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(v1.4.0.9024) is_new_episode()
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@ -49,7 +49,7 @@
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<script src="../extra.js"></script>
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<meta property="og:title" content="Determine first (weighted) isolates — first_isolate" />
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<meta property="og:description" content="Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type." />
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<meta property="og:description" content="Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use is_new_episode() that also supports grouping with the dplyr package, see Examples." />
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<meta property="og:image" content="https://msberends.github.io/AMR/logo.png" />
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@ -82,7 +82,7 @@
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</button>
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<span class="navbar-brand">
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<a class="navbar-link" href="../index.html">AMR (for R)</a>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9000</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9024</span>
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</span>
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</div>
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@ -239,7 +239,7 @@
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</div>
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<div class="ref-description">
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<p>Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type.</p>
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<p>Determine first (weighted) isolates of all microorganisms of every patient per episode and (if needed) per specimen type. To determine patient episodes not necessarily based on microorganisms, use <code>is_new_episode()</code> that also supports grouping with the <code>dplyr</code> package, see <em>Examples</em>.</p>
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</div>
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<pre class="usage"><span class='fu'>first_isolate</span><span class='op'>(</span>
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@ -278,18 +278,25 @@
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col_mo <span class='op'>=</span> <span class='cn'>NULL</span>,
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col_keyantibiotics <span class='op'>=</span> <span class='cn'>NULL</span>,
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<span class='va'>...</span>
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<span class='op'>)</span>
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<span class='fu'>is_new_episode</span><span class='op'>(</span>
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<span class='va'>.data</span>,
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episode_days <span class='op'>=</span> <span class='fl'>365</span>,
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col_date <span class='op'>=</span> <span class='cn'>NULL</span>,
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col_patient_id <span class='op'>=</span> <span class='cn'>NULL</span>
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<span class='op'>)</span></pre>
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<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
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<table class="ref-arguments">
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<colgroup><col class="name" /><col class="desc" /></colgroup>
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<tr>
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<th>x</th>
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<th>x, .data</th>
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<td><p>a <a href='https://rdrr.io/r/base/data.frame.html'>data.frame</a> containing isolates.</p></td>
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</tr>
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<tr>
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<th>col_date</th>
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<td><p>column name of the result date (or date that is was received on the lab), defaults to the first column of with a date class</p></td>
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<td><p>column name of the result date (or date that is was received on the lab), defaults to the first column with a date class</p></td>
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</tr>
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<tr>
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<th>col_patient_id</th>
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@ -366,10 +373,17 @@
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<p>A <code><a href='https://rdrr.io/r/base/logical.html'>logical</a></code> vector</p>
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<h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
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<p><strong>WHY THIS IS SO IMPORTANT</strong> <br />
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To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode <a href='https:/pubmed.ncbi.nlm.nih.gov/17304462/'>(ref)</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <a href='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p>
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<p>All isolates with a microbial ID of <code>NA</code> will be excluded as first isolate.</p>
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<p>The functions <code>filter_first_isolate()</code> and <code>filter_first_weighted_isolate()</code> are helper functions to quickly filter on first isolates. The function <code>filter_first_isolate()</code> is essentially equal to either:</p><pre> <span class='va'>x</span><span class='op'>[</span><span class='fu'>first_isolate</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span>, <span class='op'>]</span>
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<p>The <code>is_new_episode()</code> function is a wrapper around the <code>first_isolate()</code> function and can be used for data sets without isolates to just determine patient episodes based on any combination of grouping variables (using <code>dplyr</code>), please see <em>Examples</em>. Since it runs <code>first_isolate()</code> for every group, it is quite slow.</p>
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<p>All isolates with a microbial ID of <code>NA</code> will be excluded as first isolate.</p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Why this is so important</h3>
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<p>To conduct an analysis of antimicrobial resistance, you should only include the first isolate of every patient per episode <a href='https:/pubmed.ncbi.nlm.nih.gov/17304462/'>(ref)</a>. If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following week. The resistance percentage of oxacillin of all <em>S. aureus</em> isolates would be overestimated, because you included this MRSA more than once. It would be <a href='https://en.wikipedia.org/wiki/Selection_bias'>selection bias</a>.</p>
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<h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a><code>filter_*()</code> shortcuts</h3>
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<p>The functions <code>filter_first_isolate()</code> and <code>filter_first_weighted_isolate()</code> are helper functions to quickly filter on first isolates.</p>
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<p>The function <code>filter_first_isolate()</code> is essentially equal to either:</p><pre> <span class='va'>x</span><span class='op'>[</span><span class='fu'>first_isolate</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span>, <span class='op'>]</span>
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<span class='va'>x</span> <span class='op'>%>%</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span><span class='op'>(</span><span class='fu'>first_isolate</span><span class='op'>(</span><span class='va'>x</span>, <span class='va'>...</span><span class='op'>)</span><span class='op'>)</span>
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</pre>
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@ -381,6 +395,7 @@ To conduct an analysis of antimicrobial resistance, you should only include the
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/select.html'>select</a></span><span class='op'>(</span><span class='op'>-</span><span class='va'>only_weighted_firsts</span>, <span class='op'>-</span><span class='va'>keyab</span><span class='op'>)</span>
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</pre>
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<h2 class="hasAnchor" id="key-antibiotics"><a class="anchor" href="#key-antibiotics"></a>Key antibiotics</h2>
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@ -415,21 +430,22 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
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<span class='co'># basic filtering on first isolates</span>
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<span class='va'>example_isolates</span><span class='op'>[</span><span class='fu'>first_isolate</span><span class='op'>(</span><span class='va'>example_isolates</span><span class='op'>)</span>, <span class='op'>]</span>
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<span class='co'># filtering based on isolates ----------------------------------------------</span>
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<span class='co'># \donttest{</span>
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<span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://dplyr.tidyverse.org'>"dplyr"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
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<span class='co'># Filter on first isolates:</span>
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<span class='co'># filter on first isolates:</span>
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<span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span>first_isolate <span class='op'>=</span> <span class='fu'>first_isolate</span><span class='op'>(</span><span class='va'>.</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span><span class='op'>(</span><span class='va'>first_isolate</span> <span class='op'>==</span> <span class='cn'>TRUE</span><span class='op'>)</span>
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<span class='co'># Short-hand versions:</span>
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<span class='co'># short-hand versions:</span>
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<span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'>filter_first_isolate</span><span class='op'>(</span><span class='op'>)</span>
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<span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'>filter_first_weighted_isolate</span><span class='op'>(</span><span class='op'>)</span>
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<span class='co'># Now let's see if first isolates matter:</span>
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<span class='co'># now let's see if first isolates matter:</span>
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<span class='va'>A</span> <span class='op'><-</span> <span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span><span class='op'>(</span><span class='va'>hospital_id</span><span class='op'>)</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/summarise.html'>summarise</a></span><span class='op'>(</span>count <span class='op'>=</span> <span class='fu'><a href='count.html'>n_rsi</a></span><span class='op'>(</span><span class='va'>GEN</span><span class='op'>)</span>, <span class='co'># gentamicin availability</span>
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@ -446,6 +462,42 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>stable</s
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<span class='co'># Gentamicin resistance in hospital D appears to be 3.7% higher than</span>
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<span class='co'># when you (erroneously) would have used all isolates for analysis.</span>
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<span class='op'>}</span>
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<span class='co'># filtering based on any other condition -----------------------------------</span>
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<span class='kw'>if</span> <span class='op'>(</span><span class='kw'><a href='https://rdrr.io/r/base/library.html'>require</a></span><span class='op'>(</span><span class='st'><a href='https://dplyr.tidyverse.org'>"dplyr"</a></span><span class='op'>)</span><span class='op'>)</span> <span class='op'>{</span>
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<span class='co'># is_new_episode() can be used in dplyr verbs to determine patient</span>
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<span class='co'># episodes based on any (combination of) grouping variables:</span>
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<span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span>condition <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sample.html'>sample</a></span><span class='op'>(</span>x <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"A"</span>, <span class='st'>"B"</span>, <span class='st'>"C"</span><span class='op'>)</span>,
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size <span class='op'>=</span> <span class='fl'>2000</span>,
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replace <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span><span class='op'>)</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span><span class='op'>(</span><span class='va'>condition</span><span class='op'>)</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span>new_episode <span class='op'>=</span> <span class='fu'>is_new_episode</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>
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<span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span><span class='op'>(</span><span class='va'>hospital_id</span><span class='op'>)</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/summarise.html'>summarise</a></span><span class='op'>(</span>patients <span class='op'>=</span> <span class='fu'><a href='https://dplyr.tidyverse.org/reference/n_distinct.html'>n_distinct</a></span><span class='op'>(</span><span class='va'>patient_id</span><span class='op'>)</span>,
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n_episodes_365 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span><span class='op'>(</span><span class='fu'>is_new_episode</span><span class='op'>(</span>episode_days <span class='op'>=</span> <span class='fl'>365</span><span class='op'>)</span><span class='op'>)</span>,
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n_episodes_60 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span><span class='op'>(</span><span class='fu'>is_new_episode</span><span class='op'>(</span>episode_days <span class='op'>=</span> <span class='fl'>60</span><span class='op'>)</span><span class='op'>)</span>,
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n_episodes_30 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span><span class='op'>(</span><span class='fu'>is_new_episode</span><span class='op'>(</span>episode_days <span class='op'>=</span> <span class='fl'>30</span><span class='op'>)</span><span class='op'>)</span><span class='op'>)</span>
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<span class='co'># grouping on microorganisms leads to the same results as first_isolate():</span>
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<span class='va'>x</span> <span class='op'><-</span> <span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'>filter_first_isolate</span><span class='op'>(</span>include_unknown <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span>
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<span class='va'>y</span> <span class='op'><-</span> <span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span><span class='op'>(</span><span class='va'>mo</span><span class='op'>)</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span><span class='op'>(</span><span class='fu'>is_new_episode</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>
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<span class='fu'><a href='https://rdrr.io/r/base/identical.html'>identical</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>$</span><span class='va'>patient_id</span>, <span class='va'>y</span><span class='op'>$</span><span class='va'>patient_id</span><span class='op'>)</span>
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<span class='co'># but now you can group on isolates and many more:</span>
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<span class='va'>example_isolates</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/group_by.html'>group_by</a></span><span class='op'>(</span><span class='va'>mo</span>, <span class='va'>hospital_id</span>, <span class='va'>ward_icu</span><span class='op'>)</span> <span class='op'>%>%</span>
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<span class='fu'><a href='https://dplyr.tidyverse.org/reference/mutate.html'>mutate</a></span><span class='op'>(</span>flag_episode <span class='op'>=</span> <span class='fu'>is_new_episode</span><span class='op'>(</span><span class='op'>)</span><span class='op'>)</span>
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<span class='op'>}</span>
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<span class='co'># }</span>
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</pre>
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</div>
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