" stripped interpretation. using conserve_capped_values = TRUE, MIC value e.g. \">2\" always return \"R\", even breakpoint according chosen guideline \">=4\". prevent capped values raw laboratory data treated conservatively. default behaviour (conserve_capped_values = FALSE) considers \">2\" lower \">=4\" might case return \"S\" \"\". interpreting disk diffusion diameters according EUCAST CLSI. must clean disk zones first using .disk(), also gives columns new data class disk. Also, sure column microorganism names codes. found automatically, can set manually using mo argument. Using dplyr, R/SI interpretation can done easily either:   interpreting complete data set, automatic determination MIC values, disk diffusion diameters, microorganism names codes, antimicrobial test results. done simply running .rsi(your_data).","code":"your_data %>% mutate_if(is.mic, as.rsi)             # until dplyr 1.0.0 your_data %>% mutate(across(where(is.mic), as.rsi)) # since dplyr 1.0.0 your_data %>% mutate_if(is.disk, as.rsi)             # until dplyr 1.0.0 your_data %>% mutate(across(where(is.disk), as.rsi)) # since dplyr 1.0.0"},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"supported-guidelines","dir":"Reference","previous_headings":"","what":"Supported Guidelines","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"interpreting MIC values well disk diffusion diameters, currently implemented guidelines EUCAST (2011-2022) CLSI (2011-2022). Thus, guideline argument must set e.g., \"EUCAST 2022\" \"CLSI 2022\". simply using \"EUCAST\" (default) \"CLSI\" input, latest included version guideline automatically selected. can set data set using reference_data argument. guideline argument ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"after-interpretation","dir":"Reference","previous_headings":"","what":"After Interpretation","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"using .rsi(), can use eucast_rules() defined EUCAST (1) apply inferred susceptibility resistance based results antimicrobials (2) apply intrinsic resistance based taxonomic properties microorganism.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"machine-readable-interpretation-guidelines","dir":"Reference","previous_headings":"","what":"Machine-Readable Interpretation Guidelines","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"repository package contains machine-readable version guidelines. CSV file consisting 20,369 rows 11 columns. file machine-readable, since contains one row every unique combination test method (MIC disk diffusion), antimicrobial agent microorganism. allows easy implementation rules laboratory information systems (LIS). Note contains interpretation guidelines humans - interpretation guidelines CLSI animals removed.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"other","dir":"Reference","previous_headings":"","what":"Other","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"function .rsi() detects input contains class <rsi>. input data.frame, iterates columns returns logical vector. function .rsi.eligible() returns TRUE columns contains 5% invalid antimicrobial interpretations (S //R), FALSE otherwise. threshold 5% can set threshold argument. input data.frame, iterates columns returns logical vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/as.rsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Interpret MIC and Disk Values, or Clean Raw R/SI Data — as.rsi","text":"","code":"example_isolates #> # A tibble: 2,000 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  4 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  5 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  6 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  7 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  8 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  9 2002-01-16 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 10 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #> # … with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names summary(example_isolates) # see all R/SI results at a glance #>       date            hospital_id  ward_icu       ward_clinical   #>  Min.   :2002-01-02   A:321       Mode :logical   Mode :logical   #>  1st Qu.:2005-07-31   B:663       FALSE:1354      FALSE:536       #>  Median :2009-07-31   C:254       TRUE :646       TRUE :1464      #>  Mean   :2009-11-20   D:762                                       #>  3rd Qu.:2014-05-30                                               #>  Max.   :2017-12-28                                               #>  ward_outpatient      age           gender           patient_id        #>  Mode :logical   Min.   : 0.00   Length:2000        Length:2000        #>  FALSE:1880      1st Qu.:63.00   Class :character   Class :character   #>  TRUE :120       Median :74.00   Mode  :character   Mode  :character   #>                  Mean   :70.69                                         #>                  3rd Qu.:82.00                                         #>                  Max.   :97.00                                         #>       mo                  PEN                    OXA                #>  Class :mo             Class:rsi              Class:rsi             #>  <NA>  :0              %R   :73.7% (n=1201)   %R   :31.2% (n=114)   #>  Unique:90             %SI  :26.3% (n=428)    %SI  :68.8% (n=251)   #>  #1    :B_ESCHR_COLI   - %S :25.6% (n=417)    - %S :68.8% (n=251)   #>  #2    :B_STPHY_CONS   - %I : 0.7% (n=11)     - %I : 0.0% (n=0)     #>  #3    :B_STPHY_AURS                                                #>     FLC                   AMX                   AMC                 #>  Class:rsi             Class:rsi             Class:rsi              #>  %R   :29.5% (n=278)   %R   :59.6% (n=804)   %R   :23.7% (n=446)    #>  %SI  :70.5% (n=665)   %SI  :40.4% (n=546)   %SI  :76.3% (n=1433)   #>  - %S :70.5% (n=665)   - %S :40.2% (n=543)   - %S :71.4% (n=1342)   #>  - %I : 0.0% (n=0)     - %I : 0.2% (n=3)     - %I : 4.8% (n=91)     #>                                                                     #>     AMP                   TZP                   CZO                #>  Class:rsi             Class:rsi             Class:rsi             #>  %R   :59.6% (n=804)   %R   :12.6% (n=126)   %R   :44.6% (n=199)   #>  %SI  :40.4% (n=546)   %SI  :87.4% (n=875)   %SI  :55.4% (n=247)   #>  - %S :40.2% (n=543)   - %S :86.1% (n=862)   - %S :54.9% (n=245)   #>  - %I : 0.2% (n=3)     - %I : 1.3% (n=13)    - %I : 0.4% (n=2)     #>                                                                    #>     FEP                   CXM                    FOX                #>  Class:rsi             Class:rsi              Class:rsi             #>  %R   :14.2% (n=103)   %R   :26.3% (n=470)    %R   :27.4% (n=224)   #>  %SI  :85.8% (n=621)   %SI  :73.7% (n=1319)   %SI  :72.6% (n=594)   #>  - %S :85.6% (n=620)   - %S :72.5% (n=1297)   - %S :71.6% (n=586)   #>  - %I : 0.1% (n=1)     - %I : 1.2% (n=22)     - %I : 1.0% (n=8)     #>                                                                     #>     CTX                   CAZ                    CRO                #>  Class:rsi             Class:rsi              Class:rsi             #>  %R   :15.5% (n=146)   %R   :66.5% (n=1204)   %R   :15.5% (n=146)   #>  %SI  :84.5% (n=797)   %SI  :33.5% (n=607)    %SI  :84.5% (n=797)   #>  - %S :84.4% (n=796)   - %S :33.5% (n=607)    - %S :84.4% (n=796)   #>  - %I : 0.1% (n=1)     - %I : 0.0% (n=0)      - %I : 0.1% (n=1)     #>                                                                     #>     GEN                    TOB                   AMK                #>  Class:rsi              Class:rsi             Class:rsi             #>  %R   :24.6% (n=456)    %R   :34.4% (n=465)   %R   :63.7% (n=441)   #>  %SI  :75.4% (n=1399)   %SI  :65.6% (n=886)   %SI  :36.3% (n=251)   #>  - %S :74.0% (n=1372)   - %S :65.1% (n=879)   - %S :36.3% (n=251)   #>  - %I : 1.5% (n=27)     - %I : 0.5% (n=7)     - %I : 0.0% (n=0)     #>                                                                     #>     KAN                    TMP                   SXT                 #>  Class:rsi              Class:rsi             Class:rsi              #>  %R   :100.0% (n=471)   %R   :38.1% (n=571)   %R   :20.5% (n=361)    #>  %SI  : 0.0% (n=0)      %SI  :61.9% (n=928)   %SI  :79.5% (n=1398)   #>  - %S : 0.0% (n=0)      - %S :61.2% (n=918)   - %S :79.1% (n=1392)   #>  - %I : 0.0% (n=0)      - %I : 0.7% (n=10)    - %I : 0.3% (n=6)      #>                                                                      #>     NIT                   FOS                   LNZ                #>  Class:rsi             Class:rsi             Class:rsi             #>  %R   :17.1% (n=127)   %R   :42.2% (n=148)   %R   :69.3% (n=709)   #>  %SI  :82.9% (n=616)   %SI  :57.8% (n=203)   %SI  :30.7% (n=314)   #>  - %S :76.0% (n=565)   - %S :57.8% (n=203)   - %S :30.7% (n=314)   #>  - %I : 6.9% (n=51)    - %I : 0.0% (n=0)     - %I : 0.0% (n=0)     #>                                                                    #>     CIP                    MFX                   VAN                 #>  Class:rsi              Class:rsi             Class:rsi              #>  %R   :16.2% (n=228)    %R   :33.6% (n=71)    %R   :38.3% (n=712)    #>  %SI  :83.8% (n=1181)   %SI  :66.4% (n=140)   %SI  :61.7% (n=1149)   #>  - %S :78.9% (n=1112)   - %S :64.5% (n=136)   - %S :61.7% (n=1149)   #>  - %I : 4.9% (n=69)     - %I : 1.9% (n=4)     - %I : 0.0% (n=0)      #>                                                                      #>     TEC                   TCY                   TGC                #>  Class:rsi             Class:rsi             Class:rsi             #>  %R   :75.7% (n=739)   %R   :29.8% (n=357)   %R   :12.7% (n=101)   #>  %SI  :24.3% (n=237)   %SI  :70.3% (n=843)   %SI  :87.3% (n=697)   #>  - %S :24.3% (n=237)   - %S :68.3% (n=820)   - %S :87.3% (n=697)   #>  - %I : 0.0% (n=0)     - %I : 1.9% (n=23)    - %I : 0.0% (n=0)     #>                                                                    #>     DOX                   ERY                    CLI                #>  Class:rsi             Class:rsi              Class:rsi             #>  %R   :27.7% (n=315)   %R   :57.2% (n=1084)   %R   :61.2% (n=930)   #>  %SI  :72.3% (n=821)   %SI  :42.8% (n=810)    %SI  :38.8% (n=590)   #>  - %S :71.7% (n=814)   - %S :42.3% (n=801)    - %S :38.6% (n=586)   #>  - %I : 0.6% (n=7)     - %I : 0.5% (n=9)      - %I : 0.3% (n=4)     #>                                                                     #>     AZM                    IPM                   MEM                #>  Class:rsi              Class:rsi             Class:rsi             #>  %R   :57.2% (n=1084)   %R   : 6.2% (n=55)    %R   : 5.9% (n=49)    #>  %SI  :42.8% (n=810)    %SI  :93.8% (n=834)   %SI  :94.1% (n=780)   #>  - %S :42.3% (n=801)    - %S :92.7% (n=824)   - %S :94.1% (n=780)   #>  - %I : 0.5% (n=9)      - %I : 1.1% (n=10)    - %I : 0.0% (n=0)     #>                                                                     #>     MTR                  CHL                   COL                 #>  Class:rsi            Class:rsi             Class:rsi              #>  %R   :14.7% (n=5)    %R   :21.4% (n=33)    %R   :81.2% (n=1331)   #>  %SI  :85.3% (n=29)   %SI  :78.6% (n=121)   %SI  :18.8% (n=309)    #>  - %S :85.3% (n=29)   - %S :78.6% (n=121)   - %S :18.8% (n=309)    #>  - %I : 0.0% (n=0)    - %I : 0.0% (n=0)     - %I : 0.0% (n=0)      #>                                                                    #>     MUP                   RIF                #>  Class:rsi             Class:rsi             #>  %R   : 5.9% (n=16)    %R   :69.6% (n=698)   #>  %SI  :94.1% (n=254)   %SI  :30.4% (n=305)   #>  - %S :93.0% (n=251)   - %S :30.2% (n=303)   #>  - %I : 1.1% (n=3)     - %I : 0.2% (n=2)     #>                                               # For INTERPRETING disk diffusion and MIC values -----------------------         # a whole data set, even with combined MIC values and disk zones df <- data.frame(microorganism = \"Escherichia coli\",                  AMP = as.mic(8),                  CIP = as.mic(0.256),                  GEN = as.disk(18),                  TOB = as.disk(16),                  NIT = as.mic(32),                  ERY = \"R\") as.rsi(df) #> ℹ Using column 'microorganism' as input for `col_mo`. #> => Interpreting MIC values of column 'AMP' (AMP, ampicillin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting MIC values of column 'CIP' (CIP, ciprofloxacin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of column 'GEN' (GEN, gentamicin) #>    according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of column 'TOB' (TOB, tobramycin) #>    according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of column 'NIT' (NIT, nitrofurantoin) according #>    to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of nitrofurantoin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Assigning class <rsi> to already clean column 'ERY' (erythromycin)... #>  OK. #>      microorganism AMP CIP GEN TOB  NIT ERY #> 1 Escherichia coli   S   I   S   S <NA>   R  # for single values as.rsi(x = as.mic(2),        mo = as.mo(\"S. pneumoniae\"),        ab = \"AMP\",        guideline = \"EUCAST\") #> ℹ Function `as.mo()` is uncertain about \"S. pneumoniae\" (assuming #>   Streptococcus pneumoniae). Run `mo_uncertainties()` to review this. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] R  as.rsi(x = as.disk(18),        mo = \"Strep pneu\",  # `mo` will be coerced with as.mo()        ab = \"ampicillin\",  # and `ab` with as.ab()        guideline = \"EUCAST\") #> => Interpreting disk diffusion zones of 'ampicillin' according to EUCAST #>    2022... #>  OK. #> Class <rsi> #> [1] R  # \\donttest{ # the dplyr way if (require(\"dplyr\")) {   df %>% mutate_if(is.mic, as.rsi)   df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.rsi)   df %>% mutate(across(where(is.mic), as.rsi))   df %>% mutate_at(vars(AMP:TOB), as.rsi)   df %>% mutate(across(AMP:TOB, as.rsi))     df %>%     mutate_at(vars(AMP:TOB), as.rsi, mo = .$microorganism)        # to include information about urinary tract infections (UTI)   data.frame(mo = \"E. coli\",              NIT = c(\"<= 2\", 32),              from_the_bladder = c(TRUE, FALSE)) %>%     as.rsi(uti = \"from_the_bladder\")        data.frame(mo = \"E. coli\",              NIT = c(\"<= 2\", 32),              specimen = c(\"urine\", \"blood\")) %>%     as.rsi() # automatically determines urine isolates      df %>%     mutate_at(vars(AMP:NIT), as.rsi, mo = \"E. coli\", uti = TRUE)   } #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'NIT' (NIT, nitrofurantoin) based on column #>    'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of nitrofurantoin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'NIT' (NIT, nitrofurantoin) based on column #>    'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of nitrofurantoin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'NIT' (NIT, nitrofurantoin) based on column #>    'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of nitrofurantoin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) based on column #>    'microorganism' according to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) based on #>    column 'microorganism' according to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) according #>    to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of gentamicin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) according #>    to EUCAST 2022... #> Warning: in `as.rsi()`: interpretation of tobramycin is only available for #> (uncomplicated) urinary tract infections (UTI) for some microorganisms. Use #> argument `uti` to set which isolates are from urine. See ?as.rsi. #> WARNING. #> ℹ Using column 'mo' as input for `col_mo`. #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> => Interpreting MIC values of column 'NIT' (NIT, nitrofurantoin) according #>    to EUCAST 2022... #>  OK. #> ℹ Assuming value \"urine\" in column 'specimen' reflects a urinary tract #>   infection. #>   Use `as.rsi(uti = FALSE)` to prevent this. #> ℹ Using column 'mo' as input for `col_mo`. #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> => Interpreting MIC values of column 'NIT' (NIT, nitrofurantoin) according #>    to EUCAST 2022... #>  OK. #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> => Interpreting MIC values of 'AMP' (AMP, ampicillin) according to EUCAST #>    2022... #>  OK. #> => Interpreting MIC values of 'CIP' (CIP, ciprofloxacin) according to #>    EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'GEN' (GEN, gentamicin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting disk diffusion zones of 'TOB' (TOB, tobramycin) according #>    to EUCAST 2022... #>  OK. #> => Interpreting MIC values of 'NIT' (NIT, nitrofurantoin) according to #>    EUCAST 2022... #>  OK. #>      microorganism AMP CIP GEN TOB NIT ERY #> 1 Escherichia coli   S   I   S   S   S   R  # For CLEANING existing R/SI values ------------------------------------  as.rsi(c(\"S\", \"I\", \"R\", \"A\", \"B\", \"C\")) #> Warning: in `as.rsi()`: 3 results truncated (50%) that were invalid antimicrobial #> interpretations: \"A\", \"B\" and \"C\" #> Class <rsi> #> [1] S    I    R    <NA> <NA> <NA> as.rsi(\"<= 0.002; S\") # will return \"S\" #> Class <rsi> #> [1] S rsi_data <- as.rsi(c(rep(\"S\", 474), rep(\"I\", 36), rep(\"R\", 370))) is.rsi(rsi_data) #> [1] TRUE plot(rsi_data)    # for percentages  barplot(rsi_data) # for frequencies   # the dplyr way if (require(\"dplyr\")) {   example_isolates %>%     mutate_at(vars(PEN:RIF), as.rsi)   # same:      example_isolates %>%     as.rsi(PEN:RIF)     # fastest way to transform all columns with already valid AMR results to class `rsi`:   example_isolates %>%     mutate_if(is.rsi.eligible, as.rsi)        # since dplyr 1.0.0, this can also be:    # example_isolates %>%   #   mutate(across(where(is.rsi.eligible), as.rsi)) } #> # A tibble: 2,000 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  4 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  5 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  6 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  7 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  8 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  9 2002-01-16 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 10 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #> # … with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # }"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":null,"dir":"Reference","previous_headings":"","what":"Get ATC Properties from WHOCC Website — atc_online_property","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Gets data WHOCC website determine properties Anatomical Therapeutic Chemical (ATC) (e.g. antibiotic), name, defined daily dose (DDD) standard unit.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"atc_online_property(   atc_code,   property,   administration = \"O\",   url = \"https://www.whocc.no/atc_ddd_index/?code=%s&showdescription=no\",   url_vet = \"https://www.whocc.no/atcvet/atcvet_index/?code=%s&showdescription=no\" )  atc_online_groups(atc_code, ...)  atc_online_ddd(atc_code, ...)  atc_online_ddd_units(atc_code, ...)"},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"https://www.whocc./atc_ddd_alterations__cumulative/ddd_alterations/abbrevations/","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"atc_code character (vector) ATC code(s) antibiotics, coerced .ab() ab_atc() internally valid ATC code property property ATC code. Valid values \"ATC\", \"Name\", \"DDD\", \"U\" (\"unit\"), \"Adm.R\", \"Note\" groups. last option, hierarchical groups ATC code returned, see Examples. administration type administration using property = \"Adm.R\", see Details url url website WHOCC. sign %s can used placeholder ATC codes. url_vet url website WHOCC veterinary medicine. sign %s can used placeholder ATC_vet codes (start \"Q\"). ... arguments pass atc_property","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"Options argument administration: \"Implant\" = Implant \"Inhal\" = Inhalation \"Instill\" = Instillation \"N\" = nasal \"O\" = oral \"P\" = parenteral \"R\" = rectal \"SL\" = sublingual/buccal \"TD\" = transdermal \"V\" = vaginal Abbreviations return values using property = \"U\" (unit): \"g\" = gram \"mg\" = milligram \"mcg\" = microgram \"U\" = unit \"TU\" = thousand units \"MU\" = million units \"mmol\" = millimole \"ml\" = millilitre (e.g. eyedrops) N.B. function requires internet connection works following packages installed: curl, rvest, xml2.","code":""},{"path":"https://msberends.github.io/AMR/reference/atc_online.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get ATC Properties from WHOCC Website — atc_online_property","text":"","code":"# \\donttest{ if (requireNamespace(\"curl\") && requireNamespace(\"rvest\") && requireNamespace(\"xml2\")) {    # oral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"O\")   atc_online_ddd(ab_atc(\"amox\"))    # parenteral DDD (Defined Daily Dose) of amoxicillin   atc_online_property(\"J01CA04\", \"DDD\", \"P\")    atc_online_property(\"J01CA04\", property = \"groups\") # search hierarchical groups of amoxicillin } #> Loading required namespace: rvest #> [1] \"ANTIINFECTIVES FOR SYSTEMIC USE\"         #> [2] \"ANTIBACTERIALS FOR SYSTEMIC USE\"         #> [3] \"BETA-LACTAM ANTIBACTERIALS, PENICILLINS\" #> [4] \"Penicillins with extended spectrum\"      # }"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":null,"dir":"Reference","previous_headings":"","what":"Check Availability of Columns — availability","title":"Check Availability of Columns — availability","text":"Easy check data availability columns data set. makes easy get idea antimicrobial combinations can used calculation e.g. susceptibility() resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check Availability of Columns — availability","text":"","code":"availability(tbl, width = NULL)"},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check Availability of Columns — availability","text":"tbl data.frame list width number characters present visual availability, defaults filling width console","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check Availability of Columns — availability","text":"data.frame column names tbl row names","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Check Availability of Columns — availability","text":"function returns data.frame columns \"resistant\" \"visual_resistance\". values columns calculated resistance().","code":""},{"path":"https://msberends.github.io/AMR/reference/availability.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Check Availability of Columns — availability","text":"","code":"availability(example_isolates) #>                 count available visual_availabilty resistant  visual_resistance #> date             2000    100.0% |################|                              #> hospital_id      2000    100.0% |################|                              #> ward_icu         2000    100.0% |################|                              #> ward_clinical    2000    100.0% |################|                              #> ward_outpatient  2000    100.0% |################|                              #> age              2000    100.0% |################|                              #> gender           2000    100.0% |################|                              #> patient_id       2000    100.0% |################|                              #> mo               2000    100.0% |################|                              #> PEN              1629     81.5% |############----|     73.7% |###########-----| #> OXA               365     18.3% |##--------------|     31.2% |#####-----------| #> FLC               943     47.2% |#######---------|     29.5% |####------------| #> AMX              1350     67.5% |##########------|     59.6% |#########-------| #> AMC              1879     94.0% |###############-|     23.7% |###-------------| #> AMP              1350     67.5% |##########------|     59.6% |#########-------| #> TZP              1001     50.0% |########--------|     12.6% |##--------------| #> CZO               446     22.3% |###-------------|     44.6% |#######---------| #> FEP               724     36.2% |#####-----------|     14.2% |##--------------| #> CXM              1789     89.5% |##############--|     26.3% |####------------| #> FOX               818     40.9% |######----------|     27.4% |####------------| #> CTX               943     47.2% |#######---------|     15.5% |##--------------| #> CAZ              1811     90.6% |##############--|     66.5% |##########------| #> CRO               943     47.2% |#######---------|     15.5% |##--------------| #> GEN              1855     92.8% |##############--|     24.6% |###-------------| #> TOB              1351     67.6% |##########------|     34.4% |#####-----------| #> AMK               692     34.6% |#####-----------|     63.7% |##########------| #> KAN               471     23.6% |###-------------|    100.0% |################| #> TMP              1499     75.0% |############----|     38.1% |######----------| #> SXT              1759     88.0% |##############--|     20.5% |###-------------| #> NIT               743     37.2% |#####-----------|     17.1% |##--------------| #> FOS               351     17.6% |##--------------|     42.2% |######----------| #> LNZ              1023     51.2% |########--------|     69.3% |###########-----| #> CIP              1409     70.5% |###########-----|     16.2% |##--------------| #> MFX               211     10.6% |#---------------|     33.6% |#####-----------| #> VAN              1861     93.1% |##############--|     38.3% |######----------| #> TEC               976     48.8% |#######---------|     75.7% |############----| #> TCY              1200     60.0% |#########-------|     29.8% |####------------| #> TGC               798     39.9% |######----------|     12.7% |##--------------| #> DOX              1136     56.8% |#########-------|     27.7% |####------------| #> ERY              1894     94.7% |###############-|     57.2% |#########-------| #> CLI              1520     76.0% |############----|     61.2% |#########-------| #> AZM              1894     94.7% |###############-|     57.2% |#########-------| #> IPM               889     44.5% |#######---------|      6.2% |----------------| #> MEM               829     41.5% |######----------|      5.9% |----------------| #> MTR                34      1.7% |----------------|     14.7% |##--------------| #> CHL               154      7.7% |#---------------|     21.4% |###-------------| #> COL              1640     82.0% |#############---|     81.2% |############----| #> MUP               270     13.5% |##--------------|      5.9% |----------------| #> RIF              1003     50.2% |########--------|     69.6% |###########-----| # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     select_if(is.rsi) %>%     availability() } #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #>     count available       visual_availabilty resistant        visual_resistance #> PEN   467    100.0% |######################|    100.0% |######################| #> OXA     0      0.0% |----------------------|                                    #> FLC     0      0.0% |----------------------|                                    #> AMX   392     83.9% |##################----|     50.0% |###########-----------| #> AMC   467    100.0% |######################|     13.1% |##--------------------| #> AMP   392     83.9% |##################----|     50.0% |###########-----------| #> TZP   416     89.1% |###################---|      5.5% |#---------------------| #> CZO    82     17.6% |###-------------------|      2.4% |----------------------| #> FEP   317     67.9% |##############--------|      2.8% |----------------------| #> CXM   465     99.6% |######################|      5.4% |#---------------------| #> FOX   377     80.7% |#################-----|      6.9% |#---------------------| #> CTX   459     98.3% |#####################-|      2.4% |----------------------| #> CAZ   460     98.5% |#####################-|      2.4% |----------------------| #> CRO   459     98.3% |#####################-|      2.4% |----------------------| #> GEN   460     98.5% |#####################-|      2.0% |----------------------| #> TOB   462     98.9% |#####################-|      2.6% |----------------------| #> AMK   171     36.6% |########--------------|      0.0% |----------------------| #> KAN     0      0.0% |----------------------|                                    #> TMP   396     84.8% |##################----|     39.1% |########--------------| #> SXT   465     99.6% |######################|     31.6% |######----------------| #> NIT   458     98.1% |#####################-|      2.8% |----------------------| #> FOS    61     13.1% |##--------------------|      0.0% |----------------------| #> LNZ   467    100.0% |######################|    100.0% |######################| #> CIP   456     97.6% |#####################-|     12.5% |##--------------------| #> MFX    57     12.2% |##--------------------|    100.0% |######################| #> VAN   467    100.0% |######################|    100.0% |######################| #> TEC   467    100.0% |######################|    100.0% |######################| #> TCY     3      0.6% |----------------------|     66.7% |##############--------| #> TGC    68     14.6% |###-------------------|      0.0% |----------------------| #> DOX     0      0.0% |----------------------|                                    #> ERY   467    100.0% |######################|    100.0% |######################| #> CLI   467    100.0% |######################|    100.0% |######################| #> AZM   467    100.0% |######################|    100.0% |######################| #> IPM   422     90.4% |###################---|      0.0% |----------------------| #> MEM   418     89.5% |###################---|      0.0% |----------------------| #> MTR     2      0.4% |----------------------|      0.0% |----------------------| #> CHL     0      0.0% |----------------------|                                    #> COL   240     51.4% |###########-----------|      0.0% |----------------------| #> MUP     0      0.0% |----------------------|                                    #> RIF   467    100.0% |######################|    100.0% |######################| # }"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Bug-Drug Combinations — bug_drug_combinations","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"Determine antimicrobial resistance (AMR) bug-drug combinations data set least 30 (default) isolates available per species. Use format() result prettify publishable/printable format, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)  # S3 method for bug_drug_combinations format(   x,   translate_ab = \"name (ab, atc)\",   language = get_AMR_locale(),   minimum = 30,   combine_SI = TRUE,   combine_IR = FALSE,   add_ab_group = TRUE,   remove_intrinsic_resistant = FALSE,   decimal.mark = getOption(\"OutDec\"),   big.mark = ifelse(decimal.mark == \",\", \".\", \",\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"x data antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). FUN function call mo column transform microorganism codes, defaults mo_shortname() ... arguments passed FUN translate_ab character length 1 containing column names antibiotics data set language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant). used argument combine_IR, now follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . Default TRUE. combine_IR logical indicate whether values R summed add_ab_group logical indicate group antimicrobials must included first column remove_intrinsic_resistant logical indicate rows columns 100% resistance tested antimicrobials must removed table decimal.mark character used indicate numeric     decimal point. big.mark character; empty used mark every     big.interval decimals (hence big)     decimal point.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function bug_drug_combinations() returns data.frame columns \"mo\", \"ab\", \"S\", \"\", \"R\" \"total\".","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"function format() calculates resistance per bug-drug combination. Use combine_IR = FALSE (default) test R vs. S+combine_IR = TRUE test R+vs. S.","code":""},{"path":"https://msberends.github.io/AMR/reference/bug_drug_combinations.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Bug-Drug Combinations — bug_drug_combinations","text":"","code":"# \\donttest{ x <- bug_drug_combinations(example_isolates) #> ℹ Using column 'mo' as input for `col_mo`. head(x) #>                  mo  ab  S I R total #> 1 (unknown species) PEN 14 0 1    15 #> 2 (unknown species) OXA  0 0 1     1 #> 3 (unknown species) FLC  0 0 0     0 #> 4 (unknown species) AMX 15 0 1    16 #> 5 (unknown species) AMC 15 0 0    15 #> 6 (unknown species) AMP 15 0 1    16 #> Use 'format()' on this result to get a publishable/printable format. format(x, translate_ab = \"name (atc)\") #>                        Group                                    Drug #> 1            Aminoglycosides                      Amikacin (J01GB06) #> 2                                               Gentamicin (J01GB03) #> 3                                                Kanamycin (J01GB04) #> 4                                               Tobramycin (J01GB01) #> 5                Amphenicols               Chloramphenicol (J01BA01) #> 6         Antimycobacterials                    Rifampicin (J04AB02) #> 7   Beta-lactams/penicillins                   Amoxicillin (J01CA04) #> 8                              Amoxicillin/clavulanic acid (J01CR02) #> 9                                               Ampicillin (J01CA01) #> 10                                        Benzylpenicillin (J01CE01) #> 11                                          Flucloxacillin (J01CF05) #> 12                                               Oxacillin (J01CF04) #> 13                                 Piperacillin/tazobactam (J01CR05) #> 14               Carbapenems                      Imipenem (J01DH51) #> 15                                               Meropenem (J01DH02) #> 16 Cephalosporins (1st gen.)                     Cefazolin (J01DB04) #> 17 Cephalosporins (2nd gen.)                     Cefoxitin (J01DC01) #> 18                                              Cefuroxime (J01DC02) #> 19 Cephalosporins (3rd gen.)                    Cefotaxime (J01DD01) #> 20                                             Ceftazidime (J01DD02) #> 21                                             Ceftriaxone (J01DD04) #> 22 Cephalosporins (4th gen.)                      Cefepime (J01DE01) #> 23             Glycopeptides                   Teicoplanin (J01XA02) #> 24                                              Vancomycin (J01XA01) #> 25   Macrolides/lincosamides                  Azithromycin (J01FA10) #> 26                                             Clindamycin (J01FF01) #> 27                                            Erythromycin (J01FA01) #> 28      Other antibacterials                    Fosfomycin (J01XX01) #> 29                                               Mupirocin (D06AX09) #> 30                                          Nitrofurantoin (J01XE01) #> 31            Oxazolidinones                     Linezolid (J01XX08) #> 32                Polymyxins                      Colistin (J01XB01) #> 33                Quinolones                 Ciprofloxacin (J01MA02) #> 34                                            Moxifloxacin (J01MA14) #> 35             Tetracyclines                   Doxycycline (J01AA02) #> 36                                            Tetracycline (J01AA07) #> 37                                             Tigecycline (J01AA12) #> 38             Trimethoprims                  Trimethoprim (J01EA01) #> 39                           Trimethoprim/sulfamethoxazole (J01EE01) #>                CoNS          E. coli    E. faecalis  K. pneumoniae #> 1    100.0% (43/43)     0.0% (0/171) 100.0% (39/39)                #> 2    13.6% (42/309)     2.0% (9/460) 100.0% (39/39)   10.3% (6/58) #> 3    100.0% (43/43)                  100.0% (39/39)                #> 4     78.2% (43/55)    2.6% (12/462) 100.0% (39/39)   10.3% (6/58) #> 5                                                                  #> 6                   100.0% (467/467)                100.0% (58/58) #> 7   93.0% (132/142)  50.0% (196/392)                100.0% (58/58) #> 8   42.6% (132/310)   13.1% (61/467)                  10.3% (6/58) #> 9   93.0% (132/142)  50.0% (196/392)                100.0% (58/58) #> 10  77.6% (228/294) 100.0% (467/467)                100.0% (58/58) #> 11  42.8% (134/313)                                                #> 12    54.8% (34/62)                                                #> 13    69.7% (23/33)    5.5% (23/416)                  11.3% (6/53) #> 14    47.9% (23/48)     0.0% (0/422)    0.0% (0/38)    0.0% (0/51) #> 15    47.9% (23/48)     0.0% (0/418)                   0.0% (0/53) #> 16    47.9% (23/48)      2.4% (2/82) 100.0% (39/39)                #> 17    47.9% (23/48)    6.9% (26/377) 100.0% (39/39)    2.2% (1/46) #> 18  42.6% (133/312)    5.4% (25/465) 100.0% (39/39)   10.3% (6/58) #> 19    47.9% (23/48)    2.4% (11/459) 100.0% (39/39)    5.2% (3/58) #> 20 100.0% (313/313)    2.4% (11/460) 100.0% (39/39)    5.2% (3/58) #> 21    47.9% (23/48)    2.4% (11/459) 100.0% (39/39)    5.2% (3/58) #> 22    47.9% (23/48)     2.8% (9/317) 100.0% (39/39)    5.3% (2/38) #> 23                  100.0% (467/467)                100.0% (58/58) #> 24     0.3% (1/304) 100.0% (467/467)    0.0% (0/39) 100.0% (58/58) #> 25  44.1% (138/313) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58) #> 26   33.5% (59/176) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58) #> 27  44.1% (138/313) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58) #> 28                       0.0% (0/61)                               #> 29                                                                 #> 30                     2.8% (13/458)                 19.0% (11/58) #> 31                  100.0% (467/467)                100.0% (58/58) #> 32 100.0% (313/313)     0.0% (0/240) 100.0% (39/39)    5.9% (2/34) #> 33   27.4% (69/252)   12.5% (57/456)                   3.6% (2/55) #> 34                    100.0% (57/57)                               #> 35   22.2% (67/302)                                                #> 36   22.5% (59/262)                                                #> 37     0.0% (0/195)      0.0% (0/68)                               #> 38  41.4% (126/304)  39.1% (155/396) 100.0% (39/39)   18.4% (9/49) #> 39   12.2% (30/246)  31.6% (147/465) 100.0% (39/39)   10.3% (6/58) #>     P. aeruginosa   P. mirabilis        S. aureus   S. epidermidis #> 1                                                   100.0% (44/44) #> 2     0.0% (0/30)    5.9% (2/34)     0.9% (2/233)   21.5% (35/163) #> 3  100.0% (30/30)                                   100.0% (44/44) #> 4     0.0% (0/30)    5.9% (2/34)      2.3% (2/86)    49.4% (44/89) #> 5  100.0% (30/30)                     0.0% (0/46)      3.1% (1/32) #> 6  100.0% (30/30) 100.0% (36/36)     0.0% (0/108)      2.7% (2/73) #> 7  100.0% (30/30)                 93.9% (123/131)    98.9% (90/91) #> 8  100.0% (30/30)    2.8% (1/36)     0.4% (1/235)   54.5% (90/165) #> 9  100.0% (30/30)                 93.9% (123/131)    98.9% (90/91) #> 10 100.0% (30/30) 100.0% (36/36)  80.9% (123/152)   89.4% (93/104) #> 11                                   0.4% (1/235)   55.7% (97/174) #> 12                                    0.0% (0/99)    50.0% (37/74) #> 13                                                                 #> 14                   6.3% (2/32)                                   #> 15                                                                 #> 16 100.0% (30/30)                                                  #> 17 100.0% (30/30)                                                  #> 18 100.0% (30/30)    0.0% (0/36)     0.4% (1/235)   56.1% (97/173) #> 19 100.0% (30/30)    0.0% (0/36)                                   #> 20    3.3% (1/30)    0.0% (0/36) 100.0% (235/235) 100.0% (174/174) #> 21 100.0% (30/30)    0.0% (0/36)                                   #> 22                                                                 #> 23 100.0% (30/30) 100.0% (36/36)      0.0% (0/80)    64.1% (25/39) #> 24 100.0% (30/30) 100.0% (36/36)     0.0% (0/232)     0.0% (0/171) #> 25 100.0% (30/30) 100.0% (36/36)    8.9% (21/235)   53.8% (93/173) #> 26 100.0% (30/30) 100.0% (36/36)     6.0% (9/151)   37.5% (42/112) #> 27 100.0% (30/30) 100.0% (36/36)    8.9% (21/235)   53.8% (93/173) #> 28                                    0.0% (0/80)    21.9% (14/64) #> 29                                    0.0% (0/84)      8.7% (6/69) #> 30                100.0% (36/36)                                   #> 31 100.0% (30/30) 100.0% (36/36)      0.0% (0/82)      0.0% (0/68) #> 32                100.0% (36/36) 100.0% (235/235) 100.0% (174/174) #> 33    0.0% (0/30)    5.6% (2/36)   10.5% (20/191)   36.0% (49/136) #> 34                                    2.2% (1/46)      0.0% (0/31) #> 35 100.0% (30/30) 100.0% (36/36)     3.0% (7/231)   32.4% (55/170) #> 36 100.0% (30/30) 100.0% (36/36)     3.2% (7/217)   32.9% (55/167) #> 37 100.0% (30/30) 100.0% (36/36)     0.0% (0/209)     0.0% (0/109) #> 38 100.0% (30/30)  36.4% (12/33)    8.4% (13/155)   58.5% (69/118) #> 39 100.0% (30/30)   25.0% (9/36)     3.9% (9/231)   17.5% (24/137) #>        S. hominis    S. pneumoniae #> 1                 100.0% (117/117) #> 2     7.5% (6/80) 100.0% (117/117) #> 3                 100.0% (117/117) #> 4    14.5% (9/62) 100.0% (117/117) #> 5     6.5% (2/31)                  #> 6     0.0% (0/61)                  #> 7                     0.0% (0/112) #> 8   35.0% (28/80)     0.0% (0/112) #> 9                     0.0% (0/112) #> 10                    0.0% (0/117) #> 11  34.6% (28/81)                  #> 12  39.7% (23/58)                  #> 13                    0.0% (0/112) #> 14                                 #> 15                                 #> 16                                 #> 17                                 #> 18  33.3% (27/81)      0.0% (0/61) #> 19                                 #> 20 100.0% (81/81) 100.0% (117/117) #> 21                                 #> 22                                 #> 23    6.8% (4/59)                  #> 24    0.0% (0/81)      0.0% (0/94) #> 25  40.7% (33/81)     7.8% (9/116) #> 26  29.4% (20/68)      6.5% (4/62) #> 27  40.7% (33/81)     7.8% (9/116) #> 28 100.0% (59/59)                  #> 29    1.6% (1/61)                  #> 30                                 #> 31    0.0% (0/60)                  #> 32 100.0% (81/81) 100.0% (117/117) #> 33  20.0% (14/70)                  #> 34    0.0% (0/32)                  #> 35  55.0% (44/80)     4.5% (5/112) #> 36  54.3% (44/81)     4.7% (5/106) #> 37    0.0% (0/36)                  #> 38  57.9% (22/38)    17.9% (17/95) #> 39  24.7% (20/81)     7.3% (8/109)  # Use FUN to change to transformation of microorganism codes bug_drug_combinations(example_isolates,                        FUN = mo_gramstain) #> ℹ Using column 'mo' as input for `col_mo`. #>               mo  ab    S  I    R total #> 1  Gram-negative PEN    8  0  717   725 #> 2  Gram-negative OXA    6  0    0     6 #> 3  Gram-negative FLC    6  0    0     6 #> 4  Gram-negative AMX  226  0  405   631 #> 5  Gram-negative AMC  463 89  174   726 #> 6  Gram-negative AMP  226  0  405   631 #> 7  Gram-negative TZP  554 11   76   641 #> 8  Gram-negative CZO   94  2  110   206 #> 9  Gram-negative FEP  470  1   14   485 #> 10 Gram-negative CXM  539 22  142   703 #> 11 Gram-negative FOX  435  8  135   578 #> 12 Gram-negative CTX  578  1   57   636 #> 13 Gram-negative CAZ  607  0   27   634 #> 14 Gram-negative CRO  578  1   57   636 #> 15 Gram-negative GEN  651  8   25   684 #> 16 Gram-negative TOB  651  7   28   686 #> 17 Gram-negative AMK  251  0    5   256 #> 18 Gram-negative KAN    0  0   35    35 #> 19 Gram-negative TMP  364  1  223   588 #> 20 Gram-negative SXT  506  0  197   703 #> 21 Gram-negative NIT  491 51  122   664 #> 22 Gram-negative FOS   71  0    7    78 #> 23 Gram-negative LNZ    0  0  707   707 #> 24 Gram-negative CIP  610 11   63   684 #> 25 Gram-negative MFX    0  0   63    63 #> 26 Gram-negative VAN    0  0  707   707 #> 27 Gram-negative TEC    0  0  707   707 #> 28 Gram-negative TCY   15  1  110   126 #> 29 Gram-negative TGC   87  0  101   188 #> 30 Gram-negative DOX   10  0  108   118 #> 31 Gram-negative ERY    1  2  696   699 #> 32 Gram-negative CLI   18  1  709   728 #> 33 Gram-negative AZM    1  2  696   699 #> 34 Gram-negative IPM  616 10    5   631 #> 35 Gram-negative MEM  624  0    2   626 #> 36 Gram-negative MTR   21  0    2    23 #> 37 Gram-negative CHL    1  0   30    31 #> 38 Gram-negative COL  309  0   78   387 #> 39 Gram-negative MUP    0  0    0     0 #> 40 Gram-negative RIF    1  0  695   696 #> 41 Gram-positive PEN  395 11  483   889 #> 42 Gram-positive OXA  245  0  113   358 #> 43 Gram-positive FLC  659  0  278   937 #> 44 Gram-positive AMX  302  3  398   703 #> 45 Gram-positive AMC  864  2  272  1138 #> 46 Gram-positive AMP  302  3  398   703 #> 47 Gram-positive TZP  294  2   49   345 #> 48 Gram-positive CZO  151  0   89   240 #> 49 Gram-positive FEP  150  0   89   239 #> 50 Gram-positive CXM  756  0  328  1084 #> 51 Gram-positive FOX  151  0   89   240 #> 52 Gram-positive CTX  218  0   89   307 #> 53 Gram-positive CAZ    0  0 1177  1177 #> 54 Gram-positive CRO  218  0   89   307 #> 55 Gram-positive GEN  721 19  430  1170 #> 56 Gram-positive TOB  228  0  437   665 #> 57 Gram-positive AMK    0  0  436   436 #> 58 Gram-positive KAN    0  0  436   436 #> 59 Gram-positive TMP  553  9  343   905 #> 60 Gram-positive SXT  883  6  163  1052 #> 61 Gram-positive NIT   73  0    5    78 #> 62 Gram-positive FOS  132  0  141   273 #> 63 Gram-positive LNZ  312  0    2   314 #> 64 Gram-positive CIP  502 58  164   724 #> 65 Gram-positive MFX  136  4    8   148 #> 66 Gram-positive VAN 1141  0    5  1146 #> 67 Gram-positive TEC  237  0   32   269 #> 68 Gram-positive TCY  800 22  245  1067 #> 69 Gram-positive TGC  610  0    0   610 #> 70 Gram-positive DOX  802  7  206  1015 #> 71 Gram-positive ERY  797  7  385  1189 #> 72 Gram-positive CLI  556  3  220   779 #> 73 Gram-positive AZM  797  7  385  1189 #> 74 Gram-positive IPM  207  0   50   257 #> 75 Gram-positive MEM  156  0   47   203 #> 76 Gram-positive MTR    1  0    2     3 #> 77 Gram-positive CHL  120  0    3   123 #> 78 Gram-positive COL    0  0 1237  1237 #> 79 Gram-positive MUP  251  3   16   270 #> 80 Gram-positive RIF  301  2    3   306 #> Use 'format()' on this result to get a publishable/printable format.                             bug_drug_combinations(example_isolates,                       FUN = function(x) ifelse(x == as.mo(\"E. coli\"),                                                \"E. coli\",                                                \"Others\")) #> ℹ Using column 'mo' as input for `col_mo`. #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #>         mo  ab    S  I    R total #> 1  E. coli PEN    0  0  467   467 #> 2  E. coli OXA    0  0    0     0 #> 3  E. coli FLC    0  0    0     0 #> 4  E. coli AMX  196  0  196   392 #> 5  E. coli AMC  332 74   61   467 #> 6  E. coli AMP  196  0  196   392 #> 7  E. coli TZP  388  5   23   416 #> 8  E. coli CZO   79  1    2    82 #> 9  E. coli FEP  308  0    9   317 #> 10 E. coli CXM  425 15   25   465 #> 11 E. coli FOX  347  4   26   377 #> 12 E. coli CTX  448  0   11   459 #> 13 E. coli CAZ  449  0   11   460 #> 14 E. coli CRO  448  0   11   459 #> 15 E. coli GEN  451  0    9   460 #> 16 E. coli TOB  450  0   12   462 #> 17 E. coli AMK  171  0    0   171 #> 18 E. coli KAN    0  0    0     0 #> 19 E. coli TMP  241  0  155   396 #> 20 E. coli SXT  318  0  147   465 #> 21 E. coli NIT  429 16   13   458 #> 22 E. coli FOS   61  0    0    61 #> 23 E. coli LNZ    0  0  467   467 #> 24 E. coli CIP  398  1   57   456 #> 25 E. coli MFX    0  0   57    57 #> 26 E. coli VAN    0  0  467   467 #> 27 E. coli TEC    0  0  467   467 #> 28 E. coli TCY    1  0    2     3 #> 29 E. coli TGC   68  0    0    68 #> 30 E. coli DOX    0  0    0     0 #> 31 E. coli ERY    0  0  467   467 #> 32 E. coli CLI    0  0  467   467 #> 33 E. coli AZM    0  0  467   467 #> 34 E. coli IPM  422  0    0   422 #> 35 E. coli MEM  418  0    0   418 #> 36 E. coli MTR    2  0    0     2 #> 37 E. coli CHL    0  0    0     0 #> 38 E. coli COL  240  0    0   240 #> 39 E. coli MUP    0  0    0     0 #> 40 E. coli RIF    0  0  467   467 #> 41  Others PEN  417 11  734  1162 #> 42  Others OXA  251  0  114   365 #> 43  Others FLC  665  0  278   943 #> 44  Others AMX  347  3  608   958 #> 45  Others AMC 1010 17  385  1412 #> 46  Others AMP  347  3  608   958 #> 47  Others TZP  474  8  103   585 #> 48  Others CZO  166  1  197   364 #> 49  Others FEP  312  1   94   407 #> 50  Others CXM  872  7  445  1324 #> 51  Others FOX  239  4  198   441 #> 52  Others CTX  348  1  135   484 #> 53  Others CAZ  158  0 1193  1351 #> 54  Others CRO  348  1  135   484 #> 55  Others GEN  921 27  447  1395 #> 56  Others TOB  429  7  453   889 #> 57  Others AMK   80  0  441   521 #> 58  Others KAN    0  0  471   471 #> 59  Others TMP  677 10  416  1103 #> 60  Others SXT 1074  6  214  1294 #> 61  Others NIT  136 35  114   285 #> 62  Others FOS  142  0  148   290 #> 63  Others LNZ  314  0  242   556 #> 64  Others CIP  714 68  171   953 #> 65  Others MFX  136  4   14   154 #> 66  Others VAN 1149  0  245  1394 #> 67  Others TEC  237  0  272   509 #> 68  Others TCY  819 23  355  1197 #> 69  Others TGC  629  0  101   730 #> 70  Others DOX  814  7  315  1136 #> 71  Others ERY  801  9  617  1427 #> 72  Others CLI  586  4  463  1053 #> 73  Others AZM  801  9  617  1427 #> 74  Others IPM  402 10   55   467 #> 75  Others MEM  362  0   49   411 #> 76  Others MTR   27  0    5    32 #> 77  Others CHL  121  0   33   154 #> 78  Others COL   69  0 1331  1400 #> 79  Others MUP  251  3   16   270 #> 80  Others RIF  303  2  231   536 #> Use 'format()' on this result to get a publishable/printable format. # }"},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life.html","id":null,"dir":"Reference","previous_headings":"","what":"The Catalogue of Life — catalogue_of_life","title":"The Catalogue of Life — catalogue_of_life","text":"package contains complete taxonomic tree (last updated: 5 October 2021) almost microorganisms authoritative comprehensive Catalogue Life (CoL), supplemented data List Prokaryotic names Standing Nomenclature (LPSN).","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"The Catalogue of Life — catalogue_of_life","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life.html","id":"included-taxa","dir":"Reference","previous_headings":"","what":"Included Taxa","title":"The Catalogue of Life — catalogue_of_life","text":"Included : ~58,000 (sub)species kingdoms Archaea, Bacteria, Chromista Protozoa ~5,000 (sub)species orders kingdom Fungi: Eurotiales, Microascales, Mucorales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales Tremellales, well ~4,600 fungal (sub)species. kingdom Fungi large taxon almost 300,000 different (sub)species, microbial (rather macroscopic, like mushrooms). , fungi fit scope package including everything tremendously slow algorithms . including aforementioned taxonomic orders, relevant fungi covered (species Aspergillus, Candida, Cryptococcus, Histplasma, Pneumocystis, Saccharomyces Trichophyton). ~2,200 (sub)species ~50 relevant genera kingdom Animalia (Strongyloides Taenia) ~14,000 previously accepted names included (sub)species (taxonomically renamed) complete taxonomic tree included (sub)species: kingdom subspecies responsible author(s) year scientific publication Catalogue Life (http://www.catalogueoflife.org) comprehensive authoritative global index species currently available. holds essential information names, relationships distributions 1.9 million species. Catalogue Life used support major biodiversity conservation information services Global Biodiversity Information Facility (GBIF), Encyclopedia Life (EoL) International Union Conservation Nature Red List. recognised Convention Biological Diversity significant component Global Taxonomy Initiative contribution Target 1 Global Strategy Plant Conservation. syntax used transform original data cleansed R format, can found : https://github.com/msberends/AMR/blob/main/data-raw/reproduction_of_microorganisms.R.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"The Catalogue of Life — catalogue_of_life","text":"","code":"# Get version info of included data set catalogue_of_life_version() #> Included in this AMR package (v1.8.1.9021) are: #>  #> Catalogue of Life: 2019 Annual Checklist #>   Available at: http://www.catalogueoflife.org #>   Number of included microbial species: 49,029 #> List of Prokaryotic names with Standing in Nomenclature (5 October 2021) #>   Available at: https://lpsn.dsmz.de #>   Number of included bacterial species: 21,701 #>  #> => Total number of species included:  70,764 #> => Total number of synonyms included: 14,338 #>  #> See for more info `?microorganisms` and `?catalogue_of_life`.   # Get a note when a species was renamed mo_shortname(\"Chlamydophila psittaci\") #> ℹ Chlamydophila psittaci (Everett et al. , 1999) was renamed back to #>   Chlamydia psittaci (Page, 1968) [B_CHLMY_PSTT] #> [1] \"C. psittaci\"  # Get any property from the entire taxonomic tree for all included species mo_class(\"E. coli\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Gammaproteobacteria\"  mo_family(\"E. coli\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Enterobacteriaceae\"  mo_gramstain(\"E. coli\") # based on kingdom and phylum, see ?mo_gramstain #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Gram-negative\"  mo_ref(\"E. coli\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> [1] \"Castellani et al., 1919\"  # Do not get mistaken - this package is about microorganisms mo_kingdom(\"C. elegans\") #> ℹ Function `as.mo()` is uncertain about \"C. elegans\" (assuming Cladosporium #>   elegans). Run `mo_uncertainties()` to review this. #> [1] \"Fungi\" mo_name(\"C. elegans\") #> ℹ Function `as.mo()` is uncertain about \"C. elegans\" (assuming Cladosporium #>   elegans). Run `mo_uncertainties()` to review this. #> [1] \"Cladosporium elegans\""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":null,"dir":"Reference","previous_headings":"","what":"Version info of included Catalogue of Life — catalogue_of_life_version","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"function returns information included data Catalogue Life.","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"","code":"catalogue_of_life_version()"},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"list, prints pretty format","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"LPSN, see microorganisms.","code":""},{"path":"https://msberends.github.io/AMR/reference/catalogue_of_life_version.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Version info of included Catalogue of Life — catalogue_of_life_version","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":null,"dir":"Reference","previous_headings":"","what":"Count Available Isolates — count","title":"Count Available Isolates — count","text":"functions can used count resistant/susceptible microbial isolates. functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. count_resistant() used count resistant isolates, count_susceptible() used count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Count Available Isolates — count","text":"","code":"count_resistant(..., only_all_tested = FALSE)  count_susceptible(..., only_all_tested = FALSE)  count_R(..., only_all_tested = FALSE)  count_IR(..., only_all_tested = FALSE)  count_I(..., only_all_tested = FALSE)  count_SI(..., only_all_tested = FALSE)  count_S(..., only_all_tested = FALSE)  count_all(..., only_all_tested = FALSE)  n_rsi(..., only_all_tested = FALSE)  count_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   combine_SI = TRUE,   combine_IR = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Count Available Isolates — count","text":"... one vectors (columns) antibiotic interpretations. transformed internally .rsi() needed. only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class rsi (see .rsi()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant). used argument combine_IR, now follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . Default TRUE. combine_IR logical indicate whether values R must merged one, output consists S vs. +R (susceptible vs. non-susceptible). outdated, see argument combine_SI.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Count Available Isolates — count","text":"integer","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Count Available Isolates — count","text":"functions meant count isolates. Use resistance()/susceptibility() functions calculate microbial resistance/susceptibility. function count_resistant() equal function count_R(). function count_susceptible() equal function count_SI(). function n_rsi() alias count_all(). can used count available isolates, .e. input antibiotics available result (S, R). use equal n_distinct(). function equal count_susceptible(...) + count_resistant(...). function count_df() takes variable data rsi class (created .rsi()) counts number S's, 's R's. also supports grouped variables. function rsi_df() works exactly like count_df(), adds percentage S, R.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Count Available Isolates — count","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Count Available Isolates — count","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/count.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Count Available Isolates — count","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ count_resistant(example_isolates$AMX)   # counts \"R\" #> [1] 804 count_susceptible(example_isolates$AMX) # counts \"S\" and \"I\" #> [1] 546 count_all(example_isolates$AMX)         # counts \"S\", \"I\" and \"R\" #> [1] 1350  # be more specific count_S(example_isolates$AMX) #> Using `count_S()` is discouraged; use `count_susceptible()` instead to also #> consider \"I\" being susceptible. This note will be shown once for this #> session. #> [1] 543 count_SI(example_isolates$AMX) #> [1] 546 count_I(example_isolates$AMX) #> [1] 3 count_IR(example_isolates$AMX) #> Using `count_IR()` is discouraged; use `count_resistant()` instead to not #> consider \"I\" being resistant. This note will be shown once for this #> session. #> [1] 807 count_R(example_isolates$AMX) #> [1] 804  # Count all available isolates count_all(example_isolates$AMX) #> [1] 1350 n_rsi(example_isolates$AMX) #> [1] 1350  # n_rsi() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) #> [1] 546 susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX) #> [1] 546  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(hospital_id) %>%     summarise(R  = count_R(CIP),               I  = count_I(CIP),               S  = count_S(CIP),               n1 = count_all(CIP),  # the actual total; sum of all three               n2 = n_rsi(CIP),      # same - analogous to n_distinct               total = n())          # NOT the number of tested isolates!                  # Number of available isolates for a whole antibiotic class   # (i.e., in this data set columns GEN, TOB, AMK, KAN)   example_isolates %>%     group_by(hospital_id) %>%     summarise(across(aminoglycosides(), n_rsi))     # Count co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy.   # Please mind that `susceptibility()` calculates percentages right away instead.   example_isolates %>% count_susceptible(AMC) # 1433   example_isolates %>% count_all(AMC)         # 1879     example_isolates %>% count_susceptible(GEN) # 1399   example_isolates %>% count_all(GEN)         # 1855     example_isolates %>% count_susceptible(AMC, GEN) # 1764   example_isolates %>% count_all(AMC, GEN)         # 1936     # Get number of S+I vs. R immediately of selected columns   example_isolates %>%     select(AMX, CIP) %>%     count_df(translate = FALSE)     # It also supports grouping variables   example_isolates %>%     select(hospital_id, AMX, CIP) %>%     group_by(hospital_id) %>%     count_df(translate = FALSE) } #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #>   (tobramycin), 'AMK' (amikacin) and 'KAN' (kanamycin) #>    hospital_id antibiotic interpretation value #> 1            A        AMX             SI    70 #> 2            A        AMX              R   108 #> 3            A        CIP             SI   181 #> 4            A        CIP              R    26 #> 5            B        AMX             SI   170 #> 6            B        AMX              R   271 #> 7            B        CIP             SI   414 #> 8            B        CIP              R    85 #> 9            C        AMX             SI    65 #> 10           C        AMX              R   110 #> 11           C        CIP             SI   161 #> 12           C        CIP              R    41 #> 13           D        AMX             SI   241 #> 14           D        AMX              R   315 #> 15           D        CIP             SI   425 #> 16           D        CIP              R    76 # }"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Define Custom EUCAST Rules — custom_eucast_rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"Define custom EUCAST rules organisation specific analysis use output function eucast_rules().","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"custom_eucast_rules(...)"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"... rules formula notation, see Examples","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"list containing custom rules","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"organisations adoption EUCAST rules. function can used define custom EUCAST rules used eucast_rules() function.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"basics","dir":"Reference","previous_headings":"","what":"Basics","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   two custom EUCAST rules: TZP (piperacillin/tazobactam) \"S\", aminopenicillins (ampicillin amoxicillin) must made \"S\", TZP \"R\", aminopenicillins must made \"R\". rules can also printed console, immediately clear work:   rules (part tilde, example TZP == \"S\" TZP == \"R\") must evaluable data set: able run filter data set without errors. means example column TZP must exist. create sample data set test rules set:","code":"x <- custom_eucast_rules(TZP == \"S\" ~ aminopenicillins == \"S\",                          TZP == \"R\" ~ aminopenicillins == \"R\") x #> A set of custom EUCAST rules: #>  #>   1. If TZP is \"S\" then set to  S : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If TZP is \"R\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) df <- data.frame(mo = c(\"Escherichia coli\", \"Klebsiella pneumoniae\"),                  TZP = as.rsi(\"R\"),                  ampi = as.rsi(\"S\"),                  cipro = as.rsi(\"S\")) df #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    S     S  eucast_rules(df, rules = \"custom\", custom_rules = x, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    R     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"using-taxonomic-properties-in-rules","dir":"Reference","previous_headings":"","what":"Using taxonomic properties in rules","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"one exception variables used rules: column names microorganisms data set can also used, exist data set. column names : \"mo\", \"fullname\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"species_id\", \"source\", \"prevalence\" \"snomed\". Thus, next example work well, despite fact df data set contain column genus:","code":"y <- custom_eucast_rules(TZP == \"S\" & genus == \"Klebsiella\" ~ aminopenicillins == \"S\",                          TZP == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\")  eucast_rules(df, rules = \"custom\", custom_rules = y, info = FALSE) #>                      mo TZP ampi cipro #> 1      Escherichia coli   R    S     S #> 2 Klebsiella pneumoniae   R    R     S"},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"usage-of-antibiotic-group-names","dir":"Reference","previous_headings":"","what":"Usage of antibiotic group names","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"possible define antibiotic groups instead single antibiotics rule consequence, part tilde. examples, antibiotic group aminopenicillins used include ampicillin amoxicillin. following groups allowed (case-insensitive). Within parentheses agents matched running rule. \"aminoglycosides\"(amikacin, amikacin/fosfomycin, amphotericin B-high, apramycin, arbekacin, astromicin, bekanamycin, dibekacin, framycetin, gentamicin, gentamicin-high, habekacin, hygromycin, isepamicin, kanamycin, kanamycin-high, kanamycin/cephalexin, micronomicin, neomycin, netilmicin, pentisomicin, plazomicin, propikacin, ribostamycin, sisomicin, streptoduocin, streptomycin, streptomycin-high, tobramycin tobramycin-high) \"aminopenicillins\"(amoxicillin ampicillin) \"antifungals\"(5-fluorocytosine, amphotericin B, anidulafungin, butoconazole, caspofungin, ciclopirox, clotrimazole, econazole, fluconazole, fosfluconazole, griseofulvin, hachimycin, ibrexafungerp, isavuconazole, isoconazole, itraconazole, ketoconazole, manogepix, micafungin, miconazole, nystatin, pimaricin, posaconazole, rezafungin, ribociclib, sulconazole, terbinafine, terconazole voriconazole) \"antimycobacterials\"(4-aminosalicylic acid, calcium aminosalicylate, capreomycin, clofazimine, delamanid, enviomycin, ethambutol, ethambutol/isoniazid, ethionamide, isoniazid, morinamide, p-aminosalicylic acid, pretomanid, prothionamide, pyrazinamide, rifabutin, rifampicin, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, rifapentine, simvastatin/fenofibrate, sodium aminosalicylate, streptomycin/isoniazid, terizidone, thioacetazone/isoniazid, tiocarlide viomycin) \"betalactams\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, biapenem, carbenicillin, carindacillin, cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/nacubactam, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, ciclacillin, clometocillin, cloxacillin, dicloxacillin, doripenem, epicillin, ertapenem, flucloxacillin, hetacillin, imipenem, imipenem/EDTA, imipenem/relebactam, latamoxef, lenampicillin, loracarbef, mecillinam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, metampicillin, methicillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, panipenem, penamecillin, penicillin/novobiocin, penicillin/sulbactam, phenethicillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, razupenem, ritipenem, ritipenem acoxil, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, tebipenem, temocillin, ticarcillin ticarcillin/clavulanic acid) \"carbapenems\"(biapenem, doripenem, ertapenem, imipenem, imipenem/EDTA, imipenem/relebactam, meropenem, meropenem/nacubactam, meropenem/vaborbactam, panipenem, razupenem, ritipenem, ritipenem acoxil tebipenem) \"cephalosporins\"(cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, latamoxef loracarbef) \"cephalosporins_1st\"(cefacetrile, cefadroxil, cefaloridine, cefatrizine, cefazedone, cefazolin, cefroxadine, ceftezole, cephalexin, cephalothin, cephapirin cephradine) \"cephalosporins_2nd\"(cefaclor, cefamandole, cefmetazole, cefonicid, ceforanide, cefotetan, cefotiam, cefoxitin, cefoxitin screening, cefprozil, cefuroxime, cefuroxime axetil loracarbef) \"cephalosporins_3rd\"(cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefetamet, cefetamet pivoxil, cefixime, cefmenoxime, cefodizime, cefoperazone, cefoperazone/sulbactam, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotiam hexetil, cefovecin, cefpimizole, cefpiramide, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefsulodin, ceftazidime, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftriaxone latamoxef) \"cephalosporins_4th\"(cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetecol, cefoselis, cefozopran, cefpirome cefquinome) \"cephalosporins_5th\"(ceftaroline, ceftaroline/avibactam, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor ceftolozane/tazobactam) \"cephalosporins_except_caz\"(cefacetrile, cefaclor, cefadroxil, cefaloridine, cefamandole, cefatrizine, cefazedone, cefazolin, cefcapene, cefcapene pivoxil, cefdinir, cefditoren, cefditoren pivoxil, cefepime, cefepime/clavulanic acid, cefepime/tazobactam, cefetamet, cefetamet pivoxil, cefetecol, cefetrizole, cefixime, cefmenoxime, cefmetazole, cefodizime, cefonicid, cefoperazone, cefoperazone/sulbactam, ceforanide, cefoselis, cefotaxime, cefotaxime/clavulanic acid, cefotaxime/sulbactam, cefotetan, cefotiam, cefotiam hexetil, cefovecin, cefoxitin, cefoxitin screening, cefozopran, cefpimizole, cefpiramide, cefpirome, cefpodoxime, cefpodoxime proxetil, cefpodoxime/clavulanic acid, cefprozil, cefquinome, cefroxadine, cefsulodin, cefsumide, ceftaroline, ceftaroline/avibactam, ceftazidime/avibactam, ceftazidime/clavulanic acid, cefteram, cefteram pivoxil, ceftezole, ceftibuten, ceftiofur, ceftizoxime, ceftizoxime alapivoxil, ceftobiprole, ceftobiprole medocaril, ceftolozane/enzyme inhibitor, ceftolozane/tazobactam, ceftriaxone, cefuroxime, cefuroxime axetil, cephalexin, cephalothin, cephapirin, cephradine, latamoxef loracarbef) \"fluoroquinolones\"(besifloxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nifuroquine, norfloxacin, ofloxacin, orbifloxacin, pazufloxacin, pefloxacin, pradofloxacin, premafloxacin, prulifloxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"glycopeptides\"(avoparcin, dalbavancin, norvancomycin, oritavancin, ramoplanin, teicoplanin, teicoplanin-macromethod, telavancin, vancomycin vancomycin-macromethod) \"glycopeptides_except_lipo\"(avoparcin, norvancomycin, ramoplanin, teicoplanin, teicoplanin-macromethod, vancomycin vancomycin-macromethod) \"lincosamides\"(acetylmidecamycin, acetylspiramycin, clindamycin, gamithromycin, kitasamycin, lincomycin, meleumycin, nafithromycin, pirlimycin, primycin, solithromycin, tildipirosin, tilmicosin, tulathromycin, tylosin tylvalosin) \"lipoglycopeptides\"(dalbavancin, oritavancin telavancin) \"macrolides\"(acetylmidecamycin, acetylspiramycin, azithromycin, clarithromycin, dirithromycin, erythromycin, flurithromycin, gamithromycin, josamycin, kitasamycin, meleumycin, midecamycin, miocamycin, nafithromycin, oleandomycin, pirlimycin, primycin, rokitamycin, roxithromycin, solithromycin, spiramycin, telithromycin, tildipirosin, tilmicosin, troleandomycin, tulathromycin, tylosin tylvalosin) \"oxazolidinones\"(cadazolid, cycloserine, linezolid, tedizolid thiacetazone) \"penicillins\"(amoxicillin, amoxicillin/clavulanic acid, amoxicillin/sulbactam, ampicillin, ampicillin/sulbactam, apalcillin, aspoxicillin, avibactam, azidocillin, azlocillin, aztreonam, aztreonam/avibactam, aztreonam/nacubactam, bacampicillin, benzathine benzylpenicillin, benzathine phenoxymethylpenicillin, benzylpenicillin, carbenicillin, carindacillin, cefepime/nacubactam, ciclacillin, clometocillin, cloxacillin, dicloxacillin, epicillin, flucloxacillin, hetacillin, lenampicillin, mecillinam, metampicillin, methicillin, mezlocillin, mezlocillin/sulbactam, nacubactam, nafcillin, oxacillin, penamecillin, penicillin/novobiocin, penicillin/sulbactam, phenethicillin, phenoxymethylpenicillin, piperacillin, piperacillin/sulbactam, piperacillin/tazobactam, piridicillin, pivampicillin, pivmecillinam, procaine benzylpenicillin, propicillin, sarmoxicillin, sulbactam, sulbenicillin, sultamicillin, talampicillin, tazobactam, temocillin, ticarcillin ticarcillin/clavulanic acid) \"polymyxins\"(colistin, polymyxin B polymyxin B/polysorbate 80) \"quinolones\"(besifloxacin, cinoxacin, ciprofloxacin, clinafloxacin, danofloxacin, delafloxacin, difloxacin, enoxacin, enrofloxacin, finafloxacin, fleroxacin, flumequine, garenoxacin, gatifloxacin, gemifloxacin, grepafloxacin, levofloxacin, levonadifloxacin, lomefloxacin, marbofloxacin, metioxate, miloxacin, moxifloxacin, nadifloxacin, nalidixic acid, nifuroquine, nitroxoline, norfloxacin, ofloxacin, orbifloxacin, oxolinic acid, pazufloxacin, pefloxacin, pipemidic acid, piromidic acid, pradofloxacin, premafloxacin, prulifloxacin, rosoxacin, rufloxacin, sarafloxacin, sitafloxacin, sparfloxacin, temafloxacin, tilbroquinol, tioxacin, tosufloxacin trovafloxacin) \"streptogramins\"(pristinamycin quinupristin/dalfopristin) \"tetracyclines\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, tetracycline tigecycline) \"tetracyclines_except_tgc\"(cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline tetracycline) \"trimethoprims\"(brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim trimethoprim/sulfamethoxazole) \"ureidopenicillins\"(azlocillin, mezlocillin, piperacillin piperacillin/tazobactam)","code":""},{"path":"https://msberends.github.io/AMR/reference/custom_eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Define Custom EUCAST Rules — custom_eucast_rules","text":"","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\") x #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP)  # run the custom rule set (verbose = TRUE will return a logbook instead of the data set): eucast_rules(example_isolates,              rules = \"custom\",              custom_rules = x,              info = FALSE,              verbose = TRUE) #>    row col           mo_fullname old new #> 1   33 AMP Klebsiella pneumoniae   R   I #> 2   33 AMX Klebsiella pneumoniae   R   I #> 3   34 AMP Klebsiella pneumoniae   R   I #> 4   34 AMX Klebsiella pneumoniae   R   I #> 5  531 AMP Klebsiella pneumoniae   R   I #> 6  531 AMX Klebsiella pneumoniae   R   I #> 7 1485 AMP    Klebsiella oxytoca   R   I #> 8 1485 AMX    Klebsiella oxytoca   R   I #>                                                                               rule #> 1 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 2 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 3 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 4 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 5 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 6 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 7 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #> 8 report as 'I' when AMC is \"I\" and genus is \"Klebsiella\": amoxicillin, ampicillin #>            rule_group            rule_name #> 1 Custom EUCAST rules Custom EUCAST rule 2 #> 2 Custom EUCAST rules Custom EUCAST rule 2 #> 3 Custom EUCAST rules Custom EUCAST rule 2 #> 4 Custom EUCAST rules Custom EUCAST rule 2 #> 5 Custom EUCAST rules Custom EUCAST rule 2 #> 6 Custom EUCAST rules Custom EUCAST rule 2 #> 7 Custom EUCAST rules Custom EUCAST rule 2 #> 8 Custom EUCAST rules Custom EUCAST rule 2 #>                               rule_source #> 1 Object 'x' consisting of 2 custom rules #> 2 Object 'x' consisting of 2 custom rules #> 3 Object 'x' consisting of 2 custom rules #> 4 Object 'x' consisting of 2 custom rules #> 5 Object 'x' consisting of 2 custom rules #> 6 Object 'x' consisting of 2 custom rules #> 7 Object 'x' consisting of 2 custom rules #> 8 Object 'x' consisting of 2 custom rules               # combine rule sets x2 <- c(x,         custom_eucast_rules(TZP == \"R\" ~ carbapenems == \"R\")) x2 #> A set of custom EUCAST rules: #>  #>   1. If AMC is \"R\" and genus is \"Klebsiella\" then set to  R : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   2. If AMC is \"I\" and genus is \"Klebsiella\" then set to  I : #>      amoxicillin (AMX), ampicillin (AMP) #>  #>   3. If TZP is \"R\" then set to  R : #>      biapenem (BIA), doripenem (DOR), ertapenem (ETP), imipenem (IPM), #>      imipenem/EDTA (IPE), imipenem/relebactam (IMR), meropenem (MEM), #>      meropenem/nacubactam (MNC), meropenem/vaborbactam (MEV), panipenem (PAN), #>      razupenem (RZM), ritipenem (RIT), ritipenem acoxil (RIA), tebipenem (TBP)"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"EUCAST breakpoints used package based dosages data set. can retrieved eucast_dosage().","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"dosage"},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"data.frame 169 observations 9 variables: ab Antibiotic ID used package (AMC), using official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes available name Official name antimicrobial agent used WHONET/EARS-Net type Type dosage, either \"high_dosage\", \"standard_dosage\" \"uncomplicated_uti\" dose Dose, \"2 g\" \"25 mg/kg\" dose_times Number times dose must administered administration Route administration, either \"im\", \"iv\" \"oral\" notes Additional dosage notes original_txt Original text PDF file EUCAST eucast_version Version number EUCAST Clinical Breakpoints guideline dosages apply","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"'EUCAST Clinical Breakpoint Tables' v11.0 (2021) based dosages data set.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/dosage.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Treatment Dosages as Defined by EUCAST — dosage","text":"","code":"head(dosage) #>    ab        name              type        dose dose_times administration notes #> 1 AMK    Amikacin   standard_dosage 25-30 mg/kg          1             iv       #> 2 AMX Amoxicillin       high_dosage         2 g          6             iv       #> 3 AMX Amoxicillin   standard_dosage         1 g          3             iv       #> 4 AMX Amoxicillin       high_dosage    0.75-1 g          3           oral       #> 5 AMX Amoxicillin   standard_dosage       0.5 g          3           oral       #> 6 AMX Amoxicillin uncomplicated_uti       0.5 g          3           oral       #>         original_txt eucast_version #> 1 25-30 mg/kg x 1 iv             11 #> 2         2 g x 6 iv             11 #> 3       1 g x 3-4 iv             11 #> 4  0.75-1 g x 3 oral             11 #> 5     0.5 g x 3 oral             11 #> 6     0.5 g x 3 oral             11"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply EUCAST Rules — eucast_rules","title":"Apply EUCAST Rules — eucast_rules","text":"Apply rules clinical breakpoints intrinsic resistance defined European Committee Antimicrobial Susceptibility Testing (EUCAST, https://eucast.org), see Source. Use eucast_dosage() get data.frame advised dosages certain bug-drug combination, based dosage data set. improve interpretation antibiogram EUCAST rules applied, non-EUCAST rules can applied default, see Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"eucast_rules(   x,   col_mo = NULL,   info = interactive(),   rules = getOption(\"AMR_eucastrules\", default = c(\"breakpoints\", \"expert\")),   verbose = FALSE,   version_breakpoints = 11,   version_expertrules = 3.3,   ampc_cephalosporin_resistance = NA,   only_rsi_columns = FALSE,   custom_rules = NULL,   ... )  eucast_dosage(ab, administration = \"iv\", version_breakpoints = 11)"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Apply EUCAST Rules — eucast_rules","text":"EUCAST Expert Rules. Version 2.0, 2012. Leclercq et al. EUCAST expert rules antimicrobial susceptibility testing. Clin Microbiol Infect. 2013;19(2):141-60; doi:10.1111/j.1469-0691.2011.03703.x EUCAST Expert Rules, Intrinsic Resistance Exceptional Phenotypes Tables. Version 3.1, 2016. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.2, 2020. (link) EUCAST Intrinsic Resistance Unusual Phenotypes. Version 3.3, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 9.0, 2019. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 10.0, 2020. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 11.0, 2021. (link) EUCAST Breakpoint tables interpretation MICs zone diameters. Version 12.0, 2022. (link)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply EUCAST Rules — eucast_rules","text":"x data antibiotic columns, amox, AMX AMC col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions rules character vector specifies rules applied. Must one \"breakpoints\", \"expert\", \"\", \"custom\", \"\", defaults c(\"breakpoints\", \"expert\"). default value can set another value, e.g. using options(AMR_eucastrules = \"\"). using \"custom\", sure fill argument custom_rules . Custom rules can created custom_eucast_rules(). verbose logical turn Verbose mode (default ). Verbose mode, function apply rules data, instead returns data set logbook form extensive info rows columns effected way. Using Verbose mode takes lot time. version_breakpoints version number use EUCAST Clinical Breakpoints guideline. Can either \"11.0\" \"10.0\". version_expertrules version number use EUCAST Expert Rules Intrinsic Resistance guideline. Can either \"3.3\", \"3.2\" \"3.1\". ampc_cephalosporin_resistance character value applied cefotaxime, ceftriaxone ceftazidime AmpC de-repressed cephalosporin-resistant mutants, defaults NA. Currently works version_expertrules 3.2 higher; version 'EUCAST Expert Rules Enterobacterales' state results cefotaxime, ceftriaxone ceftazidime reported note, results suppressed (emptied) three agents. value NA (default) argument remove results three agents, e.g. value \"R\" make results agents resistant. Use NULL FALSE alter results three agents AmpC de-repressed cephalosporin-resistant mutants. Using TRUE equal using \"R\".  EUCAST Expert Rules v3.2, rule applies : Citrobacter braakii, Citrobacter freundii, Citrobacter gillenii, Citrobacter murliniae, Citrobacter rodenticum, Citrobacter sedlakii, Citrobacter werkmanii, Citrobacter youngae, Enterobacter, Hafnia alvei, Klebsiella aerogenes, Morganella morganii, Providencia Serratia. only_rsi_columns logical indicate whether antibiotic columns must detected transformed class <rsi> (see .rsi()) beforehand (defaults FALSE) custom_rules custom rules apply, created custom_eucast_rules() ... column name antibiotic, see section Antibiotics ab (vector ) text can coerced valid antibiotic code .ab() administration route administration, either \"im\", \"iv\" \"oral\"","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply EUCAST Rules — eucast_rules","text":"input x, possibly edited values antibiotics. , verbose = TRUE, data.frame original new values affected bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Apply EUCAST Rules — eucast_rules","text":"Note: function translate MIC values RSI values. Use .rsi() . Note: ampicillin (AMP, J01CA01) available amoxicillin (AMX, J01CA04) , latter used rules dependency ampicillin. drugs interchangeable comes expression antimicrobial resistance. file containing EUCAST rules located : https://github.com/msberends/AMR/blob/main/data-raw/eucast_rules.tsv.  Note: Old taxonomic names replaced current taxonomy applicable. example, Ochrobactrum anthropi renamed Brucella anthropi 2020; original EUCAST rules v3.1 v3.2 yet contain new taxonomic name. file used input AMR package contains taxonomy updated 5 October 2021.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"custom-rules","dir":"Reference","previous_headings":"","what":"Custom Rules","title":"Apply EUCAST Rules — eucast_rules","text":"Custom rules can created using custom_eucast_rules(), e.g.:","code":"x <- custom_eucast_rules(AMC == \"R\" & genus == \"Klebsiella\" ~ aminopenicillins == \"R\",                          AMC == \"I\" & genus == \"Klebsiella\" ~ aminopenicillins == \"I\")  eucast_rules(example_isolates, rules = \"custom\", custom_rules = x, info = FALSE) #> # A tibble: 2,000 x 49 #>    date       hospit~1 ward_~2 ward_~3 ward_~4   age gender patie~5 mo           #>  * <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  4 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  5 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  6 2002-01-13 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  7 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  8 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  9 2002-01-16 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 10 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #> # ... with 1,990 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, ... #> # i Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names"},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"-other-rules","dir":"Reference","previous_headings":"","what":"'Other' Rules","title":"Apply EUCAST Rules — eucast_rules","text":"processing, two non-EUCAST rules drug combinations can applied improve efficacy EUCAST rules, reliability data (analysis). rules : drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Important examples include amoxicillin amoxicillin/clavulanic acid, trimethoprim trimethoprim/sulfamethoxazole. Needless say, rules work, drugs must available data set. Since rules officially approved EUCAST, applied default. use rules, include \"\" rules argument, use eucast_rules(..., rules = \"\"). can also set option AMR_eucastrules, .e. run options(AMR_eucastrules = \"\").","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Apply EUCAST Rules — eucast_rules","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA12), aztreonam (ATM, J01MA24), bacampicillin (BAM, J01MA07), bekanamycin (BEK, J01MA14), benzathine benzylpenicillin (BNB, D10AF05), benzathine phenoxymethylpenicillin (BNP, J01MA06), benzylpenicillin (PEN, J01MA01), besifloxacin (BES, J01MA18), biapenem (BIA, J01MA03), carbenicillin (CRB, J01MA17), carindacillin (CRN, J01MA10), cefacetrile (CAC, J01MA21), cefaclor (CEC, J01MA09), cefadroxil (CFR, J01MA05), cefaloridine (RID, P01AA05), cefamandole (MAN, J01MA22), cefatrizine (CTZ, J01MA13), cefazedone (CZD, J01CA01), cefazolin (CZO, J01CA04), cefcapene (CCP, J01CA12), cefdinir (CDR, J01CR05), cefditoren (DIT, J01CA13), cefepime (FEP, J01AA02), cefetamet (CAT, J01FA10), cefixime (CFM, J01FA09), cefmenoxime (CMX, J01CR02), cefmetazole (CMZ, J01AA08), cefodizime (DIZ, J01FA06), cefonicid (CID, J01CF04), cefoperazone (CFP, J01CF05), cefoperazone/sulbactam (CSL, J01CR01), ceforanide (CND, J01CA19), cefotaxime (CTX, J01CE04), cefotetan (CTT, J01CA09), cefotiam (CTF, J01DF01), cefoxitin (FOX, J01CA06), cefozopran (ZOP, J01CE08), cefpiramide (CPM, J01CE10), cefpirome (CPO, J01CE01), cefpodoxime (CPD, J01CA03), cefprozil (CPR, J01CA05), cefroxadine (CRD, J01CE07), cefsulodin (CFS, J01CF02), ceftaroline (CPT, J01CF01), ceftazidime (CAZ, J01CA07), ceftazidime/clavulanic acid (CCV, J01CA18), cefteram (CEM, J01CA11), ceftezole (CTL, J01CA14), ceftibuten (CTB, J01CF03), ceftizoxime (CZX, J01CA10), ceftobiprole medocaril (CFM1, J01CF06), ceftolozane/enzyme inhibitor (CEI, J01CE06), ceftriaxone (CRO, J01CE05), cefuroxime (CXM, J01CE02), cephalexin (LEX, J01CA02), cephalothin (CEP, J01CA08), cephapirin (HAP, J01CE09), cephradine (CED, J01CE03), chloramphenicol (CHL, J01CG01), ciprofloxacin (CIP, J01CA16), clarithromycin (CLR, J01CR04), clindamycin (CLI, J01CA15), clometocillin (CLM, J01CG02), cloxacillin (CLO, J01CA17), colistin (COL, J01CR03), cycloserine (CYC, J01DB10), dalbavancin (DAL, J01DC04), daptomycin (DAP, J01DB05), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DC03), dicloxacillin (DIC, J01DB07), dirithromycin (DIR, J01DB06), doripenem (DOR, J01DB04), doxycycline (DOX, J01DD17), enoxacin (ENX, J01DD15), epicillin (EPC, J01DD16), ertapenem (ETP, J01DE01), erythromycin (ERY, J01DD10), fleroxacin (FLE, J01DD08), flucloxacillin (FLC, J01DD05), flurithromycin (FLR1, J01DC09), fosfomycin (FOS, J01DD09), framycetin (FRM, J01DC06), fusidic acid (FUS, J01DD12), garenoxacin (GRN, J01DD62), gatifloxacin (GAT, J01DC11), gemifloxacin (GEM, J01DD01), gentamicin (GEN, J01DC05), grepafloxacin (GRX, J01DC07), hetacillin (HET, J01DC01), imipenem (IPM, J01DE03), isepamicin (ISE, J01DD11), josamycin (JOS, J01DE02), kanamycin (KAN, J01DD13), latamoxef (LTM, J01DC10), levofloxacin (LVX, J01DB11), levonadifloxacin (LND, J01DD03), lincomycin (LIN, J01DI02), linezolid (LNZ, J01DD02), lomefloxacin (LOM, J01DD52), loracarbef (LOR, J01DD18), mecillinam (MEC, J01DB12), meropenem (MEM, J01DD14), meropenem/vaborbactam (MEV, J01DD07), metampicillin (MTM, J01DI01), methicillin (MET, J01DI54), mezlocillin (MEZ, J01DD04), micronomicin (MCR, J01DC02), midecamycin (MID, J01DB01), minocycline (MNO, J01DB03), miocamycin (MCM, J01DB08), moxifloxacin (MFX, J01DB09), nadifloxacin (NAD, J01DD06), nafcillin (NAF, J01DC08), nalidixic acid (NAL, J01DH05), neomycin (NEO, J01DH04), netilmicin (NET, J01DH03), nitrofurantoin (NIT, J01DH51), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01XA02), oritavancin (ORI, J01XA01), oxacillin (OXA, J01XC01), pazufloxacin (PAZ, J01FA13), pefloxacin (PEF, J01FA01), penamecillin (PNM, J01FA14), phenethicillin (PHE, J01FA07), phenoxymethylpenicillin (PHN, J01FA03), piperacillin (PIP, J01FA11), piperacillin/tazobactam (TZP, J01FA05), pivampicillin (PVM, J01FA12), pivmecillinam (PME, J01FA16), plazomicin (PLZ, J01FA02), polymyxin B (PLB, J01FA15), pristinamycin (PRI, J01FA08), procaine benzylpenicillin (PRB, J01FF02), propicillin (PRP, J01FG01), prulifloxacin (PRU, J01FG02), quinupristin/dalfopristin (QDA, J04AB02), ribostamycin (RST, J01XX09), rifampicin (RIF, J01XX08), rokitamycin (ROK, J01AA07), roxithromycin (RXT, J01XB01), rufloxacin (RFL, J01XB02), sisomicin (SIS, J01XE01), sitafloxacin (SIT, J01AA12), solithromycin (SOL, J01EA01), sparfloxacin (SPX, J01XX01), spiramycin (SPI, J01BA01), streptoduocin (STR, J01GB06), streptomycin (STR1, J01GB12), sulbactam (SUL, J01GB13), sulbenicillin (SBC, J01GB09), sulfadiazine (SDI, D09AA01), sulfadiazine/trimethoprim (SLT1, J01GB03), sulfadimethoxine (SUD, J01GB11), sulfadimidine (SDM, J01GB04), sulfadimidine/trimethoprim (SLT2, S01AA22), sulfafurazole (SLF, J01GB05), sulfaisodimidine (SLF1, J01GB07), sulfalene (SLF2, J01GB14), sulfamazone (SZO, J01GB10), sulfamerazine (SLF3, J01GB08), sulfamerazine/trimethoprim (SLT3, J01GA02), sulfamethizole (SLF4, J01GA01), sulfamethoxazole (SMX, J01GB01), sulfamethoxypyridazine (SLF5, J01EE01), sulfametomidine (SLF6, J01MB02), sulfametoxydiazine (SLF7, J01FF01), sulfametrole/trimethoprim (SLT4, J01XA04), sulfamoxole (SLF8, J01XA05), sulfamoxole/trimethoprim (SLT5, J01XA03), sulfanilamide (SLF9, J04AB01), sulfaperin (SLF10, J01XX11), sulfaphenazole (SLF11, J01EC02), sulfapyridine (SLF12, J01ED01), sulfathiazole (SUT, J01EB03), sulfathiourea (SLF13, J01EB05), sultamicillin (SLT6, J01EB01), talampicillin (TAL, J01ED02), tazobactam (TAZ, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Apply EUCAST Rules — eucast_rules","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/eucast_rules.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply EUCAST Rules — eucast_rules","text":"","code":"# \\donttest{ a <- data.frame(mo = c(\"Staphylococcus aureus\",                        \"Enterococcus faecalis\",                        \"Escherichia coli\",                        \"Klebsiella pneumoniae\",                        \"Pseudomonas aeruginosa\"),                 VAN = \"-\",       # Vancomycin                 AMX = \"-\",       # Amoxicillin                 COL = \"-\",       # Colistin                 CAZ = \"-\",       # Ceftazidime                 CXM = \"-\",       # Cefuroxime                 PEN = \"S\",       # Benzylpenicillin                 FOX = \"S\",       # Cefoxitin                 stringsAsFactors = FALSE)  head(a) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   -   -   -   -   S   S #> 2  Enterococcus faecalis   -   -   -   -   -   S   S #> 3       Escherichia coli   -   -   -   -   -   S   S #> 4  Klebsiella pneumoniae   -   -   -   -   -   S   S #> 5 Pseudomonas aeruginosa   -   -   -   -   -   S   S   # apply EUCAST rules: some results wil be changed b <- eucast_rules(a) #> Warning: in `eucast_rules()`: not all columns with antimicrobial results are of #> class <rsi> . Transform them on beforehand, with e.g.: #>   - a %>% as.rsi(CXM:AMX) #>   - a %>% mutate_if(is.rsi.eligible, as.rsi) #>   - a %>% mutate(across(where(is.rsi.eligible), as.rsi))  head(b) #>                       mo VAN AMX COL CAZ CXM PEN FOX #> 1  Staphylococcus aureus   -   S   R   R   S   S   S #> 2  Enterococcus faecalis   -   -   R   R   R   S   R #> 3       Escherichia coli   R   -   -   -   -   R   S #> 4  Klebsiella pneumoniae   R   R   -   -   -   R   S #> 5 Pseudomonas aeruginosa   R   R   -   -   R   R   R   # do not apply EUCAST rules, but rather get a data.frame # containing all details about the transformations: c <- eucast_rules(a, verbose = TRUE) #> Warning: in `eucast_rules()`: not all columns with antimicrobial results are of #> class <rsi> . Transform them on beforehand, with e.g.: #>   - a %>% as.rsi(CXM:AMX) #>   - a %>% mutate_if(is.rsi.eligible, as.rsi) #>   - a %>% mutate(across(where(is.rsi.eligible), as.rsi)) head(c) #>   row col           mo_fullname old new rule   rule_group #> 1   1 AMX Staphylococcus aureus   -   S       Breakpoints #> 2   1 CXM Staphylococcus aureus   -   S       Breakpoints #> 3   1 CAZ Staphylococcus aureus   -   R      Expert Rules #> 4   1 COL Staphylococcus aureus   -   R      Expert Rules #> 5   2 CAZ Enterococcus faecalis   -   R      Expert Rules #> 6   2 COL Enterococcus faecalis   -   R      Expert Rules #>                                                 rule_name #> 1                                          Staphylococcus #> 2                                          Staphylococcus #> 3 Table 4: Intrinsic resistance in gram-positive bacteria #> 4 Table 4: Intrinsic resistance in gram-positive bacteria #> 5 Table 4: Intrinsic resistance in gram-positive bacteria #> 6 Table 4: Intrinsic resistance in gram-positive bacteria #>                                                                                 rule_source #> 1                                           'EUCAST Clinical Breakpoint Tables' v11.0, 2021 #> 2                                           'EUCAST Clinical Breakpoint Tables' v11.0, 2021 #> 3 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 4 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 5 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 #> 6 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3, 2021 # }  # Dosage guidelines:  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\") #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v11.0 (2021). This note will be shown once per session. #>    ab          name  standard_dosage  high_dosage #> 1 TOB    Tobramycin 6-7 mg/kg x 1 iv         <NA> #> 2 GEN    Gentamicin 6-7 mg/kg x 1 iv         <NA> #> 3 CIP Ciprofloxacin     0.4 g x 2 iv 0.4 g x 3 iv  eucast_dosage(c(\"tobra\", \"genta\", \"cipro\"), \"iv\", version_breakpoints = 10) #> ℹ Dosages for antimicrobial drugs, as meant for 'EUCAST Clinical Breakpoint #>   Tables' v10.0 (2020). This note will be shown once per session. #>    ab          name  standard_dosage  high_dosage #> 1 TOB    Tobramycin 6-7 mg/kg x 1 iv         <NA> #> 2 GEN    Gentamicin 6-7 mg/kg x 1 iv         <NA> #> 3 CIP Ciprofloxacin     0.4 g x 2 iv 0.4 g x 3 iv"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 2,000 Example Isolates — example_isolates","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data set containing 2,000 microbial isolates full antibiograms. data set reflects reality can used practice AMR data analysis. examples, please read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"example_isolates"},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"data.frame 2,000 observations 49 variables: date date receipt laboratory hospital_id ID hospital, D ward_iculogical determine ward intensive care unit ward_clinicallogical determine ward regular clinical ward ward_outpatientlogical determine ward outpatient clinic age age patient gender gender patient patient_id ID patient mo ID microorganism created .mo(), see also microorganisms PEN:RIF 40 different antibiotics class rsi (see .rsi()); column names occur antibiotics data set can translated ab_name()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 2,000 Example Isolates — example_isolates","text":"","code":"head(example_isolates) #> # A tibble: 6 × 49 #>   date       hospita…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>   <date>     <fct>     <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #> 1 2002-01-02 D         FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #> 2 2002-01-03 D         FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #> 3 2002-01-07 B         TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 4 2002-01-07 B         TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 5 2002-01-13 B         TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> 6 2002-01-13 B         TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #> # … with 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, AMX <rsi>, #> #   AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, CXM <rsi>, #> #   FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, TOB <rsi>, #> #   AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, FOS <rsi>, #> #   LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, TCY <rsi>, #> #   TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, IPM <rsi>, #> #   MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, RIF <rsi>, and … #> # ℹ Use `colnames()` to see all variable names"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Unclean Data — example_isolates_unclean","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data set containing 3,000 microbial isolates cleaned consequently ready AMR data analysis. data set can used practice.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"example_isolates_unclean"},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Unclean Data — example_isolates_unclean","text":"data.frame 3,000 observations 8 variables: patient_id ID patient date date receipt laboratory hospital ID hospital, C bacteria info microorganism can transformed .mo(), see also microorganisms AMX:GEN 4 different antibiotics transformed .rsi()","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with Unclean Data — example_isolates_unclean","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/example_isolates_unclean.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Unclean Data — example_isolates_unclean","text":"","code":"head(example_isolates_unclean) #>   patient_id hospital       date      bacteria AMX AMC CIP GEN #> 1         J3        A 2012-11-21       E. coli   R   I   S   S #> 2         R7        A 2018-04-03 K. pneumoniae   R   I   S   S #> 3         P3        A 2014-09-19       E. coli   R   S   S   S #> 4        P10        A 2015-12-10       E. coli   S   I   S   S #> 5         B7        A 2015-03-02       E. coli   S   S   S   S #> 6         W3        A 2018-03-31     S. aureus   R   S   R   S"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine First Isolates — first_isolate","title":"Determine First Isolates — first_isolate","text":"Determine first isolates microorganisms every patient per episode (needed) per specimen type. functions support four methods summarised Hindler et al. 2007 (doi:10.1086/511864 ). determine patient episodes necessarily based microorganisms, use is_new_episode() also supports grouping dplyr package.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine First Isolates — first_isolate","text":"","code":"first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   col_testcode = NULL,   col_specimen = NULL,   col_icu = NULL,   col_keyantimicrobials = NULL,   episode_days = 365,   testcodes_exclude = NULL,   icu_exclude = FALSE,   specimen_group = NULL,   type = \"points\",   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ignore_I = TRUE,   points_threshold = 2,   info = interactive(),   include_unknown = FALSE,   include_untested_rsi = TRUE,   ... )  filter_first_isolate(   x = NULL,   col_date = NULL,   col_patient_id = NULL,   col_mo = NULL,   episode_days = 365,   method = c(\"phenotype-based\", \"episode-based\", \"patient-based\", \"isolate-based\"),   ... )"},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine First Isolates — first_isolate","text":"Methodology function strictly based : M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/. Hindler JF Stelling J (2007). Analysis Presentation Cumulative Antibiograms: New Consensus Guideline Clinical Laboratory Standards Institute. Clinical Infectious Diseases, 44(6), 867-873. doi:10.1086/511864","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine First Isolates — first_isolate","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_date column name result date (date received lab), defaults first column date class col_patient_id column name unique IDs patients, defaults first column starts 'patient' 'patid' (case insensitive) col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). col_testcode column name test codes. Use col_testcode = NULL exclude certain test codes (test codes screening). case testcodes_exclude ignored. col_specimen column name specimen type group col_icu column name logicals (TRUE/FALSE) whether ward department Intensive Care Unit (ICU) col_keyantimicrobials (useful method = \"phenotype-based\") column name key antimicrobials determine first isolates, see key_antimicrobials(). Defaults first column starts 'key' followed 'ab' 'antibiotics' 'antimicrobials' (case insensitive). Use col_keyantimicrobials = FALSE prevent . Can also output key_antimicrobials(). episode_days episode days genus/species combination determined 'first isolate' . default 365 days based guideline CLSI, see Source. testcodes_exclude character vector test codes excluded (case-insensitive) icu_exclude logical indicate whether ICU isolates excluded (rows value TRUE column set col_icu) specimen_group value column set col_specimen filter type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details method method apply, either \"phenotype-based\", \"episode-based\", \"patient-based\" \"isolate-based\" (can abbreviated), see Details. default \"phenotype-based\" antimicrobial test results present data, \"episode-based\" otherwise. ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details info logical indicate info printed, defaults TRUE interactive mode include_unknown logical indicate whether 'unknown' microorganisms included , .e. microbial code \"UNKNOWN\", defaults FALSE. WHONET users, means records organism code \"con\" (contamination) excluded default. Isolates microbial ID NA always excluded first isolate. include_untested_rsi logical indicate whether also rows without antibiotic results still eligible becoming first isolate. Use include_untested_rsi = FALSE always return FALSE rows. checks data set columns class <rsi> consequently requires transforming columns antibiotic results using .rsi() first. ... arguments passed first_isolate() using filter_first_isolate(), otherwise arguments passed key_antimicrobials() (universal, gram_negative, gram_positive)","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine First Isolates — first_isolate","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine First Isolates — first_isolate","text":"conduct epidemiological analyses antimicrobial resistance data, -called first isolates included prevent overestimation underestimation antimicrobial resistance. Different methods can used , see . functions context-aware. means x argument can left blank used inside data.frame call, see Examples. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names. isolates microbial ID NA excluded first isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"different-methods","dir":"Reference","previous_headings":"","what":"Different methods","title":"Determine First Isolates — first_isolate","text":"According Hindler et al. (2007, doi:10.1086/511864 ), different methods (algorithms) select first isolates increasing reliability: isolate-based, patient-based, episode-based phenotype-based. methods select combination taxonomic genus species (subspecies). mentioned methods covered first_isolate() function:","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"isolate-based","dir":"Reference","previous_headings":"","what":"Isolate-based","title":"Determine First Isolates — first_isolate","text":"method require selection, isolates included. , however, respect arguments set first_isolate() function. example, default setting include_unknown (FALSE) omit selection rows without microbial ID.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"patient-based","dir":"Reference","previous_headings":"","what":"Patient-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient , set episode_days Inf. Although often inappropriate, method makes sure duplicate isolates selected patient. large longitudinal data set, mean isolates excluded found years initial isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"episode-based","dir":"Reference","previous_headings":"","what":"Episode-based","title":"Determine First Isolates — first_isolate","text":"include every genus-species combination per patient episode , set episode_days sensible number days. Depending type analysis, 14, 30, 60 365. Short episodes common analysing specific hospital ward data, long episodes common analysing regional national data. common method correct duplicate isolates. Patients categorised episodes based ID dates (e.g., date specimen receipt laboratory result). common method, take account antimicrobial test results. means e.g. methicillin-resistant Staphylococcus aureus (MRSA) isolate differentiated wildtype Staphylococcus aureus isolate.","code":""},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"phenotype-based","dir":"Reference","previous_headings":"","what":"Phenotype-based","title":"Determine First Isolates — first_isolate","text":"reliable method, since also weighs antibiogram (antimicrobial test results) yielding -called 'first weighted isolates'. two different methods weigh antibiogram: Using type = \"points\" argument points_threshold (default) method weighs antimicrobial agents available data set. difference S R (vice versa) counts 0.5 points, difference S R (vice versa) counts 1 point. sum points exceeds points_threshold, defaults 2, isolate selected first weighted isolate. antimicrobials internally selected using all_antimicrobials() function. output function need passed first_isolate() function. Using type = \"keyantimicrobials\" argument ignore_I method weighs specific antimicrobial agents, called key antimicrobials. difference S R (vice versa) key antimicrobials select isolate first weighted isolate. ignore_I = FALSE, also differences S R (vice versa) lead . Key antimicrobials internally selected using key_antimicrobials() function, can also added manually variable data set col_keyantimicrobials argument. Another option pass output key_antimicrobials() function directly col_keyantimicrobials argument. default method phenotype-based (using type = \"points\") episode-based (using episode_days = 365). makes sure every genus-species combination selected per patient per year, taking account antimicrobial test results. antimicrobial test results available data set, episode-based method applied default.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/first_isolate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine First Isolates — first_isolate","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  example_isolates[first_isolate(), ] #> # A tibble: 1,379 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-07 B        TRUE    FALSE   FALSE      45 F      067927  B_STPHY_EPDR #>  3 2002-01-14 D        FALSE   TRUE    FALSE      78 M      462729  B_STPHY_AURS #>  4 2002-01-17 B        TRUE    FALSE   FALSE      79 F      858515  B_STPHY_EPDR #>  5 2002-01-17 D        FALSE   TRUE    FALSE      67 M      495616  B_STPHY_EPDR #>  6 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI #>  7 2002-01-21 D        FALSE   TRUE    FALSE      75 F      462081  B_CTRBC_FRND #>  8 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL #>  9 2002-02-03 B        TRUE    FALSE   FALSE      76 M      481442  B_STPHY_CONS #> 10 2002-02-05 A        FALSE   TRUE    FALSE      50 M      023456  B_STPHY_HMNS #> # … with 1,369 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # \\donttest{ # get all first Gram-negatives example_isolates[which(first_isolate(info = FALSE) & mo_is_gram_negative()), ] #> ℹ Using column 'mo' as input for `mo_is_gram_negative()` #> # A tibble: 437 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI #>  3 2002-01-21 D        FALSE   TRUE    FALSE      75 F      462081  B_CTRBC_FRND #>  4 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL #>  5 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS #>  6 2002-02-27 D        FALSE   TRUE    FALSE      85 F      066895  B_KLBSL_PNMN #>  7 2002-03-08 C        FALSE   TRUE    FALSE      69 M      4FC193  B_ESCHR_COLI #>  8 2002-03-16 C        FALSE   TRUE    FALSE      69 M      4FC193  B_PSDMN_AERG #>  9 2002-04-01 B        TRUE    FALSE   FALSE      46 F      496896  B_ESCHR_COLI #> 10 2002-04-23 B        TRUE    FALSE   FALSE      69 F      EE2510  B_ESCHR_COLI #> # … with 427 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  if (require(\"dplyr\")) {   # filter on first isolates using dplyr:   example_isolates %>%     filter(first_isolate())     # short-hand version:   example_isolates %>%     filter_first_isolate(info = FALSE)       # flag the first isolates per group:  example_isolates %>%    group_by(hospital_id) %>%    mutate(first = first_isolate()) %>%    select(hospital_id, date, patient_id, mo, first)      # now let's see if first isolates matter:   A <- example_isolates %>%     group_by(hospital_id) %>%     summarise(count = n_rsi(GEN),            # gentamicin availability               resistance = resistance(GEN))  # gentamicin resistance     B <- example_isolates %>%     filter_first_isolate() %>%               # the 1st isolate filter     group_by(hospital_id) %>%     summarise(count = n_rsi(GEN),            # gentamicin availability               resistance = resistance(GEN))  # gentamicin resistance     # Have a look at A and B.   A   B      # B is more reliable because every isolate is counted only once.   # Gentamicin resistance in hospital D appears to be 4.2% higher than   # when you (erroneously) would have used all isolates for analysis. } #> # A tibble: 4 × 3 #>   hospital_id count resistance #>   <fct>       <int>      <dbl> #> 1 A             216      0.204 #> 2 B             407      0.270 #> 3 C             202      0.218 #> 4 D             457      0.293 # }"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":null,"dir":"Reference","previous_headings":"","what":"G-test for Count Data — g.test","title":"G-test for Count Data — g.test","text":"g.test() performs chi-squared contingency table tests goodness--fit tests, just like chisq.test() reliable (1). G-test can used see whether number observations category fits theoretical expectation (called G-test goodness--fit), see whether proportions one variable different different values variable (called G-test independence).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"G-test for Count Data — g.test","text":"","code":"g.test(x, y = NULL, p = rep(1/length(x), length(x)), rescale.p = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"G-test for Count Data — g.test","text":"code function identical chisq.test(), except : calculation statistic changed \\(2 * sum(x * log(x / E))\\) Yates' continuity correction removed apply G-test possibility simulate p values simulate.p.value removed","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"G-test for Count Data — g.test","text":"x numeric vector matrix. x y can also     factors. y numeric vector; ignored x matrix.      x factor, y factor length. p vector probabilities length x.     error given entry p negative. rescale.p logical scalar; TRUE p rescaled     (necessary) sum 1.  rescale.p FALSE,     p sum 1, error given.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"G-test for Count Data — g.test","text":"list class \"htest\" containing following   components: statistic value chi-squared test statistic. parameter degrees freedom approximate     chi-squared distribution test statistic, NA     p-value computed Monte Carlo simulation. p.value p-value test. method character string indicating type test     performed, whether Monte Carlo simulation continuity     correction used. data.name character string giving name(s) data. observed observed counts. expected expected counts null hypothesis. residuals Pearson residuals,     (observed - expected) / sqrt(expected). stdres standardized residuals,     (observed - expected) / sqrt(V), V residual cell variance (Agresti, 2007,     section 2.4.5 case x matrix, n * p * (1 - p) otherwise).","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"G-test for Count Data — g.test","text":"x matrix one row column, x vector y given, goodness--fit test performed (x treated one-dimensional contingency table). entries x must non-negative integers. case, hypothesis tested whether population probabilities equal p, equal p given. x matrix least two rows columns, taken two-dimensional contingency table: entries x must non-negative integers.  Otherwise, x y must vectors factors length; cases missing values removed, objects coerced factors, contingency table computed .  Pearson's chi-squared test performed null hypothesis joint distribution cell counts 2-dimensional contingency table product row column marginals. p-value computed asymptotic chi-squared distribution test statistic. contingency table case simulation done random sampling set contingency tables given marginals, works marginals strictly positive. Note usual sampling situation assumed chi-squared test (G-test) rather Fisher's exact test. goodness--fit case simulation done random sampling discrete distribution specified p, sample size n = sum(x). simulation done R may slow.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-goodness-of-fit-likelihood-ratio-test-","dir":"Reference","previous_headings":"","what":"G-test Of Goodness-of-Fit (Likelihood Ratio Test)","title":"G-test for Count Data — g.test","text":"Use G-test goodness--fit one nominal variable two values (male female, red, pink white flowers). compare observed counts numbers observations category expected counts, calculate using kind theoretical expectation (1:1 sex ratio 1:2:1 ratio genetic cross). expected number observations category small, G-test may give inaccurate results, use exact test instead (fisher.test()). G-test goodness--fit alternative chi-square test goodness--fit (chisq.test()); tests advantages disadvantages, results two tests usually similar.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"g-test-of-independence","dir":"Reference","previous_headings":"","what":"G-test of Independence","title":"G-test for Count Data — g.test","text":"Use G-test independence two nominal variables, two possible values. want know whether proportions one variable different among values variable. also possible G-test independence two nominal variables. example, Jackson et al. (2013) also data children 3, analysis old vs. young, thigh vs. arm, reaction vs. reaction, analyzed together. Fisher's exact test (fisher.test()) exact test, G-test still approximation. 2x2 table, Fisher's Exact test may slower still run seconds, even sum observations multiple millions. G-test independence alternative chi-square test independence (chisq.test()), give approximately results.","code":""},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"how-the-test-works","dir":"Reference","previous_headings":"","what":"How the Test Works","title":"G-test for Count Data — g.test","text":"Unlike exact test goodness--fit (fisher.test()), G-test directly calculate probability obtaining observed results something extreme. Instead, like almost statistical tests, G-test intermediate step; uses data calculate test statistic measures far observed data null expectation. use mathematical relationship, case chi-square distribution, estimate probability obtaining value test statistic. G-test uses log ratio two likelihoods test statistic, also called likelihood ratio test log-likelihood ratio test. formula calculate G-statistic : \\(G = 2 * sum(x * log(x / E))\\) E expected values. Since chi-square distributed, p value can calculated R :   df degrees freedom. two categories want find ones significantly different null expectation, can use method testing category vs. sum categories, Bonferroni correction. use G-tests category, course.","code":"p <- stats::pchisq(G, df, lower.tail = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"G-test for Count Data — g.test","text":"McDonald, J.H. 2014. Handbook Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland. http://www.biostathandbook.com/gtestgof.html.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/g.test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"G-test for Count Data — g.test","text":"","code":"# = EXAMPLE 1 = # Shivrain et al. (2006) crossed clearfield rice (which are resistant # to the herbicide imazethapyr) with red rice (which are susceptible to # imazethapyr). They then crossed the hybrid offspring and examined the # F2 generation, where they found 772 resistant plants, 1611 moderately # resistant plants, and 737 susceptible plants. If resistance is controlled # by a single gene with two co-dominant alleles, you would expect a 1:2:1 # ratio.  x <- c(772, 1611, 737) g.test(x, p = c(1, 2, 1) / 4) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 4.1471, p-value = 0.1257 #>   # There is no significant difference from a 1:2:1 ratio. # Meaning: resistance controlled by a single gene with two co-dominant # alleles, is plausible.   # = EXAMPLE 2 = # Red crossbills (Loxia curvirostra) have the tip of the upper bill either # right or left of the lower bill, which helps them extract seeds from pine # cones. Some have hypothesized that frequency-dependent selection would # keep the number of right and left-billed birds at a 1:1 ratio. Groth (1992) # observed 1752 right-billed and 1895 left-billed crossbills.  x <- c(1752, 1895) g.test(x) #>  #> \tG-test of goodness-of-fit (likelihood ratio test) #>  #> data:  x #> X-squared = 5.6085, p-value = 0.01787 #>   # There is a significant difference from a 1:1 ratio. # Meaning: there are significantly more left-billed birds."},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine (New) Episodes for Patients — get_episode","title":"Determine (New) Episodes for Patients — get_episode","text":"functions determine items vector can considered (start ) new episode, based argument episode_days. can used determine clinical episodes epidemiological analysis. get_episode() function returns index number episode per group, is_new_episode() function returns values TRUE/FALSE indicate whether item vector start new episode.","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"get_episode(x, episode_days, ...)  is_new_episode(x, episode_days, ...)"},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine (New) Episodes for Patients — get_episode","text":"x vector dates (class Date POSIXt), sorted internally determine episodes episode_days required episode length days, can also less day Inf, see Details ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine (New) Episodes for Patients — get_episode","text":"get_episode(): double vector is_new_episode(): logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine (New) Episodes for Patients — get_episode","text":"Dates first sorted old new. oldest date mark start first episode. date, next date marked least episode_days days later start first episode. second marked date , next date marked least episode_days days later start second episode start third episode, . vector returned, original order restored. first_isolate() function wrapper around is_new_episode() function, efficient data sets containing microorganism codes names allows different isolate selection methods. dplyr package required functions work, functions support variable grouping work conveniently inside dplyr verbs filter(), mutate() summarise().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/get_episode.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine (New) Episodes for Patients — get_episode","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates df <- example_isolates[sample(seq_len(2000), size = 200), ]  get_episode(df$date, episode_days = 60)    # indices #>   [1] 63 42 61 46 22 21 21 10 46 12 31 47 53 64  9 27 12  7 30 25 15 61 52 20 18 #>  [26] 46 53 39 38 54 61 58 12 58 39 21 27 35 66 46 59 57 13 43 30 29 60 51 25 35 #>  [51] 22  4 56 20  5 18 18 22  9 21 50  9 58 38 59 44 46 11 41 55 55  5 14 56 26 #>  [76] 53 63 33 20  5 31 32 10  1 51 46  2 28 24 40 10 13 26 55 12 49 54 16 43 26 #> [101] 51 37 60 27 49 23 42 63  5 60  7 23  4 37 47 22 52 45 65 56 11 38 17 36 12 #> [126]  9 47 25 14  1 56 38 66 42 53 19 51 10 34 16 11 15 62 31 40 22 63 49 32 62 #> [151] 45  6 48  5 58 57  7 56 46 13 58 65 15 21  7 42  8  5  6  6 28 29  4 26 19 #> [176]  3 56  3 60 61 63 63 56 65 16 21 40 57 23 16 47 58 61 21 32  9 60  3 17 62 is_new_episode(df$date, episode_days = 60) # TRUE/FALSE #>   [1] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE #>  [13] FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE  TRUE #>  [25] FALSE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE #>  [37] FALSE  TRUE  TRUE FALSE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE  TRUE #>  [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE #>  [61]  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE #>  [73] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE #>  [85] FALSE FALSE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE FALSE FALSE FALSE #>  [97] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE #> [109]  TRUE FALSE  TRUE  TRUE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE #> [121] FALSE FALSE FALSE  TRUE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE FALSE #> [133] FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE FALSE  TRUE  TRUE #> [145]  TRUE  TRUE FALSE  TRUE FALSE FALSE  TRUE  TRUE  TRUE FALSE FALSE FALSE #> [157] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE #> [169] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE  TRUE FALSE FALSE  TRUE  TRUE #> [181]  TRUE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE  TRUE FALSE  TRUE #> [193] FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE FALSE  # filter on results from the third 60-day episode only, using base R df[which(get_episode(df$date, 60) == 3), ] #> # A tibble: 3 × 49 #>   date       hospita…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>   <date>     <fct>     <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #> 1 2002-06-06 D         FALSE   TRUE    FALSE      20 F      24D393  B_ESCHR_COLI #> 2 2002-06-18 B         TRUE    FALSE   FALSE      30 M      012595  B_CRYNB      #> 3 2002-07-23 B         TRUE    FALSE   FALSE      51 M      F35553  B_STPHY_AURS #> # … with 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, AMX <rsi>, #> #   AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, CXM <rsi>, #> #   FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, TOB <rsi>, #> #   AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, FOS <rsi>, #> #   LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, TCY <rsi>, #> #   TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, IPM <rsi>, #> #   MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, RIF <rsi>, and … #> # ℹ Use `colnames()` to see all variable names  # the functions also work for less than a day, e.g. to include one per hour: get_episode(c(Sys.time(),               Sys.time() + 60 * 60),             episode_days = 1/24) #> [1] 1 2  # \\donttest{ if (require(\"dplyr\")) {   # is_new_episode() can also be used in dplyr verbs to determine patient   # episodes based on any (combination of) grouping variables:   df %>%     mutate(condition = sample(x = c(\"A\", \"B\", \"C\"),                                size = 2000,                               replace = TRUE)) %>%      group_by(condition) %>%     mutate(new_episode = is_new_episode(date, 365)) %>%     select(patient_id, date, condition, new_episode)        df %>%     group_by(hospital_id, patient_id) %>%     transmute(date,                patient_id,               new_index = get_episode(date, 60),               new_logical = is_new_episode(date, 60))      df %>%     group_by(hospital_id) %>%      summarise(n_patients = n_distinct(patient_id),               n_episodes_365 = sum(is_new_episode(date, episode_days = 365)),               n_episodes_60  = sum(is_new_episode(date, episode_days = 60)),               n_episodes_30  = sum(is_new_episode(date, episode_days = 30)))             # grouping on patients and microorganisms leads to the same   # results as first_isolate() when using 'episode-based':   x <- df %>%     filter_first_isolate(include_unknown = TRUE,                          method = \"episode-based\")        y <- df %>%     group_by(patient_id, mo) %>%     filter(is_new_episode(date, 365)) %>%     ungroup()    identical(x, y)      # but is_new_episode() has a lot more flexibility than first_isolate(),   # since you can now group on anything that seems relevant:   df %>%     group_by(patient_id, mo, hospital_id, ward_icu) %>%     mutate(flag_episode = is_new_episode(date, 365)) %>%     select(group_vars(.), flag_episode) } #> Error in mutate(., condition = sample(x = c(\"A\", \"B\", \"C\"), size = 2000,     replace = TRUE)): Problem while computing `condition = sample(x = c(\"A\", \"B\", \"C\"), size = #> 2000, replace = TRUE)`. #> ✖ `condition` must be size 200 or 1, not 2000. # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"PCA Biplot with ggplot2 — ggplot_pca","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"Produces ggplot2 variant -called biplot PCA (principal component analysis), flexible appealing base R biplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"ggplot_pca(   x,   choices = 1:2,   scale = 1,   pc.biplot = TRUE,   labels = NULL,   labels_textsize = 3,   labels_text_placement = 1.5,   groups = NULL,   ellipse = TRUE,   ellipse_prob = 0.68,   ellipse_size = 0.5,   ellipse_alpha = 0.5,   points_size = 2,   points_alpha = 0.25,   arrows = TRUE,   arrows_colour = \"darkblue\",   arrows_size = 0.5,   arrows_textsize = 3,   arrows_textangled = TRUE,   arrows_alpha = 0.75,   base_textsize = 10,   ... )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"ggplot_pca() function based ggbiplot() function ggbiplot package Vince Vu, found GitHub: https://github.com/vqv/ggbiplot (retrieved: 2 March 2020, latest commit: 7325e88; 12 February 2015). per GPL-2 licence demands documentation code changes, changes made based source code : Rewritten code remove dependency packages plyr, scales grid Parametrised options, like arrow ellipse settings Hardened input possibilities defining exact type user input every argument Added total amount explained variance caption plot Cleaned syntax based lintr package, fixed grammatical errors added integrity checks Updated documentation","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"x object returned pca(), prcomp() princomp() choices length 2 vector specifying components plot. default     biplot strict sense. scale variables scaled lambda ^ scale     observations scaled lambda ^ (1-scale)     lambda singular values computed     princomp. Normally 0 <= scale <= 1, warning     issued specified scale outside range. pc.biplot true, use Gabriel (1971) refers \"principal component     biplot\", lambda = 1 observations scaled sqrt(n)     variables scaled sqrt(n).  inner products     variables approximate covariances distances observations     approximate Mahalanobis distance. labels optional vector labels observations. set, labels placed respective points. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). labels_textsize size text used labels labels_text_placement adjustment factor placement variable names (>=1 means away arrow head) groups optional vector groups labels, length labels. set, points labels coloured according groups. using pca() function input x, determined automatically based attribute non_numeric_cols, see pca(). ellipse logical indicate whether normal data ellipse drawn group (set groups) ellipse_prob statistical size ellipse normal probability ellipse_size size ellipse line ellipse_alpha alpha (transparency) ellipse line points_size size points points_alpha alpha (transparency) points arrows logical indicate whether arrows drawn arrows_colour colour arrow text arrows_size size (thickness) arrow lines arrows_textsize size text end arrows arrows_textangled logical whether text end arrows angled arrows_alpha alpha (transparency) arrows text base_textsize text size plot elements except labels arrows ... arguments passed functions","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"colours labels points can changed adding another scale layer colour, scale_colour_viridis_d() scale_colour_brewer().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"PCA Biplot with ggplot2 — ggplot_pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first    resistance_data <- example_isolates %>%      group_by(order = mo_order(mo),       # group on anything, like order              genus = mo_genus(mo)) %>%   #   and genus as we do here;     filter(n() >= 30) %>%                # filter on only 30 results per group     summarise_if(is.rsi, resistance)     # then get resistance of all drugs        # now conduct PCA for certain antimicrobial agents   pca_result <- resistance_data %>%              pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)         summary(pca_result)      # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   ggplot_pca(pca_result)      if (require(\"ggplot2\")) {     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #>   and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>  #> Loading required package: ggplot2   # }"},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":null,"dir":"Reference","previous_headings":"","what":"AMR Plots with ggplot2 — ggplot_rsi","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"Use functions create bar plots AMR data analysis. functions rely ggplot2 functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"","code":"ggplot_rsi(   data,   position = NULL,   x = \"antibiotic\",   fill = \"interpretation\",   facet = NULL,   breaks = seq(0, 1, 0.1),   limits = NULL,   translate_ab = \"name\",   combine_SI = TRUE,   combine_IR = FALSE,   minimum = 30,   language = get_AMR_locale(),   nrow = NULL,   colours = c(S = \"#3CAEA3\", SI = \"#3CAEA3\", I = \"#F6D55C\", IR = \"#ED553B\", R =     \"#ED553B\"),   datalabels = TRUE,   datalabels.size = 2.5,   datalabels.colour = \"grey15\",   title = NULL,   subtitle = NULL,   caption = NULL,   x.title = \"Antimicrobial\",   y.title = \"Proportion\",   ... )  geom_rsi(   position = NULL,   x = c(\"antibiotic\", \"interpretation\"),   fill = \"interpretation\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   combine_IR = FALSE,   ... )  facet_rsi(facet = c(\"interpretation\", \"antibiotic\"), nrow = NULL)  scale_y_percent(breaks = seq(0, 1, 0.1), limits = NULL)  scale_rsi_colours(..., aesthetics = \"fill\")  theme_rsi()  labels_rsi_count(   position = NULL,   x = \"antibiotic\",   translate_ab = \"name\",   minimum = 30,   language = get_AMR_locale(),   combine_SI = TRUE,   combine_IR = FALSE,   datalabels.size = 3,   datalabels.colour = \"grey15\" )"},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"data data.frame column(s) class rsi (see .rsi()) position position adjustment bars, either \"fill\", \"stack\" \"dodge\" x variable show x axis, either \"antibiotic\" (default) \"interpretation\" grouping variable fill variable categorise using plots legend, either \"antibiotic\" (default) \"interpretation\" grouping variable facet variable split plots , either \"interpretation\" (default) \"antibiotic\" grouping variable breaks numeric vector positions limits numeric vector length two providing limits scale, use NA refer existing minimum maximum translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant). used argument combine_IR, now follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . Default TRUE. combine_IR logical indicate whether values R must merged one, output consists S vs. +R (susceptible vs. non-susceptible). outdated, see argument combine_SI. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. nrow (using facet) number rows colours named vactor colour used filling. default colours colour-blind friendly. datalabels show datalabels using labels_rsi_count() datalabels.size size datalabels datalabels.colour colour datalabels title text show title plot subtitle text show subtitle plot caption text show caption plot x.title text show x axis description y.title text show y axis description ... arguments passed geom_rsi() , case scale_rsi_colours(), named values set colours. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. See Examples. aesthetics aesthetics apply colours , defaults \"fill\" can also (combination ) \"alpha\", \"colour\", \"fill\", \"linetype\", \"shape\" \"size\"","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"default, names antibiotics shown plots using ab_name(). can set translate_ab argument. See count_df().","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"the-functions","dir":"Reference","previous_headings":"","what":"The Functions","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"geom_rsi() take variable data rsi class (created .rsi()) using rsi_df() plot bars percentage R, S. default behaviour bars stacked different antibiotics x axis. facet_rsi() creates 2d plots (default based S//R) using ggplot2::facet_wrap(). scale_y_percent() transforms y axis 0 100% range using ggplot2::scale_y_continuous(). scale_rsi_colours() sets colours bars (green S, yellow , red R). multilingual support. default colours colour-blind friendly, maintaining convention e.g. 'susceptible' green 'resistant' red. theme_rsi() [ggplot2 theme][ggplot2::theme() minimal distraction. labels_rsi_count() print datalabels bars percentage amount isolates using ggplot2::geom_text(). ggplot_rsi() wrapper around functions uses data first input. makes possible use function pipe (%>%). See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/ggplot_rsi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"AMR Plots with ggplot2 — ggplot_rsi","text":"","code":"# \\donttest{ if (require(\"ggplot2\") & require(\"dplyr\")) {     # get antimicrobial results for drugs against a UTI:   ggplot(example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)) +     geom_rsi()     # prettify the plot using some additional functions:   df <- example_isolates %>% select(AMX, NIT, FOS, TMP, CIP)   ggplot(df) +     geom_rsi() +     scale_y_percent() +     scale_rsi_colours() +     labels_rsi_count() +     theme_rsi()     # or better yet, simplify this using the wrapper function - a single command:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_rsi()     # get only proportions and no counts:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_rsi(datalabels = FALSE)     # add other ggplot2 arguments as you like:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_rsi(width = 0.5,                colour = \"black\",                size = 1,                linetype = 2,                alpha = 0.25)    # you can alter the colours with colour names:   example_isolates %>%     select(AMX) %>%     ggplot_rsi(colours = c(SI = \"yellow\"))    # but you can also use the built-in colour-blind friendly colours for   # your plots, where \"S\" is green, \"I\" is yellow and \"R\" is red:   data.frame(x = c(\"Value1\", \"Value2\", \"Value3\"),              y = c(1, 2, 3),              z = c(\"Value4\", \"Value5\", \"Value6\")) %>%     ggplot() +     geom_col(aes(x = x, y = y, fill = z)) +     scale_rsi_colours(Value4 = \"S\", Value5 = \"I\", Value6 = \"R\")      # resistance of ciprofloxacine per age group   example_isolates %>%     mutate(first_isolate = first_isolate()) %>%     filter(first_isolate == TRUE,            mo == as.mo(\"E. coli\")) %>%     # age_groups() is also a function in this AMR package:     group_by(age_group = age_groups(age)) %>%     select(age_group, CIP) %>%     ggplot_rsi(x = \"age_group\")      # a shorter version which also adjusts data label colours:   example_isolates %>%     select(AMX, NIT, FOS, TMP, CIP) %>%     ggplot_rsi(colours = FALSE)     # it also supports groups (don't forget to use the group var on `x` or `facet`):   example_isolates %>%     filter(mo_is_gram_negative()) %>%      # select only UTI-specific drugs     select(hospital_id, AMX, NIT, FOS, TMP, CIP) %>%     group_by(hospital_id) %>%     ggplot_rsi(x = \"hospital_id\",                facet = \"antibiotic\",                nrow = 1,                title = \"AMR of Anti-UTI Drugs Per Hospital\",                x.title = \"Hospital\",                datalabels = FALSE) } #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> ℹ Using column 'mo' as input for `mo_is_gram_negative()` #> Warning: Removed 6 rows containing missing values (position_stack).  # }"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":null,"dir":"Reference","previous_headings":"","what":"Guess Antibiotic Column — guess_ab_col","title":"Guess Antibiotic Column — guess_ab_col","text":"tries find column name data set based information antibiotics data set. Also supports WHONET abbreviations.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"guess_ab_col(   x = NULL,   search_string = NULL,   verbose = FALSE,   only_rsi_columns = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Guess Antibiotic Column — guess_ab_col","text":"x data.frame search_string text search x , checked .ab() value column x verbose logical indicate whether additional info printed only_rsi_columns logical indicate whether antibiotic columns must detected transformed class <rsi> (see .rsi()) beforehand (defaults FALSE)","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Guess Antibiotic Column — guess_ab_col","text":"column name x, NULL result found.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Guess Antibiotic Column — guess_ab_col","text":"can look antibiotic (trade) name abbreviation search x antibiotics data set column containing name code antibiotic. Longer columns names take precedence shorter column names.","code":""},{"path":"https://msberends.github.io/AMR/reference/guess_ab_col.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Guess Antibiotic Column — guess_ab_col","text":"","code":"df <- data.frame(amox = \"S\",                  tetr = \"R\")  guess_ab_col(df, \"amoxicillin\") #> [1] \"amox\" # [1] \"amox\" guess_ab_col(df, \"J01AA07\") # ATC code of tetracycline #> [1] \"tetr\" # [1] \"tetr\"  guess_ab_col(df, \"J01AA07\", verbose = TRUE) #> Auto-guessing columns suitable for analysis #> ... #>  OK. #> ℹ Using column 'amox' as input for AMX (amoxicillin). #> ℹ Using column 'tetr' as input for TCY (tetracycline). #> ℹ Using column 'tetr' as input for J01AA07 (tetracycline). #> [1] \"tetr\" # NOTE: Using column 'tetr' as input for J01AA07 (tetracycline). # [1] \"tetr\"  # WHONET codes df <- data.frame(AMP_ND10 = \"R\",                  AMC_ED20 = \"S\") guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ND10\" # [1] \"AMP_ND10\" guess_ab_col(df, \"J01CR02\") #> [1] \"AMC_ED20\" # [1] \"AMC_ED20\" guess_ab_col(df, as.ab(\"augmentin\")) #> [1] \"AMC_ED20\" # [1] \"AMC_ED20\"  # Longer names take precendence: df <- data.frame(AMP_ED2 = \"S\",                  AMP_ED20 = \"S\") guess_ab_col(df, \"ampicillin\") #> [1] \"AMP_ED2\" # [1] \"AMP_ED20\""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"Data set containing defined intrinsic resistance EUCAST bug-drug combinations.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"intrinsic_resistant"},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"data.frame 134,956 observations 2 variables: mo Microorganism ID ab Antibiotic ID","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"repository AMR package contains file comprising data set full taxonomic antibiotic names: https://github.com/msberends/AMR/blob/main/data-raw/intrinsic_resistant.txt. file allows machine reading EUCAST guidelines intrinsic resistance, almost impossible Excel PDF files distributed EUCAST. file updated automatically. data set based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021).","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/intrinsic_resistant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Bacterial Intrinsic Resistance — intrinsic_resistant","text":"","code":"head(intrinsic_resistant) #>        mo  ab #> 1 B_GRAMP ATM #> 2 B_GRAMP COL #> 3 B_GRAMP NAL #> 4 B_GRAMP PLB #> 5 B_GRAMP TEM #> 6 B_ABTRP ATM"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":null,"dir":"Reference","previous_headings":"","what":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"According binomial nomenclature, lowest four taxonomic levels (family, genus, species, subspecies) printed italics. function finds taxonomic names within strings makes italic.","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(string, type = c(\"markdown\", \"ansi\"))  italicize_taxonomy(string, type = c(\"markdown\", \"ansi\"))"},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"string character (vector) type type conversion taxonomic names, either \"markdown\" \"ansi\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"function finds taxonomic names makes italic based microorganisms data set. taxonomic names can italicised using markdown (default) adding * taxonomic names, using ANSI colours adding \\033[3m \\033[23m taxonomic names. multiple ANSI colours available, conversion occur. function also supports abbreviation genus followed species, \"E. coli\" \"K. pneumoniae ozaenae\".","code":""},{"path":"https://msberends.github.io/AMR/reference/italicise_taxonomy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Italicise Taxonomic Families, Genera, Species, Subspecies — italicise_taxonomy","text":"","code":"italicise_taxonomy(\"An overview of Staphylococcus aureus isolates\") #> [1] \"An overview of *Staphylococcus aureus* isolates\" italicise_taxonomy(\"An overview of S. aureus isolates\") #> [1] \"An overview of *S. aureus* isolates\"  cat(italicise_taxonomy(\"An overview of S. aureus isolates\", type = \"ansi\")) #> An overview of S. aureus isolates"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":null,"dir":"Reference","previous_headings":"","what":"Join microorganisms to a Data Set — join","title":"Join microorganisms to a Data Set — join","text":"Join data set microorganisms easily existing data set character vector.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Join microorganisms to a Data Set — join","text":"","code":"inner_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  left_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  right_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  full_join_microorganisms(x, by = NULL, suffix = c(\"2\", \"\"), ...)  semi_join_microorganisms(x, by = NULL, ...)  anti_join_microorganisms(x, by = NULL, ...)"},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Join microorganisms to a Data Set — join","text":"x existing data set join, character vector. case character vector, resulting data.frame contain column 'x' values. variable join - left empty search column class mo (created .mo()) \"mo\" column name exists x, otherwise column name x values exist microorganisms$mo (= \"bacteria_id\"), another column microorganisms (named, like = c(\"bacteria_id\" = \"fullname\")) suffix non-joined duplicate variables x y, suffixes added output disambiguate . character vector length 2. ... ignored, place allow future extensions","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Join microorganisms to a Data Set — join","text":"data.frame","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Join microorganisms to a Data Set — join","text":"Note: opposed join() functions dplyr, character vectors supported default existing columns get suffix \"2\" newly joined columns get suffix. dplyr package installed, join functions used. Otherwise, much slower merge() interaction() functions base R used.","code":""},{"path":"https://msberends.github.io/AMR/reference/join.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Join microorganisms to a Data Set — join","text":"","code":"left_join_microorganisms(as.mo(\"K. pneumoniae\")) #> ℹ Function `as.mo()` is uncertain about \"K. pneumoniae\" (assuming #>   Klebsiella pneumoniae). Run `mo_uncertainties()` to review this. #>             mo              fullname  kingdom         phylum #> 1 B_KLBSL_PNMN Klebsiella pneumoniae Bacteria Proteobacteria #>                 class            order             family      genus    species #> 1 Gammaproteobacteria Enterobacterales Enterobacteriaceae Klebsiella pneumoniae #>   subspecies    rank            ref species_id source prevalence #> 1            species Trevisan, 1887     777151   LPSN          1 #>                                                                          snomed #> 1 1098101000112102, 446870005, 1098201000112108, 409801009, 56415008, 714315002 left_join_microorganisms(\"B_KLBSL_PNMN\") #>             mo              fullname  kingdom         phylum #> 1 B_KLBSL_PNMN Klebsiella pneumoniae Bacteria Proteobacteria #>                 class            order             family      genus    species #> 1 Gammaproteobacteria Enterobacterales Enterobacteriaceae Klebsiella pneumoniae #>   subspecies    rank            ref species_id source prevalence #> 1            species Trevisan, 1887     777151   LPSN          1 #>                                                                          snomed #> 1 1098101000112102, 446870005, 1098201000112108, 409801009, 56415008, 714315002  # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     left_join_microorganisms() %>%      colnames()     df <- data.frame(date = seq(from = as.Date(\"2018-01-01\"),                               to = as.Date(\"2018-01-07\"),                               by = 1),                    bacteria = as.mo(c(\"S. aureus\", \"MRSA\", \"MSSA\", \"STAAUR\",                                       \"E. coli\", \"E. coli\", \"E. coli\")),                    stringsAsFactors = FALSE)   colnames(df)   df_joined <- left_join_microorganisms(df, \"bacteria\")   colnames(df_joined) } #> Joining, by = \"mo\" #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli) and \"S. aureus\" (assuming Staphylococcus aureus). Run #>   `mo_uncertainties()` to review these uncertainties. #>  [1] \"date\"       \"bacteria\"   \"fullname\"   \"kingdom\"    \"phylum\"     #>  [6] \"class\"      \"order\"      \"family\"     \"genus\"      \"species\"    #> [11] \"subspecies\" \"rank\"       \"ref\"        \"species_id\" \"source\"     #> [16] \"prevalence\" \"snomed\"     # }"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":null,"dir":"Reference","previous_headings":"","what":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"functions can used determine first weighted isolates considering phenotype isolate selection (see first_isolate()). Using phenotype-based method determine first isolates reliable methods disregard phenotypes.","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"key_antimicrobials(   x = NULL,   col_mo = NULL,   universal = c(\"ampicillin\", \"amoxicillin/clavulanic acid\", \"cefuroxime\",     \"piperacillin/tazobactam\", \"ciprofloxacin\", \"trimethoprim/sulfamethoxazole\"),   gram_negative = c(\"gentamicin\", \"tobramycin\", \"colistin\", \"cefotaxime\", \"ceftazidime\",     \"meropenem\"),   gram_positive = c(\"vancomycin\", \"teicoplanin\", \"tetracycline\", \"erythromycin\",     \"oxacillin\", \"rifampin\"),   antifungal = c(\"anidulafungin\", \"caspofungin\", \"fluconazole\", \"miconazole\", \"nystatin\",     \"voriconazole\"),   only_rsi_columns = FALSE,   ... )  all_antimicrobials(x = NULL, only_rsi_columns = FALSE, ...)  antimicrobials_equal(   y,   z,   type = c(\"points\", \"keyantimicrobials\"),   ignore_I = TRUE,   points_threshold = 2,   ... )"},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"x data.frame antibiotics columns, like AMX amox. Can left blank determine automatically col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). universal names broad-spectrum antimicrobial agents, case-insensitive. Set NULL ignore. See Details default agents. gram_negative names antibiotic agents Gram-positives, case-insensitive. Set NULL ignore. See Details default agents. gram_positive names antibiotic agents Gram-negatives, case-insensitive. Set NULL ignore. See Details default agents. antifungal names antifungal agents fungi, case-insensitive. Set NULL ignore. See Details default agents. only_rsi_columns logical indicate whether columns must included transformed class <rsi> (see .rsi()) beforehand (defaults FALSE) ... ignored, place allow future extensions y, z character vectors compare type type determine weighed isolates; can \"keyantimicrobials\" \"points\", see Details ignore_I logical indicate whether antibiotic interpretations \"\" ignored type = \"keyantimicrobials\", see Details points_threshold minimum number points require differences antibiogram lead inclusion isolate type = \"points\", see Details","code":""},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"key_antimicrobials() all_antimicrobials() functions context-aware. means x argument can left blank used inside data.frame call, see Examples. function key_antimicrobials() returns character vector 12 antimicrobial results every isolate. function all_antimicrobials() returns character vector antimicrobial results every isolate. vectors can compared using antimicrobials_equal(), check two isolates generally antibiogram. Missing invalid values replaced dot (\".\") key_antimicrobials() ignored antimicrobials_equal(). Please see first_isolate() function important functions enable 'phenotype-based' method determination first isolates. default antimicrobial agents used rows (set universal) : Ampicillin Amoxicillin/clavulanic acid Cefuroxime Ciprofloxacin Piperacillin/tazobactam Trimethoprim/sulfamethoxazole default antimicrobial agents used Gram-negative bacteria (set gram_negative) : Cefotaxime Ceftazidime Colistin Gentamicin Meropenem Tobramycin default antimicrobial agents used Gram-positive bacteria (set gram_positive) : Erythromycin Oxacillin Rifampin Teicoplanin Tetracycline Vancomycin default antimicrobial agents used fungi (set antifungal) : Anidulafungin Caspofungin Fluconazole Miconazole Nystatin Voriconazole","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/key_antimicrobials.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"(Key) Antimicrobials for First Weighted Isolates — key_antimicrobials","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # output of the `key_antimicrobials()` function could be like this: strainA <- \"SSSRR.S.R..S\" strainB <- \"SSSIRSSSRSSS\"  # those strings can be compared with: antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\") #> [1] TRUE # TRUE, because I is ignored (as well as missing values)  antimicrobials_equal(strainA, strainB, type = \"keyantimicrobials\", ignore_I = FALSE) #> [1] FALSE # FALSE, because I is not ignored and so the 4th [character] differs  # \\donttest{ if (require(\"dplyr\")) {   # set key antibiotics to a new variable   my_patients <- example_isolates %>%     mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`     mutate(       # now calculate first isolates       first_regular = first_isolate(col_keyantimicrobials = FALSE),       # and first WEIGHTED isolates       first_weighted = first_isolate(col_keyantimicrobials = \"keyab\")     )     # Check the difference in this data set, 'weighted' results in more isolates:   sum(my_patients$first_regular, na.rm = TRUE)   sum(my_patients$first_weighted, na.rm = TRUE) } #> [1] 1395 # }"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":null,"dir":"Reference","previous_headings":"","what":"Kurtosis of the Sample — kurtosis","title":"Kurtosis of the Sample — kurtosis","text":"Kurtosis measure \"tailedness\" probability distribution real-valued random variable. normal distribution kurtosis 3 excess kurtosis 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for default kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for matrix kurtosis(x, na.rm = FALSE, excess = FALSE)  # S3 method for data.frame kurtosis(x, na.rm = FALSE, excess = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Kurtosis of the Sample — kurtosis","text":"x vector values, matrix data.frame na.rm logical indicate whether NA values stripped computation proceeds excess logical indicate whether excess kurtosis returned, defined kurtosis minus 3.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/kurtosis.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Kurtosis of the Sample — kurtosis","text":"","code":"kurtosis(rnorm(10000)) #> [1] 3.031812 kurtosis(rnorm(10000), excess = TRUE) #> [1] -0.03994734"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":null,"dir":"Reference","previous_headings":"","what":"Vectorised Pattern Matching with Keyboard Shortcut — like","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Convenient wrapper around grepl() match pattern: x %like% pattern. always returns logical vector always case-insensitive (use x %like_case% pattern case-sensitive matching). Also, pattern can long x compare items index vectors, can length iterate cases.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"like(x, pattern, ignore.case = TRUE)  x %like% pattern  x %unlike% pattern  x %like_case% pattern  x %unlike_case% pattern"},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"Idea like function data.table package, although altered explained Details.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"x character vector matches sought, object can coerced .character() character vector. pattern character vector containing regular expressions (character string fixed = TRUE) matched given character vector. Coerced .character() character string possible. ignore.case FALSE, pattern matching case sensitive TRUE, case ignored matching.","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"logical vector","code":""},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"like() %like%/%unlike% functions: case-insensitive (use %like_case%/%unlike_case% case-sensitive matching) Support multiple patterns Check pattern valid regular expression sets fixed = TRUE , greatly improve speed (vectorised pattern) Always use compatibility Perl unless fixed = TRUE, greatly improve speed Using RStudio? %like%/%unlike% functions can also directly inserted code Addins menu can keyboard shortcut like Shift+Ctrl+L Shift+Cmd+L (see menu Tools > Modify Keyboard Shortcuts...). keep pressing shortcut, inserted text iterated %like% -> %unlike% -> %like_case% -> %unlike_case%.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/like.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Vectorised Pattern Matching with Keyboard Shortcut — like","text":"","code":"a <- \"This is a test\" b <- \"TEST\" a %like% b #> [1] TRUE b %like% a #> [1] FALSE  # also supports multiple patterns a <- c(\"Test case\", \"Something different\", \"Yet another thing\") b <- c(     \"case\",           \"diff\",      \"yet\") a %like% b #> [1] TRUE TRUE TRUE a %unlike% b #> [1] FALSE FALSE FALSE  a[1] %like% b #> [1]  TRUE FALSE FALSE a %like% b[1] #> [1]  TRUE FALSE FALSE  # get isolates whose name start with 'Ent' or 'ent' example_isolates[which(mo_name(example_isolates$mo) %like% \"^ent\"), ] #> # A tibble: 106 × 49 #>    date       hospi…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo            #>    <date>     <fct>   <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>          #>  1 2002-02-21 C       FALSE   TRUE    FALSE      69 M      4FC193  B_ENTRC_FACM  #>  2 2002-04-08 A       TRUE    TRUE    FALSE      78 M      130252  B_ENTRC_FCLS  #>  3 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  4 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  5 2003-04-20 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  6 2003-04-21 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  7 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #>  8 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC_FCLS  #>  9 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC       #> 10 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # \\donttest{ # faster way, since mo_name() is context-aware: example_isolates[which(mo_name() %like% \"^ent\"), ] #> ℹ Using column 'mo' as input for `mo_name()` #> # A tibble: 106 × 49 #>    date       hospi…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo            #>    <date>     <fct>   <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>          #>  1 2002-02-21 C       FALSE   TRUE    FALSE      69 M      4FC193  B_ENTRC_FACM  #>  2 2002-04-08 A       TRUE    TRUE    FALSE      78 M      130252  B_ENTRC_FCLS  #>  3 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  4 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  5 2003-04-20 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  6 2003-04-21 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  7 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #>  8 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC_FCLS  #>  9 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC       #> 10 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names  if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_name() %like% \"^ent\") } #> ℹ Using column 'mo' as input for `mo_name()` #> # A tibble: 106 × 49 #>    date       hospi…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo            #>    <date>     <fct>   <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>          #>  1 2002-02-21 C       FALSE   TRUE    FALSE      69 M      4FC193  B_ENTRC_FACM  #>  2 2002-04-08 A       TRUE    TRUE    FALSE      78 M      130252  B_ENTRC_FCLS  #>  3 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  4 2002-06-23 D       FALSE   TRUE    FALSE      82 M      798871  B_ENTRC_FCLS  #>  5 2003-04-20 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  6 2003-04-21 D       TRUE    TRUE    FALSE      62 M      6BC362  B_ENTRC       #>  7 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #>  8 2003-08-13 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC_FCLS  #>  9 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRC       #> 10 2003-09-05 B       TRUE    FALSE   FALSE      52 M      F35553  B_ENTRBC_CLOC #> # … with 96 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names # }"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Determine isolates multidrug-resistant organisms (MDRO) according international, national custom guidelines.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"mdro(   x = NULL,   guideline = \"CMI2012\",   col_mo = NULL,   info = interactive(),   pct_required_classes = 0.5,   combine_SI = TRUE,   verbose = FALSE,   only_rsi_columns = FALSE,   ... )  custom_mdro_guideline(..., as_factor = TRUE)  brmo(x = NULL, only_rsi_columns = FALSE, ...)  mrgn(x = NULL, only_rsi_columns = FALSE, ...)  mdr_tb(x = NULL, only_rsi_columns = FALSE, ...)  mdr_cmi2012(x = NULL, only_rsi_columns = FALSE, ...)  eucast_exceptional_phenotypes(x = NULL, only_rsi_columns = FALSE, ...)"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"See supported guidelines list publications used function.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"x data.frame antibiotics columns, like AMX amox. Can left blank automatic determination. guideline specific guideline follow, see sections Supported international / national guidelines Using Custom Guidelines . left empty, publication Magiorakos et al. (see ) followed. col_mo column name IDs microorganisms (see .mo()), defaults first column class mo. Values coerced using .mo(). info logical indicate whether progress printed console, defaults print interactive sessions pct_required_classes minimal required percentage antimicrobial classes must available per isolate, rounded . example, default guideline, 17 antimicrobial classes must available S. aureus. Setting pct_required_classes argument 0.5 (default) means every S. aureus isolate least 8 different classes must available. lower number available classes return NA isolate. combine_SI logical indicate whether values S must merged one, resistance considered isolates R, . default behaviour mdro() function, follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . using combine_SI = FALSE, resistance considered isolates R . verbose logical turn Verbose mode (default ). Verbose mode, function return MDRO results, instead returns data set logbook form extensive info isolates MDRO-positive, . only_rsi_columns logical indicate whether antibiotic columns must detected transformed class <rsi> (see .rsi()) beforehand (defaults FALSE) ... case custom_mdro_guideline(): set rules, see section Using Custom Guidelines . Otherwise: column name antibiotic, see section Antibiotics . as_factor logical indicate whether returned value ordered factor (TRUE, default), otherwise character vector","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"CMI 2012 paper - function mdr_cmi2012() mdro(): Ordered factor levels Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant (XDR) < Pandrug-resistant (PDR) TB guideline - function mdr_tb() mdro(..., guideline = \"TB\"): Ordered factor levels Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant < Extensively drug-resistant German guideline - function mrgn() mdro(..., guideline = \"MRGN\"): Ordered factor levels Negative < 3MRGN < 4MRGN Everything else, except custom guidelines: Ordered factor levels Negative < Positive, unconfirmed < Positive. value \"Positive, unconfirmed\" means , according guideline, entirely sure isolate multi-drug resistant confirmed additional (e.g. molecular) tests","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"functions context-aware. means x argument can left blank used inside data.frame call, see Examples. pct_required_classes argument, values 1 divided 100. support fractions (0.75 3/4) percentages (75). Note: Every test involves Enterobacteriaceae family, internally performed using newly named order Enterobacterales, since Enterobacteriaceae family taxonomically reclassified Adeolu et al. 2016. , Enterobacteriaceae family Enterobacteriales () order. species old Enterobacteriaceae family still new Enterobacterales (without ) order, divided multiple families. way tests performed now mdro() function makes sure results 2016 2016 identical.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"supported-international-national-guidelines","dir":"Reference","previous_headings":"","what":"Supported International / National Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Currently supported guidelines (case-insensitive): guideline = \"CMI2012\" (default) Magiorakos AP, Srinivasan et al. \"Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.\" Clinical Microbiology Infection (2012) (link) guideline = \"EUCAST3.3\" (simply guideline = \"EUCAST\") European international guideline - EUCAST Expert Rules Version 3.3 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.2\" European international guideline - EUCAST Expert Rules Version 3.2 \"Intrinsic Resistance Unusual Phenotypes\" (link) guideline = \"EUCAST3.1\" European international guideline - EUCAST Expert Rules Version 3.1 \"Intrinsic Resistance Exceptional Phenotypes Tables\" (link) guideline = \"TB\" international guideline multi-drug resistant tuberculosis - World Health Organization \"Companion handbook guidelines programmatic management drug-resistant tuberculosis\" (link) guideline = \"MRGN\" German national guideline - Mueller et al. (2015) Antimicrobial Resistance Infection Control 4:7; doi:10.1186/s13756-015-0047-6 guideline = \"BRMO\" Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu \"WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)\" (link) Please suggest (country-specific) guidelines letting us know: https://github.com/msberends/AMR/issues/new.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"using-custom-guidelines","dir":"Reference","previous_headings":"","what":"Using Custom Guidelines","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"Custom guidelines can set custom_mdro_guideline() function. great importance custom rules determine MDROs hospital, e.g., rules dependent ward, state contact isolation variables data. familiar case_when() function dplyr package, recognise input method set rules. Rules must set using R considers 'formula notation'. rule written tilde (~) consequence rule written tilde:   row/isolate matches first rule, value first ~ (case 'Elderly Type ') set MDRO value. Otherwise, second rule tried . number rules unlimited. can print rules set console overview. Colours help reading console supports colours.   outcome function can used guideline argument mdro() function:   Rules can also combined custom rules using c():   rules set (custom object case) exported shared file location using saveRDS() collaborate multiple users. custom rules set imported using readRDS().","code":"custom <- custom_mdro_guideline(CIP == \"R\" & age > 60 ~ \"Elderly Type A\",                                 ERY == \"R\" & age > 60 ~ \"Elderly Type B\") custom #> A set of custom MDRO rules: #>   1. CIP is \"R\" and age is higher than 60 -> Elderly Type A #>   2. ERY is \"R\" and age is higher than 60 -> Elderly Type B #>   3. Otherwise -> Negative #>  #> Unmatched rows will return NA. x <- mdro(example_isolates,           guideline = custom) table(x) #>       Negative Elderly Type A Elderly Type B #>           1070            198            732 x <- mdro(example_isolates,           guideline = c(custom,                          custom_mdro_guideline(ERY == \"R\" & age > 50 ~ \"Elderly Type C\"))) table(x) #>       Negative Elderly Type A Elderly Type B Elderly Type C  #>            961            198            732            109"},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"antibiotics","dir":"Reference","previous_headings":"","what":"Antibiotics","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"define antibiotics column names, leave determine automatically guess_ab_col() input text (case-insensitive), use NULL skip column (e.g. TIC = NULL skip ticarcillin). Manually defined non-existing columns skipped warning. following antibiotics eligible functions eucast_rules() mdro(). shown format 'name (antimicrobial ID, ATC code)', sorted alphabetically: Amikacin (AMK, S01AE08), amoxicillin (AMX, J01MA02), amoxicillin/clavulanic acid (AMC, J01MA23), ampicillin (AMP, J01MA04), ampicillin/sulbactam (SAM, J01MA08), arbekacin (ARB, J01MA19), aspoxicillin (APX, J01MA16), azidocillin (AZD, J01MA15), azithromycin (AZM, J01MA11), azlocillin (AZL, J01MA12), aztreonam (ATM, J01MA24), bacampicillin (BAM, J01MA07), bekanamycin (BEK, J01MA14), benzathine benzylpenicillin (BNB, D10AF05), benzathine phenoxymethylpenicillin (BNP, J01MA06), benzylpenicillin (PEN, J01MA01), besifloxacin (BES, J01MA18), biapenem (BIA, J01MA03), carbenicillin (CRB, J01MA17), carindacillin (CRN, J01MA10), cefacetrile (CAC, J01MA21), cefaclor (CEC, J01MA09), cefadroxil (CFR, J01MA05), cefaloridine (RID, P01AA05), cefamandole (MAN, J01MA22), cefatrizine (CTZ, J01MA13), cefazedone (CZD, J01CA01), cefazolin (CZO, J01CA04), cefcapene (CCP, J01CA12), cefdinir (CDR, J01CR05), cefditoren (DIT, J01CA13), cefepime (FEP, J01AA02), cefetamet (CAT, J01FA10), cefixime (CFM, J01FA09), cefmenoxime (CMX, J01CR02), cefmetazole (CMZ, J01AA08), cefodizime (DIZ, J01FA06), cefonicid (CID, J01CF04), cefoperazone (CFP, J01CF05), cefoperazone/sulbactam (CSL, J01CR01), ceforanide (CND, J01CA19), cefotaxime (CTX, J01CE04), cefotetan (CTT, J01CA09), cefotiam (CTF, J01DF01), cefoxitin (FOX, J01CA06), cefozopran (ZOP, J01CE08), cefpiramide (CPM, J01CE10), cefpirome (CPO, J01CE01), cefpodoxime (CPD, J01CA03), cefprozil (CPR, J01CA05), cefroxadine (CRD, J01CE07), cefsulodin (CFS, J01CF02), ceftaroline (CPT, J01CF01), ceftazidime (CAZ, J01CA07), ceftazidime/clavulanic acid (CCV, J01CA18), cefteram (CEM, J01CA11), ceftezole (CTL, J01CA14), ceftibuten (CTB, J01CF03), ceftizoxime (CZX, J01CA10), ceftobiprole medocaril (CFM1, J01CF06), ceftolozane/enzyme inhibitor (CEI, J01CE06), ceftriaxone (CRO, J01CE05), cefuroxime (CXM, J01CE02), cephalexin (LEX, J01CA02), cephalothin (CEP, J01CA08), cephapirin (HAP, J01CE09), cephradine (CED, J01CE03), chloramphenicol (CHL, J01CG01), ciprofloxacin (CIP, J01CA16), clarithromycin (CLR, J01CR04), clindamycin (CLI, J01CA15), clometocillin (CLM, J01CG02), cloxacillin (CLO, J01CA17), colistin (COL, J01CR03), cycloserine (CYC, J01DB10), dalbavancin (DAL, J01DC04), daptomycin (DAP, J01DB05), delafloxacin (DFX, J01DB02), dibekacin (DKB, J01DC03), dicloxacillin (DIC, J01DB07), dirithromycin (DIR, J01DB06), doripenem (DOR, J01DB04), doxycycline (DOX, J01DD17), enoxacin (ENX, J01DD15), epicillin (EPC, J01DD16), ertapenem (ETP, J01DE01), erythromycin (ERY, J01DD10), fleroxacin (FLE, J01DD08), flucloxacillin (FLC, J01DD05), flurithromycin (FLR1, J01DC09), fosfomycin (FOS, J01DD09), framycetin (FRM, J01DC06), fusidic acid (FUS, J01DD12), garenoxacin (GRN, J01DD62), gatifloxacin (GAT, J01DC11), gemifloxacin (GEM, J01DD01), gentamicin (GEN, J01DC05), grepafloxacin (GRX, J01DC07), hetacillin (HET, J01DC01), imipenem (IPM, J01DE03), isepamicin (ISE, J01DD11), josamycin (JOS, J01DE02), kanamycin (KAN, J01DD13), latamoxef (LTM, J01DC10), levofloxacin (LVX, J01DB11), levonadifloxacin (LND, J01DD03), lincomycin (LIN, J01DI02), linezolid (LNZ, J01DD02), lomefloxacin (LOM, J01DD52), loracarbef (LOR, J01DD18), mecillinam (MEC, J01DB12), meropenem (MEM, J01DD14), meropenem/vaborbactam (MEV, J01DD07), metampicillin (MTM, J01DI01), methicillin (MET, J01DI54), mezlocillin (MEZ, J01DD04), micronomicin (MCR, J01DC02), midecamycin (MID, J01DB01), minocycline (MNO, J01DB03), miocamycin (MCM, J01DB08), moxifloxacin (MFX, J01DB09), nadifloxacin (NAD, J01DD06), nafcillin (NAF, J01DC08), nalidixic acid (NAL, J01DH05), neomycin (NEO, J01DH04), netilmicin (NET, J01DH03), nitrofurantoin (NIT, J01DH51), norfloxacin (, J01DH02), ofloxacin (OFX, J01DH52), oleandomycin (OLE, J01XA02), oritavancin (ORI, J01XA01), oxacillin (OXA, J01XC01), pazufloxacin (PAZ, J01FA13), pefloxacin (PEF, J01FA01), penamecillin (PNM, J01FA14), phenethicillin (PHE, J01FA07), phenoxymethylpenicillin (PHN, J01FA03), piperacillin (PIP, J01FA11), piperacillin/tazobactam (TZP, J01FA05), pivampicillin (PVM, J01FA12), pivmecillinam (PME, J01FA16), plazomicin (PLZ, J01FA02), polymyxin B (PLB, J01FA15), pristinamycin (PRI, J01FA08), procaine benzylpenicillin (PRB, J01FF02), propicillin (PRP, J01FG01), prulifloxacin (PRU, J01FG02), quinupristin/dalfopristin (QDA, J04AB02), ribostamycin (RST, J01XX09), rifampicin (RIF, J01XX08), rokitamycin (ROK, J01AA07), roxithromycin (RXT, J01XB01), rufloxacin (RFL, J01XB02), sisomicin (SIS, J01XE01), sitafloxacin (SIT, J01AA12), solithromycin (SOL, J01EA01), sparfloxacin (SPX, J01XX01), spiramycin (SPI, J01BA01), streptoduocin (STR, J01GB06), streptomycin (STR1, J01GB12), sulbactam (SUL, J01GB13), sulbenicillin (SBC, J01GB09), sulfadiazine (SDI, D09AA01), sulfadiazine/trimethoprim (SLT1, J01GB03), sulfadimethoxine (SUD, J01GB11), sulfadimidine (SDM, J01GB04), sulfadimidine/trimethoprim (SLT2, S01AA22), sulfafurazole (SLF, J01GB05), sulfaisodimidine (SLF1, J01GB07), sulfalene (SLF2, J01GB14), sulfamazone (SZO, J01GB10), sulfamerazine (SLF3, J01GB08), sulfamerazine/trimethoprim (SLT3, J01GA02), sulfamethizole (SLF4, J01GA01), sulfamethoxazole (SMX, J01GB01), sulfamethoxypyridazine (SLF5, J01EE01), sulfametomidine (SLF6, J01MB02), sulfametoxydiazine (SLF7, J01FF01), sulfametrole/trimethoprim (SLT4, J01XA04), sulfamoxole (SLF8, J01XA05), sulfamoxole/trimethoprim (SLT5, J01XA03), sulfanilamide (SLF9, J04AB01), sulfaperin (SLF10, J01XX11), sulfaphenazole (SLF11, J01EC02), sulfapyridine (SLF12, J01ED01), sulfathiazole (SUT, J01EB03), sulfathiourea (SLF13, J01EB05), sultamicillin (SLT6, J01EB01), talampicillin (TAL, J01ED02), tazobactam (TAZ, J01ED09), tedizolid (TZD, J01ED07), teicoplanin (TEC, J01EB02), telavancin (TLV, J01EC01), telithromycin (TLT, J01ED05), temafloxacin (TMX, J01ED03), temocillin (TEM, J01ED04), tetracycline (TCY, J01EC03), ticarcillin (TIC, J01EB06), ticarcillin/clavulanic acid (TCC, J01ED06), tigecycline (TGC, J01ED08), tilbroquinol (TBQ, J01EB04), tobramycin (TOB, J01EB07), tosufloxacin (TFX, J01EB08), trimethoprim (TMP, J01EE02), trimethoprim/sulfamethoxazole (SXT, J01EE05), troleandomycin (TRL, J01EE07), trovafloxacin (TVA, J01EE03), vancomycin (VAN, J01EE04)","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/mdro.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Determine Multidrug-Resistant Organisms (MDRO) — mdro","text":"","code":"out <- mdro(example_isolates, guideline = \"EUCAST\") #>  (16 isolates had no test results) str(out) #>  Ord.factor w/ 3 levels \"Negative\"<\"Positive, unconfirmed\"<..: 1 1 1 1 1 1 1 1 1 1 ... table(out) #> out #>              Negative Positive, unconfirmed              Positive  #>                  1978                     0                     6   out <- mdro(example_isolates,             guideline = custom_mdro_guideline(AMX == \"R\" ~ \"Custom MDRO 1\",                                               VAN == \"R\" ~ \"Custom MDRO 2\")) table(out) #> out #>      Negative Custom MDRO 1 Custom MDRO 2  #>           870           804           326   # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     mdro() %>%     table()      # no need to define `x` when used inside dplyr verbs:   example_isolates %>%     mutate(MDRO = mdro()) %>%     pull(MDRO) %>%     table()             } #>  (16 isolates had no test results) #> Warning: in `mdro()`: NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with `pct_required_classes`) #>  (16 isolates had no test results) #> Warning: in `mdro()`: NA introduced for isolates where the available percentage of #> antimicrobial classes was below 50% (set with `pct_required_classes`) #> . #>                         Negative       Multi-drug-resistant (MDR)  #>                             1601                              128  #> Extensively drug-resistant (XDR)          Pandrug-resistant (PDR)  #>                                0                                0  # }"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"data set containing commonly used codes microorganisms, laboratory systems WHONET. Define set_mo_source(). searched using .mo() consequently mo_* functions.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"","code":"microorganisms.codes"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"data.frame 5,604 observations 2 variables: code Commonly used code microorganism mo ID microorganism microorganisms data set","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.codes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 5,604 Common Microorganism Codes — microorganisms.codes","text":"","code":"head(microorganisms.codes) #>      code      mo #> 1 _FAM_A- B_GRAMN #> 2 _FAM_A+ B_GRAMP #> 3 _FAM_AC B_GRAMP #> 4 _FAM_AE B_GRAMN #> 5 _FAM_AN B_GRAMN #> 6 _FAM_AO B_GRAMP"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with 70,764 Microorganisms — microorganisms","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"data set containing full microbial taxonomy (last updated: 5 October 2021) six kingdoms Catalogue Life (CoL) List Prokaryotic names Standing Nomenclature (LPSN). MO codes can looked using .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"","code":"microorganisms"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"data.frame 70,764 observations 16 variables: mo ID microorganism used package fullname Full name, like \"Escherichia coli\" kingdom, phylum, class, order, family, genus, species, subspecies Taxonomic rank microorganism rank Text taxonomic rank microorganism, like \"species\" \"genus\" ref Author(s) year concerning scientific publication species_id ID species used Catalogue Life source Either \"CoL\", \"LPSN\" \"manually added\" (see Source) prevalence Prevalence microorganism, see .mo() snomed Systematized Nomenclature Medicine (SNOMED) code microorganism, according US Edition SNOMED CT 1 September 2020 (see Source). Use mo_snomed() retrieve quickly, see mo_property().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"Catalogue Life: 2019 Annual Checklist currently implemented AMR package: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org List Prokaryotic names Standing Nomenclature (5 October 2021) currently implemented AMR package: Parte, .C., Sarda Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. Goker, M. (2020). List Prokaryotic names Standing Nomenclature (LPSN) moves DSMZ. International Journal Systematic Evolutionary Microbiology, 70, 5607-5612; doi:10.1099/ijsem.0.004332 Parte, .C. (2018). LPSN - List Prokaryotic names Standing Nomenclature (bacterio.net), 20 years . International Journal Systematic Evolutionary Microbiology, 68, 1825-1829; doi:10.1099/ijsem.0.002786 Parte, .C. (2014). LPSN - List Prokaryotic names Standing Nomenclature. Nucleic Acids Research, 42, Issue D1, D613-D616; doi:10.1093/nar/gkt1111 Euzeby, J.P. (1997). List Bacterial Names Standing Nomenclature: Folder Available Internet. International Journal Systematic Bacteriology, 47, 590-592; doi:10.1099/00207713-47-2-590 US Edition SNOMED CT 1 September 2020 currently implemented AMR package: Retrieved Public Health Information Network Vocabulary Access Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"Please note entries based Catalogue Life LPSN (see ). Since sources incorporate entries based (recent) publications International Journal Systematic Evolutionary Microbiology (IJSEM), can happen year publication sometimes later one might expect. example, Staphylococcus pettenkoferi described first time Diagnostic Microbiology Infectious Disease 2002 (doi:10.1016/s0732-8893(02)00399-1 ), 2007 publication IJSEM followed (doi:10.1099/ijs.0.64381-0 ). Consequently, AMR package returns 2007 mo_year(\"S. pettenkoferi\").","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"manual-additions","dir":"Reference","previous_headings":"","what":"Manual additions","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"convenience, entries added manually: 11 entries Streptococcus (beta-haemolytic: groups , B, C, D, F, G, H, K unspecified; : viridans, milleri) 2 entries Staphylococcus (coagulase-negative (CoNS) coagulase-positive (CoPS)) 3 entries Trichomonas (T. vaginalis, family genus) 4 entries Toxoplasma (T. gondii, order, family genus) 1 entry Candida (C.  krusei), (yet) Catalogue Life 1 entry Blastocystis (B.  hominis), although officially exist (Noel et al. 2005, PMID 15634993) 1 entry Moraxella (M. catarrhalis), formally named Branhamella catarrhalis (Catlin, 1970) though change never accepted within field clinical microbiology 5 'undefined' entries (unknown, unknown Gram negatives, unknown Gram positives, unknown yeast unknown fungus) 6 families Enterobacterales order, according Adeolu et al. (2016, PMID 27620848), (yet) Catalogue Life","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"direct-download","dir":"Reference","previous_headings":"","what":"Direct download","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"data set available 'flat file' use even without R - can find file : https://github.com/msberends/AMR/raw/main/data-raw/microorganisms.txt. file R format (preserved data structure) can found : https://github.com/msberends/AMR/raw/main/data/microorganisms.rda.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"about-the-records-from-lpsn-see-source-","dir":"Reference","previous_headings":"","what":"About the Records from LPSN (see Source)","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"List Prokaryotic names Standing Nomenclature (LPSN) provides comprehensive information nomenclature prokaryotes. LPSN free use service founded Jean P. Euzeby 1997 later maintained Aidan C. Parte. February 2020, regularly augmented LPSN database DSMZ basis new LPSN service. new database implemented Type-Strain Genome Server augmented 2018 store kinds nomenclatural information. Data previous version LPSN Prokaryotic Nomenclature --date (PNU) service imported new system. PNU established 1993 service Leibniz Institute DSMZ, curated Norbert Weiss, Manfred Kracht Dorothea Gleim.","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with 70,764 Microorganisms — microorganisms","text":"","code":"head(microorganisms) #>                 mo                 fullname           kingdom           phylum #> 1         F_FUNGUS         (unknown fungus)             Fungi (unknown phylum) #> 2          B_GRAMN (unknown Gram-negatives)          Bacteria (unknown phylum) #> 3          B_GRAMP (unknown Gram-positives)          Bacteria (unknown phylum) #> 4          UNKNOWN           (unknown name) (unknown kingdom) (unknown phylum) #> 5          F_YEAST          (unknown yeast)             Fungi (unknown phylum) #> 6 B_[FAM]_ABDTBCTR       Abditibacteriaceae          Bacteria Abditibacteriota #>             class             order             family                    genus #> 1 (unknown class)   (unknown order)   (unknown family)          (unknown genus) #> 2 (unknown class)   (unknown order)   (unknown family) (unknown Gram-negatives) #> 3 (unknown class)   (unknown order)   (unknown family) (unknown Gram-positives) #> 4 (unknown class)   (unknown order)   (unknown family)          (unknown genus) #> 5 (unknown class)   (unknown order)   (unknown family)          (unknown genus) #> 6  Abditibacteria Abditibacteriales Abditibacteriaceae                          #>             species           subspecies           rank  ref species_id #> 1 (unknown species) (unknown subspecies)        species <NA>         NA #> 2 (unknown species) (unknown subspecies)        species <NA>         NA #> 3 (unknown species) (unknown subspecies)        species <NA>         NA #> 4 (unknown species) (unknown subspecies) (unknown rank) <NA>         NA #> 5 (unknown species) (unknown subspecies)        species <NA>         NA #> 6                                                family <NA>         NA #>           source prevalence snomed #> 1 manually added          3        #> 2 manually added          3        #> 3 manually added          3        #> 4 manually added          3        #> 5 manually added          3        #> 6            CoL          2"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"data set containing old (previously valid accepted) taxonomic names according Catalogue Life. data set used internally .mo().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"","code":"microorganisms.old"},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"data.frame 14,338 observations 4 variables: fullname Old full taxonomic name microorganism fullname_new New full taxonomic name microorganism ref Author(s) year concerning scientific publication prevalence Prevalence microorganism, see .mo()","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"Catalogue Life: Annual Checklist (public online taxonomic database), http://www.catalogueoflife.org (check included annual version catalogue_of_life_version()). Parte, .C. (2018). LPSN - List Prokaryotic names Standing Nomenclature (bacterio.net), 20 years . International Journal Systematic Evolutionary Microbiology, 68, 1825-1829; doi:10.1099/ijsem.0.002786","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/microorganisms.old.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set with Previously Accepted Taxonomic Names — microorganisms.old","text":"","code":"head(microorganisms.old) #>                     fullname                 fullname_new #> 1    Abaphospora petrucciana          Nectria petrucciana #> 2      Abathostoma stercoris    Echinocyclidium stercoris #> 3  Abbottina tannerbankensis       Nonion tannerbankensis #> 4 Abditodentrix rhomboidalis  Tortoplectella rhomboidalis #> 5       Abiotrophia adiacens      Granulicatella adiacens #> 6  Abiotrophia balaenopterae Granulicatella balaenopterae #>                       ref prevalence #> 1          Kirschst, 1939          2 #> 2 Berger in Corliss, 1979          3 #> 3         McCulloch, 1977          3 #> 4           Millett, 1899          3 #> 5   Kawamura et al., 1995          2 #> 6     Lawson et al., 1999          2"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the Matching Score for Microorganisms — mo_matching_score","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"algorithm used .mo() mo_* functions determine probable match taxonomic records based user input.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"mo_matching_score(x, n)"},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"x user input value(s) n full taxonomic name, exists microorganisms$fullname","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function, counts insertion, deletion substitution 1 needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based experience several microbiological laboratories Netherlands conjunction international reports pathogen prevalence. Group 1 (prevalent microorganisms) consists microorganisms taxonomic class Gammaproteobacteria taxonomic genus Enterococcus, Staphylococcus Streptococcus. group consequently contains common Gram-negative bacteria, Pseudomonas Legionella species within order Enterobacterales. Group 2 consists microorganisms taxonomic phylum Proteobacteria, Firmicutes, Actinobacteria Sarcomastigophora, taxonomic genus Absidia, Acremonium, Actinotignum, Alternaria, Anaerosalibacter, Apophysomyces, Arachnia, Aspergillus, Aureobacterium, Aureobasidium, Bacteroides, Basidiobolus, Beauveria, Blastocystis, Branhamella, Calymmatobacterium, Candida, Capnocytophaga, Catabacter, Chaetomium, Chryseobacterium, Chryseomonas, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Cryptococcus, Curvularia, Exophiala, Exserohilum, Flavobacterium, Fonsecaea, Fusarium, Fusobacterium, Hendersonula, Hypomyces, Koserella, Lelliottia, Leptosphaeria, Leptotrichia, Malassezia, Malbranchea, Mortierella, Mucor, Mycocentrospora, Mycoplasma, Nectria, Ochroconis, Oidiodendron, Phoma, Piedraia, Pithomyces, Pityrosporum, Prevotella, Pseudallescheria, Rhizomucor, Rhizopus, Rhodotorula, Scolecobasidium, Scopulariopsis, Scytalidium, Sporobolomyces, Stachybotrys, Stomatococcus, Treponema, Trichoderma, Trichophyton, Trichosporon, Tritirachium Ureaplasma. Group 3 consists microorganisms. characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.079\\), less prevalent microorganism humans), although latter alphabetically come first. Since AMR version 1.8.1, common microorganism abbreviations ignored determining matching score. abbreviations currently: AIEC, ATEC, BORSA, CRSM, DAEC, EAEC, EHEC, EIEC, EPEC, ETEC, GISA, MRPA, MRSA, MRSE, MSSA, MSSE, NMEC, PISP, PRSP, STEC, UPEC, VISA, VISP, VRE, VRSA VRSP.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"Dr Matthijs Berends","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_matching_score.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the Matching Score for Microorganisms — mo_matching_score","text":"","code":"as.mo(\"E. coli\") #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this. #> Class <mo> #> [1] B_ESCHR_COLI mo_uncertainties() #> Matching scores are based on pathogenicity in humans and the resemblance #> between the input and the full taxonomic name. See `?mo_matching_score`. #>  #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Entamoeba coli (0.079) #>   mo_matching_score(x = \"E. coli\",                   n = c(\"Escherichia coli\", \"Entamoeba coli\")) #> [1] 0.68750000 0.07936508"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Properties of a Microorganism — mo_property","title":"Get Properties of a Microorganism — mo_property","text":"Use functions return specific property microorganism based latest accepted taxonomy. input values evaluated internally .mo(), makes possible use microbial abbreviations, codes names input. See Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Properties of a Microorganism — mo_property","text":"","code":"mo_name(x, language = get_AMR_locale(), ...)  mo_fullname(x, language = get_AMR_locale(), ...)  mo_shortname(x, language = get_AMR_locale(), ...)  mo_subspecies(x, language = get_AMR_locale(), ...)  mo_species(x, language = get_AMR_locale(), ...)  mo_genus(x, language = get_AMR_locale(), ...)  mo_family(x, language = get_AMR_locale(), ...)  mo_order(x, language = get_AMR_locale(), ...)  mo_class(x, language = get_AMR_locale(), ...)  mo_phylum(x, language = get_AMR_locale(), ...)  mo_kingdom(x, language = get_AMR_locale(), ...)  mo_domain(x, language = get_AMR_locale(), ...)  mo_type(x, language = get_AMR_locale(), ...)  mo_gramstain(x, language = get_AMR_locale(), ...)  mo_is_gram_negative(x, language = get_AMR_locale(), ...)  mo_is_gram_positive(x, language = get_AMR_locale(), ...)  mo_is_yeast(x, language = get_AMR_locale(), ...)  mo_is_intrinsic_resistant(x, ab, language = get_AMR_locale(), ...)  mo_snomed(x, language = get_AMR_locale(), ...)  mo_ref(x, language = get_AMR_locale(), ...)  mo_authors(x, language = get_AMR_locale(), ...)  mo_year(x, language = get_AMR_locale(), ...)  mo_lpsn(x, language = get_AMR_locale(), ...)  mo_rank(x, language = get_AMR_locale(), ...)  mo_taxonomy(x, language = get_AMR_locale(), ...)  mo_synonyms(x, language = get_AMR_locale(), ...)  mo_info(x, language = get_AMR_locale(), ...)  mo_url(x, open = FALSE, language = get_AMR_locale(), ...)  mo_property(x, property = \"fullname\", language = get_AMR_locale(), ...)"},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Properties of a Microorganism — mo_property","text":"x character (vector) can coerced valid microorganism code .mo(). Can left blank auto-guessing column containing microorganism codes used data set, see Examples. language language returned text, defaults system language (see get_AMR_locale()) can overwritten setting option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Also used translate text like \"growth\". Use language = NULL language = \"\" prevent translation. ... arguments passed .mo(), 'allow_uncertain' 'ignore_pattern' ab (vector ) text can coerced valid antibiotic code .ab() open browse URL using browseURL() property one column names microorganisms data set: \"mo\", \"fullname\", \"kingdom\", \"phylum\", \"class\", \"order\", \"family\", \"genus\", \"species\", \"subspecies\", \"rank\", \"ref\", \"species_id\", \"source\", \"prevalence\" \"snomed\", must \"shortname\"","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get Properties of a Microorganism — mo_property","text":"integer case mo_year() list case mo_taxonomy() mo_info() named character case mo_url() numeric case mo_snomed() character cases","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Properties of a Microorganism — mo_property","text":"functions return recently known taxonomic property according Catalogue Life, except mo_ref(), mo_authors() mo_year(). Please refer example, knowing Escherichia blattae renamed Shimwellia blattae 2010: mo_name(\"Escherichia blattae\") return \"Shimwellia blattae\" (message renaming) mo_ref(\"Escherichia blattae\") return \"Burgess et al., 1973\" (message renaming) mo_ref(\"Shimwellia blattae\") return \"Priest et al., 2010\" (without message) short name - mo_shortname() - almost always returns first character genus full species, like \"E. coli\". Exceptions abbreviations staphylococci (\"CoNS\", Coagulase-Negative Staphylococci) beta-haemolytic streptococci (\"GBS\", Group B Streptococci). Please bear mind e.g. E. coli mean Escherichia coli (kingdom Bacteria) well Entamoeba coli (kingdom Protozoa). Returning full name done using .mo() internally, giving priority bacteria human pathogens, .e. \"E. coli\" considered Escherichia coli. words, mo_fullname(mo_shortname(\"Entamoeba coli\")) returns \"Escherichia coli\". Since top-level taxonomy sometimes referred 'kingdom' sometimes 'domain', functions mo_kingdom() mo_domain() return exact results. Gram stain - mo_gramstain() - determined based taxonomic kingdom phylum. According Cavalier-Smith (2002, PMID 11837318), defined subkingdoms Negibacteria Posibacteria, phyla Posibacteria: Actinobacteria, Chloroflexi, Firmicutes Tenericutes. bacteria considered Gram-positive, except members class Negativicutes Gram-negative. Members bacterial phyla considered Gram-negative. Species outside kingdom Bacteria return value NA. Functions mo_is_gram_negative() mo_is_gram_positive() always return TRUE FALSE (except input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Determination yeasts - mo_is_yeast() - based taxonomic kingdom class. Budding yeasts fungi phylum Ascomycetes, class Saccharomycetes (also called Hemiascomycetes). True yeasts aggregated underlying order Saccharomycetales. Thus, microorganisms fungi member taxonomic class Saccharomycetes, function return TRUE. returns FALSE otherwise (except input NA MO code UNKNOWN). Intrinsic resistance - mo_is_intrinsic_resistant() - determined based intrinsic_resistant data set, based 'EUCAST Expert Rules' 'EUCAST Intrinsic Resistance Unusual Phenotypes' v3.3 (2021). mo_is_intrinsic_resistant() functions can vectorised arguments x (input microorganisms) ab (input antibiotics). output translated possible. function mo_url() return direct URL online database entry, also shows scientific reference concerned species. SNOMED codes - mo_snomed() - US Edition SNOMED CT 1 September 2020. See Source microorganisms data set info.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"matching-score-for-microorganisms","dir":"Reference","previous_headings":"","what":"Matching Score for Microorganisms","title":"Get Properties of a Microorganism — mo_property","text":"ambiguous user input .mo() mo_* functions, returned results chosen based matching score using mo_matching_score(). matching score \\(m\\), calculated :  : x user input; n taxonomic name (genus, species, subspecies); ln length n; lev Levenshtein distance function, counts insertion, deletion substitution 1 needed change x n; pn human pathogenic prevalence group n, described ; kn taxonomic kingdom n, set Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5. grouping human pathogenic prevalence (\\(p\\)) based experience several microbiological laboratories Netherlands conjunction international reports pathogen prevalence. Group 1 (prevalent microorganisms) consists microorganisms taxonomic class Gammaproteobacteria taxonomic genus Enterococcus, Staphylococcus Streptococcus. group consequently contains common Gram-negative bacteria, Pseudomonas Legionella species within order Enterobacterales. Group 2 consists microorganisms taxonomic phylum Proteobacteria, Firmicutes, Actinobacteria Sarcomastigophora, taxonomic genus Absidia, Acremonium, Actinotignum, Alternaria, Anaerosalibacter, Apophysomyces, Arachnia, Aspergillus, Aureobacterium, Aureobasidium, Bacteroides, Basidiobolus, Beauveria, Blastocystis, Branhamella, Calymmatobacterium, Candida, Capnocytophaga, Catabacter, Chaetomium, Chryseobacterium, Chryseomonas, Chrysonilia, Cladophialophora, Cladosporium, Conidiobolus, Cryptococcus, Curvularia, Exophiala, Exserohilum, Flavobacterium, Fonsecaea, Fusarium, Fusobacterium, Hendersonula, Hypomyces, Koserella, Lelliottia, Leptosphaeria, Leptotrichia, Malassezia, Malbranchea, Mortierella, Mucor, Mycocentrospora, Mycoplasma, Nectria, Ochroconis, Oidiodendron, Phoma, Piedraia, Pithomyces, Pityrosporum, Prevotella, Pseudallescheria, Rhizomucor, Rhizopus, Rhodotorula, Scolecobasidium, Scopulariopsis, Scytalidium, Sporobolomyces, Stachybotrys, Stomatococcus, Treponema, Trichoderma, Trichophyton, Trichosporon, Tritirachium Ureaplasma. Group 3 consists microorganisms. characters \\(x\\) \\(n\\) ignored -Z, -z, 0-9, spaces parentheses. matches sorted descending matching score user input values, top match returned. lead effect e.g., \"E. coli\" return microbial ID Escherichia coli (\\(m = 0.688\\), highly prevalent microorganism found humans) Entamoeba coli (\\(m = 0.079\\), less prevalent microorganism humans), although latter alphabetically come first. Since AMR version 1.8.1, common microorganism abbreviations ignored determining matching score. abbreviations currently: AIEC, ATEC, BORSA, CRSM, DAEC, EAEC, EHEC, EIEC, EPEC, ETEC, GISA, MRPA, MRSA, MRSE, MSSA, MSSE, NMEC, PISP, PRSP, STEC, UPEC, VISA, VISP, VRE, VRSA VRSP.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"catalogue-of-life","dir":"Reference","previous_headings":"","what":"Catalogue of Life","title":"Get Properties of a Microorganism — mo_property","text":"package contains complete taxonomic tree almost microorganisms (~71,000 species) authoritative comprehensive Catalogue Life (CoL, http://www.catalogueoflife.org). CoL comprehensive authoritative global index species currently available. Nonetheless, supplemented CoL data data List Prokaryotic names Standing Nomenclature (LPSN, lpsn.dsmz.de). supplementation needed CoL+ project finished, await. Click information included taxa. Check versions CoL LPSN included package catalogue_of_life_version().","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Get Properties of a Microorganism — mo_property","text":"Becker K et al. Coagulase-Negative Staphylococci. 2014. Clin Microbiol Rev. 27(4): 870-926; doi:10.1128/CMR.00109-13 Becker K et al. Implications identifying recently defined members S. aureus complex, S. argenteus S. schweitzeri: position paper members ESCMID Study Group staphylococci Staphylococcal Diseases (ESGS). 2019. Clin Microbiol Infect; doi:10.1016/j.cmi.2019.02.028 Becker K et al. Emergence coagulase-negative staphylococci 2020. Expert Rev Anti Infect Ther. 18(4):349-366; doi:10.1080/14787210.2020.1730813 Lancefield RC serological differentiation human groups hemolytic streptococci. 1933. J Exp Med. 57(4): 571-95; doi:10.1084/jem.57.4.571 Catalogue Life: 2019 Annual Checklist, http://www.catalogueoflife.org List Prokaryotic names Standing Nomenclature (5 October 2021), doi:10.1099/ijsem.0.004332 US Edition SNOMED CT 1 September 2020, retrieved Public Health Information Network Vocabulary Access Distribution System (PHIN VADS), OID 2.16.840.1.114222.4.11.1009, version 12; url: https://phinvads.cdc.gov/vads/ViewValueSet.action?oid=2.16.840.1.114222.4.11.1009","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Get Properties of a Microorganism — mo_property","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/mo_property.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get Properties of a Microorganism — mo_property","text":"","code":"# taxonomic tree ----------------------------------------------------------- mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_phylum(\"Klebsiella pneumoniae\") #> [1] \"Proteobacteria\" mo_class(\"Klebsiella pneumoniae\") #> [1] \"Gammaproteobacteria\" mo_order(\"Klebsiella pneumoniae\") #> [1] \"Enterobacterales\" mo_family(\"Klebsiella pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"Klebsiella pneumoniae\") #> [1] \"pneumoniae\" mo_subspecies(\"Klebsiella pneumoniae\") #> [1] \"\"  # colloquial properties ---------------------------------------------------- mo_name(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_fullname(\"Klebsiella pneumoniae\") #> [1] \"Klebsiella pneumoniae\" mo_shortname(\"Klebsiella pneumoniae\") #> [1] \"K. pneumoniae\"  # other properties --------------------------------------------------------- mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_snomed(\"Klebsiella pneumoniae\") #> [1] 1.098101e+15 4.468700e+08 1.098201e+15 4.098010e+08 5.641501e+07 #> [6] 7.143150e+08 mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_rank(\"Klebsiella pneumoniae\") #> [1] \"species\" mo_url(\"Klebsiella pneumoniae\") #>                                Klebsiella pneumoniae  #> \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\"  mo_synonyms(\"Klebsiella pneumoniae\") #> NULL  # scientific reference ----------------------------------------------------- mo_ref(\"Klebsiella pneumoniae\") #> [1] \"Trevisan, 1887\" mo_authors(\"Klebsiella pneumoniae\") #> [1] \"Trevisan\" mo_year(\"Klebsiella pneumoniae\") #> [1] 1887 mo_lpsn(\"Klebsiella pneumoniae\") #> [1] 777151  # abbreviations known in the field ----------------------------------------- mo_genus(\"MRSA\") #> [1] \"Staphylococcus\" mo_species(\"MRSA\") #> [1] \"aureus\" mo_shortname(\"VISA\") #> [1] \"S. aureus\" mo_gramstain(\"VISA\") #> [1] \"Gram-positive\"  mo_genus(\"EHEC\") #> [1] \"Escherichia\" mo_species(\"EHEC\") #> [1] \"coli\"  # known subspecies --------------------------------------------------------- mo_name(\"doylei\") #> [1] \"Campylobacter jejuni doylei\" mo_genus(\"doylei\") #> [1] \"Campylobacter\" mo_species(\"doylei\") #> [1] \"jejuni\" mo_subspecies(\"doylei\") #> [1] \"doylei\"  mo_fullname(\"K. pneu rh\") #> [1] \"Klebsiella pneumoniae rhinoscleromatis\" mo_shortname(\"K. pneu rh\") #> [1] \"K. pneumoniae\"  # \\donttest{ # Becker classification, see ?as.mo ---------------------------------------- mo_fullname(\"S. epi\") #> ℹ Function `as.mo()` is uncertain about \"S. epi\" (assuming Staphylococcus #>   epidermidis). Run `mo_uncertainties()` to review this. #> [1] \"Staphylococcus epidermidis\" mo_fullname(\"S. epi\", Becker = TRUE) #> ℹ Function `as.mo()` is uncertain about \"S. epi\" (assuming Staphylococcus #>   epidermidis). Run `mo_uncertainties()` to review this. #> [1] \"Coagulase-negative Staphylococcus (CoNS)\" mo_shortname(\"S. epi\") #> ℹ Function `as.mo()` is uncertain about \"S. epi\" (assuming Staphylococcus #>   epidermidis). Run `mo_uncertainties()` to review this. #> [1] \"S. epidermidis\" mo_shortname(\"S. epi\", Becker = TRUE) #> ℹ Function `as.mo()` is uncertain about \"S. epi\" (assuming Staphylococcus #>   epidermidis). Run `mo_uncertainties()` to review this. #> [1] \"CoNS\"  # Lancefield classification, see ?as.mo ------------------------------------ mo_fullname(\"S. pyo\") #> [1] \"Streptococcus pyogenes\" mo_fullname(\"S. pyo\", Lancefield = TRUE) #> [1] \"Streptococcus group A\" mo_shortname(\"S. pyo\") #> [1] \"S. pyogenes\" mo_shortname(\"S. pyo\", Lancefield = TRUE) #> [1] \"GAS\"   # language support  -------------------------------------------------------- mo_gramstain(\"Klebsiella pneumoniae\", language = \"de\") #> [1] \"Gramnegativ\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"nl\") #> [1] \"Gram-negatief\" mo_gramstain(\"Klebsiella pneumoniae\", language = \"es\") #> [1] \"Gram negativo\"  # mo_type is equal to mo_kingdom, but mo_kingdom will remain official mo_kingdom(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\" mo_type(\"Klebsiella pneumoniae\") #> [1] \"Bacteria\"  mo_fullname(\"S. pyogenes\",             Lancefield = TRUE,             language = \"de\") #> [1] \"Streptococcus Gruppe A\" mo_fullname(\"S. pyogenes\",             Lancefield = TRUE,             language = \"nl\") #> [1] \"Streptococcus groep A\"   # other --------------------------------------------------------------------  mo_is_yeast(c(\"Candida\", \"Trichophyton\", \"Klebsiella\")) #> [1]  TRUE FALSE FALSE  # gram stains and intrinsic resistance can be used as a filter in dplyr verbs if (require(\"dplyr\")) {   example_isolates %>%     filter(mo_is_gram_positive())        example_isolates %>%     filter(mo_is_intrinsic_resistant(ab = \"vanco\")) } #> ℹ Using column 'mo' as input for `mo_is_gram_positive()` #> ℹ Using column 'mo' as input for `mo_is_intrinsic_resistant()` #> # A tibble: 709 × 49 #>    date       hospit…¹ ward_…² ward_…³ ward_…⁴   age gender patie…⁵ mo           #>    <date>     <fct>    <lgl>   <lgl>   <lgl>   <dbl> <chr>  <chr>   <mo>         #>  1 2002-01-02 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  2 2002-01-03 D        FALSE   TRUE    FALSE      65 F      A77334  B_ESCHR_COLI #>  3 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI #>  4 2002-01-19 D        FALSE   TRUE    FALSE      71 M      738003  B_ESCHR_COLI #>  5 2002-01-21 D        FALSE   TRUE    FALSE      75 F      462081  B_CTRBC_FRND #>  6 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL #>  7 2002-01-22 B        TRUE    FALSE   FALSE      50 M      F35553  B_PROTS_MRBL #>  8 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS #>  9 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS #> 10 2002-02-05 B        TRUE    FALSE   FALSE      45 F      067927  B_SERRT_MRCS #> # … with 699 more rows, 40 more variables: PEN <rsi>, OXA <rsi>, FLC <rsi>, #> #   AMX <rsi>, AMC <rsi>, AMP <rsi>, TZP <rsi>, CZO <rsi>, FEP <rsi>, #> #   CXM <rsi>, FOX <rsi>, CTX <rsi>, CAZ <rsi>, CRO <rsi>, GEN <rsi>, #> #   TOB <rsi>, AMK <rsi>, KAN <rsi>, TMP <rsi>, SXT <rsi>, NIT <rsi>, #> #   FOS <rsi>, LNZ <rsi>, CIP <rsi>, MFX <rsi>, VAN <rsi>, TEC <rsi>, #> #   TCY <rsi>, TGC <rsi>, DOX <rsi>, ERY <rsi>, CLI <rsi>, AZM <rsi>, #> #   IPM <rsi>, MEM <rsi>, MTR <rsi>, CHL <rsi>, COL <rsi>, MUP <rsi>, … #> # ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names   # get a list with the complete taxonomy (from kingdom to subspecies) mo_taxonomy(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Proteobacteria\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>   # get a list with the taxonomy, the authors, Gram-stain, # SNOMED codes, and URL to the online database mo_info(\"Klebsiella pneumoniae\") #> $kingdom #> [1] \"Bacteria\" #>  #> $phylum #> [1] \"Proteobacteria\" #>  #> $class #> [1] \"Gammaproteobacteria\" #>  #> $order #> [1] \"Enterobacterales\" #>  #> $family #> [1] \"Enterobacteriaceae\" #>  #> $genus #> [1] \"Klebsiella\" #>  #> $species #> [1] \"pneumoniae\" #>  #> $subspecies #> [1] \"\" #>  #> $synonyms #> NULL #>  #> $gramstain #> [1] \"Gram-negative\" #>  #> $url #> [1] \"https://lpsn.dsmz.de/species/klebsiella-pneumoniae\" #>  #> $ref #> [1] \"Trevisan, 1887\" #>  #> $snomed #> [1] 1.098101e+15 4.468700e+08 1.098201e+15 4.098010e+08 5.641501e+07 #> [6] 7.143150e+08 #>  # }"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":null,"dir":"Reference","previous_headings":"","what":"User-Defined Reference Data Set for Microorganisms — mo_source","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"functions can used predefine reference used .mo() consequently mo_* functions (mo_genus() mo_gramstain()). fastest way organisation (analysis) specific codes picked translated package, since bother setting .","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"","code":"set_mo_source(   path,   destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\") )  get_mo_source(destination = getOption(\"AMR_mo_source\", \"~/mo_source.rds\"))"},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"path location reference file, can text file (comma-, tab- pipe-separated) Excel file (see Details). Can also \"\", NULL FALSE delete reference file. destination destination compressed data file, default user's home directory.","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"reference file can text file separated commas (CSV) tabs pipes, Excel file (either 'xls' 'xlsx' format) R object file (extension '.rds'). use Excel file, need readxl package installed. set_mo_source() check file validity: must data.frame, must column named \"mo\" contains values microorganisms$mo microorganisms$fullname must reference column defined values. tests pass, set_mo_source() read file R ask export \"~/mo_source.rds\". CRAN policy disallows packages write file system, although 'exceptions may allowed interactive sessions package obtains confirmation user'. reason, function works interactive sessions user can specifically confirm allow file created. destination file can set destination argument defaults user's home directory. can also set R option, using options(AMR_mo_source = \"/location/file.rds\"). created compressed data file \"mo_source.rds\" used default MO determination (function .mo() consequently mo_* functions like mo_genus() mo_gramstain()). location timestamp original file saved attribute compressed data file. function get_mo_source() return data set reading \"mo_source.rds\" readRDS(). original file changed (checking location timestamp original file), call set_mo_source() update data file automatically used interactive session. Reading Excel file (.xlsx) one row size 8-9 kB. compressed file created set_mo_source() size 0.1 kB can read get_mo_source() couple microseconds (millionths second).","code":""},{"path":"https://msberends.github.io/AMR/reference/mo_source.html","id":"how-to-setup","dir":"Reference","previous_headings":"","what":"How to Setup","title":"User-Defined Reference Data Set for Microorganisms — mo_source","text":"Imagine data sheet Excel file. first column contains organisation specific codes, second column contains valid taxonomic names:   save \"home//ourcodes.xlsx\". Now set source:   now created file \"~/mo_source.rds\" contents Excel file. first column foreign values 'mo' column kept creating RDS file. now can use functions:   edit Excel file , say, adding row 4 like :   ...new usage MO function package update data file:   delete reference data file, just use \"\", NULL FALSE input set_mo_source():   original file (previous case Excel file) moved deleted, mo_source.rds file removed upon next use .mo() mo_* function.","code":"|         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 |                    |                       | set_mo_source(\"home/me/ourcodes.xlsx\") #> NOTE: Created mo_source file '/Users/me/mo_source.rds' (0.3 kB) from #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns  #>       \"Organisation XYZ\" and \"mo\" as.mo(\"lab_mo_ecoli\") #> Class <mo> #> [1] B_ESCHR_COLI  mo_genus(\"lab_mo_kpneumoniae\") #> [1] \"Klebsiella\"  # other input values still work too as.mo(c(\"Escherichia coli\", \"E. coli\", \"lab_mo_ecoli\")) #> NOTE: Translation to one microorganism was guessed with uncertainty. #>       Use mo_uncertainties() to review it. #> Class <mo> #> [1] B_ESCHR_COLI B_ESCHR_COLI B_ESCHR_COLI |         A          |            B          | --|--------------------|-----------------------| 1 | Organisation XYZ   | mo                    | 2 | lab_mo_ecoli       | Escherichia coli      | 3 | lab_mo_kpneumoniae | Klebsiella pneumoniae | 4 | lab_Staph_aureus   | Staphylococcus aureus | 5 |                    |                       | as.mo(\"lab_mo_ecoli\") #> NOTE: Updated mo_source file '/Users/me/mo_source.rds' (0.3 kB) from  #>       '/Users/me/Documents/ourcodes.xlsx' (9 kB), columns #>        \"Organisation XYZ\" and \"mo\" #> Class <mo> #> [1] B_ESCHR_COLI  mo_genus(\"lab_Staph_aureus\") #> [1] \"Staphylococcus\" set_mo_source(NULL) #> Removed mo_source file '/Users/me/mo_source.rds'"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Principal Component Analysis (for AMR) — pca","title":"Principal Component Analysis (for AMR) — pca","text":"Performs principal component analysis (PCA) based data set automatic determination afterwards plotting groups labels, automatic filtering suitable (.e. non-empty numeric) variables.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"pca(   x,   ...,   retx = TRUE,   center = TRUE,   scale. = TRUE,   tol = NULL,   rank. = NULL )"},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Principal Component Analysis (for AMR) — pca","text":"x data.frame containing numeric columns ... columns x selected PCA, can unquoted since supports quasiquotation. retx logical value indicating whether rotated variables     returned. center logical value indicating whether variables     shifted zero centered. Alternately, vector     length equal number columns x can supplied.     value passed scale. scale. logical value indicating whether variables     scaled unit variance analysis takes     place.  default FALSE consistency S,     general scaling advisable.  Alternatively, vector length     equal number columns x can supplied.      value passed scale. tol value indicating magnitude components     omitted. (Components omitted     standard deviations less equal tol times     standard deviation first component.)  default null     setting, components omitted (unless rank. specified     less min(dim(x)).).  settings tol     tol = 0 tol = sqrt(.Machine$double.eps),     omit essentially constant components. rank. optionally, number specifying maximal rank, .e.,     maximal number principal components used.  Can set     alternative addition tol, useful notably     desired rank considerably smaller dimensions matrix.","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Principal Component Analysis (for AMR) — pca","text":"object classes pca prcomp","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Principal Component Analysis (for AMR) — pca","text":"pca() function takes data.frame input performs actual PCA R function prcomp(). result pca() function prcomp object, additional attribute non_numeric_cols vector column names columns contain numeric values. probably groups labels, used ggplot_pca().","code":""},{"path":"https://msberends.github.io/AMR/reference/pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Principal Component Analysis (for AMR) — pca","text":"","code":"# `example_isolates` is a data set available in the AMR package. # See ?example_isolates.  # \\donttest{ if (require(\"dplyr\")) {   # calculate the resistance per group first    resistance_data <- example_isolates %>%      group_by(order = mo_order(mo),       # group on anything, like order              genus = mo_genus(mo)) %>%   #   and genus as we do here;     filter(n() >= 30) %>%                # filter on only 30 results per group     summarise_if(is.rsi, resistance)     # then get resistance of all drugs        # now conduct PCA for certain antimicrobial agents   pca_result <- resistance_data %>%              pca(AMC, CXM, CTX, CAZ, GEN, TOB, TMP, SXT)         pca_result   summary(pca_result)      # old base R plotting method:   biplot(pca_result)   # new ggplot2 plotting method using this package:   ggplot_pca(pca_result)      if (require(\"ggplot2\")) {     ggplot_pca(pca_result) +       scale_colour_viridis_d() +       labs(title = \"Title here\")   } } #> Warning: Introducing NA: only 14 results available for PEN in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 13 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 15 results available for OXA in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for OXA in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 13 results available for FLC in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FLC in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for TZP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 12 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 5 results available for CZO in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for FEP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 23 results available for FEP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 29 results available for FOX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for AMK in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 17 results available for AMK in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for KAN in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for NIT in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 17 results available for NIT in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 8 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for FOS in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for FOS in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for LNZ in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 5 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 23 results available for CIP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MFX in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for MFX in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MFX in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 6 results available for TEC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 3 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for TCY in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 18 results available for TGC in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 7 results available for TGC in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for DOX in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for DOX in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 27 results available for IPM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: only 25 results available for MEM in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 26 results available for MEM in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Caryophanales\", genus = \"Staphylococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 2 results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MTR in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for CHL in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: only 9 results available for COL in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Escherichia\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Klebsiella\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Enterobacterales\", genus = \"Proteus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for MUP in group: order = #> \"Pseudomonadales\", genus = \"Pseudomonas\" (`minimum` = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Enterococcus\" (`minimum` = 30). #> Warning: Introducing NA: no results available for RIF in group: order = #> \"Lactobacillales\", genus = \"Streptococcus\" (`minimum` = 30). #> ℹ Columns selected for PCA: \"AMC\", \"CAZ\", \"CTX\", \"CXM\", \"GEN\", \"SXT\", \"TMP\" #>   and \"TOB\". Total observations available: 7. #> Groups (n=4, named as 'order'): #> [1] \"Caryophanales\"    \"Enterobacterales\" \"Lactobacillales\"  \"Pseudomonadales\"  #>    # }"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plotting for Classes rsi, mic and disk — plot","title":"Plotting for Classes rsi, mic and disk — plot","text":"Functions plot classes rsi, mic disk, support base R ggplot2.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plotting for Classes rsi, mic and disk — plot","text":"","code":"# S3 method for mic plot(   x,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   main = deparse(substitute(x)),   ylab = \"Frequency\",   xlab = \"Minimum Inhibitory Concentration (mg/L)\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic autoplot(   object,   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   title = deparse(substitute(object)),   ylab = \"Frequency\",   xlab = \"Minimum Inhibitory Concentration (mg/L)\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for mic fortify(object, ...)  # S3 method for disk plot(   x,   main = deparse(substitute(x)),   ylab = \"Frequency\",   xlab = \"Disk diffusion diameter (mm)\",   mo = NULL,   ab = NULL,   guideline = \"EUCAST\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk autoplot(   object,   mo = NULL,   ab = NULL,   title = deparse(substitute(object)),   ylab = \"Frequency\",   xlab = \"Disk diffusion diameter (mm)\",   guideline = \"EUCAST\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   expand = TRUE,   ... )  # S3 method for disk fortify(object, ...)  # S3 method for rsi plot(   x,   ylab = \"Percentage\",   xlab = \"Antimicrobial Interpretation\",   main = deparse(substitute(x)),   language = get_AMR_locale(),   ... )  # S3 method for rsi autoplot(   object,   title = deparse(substitute(object)),   xlab = \"Antimicrobial Interpretation\",   ylab = \"Frequency\",   colours_RSI = c(\"#ED553B\", \"#3CAEA3\", \"#F6D55C\"),   language = get_AMR_locale(),   ... )  # S3 method for rsi fortify(object, ...)"},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plotting for Classes rsi, mic and disk — plot","text":"x, object values created .mic(), .disk() .rsi() (random_* variants, random_mic()) mo (vector ) text can coerced valid microorganism code .mo() ab (vector ) text can coerced valid antimicrobial code .ab() guideline interpretation guideline use, defaults latest included EUCAST guideline, see Details main, title title plot xlab, ylab axis title colours_RSI colours use filling bars, must vector three values (order R, S ). default colours colour-blind friendly. language language used translate 'Susceptible', 'Increased exposure'/'Intermediate' 'Resistant', defaults system language (see get_AMR_locale()) can overwritten setting option AMR_locale, e.g. options(AMR_locale = \"de\"), see translate. Use language = NULL language = \"\" prevent translation. expand logical indicate whether range x axis expanded lowest highest value. MIC values, intermediate values factors 2 starting highest MIC value. disk diameters, whole diameter range filled. ... arguments passed methods","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plotting for Classes rsi, mic and disk — plot","text":"autoplot() functions return ggplot model extendible ggplot2 function. fortify() functions return data.frame extension usage ggplot2::ggplot() function.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plotting for Classes rsi, mic and disk — plot","text":"interpretation \"\" named \"Increased exposure\" EUCAST guidelines since 2019, named \"Intermediate\" cases. interpreting MIC values well disk diffusion diameters, supported guidelines used input guideline argument : \"EUCAST 2022\", \"EUCAST 2021\", \"EUCAST 2020\", \"EUCAST 2019\", \"EUCAST 2018\", \"EUCAST 2017\", \"EUCAST 2016\", \"EUCAST 2015\", \"EUCAST 2014\", \"EUCAST 2013\", \"EUCAST 2012\", \"EUCAST 2011\", \"CLSI 2022\", \"CLSI 2021\", \"CLSI 2020\", \"CLSI 2019\", \"CLSI 2018\", \"CLSI 2017\", \"CLSI 2016\", \"CLSI 2015\", \"CLSI 2014\", \"CLSI 2013\", \"CLSI 2012\" \"CLSI 2011\". Simply using \"CLSI\" \"EUCAST\" input automatically select latest version guideline.","code":""},{"path":"https://msberends.github.io/AMR/reference/plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plotting for Classes rsi, mic and disk — plot","text":"","code":"some_mic_values <- random_mic(size = 100) some_disk_values <- random_disk(size = 100, mo = \"Escherichia coli\", ab = \"cipro\") some_rsi_values <- random_rsi(50, prob_RSI = c(0.30, 0.55, 0.05))  plot(some_mic_values)  plot(some_disk_values)  plot(some_rsi_values)   # when providing the microorganism and antibiotic, colours will show interpretations: plot(some_mic_values, mo = \"S. aureus\", ab = \"ampicillin\") #> ℹ Function `as.mo()` is uncertain about \"S. aureus\" (assuming #>   Staphylococcus aureus). Run `mo_uncertainties()` to review this.  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")  plot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\", language = \"uk\")   # \\donttest{ if (require(\"ggplot2\")) {   autoplot(some_mic_values)   autoplot(some_disk_values, mo = \"Escherichia coli\", ab = \"cipro\")   autoplot(some_rsi_values) }  # }"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Microbial Resistance — proportion","title":"Calculate Microbial Resistance — proportion","text":"functions can used calculate (co-)resistance susceptibility microbial isolates (.e. percentage S, SI, , IR R). functions support quasiquotation pipes, can used summarise() dplyr package also support grouped variables, see Examples. resistance() used calculate resistance, susceptibility() used calculate susceptibility.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Microbial Resistance — proportion","text":"","code":"resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)  proportion_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   minimum = 30,   as_percent = FALSE,   combine_SI = TRUE,   combine_IR = FALSE )  rsi_df(   data,   translate_ab = \"name\",   language = get_AMR_locale(),   minimum = 30,   as_percent = FALSE,   combine_SI = TRUE,   combine_IR = FALSE )"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Calculate Microbial Resistance — proportion","text":"M39 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Microbial Resistance — proportion","text":"... one vectors (columns) antibiotic interpretations. transformed internally .rsi() needed. Use multiple columns calculate (lack ) co-resistance: probability one two drugs resistant susceptible result. See Examples. minimum minimum allowed number available (tested) isolates. isolate count lower minimum return NA warning. default number 30 isolates advised Clinical Laboratory Standards Institute (CLSI) best practice, see Source. as_percent logical indicate whether output must returned hundred fold % sign (character). value 0.123456 returned \"12.3%\". only_all_tested (combination therapies, .e. using one variable ...): logical indicate isolates must tested antibiotics, see section Combination Therapy data data.frame containing columns class rsi (see .rsi()) translate_ab column name antibiotics data set translate antibiotic abbreviations , using ab_property() language language returned text, defaults system language (see get_AMR_locale()) can also set getOption(\"AMR_locale\"). Use language = NULL language = \"\" prevent translation. combine_SI logical indicate whether values S must merged one, output consists S+vs. R (susceptible vs. resistant). used argument combine_IR, now follows redefinition EUCAST interpretation (increased exposure) 2019, see section 'Interpretation S, R' . Default TRUE. combine_IR logical indicate whether values R must merged one, output consists S vs. +R (susceptible vs. non-susceptible). outdated, see argument combine_SI.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Microbial Resistance — proportion","text":"double , as_percent = TRUE, character.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate Microbial Resistance — proportion","text":"function resistance() equal function proportion_R(). function susceptibility() equal function proportion_SI(). Remember filter data let contain first isolates! needed exclude duplicates reduce selection bias. Use first_isolate() determine data set. functions meant count isolates, calculate proportion resistance/susceptibility. Use count() functions count isolates. function susceptibility() essentially equal count_susceptible() / count_all(). Low counts can influence outcome - proportion functions may camouflage , since return proportion (albeit dependent minimum argument). function proportion_df() takes variable data rsi class (created .rsi()) calculates proportions R, S. also supports grouped variables. function rsi_df() works exactly like proportion_df(), adds number isolates.","code":""},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"combination-therapy","dir":"Reference","previous_headings":"","what":"Combination Therapy","title":"Calculate Microbial Resistance — proportion","text":"using one variable ... (= combination therapy), use only_all_tested count isolates tested antibiotics/variables test . See example two antibiotics, Drug Drug B, susceptibility() works calculate %SI:   Please note , combination therapies, only_all_tested = TRUE applies :   , combination therapies, only_all_tested = FALSE applies :   Using only_all_tested impact using one antibiotic input.","code":"--------------------------------------------------------------------                     only_all_tested = FALSE  only_all_tested = TRUE                     -----------------------  -----------------------  Drug A    Drug B   include as  include as   include as  include as                     numerator   denominator  numerator   denominator --------  --------  ----------  -----------  ----------  -----------  S or I    S or I       X            X            X            X    R       S or I       X            X            X            X   <NA>     S or I       X            X            -            -  S or I      R          X            X            X            X    R         R          -            X            -            X   <NA>       R          -            -            -            -  S or I     <NA>        X            X            -            -    R        <NA>        -            -            -            -   <NA>      <NA>        -            -            -            - -------------------------------------------------------------------- count_S()    +   count_I()    +   count_R()    = count_all()   proportion_S() + proportion_I() + proportion_R() = 1 count_S()    +   count_I()    +   count_R()    >= count_all()   proportion_S() + proportion_I() + proportion_R() >= 1"},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Calculate Microbial Resistance — proportion","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Microbial Resistance — proportion","text":"","code":"# example_isolates is a data set available in the AMR package. # run ?example_isolates for more info.  # base R ------------------------------------------------------------ resistance(example_isolates$AMX)     # determines %R #> [1] 0.5955556 susceptibility(example_isolates$AMX) # determines %S+I #> [1] 0.4044444  # be more specific proportion_S(example_isolates$AMX) #> [1] 0.4022222 proportion_SI(example_isolates$AMX) #> [1] 0.4044444 proportion_I(example_isolates$AMX) #> [1] 0.002222222 proportion_IR(example_isolates$AMX) #> [1] 0.5977778 proportion_R(example_isolates$AMX) #> [1] 0.5955556  # dplyr ------------------------------------------------------------- # \\donttest{ if (require(\"dplyr\")) {   example_isolates %>%     group_by(hospital_id) %>%     summarise(r = resistance(CIP),               n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi     example_isolates %>%     group_by(hospital_id) %>%     summarise(R  = resistance(CIP, as_percent = TRUE),               SI = susceptibility(CIP, as_percent = TRUE),               n1 = count_all(CIP),  # the actual total; sum of all three               n2 = n_rsi(CIP),      # same - analogous to n_distinct               total = n())          # NOT the number of tested isolates!     # Calculate co-resistance between amoxicillin/clav acid and gentamicin,   # so we can see that combination therapy does a lot more than mono therapy:   example_isolates %>% susceptibility(AMC)  # %SI = 76.3%   example_isolates %>% count_all(AMC)       #   n = 1879     example_isolates %>% susceptibility(GEN)  # %SI = 75.4%   example_isolates %>% count_all(GEN)       #   n = 1855     example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%   example_isolates %>% count_all(AMC, GEN)      #   n = 1939       # See Details on how `only_all_tested` works. Example:   example_isolates %>%     summarise(numerator = count_susceptible(AMC, GEN),               denominator = count_all(AMC, GEN),               proportion = susceptibility(AMC, GEN))     example_isolates %>%     summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),               denominator = count_all(AMC, GEN, only_all_tested = TRUE),               proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))       example_isolates %>%     group_by(hospital_id) %>%     summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),               cipro_n = count_all(CIP),               genta_p = susceptibility(GEN, as_percent = TRUE),               genta_n = count_all(GEN),               combination_p = susceptibility(CIP, GEN, as_percent = TRUE),               combination_n = count_all(CIP, GEN))     # Get proportions S/I/R immediately of all rsi columns   example_isolates %>%     select(AMX, CIP) %>%     proportion_df(translate = FALSE)     # It also supports grouping variables   # (use rsi_df to also include the count)   example_isolates %>%     select(hospital_id, AMX, CIP) %>%     group_by(hospital_id) %>%     rsi_df(translate = FALSE) } #>    hospital_id antibiotic interpretation     value isolates #> 1            A        AMX             SI 0.3932584       70 #> 2            A        AMX              R 0.6067416      108 #> 3            A        CIP             SI 0.8743961      181 #> 4            A        CIP              R 0.1256039       26 #> 5            B        AMX             SI 0.3854875      170 #> 6            B        AMX              R 0.6145125      271 #> 7            B        CIP             SI 0.8296593      414 #> 8            B        CIP              R 0.1703407       85 #> 9            C        AMX             SI 0.3714286       65 #> 10           C        AMX              R 0.6285714      110 #> 11           C        CIP             SI 0.7970297      161 #> 12           C        CIP              R 0.2029703       41 #> 13           D        AMX             SI 0.4334532      241 #> 14           D        AMX              R 0.5665468      315 #> 15           D        CIP             SI 0.8483034      425 #> 16           D        CIP              R 0.1516966       76 # }"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":null,"dir":"Reference","previous_headings":"","what":"Random MIC Values/Disk Zones/RSI Generation — random","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"functions can used generating random MIC values disk diffusion diameters, AMR data analysis practice. providing microorganism antimicrobial agent, generated results reflect reality much possible.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"","code":"random_mic(size = NULL, mo = NULL, ab = NULL, ...)  random_disk(size = NULL, mo = NULL, ab = NULL, ...)  random_rsi(size = NULL, prob_RSI = c(0.33, 0.33, 0.33), ...)"},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"size desired size returned vector. used data.frame call dplyr verb, get current (group) size left blank. mo character can coerced valid microorganism code .mo() ab character can coerced valid antimicrobial agent code .ab() ... ignored, place allow future extensions prob_RSI vector length 3: probabilities \"R\" (1st value), \"S\" (2nd value) \"\" (3rd value)","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"class <mic> random_mic() (see .mic()) class <disk> random_disk() (see .disk())","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"base R function sample() used generating values. Generated values based EUCAST 2022 guideline implemented rsi_translation data set. create specific generated values per bug drug, set mo /ab argument.","code":""},{"path":"https://msberends.github.io/AMR/reference/random.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Random MIC Values/Disk Zones/RSI Generation — random","text":"","code":"random_mic(25) #> Class <mic> #>  [1] 8     64    8     0.25  8     2     0.005 16    256   32    16    0.125 #> [13] 0.125 64    32    0.25  256   4     1     0.125 32    0.5   0.025 128   #> [25] 0.25  random_disk(25) #> Class <disk> #>  [1] 27 41 44 30  6 49 34 45 48 31 48 31 31 41 49 46 14 24 31 12 45 24 44 36 38 random_rsi(25) #> Class <rsi> #>  [1] I I R S S R S S S I I R I R I I S R R R S I S I S  # \\donttest{ # make the random generation more realistic by setting a bug and/or drug: random_mic(25, \"Klebsiella pneumoniae\")                 # range 0.0625-64 #> Class <mic> #>  [1] 0.125  0.25   0.125  0.002  4      2      8      32     0.0625 0.002  #> [11] 0.125  0.005  1      0.002  0.001  16     0.025  0.25   0.5    128    #> [21] 2      0.005  128    4      0.002  random_mic(25, \"Klebsiella pneumoniae\", \"meropenem\")    # range 0.0625-16 #> Class <mic> #>  [1] <=1  4    8    8    8    8    8    >=16 2    2    4    2    <=1  8    2    #> [16] <=1  >=16 2    2    8    4    4    8    <=1  <=1  random_mic(25, \"Streptococcus pneumoniae\", \"meropenem\") # range 0.0625-4 #> Class <mic> #>  [1] 0.125  0.25   2      0.5    0.0625 8      0.25   0.25   0.125  1      #> [11] 2      0.125  0.0625 4      0.5    0.125  2      8      4      0.0625 #> [21] 0.5    0.25   0.5    8      0.25    random_disk(25, \"Klebsiella pneumoniae\")                  # range 8-50 #> Class <disk> #>  [1] 42 30 13 25 19 37 49 26 38  8 46 40 23 25 44 11 40 13 22 34 15 36 47 49 49 random_disk(25, \"Klebsiella pneumoniae\", \"ampicillin\")    # range 11-17 #> Class <disk> #>  [1] 15 14 17 16 15 16 15 11 16 16 15 15 13 13 15 11 15 15 15 17 13 11 15 17 11 random_disk(25, \"Streptococcus pneumoniae\", \"ampicillin\") # range 12-27 #> Class <disk> #>  [1] 20 25 18 16 15 19 16 21 23 19 25 18 16 16 16 18 16 27 17 27 23 22 17 19 19 # }"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Predict Antimicrobial Resistance — resistance_predict","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Create prediction model predict antimicrobial resistance next years statistical solid ground. Standard errors (SE) returned columns se_min se_max. See Examples real live example.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"resistance_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  rsi_predict(   x,   col_ab,   col_date = NULL,   year_min = NULL,   year_max = NULL,   year_every = 1,   minimum = 30,   model = NULL,   I_as_S = TRUE,   preserve_measurements = TRUE,   info = interactive(),   ... )  # S3 method for resistance_predict plot(x, main = paste(\"Resistance Prediction of\", x_name), ...)  ggplot_rsi_predict(   x,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )  # S3 method for resistance_predict autoplot(   object,   main = paste(\"Resistance Prediction of\", x_name),   ribbon = TRUE,   ... )"},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Predict Antimicrobial Resistance — resistance_predict","text":"x data.frame containing isolates. Can left blank automatic determination, see Examples. col_ab column name x containing antimicrobial interpretations (\"R\", \"\" \"S\") col_date column name date, used calculate years column consist years already, defaults first column date class year_min lowest year use prediction model, dafaults lowest year col_date year_max highest year use prediction model, defaults 10 years today year_every unit sequence lowest year found data year_max minimum minimal amount available isolates per year include. Years containing less observations estimated model. model statistical model choice. generalised linear regression model binomial distribution (.e. using glm(..., family = binomial), assuming period zero resistance followed period increasing resistance leading slowly resistance. See Details valid options. I_as_S logical indicate whether values \"\" treated \"S\" (otherwise treated \"R\"). default, TRUE, follows redefinition EUCAST interpretation (increased exposure) 2019, see section Interpretation S, R . preserve_measurements logical indicate whether predictions years actually available data overwritten original data. standard errors years NA. info logical indicate whether textual analysis printed name summary() statistical model. ... arguments passed functions main title plot ribbon logical indicate whether ribbon shown (default) error bars object model data plotted","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Predict Antimicrobial Resistance — resistance_predict","text":"data.frame extra class resistance_predict columns: year value, estimated preserve_measurements = FALSE, combination observed estimated otherwise se_min, lower bound standard error minimum 0 (standard error never go 0%) se_max upper bound standard error maximum 1 (standard error never go 100%) observations, total number available observations year, .e. \\(S + + R\\) observed, original observed resistant percentages estimated, estimated resistant percentages, calculated model Furthermore, model available attribute: attributes(x)$model, see Examples.","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Predict Antimicrobial Resistance — resistance_predict","text":"Valid options statistical model (argument model) : \"binomial\" \"binom\" \"logit\": generalised linear regression model binomial distribution \"loglin\" \"poisson\": generalised log-linear regression model poisson distribution \"lin\" \"linear\": linear regression model","code":""},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"interpretation-of-r-and-s-i","dir":"Reference","previous_headings":"","what":"Interpretation of R and S/I","title":"Predict Antimicrobial Resistance — resistance_predict","text":"2019, European Committee Antimicrobial Susceptibility Testing (EUCAST) decided change definitions susceptibility testing categories R S/shown (https://www.eucast.org/newsiandr/). R = Resistant microorganism categorised Resistant high likelihood therapeutic failure even increased exposure. Exposure function mode administration, dose, dosing interval, infusion time, well distribution excretion antimicrobial agent influence infecting organism site infection. S = Susceptible microorganism categorised Susceptible, standard dosing regimen, high likelihood therapeutic success using standard dosing regimen agent. = Susceptible, Increased exposure microorganism categorised Susceptible, Increased exposure high likelihood therapeutic success exposure agent increased adjusting dosing regimen concentration site infection. AMR package honours (new) insight. Use susceptibility() (equal proportion_SI()) determine antimicrobial susceptibility count_susceptible() (equal count_SI()) count susceptible isolates.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/resistance_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Predict Antimicrobial Resistance — resistance_predict","text":"","code":"x <- resistance_predict(example_isolates,                          col_ab = \"AMX\",                         year_min = 2010,                         model = \"binomial\") #> ℹ Using column 'date' as input for `col_date`. plot(x)  # \\donttest{ if (require(\"ggplot2\")) {   ggplot_rsi_predict(x) }   # using dplyr: if (require(\"dplyr\")) {   x <- example_isolates %>%     filter_first_isolate() %>%     filter(mo_genus(mo) == \"Staphylococcus\") %>%     resistance_predict(\"PEN\", model = \"binomial\")   plot(x)    # get the model from the object   mymodel <- attributes(x)$model   summary(mymodel) } #> ℹ Using column 'date' as input for `col_date`.  #>  #> Call: #> glm(formula = df_matrix ~ year, family = binomial) #>  #> Deviance Residuals:  #>     Min       1Q   Median       3Q      Max   #> -1.4150  -0.1855   0.0000   0.4096   0.9277   #>  #> Coefficients: #>             Estimate Std. Error z value Pr(>|z|) #> (Intercept) 47.02915   72.98739   0.644    0.519 #> year        -0.02280    0.03637  -0.627    0.531 #>  #> (Dispersion parameter for binomial family taken to be 1) #>  #>     Null deviance: 5.9620  on 11  degrees of freedom #> Residual deviance: 5.5701  on 10  degrees of freedom #> AIC: 50.533 #>  #> Number of Fisher Scoring iterations: 4 #>   # create nice plots with ggplot2 yourself if (require(\"dplyr\") & require(\"ggplot2\")) {    data <- example_isolates %>%     filter(mo == as.mo(\"E. coli\")) %>%     resistance_predict(col_ab = \"AMX\",                        col_date = \"date\",                        model = \"binomial\",                        info = FALSE,                        minimum = 15)   head(data)   autoplot(data) } #> ℹ Function `as.mo()` is uncertain about \"E. coli\" (assuming Escherichia #>   coli). Run `mo_uncertainties()` to review this.  # }"},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":null,"dir":"Reference","previous_headings":"","what":"Data Set for R/SI Interpretation — rsi_translation","title":"Data Set for R/SI Interpretation — rsi_translation","text":"Data set containing reference data interpret MIC disk diffusion R/SI values, according international guidelines. Currently implemented guidelines EUCAST (2011-2022) CLSI (2011-2022). Use .rsi() transform MICs disks measurements R/SI values.","code":""},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data Set for R/SI Interpretation — rsi_translation","text":"","code":"rsi_translation"},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data Set for R/SI Interpretation — rsi_translation","text":"data.frame 20,369 observations 11 variables: guideline Name guideline method Either \"DISK\" \"MIC\" site Body site, e.g. \"Oral\" \"Respiratory\" mo Microbial ID, see .mo() rank_index Taxonomic rank index mo 1 (subspecies/infraspecies) 5 (unknown microorganism) ab Antibiotic ID, see .ab() ref_tbl Info guideline rule can found disk_dose Dose used disk diffusion method breakpoint_S Lowest MIC value highest number millimetres leads \"S\" breakpoint_R Highest MIC value lowest number millimetres leads \"R\" uti logical value (TRUE/FALSE) indicate whether rule applies urinary tract infection (UTI)","code":""},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Data Set for R/SI Interpretation — rsi_translation","text":"repository AMR package contains file comprising exact data set: https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt. file allows machine reading EUCAST CLSI guidelines, almost impossible Excel PDF files distributed EUCAST CLSI. file updated automatically mo ab columns transformed contain full official names instead codes.","code":""},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"reference-data-publicly-available","dir":"Reference","previous_headings":"","what":"Reference Data Publicly Available","title":"Data Set for R/SI Interpretation — rsi_translation","text":"reference data sets (microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) AMR package publicly freely available. continually export data sets formats use R, SPSS, SAS, Stata Excel. also supply flat files machine-readable suitable input software program, laboratory information systems. Please find download links website, automatically updated every code change.","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/rsi_translation.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data Set for R/SI Interpretation — rsi_translation","text":"","code":"head(rsi_translation) #>     guideline method site           mo rank_index  ab     ref_tbl disk_dose #> 1 EUCAST 2022    MIC <NA> F_ASPRG_MGTS          2 AMB Aspergillus      <NA> #> 2 EUCAST 2022    MIC <NA> F_ASPRG_NIGR          2 AMB Aspergillus      <NA> #> 3 EUCAST 2022    MIC <NA>      F_CANDD          3 AMB     Candida      <NA> #> 4 EUCAST 2022    MIC <NA> F_CANDD_ALBC          2 AMB     Candida      <NA> #> 5 EUCAST 2022    MIC <NA> F_CANDD_DBLN          2 AMB     Candida      <NA> #> 6 EUCAST 2022    MIC <NA> F_CANDD_KRUS          2 AMB     Candida      <NA> #>   breakpoint_S breakpoint_R   uti #> 1            1            1 FALSE #> 2            1            1 FALSE #> 3            1            1 FALSE #> 4            1            1 FALSE #> 5            1            1 FALSE #> 6            1            1 FALSE"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":null,"dir":"Reference","previous_headings":"","what":"Skewness of the Sample — skewness","title":"Skewness of the Sample — skewness","text":"Skewness measure asymmetry probability distribution real-valued random variable mean. negative ('left-skewed'): left tail longer; mass distribution concentrated right histogram. positive ('right-skewed'): right tail longer; mass distribution concentrated left histogram. normal distribution skewness 0.","code":""},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Skewness of the Sample — skewness","text":"","code":"skewness(x, na.rm = FALSE)  # S3 method for default skewness(x, na.rm = FALSE)  # S3 method for matrix skewness(x, na.rm = FALSE)  # S3 method for data.frame skewness(x, na.rm = FALSE)"},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Skewness of the Sample — skewness","text":"x vector values, matrix data.frame na.rm logical value indicating whether NA values stripped computation proceeds","code":""},{"path":[]},{"path":"https://msberends.github.io/AMR/reference/skewness.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Skewness of the Sample — skewness","text":"","code":"skewness(runif(1000)) #> [1] 0.06804385"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":null,"dir":"Reference","previous_headings":"","what":"Translate Strings from the AMR Package — translate","title":"Translate Strings from the AMR Package — translate","text":"language-dependent output AMR functions, like mo_name(), mo_gramstain(), mo_type() ab_name().","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Translate Strings from the AMR Package — translate","text":"","code":"get_AMR_locale()  set_AMR_locale(lang)  reset_AMR_locale()  translate_AMR(x, language = get_AMR_locale())"},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Translate Strings from the AMR Package — translate","text":"lang language choose. Use one supported language names ISO-639-1 codes: \"English\" (\"en\"), \"Chinese\" (\"zh\"), \"Danish\" (\"da\"), \"Dutch\" (\"nl\"), \"French\" (\"fr\"), \"German\" (\"de\"), \"Greek\" (\"el\"), \"Italian\" (\"\"), \"Japanese\" (\"ja\"), \"Polish\" (\"pl\"), \"Portuguese\" (\"pt\"), \"Russian\" (\"ru\"), \"Spanish\" (\"es\"), \"Swedish\" (\"sv\"), \"Turkish\" (\"tr\"), \"Ukrainian\" (\"uk\"). x text translate","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Translate Strings from the AMR Package — translate","text":"currently 16 supported languages English, Chinese, Danish, Dutch, French, German, Greek, Italian, Japanese, Polish, Portuguese, Russian, Spanish, Swedish, Turkish Ukrainian. languages translations available antimicrobial agents colloquial microorganism names. Please read adding updating language Wiki.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"changing-the-default-language","dir":"Reference","previous_headings":"","what":"Changing the Default Language","title":"Translate Strings from the AMR Package — translate","text":"system language used default (returned Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(\"LC_COLLATE\")), language supported. language used can overwritten two ways checked order: Setting R option AMR_locale, either using set_AMR_locale() running e.g. options(AMR_locale = \"de\"). Note setting R option works session. Save command options(AMR_locale = \"(language)\") .Rprofile file apply every session. Setting system variable LANGUAGE LANG, e.g. adding LANGUAGE=\"de_DE.utf8\" .Renviron file home directory. Thus, R option AMR_locale set, system variables LANGUAGE LANG ignored.","code":""},{"path":"https://msberends.github.io/AMR/reference/translate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Translate Strings from the AMR Package — translate","text":"","code":"# Current settings (based on system language) ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacin\" mo_name(\"Coagulase-negative Staphylococcus\") #> [1] \"Coagulase-negative Staphylococcus (CoNS)\"  # setting another language set_AMR_locale(\"Greek\") #> ℹ Using the Greek language (Ελληνικά) for the AMR package for this session. ab_name(\"Ciprofloxacin\") #> [1] \"Σιπροφλοξασίνη\" mo_name(\"Coagulase-negative Staphylococcus\") #> [1] \"Σταφυλόκοκκος με αρνητική πηκτικότητα (CoNS)\"  set_AMR_locale(\"Spanish\") #> ℹ Using the Spanish language (Español) for the AMR package for this #>   session. ab_name(\"Ciprofloxacin\") #> [1] \"Ciprofloxacina\" mo_name(\"Coagulase-negative Staphylococcus\") #> [1] \"Staphylococcus coagulasa negativo (SCN)\"  # set_AMR_locale() understands endonyms, English exonyms, and ISO-639-1: set_AMR_locale(\"Deutsch\") #> ℹ Using the German language (Deutsch) for the AMR package for this session. set_AMR_locale(\"German\") #> ℹ Using the German language (Deutsch) for the AMR package for this session. set_AMR_locale(\"de\") #> ℹ Using the German language (Deutsch) for the AMR package for this session.  # reset to system default reset_AMR_locale()"},{"path":[]},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-8-1-9021","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.8.1.9021","text":"EUCAST 2022 CLSI 2022 guidelines added .rsi(). EUCAST 2022 now new default guideline MIC disks diffusion interpretations. Support following languages: Chinese, Greek, Japanese, Polish, Turkish Ukrainian. AMR package now available 16 languages.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-8-1-9021","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.8.1.9021","text":"Fix .rsi() certain EUCAST breakpoints MIC values Removed .integer() MIC values, since MIC integer values running table() MIC values consequently failed able retrieve level position (’s normally .integer() factors work) droplevels() MIC now return common factor default lose <mic> class. Use droplevels(..., .mic = TRUE) keep <mic> class. Small fix using ab_from_text() Fixes reading text files using set_mo_source(), now also allows source file contain valid taxonomic names instead valid microorganism ID package Using random_*() function (random_mic()) now possible directly calling package without loading first: AMR::random_mic(10) Added Toxoplasma gondii (P_TXPL_GOND) microorganisms data set, together genus, family, order Changed value column prevalence microorganisms data set 3 2 genera: Acholeplasma, Alistipes, Alloprevotella, Bergeyella, Borrelia, Brachyspira, Butyricimonas, Cetobacterium, Chlamydia, Chlamydophila, Deinococcus, Dysgonomonas, Elizabethkingia, Empedobacter, Haloarcula, Halobacterium, Halococcus, Myroides, Odoribacter, Ornithobacterium, Parabacteroides, Pedobacter, Phocaeicola, Porphyromonas, Riemerella, Sphingobacterium, Streptobacillus, Tenacibaculum, Terrimonas, Victivallis, Wautersiella, Weeksella Fix using form df[carbapenems() == \"R\", ] using latest vctrs package Fix using info = FALSE mdro()","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-1-9021","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.1.9021","text":"New website make use new Bootstrap 5 pkgdown v2.0. website now contains results examples automatically regenerated every change repository, using GitHub Actions Added Peter Dutey-Magni Anton Mymrikov contributors, thank valuable input","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-181","dir":"Changelog","previous_headings":"","what":"AMR 1.8.1","title":"AMR 1.8.1","text":"CRAN release: 2022-03-24","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-8-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.8.1","text":"Fix using .rsi() values containing capped values (>=), sometimes leading NA Support antibiotic interpretations MIPS laboratory system: \"U\" S (‘susceptible urine’), \"D\" (‘susceptible dose-dependent’) Improved algorithm .mo(), especially ignoring non-taxonomic text, : informative warning messages Added 192 valid MIC Updated MIC printing tibbles Increased speed loading package","code":"mo_name(\"methicillin-resistant S. aureus (MRSA)\") #> [1] \"Staphylococcus aureus\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-1","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.1","text":"Fix unit testing R 3.3 Fix size image elements, requested CRAN","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-180","dir":"Changelog","previous_headings":"","what":"AMR 1.8.0","title":"AMR 1.8.0","text":"CRAN release: 2022-01-07","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-changes-1-8-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"AMR 1.8.0","text":"Removed p_symbol() filter_*() functions (except filter_first_isolate()), deprecated previous package version Removed key_antibiotics() key_antibiotics_equal() functions, deprecated superseded key_antimicrobials() antimicrobials_equal() Removed previously implemented ggplot2::ggplot() generics classes <mic>, <disk>, <rsi> <resistance_predict> follow ggplot2 logic. replaced ggplot2::autoplot() generics. Renamed function get_locale() get_AMR_locale() prevent conflicts packages","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-8-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.8.0","text":"Support CLSI 2021 guideline interpreting MIC/disk diffusion values, incorporated rsi_translation data set. data set now strictly follows WHONET software well. Support EUCAST Intrinsic Resistance Unusual Phenotypes v3.3 (October 2021). now default EUCAST guideline package (older guidelines still available) eucast_rules(), mo_is_intrinsic_resistant() mdro(). intrinsic_resistant data set also updated accordingly. Support antimicrobial drug (group) names colloquial microorganism names Danish, Dutch, English, French, German, Italian, Portuguese, Russian, Spanish Swedish Function set_ab_names() rename data set columns resemble antimicrobial drugs. allows quickly renaming columns official names, ATC codes, etc. second argument can tidyverse way selecting: Function mo_lpsn() retrieve LPSN record ID Function ab_ddd_units() get units DDDs (daily defined doses), deprecating use ab_ddd(..., units = TRUE) consistent data types function output","code":"example_isolates %>% set_ab_names(where(is.rsi)) example_isolates %>% set_ab_names(AMC:GEN, property = \"atc\")"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-8-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.8.0","text":"Updated bacterial taxonomy 5 October 2021 (according LPSN), including 11 new staphylococcal species named since 1 January last year drugs now contain multiple ATC codes (e.g., metronidazole contains 5) antibiotics$atc now list containing character vectors, atc column moved 5th position antibiotics data set ab_atc() always return character vector length 1, returns list input larger length 1 ab_info() slightly different output DDDs (daily defined doses) added updated according newly included ATC codes now also work R-3.0 R-3.1, supporting every version R since 2013 like rest package Added selectors antibiotic classes: aminopenicillins(), antifungals(), antimycobacterials(), lincosamides(), lipoglycopeptides(), polymyxins(), quinolones(), streptogramins(), trimethoprims() ureidopenicillins() Added specific selectors certain types treatment: administrable_per_os() administrable_iv(), based available Defined Daily Doses (DDDs), defined WHOCC. ideal e.g. analysing pathogens primary care IV treatment option. can combined AB selectors, e.g. select penicillins administrable per os (.e., orally): Added selector ab_selector(), accepts filter used internally antibiotics data set, yielding great flexibility drug properties, selecting antibiotic columns oral DDD least 1 gram: Added selector not_intrinsic_resistant(), keeps antibiotic columns intrinsic resistant microorganisms data set, based latest EUCAST guideline intrinsic resistance. example, data set contains microorganism codes names E. coli K. pneumoniae contains column “vancomycin”, column removed (rather, unselected) using function. Added argument only_treatable, defaults TRUE exclude drugs laboratory tests treating patients (imipenem/EDTA gentamicin-high) Fix using selectors multiple times one call (e.g., using dplyr::filter() immediately dplyr::select()) Fix using multiple columns coerced antibiotic agent Fixed using () () antibiotic selectors R Markdown file Added following antimicrobial agents now covered : aztreonam/nacubactam (ANC), cefepime/nacubactam (FNC), exebacase (EXE), ozenoxacin (OZN), zoliflodacin (ZFD), manogepix (MGX), ibrexafungerp (IBX), rezafungin (RZF). None agents ATC code yet. Fixed Gram stain (mo_gramstain()) determination taxonomic class Negativicutes within phylum Firmicutes - considered Gram-positives phylum actually Gram-negative. impacts 137 taxonomic species, genera families, Negativicoccus Veillonella. Dramatic speed improvement first_isolate() Fix prevent introducing NAs old MO codes running .mo() Added informative error messages proportion_*() count_*() functions fail printing tibble old MO code, warning thrown old codes updated using .mo() Improved automatic column selector col_* arguments left blank, e.g. first_isolate() right input types random_mic(), random_disk() random_rsi() now enforced .rsi() improved algorithm can now also correct textual input (“Susceptible”, “Resistant”) supported languages .mic() improved algorithm warnings thrown isolates count_*(), proportion_*() function (resistant() susceptible()), dplyr group shown, available Fix legends created scale_rsi_colours() using ggplot2 v3.3.4 higher (ggplot2 bug 4511, soon fixed) Fix minor translation errors Fix MIC interpretation Morganellaceae (Morganella Proteus) using EUCAST 2021 guideline Improved algorithm .mo() Improved algorithm generating random MICs random_mic() Improved plot legends MICs disk diffusion values Improved speed .ab() ab_*() functions Added fortify() extensions plotting methods NA values classes <mic>, <disk> <rsi> now exported objects package, e.g. NA_mic_ NA class mic (just like base R NA_character_ NA class character) proportion_df(), count_df() rsi_df() functions now return additional S3 class ‘rsi_df’ can extended packages mdro() function now returns NA rows test results species_id column microorganisms data set now contains LPSN record numbers. reason, column now numeric instead character, mo_url() updated reflect change. Fixed small bug functions get_episode() is_new_episode() get_episode() is_new_episode() can now cope NAs","code":"example_isolates[, penicillins() & administrable_per_os()]          # base R example_isolates %>% select(penicillins() & administrable_per_os()) # dplyr example_isolates[, ab_selector(oral_ddd > 1 & oral_units == \"g\")]          # base R example_isolates %>% select(ab_selector(oral_ddd > 1 & oral_units == \"g\")) # dplyr"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-8-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.8.0","text":"package now maintained two epidemiologists data scientist two different non-profit healthcare organisations.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-171","dir":"Changelog","previous_headings":"","what":"AMR 1.7.1","title":"AMR 1.7.1","text":"CRAN release: 2021-06-03","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-change-1-7-1","dir":"Changelog","previous_headings":"","what":"Breaking change","title":"AMR 1.7.1","text":"antibiotic class selectors (carbapenems(), aminoglycosides()) can now used filtering well, making accompanying filter_*() functions redundant (filter_carbapenems(), filter_aminoglycosides()). functions now deprecated removed next release. Examples selectors can used filtering:","code":"# select columns with results for carbapenems example_isolates[, carbapenems()]           # base R example_isolates %>% select(carbapenems())  # dplyr  # filter rows for resistance in any carbapenem example_isolates[any(carbapenems() == \"R\"), ]                  # base R example_isolates %>% filter(any(carbapenems() == \"R\"))         # dplyr example_isolates %>% filter(if_any(carbapenems(), ~.x == \"R\")) # dplyr (formal)  # filter rows for resistance in all carbapenems example_isolates[all(carbapenems() == \"R\"), ]           # base R example_isolates[carbapenems() == \"R\", ] example_isolates %>% filter(all(carbapenems() == \"R\"))  # dplyr example_isolates %>% filter(carbapenems() == \"R\")"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-7-1","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.7.1","text":"Support CLSI 2020 guideline interpreting MICs disk diffusion values (using .rsi()) Function custom_eucast_rules() brings support custom AMR rules eucast_rules() Function italicise_taxonomy() make taxonomic names within string italic, support markdown ANSI first_isolate() function gained argument method “phenotype-based”, “episode-based”, “patient-based”, “isolate-based”. old behaviour equal “episode-based”. new default “phenotype-based” antimicrobial test results available, “episode-based” otherwise. new default yield slightly isolates selection (good thing). Since fungal isolates can also selected, functions key_antibiotics() key_antibiotics_equal() now deprecated favour key_antimicrobials() antimicrobials_equal() functions. Also, new all_antimicrobials() function works like old key_antibiotics() function, includes column antimicrobial test results. Using key_antimicrobials() still selects six preferred antibiotics Gram-negatives, six Gram-positives, six universal antibiotics. new antifungal argument set antifungal agents (antimycotics). Using type == \"points\" first_isolate() function phenotype-based selection now consider antimicrobial drugs data set, using new all_antimicrobials() first_isolate() function can now take vector values col_keyantibiotics can episode length Inf Since phenotype-based method new default, filter_first_isolate() renders filter_first_weighted_isolate() function redundant. reason, filter_first_weighted_isolate() now deprecated. documentation first_isolate() key_antimicrobials() functions completely rewritten. Function betalactams() additional antbiotic column selector function filter_betalactams() additional antbiotic column filter. group betalactams consists carbapenems, cephalosporins penicillins. ggplot() method resistance_predict()","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-7-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.7.1","text":"bug_drug_combinations() now supports grouping using dplyr package Custom MDRO guidelines can now combined custom MDRO guidelines using c() Fix applying rules; previous versions, rows interpreted according last matched rule. Now, rows interpreted according first matched rule Fix age_groups() persons aged zero example_isolates data set now contains (fictitious) zero-year old patients Fix minor translation errors Printing microbial codes data.frame tibble now gives warning data contains old microbial codes (previous AMR package version) Now checks pattern valid regular expression Added %unlike% %unlike_case% (negations existing %like% %like_case%). greatly improves readability: Altered RStudio addin, now iterates %like% -> %unlike% -> %like_case% -> %unlike_case% keep pressing keyboard shortcut Fixed installation error R-3.0 Added info argument .mo() turn /progress bar Fixed bug col_mo functions (esp. eucast_rules() mdro()) column name microorganisms data set throw error Fix transforming numeric values RSI (.rsi()) vctrs package loaded (.e., using tidyverse) Colour fix using barplot() RSI class Added 25 common system codes bacteria microorganisms.codes data set Added 16 common system codes antimicrobial agents antibiotics data set Fix using skimr::skim() classes mo, mic disk using just released dplyr v1.0.6 Updated skimr::skim() usage MIC values also include 25th 75th percentiles Fix plotting missing MIC/disk diffusion values Updated join functions always use dplyr join functions dplyr package installed - now also preserving grouped variables Antibiotic class selectors (cephalosporins()) now maintain column order original data Fix selecting columns using fluoroquinolones() age() now vectorises x reference","code":"if (!grepl(\"EUCAST\", guideline)) ... # same: if (guideline %unlike% \"EUCAST\") ..."},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-7-1","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.7.1","text":"requested CRAN administrators: decreased package size 3 MB costs slower loading time package unit tests now processed tinytest package, instead testthat package. testthat package unfortunately requires tons dependencies also heavy usable recent R versions, disallowing developers test package R 3.* version. contrary, tinytest package lightweight dependency-free.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-160","dir":"Changelog","previous_headings":"","what":"AMR 1.6.0","title":"AMR 1.6.0","text":"CRAN release: 2021-03-14","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-6-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.6.0","text":"Support EUCAST Clinical Breakpoints v11.0 (2021), effective eucast_rules() function .rsi() interpret MIC disk diffusion values. now default guideline package. Added function eucast_dosage() get data.frame advised dosages certain bug-drug combination, based new dosage data set Added data set dosage fuel new eucast_dosage() function make data available structured way Existing data set example_isolates now reflects latest EUCAST rules Added argument only_rsi_columns functions, defaults FALSE, indicate functions must applied columns class <rsi> (.e., transformed .rsi()). increases speed since automatic determination antibiotic columns needed anymore. Affected functions : antibiotic selector functions (ab_class() wrappers, aminoglycosides(), carbapenems(), penicillins()) antibiotic filter functions (filter_ab_class() wrappers, filter_aminoglycosides(), filter_carbapenems(), filter_penicillins()) eucast_rules() mdro() (including wrappers brmo(), mrgn() eucast_exceptional_phenotypes()) guess_ab_col() Functions oxazolidinones() (antibiotic selector function) filter_oxazolidinones() (antibiotic filter function) select/filter e.g. linezolid tedizolid Support custom MDRO guidelines, using new custom_mdro_guideline() function, please see mdro() additional info ggplot() generics classes <mic> <disk> Function mo_is_yeast(), determines whether microorganism member taxonomic class Saccharomycetes taxonomic order Saccharomycetales: mo_type() function also updated reflect change: Added Pretomanid (PMD, J04AK08) antibiotics data set MIC values (see .mic()) can now used mathematical processing, usage inside functions min(), max(), range(), binary operators (+, -, etc.). allows easy distribution analysis fast filtering MIC values:","code":"library(dplyr) x <- example_isolates %>% select(date, hospital_id, oxazolidinones()) #> Selecting oxazolidinones: column 'LNZ' (linezolid)  x <- example_isolates %>% filter_oxazolidinones() #> Filtering on oxazolidinones: value in column `LNZ` (linezolid) is either \"R\", \"S\" or \"I\" mo_kingdom(c(\"Aspergillus\", \"Candida\")) #> [1] \"Fungi\" \"Fungi\"  mo_is_yeast(c(\"Aspergillus\", \"Candida\")) #> [1] FALSE  TRUE  # usage for filtering data: example_isolates[which(mo_is_yeast()), ]   # base R example_isolates %>% filter(mo_is_yeast()) # dplyr mo_type(c(\"Aspergillus\", \"Candida\")) # [1] \"Fungi\"  \"Yeasts\" mo_type(c(\"Aspergillus\", \"Candida\"), language = \"es\") # also supported: de, nl, fr, it, pt #> [1] \"Hongos\"    \"Levaduras\" x <- random_mic(10) x #> Class <mic> #>  [1] 128   0.5   2     0.125 64    0.25  >=256 8     16    4 x[x > 4] #> Class <mic> #> [1] 128   64    >=256 8     16 range(x) #> [1]   0.125 256.000 range(log2(x)) #> [1] -3  8"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-6-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.6.0","text":"Added 3,372 new species 1,523 existing species became synomyms URL bacterial species (mo_url()) now lead https://lpsn.dsmz.de Plotting MIC disk diffusion values now support interpretation colouring supply microorganism antimicrobial agent colours updated colour-blind friendly versions values R, S plot methods (also applies tibble printing) Interpretation MIC disk diffusion values R/SI now translated system language German, Dutch Spanish (see translate) Plotting now possible base R using plot() ggplot2 using ggplot() vector MIC disk diffusion values Updated SNOMED codes US Edition SNOMED CT 1 September 2020 added source help page microorganisms data set .rsi() .rsi.eligible() now return vector TRUE/FALSE input data set, iterating columns Using functions without setting data set (e.g., mo_is_gram_negative(), mo_is_gram_positive(), mo_is_intrinsic_resistant(), first_isolate(), mdro()) now work dplyrs group_by() first_isolate() can used group_by() (also using dot . input data) now returns names groups Updated data set microorganisms.codes (contains popular LIS WHONET codes microorganisms) species Mycobacterium previously incorrectly returned M. africanum WHONET code \"PNV\" now correctly interpreted PHN, antibiotic code phenoxymethylpenicillin (‘peni V’) Fix verbose output mdro(..., verbose = TRUE) German guideline (3MGRN 4MGRN) Dutch guideline (BRMO, P. aeruginosa) .rsi.eligible() now detects column name resembles antibiotic name code now returns TRUE immediately input contains values “R”, “S” “”. drastically improves speed, also lot functions rely automatic determination antibiotic columns. Functions get_episode() is_new_episode() now support less day value argument episode_days (e.g., include one patient/test per hour) Argument ampc_cephalosporin_resistance eucast_rules() now also applies value “” (“S”) Functions print() summary() Principal Components Analysis object (pca()) now print additional group info original data grouped using dplyr::group_by() Improved speed reliability guess_ab_col(). also internally improves reliability first_isolate() mdro(), might slight impact results functions. Fix mo_name() used languages English like() function (fast alias %like%) now always use Perl compatibility, improving speed many functions package (e.g., .mo() now 4 times faster) Staphylococcus cornubiensis now correctly categorised coagulase-positive random_disk() random_mic() now expanded range randomisation Support GISA (glycopeptide-intermediate S. aureus), e.g. mo_genus(\"GISA\") return \"Staphylococcus\" Added translations German Spanish 200 antimicrobial drugs Speed improvement .ab() input official name ATC code Added argument include_untested_rsi first_isolate() functions (defaults TRUE keep existing behaviour), able exclude rows R/SI values (class <rsi>, see .rsi()) empty","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-6-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.6.0","text":"Big documentation updates Loading package (.e., library(AMR)) now ~50 times faster , costs package size (increased ~3 MB)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-150","dir":"Changelog","previous_headings":"","what":"AMR 1.5.0","title":"AMR 1.5.0","text":"CRAN release: 2021-01-06","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-5-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.5.0","text":"Functions get_episode() is_new_episode() determine (patient) episodes necessarily based microorganisms. get_episode() function returns index number episode per group, is_new_episode() function returns values TRUE/FALSE indicate whether item vector start new episode. also support dplyrs grouping (.e. using group_by()): Functions mo_is_gram_negative() mo_is_gram_positive() wrappers around mo_gramstain(). always return TRUE FALSE (except input NA MO code UNKNOWN), thus always return FALSE species outside taxonomic kingdom Bacteria. Function mo_is_intrinsic_resistant() test intrinsic resistance, based EUCAST Intrinsic Resistance Unusual Phenotypes v3.2 2020. Functions random_mic(), random_disk() random_rsi() random value generation. functions random_mic() random_disk() take microorganism names antibiotic names input make generation realistic.","code":"library(dplyr) example_isolates %>%   group_by(patient_id, hospital_id) %>%   filter(is_new_episode(date, episode_days = 60))"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-5-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.5.0","text":"New argument ampc_cephalosporin_resistance eucast_rules() correct AmpC de-repressed cephalosporin-resistant mutants Interpretation antimicrobial resistance - .rsi(): Reference data used .rsi() can now set user, using reference_data argument. allows using interpretation guidelines. user-set data must structure rsi_translation. Better determination disk zones MIC values running .rsi() data.frame Fix using .rsi() data.frame older R versions .rsi() data.frame print message anymore values already clean R/SI values using .rsi() MICs disk diffusion intrinsic antimicrobial resistance, warning thrown remind Fix using .rsi() data.frame contains one column antibiotic interpretations functions now context-aware used inside dplyr verbs, filter(), mutate() summarise(). means data argument need set anymore. case new functions: mo_is_gram_negative() mo_is_gram_positive() mo_is_intrinsic_resistant() … existing functions: first_isolate(), key_antibiotics(), mdro(), brmo(), mrgn(), mdr_tb(), mdr_cmi2012(), eucast_exceptional_phenotypes() antibiotic selection functions (cephalosporins(), aminoglycosides()) select columns based certain antibiotic group, dependency tidyselect package removed, meaning can now also used without need package installed now also work base R function calls (rely R 3.2 later): function arguments code, now defined exact type user input (inspired typed package). user input certain function meet requirements specific argument (class length), informative error thrown. makes package robust use reproducible reliable. total, 420 arguments defined. Fix set_mo_source(), previously remember file location original file Deprecated function p_symbol() really fits scope package. removed future version. See source code preserve . Updated coagulase-negative staphylococci determination Becker et al. 2020 (PMID 32056452), meaning species S. argensis, S. caeli, S. debuckii, S. edaphicus S. pseudoxylosus now considered CoNS Fix using argument reference_df .mo() mo_*() functions contain old microbial codes (previous package versions) Fixed bug mo_uncertainties() return results based MO matching score Fixed bug .mo() return results known laboratory codes microorganisms Fixed bug .ab() sometimes fail Better tibble printing MIC values Fix plotting MIC values plot() Added plot() generic class <disk> LA-MRSA CA-MRSA now recognised abbreviation Staphylococcus aureus, meaning e.g. mo_genus(\"LA-MRSA\") return \"Staphylococcus\" mo_is_gram_positive(\"LA-MRSA\") return TRUE. Fix printing class  tibbles values NA Fix mo_shortname() input contains NA .mo() takes 30 seconds, suggestions done improve speed","code":"# to select first isolates that are Gram-negative  # and view results of cephalosporins and aminoglycosides: library(dplyr) example_isolates %>%   filter(first_isolate(), mo_is_gram_negative()) %>%    select(mo, cephalosporins(), aminoglycosides()) %>%    as_tibble() # above example in base R: example_isolates[which(first_isolate() & mo_is_gram_negative()),                  c(\"mo\", cephalosporins(), aminoglycosides())]"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-5-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.5.0","text":"messages warnings thrown package now break sentences whole words extensive unit tests Internal calls options() removed favour new internal environment pkg_env Improved internal type setting (among things: replaced sapply() calls vapply()) Added CodeFactor continuous code review package: https://www.codefactor.io/repository/github/msberends/amr/ Added Dr. Rogier Schade contributor","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-140","dir":"Changelog","previous_headings":"","what":"AMR 1.4.0","title":"AMR 1.4.0","text":"CRAN release: 2020-10-08","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-4-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.4.0","text":"Support ‘EUCAST Expert Rules’ / ‘EUCAST Intrinsic Resistance Unusual Phenotypes’ version 3.2 May 2020. addition previously implemented version 3.1 2016, eucast_rules() function can now correct 180 different antibiotics mdro() function can determine multidrug resistance based 150 different antibiotics. previously implemented versions EUCAST rules now maintained kept available package. eucast_rules() function consequently gained arguments version_breakpoints (moment defaults v10.0, 2020) version_expertrules (moment defaults v3.2, 2020). example_isolates data set now also reflects change v3.1 v3.2. mdro() function now accepts guideline == \"EUCAST3.1\" guideline == \"EUCAST3.2\". new vignette website page info public freely available data sets, can downloaded flat files formats use R, SPSS, SAS, Stata Excel: https://msberends.github.io/AMR/articles/datasets.html Data set intrinsic_resistant. data set contains bug-drug combinations ‘bug’ intrinsic resistant ‘drug’ according latest EUCAST insights. contains just two columns: microorganism antibiotic. Curious enterococci actually intrinsic resistant vancomycin? Support veterinary ATC codes Support skimming classes <rsi>, <mic>, <disk> <mo> skimr package","code":"library(AMR) library(dplyr) intrinsic_resistant %>%   filter(antibiotic == \"Vancomycin\", microorganism %like% \"Enterococcus\") %>%    pull(microorganism) #> [1] \"Enterococcus casseliflavus\" \"Enterococcus gallinarum\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-4-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.4.0","text":"Although advertised package work R 3.0.0, still dependency R 3.6.0. fixed, meaning package now work R 3.0.0. Improvements .rsi(): Support using dplyr’s across() interpret MIC values disk zone diameters, also automatically determines column microorganism names codes. Cleaning columns data.frame now allows specify columns tidy selection, e.g. .rsi(df, col1:col9) Big speed improvement interpreting MIC values disk zone diameters. interpreting 5,000 MIC values two antibiotics (10,000 values total), benchmarks showed total run time going 80.7-85.1 seconds 1.8-2.0 seconds. Added argument ‘add_intrinsic_resistance’ (defaults FALSE), considers intrinsic resistance according EUCAST Fixed bug EUCAST rules breakpoint R interpreted “>=” “<” Added intelligent data cleaning .disk(), numbers can also extracted text decimal numbers always rounded : Improvements .mo(): completely new matching score ambiguous user input, using mo_matching_score(). user input value mean one taxonomic entry now considered ‘uncertain’. Instead warning, message thrown accompanying mo_uncertainties() changed completely; now prints possible candidates matching score. Big speed improvement already valid microorganism ID. also means significant speed improvement using mo_* functions like mo_name() microoganism IDs. Added argument ignore_pattern .mo() can also given mo_* functions like mo_name(), exclude known non-relevant input analysing. can also set option AMR_ignore_pattern. get_locale() now uses default Sys.getenv(\"LANG\") , LANG set, Sys.getlocale(). can overwritten setting option AMR_locale. Big speed improvement eucast_rules() Overall speed improvement tweaking joining functions Function mo_shortname() now returns genus input species unknown BORSA now recognised abbreviation Staphylococcus aureus, meaning e.g. mo_genus(\"BORSA\") return “Staphylococcus” Added feature AMR 1.1.0 earlier , now without package dependencies: tibble printing support classes <rsi>, <mic>, <disk>, <ab> <mo>. using tibbles containing antimicrobial columns (class <rsi>), “S” print green, “” print yellow “R” print red. Microbial IDs (class <mo>) emphasise genus species, kingdom. Names antiviral agents data set antivirals now starting capital letter, like case antibiotics data set Updated documentation WHONET data set clarify patient names fictitious Small .ab() algorithm improvements Fix combining MIC values raw numbers, .e. c(.mic(2), 2) previously failed now returns valid MIC class ggplot_rsi() geom_rsi() gained arguments minimum language, influence internal use rsi_df() Changes antibiotics data set: Updated oral parental DDDs WHOCC Added abbreviation “piptazo” ‘Piperacillin/tazobactam’ (TZP) ‘Penicillin G’ (intravenous use) now named ‘Benzylpenicillin’ (code PEN) ‘Penicillin V’ (oral use, code PNV) removed, since actual entry ‘Phenoxymethylpenicillin’ (code PHN) already existed group name (antibiotics$group) ‘Linezolid’ (LNZ), ‘Cycloserine’ (CYC), ‘Tedizolid’ (TZD) ‘Thiacetazone’ (THA) now “Oxazolidinones” instead “antibacterials” Added support using unique() classes <rsi>, <mic>, <disk>, <ab> <mo> Added argument excess kurtosis() function (defaults FALSE), return excess kurtosis, defined kurtosis minus three.","code":"# until dplyr 1.0.0 your_data %>% mutate_if(is.mic, as.rsi) your_data %>% mutate_if(is.disk, as.rsi)  # since dplyr 1.0.0 your_data %>% mutate(across(where(is.mic), as.rsi)) your_data %>% mutate(across(where(is.disk), as.rsi)) as.disk(c(\"disk zone: 23.4 mm\", 23.4)) #> Class <disk> #> [1] 24 24"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-4-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.4.0","text":"Removed functions portion_R(), portion_S() portion_I() deprecated since version 0.9.0 (November 2019) replaced proportion_R(), proportion_S() proportion_I() Removed unnecessary references base package Added packages useful functions Suggests field DESCRIPTION file","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-130","dir":"Changelog","previous_headings":"","what":"AMR 1.3.0","title":"AMR 1.3.0","text":"CRAN release: 2020-07-31","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-3-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.3.0","text":"Function ab_from_text() retrieve antimicrobial drug names, doses forms administration clinical texts e.g. health care records, also corrects misspelling since uses .ab() internally Tidyverse selection helpers antibiotic classes, help select columns antibiotics specific antibiotic class, without need define columns antibiotic abbreviations. can used function allows selection helpers, like dplyr::select() tidyr::pivot_longer(): Added mo_domain() alias mo_kingdom() Added function filter_penicillins() filter isolates specific result column name antimicrobial ‘penicillins’ class (specific: ATC subgroup Beta-lactam antibacterials, penicillins) Added official antimicrobial names filter_ab_class() functions, filter_aminoglycosides() Added antibiotics code “FOX1” cefoxitin screening (abbreviation “cfsc”) antibiotics data set Added Monuril trade name fosfomycin Added argument conserve_capped_values .rsi() interpreting MIC values - makes sure values starting “<” (“<=”) always return “S” values starting “>” (“>=”) always return “R”. default behaviour .rsi() changed, need specifically .rsi(..., conserve_capped_values = TRUE).","code":"library(dplyr)  # Columns 'IPM' and 'MEM' are in the example_isolates data set example_isolates %>%    select(carbapenems()) #> Selecting carbapenems: `IPM` (imipenem), `MEM` (meropenem)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-3-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.3.0","text":"Big speed improvement using function microorganism codes earlier package versions (prior AMR v1.2.0), .mo(), mo_name(), first_isolate(), eucast_rules(), mdro(), etc. consequence, old microbial codes (AMR v0.5.0 lower) supported anymore. Use .mo() microorganism names codes transform current abbreviations used package. Improvements susceptibility() resistance() count_*(), proportion_*() functions: 95% speed improvement using base R functions calculation Using unexisting columns wil now return error instead dropping silently Using variables column names (well selectors like dplyr::all_of()) now works Improvements .ab(): Dramatic improvement algorithm behind .ab(), making many input errors translatable, digitalised health care records, using many vowels consonants many Added progress bar Fixed bug .ab() return error invalid input values .ab() function now throw note 1 antimicrobial drug retrieved single input value. Fixed bug eucast_rules() work tibble tibble dplyr package loaded Fixed bug CLSI 2019 guidelines (using .rsi()), also included results animals. now contains interpretation guidelines humans. *_join_microorganisms() functions bug_drug_combinations() now return original data class (e.g. tibbles data.tables) functions rsi_df(), proportion_df() count_df(): Fixed bug using grouped versions Fixed bug different antimicrobial results added rows Fixed bug calculating counts (count_df()) antibiotics data set NAs Improved auto-determination columns types <mo> <Date> Fixed bug bug_drug_combinations() one antibiotic input data Changed summary class <rsi>, highlight %SI vs. %R Improved error handling, giving useful info functions return error progress bar now show interactive mode (.e. R Markdown) Speed improvement mdro() filter_ab_class() New option arrows_textangled ggplot_pca() indicate whether text end arrows angled (defaults TRUE, previous versions) Added parenteral DDD benzylpenicillin Fixed bug .mic() handle dots without leading zero (like \"<=.25)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-3-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.3.0","text":"Moved primary location project GitLab GitHub, giving us native support automated syntax checking without dependent external services AppVeyor Travis CI.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-120","dir":"Changelog","previous_headings":"","what":"AMR 1.2.0","title":"AMR 1.2.0","text":"CRAN release: 2020-05-28","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-1-2-0","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 1.2.0","text":"Removed code dependency R packages, making package fully independent development process others. major code change, probably noticeable users. Making package independent especially tidyverse (e.g. packages dplyr tidyr) tremendously increases sustainability long term, since tidyverse functions change quite often. Good users, hard package maintainers. functions replaced versions rely base R, keeps package fully functional many years come, without requiring lot maintenance keep packages anymore. Another upside package can now used versions R since R-3.0.0 (April 2013). package used settings resources limited. Fewer dependencies newer software helpful settings. Negative effects change : Function freq() borrowed cleaner package removed. Use cleaner::freq(), run library(\"cleaner\") use freq(). Printing values class mo rsi tibble longer colour printing rsi tibble show class <ord>, <rsi> anymore. purely visual effect. functions mo_* family (like mo_name() mo_gramstain()) noticeably slower running hundreds thousands rows. developers: classes mo ab now also inherit class character, support data transformation. change invalidates code checks class length == 1.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.2.0","text":"Updated taxonomy microorganisms May 2020, using Catalogue Life (CoL), Global Biodiversity Information Facility (GBIF) List Prokaryotic names Standing Nomenclature (LPSN, hosted DSMZ since February 2020). Note: taxonomic update may always impact determination first isolates (using first_isolate()), since bacterial names might renamed genera (sub)species. expected behaviour. Removed Catalogue Life IDs (like 776351), since now work species ID (hexadecimal string) eucast_rules() function longer applies “” rules default made available package (like setting ampicillin = R ampicillin + enzyme inhibitor = R). default input value rules now c(\"breakpoints\", \"expert\") instead \"\", can changed user. return old behaviour, set options(AMR.eucast_rules = \"\"). Fixed bug checking antimicrobial results original data regarded valid R/SI values drug enzyme inhibitor set S drug without enzyme inhibitor S drug without enzyme inhibitor set R drug enzyme inhibitor R Added official drug names verbose output eucast_rules() Added function ab_url() return direct URL antimicrobial agent official website Improvements algorithm .ab(), e.g. .ab(\"ampi sul\") ab_name(\"ampi sul\") work Functions ab_atc() ab_group() now return NA antimicrobial agent found Small fix text input coerced valid MIC values Fix interpretation generic CLSI interpretation rules (thanks Anthony Underwood) Fix set_mo_source() make sure column mo always second column Added abbreviation “cfsc” Cefoxitin “cfav” Ceftazidime/avibactam","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-2-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.2.0","text":"Removed previously deprecated function p.symbol() - replaced p_symbol() Removed function read.4d(), useful reading data old test database.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-110","dir":"Changelog","previous_headings":"","what":"AMR 1.1.0","title":"AMR 1.1.0","text":"CRAN release: 2020-04-15","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-1-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.1.0","text":"Support easy principal component analysis AMR, using new pca() function Plotting biplots principal component analysis using new ggplot_pca() function","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-1-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.1.0","text":"Support codes ending SPE species, like \"ESCSPE\" Escherichia coli Support encoding, means language-specific character accents can used input Support arbitrary IDs used laboratory information systems Small fix preventing viruses treated bacteria Small fix preventing contamination lack growth treated valid microorganisms Support abbreviations antibiotics antimycotics used Netherlands National Institute Public Health Environment (Rijksinstituut voor Volksgezondheid en Milieu; RIVM) Added abbreviations antibiotics data set Reloaded original EUCAST master tables 2019 (2020 already available). seems reliable data used WHONET. Added generic CLSI rules R/SI interpretation using .rsi() years 2010-2019 (thanks Anthony Underwood)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-1-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.1.0","text":"Support upcoming dplyr version 1.0.0 robust assigning classes rsi mic","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-101","dir":"Changelog","previous_headings":"","what":"AMR 1.0.1","title":"AMR 1.0.1","text":"CRAN release: 2020-02-23","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-1-0-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 1.0.1","text":"Fixed important floating point error MIC comparisons EUCAST 2020 guideline Interpretation MIC values (disk zones) R/SI can now used mutate_at() dplyr package: Added antibiotic abbreviations laboratory manufacturer (GLIMS) cefuroxime, cefotaxime, ceftazidime, cefepime, cefoxitin trimethoprim/sulfamethoxazole Added uti (abbreviation urinary tract infections) argument .rsi(), interpretation MIC values disk zones can made dependent isolates specifically UTIs Info printing functions eucast_rules(), first_isolate(), mdro() resistance_predict() now default print R interactive mode (.e. RMarkdown)","code":"yourdata %>%    mutate_at(vars(antibiotic1:antibiotic25), as.rsi, mo = \"E. coli\")  yourdata %>%    mutate_at(vars(antibiotic1:antibiotic25), as.rsi, mo = .$mybacteria)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-100","dir":"Changelog","previous_headings":"","what":"AMR 1.0.0","title":"AMR 1.0.0","text":"CRAN release: 2020-02-17 software now beta considered stable. Nonetheless, package developed continually.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-1-0-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 1.0.0","text":"Support newest EUCAST Clinical Breakpoint Tables v.10.0, valid 1 January 2020. affects translation MIC disk zones using .rsi() inferred resistance susceptibility using eucast_rules(). repository package now contains clean version EUCAST CLSI guidelines 2011-2020 translate MIC disk diffusion values R/SI: https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt. allows machine reading guidelines, almost impossible Excel PDF files distributed EUCAST CLSI. file used process EUCAST Clinical Breakpoints Excel file can found . Support LOINC codes antibiotics data set. Use ab_loinc() retrieve LOINC codes, use LOINC code input ab_* function: Support SNOMED CT codes microorganisms data set. Use mo_snomed() retrieve SNOMED codes, use SNOMED code input mo_* function:","code":"ab_loinc(\"ampicillin\") #> [1] \"21066-6\" \"3355-5\"  \"33562-0\" \"33919-2\" \"43883-8\" \"43884-6\" \"87604-5\" ab_name(\"21066-6\") #> [1] \"Ampicillin\" ab_atc(\"21066-6\") #> [1] \"J01CA01\" mo_snomed(\"S. aureus\") #> [1] 115329001   3092008 113961008 mo_name(115329001) #> [1] \"Staphylococcus aureus\" mo_gramstain(115329001) #> [1] \"Gram-positive\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 1.0.0","text":".mo() function previously wrote package folder improve calculation speed previously calculated results. longer case, comply CRAN policies. Consequently, function clear_mo_history() removed. Bugfix WHONET microorganism codes interpreted correctly using .rsi() Support missing spaces, e.g. .mo(\"Methicillin-resistant S.aureus\") Better support determination Salmonella biovars Speed improvements, especially G. species format (G genus), like E. coli K pneumoniae Support common codes used laboratory information systems Input values .disk() limited maximum 50 millimeters Added lifecycle state every function, following lifecycle circle tidyverse .ab(): support drugs starting “co-” like co-amoxiclav, co-trimoxazole, co-trimazine co-trimazole (thanks Peter Dutey) Added synonyms colistin, imipenem piperacillin/tazobactam Moved synonyms Rifinah Rimactazid rifampicin (RIF) rifampicin/isoniazid (RFI). Please note combination rifampicin/isoniazid DDDs defined, e.g. ab_ddd(\"Rimactazid\") now return NA. Moved synonyms Bactrimel Cotrimazole sulfamethoxazole (SMX) trimethoprim/sulfamethoxazole (SXT)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-1-0-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 1.0.0","text":"Add CITATION file Full support upcoming R 4.0 Removed unnecessary AMR:: calls","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-090","dir":"Changelog","previous_headings":"","what":"AMR 0.9.0","title":"AMR 0.9.0","text":"CRAN release: 2019-11-29","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-0-9-0","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 0.9.0","text":"dependent old Enterobacteriaceae family e.g. using code: please adjust :","code":"if (mo_family(somebugs) == \"Enterobacteriaceae\") ... if (mo_order(somebugs) == \"Enterobacterales\") ..."},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-9-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.9.0","text":"Functions susceptibility() resistance() aliases proportion_SI() proportion_R(), respectively. functions added make clear “” considered susceptible resistant. Support new MDRO guideline: Magiorakos AP, Srinivasan et al. “Multidrug-resistant, extensively drug-resistant pandrug-resistant bacteria: international expert proposal interim standard definitions acquired resistance.” Clinical Microbiology Infection (2012). now new default guideline mdro() function new Verbose mode (mdro(...., verbose = TRUE)) returns informative data set reason MDRO determination given every isolate, list resistant antimicrobial agents Data set antivirals, containing entries ATC J05 group DDDs oral parenteral treatment","code":"library(dplyr) example_isolates %>%   group_by(bug = mo_name(mo)) %>%    summarise(amoxicillin = resistance(AMX),             amox_clav   = resistance(AMC)) %>%   filter(!is.na(amoxicillin) | !is.na(amox_clav))"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changes-0-9-0","dir":"Changelog","previous_headings":"","what":"Changes","title":"AMR 0.9.0","text":"Now allows “ou” “au” used vice versa intelligent way coping consonants like “l” “r” Added score (certainty percentage) mo_uncertainties(), calculated using Levenshtein distance: Removed previously deprecated function .atc() - function replaced ab_atc() Renamed portion_* functions proportion_*. portion_* functions still available deprecated functions, return warning used. running .rsi() data set, now print guideline used specified user Fix Stenotrophomonas maltophilia always become ceftazidime R (following EUCAST v3.1) Fix Leuconostoc Pediococcus always become glycopeptides R non-EUCAST rules eucast_rules() now applied first last anymore. improve dependency certain antibiotics official EUCAST rules. Please see ?eucast_rules. Fix interpreting MIC values .rsi() input NA Added “imi” “imp” allowed abbreviation Imipenem (IPM) Fix automatically determining columns antibiotic results mdro() eucast_rules() Added ATC codes ceftaroline, ceftobiprole faropenem fixed two typos antibiotics data set robust way determining valid MIC values Small changed example_isolates data set better reflect reality Added microorganisms codes laboratory systems (esp. species Pseudescherichia Rodentibacter) Added Gram-stain mo_info()","code":"as.mo(c(\"Stafylococcus aureus\",         \"staphylokok aureuz\")) #> Warning:  #> Results of two values were guessed with uncertainty. Use mo_uncertainties() to review them. #> Class 'mo' #> [1] B_STPHY_AURS B_STPHY_AURS  mo_uncertainties() #> \"Stafylococcus aureus\" -> Staphylococcus aureus (B_STPHY_AURS, score: 95.2%) #> \"staphylokok aureuz\"   -> Staphylococcus aureus (B_STPHY_AURS, score: 85.7%)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-9-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.9.0","text":"Rewrote complete documentation markdown format, able use latest version great Roxygen2, released November 2019. tremously improved documentation quality, since rewrite forced us go texts make changes needed. Change dependency clean cleaner, package renamed accordingly upon CRAN request Added Dr. Sofia Ny contributor","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-080","dir":"Changelog","previous_headings":"","what":"AMR 0.8.0","title":"AMR 0.8.0","text":"CRAN release: 2019-10-15","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"breaking-0-8-0","dir":"Changelog","previous_headings":"","what":"Breaking","title":"AMR 0.8.0","text":"Determination first isolates now excludes ‘unknown’ microorganisms default, .e. microbial code \"UNKNOWN\". can included new argument include_unknown: WHONET users, means records/isolates organism code \"con\" (contamination) excluded default, since .mo(\"con\") = \"UNKNOWN\". function always shows note number ‘unknown’ microorganisms included excluded. code consistency, classes ab mo now preserved subsetting assignment. sake data integrity, means invalid assignments now result NA: important, value like \"testvalue\" never understood e.g. mo_name(), although class suggest valid microbial code. Function freq() moved new package, clean (CRAN link), since creating frequency tables actually fit scope package. freq() function still works, since re-exported clean package (installed automatically upon updating AMR package). Renamed data set septic_patients example_isolates","code":"first_isolate(..., include_unknown = TRUE) # how it works in base R: x <- factor(\"A\") x[1] <- \"B\" #> Warning message: #> invalid factor level, NA generated  # how it now works similarly for classes 'mo' and 'ab': x <- as.mo(\"E. coli\") x[1] <- \"testvalue\" #> Warning message: #> invalid microorganism code, NA generated"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-8-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.8.0","text":"Function bug_drug_combinations() quickly get data.frame results bug-drug combinations data set. column containing microorganism codes guessed automatically input transformed mo_shortname() default: can format printable format, ready reporting exporting e.g. Excel base R format() function: Additional way calculate co-resistance, .e. using multiple antimicrobials input portion_* functions count_* functions. can used determine empiric susceptibility combination therapy. new argument only_all_tested (defaults FALSE) replaces old also_single_tested can used select one two methods count isolates calculate portions. difference can seen example table (also portion count help pages), %SI determined: Since major change, usage old also_single_tested throw informative error replaced only_all_tested. tibble printing support classes rsi, mic, disk, ab mo. using tibbles containing antimicrobial columns, values S print green, values print yellow values R print red. Microbial IDs (class mo) emphasise genus species, kingdom.","code":"x <- bug_drug_combinations(example_isolates) #> NOTE: Using column `mo` as input for `col_mo`. x[1:4, ] #>             mo  ab S I R total #> 1 A. baumannii AMC 0 0 3     3 #> 2 A. baumannii AMK 0 0 0     0 #> 3 A. baumannii AMP 0 0 3     3 #> 4 A. baumannii AMX 0 0 3     3 #> NOTE: Use 'format()' on this result to get a publicable/printable format.  # change the transformation with the FUN argument to anything you like: x <- bug_drug_combinations(example_isolates, FUN = mo_gramstain) #> NOTE: Using column `mo` as input for `col_mo`. x[1:4, ] #>              mo  ab   S  I   R total #> 1 Gram-negative AMC 469 89 174   732 #> 2 Gram-negative AMK 251  0   2   253 #> 3 Gram-negative AMP 227  0 405   632 #> 4 Gram-negative AMX 227  0 405   632 #> NOTE: Use 'format()' on this result to get a publicable/printable format. format(x, combine_IR = FALSE) # -------------------------------------------------------------------- #                     only_all_tested = FALSE  only_all_tested = TRUE #                     -----------------------  ----------------------- #  Drug A    Drug B   include as  include as   include as  include as #                     numerator   denominator  numerator   denominator # --------  --------  ----------  -----------  ----------  ----------- #  S or I    S or I       X            X            X            X #    R       S or I       X            X            X            X #   <NA>     S or I       X            X            -            - #  S or I      R          X            X            X            X #    R         R          -            X            -            X #   <NA>       R          -            -            -            - #  S or I     <NA>        X            X            -            - #    R        <NA>        -            -            -            - #   <NA>      <NA>        -            -            -            - # -------------------------------------------------------------------- # (run this on your own console, as this page does not support colour printing) library(dplyr) example_isolates %>%   select(mo:AMC) %>%    as_tibble()"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-8-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.8.0","text":"Self-learning algorithm - function now gains experience previously determined microorganism IDs learns (yielding 80-95% speed improvement guess first try) Big improvement misspelled input new trivial names known field now understood: meningococcus, gonococcus, pneumococcus Updated latest taxonomic data (updated August 2019, International Journal Systematic Evolutionary Microbiology Added support Viridans Group Streptococci (VGS) Milleri Group Streptococci (MGS) Added support Blastocystis Added support 5,000 new fungi Added support unknown yeasts fungi Changed microorganism IDs improve readability. example, old code B_ENTRC_FAE E. faecalis E. faecium. new code B_ENTRC_FCLS E. faecium become B_ENTRC_FACM. Also, Latin character ae now preserved start genus species abbreviation. example, old code Aerococcus urinae B_ARCCC_NAE. now B_AERCC_URIN. IMPORTANT: Old microorganism IDs still supported, support dropped future version. Use .mo() old codes transform new format. Using functions mo_* family (like mo_name() mo_gramstain()) old codes, throw warning. intelligent guessing .ab(), including bidirectional language support Added support German national guideline (3MRGN/4MRGN) mdro() function, determine multi-drug resistant organisms Fixed bug Yersinia pseudotuberculosis Added informative errors warnings Printed info now distinguishes added changes values Using Verbose mode (.e. eucast_rules(..., verbose = TRUE)) returns informative readable output Using factors input now adds missing factors levels function changes antibiotic results Improved internal auto-guessing function determining antimicrobials data set (AMR:::get_column_abx()) Removed class atc - using .atc() now deprecated favour ab_atc() return character, atc class anymore Removed deprecated functions abname(), ab_official(), atc_name(), atc_official(), atc_property(), atc_tradenames(), atc_trivial_nl() Fix speed improvement mo_shortname() Fix using mo_* functions coercion uncertainties failures available mo_uncertainties() mo_failures() anymore Deprecated country argument mdro() favour already existing guideline argument support multiple guidelines within one country name RIF now Rifampicin instead Rifampin antibiotics data set now sorted name cephalosporins now generation brackets Speed improvement guess_ab_col() now 30 times faster antibiotic abbreviations Improved filter_ab_class() reliable support 5th generation cephalosporins Function availability() now uses portion_R() instead portion_IR(), comply EUCAST insights Functions age() age_groups() now na.rm argument remove empty values Renamed function p.symbol() p_symbol() (former now deprecated removed future version) Using negative values x age_groups() now introduce NAs return error anymore Fix determining system’s language Fix key_antibiotics() foreign systems Added 80 new LIS codes microorganisms Relabeled factor levels mdr_tb() Added MIC factor levels (.mic())","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-8-0","dir":"Changelog","previous_headings":"Changed","what":"Other","title":"AMR 0.8.0","text":"Added Prof. Dr. Casper Albers doctoral advisor added Dr. Judith Fonville, Eric Hazenberg, Dr. Bart Meijer, Dr. Dennis Souverein Annick Lenglet contributors Cleaned coding style every single syntax line package help lintr package","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-071","dir":"Changelog","previous_headings":"","what":"AMR 0.7.1","title":"AMR 0.7.1","text":"CRAN release: 2019-06-23","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-7-1","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.7.1","text":"Function rsi_df() transform data.frame data set containing microbial interpretation (S, , R), antibiotic, percentage S//R number available isolates. convenient combination existing functions count_df() portion_df() immediately show resistance percentages number available isolates: Support scientifically published pathotypes E. coli date (find). Supported : AIEC (Adherent-Invasive E. coli) ATEC (Atypical Entero-pathogenic E. coli) DAEC (Diffusely Adhering E. coli) EAEC (Entero-Aggresive E. coli) EHEC (Entero-Haemorrhagic E. coli) EIEC (Entero-Invasive E. coli) EPEC (Entero-Pathogenic E. coli) ETEC (Entero-Toxigenic E. coli) NMEC (Neonatal Meningitis-causing E. coli) STEC (Shiga-toxin producing E. coli) UPEC (Uropathogenic E. coli) lead microbial ID E. coli: Function mo_info() analogy ab_info(). mo_info() prints list full taxonomy, authors, URL online database microorganism Function mo_synonyms() get previously accepted taxonomic names microorganism","code":"septic_patients %>%   select(AMX, CIP) %>%   rsi_df() #      antibiotic  interpretation      value  isolates # 1   Amoxicillin              SI  0.4442636       546 # 2   Amoxicillin               R  0.5557364       683 # 3 Ciprofloxacin              SI  0.8381831      1181 # 4 Ciprofloxacin               R  0.1618169       228 as.mo(\"UPEC\") # B_ESCHR_COL mo_name(\"UPEC\") # \"Escherichia coli\" mo_gramstain(\"EHEC\") # \"Gram-negative\""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-7-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.7.1","text":"Column names output count_df() portion_df() now lowercase Fixed bug translation microorganism names Fixed bug determining taxonomic kingdoms Algorithm improvements .ab() .mo() understand even severely misspelled input Function .ab() now allows spaces coercing antibiotics names Added ggplot2 methods automatically determining scale type classes mo ab Added names object header frequency tables, even using pipes Prevented \"bacteria\" getting coerced .ab() Bacterial brand name trimethoprim (TMP) Fixed bug setting antibiotic work eucast_rules() mdro() Fixed EUCAST rule Staphylococci, amikacin resistance inferred tobramycin Removed latest_annual_release catalogue_of_life_version() function Removed antibiotic code PVM1 antibiotics data set duplicate PME Fixed bug old taxonomic names printed, using vector input .mo() Manually added Trichomonas vaginalis kingdom Protozoa, missing Catalogue Life Small improvements plot() barplot() MIC RSI classes Allow Catalogue Life IDs coerced .mo()","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-7-1","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.7.1","text":"Fixed note thrown CRAN tests","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-070","dir":"Changelog","previous_headings":"","what":"AMR 0.7.0","title":"AMR 0.7.0","text":"CRAN release: 2019-06-03","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-7-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.7.0","text":"Support translation disk diffusion MIC values RSI values (.e. antimicrobial interpretations). Supported guidelines EUCAST (2011 2019) CLSI (2011 2019). Use .rsi() MIC value (created .mic()), disk diffusion value (created new .disk()) complete date set containing columns MIC disk diffusion values. Function mo_name() alias mo_fullname() Added guidelines determine multi-drug resistance (MDR) TB (mdr_tb()) added new vignette MDR. Read tutorial website.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-7-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.7.0","text":"Fixed critical bug first_isolate() missing species lead incorrect FALSEs. bug present AMR v0.5.0, v0.6.0 v0.6.1. Fixed bug eucast_rules() antibiotics WHONET software recognised Column ab contains human readable EARS-Net code, used ECDC /WHONET - primary identifier used package Column atc contains ATC code, used /WHOCC Column cid contains CID code (Compound ID), used PubChem Based Compound ID, almost 5,000 official brand names added many different countries references antibiotics package now use EARS-Net codes, like AMX amoxicillin Functions atc_certe, ab_umcg atc_trivial_nl removed atc_* functions superseded ab_* functions output translated using included translation file can viewed New argument colours set bar colours New arguments title, subtitle, caption, x.title y.title set titles axis descriptions Improved intelligence looking antibiotic columns data set using guess_ab_col() Added ~5,000 old taxonomic names microorganisms.old data set, leads better results finding using .mo() function package now honours new EUCAST insight (2019) S classified susceptible, defined ‘increased exposure’ ‘intermediate’ anymore. functions like portion_df() count_df() means new argument combine_SI TRUE default. plotting function ggplot_rsi() also reflects change since uses count_df() internally. age() function gained new argument exact determine ages decimals Removed deprecated functions guess_mo(), guess_atc(), EUCAST_rules(), interpretive_reading(), rsi() speed improvement microbial IDs fixed factor level names R Markdown values unique now shows message instead warning support boxplots: Removed hardcoded EUCAST rules replaced new reference file can viewed Added ceftazidim intrinsic resistance Streptococci Changed default settings age_groups(), let groups fives tens end 100+ instead 120+ Fix freq() values NA Fix first_isolate() dates missing Improved speed guess_ab_col() Function .mo() now gently interprets number whitespace characters (like tabs) one space Function .mo() now returns UNKNOWN \"con\" (WHONET ID ‘contamination’) returns NA \"xxx\"(WHONET ID ‘growth’) Small algorithm fix .mo() Removed viruses data set microorganisms.codes cleaned Fix mo_shortname() species determined correctly","code":"septic_patients %>%    freq(age) %>%    boxplot() # grouped boxplots: septic_patients %>%    group_by(hospital_id) %>%    freq(age) %>%   boxplot()"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-7-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.7.0","text":"Support R 3.6.0 later providing support staged install","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-061","dir":"Changelog","previous_headings":"","what":"AMR 0.6.1","title":"AMR 0.6.1","text":"CRAN release: 2019-03-29","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-6-1","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.6.1","text":"Fixed critical bug using eucast_rules() verbose = TRUE Coercion microbial IDs now written package namespace instead user’s home folder, comply CRAN policy","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-060","dir":"Changelog","previous_headings":"","what":"AMR 0.6.0","title":"AMR 0.6.0","text":"CRAN release: 2019-03-27 New website! ’ve got new website: https://msberends.gitlab.io/AMR (built great pkgdown) Contains complete manual package functions explanation arguments Contains comprehensive tutorial conduct AMR data analysis, import data WHONET SPSS many .","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-6-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.6.0","text":"BREAKING: removed deprecated functions, arguments references ‘bactid’. Use .mo() identify MO code. Catalogue Life new taxonomic source data microorganisms, also contains ITIS data used previously. microorganisms data set now contains: ~55,000 (sub)species kingdoms Archaea, Bacteria Protozoa ~3,000 (sub)species orders kingdom Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales Schizosaccharomycetales (covering least like species Aspergillus, Candida, Pneumocystis, Saccharomyces Trichophyton) ~2,000 (sub)species ~100 relevant genera, kingdoms Animalia Plantae (like Strongyloides Taenia) ~15,000 previously accepted names included (sub)species taxonomically renamed responsible author(s) year scientific publication data updated annually - check included version new function catalogue_of_life_version(). Due change, mo codes changed (e.g. Streptococcus changed B_STRPTC B_STRPT). translation table used internally support older microorganism IDs, users notice difference. New function mo_rank() taxonomic rank (genus, species, infraspecies, etc.) New function mo_url() get direct URL species Catalogue Life Support data WHONET EARS-Net (European Antimicrobial Resistance Surveillance Network): Exported files WHONET can read used package. functions like first_isolate() eucast_rules(), arguments filled automatically. package now knows antibiotic abbrevations EARS-Net (also used WHONET) - antibiotics data set now contains column ears_net. function .mo() now knows WHONET species abbreviations , almost 2,000 microbial abbreviations added microorganisms.codes data set. New filters antimicrobial classes. Use functions filter isolates results one antibiotics specific class: antibiotics data set searched, input data checked column names value abbreviations, codes official names found antibiotics data set. example: ab_* functions deprecated replaced atc_* functions: functions use .atc() internally. old atc_property renamed atc_online_property(). done two reasons: firstly, ATC codes antibiotics (ab) can also antivirals antifungals. Secondly, input must class atc must coerable class. Properties classes start class name, analogous .mo() e.g. mo_genus. New functions set_mo_source() get_mo_source() use predefined MO codes input .mo() consequently mo_* functions Support upcoming dplyr version 0.8.0 New function guess_ab_col() find antibiotic column table New function mo_failures() review values coerced valid MO code, using .mo(). latter function now show maximum 10 uncoerced values refer mo_failures(). New function mo_uncertainties() review values coerced valid MO code using .mo(), uncertainty. New function mo_renamed() get list returned values .mo() taxonomic renaming New function age() calculate (patients) age years New function age_groups() split ages custom predefined groups (like children elderly). allows easier demographic AMR data analysis per age group. New function ggplot_rsi_predict() well base R plot() function can now used resistance prediction calculated resistance_predict(): Functions filter_first_isolate() filter_first_weighted_isolate() shorten fasten filtering data sets antimicrobial results, e.g.: equal : New function availability() check number available (non-empty) results data.frame New vignettes conduct AMR analysis, predict antimicrobial resistance, use G-test . also available (even easier readable) website: https://msberends.gitlab.io/AMR.","code":"filter_aminoglycosides() filter_carbapenems() filter_cephalosporins() filter_1st_cephalosporins() filter_2nd_cephalosporins() filter_3rd_cephalosporins() filter_4th_cephalosporins() filter_fluoroquinolones() filter_glycopeptides() filter_macrolides() filter_tetracyclines() septic_patients %>% filter_glycopeptides(result = \"R\") # Filtering on glycopeptide antibacterials: any of `vanc` or `teic` is R septic_patients %>% filter_glycopeptides(result = \"R\", scope = \"all\") # Filtering on glycopeptide antibacterials: all of `vanc` and `teic` is R ab_property -> atc_property() ab_name -> atc_name() ab_official -> atc_official() ab_trivial_nl -> atc_trivial_nl() ab_certe -> atc_certe() ab_umcg -> atc_umcg() ab_tradenames -> atc_tradenames() x <- resistance_predict(septic_patients, col_ab = \"amox\") plot(x) ggplot_rsi_predict(x) septic_patients %>% filter_first_isolate(...) # or filter_first_isolate(septic_patients, ...) septic_patients %>%   mutate(only_firsts = first_isolate(septic_patients, ...)) %>%   filter(only_firsts == TRUE) %>%   select(-only_firsts)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-6-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.6.0","text":"Updated EUCAST Clinical breakpoints version 9.0 1 January 2019, data set septic_patients now reflects changes Fixed critical bug rules depend previous applied rules applied adequately Emphasised manual penicillin meant benzylpenicillin (ATC J01CE01) New info returned running function, stating exactly changed added. Use eucast_rules(..., verbose = TRUE) get data set changed per bug drug combination. Removed data sets microorganisms.oldDT, microorganisms.prevDT, microorganisms.unprevDT microorganismsDT since longer needed contained info already available microorganisms data set Added 65 antibiotics antibiotics data set, Pharmaceuticals Community Register European Commission Removed columns atc_group1_nl atc_group2_nl antibiotics data set Functions atc_ddd() atc_groups() renamed atc_online_ddd() atc_online_groups(). old functions deprecated removed future version. Function guess_mo() now deprecated favour .mo() removed future versions Function guess_atc() now deprecated favour .atc() removed future versions Now handles incorrect spelling, like instead y f instead ph: Uncertainty algorithm now divided four levels, 0 3, default allow_uncertain = TRUE equal uncertainty level 2. Run ?.mo info levels. Using .mo(..., allow_uncertain = 3) lead unreliable results. Implemented latest publication Becker et al. (2019), categorising coagulase-negative Staphylococci microbial IDs found now saved local file ~/.Rhistory_mo. Use new function clean_mo_history() delete file, resets algorithms. Incoercible results now considered ‘unknown’, MO code UNKNOWN. foreign systems, properties translated languages already previously supported: German, Dutch, French, Italian, Spanish Portuguese. Fix vector containing empty values Finds better results input languages Better handling subspecies Better handling Salmonellae, especially ‘city like’ serovars like Salmonella London Understanding highly virulent E. coli strains like EIEC, EPEC STEC looked uncertain results default - results returned informative warning Manual (help page) now contains info algorithms Progress bar shown takes 3 seconds get results Support formatted console text Console return percentage uncoercable input Fixed bug distances dates calculated right - septic_patients data set yielded difference 0.15% isolates now use column named like “patid” patient ID (argument col_patientid), argument left blank now use column named like “key(…)ab” “key(…)antibiotics” key antibiotics (argument col_keyantibiotics()), argument left blank Removed argument output_logical, function now always return logical value Renamed argument filter_specimen specimen_group, although using filter_specimen still work note manual pages portion functions, low counts can influence outcome portion functions may camouflage , since return portion (albeit dependent minimum argument) Merged data sets microorganisms.certe microorganisms.umcg microorganisms.codes Function mo_taxonomy() now contains kingdom Reduce false positives .rsi.eligible() using new threshold argument New colours scale_rsi_colours() Summaries class mo now return top 3 unique count, e.g. using summary(mo) Small text updates summaries class rsi mic Now gives warning inputting MIC values Now accepts high low resistance: \"HIGH S\" return S Support tidyverse quasiquotation! Now can create frequency tables function outcomes: Header info now available list, header function argument header now set TRUE default, even markdown Added header info class mo show unique count families, genera species Now honours decimal.mark setting, just like format defaults getOption(\"OutDec\") new big.mark argument default \",\" decimal.mark = \".\" \".\" otherwise Fix header text observations NA New argument droplevels exclude empty factor levels input factor Factor levels header present input data (maximum 5) Fix using select() frequency tables Function scale_y_percent() now contains limits argument Automatic argument filling mdro(), key_antibiotics() eucast_rules() Updated examples resistance prediction (resistance_predict() function) Fix .mic() support values ending (several) zeroes using different lengths pattern x %like%, now return call","code":"# mo_fullname() uses as.mo() internally  mo_fullname(\"Sthafilokockus aaureuz\") #> [1] \"Staphylococcus aureus\"  mo_fullname(\"S. klossi\") #> [1] \"Staphylococcus kloosii\" # equal: as.mo(..., allow_uncertain = TRUE) as.mo(..., allow_uncertain = 2)  # also equal: as.mo(..., allow_uncertain = FALSE) as.mo(..., allow_uncertain = 0) # Determine genus of microorganisms (mo) in `septic_patients` data set: # OLD WAY septic_patients %>%   mutate(genus = mo_genus(mo)) %>%   freq(genus) # NEW WAY septic_patients %>%    freq(mo_genus(mo))  # Even supports grouping variables: septic_patients %>%   group_by(gender) %>%    freq(mo_genus(mo))"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-6-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.6.0","text":"Updated licence text emphasise GPL 2.0 R package.","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-050","dir":"Changelog","previous_headings":"","what":"AMR 0.5.0","title":"AMR 0.5.0","text":"CRAN release: 2018-11-30","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-5-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.5.0","text":"Repository moved GitLab Function count_all get available isolates (like portion_* count_* functions also supports summarise group_by), old n_rsi now alias count_all Function get_locale determine language language-dependent output mo_* functions. now default value language argument, system language used default. Data sets microorganismsDT, microorganisms.prevDT, microorganisms.unprevDT microorganisms.oldDT improve speed .mo. reference , since primarily internal use .mo. Function read.4D read 4D database MMB department UMCG Functions mo_authors mo_year get specific values scientific reference taxonomic entry","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-5-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.5.0","text":"Functions MDRO, BRMO, MRGN EUCAST_exceptional_phenotypes renamed mdro, brmo, mrgn eucast_exceptional_phenotypes EUCAST_rules renamed eucast_rules, old function still exists deprecated function Big changes eucast_rules function: Now also applies rules EUCAST ‘Breakpoint tables bacteria’, version 8.1, 2018, https://www.eucast.org/clinical_breakpoints/ (see Source function) New argument rules specify rules applied (expert rules, breakpoints, others ) New argument verbose can set TRUE get specific messages columns rows affected Better error handling rules applied (.e. new values inserted) number affected values now measured per row/column combination Data set septic_patients now reflects changes Added argument pipe piperacillin (J01CA12), also mdro function Small fixes EUCAST clinical breakpoint rules Added column kingdom microorganisms data set, function mo_kingdom look values Tremendous speed improvement .mo (subsequently mo_* functions), empty values wil ignored priori Fewer 3 characters input .mo return NA Function .mo (mo_* wrappers) now supports genus abbreviations “species” attached Added argument combine_IR (TRUE/FALSE) functions portion_df count_df, indicate values R must merged one, output consists S vs. IR (susceptible vs. non-susceptible) Fix portion_*(..., as_percent = TRUE) minimal number isolates met Added argument also_single_tested portion_* count_* functions also include cases antibiotics tested least one tested antibiotics includes target antimicribial interpretation, see ?portion Using portion_* functions now throws warning total available isolate argument minimum Functions .mo, .rsi, .mic, .atc freq set package name attribute anymore Frequency tables - freq(): Support grouping variables, test : Support (un)selecting columns: Check hms::.hms Now prints markdown default non-interactive sessions longer adds factor level column sorts factors count Support class difftime New argument na, choose character print empty values New argument header turn header info (default markdown = TRUE) New argument title manually setbthe title frequency table first_isolate now tries find columns use input arguments left blank Improvements MDRO algorithm (function mdro) Data set septic_patients now data.frame, tibble anymore Removed diacritics authors (columns microorganisms$ref microorganisms.old$ref) comply CRAN policy allow ASCII characters Fix mo_property working properly Fix eucast_rules Streptococci become ceftazidime R EUCAST rule 4.5 Support named vectors class mo, useful top_freq() ggplot_rsi scale_y_percent breaks argument AI improvements .mo: \"CRS\" -> Stenotrophomonas maltophilia \"CRSM\" -> Stenotrophomonas maltophilia \"MSSA\" -> Staphylococcus aureus \"MSSE\" -> Staphylococcus epidermidis Fix join functions Speed improvement .rsi.eligible, now 15-20 times faster g.test, sum(x) 1000 expected values 5, Fisher’s Exact Test suggested ab_name try fall back .atc results found Removed addin view data sets Percentages now rounded logically (e.g. freq function)","code":"as.mo(\"E. species\")        # B_ESCHR mo_fullname(\"E. spp.\")     # \"Escherichia species\" as.mo(\"S. spp\")            # B_STPHY mo_fullname(\"S. species\")  # \"Staphylococcus species\" septic_patients %>%    group_by(hospital_id) %>%    freq(gender) septic_patients %>%    freq(hospital_id) %>%    select(-count, -cum_count) # only get item, percent, cum_percent"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-5-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.5.0","text":"New dependency package crayon, support formatted text console Dependency tidyr now mandatory (went Import field) since portion_df count_df rely Updated vignettes comply README","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-040","dir":"Changelog","previous_headings":"","what":"AMR 0.4.0","title":"AMR 0.4.0","text":"CRAN release: 2018-10-01","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-4-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.4.0","text":"data set microorganisms now contains microbial taxonomic data ITIS (kingdoms Bacteria, Fungi Protozoa), Integrated Taxonomy Information System, available via https://itis.gov. data set now contains 18,000 microorganisms known bacteria, fungi protozoa according ITIS genus, species, subspecies, family, order, class, phylum subkingdom. new data set microorganisms.old contains previously known taxonomic names kingdoms. New functions based existing function mo_property: Taxonomic names: mo_phylum, mo_class, mo_order, mo_family, mo_genus, mo_species, mo_subspecies Semantic names: mo_fullname, mo_shortname Microbial properties: mo_type, mo_gramstain Author year: mo_ref also come support German, Dutch, French, Italian, Spanish Portuguese: Furthermore, former taxonomic names give note current taxonomic name: Functions count_R, count_IR, count_I, count_SI count_S selectively count resistant susceptible isolates Extra function count_df (works like portion_df) get counts S, R data set antibiotic columns, support grouped variables Function .rsi.eligible check columns valid antimicrobial results, rsi class yet. Transform columns raw data : data %>% mutate_if(.rsi.eligible, .rsi) Functions .mo .mo replacements .bactid .bactid (since microoganisms data set contains bacteria). last two functions deprecated removed future release. .mo function determines microbial IDs using intelligent rules: great speed - quite regular Linux server 2007 takes us less 0.02 seconds transform 25,000 items: Added argument reference_df .mo, users can supply microbial IDs, name codes reference table Renamed previous references bactid mo, like: Column names inputs EUCAST_rules, first_isolate key_antibiotics Column names datasets microorganisms septic_patients old syntaxes still work version, throw warnings Function labels_rsi_count print datalabels RSI ggplot2 model Functions .atc .atc transform/look antibiotic ATC codes defined . existing function guess_atc now alias .atc. Function ab_property aliases: ab_name, ab_tradenames, ab_certe, ab_umcg ab_trivial_nl Introduction AMR vignette Removed clipboard functions violated CRAN policy Renamed septic_patients$sex septic_patients$gender","code":"mo_gramstain(\"E. coli\") # [1] \"Gram negative\" mo_gramstain(\"E. coli\", language = \"de\") # German # [1] \"Gramnegativ\" mo_gramstain(\"E. coli\", language = \"es\") # Spanish # [1] \"Gram negativo\" mo_fullname(\"S. group A\", language = \"pt\") # Portuguese # [1] \"Streptococcus grupo A\" mo_gramstain(\"Esc blattae\") # Note: 'Escherichia blattae' (Burgess et al., 1973) was renamed 'Shimwellia blattae' (Priest and Barker, 2010) # [1] \"Gram negative\" as.mo(\"E. coli\") # [1] B_ESCHR_COL as.mo(\"MRSA\") # [1] B_STPHY_AUR as.mo(\"S group A\") # [1] B_STRPTC_GRA thousands_of_E_colis <- rep(\"E. coli\", 25000) microbenchmark::microbenchmark(as.mo(thousands_of_E_colis), unit = \"s\") # Unit: seconds #         min       median         max  neval #  0.01817717  0.01843957  0.03878077    100"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-4-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.4.0","text":"Added three antimicrobial agents antibiotics data set: Terbinafine (D01BA02), Rifaximin (A07AA11) Isoconazole (D01AC05) Added 163 trade names antibiotics data set, now contains 298 different trade names total, e.g.: first_isolate, rows ignored ’s species available Function ratio now deprecated removed future release, really scope package Fix .mic values ending zeroes real number Small fix B. fragilis found microorganisms.umcg data set Added prevalence column microorganisms data set Added arguments minimum as_percent portion_df Support quasiquotation functions series count_* portions_*, n_rsi. allows check 2 vectors columns. Edited ggplot_rsi geom_rsi can cope count_df. new fun argument value portion_df default, can set count_df. Fix ggplot_rsi ggplot2 package loaded Added datalabels function labels_rsi_count ggplot_rsi Added possibility set argument geom_rsi (ggplot_rsi) can set preferences Fix joins, predefined suffices honoured Added argument quote freq function Added generic function diff frequency tables Added longest en shortest character length frequency table (freq) header class character Support types (classes) list matrix freq lists, subsetting possible:","code":"ab_official(\"Bactroban\") # [1] \"Mupirocin\" ab_name(c(\"Bactroban\", \"Amoxil\", \"Zithromax\", \"Floxapen\")) # [1] \"Mupirocin\" \"Amoxicillin\" \"Azithromycin\" \"Flucloxacillin\" ab_atc(c(\"Bactroban\", \"Amoxil\", \"Zithromax\", \"Floxapen\")) # [1] \"R01AX06\" \"J01CA04\" \"J01FA10\" \"J01CF05\" septic_patients %>% select(amox, cipr) %>% count_IR() # which is the same as: septic_patients %>% count_IR(amox, cipr)  septic_patients %>% portion_S(amcl) septic_patients %>% portion_S(amcl, gent) septic_patients %>% portion_S(amcl, gent, pita) my_matrix = with(septic_patients, matrix(c(age, gender), ncol = 2)) freq(my_matrix) my_list = list(age = septic_patients$age, gender = septic_patients$gender) my_list %>% freq(age) my_list %>% freq(gender)"},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-4-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.4.0","text":"unit tests ensure better integrity functions","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-030","dir":"Changelog","previous_headings":"","what":"AMR 0.3.0","title":"AMR 0.3.0","text":"CRAN release: 2018-08-14","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-3-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.3.0","text":"New function portion_df get portions S, R data set antibiotic columns, support grouped variables Universal: amoxicillin, amoxicillin/clavlanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole Gram-positive: vancomycin, teicoplanin, tetracycline, erythromycin, oxacillin, rifampicin Gram-negative: gentamicin, tobramycin, colistin, cefotaxime, ceftazidime, meropenem New functions geom_rsi, facet_rsi, scale_y_percent, scale_rsi_colours theme_rsi septic_patients %>% select(tobr, gent) %>% ggplot_rsi show portions S, R immediately pretty plot Support grouped variables, see ?ggplot_rsi New functions .bactid .bactid transform/ look microbial ID’s. existing function guess_bactid now alias .bactid New Becker classification Staphylococcus categorise Coagulase Negative Staphylococci (CoNS) Coagulase Positve Staphylococci (CoPS) New Lancefield classification Streptococcus categorise Lancefield groups convience, new descriptive statistical functions kurtosis skewness lacking base R - generic functions support vectors, data.frames matrices Function g.test perform X2 distributed G-test, use chisq.test example: ratio(c(10, 500, 10), ratio = \"1:2:1\") return 130, 260, 130 Support Addins menu RStudio quickly insert %% %like% (give keyboard shortcuts), view datasets come package Function p.symbol transform p values related symbols: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Functions clipboard_import clipboard_export helper functions quickly copy paste /software like Excel SPSS. functions use clipr package, little altered also support headless Linux servers (can use RStudio Server) vignette explain usage Support rsi (antimicrobial resistance) use input Support table use input: freq(table(x, y)) Support existing functions hist plot use frequency table input: hist(freq(df$age)) Support .vector, .data.frame, as_tibble format Support quasiquotation: freq(mydata, mycolumn) mydata %>% freq(mycolumn) Function top_freq function return top/n items vector Header frequency tables now also show Mean Absolute Deviaton (MAD) Interquartile Range (IQR) Possibility globally set default amount items print, options(max.print.freq = n) n preset value","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-3-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.3.0","text":"Improvements forecasting resistance_predict added examples antibiotics added arguments EUCAST rules Updated version septic_patients data set better reflect reality Pretty printing tibbles removed really scope package Printing mic rsi classes now returns values - use freq check distributions Improved speed key antibiotics comparison determining first isolates Column names key_antibiotics function now generic: 6 broadspectrum ABs, 6 Gram-positive specific 6 Gram-negative specific ABs Speed improvement abname function %like% now supports multiple patterns Frequency tables now actual data.frames altered console printing make look like frequency table. , argument toConsole longer needed. Fix freq class item lost Small translational improvements septic_patients dataset column bactid now new class \"bactid\" Small improvements microorganisms dataset (especially Salmonella) column bactid now new class \"bactid\" .rsi(\"<=0.002; S\") return S .mic(\"<=0.002; S\") return <=0.002 Now possible coerce MIC values space operator value, .e. .mic(\"<= 0.002\") now works Classes rsi mic add attribute package.version anymore Added \"groups\" option atc_property(..., property). return vector ATC hierarchy defined . new function atc_groups convenient wrapper around . Build-host check atc_property requires host set url responsive Improved first_isolate algorithm exclude isolates bacteria ID genus unavailable Fix warning hybrid evaluation forced row_number (924b62) dplyr package v0.7.5 yourdata %>% select(genus, species) %>% .bactid() now also works small fixes","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-3-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.3.0","text":"Linux macOS: https://travis-ci.org/msberends/AMR Windows: https://ci.appveyor.com/project/msberends/amr Added thesis advisors DESCRIPTION file","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-020","dir":"Changelog","previous_headings":"","what":"AMR 0.2.0","title":"AMR 0.2.0","text":"CRAN release: 2018-05-03","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"new-0-2-0","dir":"Changelog","previous_headings":"","what":"New","title":"AMR 0.2.0","text":"Full support Windows, Linux macOS Full support old R versions, R-3.0.0 (April 2013) later needed (needed packages may dependencies) Function n_rsi count cases antibiotic test results available, used conjunction dplyr::summarise, see ?rsi Function guess_bactid determine ID microorganism based genus/species known abbreviations like MRSA Function guess_atc determine ATC antibiotic based name, trade name, known abbreviations Function freq create frequency tables, additional info header Exceptional resistances defined EUCAST also supported instead countries alone Functions BRMO MRGN wrappers Dutch German guidelines, respectively New algorithm determine weighted isolates, can now \"points\" \"keyantibiotics\", see ?first_isolate New print format tibbles data.tables","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"changed-0-2-0","dir":"Changelog","previous_headings":"","what":"Changed","title":"AMR 0.2.0","text":"Fixed rsi class vectors contain invalid antimicrobial interpretations Renamed dataset ablist antibiotics Renamed dataset bactlist microorganisms Added common abbreviations trade names antibiotics dataset Added microorganisms microorganisms dataset Added analysis examples help page dataset septic_patients Added support character vector join functions Added warnings join results rows join Altered %like% make case insensitive arguments functions first_isolate EUCAST_rules column names now case-insensitive Functions .rsi .mic now add package name version attributes","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"other-0-2-0","dir":"Changelog","previous_headings":"","what":"Other","title":"AMR 0.2.0","text":"Expanded README.md examples Added ORCID authors DESCRIPTION file Added unit testing testthat package Added build tests Linux macOS using Travis CI (https://travis-ci.org/msberends/AMR) Added line coverage checking using CodeCov (https://codecov.io/gh/msberends/AMR/tree/main/R)","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-011","dir":"Changelog","previous_headings":"","what":"AMR 0.1.1","title":"AMR 0.1.1","text":"CRAN release: 2018-03-14 EUCAST_rules applies amoxicillin even ampicillin missing Edited column names comply GLIMS, laboratory information system Added valid MIC values Renamed ‘Daily Defined Dose’ ‘Defined Daily Dose’ Added barplots rsi mic classes","code":""},{"path":"https://msberends.github.io/AMR/news/index.html","id":"amr-010","dir":"Changelog","previous_headings":"","what":"AMR 0.1.0","title":"AMR 0.1.0","text":"CRAN release: 2018-02-22 First submission CRAN.","code":""}]