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<a href="00-before-we-start.html">Before we start</a>
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<a href="01-intro-to-R.html">Intro to R</a>
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<a href="02-starting-with-data.html">Starting with data</a>
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<a href="03-data-frames.html">Data frames</a>
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<a href="04-dplyr.html">The dplyr package</a>
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<h1 class="title toc-ignore">Starting with data</h1>
<h4 class="author"><em>Data Carpentry contributors</em></h4>
</div>
<hr />
<blockquote>
<h2 id="learning-objectives">Learning Objectives</h2>
<ul>
<li>Load external tabular data from a .csv file into R.</li>
<li>Describe what an R data frame is.</li>
<li>Summarize the contents of a data frame in R.</li>
<li>Manipulate categorical data in R using factors.</li>
</ul>
</blockquote>
<hr />
<div id="looking-at-metadata" class="section level1">
<h1>Looking at Metadata</h1>
<p>We are studying a population of Escherichia coli (designated Ara-3), which were propagated for more than 40,000 generations in a glucose-limited minimal medium. This medium was supplemented with citrate which E. coli cannot metabolize in the aerobic conditions of the experiment. Sequencing of the populations at regular time points reveals that spontaneous citrate-using mutants (Cit+) appeared at around 31,000 generations. This metadata describes information on the Ara-3 clones and the columns represent:</p>
<table>
<thead>
<tr class="header">
<th>Column</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>sample</td>
<td>clone name</td>
</tr>
<tr class="even">
<td>generation</td>
<td>generation when sample frozen</td>
</tr>
<tr class="odd">
<td>clade</td>
<td>based on parsimony-based tree</td>
</tr>
<tr class="even">
<td>strain</td>
<td>ancestral strain</td>
</tr>
<tr class="odd">
<td>cit</td>
<td>citrate-using mutant status</td>
</tr>
<tr class="even">
<td>run</td>
<td>Sequence read archive sample ID</td>
</tr>
<tr class="odd">
<td>genome_size</td>
<td>size in Mbp (made up data for this lesson)</td>
</tr>
</tbody>
</table>
<p>The metadata file required for this lesson can be <a href="https://raw.githubusercontent.com/datacarpentry/R-genomics/gh-pages/data/Ecoli_metadata.csv">downloaded directly here</a> or <a href="./data/Ecoli_metadata.csv">viewed in Github</a>.</p>
<blockquote>
<p>Tip: If you can’t find the Ecoli_metadata.csv file, or have lost track of it, download the file directly using the R <code>download.file() function</code></p>
</blockquote>
<div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb1-1" data-line-number="1"><span class="kw">download.file</span>(<span class="st">"https://raw.githubusercontent.com/datacarpentry/R-genomics/gh-pages/data/Ecoli_metadata.csv"</span>, <span class="st">"data/Ecoli_metadata.csv"</span>)</a></code></pre></div>
<p>You are now ready to load the data. We are going to use the R function <code>read.csv()</code> to load the data file into memory (as a <code>data.frame</code>):</p>
<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb2-1" 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>This statement doesn’t produce any output because assignment doesn’t display anything. If we want to check that our data has been loaded, we can print the variable’s value: <code>metadata</code></p>
<p>Alternatively, wrapping an assignment in parentheses will perform the assignment and display it at the same time.</p>
<div class="sourceCode" id="cb3"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb3-1" 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>Wow… that was a lot of output. At least it means the data loaded properly. Let’s check the top (the first 6 lines) of this <code>data.frame</code> using the function <code>head()</code>:</p>
<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" data-line-number="1"><span class="kw">head</span>(metadata)</a></code></pre></div>
<pre><code>## sample generation clade strain cit run genome_size
## 1 REL606 0 <NA> REL606 unknown 4.62
## 2 REL1166A 2000 unknown REL606 unknown SRR098028 4.63
## 3 ZDB409 5000 unknown REL606 unknown SRR098281 4.60
## 4 ZDB429 10000 UC REL606 unknown SRR098282 4.59
## 5 ZDB446 15000 UC REL606 unknown SRR098283 4.66
## 6 ZDB458 20000 (C1,C2) REL606 unknown SRR098284 4.63</code></pre>
<p>We’ve just done two very useful things. 1. We’ve read our data in to R, so now we can work with it in R 2. We’ve created a data frame (with the read.csv command) the standard way R works with data.</p>
</div>
<div id="what-are-data-frames" class="section level1">
<h1>What are data frames?</h1>
<p><code>data.frame</code> is the <em>de facto</em> data structure for most tabular data and what we use for statistics and plotting.</p>
<p>A <code>data.frame</code> is a collection of vectors of identical lengths. Each vector represents a column, and each vector can be of a different data type (e.g., characters, integers, factors). The <code>str()</code> function is useful to inspect the data types of the columns.</p>
<p>A <code>data.frame</code> can be created by the functions <code>read.csv()</code> or <code>read.table()</code>, in other words, when importing spreadsheets from your hard drive (or the web).</p>
<p>By default, <code>data.frame</code> converts (= coerces) columns that contain characters (i.e., text) into the <code>factor</code> data type. Depending on what you want to do with the data, you may want to keep these columns as <code>character</code>. To do so, <code>read.csv()</code> and <code>read.table()</code> have an argument called <code>stringsAsFactors</code> which can be set to <code>FALSE</code>:</p>
<p>Let’s now check the __str__ucture of this <code>data.frame</code> in more details with the function <code>str()</code>:</p>
<div class="sourceCode" id="cb6"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb6-1" data-line-number="1"><span class="kw">str</span>(metadata)</a></code></pre></div>
</div>
<div id="inspecting-data.frame-objects" class="section level1">
<h1>Inspecting <code>data.frame</code> objects</h1>
<p>We already saw how the functions <code>head()</code> and <code>str()</code> can be useful to check the content and the structure of a <code>data.frame</code>. Here is a non-exhaustive list of functions to get a sense of the content/structure of the data.</p>
<ul>
<li>Size:
<ul>
<li><code>dim()</code> - returns a vector with the number of rows in the first element, and the number of columns as the second element (the __dim__ensions of the object)</li>
<li><code>nrow()</code> - returns the number of rows</li>
<li><code>ncol()</code> - returns the number of columns</li>
</ul></li>
<li>Content:
<ul>
<li><code>head()</code> - shows the first 6 rows</li>
<li><code>tail()</code> - shows the last 6 rows</li>
</ul></li>
<li>Names:
<ul>
<li><code>names()</code> - returns the column names (synonym of <code>colnames()</code> for <code>data.frame</code> objects)</li>
<li><code>rownames()</code> - returns the row names</li>
</ul></li>
<li>Summary:
<ul>
<li><code>str()</code> - structure of the object and information about the class, length and content of each column</li>
<li><code>summary()</code> - summary statistics for each column</li>
</ul></li>
</ul>
<p>Note: most of these functions are “generic”, they can be used on other types of objects besides <code>data.frame</code>.</p>
<div id="challenge" class="section level3">
<h3>Challenge</h3>
<p>Based on the given table of functions to asses data structure, can you answer the following questions?</p>
<ul>
<li>What is the class of the object <code>metadata</code>?</li>
<li>How many rows and how many columns are in this object?</li>
<li>How many citrate+ mutants have been recorded in this population?</li>
</ul>
<p>As you can see, many of the columns in our data frame are of a special class called <code>factor</code>. Before we learn more about the <code>data.frame</code> class, we are going to talk about factors. They are very useful but not necessarily intuitive, and therefore require some attention.</p>
</div>
<div id="factors" class="section level2">
<h2>Factors</h2>
<p>Factors are used to represent categorical data. Factors can be ordered or unordered and are an important class for statistical analysis and for plotting.</p>
<p>Factors are stored as integers, and have labels associated with these unique integers. While factors look (and often behave) like character vectors, they are actually integers under the hood, and you need to be careful when treating them like strings.</p>
<p>In the data frame we just imported, let’s do</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="kw">str</span>(metadata)</a></code></pre></div>
<p>We can see the names of the multiple columns. And, we see that some say things like <code>Factor w/ 30 levels</code></p>
<p>When we read in a file, any column that contains text is automatically assumed to be a factor. Once created, factors can only contain a pre-defined set values, known as <em>levels</em>. By default, R always sorts <em>levels</em> in alphabetical order.</p>
<p>For instance, we see that <code>cit</code> is a Factor w/ 3 levels, <code>minus</code>, <code>plus</code> and <code>unknown</code>.</p>
<!--
You can check this by using the function `levels()`, and check the
number of levels using `nlevels()`:
```r
levels(citrate)
nlevels(citrate)
```
Sometimes, the order of the factors does not matter, other times you might want
to specify the order because it is meaningful (e.g., "low", "medium", "high") or
it is required by particular type of analysis. Additionally, specifying the
order of the levels allows to compare levels:
```r
expression <- factor(c("low", "high", "medium", "high", "low", "medium", "high"))
levels(expression)
expression <- factor(expression, levels=c("low", "medium", "high"))
levels(expression)
min(expression) ## doesn't work
expression <- factor(expression, levels=c("low", "medium", "high"), ordered=TRUE)
levels(expression)
min(expression) ## works!
```
In R's memory, these factors are represented by numbers (1, 2, 3). They are
better than using simple integer labels because factors are self describing:
`"low"`, `"medium"`, and `"high"`" is more descriptive than `1`, `2`, `3`. Which
is low? You wouldn't be able to tell with just integer data. Factors have this
information built in. It is particularly helpful when there are many levels
(like the species in our example data set).
### Converting factors
If you need to convert a factor to a character vector, simply use
`as.character(x)`.
Converting a factor to a numeric vector is however a little trickier, and you
have to go via a character vector. Compare:
```r
f <- factor(c(1, 5, 10, 2))
as.numeric(f) ## wrong! and there is no warning...
as.numeric(as.character(f)) ## works...
as.numeric(levels(f))[f] ## The recommended way.
```
### Challenge
The function `table()` tabulates observations and can be used to create
bar plots quickly. For instance:
```r
## Question: How can you recreate this plot but by having "control"
## being listed last instead of first?
exprmt <- factor(c("treat1", "treat2", "treat1", "treat3", "treat1", "control",
"treat1", "treat2", "treat3"))
table(exprmt)
```
```
## exprmt
## control treat1 treat2 treat3
## 1 4 2 2
```
```r
barplot(table(exprmt))
```
<img src="img/r-lesson-wrong-order-1.png" width="672" />
```r
exprmt <- factor(exprmt, levels=c("treat1", "treat2", "treat3", "control"))
barplot(table(exprmt))
```
<img src="img/r-lesson-correct-order-1.png" width="672" />
--->
</div>
</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
an issue on GitHub</a>. <br/> On
Twitter: <a href="https://twitter.com/datacarpentry">@datacarpentry</a></p>
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