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<h1 class="title toc-ignore">Using data in data frames</h1>
<h4 class="author"><em>Data Carpentry contributors</em></h4>
</div>
<hr />
<blockquote>
<h2 id="learning-objectives">Learning Objectives</h2>
<ul>
<li>Extract values from vectors and data frames.</li>
<li>Perform operations on columns in a data frame.</li>
<li>Append columns to a data frame.</li>
<li>Create subsets of a data frame.</li>
</ul>
</blockquote>
<hr />
<p>In this lesson you will learn how to extract and manipulate data stored in data frames in R. We will work with the <em>E. coli</em> metadata file that we used previously. Be sure to read this file into a dataframe named <code>metadata</code>, if you haven’t already done so.</p>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1">metadata <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">'data/Ecoli_metadata.csv'</span>)</a></code></pre></div>
<p>Because the columns of a data frame are vectors, we will first learn how to extract elements from vectors and then learn how to apply this concept to select rows and columns from a data frame.</p>
<div id="extracting-values-with-indexing-and-sequences" class="section level1">
<h1>Extracting values with indexing and sequences</h1>
<div id="vectors" class="section level2">
<h2>Vectors</h2>
<p>Let’s create a vector containing the first ten letters of the alphabet.</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" data-line-number="1">ten_letters <-<span class="st"> </span><span class="kw">c</span>(<span class="st">'a'</span>, <span class="st">'b'</span>, <span class="st">'c'</span>, <span class="st">'d'</span>, <span class="st">'e'</span>, <span class="st">'f'</span>, <span class="st">'g'</span>, <span class="st">'h'</span>, <span class="st">'i'</span>, <span class="st">'j'</span>)</a></code></pre></div>
<p>In order to extract one or several values from a vector, we must provide one or several indices in square brackets, just as we do in math. R indexes start at 1. Programming languages like Fortran, MATLAB, and R start counting at 1, because that’s what human beings typically do. Languages in the C family (including C++, Java, Perl, and Python) count from 0 because that’s simpler for computers to do.</p>
<p>So, to extract the 2nd element of <code>ten_letters</code> we type:</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" data-line-number="1">ten_letters[<span class="dv">2</span>]</a></code></pre></div>
<p>We can extract multiple elements at a time by specifying mulitple indices inside the square brackets as a vector. Notice how you can use <code>:</code> to make a vector of all integers two numbers.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1">ten_letters[<span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">7</span>)]</a>
<a class="sourceLine" id="cb4-2" data-line-number="2"></a>
<a class="sourceLine" id="cb4-3" data-line-number="3">ten_letters[<span class="dv">3</span><span class="op">:</span><span class="dv">6</span>]</a>
<a class="sourceLine" id="cb4-4" data-line-number="4"></a>
<a class="sourceLine" id="cb4-5" data-line-number="5">ten_letters[<span class="dv">10</span><span class="op">:</span><span class="dv">1</span>]</a>
<a class="sourceLine" id="cb4-6" data-line-number="6"></a>
<a class="sourceLine" id="cb4-7" data-line-number="7">ten_letters[<span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">8</span><span class="op">:</span><span class="dv">10</span>)]</a></code></pre></div>
<p>Quick exercise / formative assessment: Select every other element in <code>ten_letters</code>.</p>
<p>What if we were dealing with a much longer vector? We can use the <code>seq()</code> function to quickly create sequences of numbers.</p>
<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" data-line-number="1"><span class="kw">seq</span>(<span class="dv">1</span>, <span class="dv">10</span>, <span class="dt">by =</span> <span class="dv">2</span>)</a>
<a class="sourceLine" id="cb5-2" data-line-number="2"><span class="kw">seq</span>(<span class="dv">20</span>, <span class="dv">4</span>, <span class="dt">by =</span> <span class="dv">-3</span>)</a></code></pre></div>
<!--
Consider including:
# Create sequences between two numbers, given the number of values (length.out = number of values)
seq(1, 10, length.out = 2)
seq(20, 4, length.out = 3)
and discuss why they differ.
-->
<blockquote>
<h2 id="exercise">Exercise</h2>
<p>Fill in the blank to select the even elements of ten_letters using the seq() function.</p>
<p>ten_letters[____________]</p>
<blockquote>
<h2 id="solution">Solution</h2>
<p>ten_letters[seq(2, 10, by = 2)] {: .solution} {: .challenge}</p>
</blockquote>
</blockquote>
</div>
<div id="data-frames" class="section level2">
<h2>Data frames</h2>
<p>The metadata data frame has rows and columns (it has 2 dimensions), if we want to extract some specific data from it, we need to specify the “coordinates” we want from it. Row numbers come first, followed by column numbers (i.e. [row, column]).</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1">metadata[<span class="dv">1</span>, <span class="dv">2</span>] <span class="co"># 1st element in the 2nd column </span></a>
<a class="sourceLine" id="cb6-2" data-line-number="2">metadata[<span class="dv">1</span>, <span class="dv">6</span>] <span class="co"># 1st element in the 6th column</span></a>
<a class="sourceLine" id="cb6-3" data-line-number="3">metadata[<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>, <span class="dv">7</span>] <span class="co"># First three elements in the 7th column</span></a>
<a class="sourceLine" id="cb6-4" data-line-number="4">metadata[<span class="dv">3</span>, ] <span class="co"># 3rd element for all columns</span></a>
<a class="sourceLine" id="cb6-5" data-line-number="5">metadata[, <span class="dv">7</span>] <span class="co"># Entire 7th column</span></a></code></pre></div>
<blockquote>
<h2 id="challenge">Challenge</h2>
<p>The function <code>nrow()</code> on a <code>data.frame</code> returns the number of rows. For example, try typing nrow(metadata)<code>. Use</code>nrow()<code>and</code>seq()<code>to create a new data frame called</code>meta_by_2<code>that includes all even numbered rows of</code>metadata`.</p>
<h2 id="solution-1">Solution</h2>
<blockquote>
<p>meta_data[seq(2, nrow(metadata), by = 2, ]</p>
<p>{: .solution} {: .challenge}</p>
</blockquote>
</blockquote>
<p>For larger datasets, it can be tricky to remember the column number that corresponds to a particular variable. Sometimes the column number for a particular variable can change if your analysis adds or removes columns. The best practice when working with columns in a data frame is to refer to them by name. This also makes your code easier to read and your intentions clearer.</p>
<p>There are two ways to select a column by name from a data frame:</p>
<ul>
<li>Using <code>dataframe[ , "column_name"]</code></li>
<li>Using <code>dataframe$column_name</code></li>
</ul>
<p>You can do operations on a particular column, by selecting it using the <code>$</code> sign. In this case, the entire column is a vector. You can use <code>names(metadata)</code> or <code>colnames(metadata)</code> to remind yourself of the column names. For instance, to extract all the strain information from our datasets:</p>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" data-line-number="1"><span class="co"># Select the strain column from metadata</span></a>
<a class="sourceLine" id="cb7-2" data-line-number="2">metadata[ , <span class="st">"strain"</span>]</a>
<a class="sourceLine" id="cb7-3" data-line-number="3"></a>
<a class="sourceLine" id="cb7-4" data-line-number="4"><span class="co"># Alternatively...</span></a>
<a class="sourceLine" id="cb7-5" data-line-number="5">metadata<span class="op">$</span>strain</a></code></pre></div>
<p>The first method allows you to select multiple columns at once. Suppose we wanted strain and clade information:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb8-1" data-line-number="1">metadata[, <span class="kw">c</span>(<span class="st">"strain"</span>, <span class="st">"clade"</span>)]</a></code></pre></div>
<p>You can even access columns by column name <em>and</em> select specific rows of interest. For example, if we wanted the strain and clade of just rows 4 through 7, we could do:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" data-line-number="1">metadata[<span class="dv">4</span><span class="op">:</span><span class="dv">7</span>, <span class="kw">c</span>(<span class="st">"strain"</span>, <span class="st">"clade"</span>)]</a></code></pre></div>
<p><!– Still need to address the following learning objectives: * Append columns to a data frame. * Create subsets of a data frame.</p>
<p>The following headings are just suggestions.</p>
<blockquote>
</blockquote>
</div>
</div>
<div id="manipulating-columns" class="section level1">
<h1>Manipulating columns</h1>
<div id="mathematical-operations" class="section level2">
<h2>Mathematical operations</h2>
</div>
<div id="appending-new-columns" class="section level2">
<h2>Appending new columns</h2>
</div>
</div>
<div id="creating-subsets" class="section level1">
<h1>Creating subsets</h1>
</div>
<hr/>
<p><a href="http://datacarpentry.org/">Data Carpentry</a>,
2017-2018. <a href="LICENSE.html">License</a>. <a href="CONTRIBUTING.html">Contributing</a>. <br/>
Questions? Feedback?
Please <a href="https://github.com/datacarpentry/R-genomics/issues/new">file
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Twitter: <a href="https://twitter.com/datacarpentry">@datacarpentry</a></p>
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