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<h1 class="title toc-ignore">Using data in data frames</h1>
<h4 class="author">Data Carpentry contributors</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"><span id="cb1-1"><a href="#cb1-1"></a>metadata <-<span class="st"> </span><span class="kw">read.csv</span>(<span class="st">'data/Ecoli_metadata.csv'</span>)</span></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"><span id="cb2-1"><a href="#cb2-1"></a>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>)</span></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"><span id="cb3-1"><a href="#cb3-1"></a>ten_letters[<span class="dv">2</span>]</span></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 between (and including) two numbers.</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>ten_letters[<span class="kw">c</span>(<span class="dv">1</span>,<span class="dv">7</span>)]</span>
<span id="cb4-2"><a href="#cb4-2"></a></span>
<span id="cb4-3"><a href="#cb4-3"></a>ten_letters[<span class="dv">3</span><span class="op">:</span><span class="dv">6</span>]</span>
<span id="cb4-4"><a href="#cb4-4"></a></span>
<span id="cb4-5"><a href="#cb4-5"></a>ten_letters[<span class="dv">10</span><span class="op">:</span><span class="dv">1</span>]</span>
<span id="cb4-6"><a href="#cb4-6"></a></span>
<span id="cb4-7"><a href="#cb4-7"></a>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>)]</span></code></pre></div>
<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"><span id="cb5-1"><a href="#cb5-1"></a><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>)</span>
<span id="cb5-2"><a href="#cb5-2"></a><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>)</span></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>
</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"><span id="cb6-1"><a href="#cb6-1"></a>metadata[<span class="dv">1</span>, <span class="dv">2</span>] <span class="co"># 1st element in the 2nd column </span></span>
<span id="cb6-2"><a href="#cb6-2"></a>metadata[<span class="dv">1</span>, <span class="dv">6</span>] <span class="co"># 1st element in the 6th column</span></span>
<span id="cb6-3"><a href="#cb6-3"></a>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></span>
<span id="cb6-4"><a href="#cb6-4"></a>metadata[<span class="dv">3</span>, ] <span class="co"># 3rd element for all columns</span></span>
<span id="cb6-5"><a href="#cb6-5"></a>metadata[, <span class="dv">7</span>] <span class="co"># Entire 7th column</span></span></code></pre></div>
<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"><span id="cb7-1"><a href="#cb7-1"></a><span class="co"># Select the strain column from metadata</span></span>
<span id="cb7-2"><a href="#cb7-2"></a>metadata[ , <span class="st">"strain"</span>]</span>
<span id="cb7-3"><a href="#cb7-3"></a></span>
<span id="cb7-4"><a href="#cb7-4"></a><span class="co"># Alternatively...</span></span>
<span id="cb7-5"><a href="#cb7-5"></a>metadata<span class="op">$</span>strain</span></code></pre></div>
<p>The first method allows you to select multiple columns at once. Suppose we wanted strain and clade information:</p>
<div class="sourceCode" id="cb8"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb8-1"><a href="#cb8-1"></a>metadata[, <span class="kw">c</span>(<span class="st">"strain"</span>, <span class="st">"clade"</span>)]</span></code></pre></div>
<p>You can even access columns by column name <em>and</em> select specific rows of interest. For example, if we wanted the strain and clade of just rows 4 through 7, we could do:</p>
<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a>metadata[<span class="dv">4</span><span class="op">:</span><span class="dv">7</span>, <span class="kw">c</span>(<span class="st">"strain"</span>, <span class="st">"clade"</span>)]</span></code></pre></div>
</div>
<div id="appending-new-columns" class="section level2">
<h2>Appending new columns</h2>
<p>If we want to add a new column to a data.frame, there are a few ways to do it [Note: there is usually more than one way to get the same outcome!], although all use the assignment arrow <code><-</code>.</p>
<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a>metadata<span class="op">$</span>new1 <-<span class="st"> </span><span class="dv">1</span></span>
<span id="cb10-2"><a href="#cb10-2"></a>metadata[,<span class="st">"new2"</span>] <-<span class="st"> </span><span class="dv">2</span></span>
<span id="cb10-3"><a href="#cb10-3"></a>metadata <-<span class="st"> </span><span class="kw">cbind</span>(metadata, <span class="dt">new3 =</span> <span class="dv">3</span>)</span>
<span id="cb10-4"><a href="#cb10-4"></a><span class="kw">head</span>(metadata)</span></code></pre></div>
</div>
</div>
<hr/>
<p><a href="http://datacarpentry.org/">Data Carpentry</a>,
2017. <a href="LICENSE.html">License</a>. <a href="CONTRIBUTING.html">Contributing</a>. <br/>
Questions? Feedback?
Please <a href="https://github.com/datacarpentry/R-ecology-lesson/issues/new">file
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