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(v1.4.0.9032) auto-data guessing for functions
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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">1.4.0.9030</span>
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<span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Latest development version">1.4.0.9032</span>
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</span>
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</div>
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@ -281,48 +281,7 @@ The <a href='lifecycle.html'>lifecycle</a> of this function is <strong>experimen
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<p>On our website <a href='https://msberends.github.io/AMR/'>https://msberends.github.io/AMR/</a> you can find <a href='https://msberends.github.io/AMR/articles/AMR.html'>a comprehensive tutorial</a> about how to conduct AMR analysis, the <a href='https://msberends.github.io/AMR/reference/'>complete documentation of all functions</a> and <a href='https://msberends.github.io/AMR/articles/WHONET.html'>an example analysis using WHONET data</a>. As we would like to better understand the backgrounds and needs of our users, please <a href='https://msberends.github.io/AMR/survey.html'>participate in our survey</a>!</p>
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<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
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<pre class="examples"><span class='co'># `example_isolates` is a dataset available in the AMR package.</span>
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<span class='co'># See ?example_isolates.</span>
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<span class='fu'>is_new_episode</span><span class='op'>(</span><span class='va'>example_isolates</span><span class='op'>$</span><span class='va'>date</span><span class='op'>)</span>
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<span class='fu'>is_new_episode</span><span class='op'>(</span><span class='va'>example_isolates</span><span class='op'>$</span><span class='va'>date</span>, episode_days <span class='op'>=</span> <span class='fl'>60</span><span class='op'>)</span>
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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 also 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='va'>date</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><span class='va'>date</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><span class='va'>date</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><span class='va'>date</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 patients and microorganisms leads to the same results</span>
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<span class='co'># 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'><a href='https://dplyr.tidyverse.org/reference/filter.html'>filter</a></span><span class='op'>(</span><span class='fu'><a href='first_isolate.html'>first_isolate</a></span><span class='op'>(</span><span class='va'>.</span>, include_unknown <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</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'>patient_id</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='va'>date</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 is_new_episode() has a lot more flexibility than first_isolate(),</span>
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<span class='co'># since you can now group on anything that seems relevant:</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'>patient_id</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='va'>date</span><span class='op'>)</span><span class='op'>)</span>
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<span class='op'>}</span>
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</pre>
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<pre class="examples"></pre>
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</div>
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