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<h1 class="title toc-ignore">Aggregating and analyzing data with dplyr (notes)</h1>
<h4 class="author"><em>Data Carpentry contributors</em></h4>
</div>
<div id="TOC">
<ul>
<li><a href="#key-idea">Key idea</a></li>
<li><a href="#install-and-load-the-library">Install and load the library</a></li>
<li><a href="#select-and-filter">Select and filter</a></li>
<li><a href="#pipe">Pipe</a><ul>
<li><a href="#challenge">Challenge</a></li>
</ul></li>
<li><a href="#mutate">Mutate</a><ul>
<li><a href="#challenge-1">Challenge</a></li>
<li><a href="#split-apply-combine-data-analyses-group-by-and-summarize">split-apply-combine data analyses (group-by and summarize)</a></li>
<li><a href="#challenge-2">Challenge</a></li>
<li><a href="#challenge-3">Challenge</a></li>
<li><a href="#challenge-4">Challenge</a></li>
</ul></li>
<li><a href="#data-cleaning-preparations">Data cleaning preparations</a></li>
</ul>
</div>
<div id="key-idea" class="section level2">
<h2>Key idea</h2>
<p>All that bracket-based selecting can be a bit cumbersome. Add-on package dplyr greatly simplifies the process; inspired by SQL.</p>
<p><code>select</code>, <code>filter</code>, <code>mutate</code>, <code>group_by</code>, <code>summarize</code></p>
<p>(also <code>tally</code> and <code>arrange</code>)</p>
</div>
<div id="install-and-load-the-library" class="section level2">
<h2>Install and load the library</h2>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">install.packages</span>(<span class="st">"dplyr"</span>)
<span class="kw">install.packages</span>(<span class="st">"ggplot2"</span>)</code></pre></div>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">library</span>(dplyr)</code></pre></div>
</div>
<div id="select-and-filter" class="section level2">
<h2>Select and filter</h2>
<p>Select to grab columns.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">selectedcol <-<span class="st"> </span><span class="kw">select</span>(surveys, species_id, plot_type, weight)
<span class="kw">head</span>(selectedcol)</code></pre></div>
<p>Filter to grab rows.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys2002 <-<span class="st"> </span><span class="kw">filter</span>(surveys, year==<span class="dv">2002</span>)
<span class="kw">head</span>(surveys2002)</code></pre></div>
</div>
<div id="pipe" class="section level2">
<h2>Pipe</h2>
<p>Output of one function becomes the input to the next.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">filter</span>(weight <<span class="st"> </span><span class="dv">5</span>) %>%
<span class="st"> </span><span class="kw">select</span>(species_id, sex, weight)</code></pre></div>
<p><kbd><code>Ctrl</code></kbd> + <kbd><code>Shift</code></kbd> + <kbd><code>M</code></kbd> to insert <code>%>%</code></p>
<p>Could assign this to something:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys_sml <-<span class="st"> </span>surveys %>%
<span class="st"> </span><span class="kw">filter</span>(weight <<span class="st"> </span><span class="dv">5</span>) %>%
<span class="st"> </span><span class="kw">select</span>(species_id, sex, weight)</code></pre></div>
<div id="challenge" class="section level3">
<h3>Challenge</h3>
<p>Using pipes, subset the data to include individuals collected before 1995, and retain the columns <code>year</code>, <code>sex</code>, and <code>weight.</code></p>
</div>
</div>
<div id="mutate" class="section level2">
<h2>Mutate</h2>
<p><code>mutate()</code> to derive a new column.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">weight_kg =</span> weight /<span class="st"> </span><span class="dv">1000</span>)</code></pre></div>
<p>To just look at the top:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">weight_kg =</span> weight /<span class="st"> </span><span class="dv">1000</span>) %>%
<span class="st"> </span>head</code></pre></div>
<p>Filter out <code>NA</code>s:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(weight)) %>%
<span class="st"> </span><span class="kw">mutate</span>(<span class="dt">weight_kg =</span> weight /<span class="st"> </span><span class="dv">1000</span>) %>%
<span class="st"> </span>head</code></pre></div>
<div id="challenge-1" class="section level3">
<h3>Challenge</h3>
<p>Create a new dataframe from the survey data that meets the following criteria: contains only the <code>species_id</code> column and a column that contains values that are the square-root of <code>hindfoot_length</code> values (e.g. a new column <code>hindfoot_sqrt</code>). In this <code>hindfoot_sqrt</code> column, there are no NA values and all values are < 3.</p>
<p>Hint: think about how the commands should be ordered</p>
</div>
<div id="split-apply-combine-data-analyses-group-by-and-summarize" class="section level3">
<h3>split-apply-combine data analyses (group-by and summarize)</h3>
<p>Many analyses fit a split-apply-combine pattern: split the data into groups, apply some analysis to each group, and then combine the results.</p>
<p>With dplyr, we use <code>group_by()</code> for the splitting and <code>tally</code> or <code>summarize()</code> for the rest.</p>
<p>Count individuals by sex:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">group_by</span>(sex) %>%
<span class="st"> </span><span class="kw">tally</span>()</code></pre></div>
<p>Average weight by sex:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">group_by</span>(sex) %>%
<span class="st"> </span><span class="kw">summarize</span>(<span class="dt">mean_weight =</span> <span class="kw">mean</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>))</code></pre></div>
<p>Can group by multiple columns:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">group_by</span>(sex, species_id) %>%
<span class="st"> </span><span class="kw">summarize</span>(<span class="dt">mean_weight =</span> <span class="kw">mean</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>))</code></pre></div>
<p>Maybe filter those <code>NA</code>s</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">group_by</span>(sex, species_id) %>%
<span class="st"> </span><span class="kw">summarize</span>(<span class="dt">mean_weight =</span> <span class="kw">mean</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)) %>%
<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(mean_weight))</code></pre></div>
<p>Another thing we might do here is sort rows by <code>mean_weight</code>, using <code>arrange()</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">group_by</span>(sex, species_id) %>%
<span class="st"> </span><span class="kw">summarize</span>(<span class="dt">mean_weight =</span> <span class="kw">mean</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)) %>%
<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(mean_weight)) %>%
<span class="st"> </span><span class="kw">arrange</span>(mean_weight)</code></pre></div>
<p>If you want them sorted from highest to lowest, use <code>desc()</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">group_by</span>(sex, species_id) %>%
<span class="st"> </span><span class="kw">summarize</span>(<span class="dt">mean_weight =</span> <span class="kw">mean</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)) %>%
<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(mean_weight)) %>%
<span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(mean_weight))</code></pre></div>
<p>Also note that you can include multiple summaries.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys %>%
<span class="st"> </span><span class="kw">group_by</span>(sex, species_id) %>%
<span class="st"> </span><span class="kw">summarize</span>(<span class="dt">mean_weight =</span> <span class="kw">mean</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>),
<span class="dt">min_weight =</span> <span class="kw">min</span>(weight, <span class="dt">na.rm =</span> <span class="ot">TRUE</span>)) %>%
<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(mean_weight)) %>%
<span class="st"> </span><span class="kw">arrange</span>(<span class="kw">desc</span>(mean_weight))</code></pre></div>
</div>
<div id="challenge-2" class="section level3">
<h3>Challenge</h3>
<p>How many times was each <code>plot_type</code> surveyed?</p>
</div>
<div id="challenge-3" class="section level3">
<h3>Challenge</h3>
<p>Use <code>group_by()</code> and <code>summarize()</code> to find the mean, min, and max hindfoot length for each species.</p>
</div>
<div id="challenge-4" class="section level3">
<h3>Challenge</h3>
<p>What was the heaviest animal measured in each year? Return the columns <code>year</code>, <code>genus</code>, <code>species</code>, and <code>weight</code>.</p>
</div>
</div>
<div id="data-cleaning-preparations" class="section level2">
<h2>Data cleaning preparations</h2>
<p>In preparations for the plotting, let’s do a bit of data cleaning: remove rows with missing <code>species_id</code>, <code>weight</code>, <code>hindfoot_length</code>, or <code>sex</code>.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r">surveys_complete <-<span class="st"> </span>surveys %>%
<span class="st"> </span><span class="kw">filter</span>(species_id !=<span class="st"> ""</span>, !<span class="kw">is.na</span>(weight)) %>%
<span class="st"> </span><span class="kw">filter</span>(!<span class="kw">is.na</span>(hindfoot_length), sex !=<span class="st"> ""</span>)</code></pre></div>
<p>There are a lot of species with low counts. Let’s remove the species with less than 10 counts.</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="co"># count records per species</span>
species_counts <-<span class="st"> </span>surveys_complete %>%
<span class="st"> </span><span class="kw">group_by</span>(species_id) %>%
<span class="st"> </span>tally
<span class="kw">head</span>(species_counts)
<span class="co"># get names of the species with counts >= 10</span>
frequent_species <-<span class="st"> </span>species_counts %>%
<span class="st"> </span><span class="kw">filter</span>(n >=<span class="st"> </span><span class="dv">10</span>) %>%
<span class="st"> </span><span class="kw">select</span>(species_id)
<span class="co"># filter out the less-frequent species</span>
surveys_complete <-<span class="st"> </span>surveys_complete %>%
<span class="st"> </span><span class="kw">filter</span>(species_id %in%<span class="st"> </span>frequent_species$species_id)</code></pre></div>
<p>We might save this to a file:</p>
<div class="sourceCode"><pre class="sourceCode r"><code class="sourceCode r"><span class="kw">write.csv</span>(reduced, <span class="st">"CleanData/portal_data_reduced.csv"</span>)</code></pre></div>
<p><br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/> <br/></p>
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