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@ -29,7 +29,7 @@
<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="">2.1.1.9084</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9086</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">
@ -87,7 +87,7 @@
website update since they are based on randomly created values and the
page was written in <a href="https://rmarkdown.rstudio.com/" class="external-link">R
Markdown</a>. However, the methodology remains unchanged. This page was
generated on 30 September 2024.</p>
generated on 02 October 2024.</p>
<div class="section level2">
<h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#introduction"></a>
</h2>
@ -143,21 +143,21 @@ make the structure of your data generally look like this:</p>
</tr></thead>
<tbody>
<tr class="odd">
<td align="center">2024-09-30</td>
<td align="center">2024-10-02</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">S</td>
</tr>
<tr class="even">
<td align="center">2024-09-30</td>
<td align="center">2024-10-02</td>
<td align="center">abcd</td>
<td align="center">Escherichia coli</td>
<td align="center">S</td>
<td align="center">R</td>
</tr>
<tr class="odd">
<td align="center">2024-09-30</td>
<td align="center">2024-10-02</td>
<td align="center">efgh</td>
<td align="center">Escherichia coli</td>
<td align="center">R</td>
@ -270,12 +270,12 @@ taxonomic codes. Let’s check this:</p>
<span><span class="co">#&gt; taxonomic name, and the pathogenicity in humans. See ?mo_matching_score.</span></span>
<span><span class="co">#&gt; </span></span>
<span><span class="co">#&gt; --------------------------------------------------------------------------------</span></span>
<span><span class="co">#&gt; "E. coli" -&gt; Enterococcus crotali (B_ENTRC_CRTL, 0.650)</span></span>
<span><span class="co">#&gt; Also matched: Escherichia coli (0.598), Enterococcus columbae (0.595),</span></span>
<span><span class="co">#&gt; Enterococcus camelliae (0.591), Enterococcus casseliflavus (0.577),</span></span>
<span><span class="co">#&gt; Escherichia coli coli (0.559), Enterococcus canis (0.556), Enterococcus</span></span>
<span><span class="co">#&gt; cecorum (0.550), Enterococcus canintestini (0.540), Escherichia coli</span></span>
<span><span class="co">#&gt; expressing (0.531), and Enterobacter cowanii (0.522)</span></span>
<span><span class="co">#&gt; "E. coli" -&gt; Escherichia coli (B_ESCHR_COLI, 0.688)</span></span>
<span><span class="co">#&gt; Also matched: Enterococcus crotali (0.650), Escherichia coli coli</span></span>
<span><span class="co">#&gt; (0.643), Escherichia coli expressing (0.611), Enterococcus columbae</span></span>
<span><span class="co">#&gt; (0.595), Enterococcus camelliae (0.591), Enterococcus casseliflavus</span></span>
<span><span class="co">#&gt; (0.577), Enterococcus canis (0.556), Enterococcus cecorum (0.550),</span></span>
<span><span class="co">#&gt; Enterococcus canintestini (0.540), and Enterobacter cowanii (0.522)</span></span>
<span><span class="co">#&gt; --------------------------------------------------------------------------------</span></span>
<span><span class="co">#&gt; "K. pneumoniae" -&gt; Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786)</span></span>
<span><span class="co">#&gt; Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella</span></span>
@ -333,15 +333,15 @@ dplyr:</p>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3,000 × 8</span></span></span>
<span><span class="co">#&gt; patient_id hospital date bacteria AMX AMC CIP GEN </span></span>
<span><span class="co">#&gt; <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;date&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J3 A 2012-11-21 B_ENTRC_CRTL R I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J3 A 2012-11-21 B_ESCHR_COLI R I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R7 A 2018-04-03 B_KLBSL_PNMN R I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> P3 A 2014-09-19 B_ENTRC_CRTL R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> P10 A 2015-12-10 B_ENTRC_CRTL S I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B7 A 2015-03-02 B_ENTRC_CRTL S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> P3 A 2014-09-19 B_ESCHR_COLI R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> P10 A 2015-12-10 B_ESCHR_COLI S I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B7 A 2015-03-02 B_ESCHR_COLI S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> W3 A 2018-03-31 B_STPHY_AURS R S R S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> J8 A 2016-06-14 B_ENTRC_CRTL R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> M3 A 2015-10-25 B_ENTRC_CRTL R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> J3 A 2019-06-19 B_ENTRC_CRTL S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> J8 A 2016-06-14 B_ESCHR_COLI R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> M3 A 2015-10-25 B_ESCHR_COLI R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> J3 A 2019-06-19 B_ESCHR_COLI S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> G6 A 2015-04-27 B_STPHY_AURS S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,990 more rows</span></span></span></code></pre></div>
<p>This is basically it for the cleaning, time to start the data
@ -396,9 +396,9 @@ the methods on the <code><a href="../reference/first_isolate.html">first_isolate
<span><span class="co">#&gt; ℹ Using column 'patient_id' as input for col_patient_id.</span></span>
<span><span class="co">#&gt; ℹ Basing inclusion on all antimicrobial results, using a points threshold</span></span>
<span><span class="co">#&gt; of 2</span></span>
<span><span class="co">#&gt; =&gt; Found 2,764 'phenotype-based' first isolates (92.1% of total where a</span></span>
<span><span class="co">#&gt; =&gt; Found 2,724 'phenotype-based' first isolates (90.8% of total where a</span></span>
<span><span class="co">#&gt; microbial ID was available)</span></span></code></pre></div>
<p>So only 92% is suitable for resistance analysis! We can now filter on
<p>So only 91% 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="cb9"><pre class="downlit sourceCode r">
@ -408,24 +408,24 @@ it with the <code><a href="https://dplyr.tidyverse.org/reference/filter.html" cl
<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data_1st</span> <span class="op">&lt;-</span> <span class="va">our_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 2 764 isolates for analysis. Now our data looks
<p>So we end up with 2 724 isolates for analysis. Now our data looks
like:</p>
<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_data_1st</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,764 × 9</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,724 × 9</span></span></span>
<span><span class="co">#&gt; patient_id hospital date bacteria AMX AMC CIP GEN first</span></span>
<span><span class="co">#&gt; <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;date&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J3 A 2012-11-21 B_ENTRC_CRTL R I S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> P10 A 2015-12-10 B_ENTRC_CRTL S I S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> B7 A 2015-03-02 B_ENTRC_CRTL S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> M3 A 2015-10-25 B_ENTRC_CRTL R S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> J3 A 2019-06-19 B_ENTRC_CRTL S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> P4 A 2011-06-21 B_ENTRC_CRTL S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> Z1 A 2014-09-05 B_ENTRC_CRTL S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,754 more rows</span></span></span></code></pre></div>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,714 more rows</span></span></span></code></pre></div>
<p>Time for the analysis.</p>
</div>
</div>
@ -438,44 +438,44 @@ impression, as it comes with support for the new <code>mo</code> and
<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">our_data_1st</span><span class="op">)</span></span>
<span><span class="co">#&gt; patient_id hospital date </span></span>
<span><span class="co">#&gt; Length:2764 Length:2764 Min. :2011-01-01 </span></span>
<span><span class="co">#&gt; Class :character Class :character 1st Qu.:2013-04-18 </span></span>
<span><span class="co">#&gt; Mode :character Mode :character Median :2015-06-07 </span></span>
<span><span class="co">#&gt; Mean :2015-06-16 </span></span>
<span><span class="co">#&gt; 3rd Qu.:2017-08-23 </span></span>
<span><span class="co">#&gt; Max. :2020-01-01 </span></span>
<span><span class="co">#&gt; Length:2724 Length:2724 Min. :2011-01-01 </span></span>
<span><span class="co">#&gt; Class :character Class :character 1st Qu.:2013-04-07 </span></span>
<span><span class="co">#&gt; Mode :character Mode :character Median :2015-06-03 </span></span>
<span><span class="co">#&gt; Mean :2015-06-09 </span></span>
<span><span class="co">#&gt; 3rd Qu.:2017-08-11 </span></span>
<span><span class="co">#&gt; Max. :2019-12-27 </span></span>
<span><span class="co">#&gt; bacteria AMX AMC </span></span>
<span><span class="co">#&gt; Class :mo Class:sir Class:sir </span></span>
<span><span class="co">#&gt; &lt;NA&gt; :0 %S :42.1% (n=1163) %S :53.0% (n=1464) </span></span>
<span><span class="co">#&gt; Unique:5 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) </span></span>
<span><span class="co">#&gt; #1 :B_ESCHR_COLI %I :16.2% (n=447) %I :12.2% (n=337) </span></span>
<span><span class="co">#&gt; #2 :B_STPHY_AURS %R :41.8% (n=1154) %R :34.8% (n=963) </span></span>
<span><span class="co">#&gt; #3 :B_ENTRC_CRTL %NI : 0.0% (n=0) %NI : 0.0% (n=0) </span></span>
<span><span class="co">#&gt; &lt;NA&gt; :0 %S :41.6% (n=1133) %S :52.6% (n=1432) </span></span>
<span><span class="co">#&gt; Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) </span></span>
<span><span class="co">#&gt; #1 :B_ESCHR_COLI %I :16.4% (n=446) %I :12.2% (n=333) </span></span>
<span><span class="co">#&gt; #2 :B_STPHY_AURS %R :42.0% (n=1145) %R :35.2% (n=959) </span></span>
<span><span class="co">#&gt; #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) </span></span>
<span><span class="co">#&gt; CIP GEN first </span></span>
<span><span class="co">#&gt; Class:sir Class:sir Mode:logical </span></span>
<span><span class="co">#&gt; %S :52.9% (n=1462) %S :61.5% (n=1699) TRUE:2764 </span></span>
<span><span class="co">#&gt; %S :52.5% (n=1431) %S :61.0% (n=1661) TRUE:2724 </span></span>
<span><span class="co">#&gt; %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) </span></span>
<span><span class="co">#&gt; %I : 6.3% (n=174) %I : 3.0% (n=82) </span></span>
<span><span class="co">#&gt; %R :40.8% (n=1128) %R :35.6% (n=983) </span></span>
<span><span class="co">#&gt; %I : 6.5% (n=176) %I : 3.0% (n=82) </span></span>
<span><span class="co">#&gt; %R :41.0% (n=1117) %R :36.0% (n=981) </span></span>
<span><span class="co">#&gt; %NI : 0.0% (n=0) %NI : 0.0% (n=0)</span></span>
<span></span>
<span><span class="fu"><a href="https://pillar.r-lib.org/reference/glimpse.html" class="external-link">glimpse</a></span><span class="op">(</span><span class="va">our_data_1st</span><span class="op">)</span></span>
<span><span class="co">#&gt; Rows: 2,764</span></span>
<span><span class="co">#&gt; Rows: 2,724</span></span>
<span><span class="co">#&gt; Columns: 9</span></span>
<span><span class="co">#&gt; $ patient_id <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "J3", "R7", "P10", "B7", "W3", "M3", "J3", "G6", "P4", "Z1"…</span></span>
<span><span class="co">#&gt; $ patient_id <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "J3", "R7", "P3", "P10", "B7", "W3", "M3", "J3", "G6", "P4"…</span></span>
<span><span class="co">#&gt; $ hospital <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…</span></span>
<span><span class="co">#&gt; $ date <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31…</span></span>
<span><span class="co">#&gt; $ bacteria <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ENTRC_CRTL", "B_KLBSL_PNMN", "B_ENTRC_CRTL", "B_ENTRC_CRT…</span></span>
<span><span class="co">#&gt; $ AMX <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, S, S, R, R, S, S, S, S, R, S, S, S, R, R, R, R, S, R,…</span></span>
<span><span class="co">#&gt; $ AMC <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I, I, I, S, S, S, S, S, S, S, S, S, I, S, S, S, S, R, S, S,…</span></span>
<span><span class="co">#&gt; $ CIP <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…</span></span>
<span><span class="co">#&gt; $ GEN <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S, S,…</span></span>
<span><span class="co">#&gt; $ date <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02…</span></span>
<span><span class="co">#&gt; $ bacteria <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> "B_ESCHR_COLI", "B_KLBSL_PNMN", "B_ESCHR_COLI", "B_ESCHR_COL…</span></span>
<span><span class="co">#&gt; $ AMX <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,…</span></span>
<span><span class="co">#&gt; $ AMC <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,…</span></span>
<span><span class="co">#&gt; $ CIP <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…</span></span>
<span><span class="co">#&gt; $ GEN <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,…</span></span>
<span><span class="co">#&gt; $ first <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…</span></span>
<span></span>
<span><span class="co"># number of unique values per column:</span></span>
<span><span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">sapply</a></span><span class="op">(</span><span class="va">our_data_1st</span>, <span class="va">n_distinct</span><span class="op">)</span></span>
<span><span class="co">#&gt; patient_id hospital date bacteria AMX AMC CIP </span></span>
<span><span class="co">#&gt; 260 3 1877 5 3 3 3 </span></span>
<span><span class="co">#&gt; 260 3 1854 4 3 3 3 </span></span>
<span><span class="co">#&gt; GEN first </span></span>
<span><span class="co">#&gt; 3 1</span></span></code></pre></div>
<div class="section level3">
@ -487,25 +487,23 @@ microorganisms:</p>
<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_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/count.html">count</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">bacteria</span><span class="op">)</span>, sort <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 5 × 2</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 4 × 2</span></span></span>
<span><span class="co">#&gt; `mo_name(bacteria)` n</span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Escherichia coli <span style="text-decoration: underline;">1</span>024</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Escherichia coli <span style="text-decoration: underline;">1</span>518</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Staphylococcus aureus 730</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Enterococcus crotali 494</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Streptococcus pneumoniae 426</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> Klebsiella pneumoniae 326</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Streptococcus pneumoniae 426</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae 326</span></span>
<span></span>
<span><span class="va">our_data_1st</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/count.html">count</a></span><span class="op">(</span><span class="fu"><a href="../reference/mo_property.html">mo_name</a></span><span class="op">(</span><span class="va">bacteria</span><span class="op">)</span>, sort <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 5 × 2</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 4 × 2</span></span></span>
<span><span class="co">#&gt; `mo_name(bacteria)` n</span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;int&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Escherichia coli 919</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> Escherichia coli <span style="text-decoration: underline;">1</span>321</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> Staphylococcus aureus 682</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Enterococcus crotali 442</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Streptococcus pneumoniae 402</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">5</span> Klebsiella pneumoniae 319</span></span></code></pre></div>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> Streptococcus pneumoniae 402</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">4</span> Klebsiella pneumoniae 319</span></span></code></pre></div>
</div>
<div class="section level3">
<h3 id="select-and-filter-with-antibiotic-selectors">Select and filter with antibiotic selectors<a class="anchor" aria-label="anchor" href="#select-and-filter-with-antibiotic-selectors"></a>
@ -517,113 +515,113 @@ in:</p>
<code class="sourceCode R"><span><span class="va">our_data_1st</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/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">date</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ For aminoglycosides() using column 'GEN' (gentamicin)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,764 × 2</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,724 × 2</span></span></span>
<span><span class="co">#&gt; date GEN </span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;date&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> 2012-11-21 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> 2018-04-03 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2015-12-10 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2015-03-02 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2018-03-31 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2015-10-25 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2019-06-19 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2015-04-27 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2011-06-21 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2014-09-05 S </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,754 more rows</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> 2014-09-19 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> 2015-12-10 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> 2015-03-02 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> 2018-03-31 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> 2015-10-25 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> 2019-06-19 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> 2015-04-27 S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> 2011-06-21 S </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,714 more rows</span></span></span>
<span></span>
<span><span class="va">our_data_1st</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/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">bacteria</span>, <span class="fu"><a href="../reference/antibiotic_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'</span></span>
<span><span class="co">#&gt; (amoxicillin/clavulanic acid)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,764 × 3</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,724 × 3</span></span></span>
<span><span class="co">#&gt; bacteria AMX AMC </span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> B_ENTRC_CRTL R I </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> B_ESCHR_COLI R I </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> B_KLBSL_PNMN R I </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B_ENTRC_CRTL S I </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> B_ENTRC_CRTL S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B_STPHY_AURS R S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> B_ENTRC_CRTL R S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> B_ENTRC_CRTL S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> B_STPHY_AURS S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> B_ENTRC_CRTL S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> B_ENTRC_CRTL S S </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,754 more rows</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B_ESCHR_COLI R S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> B_ESCHR_COLI S I </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B_ESCHR_COLI S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> B_STPHY_AURS R S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> B_ESCHR_COLI R S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> B_ESCHR_COLI S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> B_STPHY_AURS S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> B_ESCHR_COLI S S </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,714 more rows</span></span></span>
<span></span>
<span><span class="va">our_data_1st</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/select.html" class="external-link">select</a></span><span class="op">(</span><span class="va">bacteria</span>, <span class="fu"><a href="https://tidyselect.r-lib.org/reference/where.html" class="external-link">where</a></span><span class="op">(</span><span class="va">is.sir</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,764 × 5</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 2,724 × 5</span></span></span>
<span><span class="co">#&gt; bacteria AMX AMC CIP GEN </span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> B_ENTRC_CRTL R I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> B_ESCHR_COLI R I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> B_KLBSL_PNMN R I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B_ENTRC_CRTL S I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> B_ENTRC_CRTL S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B_STPHY_AURS R S R S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> B_ENTRC_CRTL R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> B_ENTRC_CRTL S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> B_STPHY_AURS S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> B_ENTRC_CRTL S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> B_ENTRC_CRTL S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,754 more rows</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B_ESCHR_COLI R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> B_ESCHR_COLI S I S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B_ESCHR_COLI S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> B_STPHY_AURS R S R S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> B_ESCHR_COLI R S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> B_ESCHR_COLI S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> B_STPHY_AURS S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> B_ESCHR_COLI S S S S </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 2,714 more rows</span></span></span>
<span></span>
<span><span class="co"># filtering using AB selectors is also possible:</span></span>
<span><span class="va">our_data_1st</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="https://rdrr.io/r/base/any.html" class="external-link">any</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">aminoglycosides</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ For aminoglycosides() using column 'GEN' (gentamicin)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 983 × 9</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 981 × 9</span></span></span>
<span><span class="co">#&gt; patient_id hospital date bacteria AMX AMC CIP GEN first</span></span>
<span><span class="co">#&gt; <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;date&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B3 A 2016-07-24 B_ENTRC_CRTL S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> V7 A 2012-04-03 B_ENTRC_CRTL S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> C9 A 2017-03-23 B_ENTRC_CRTL S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 973 more rows</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 971 more rows</span></span></span>
<span></span>
<span><span class="va">our_data_1st</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="https://rdrr.io/r/base/all.html" class="external-link">all</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span><span class="op">)</span></span>
<span><span class="co">#&gt; ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'</span></span>
<span><span class="co">#&gt; (amoxicillin/clavulanic acid)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 464 × 9</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 462 × 9</span></span></span>
<span><span class="co">#&gt; patient_id hospital date bacteria AMX AMC CIP GEN first</span></span>
<span><span class="co">#&gt; <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;date&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B6 A 2016-11-20 B_ENTRC_CRTL R R R R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> I7 A 2015-08-19 B_ENTRC_CRTL R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> Q2 A 2019-09-22 B_ENTRC_CRTL R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> X7 A 2011-03-20 B_ENTRC_CRTL R R S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 454 more rows</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 452 more rows</span></span></span>
<span></span>
<span><span class="co"># even works in base R (since R 3.0):</span></span>
<span><span class="va">our_data_1st</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/all.html" class="external-link">all</a></span><span class="op">(</span><span class="fu"><a href="../reference/antibiotic_class_selectors.html">betalactams</a></span><span class="op">(</span><span class="op">)</span> <span class="op">==</span> <span class="st">"R"</span><span class="op">)</span>, <span class="op">]</span></span>
<span><span class="co">#&gt; ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'</span></span>
<span><span class="co">#&gt; (amoxicillin/clavulanic acid)</span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 464 × 9</span></span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 462 × 9</span></span></span>
<span><span class="co">#&gt; patient_id hospital date bacteria AMX AMC CIP GEN first</span></span>
<span><span class="co">#&gt; <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;date&gt;</span> <span style="color: #949494; font-style: italic;">&lt;mo&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;sir&gt;</span> <span style="color: #949494; font-style: italic;">&lt;lgl&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 1</span> M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 2</span> R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 3</span> R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 4</span> R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B6 A 2016-11-20 B_ENTRC_CRTL R R R R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> I7 A 2015-08-19 B_ENTRC_CRTL R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 5</span> B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 6</span> I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 7</span> N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> Q2 A 2019-09-22 B_ENTRC_CRTL R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> X7 A 2011-03-20 B_ENTRC_CRTL R R S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 8</span> Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;"> 9</span> X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">10</span> V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE </span></span>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 454 more rows</span></span></span></code></pre></div>
<span><span class="co">#&gt; <span style="color: #949494;"># ℹ 452 more rows</span></span></span></code></pre></div>
</div>
<div class="section level3">
<h3 id="generate-antibiograms">Generate antibiograms<a class="anchor" aria-label="anchor" href="#generate-antibiograms"></a>
@ -1222,7 +1220,7 @@ I (<code><a href="../reference/proportion.html">proportion_SI()</a></code>, equa
own:</p>
<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="va">our_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">#&gt; [1] 0.4175109</span></span></code></pre></div>
<span><span class="co">#&gt; [1] 0.4203377</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="cb23"><pre class="downlit sourceCode r">
@ -1232,9 +1230,9 @@ own:</p>
<span><span class="co">#&gt; <span style="color: #949494;"># A tibble: 3 × 2</span></span></span>
<span><span class="co">#&gt; hospital amoxicillin</span></span>
<span><span class="co">#&gt; <span style="color: #949494; font-style: italic;">&lt;chr&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> A 0.342</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">1</span> A 0.340</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">2</span> B 0.551</span></span>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> C 0.362</span></span></code></pre></div>
<span><span class="co">#&gt; <span style="color: #BCBCBC;">3</span> C 0.370</span></span></code></pre></div>
<hr>
<p><em>Author: Dr. Matthijs Berends, 26th Feb 2023</em></p>
</div>

153
articles/AMR_for_Python.html
View File

@ -29,7 +29,7 @@
<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="">2.1.1.9084</small>
<small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">2.1.1.9086</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">
@ -91,13 +91,14 @@ antimicrobial resistance (AMR) data analysis, providing extensive
functionality for working with microbial and antimicrobial properties.
But what if you’re working in Python and still want to benefit from the
robust features of <code>AMR</code>?</p>
<p>Luckily, there is no need to port the package to Python! With the
help of <code>rpy2</code>, a powerful Python package, you can easily
access R from Python and call functions from the <code>AMR</code>
package to process your own data. This post will guide you through
<p>The best way is to access R directly from Python with the help of
<code>rpy2</code>, a simple yet powerful Python package. You can easily
call functions from the <code>AMR</code> package to process your own
data in your own Python environment. This post will guide you through
setting up <code>rpy2</code> and show you how to use R functions from
<code>AMR</code> in Python to supercharge your antimicrobial resistance
analysis.</p>
<p><a href="https://chatgpt.com/g/g-M4UNLwFi5-amr-for-r-assistant" class="external-link"><img src="../AMRforRGPT.svg" style="min-width: 300px; width: 10%;"></a></p>
</div>
<div class="section level2">
<h2 id="what-is-rpy2">What is <code>rpy2</code>?<a class="anchor" aria-label="anchor" href="#what-is-rpy2"></a>
@ -127,19 +128,44 @@ environment.</li>
<div class="section level3">
<h3 id="step-1-install-r">Step 1: Install R<a class="anchor" aria-label="anchor" href="#step-1-install-r"></a>
</h3>
<p>Ensure that you have R installed on your system. You can download R
from <a href="https://cran.r-project.org/" class="external-link">CRAN</a>.</p>
<p>Ensure that R is installed on your system. R has minimal dependencies
and is very simple to install:</p>
<ul>
<li>
<strong>Linux</strong>
<ul>
<li>Ubuntu / Debian:<br><code>sudo apt install r-base</code>
</li>
<li>Fedora:<br><code>sudo dnf install R</code>
</li>
<li>CentOS/RHEL:<br><code>sudo yum install R</code>
</li>
<li>Arch Linux:<br><code>sudo pacman -S r</code>
</li>
</ul>
</li>
<li>
<strong>macOS</strong> (with Homebrew):<br><code>brew install r</code>
</li>
<li>
<strong>Other Systems:</strong><br>
Visit the <a href="https://cran.r-project.org" class="external-link">CRAN download
page</a>.</li>
</ul>
</div>
<div class="section level3">
<h3 id="step-2-install-the-amr-package-in-r">Step 2: Install the <code>AMR</code> package in R<a class="anchor" aria-label="anchor" href="#step-2-install-the-amr-package-in-r"></a>
</h3>
<p>Once you have R installed, open your R console and install the
<code>AMR</code> package:</p>
<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"AMR"</span><span class="op">)</span></span></code></pre></div>
<p>You can also install the latest development version of the
<code>AMR</code> package if needed:</p>
<p>On Linux and macOS, open Terminal and run:</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" tabindex="-1"></a><span class="ex">Rscript</span> <span class="at">-e</span> <span class="st">'install.packages("AMR")'</span></span></code></pre></div>
<p>For other systems, open your R console and install the
<code>AMR</code> package by running:</p>
<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"AMR"</span><span class="op">)</span></span></code></pre></div>
<p>On any system, you can also install the latest development version of
the <code>AMR</code> package by setting <code>repos</code> to our beta
channel:</p>
<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"AMR"</span>, repos <span class="op">=</span> <span class="st">"https://msberends.r-universe.dev"</span><span class="op">)</span></span></code></pre></div>
</div>
<div class="section level3">
@ -147,58 +173,53 @@ from <a href="https://cran.r-project.org/" class="external-link">CRAN</a>.</p>
</h3>
<p>To install <code>rpy2</code>, simply run the following command in
your terminal:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" tabindex="-1"></a><span class="ex">pip</span> install rpy2</span></code></pre></div>
<div class="sourceCode" id="cb4"><pre class="sourceCode bash"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="ex">pip</span> install rpy2</span></code></pre></div>
</div>
<div class="section level3">
<h3 id="step-4-test-rpy2-installation">Step 4: Test <code>rpy2</code> Installation<a class="anchor" aria-label="anchor" href="#step-4-test-rpy2-installation"></a>
</h3>
<p>To ensure everything is set up correctly, you can test your
installation by running the following Python script:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" tabindex="-1"></a><span class="im">import</span> rpy2.robjects <span class="im">as</span> ro</span>
<span id="cb4-2"><a href="#cb4-2" tabindex="-1"></a></span>
<span id="cb4-3"><a href="#cb4-3" tabindex="-1"></a><span class="co"># Test a simple R function from Python</span></span>
<span id="cb4-4"><a href="#cb4-4" tabindex="-1"></a>ro.r(<span class="st">'1 + 1'</span>)</span></code></pre></div>
installation by running the following Python script, which essentially
runs R in the background:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> rpy2.robjects <span class="im">as</span> ro</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a></span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="co"># Test a simple R function from Python</span></span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a>ro.r(<span class="st">'1 + 1'</span>)</span></code></pre></div>
<p>If this returns <code>2</code>, you’re good to go!</p>
</div>
</div>
<div class="section level2">
<h2 id="working-with-amr-in-python-using-rpy2">Working with AMR in Python using <code>rpy2</code><a class="anchor" aria-label="anchor" href="#working-with-amr-in-python-using-rpy2"></a>
<h2 id="working-with-amr-in-python">Working with <code>AMR</code> in Python<a class="anchor" aria-label="anchor" href="#working-with-amr-in-python"></a>
</h2>
<p>Now that we have <code>rpy2</code> set up, let’s walk through some
practical examples of using the <code>AMR</code> package within
Python.</p>
<div class="section level3">
<h3 id="example-1-loading-amr-and-example-data">Example 1: Loading <code>AMR</code> and Example Data<a class="anchor" aria-label="anchor" href="#example-1-loading-amr-and-example-data"></a>
<h3 id="example-1-converting-taxonomic-data">Example 1: Converting Taxonomic Data<a class="anchor" aria-label="anchor" href="#example-1-converting-taxonomic-data"></a>
</h3>
<p>Let’s start by converting taxonomic user input to valid taxonomy
using the <code>AMR</code> package, from within Python:</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb5-2"><a href="#cb5-2" tabindex="-1"></a><span class="im">import</span> rpy2.robjects <span class="im">as</span> ro</span>
<span id="cb5-3"><a href="#cb5-3" tabindex="-1"></a><span class="im">from</span> rpy2.robjects.packages <span class="im">import</span> importr</span>
<span id="cb5-4"><a href="#cb5-4" tabindex="-1"></a><span class="im">from</span> rpy2.robjects <span class="im">import</span> pandas2ri</span>
<span id="cb5-5"><a href="#cb5-5" tabindex="-1"></a></span>
<span id="cb5-6"><a href="#cb5-6" tabindex="-1"></a><span class="co"># Enable conversion between pandas and R data frames</span></span>
<span id="cb5-7"><a href="#cb5-7" tabindex="-1"></a>pandas2ri.activate()</span>
<span id="cb5-8"><a href="#cb5-8" tabindex="-1"></a></span>
<span id="cb5-9"><a href="#cb5-9" tabindex="-1"></a><span class="co"># Load the AMR package from R</span></span>
<span id="cb5-10"><a href="#cb5-10" tabindex="-1"></a>amr <span class="op">=</span> importr(<span class="st">'AMR'</span>)</span>
<span id="cb5-11"><a href="#cb5-11" tabindex="-1"></a></span>
<span id="cb5-12"><a href="#cb5-12" tabindex="-1"></a><span class="co"># Example user dataset in Python</span></span>
<span id="cb5-13"><a href="#cb5-13" tabindex="-1"></a>data <span class="op">=</span> pd.DataFrame({</span>
<span id="cb5-14"><a href="#cb5-14" tabindex="-1"></a> <span class="st">'microorganism'</span>: [<span class="st">'E. coli'</span>, <span class="st">'S. aureus'</span>, <span class="st">'P. aeruginosa'</span>, <span class="st">'K. pneumoniae'</span>]</span>
<span id="cb5-15"><a href="#cb5-15" tabindex="-1"></a>})</span>
<span id="cb5-16"><a href="#cb5-16" tabindex="-1"></a></span>
<span id="cb5-17"><a href="#cb5-17" tabindex="-1"></a><span class="co"># Convert the Python DataFrame to an R DataFrame</span></span>
<span id="cb5-18"><a href="#cb5-18" tabindex="-1"></a>r_data <span class="op">=</span> pandas2ri.py2rpy(data)</span>
<span id="cb5-19"><a href="#cb5-19" tabindex="-1"></a></span>
<span id="cb5-20"><a href="#cb5-20" tabindex="-1"></a><span class="co"># Apply mo_name() from the AMR package to the 'microorganism' column</span></span>
<span id="cb5-21"><a href="#cb5-21" tabindex="-1"></a>ro.globalenv[<span class="st">'r_data'</span>] <span class="op">=</span> r_data</span>
<span id="cb5-22"><a href="#cb5-22" tabindex="-1"></a>ro.r(<span class="st">'r_data$mo_name &lt;- mo_name(r_data$microorganism)'</span>)</span>
<span id="cb5-23"><a href="#cb5-23" tabindex="-1"></a></span>
<span id="cb5-24"><a href="#cb5-24" tabindex="-1"></a><span class="co"># Retrieve and print the modified R DataFrame in Python</span></span>
<span id="cb5-25"><a href="#cb5-25" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(r_data)'</span>)</span>
<span id="cb5-26"><a href="#cb5-26" tabindex="-1"></a>result <span class="op">=</span> pandas2ri.rpy2py(result)</span>
<span id="cb5-27"><a href="#cb5-27" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="im">import</span> pandas <span class="im">as</span> pd</span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a><span class="im">import</span> rpy2.robjects <span class="im">as</span> ro</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a><span class="im">from</span> rpy2.robjects.packages <span class="im">import</span> importr</span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="im">from</span> rpy2.robjects <span class="im">import</span> pandas2ri</span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a></span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="co"># Load the AMR package from R</span></span>
<span id="cb6-7"><a href="#cb6-7" tabindex="-1"></a>amr <span class="op">=</span> importr(<span class="st">'AMR'</span>)</span>
<span id="cb6-8"><a href="#cb6-8" tabindex="-1"></a></span>
<span id="cb6-9"><a href="#cb6-9" tabindex="-1"></a><span class="co"># Example user dataset in Python</span></span>
<span id="cb6-10"><a href="#cb6-10" tabindex="-1"></a>data <span class="op">=</span> pd.DataFrame({</span>
<span id="cb6-11"><a href="#cb6-11" tabindex="-1"></a> <span class="st">'microorganism'</span>: [<span class="st">'E. coli'</span>, <span class="st">'S. aureus'</span>, <span class="st">'P. aeruginosa'</span>, <span class="st">'K. pneumoniae'</span>]</span>
<span id="cb6-12"><a href="#cb6-12" tabindex="-1"></a>})</span>
<span id="cb6-13"><a href="#cb6-13" tabindex="-1"></a></span>
<span id="cb6-14"><a href="#cb6-14" tabindex="-1"></a><span class="co"># Apply mo_name() from the AMR package to the 'microorganism' column</span></span>
<span id="cb6-15"><a href="#cb6-15" tabindex="-1"></a>ro.globalenv[<span class="st">'r_data'</span>] <span class="op">=</span> data</span>
<span id="cb6-16"><a href="#cb6-16" tabindex="-1"></a>ro.r(<span class="st">'r_data$mo_name &lt;- mo_name(r_data$microorganism)'</span>)</span>
<span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a></span>
<span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a><span class="co"># Retrieve and print the modified R DataFrame in Python</span></span>
<span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'r_data'</span>)</span>
<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a>result <span class="op">=</span> pandas2ri.rpy2py(result)</span>
<span id="cb6-21"><a href="#cb6-21" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<p>In this example, a Python dataset with microorganism names like
<em>E. coli</em> and <em>S. aureus</em> is passed to the R function
<code><a href="../reference/mo_property.html">mo_name()</a></code>. The result is an updated <code>DataFrame</code>
@ -212,12 +233,12 @@ that includes the standardised microorganism names based on the
generating an antibiogram, a table that summarises the antimicrobial
susceptibility of bacterial isolates. Here’s how you can generate an
antibiogram from Python:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb6-1"><a href="#cb6-1" tabindex="-1"></a><span class="co"># Run an antibiogram in R from Python</span></span>
<span id="cb6-2"><a href="#cb6-2" tabindex="-1"></a>ro.r(<span class="st">'result &lt;- antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()))'</span>)</span>
<span id="cb6-3"><a href="#cb6-3" tabindex="-1"></a></span>
<span id="cb6-4"><a href="#cb6-4" tabindex="-1"></a><span class="co"># Retrieve the result in Python</span></span>
<span id="cb6-5"><a href="#cb6-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
<span id="cb6-6"><a href="#cb6-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co"># Run an antibiogram in R from Python</span></span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>ro.r(<span class="st">'result &lt;- antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()))'</span>)</span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a></span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co"># Retrieve the result in Python</span></span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<p>In this example, we generate an antibiogram by selecting
aminoglycosides and carbapenems, two classes of antibiotics, and then
convert the resulting R data frame into a Python-readable format.</p>
@ -227,12 +248,12 @@ convert the resulting R data frame into a Python-readable format.</p>
</h3>
<p>Let’s say you want to filter the dataset for Gram-negative bacteria
and display their resistance to certain antibiotics:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb7-1"><a href="#cb7-1" tabindex="-1"></a><span class="co"># Filter for Gram-negative bacteria with intrinsic resistance to cefotaxime</span></span>
<span id="cb7-2"><a href="#cb7-2" tabindex="-1"></a>ro.r(<span class="st">'result &lt;- example_isolates[which(mo_is_gram_negative() &amp; mo_is_intrinsic_resistant(ab = "cefotax")), c("bacteria", aminoglycosides(), carbapenems())]'</span>)</span>
<span id="cb7-3"><a href="#cb7-3" tabindex="-1"></a></span>
<span id="cb7-4"><a href="#cb7-4" tabindex="-1"></a><span class="co"># Retrieve the filtered result in Python</span></span>
<span id="cb7-5"><a href="#cb7-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
<span id="cb7-6"><a href="#cb7-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="co"># Filter for Gram-negative bacteria with intrinsic resistance to cefotaxime</span></span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>ro.r(<span class="st">'result &lt;- example_isolates[which(mo_is_gram_negative() &amp; mo_is_intrinsic_resistant(ab = "cefotax")), c("bacteria", aminoglycosides(), carbapenems())]'</span>)</span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a></span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co"># Retrieve the filtered result in Python</span></span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<p>This example uses the AMR functions
<code><a href="../reference/mo_property.html">mo_is_gram_negative()</a></code> and
<code><a href="../reference/mo_property.html">mo_is_intrinsic_resistant()</a></code> to filter the dataset and
@ -243,12 +264,12 @@ returns a subset of bacteria with resistance data.</p>
</h3>
<p>You can easily customise the antibiogram by passing different
antibiotics or microorganism transformations, as shown below:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb8-1"><a href="#cb8-1" tabindex="-1"></a><span class="co"># Customise the antibiogram with different settings</span></span>
<span id="cb8-2"><a href="#cb8-2" tabindex="-1"></a>ro.r(<span class="st">'result &lt;- antibiogram(example_isolates, antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), mo_transform = "gramstain")'</span>)</span>
<span id="cb8-3"><a href="#cb8-3" tabindex="-1"></a></span>
<span id="cb8-4"><a href="#cb8-4" tabindex="-1"></a><span class="co"># Retrieve and print the result</span></span>
<span id="cb8-5"><a href="#cb8-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
<span id="cb8-6"><a href="#cb8-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<div class="sourceCode" id="cb9"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb9-1"><a href="#cb9-1" tabindex="-1"></a><span class="co"># Customise the antibiogram with different settings</span></span>
<span id="cb9-2"><a href="#cb9-2" tabindex="-1"></a>ro.r(<span class="st">'result &lt;- antibiogram(example_isolates, antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"), mo_transform = "gramstain")'</span>)</span>
<span id="cb9-3"><a href="#cb9-3" tabindex="-1"></a></span>
<span id="cb9-4"><a href="#cb9-4" tabindex="-1"></a><span class="co"># Retrieve and print the result</span></span>
<span id="cb9-5"><a href="#cb9-5" tabindex="-1"></a>result <span class="op">=</span> ro.r(<span class="st">'as.data.frame(result)'</span>)</span>
<span id="cb9-6"><a href="#cb9-6" tabindex="-1"></a><span class="bu">print</span>(result)</span></code></pre></div>
<p>Here, we use piperacillin/tazobactam (TZP) in combination with
tobramycin (TOB) and gentamicin (GEN) to see how they perform against
various Gram-negative bacteria.</p>

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@ -29,7 +29,7 @@
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@ -29,7 +29,7 @@
<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="">2.1.1.9084</small>
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<button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@ -76,7 +76,7 @@
<main id="main" class="col-md-9"><div class="page-header">
<img src="../logo.svg" class="logo" alt=""><h1>Data sets for download / own use</h1>
<h4 data-toc-skip class="date">30 September 2024</h4>
<h4 data-toc-skip class="date">02 October 2024</h4>
<small class="dont-index">Source: <a href="https://github.com/msberends/AMR/blob/main/vignettes/datasets.Rmd" class="external-link"><code>vignettes/datasets.Rmd</code></a></small>
<div class="d-none name"><code>datasets.Rmd</code></div>
@ -108,7 +108,7 @@ column names:<br><em>mo</em>, <em>fullname</em>, <em>status</em>, <em>kingdom</e
<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 30 September 2024 16:46:55 UTC. Find more info
<p>It was last updated on 2 October 2024 08:20:05 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>
@ -286,7 +286,7 @@ Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: <a hre
<td align="center"></td>
<td align="center">11158430</td>
<td align="center"></td>
<td align="center">1.15</td>
<td align="center">1</td>
<td align="center">407310004, 407251000, 407281008, …</td>
</tr>
<tr class="even">
@ -314,7 +314,7 @@ Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: <a hre
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
<td align="center">1.15</td>
<td align="center">1</td>
<td align="center"></td>
</tr>
<tr class="odd">
@ -342,7 +342,7 @@ Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: <a hre
<td align="center">5427575</td>
<td align="center"></td>
<td align="center"></td>
<td align="center">1.15</td>
<td align="center">1</td>
<td align="center">419388003</td>
</tr>
<tr class="even">
@ -370,7 +370,7 @@ Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: <a hre
<td align="center"></td>
<td align="center"></td>
<td align="center"></td>
<td align="center">1.50</td>
<td align="center">1</td>
<td align="center"></td>
</tr>
<tr class="odd">
@ -398,7 +398,7 @@ Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: <a hre
<td align="center">11286021</td>
<td align="center"></td>
<td align="center"></td>
<td align="center">1.15</td>
<td align="center">1</td>
<td align="center">1095001000112106, 715307006, 737528008, …</td>
</tr>
<tr class="even">
@ -426,7 +426,7 @@ Set Name ‘Microorganism’, OID 2.16.840.1.114222.4.11.1009 (v12). URL: <a hre
<td align="center">12233256</td>
<td align="center">11286021</td>
<td align="center"></td>
<td align="center">1.15</td>
<td align="center">1</td>
<td align="center"></td>
</tr>
</tbody>

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