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<!DOCTYPE html>
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<title>Introduction to R, from wrangling to visualization</title>
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<body>
<a class='sr-only sr-only-focusable visually-hidden-focusable' href='#learnr-tutorial-content'>Skip to Tutorial Content</a>
<div class="pageContent band">
<main class="bandContent page">
<article class="topics" id="learnr-tutorial-content">
<!-- Warning: there must always be a new line before a code chunk and end of file -->
<!-- If not the whole learnr puts odd columns across your html file -->
<!-- Also, we can have either `solutions` or `hints`, not both -->
<p><img src="images/INRES_Logo.png" style="width:2in" /> <img
src="images/Uni_Bonn_Picture.png" style="width:2in" /> <img
src="images/ZEF_Logo.png" style="width:2in" /></p>
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<div id="section-introduction" class="section level2">
<h2>Introduction</h2>
<p>Welcome to this interactive tutorial on the R programming language.
This tutorial runs in the <code>learnr</code> <span
class="citation">(Aden-Buie et al. 2023)</span> library and provides a
<code>shiny</code> interface <span class="citation">(Chang et al.
2023)</span> that will allow you the chance to interact with the
examples in the R programming language. You may follow along by simply
working in this browser. You may find the content and learning a bit
more useful if you work on your own machine as well. This will require
loading the necessary software. For this tutorial we will use functions
from <code>tidyverse</code> <span class="citation">(Wickham 2023)</span>
libraries including <code>ggplot2</code> <span class="citation">(Wickham
et al. 2024)</span>, <code>dplyr</code> <span class="citation">(Wickham
et al. 2023)</span> and <code>magrittr</code> <span
class="citation">(Bache and Wickham 2022)</span> among others in the <a
href="https://www.r-project.org/">R programming language</a> <span
class="citation">(R Core Team 2023)</span>.</p>
<p>If you are also planning to run this on your own machine then you
should start by installing and running <a
href="https://www.r-project.org/">R</a> and <a
href="https://rstudio.com/">RStudio</a>. This video walks you through
that process.</p>
<!--  -->
<iframe width="560" height="315" src="https://www.youtube.com/embed/WT3tKlzCZgo" frameborder="0" allowfullscreen>
</iframe>
<div id="section-using-this-tutorial" class="section level3">
<h3>Using this tutorial</h3>
<p>Here is an example of how this tutorial works. The code below
calculates the answer to one plus one.</p>
<div class="tutorial-exercise" data-label="example-addition"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Change this code so it computes two plus two
1 + 1</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="example-addition-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>2 + 2</code></pre>
</div>
<p>For each example there will also generally be a small exercise after
the <code>#</code> mark. We will ask you to work in this interface and
make changes to the existing code. In this interface you will see
options for looking at the solution and sometimes some hints. Feel free
to run these here in the web interface and also to run them on your own
machine.</p>
<p>You will also always have the option to get a copy of the code to run
on your own machine. To do that you will need all the libraries. If
these do not load you will also need to use the
<code>install.packages()</code> function.
i.e. <code>install.packages("datasauRus")</code>.</p>
<div class="tutorial-exercise" data-label="example-libraries"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>library(datasauRus)
library(gganimate)
library(ggplot2)
library(purrr)
library(rmarkdown)
library(tidyverse)
# part of the tidyverse:
library(dplyr)
library(magrittr)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
</div>
<div id="section-bonus-rstudio-settings" class="section level3">
<h3>Bonus RStudio settings</h3>
<p>You will see from the examples throughout the course that I use dark
mode in RStudio. You can find these settings and choose the one you like
best under <em>Global Options > Appearance</em>. For my display I use
the ‘Pastel On Dark’ option.</p>
<p><img src="images/dark_mode.png" style="width:3in" /></p>
<p>Using color in code is nice and can also help you troubleshoot code
that uses lots of nested parentheses <code>()</code>, square brackets
<code>[]</code> or curly braces <code>{}</code>. You can adjust these
settings under <em>Global Options > Code > Display</em>.</p>
<p><img src="images/rainbow_parentheses.png" style="width:3in" /></p>
</div>
</div>
<div id="section-data-wrangling-part-1" class="section level2">
<h2>Data Wrangling Part 1</h2>
<p><img src="images/wrangler.png" style="width:2in" /> <img
src="images/tidyR.png" style="width:1in" /> <img src="images/dplyr.png"
style="width:1.5in" /></p>
<p>The video below gives a brief overview of the content covered in this
section of the tutorial. All the <a
href="http://htmlpreview.github.io/?https://github.com/CWWhitney/teaching_R/blob/master/Data_Wrangling_Rpres/Data_Wrangling.html#/">Data
Wrangling slides</a> are also available as a web page.</p>
<!--  -->
<iframe width="560" height="315" src="https://www.youtube.com/embed/j6XRof3q9aM" frameborder="0" allowfullscreen>
</iframe>
<div id="section-notes-on-r" class="section level3">
<h3>Notes on R</h3>
<ul>
<li>Tidy code style using tidyR</li>
<li>Clean and intuitive functions using dplyr</li>
<li>Concise code using magrittr ‘Ceci n’est pas une pipe’</li>
</ul>
<p><img src="images/magrittr.png" style="width:1in" /></p>
</div>
<div id="section-notes-on-r-about-process" class="section level3">
<h3>Notes on R: About process</h3>
<ul>
<li><p>“[…] writing R code is a hedonistically artistic, left-brained,
paint-in-your-hair sort of experience […]</p></li>
<li><p>learn how to code the same way we learned how to catch
salamanders as children – trial and error, flipping over rocks till we
get a reward […]</p></li>
<li><p>once the ecstasy of creation has swept over us, we awake late the
next morning to find our canvas covered with 2100 lines of R code
[…]</p></li>
<li><p>Heads throbbing with a statistical absinthe hangover, we trudge
through it slowly over days, trying to figure out what we did.”</p>
<p><em>Andrew MacDonald</em></p></li>
</ul>
</div>
<div id="section-notes-on-r-focus" class="section level3">
<h3>Notes on R: Focus</h3>
<p><img src="images/Focus.png" style="width:5in" /></p>
</div>
<div id="section-notes-on-r-keeping-track-of-work"
class="section level3">
<h3>Notes on R: Keeping track of work</h3>
<p><img src="images/tidyR.png" style="width:1in" /></p>
<p>Keep it tidy</p>
<p>When writing .R files use the <code>#</code> symbol to annotate and
not run lines. This is a great way to make notes for others and for
future you.</p>
<p>We will talk later about using other file types like
<code>Rmarkdown</code> for organizing R script and other associated
languages. There it will be possible to add a lot more information and
text, citations etc. An .R file is intended for code but we can still
keep it organized in sections by ending headers with <code>----</code>
or <code>####</code> annotation.</p>
<p><code># Section 1 ----</code></p>
<p><code># Section 2 ####</code></p>
<p><code># Section 3 ####</code></p>
<p>Look for the <code>Table of contents</code> in the upper right
console of the RStudio scripting pane (next to the <code>Run</code>
button).</p>
<p><img src="images/toc_rstudio.png" style="width:2in" /></p>
<p>Read more tips in the <a href="http://style.tidyverse.org/">Tidyverse
Style guide</a>.</p>
</div>
</div>
<div id="section-data-wrangling-part-2" class="section level2">
<h2>Data Wrangling Part 2</h2>
<p><img src="images/wrangler.png" style="width:1in" /> <img
src="images/tidyR.png" style="width:1in" /> <img src="images/dplyr.png"
style="width:1.5in" /></p>
<p>The video below offers a brief overview of the content covered in
this section of the tutorial. Feel free to watch the video and follow
along or simply work through the tutorial.</p>
<!--  -->
<iframe width="560" height="315" src="https://www.youtube.com/embed/8vqQljFcMsw" frameborder="0" allowfullscreen>
</iframe>
<div id="section-notes-on-tidy-r" class="section level3">
<h3>Notes on tidy R</h3>
<p><img src="images/tidyR.png" style="width:1in" /></p>
<p>Keep it tidy</p>
<p>If you are following this tutorial by running the code on your local
machine (recommended) then it may make sense to check your R version by
running the following code in your R console:</p>
<div class="tutorial-exercise" data-label="example-version"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>version</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<p>At the time of writing this I am using R version 4.0.4
<code>Lost Library Book</code> <span class="citation">(R Core Team
2023)</span>. If you do not have this version or something newer it may
make sense to update so that you can follow along without pesky version
issues.</p>
<p>The easiest way to get libraries for today is to install the whole
tidyverse <span class="citation">(Wickham 2023)</span> by typing
<code>install.packages("tidyverse")</code> in the R console and then
running <code>library(tidyverse)</code>:</p>
<pre><code>install.packages("tidyverse")
library(tidyverse)</code></pre>
<p>If you save your work to an .R file (recommended) be sure to annotate
any code that you do not intend to run each time with the <code>#</code>
symbol. You should only need to install tidyverse once and should be
sure to either change that line of code to
<code>#install.packages("tidyverse")</code> or remove it from your
script.</p>
<p>Read more style tips in the <a
href="http://style.tidyverse.org/">tidyverse style guide</a> <span
class="citation">(Wickham 2023)</span>.</p>
</div>
<div id="section-notes-on-tidy-r-browsevignettes"
class="section level3">
<h3>Notes on tidy R browseVignettes</h3>
<p><img src="images/tidyR.png" style="width:1in" /></p>
<p>Keep it tidy</p>
<p>Get a lot of examples and details about the tidyverse by running the
following code in the R console:
<code>browseVignettes(package = "tidyverse")</code>. Nearly every R
library has a collection of vignettes that walk through examples and
show, often in explicit detail, the authors intended use of the
library.</p>
</div>
<div id="section-the-tidy-tools-manifesto" class="section level3">
<h3>The tidy tools manifesto</h3>
<p>In this tutorial we will be following the basic ideas behind the
tidyverse.</p>
<p><img src="images/tidy_paper.jpg" style="width:5in" /></p>
<p>Read the <a
href="https://cran.r-project.org/web/packages/tidyverse/vignettes/manifesto.html">full
tidyverse manifesto here</a>.</p>
</div>
<div id="section-notes-on-r-tidyr-process" class="section level3">
<h3>Notes on R: tidyR process</h3>
<p><img src="images/tidyR.png" style="width:1in" /></p>
<p>Keep it tidy</p>
<p><img src="images/tidyR_process.png" style="width:4in" /></p>
<ul>
<li>Good coding style is like correct punctuation:</li>
<li>withoutitthingsarehardtoread</li>
<li>When your data is tidy, each column is a variable, and each row is
an observation</li>
<li>Consistent structure lets you focus your struggle on questions about
the data, not fighting to get the data into the right form for different
functions</li>
</ul>
<p><img src="images/wrangler.png" style="width:2in" /></p>
<p>Read more style tipes in the <a
href="http://style.tidyverse.org/">tidyverse style guide</a> <span
class="citation">(Wickham 2023)</span>.</p>
</div>
<div id="section-notes-on-r-tidy-data" class="section level3">
<h3>Notes on R: Tidy Data</h3>
<p>Three things make a dataset tidy:</p>
<ul>
<li>Each variable with its own column.</li>
<li>Each observation with its own row.</li>
<li>Each value with its own cell.</li>
</ul>
<p><img src="images/tidydata.png" style="width:5in" /></p>
<p>Read more about this from <a
href="www.jstatsoft.org/v59/i10/paper">Wickham’s paper</a> in the
Journal of Statistical Software.</p>
</div>
<div id="section-wrangling-transform" class="section level3">
<h3>Wrangling: transform</h3>
<ul>
<li>Once you have <strong>tidy</strong> data, a common first step is to
<strong>transform</strong> it</li>
<li>narrowing in on observations of interest</li>
<li>creating new variables that are functions of existing variables</li>
<li>calculating a set of summary statistics</li>
</ul>
<p><img src="images/Wrangling_Data.png" style="width:3in" /></p>
<p>www.codeastar.com/data-wrangling/</p>
</div>
<div id="section-wrangling-dplyr-arguments" class="section level3">
<h3>Wrangling: dplyr arguments</h3>
<p>Format of <strong>dplyr</strong></p>
<p><img src="images/dplyr.png" style="width:1in" /></p>
<p>Arguments start with a data frame</p>
<ul>
<li><strong>select</strong>: return a subset of the columns</li>
<li><strong>filter</strong>: extract a subset of rows</li>
<li><strong>rename</strong>: rename variables</li>
<li><strong>mutate</strong>: add new variables and columns or
transform</li>
<li><strong>group_by</strong>: split data into groups</li>
<li><strong>summarize</strong>: generate tables of summary
statistics</li>
</ul>
<p><a href="https://dplyr.tidyverse.org/"
class="uri">https://dplyr.tidyverse.org/</a></p>
</div>
<div id="section-getting-your-data-in-r" class="section level3">
<h3>Getting your data in R</h3>
<p>Load data</p>
<p><img src="images/R_logo.png" style="width:1in" /></p>
<p>The data we will use for this course is <a
href="https://raw.githubusercontent.com/CWWhitney/teaching_R/master/participants_data.csv">on
Github</a> and you can save it as a .csv to your local folder.</p>
<ul>
<li>Load the data using the <code>read.csv</code> function</li>
</ul>
<pre><code># Use this on your machine
participants_data <- read.csv("participants_data.csv")</code></pre>
<p>Learn more about what this function does by typing
<code>?read.csv</code> in the R console.</p>
<p>You can also get the data from this Github repository by using the
<code>read_csv</code> function from the <code>readr</code> library <span
class="citation">(Wickham, Hester, and Bryan 2023)</span> and
<code>url</code> function from base R. In this case you will want to use
the ‘save as’ option for the webpage so that you can have it stored
locally as a <code>comma separated values</code> (.csv) file on your
machine.</p>
<div class="tutorial-exercise" data-label="wrangle2-git-data"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>library(readr)
urlfile = "https://raw.githubusercontent.com/CWWhitney/teaching_R/master/participants_data.csv"
participants_data <- read_csv(url(urlfile))</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<ul>
<li>Keep your data in the same folder structure as .RProj</li>
<li>at or below the level of .RProj</li>
</ul>
</div>
<div id="section-looking-at-the-data" class="section level3">
<h3>Looking at the data</h3>
<ul>
<li>View the full data in the console (see the <code>View</code>
function to see it in the Rstudio ‘Environment’)</li>
</ul>
<div class="tutorial-exercise"
data-label="wrangle2-names_participants-data" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>participants_data</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<ul>
<li>Look at the top rows of the data with the <code>head</code>
function. The default of the <code>head</code> function is to show 6
rows. This can be changed with the <code>n</code> argument.</li>
</ul>
<div class="tutorial-exercise"
data-label="wrangle2-head-participants-data" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># Change the number of rows displayed to 7
head(participants_data,
n = 4)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-head-participants-data-hint-1" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># use the ?head option to learn the details of the function
?head</code></pre>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-head-participants-data-hint-2" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># look at the 'Arguments' section for the 'n' argument
?head</code></pre>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-head-participants-data-hint-3" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># The 'n' argument should be changed from 'n = 4' to 'n = 7'
head(participants_data,
n = 7)</code></pre>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-head-participants-data-solution"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>head(participants_data,
n = 7)</code></pre>
</div>
<ul>
<li>Check the names of the variables in the data with the
<code>names</code> function</li>
</ul>
<div class="tutorial-exercise"
data-label="wrangle2-names-participants-data" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>names(participants_data)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<ul>
<li>Look at the structure of the data with the <code>str</code>
function</li>
</ul>
<div class="tutorial-exercise"
data-label="wrangle2-str-participants-data" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>str(participants_data)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<ul>
<li>Call a particular variable in your data with <code>$</code></li>
</ul>
<div class="tutorial-exercise" data-label="wrangle2-call-variable"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Change the variable to gender
participants_data$age</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-call-variable-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>participants_data$gender</code></pre>
</div>
<p>Follow these steps to see the result of the rest of the
transformations we perform with <code>tidyverse</code>.</p>
</div>
<div id="section-wrangling-dplyr-library" class="section level3">
<h3>Wrangling: dplyr library</h3>
<p>Using <strong>dplyr</strong> <img src="images/dplyr.png"
style="width:1in" /></p>
<p>Load the dplyr library by running <code>library(dplyr)</code> in the
R console. do the same for other libraries we need today
<code>library(tidyr)</code> and <code>library(magrittr)</code> <span
class="citation">Wickham et al. (2023)</span>.</p>
<p>Inspiration for many of the following materials comes from Roger
Peng’s <a href="genomicsclass.github.io/book/pages/dplyr_tutorial">dplyr
tutorial</a>.</p>
<p><img src="images/github.png" style="width:1in" /></p>
<p>Read more about the <a href="https://dplyr.tidyverse.org/">dplyr
library</a> <span class="citation">(Wickham et al. 2023)</span>.</p>
</div>
<div id="section-wrangling-dplyrselect-aca_work_set"
class="section level3">
<h3>Wrangling: dplyr::select aca_work_set</h3>
<p>Subsetting</p>
<p><img src="images/dplyr.png" style="width:1in" /></p>
<p><strong>Select</strong></p>
<p>Create a subset of the data with the <code>select</code>
function:</p>
<div class="tutorial-exercise" data-label="wrangle2-select-aca-parents"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Change the selection to batch and age
select(participants_data,
academic_parents,
working_hours_per_day)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-select-aca-parents-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>select(participants_data,
batch,
age)</code></pre>
</div>
<p><a href="https://dplyr.tidyverse.org/"
class="uri">https://dplyr.tidyverse.org/</a></p>
</div>
<div id="section-wrangling-dplyrselect-non_aca_work_filter"
class="section level3">
<h3>Wrangling: dplyr::select non_aca_work_filter</h3>
<p>Subsetting <img src="images/dplyr.png" style="width:1in" /></p>
<p><strong>Select</strong></p>
<p>Try creating a subset of the data with the <code>select</code>
function:</p>
<div class="tutorial-exercise"
data-label="wrangle2-select-non-aca-parents" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code># Change the selection
# without batch and age
select(participants_data,
-academic_parents,
-working_hours_per_day)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-select-non-aca-parents-solution"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code>select(participants_data,
-batch,
-age)</code></pre>
</div>
<p><a href="https://dplyr.tidyverse.org/"
class="uri">https://dplyr.tidyverse.org/</a></p>
</div>
<div id="section-wrangling-dplyrfilter-work_filter"
class="section level3">
<h3>Wrangling: dplyr::filter work_filter</h3>
<p>Subsetting <img src="images/dplyr.png" style="width:1in" /></p>
<p><strong>Filter</strong></p>
<p>Try creating a subset of the data with the <code>filter</code>
function:</p>
<div class="tutorial-exercise" data-label="wrangle2-filter-work"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Change the selection to
# those who work more than 5 hours a day
filter(participants_data,
working_hours_per_day >10)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-filter-work-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>filter(participants_data,
working_hours_per_day >5)</code></pre>
</div>
<p><a href="https://dplyr.tidyverse.org/"
class="uri">https://dplyr.tidyverse.org/</a></p>
</div>
<div id="section-wrangling-dplyrfilter-work_name_filter"
class="section level3">
<h3>Wrangling: dplyr::filter work_name_filter</h3>
<p>Subsetting <img src="images/dplyr.png" style="width:1in" /></p>
<p><strong>Filter</strong></p>
<p>Create a subset of the data with multiple options in the
<code>filter</code> function:</p>
<div class="tutorial-exercise" data-label="wrangle2-filter-work-name"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Change the filter to those who
# work more than 5 hours a day and
# names are longer than three letters
filter(participants_data,
working_hours_per_day >10 &
letters_in_first_name >6)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-filter-work-name-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>filter(participants_data,
working_hours_per_day >5 &
letters_in_first_name >3)</code></pre>
</div>
<p><a href="https://dplyr.tidyverse.org/"
class="uri">https://dplyr.tidyverse.org/</a></p>
</div>
<div id="section-wrangling-dplyrrename-name_length"
class="section level3">
<h3>Wrangling: dplyr::rename name_length</h3>
<p><strong>Rename</strong> <img src="images/dplyr.png"
style="width:1in" /></p>
<p>Change the names of the variables in the data with the
<code>rename</code> function:</p>
<div class="tutorial-exercise" data-label="wrangle2-rename-letters"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Rename the variable km_home_to_office as commute
rename(participants_data,
name_length = letters_in_first_name)</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-rename-letters-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>rename(participants_data,
commute = km_home_to_office)</code></pre>
</div>
<p><a href="https://dplyr.tidyverse.org/"
class="uri">https://dplyr.tidyverse.org/</a></p>
</div>
<div id="section-wrangling-dplyrmutate" class="section level3">
<h3>Wrangling: dplyr::mutate</h3>
<p><strong>Mutate</strong> <img src="images/dplyr.png"
style="width:1in" /></p>
<div class="tutorial-exercise" data-label="wrangle2-rename-work"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Mutate a new column named age_mean that is a function of the age multiplied by the mean of all ages in the group
mutate(participants_data,
labor_mean = working_hours_per_day*
mean(working_hours_per_day))</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-rename-work-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>mutate(participants_data,
age_mean = age*
mean(age))</code></pre>
</div>
<p><a href="https://dplyr.tidyverse.org/"
class="uri">https://dplyr.tidyverse.org/</a></p>
</div>
<div id="section-wrangling-dplyrmutate-1" class="section level3">
<h3>Wrangling: dplyr::mutate</h3>
<p><strong>Mutate</strong> <img src="images/dplyr.png"
style="width:1in" /></p>
<p>Create a commute category with the <code>mutate</code> function:</p>
<div class="tutorial-exercise" data-label="wrangle2-mutate-commute"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Mutate new column named response_speed
# populated by 'slow' if it took you
# more than a day to answer my email and
# 'fast' for others
mutate(participants_data,
commute =
ifelse(km_home_to_office > 10,
"commuter", "local"))</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-mutate-commute-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>mutate(participants_data,
response_speed =
ifelse(days_to_email_response > 1,
"slow", "fast"))</code></pre>
</div>
<p><a href="https://dplyr.tidyverse.org/"
class="uri">https://dplyr.tidyverse.org/</a></p>
</div>
<div id="section-wrangling-dplyrsummarize" class="section level3">
<h3>Wrangling: dplyr::summarize</h3>
<p><strong>Summarize</strong></p>
<p><img src="images/dplyr.png" style="width:1in" /></p>
<p>Get a summary of selected variables with <code>summarize</code></p>
<div class="tutorial-exercise" data-label="wrangle2-summar-commute"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Create a summary of the participants_mutate data
# with the mean number of siblings
# and median years of study
summarize(participants_data,
mean(years_of_study),
median(letters_in_first_name))</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-summar-commute-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>summarize(participants_data,
mean(number_of_siblings),
median(years_of_study))</code></pre>
</div>
</div>
<div id="section-wrangling-magrittr-use" class="section level3">
<h3>Wrangling: magrittr use</h3>
<p><strong>Pipeline %>%</strong></p>
<ul>
<li>Do all the previous with a <code>magrittr</code> pipeline %>%.
Use the <code>group_by</code> function to get these results for
comparison between groups.</li>
</ul>
<div class="tutorial-exercise" data-label="wrangle2-pipe-long"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Use the magrittr pipe to summarize
# the mean days to email response,
# median letters in first name,
# and maximum years of study by gender
participants_data %>%
group_by(research_continent) %>%
summarize(mean(days_to_email_response),
median(letters_in_first_name),
max(years_of_study))</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-pipe-long-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>participants_data %>%
group_by(gender) %>%
summarize(mean(days_to_email_response),
median(letters_in_first_name),
max(years_of_study))</code></pre>
</div>
<p>Now use the <code>mutate</code> function to subset the data and use
the <code>group_by</code> function to get these results for comparisons
between groups.</p>
<div class="tutorial-exercise" data-label="wrangle2-pipe-long2"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Use the magrittr pipe to create a new column
# called commute, where those who travel
# more than 10km to get to the office
# are called "commuter" and others are "local".
# Summarize the mean days to email response,
# median letters in first name,
# and maximum years of study.
participants_data %>%
mutate(response_speed = ifelse(
days_to_email_response > 1,
"slow", "fast")) %>%
group_by(response_speed) %>%
summarize(mean(number_of_siblings),
median(years_of_study),
max(letters_in_first_name))</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-pipe-long2-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code>participants_data %>%
mutate(commute = ifelse(
km_home_to_office > 10,
"commuter", "local")) %>%
group_by(commute) %>%
summarize(mean(days_to_email_response),
median(letters_in_first_name),
max(years_of_study))</code></pre>
</div>
</div>
<div id="section-purrr-apply-a-function-to-each-element-of-a-vector"
class="section level3">
<h3>purrr: Apply a function to each element of a vector</h3>
<p><img src="images/purrr.jpg" style="width:4in" /></p>
<p>We will use the <code>purrr</code> library to run a regression <span
class="citation">(Wickham and Henry 2023)</span>. Run the code
<code>library(purrr)</code> in your local R console to load the
library.</p>
<p><img src="images/purrr.jpg" style="width:1in" /> Now we will use the
<code>purrr</code> library for a simple linear regression <span
class="citation">(Wickham and Henry 2023)</span>. Note that when using
base R functions with the <code>magrittr</code> pipeline we use ‘.’ to
refer to the data. The functions <code>split</code> and <code>lm</code>
are from base R and stats <span class="citation">(R Core Team
2023)</span>.</p>
<p>Use purrr to solve: split a data frame into pieces, fit a model to
each piece, compute the summary, then extract the R^2.</p>
<div class="tutorial-exercise" data-label="wrangle2-purr-regression"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<pre class="text"><code># Split the data frame by batch,
# fit a linear model formula
# (days to email response as dependent
# and working hours as independent)
# to each batch, compute the summary,
# then extract the R^2.
participants_data %>%
split(.$gender) %>%
map(~
lm(number_of_publications ~
number_of_siblings,
data = .)) %>%
map(summary) %>%
map_dbl("r.squared")</code></pre>
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-purr-regression-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code> participants_data %>%
split(.$batch) %>% # from base R
map(~
lm(days_to_email_response ~
working_hours_per_day,
data = .)) %>%
map(summary) %>%
map_dbl("r.squared")</code></pre>
</div>
<p>Learn more about purrr from in <a
href="https://purrr.tidyverse.org/">the tidyverse</a> and from <a
href="http://varianceexplained.org/r/teach-tidyverse/">varianceexplained</a>.</p>
<p><a
href="https://github.com/rstudio/cheatsheets/blob/master/purrr.pdf">Check
out the purr Cheatsheet</a></p>
<p><img src="images/tidyR.png" style="width:1in" /> <img
src="images/dplyr.png" style="width:1in" /> <img
src="images/magrittr.png" style="width:1in" /> ### Test your new
skills</p>
<p><strong>Your turn to perform</strong></p>
<p>Up until this point the code has been provided for you to work on.
Now it is time for you to apply your new found skills. Please work
through the wrangling tasks we just went though. Use the
<code>diamonds</code> data and make the steps in long format
(i.e. assigning each step to an object) and short format with (i.e. with
the magrittr pipeline):</p>
<ul>
<li>select: carat and price</li>
<li>filter: only where carat is > 0.5</li>
<li>rename: rename price as cost</li>
<li>mutate: create a variable with ‘expensive’ if greater than mean of
cost and ‘cheap’ otherwise</li>
<li>group_by: split into cheap and expensive</li>
<li>summarize: give some summary statistics of your choice</li>
</ul>
<p>The diamonds data is built in with the <code>ggplot2</code> library.
It is already available in your R environment. Look at the help file
with <code>?diamonds</code> to learn more about it.</p>
<div class="tutorial-exercise" data-label="wrangle2-final"
data-completion="1" data-diagnostics="1" data-startover="1"
data-lines="0" data-pipe="|>">
<script type="application/json" data-ui-opts="1">{"engine":"r","has_checker":false,"caption":"<span data-i18n=\"text.enginecap\" data-i18n-opts=\"{"engine":"R"}\">R Code<\/span>"}</script>
</div>
<div class="tutorial-exercise-support"
data-label="wrangle2-final-solution" data-completion="1"
data-diagnostics="1" data-startover="1" data-lines="0"
data-pipe="|>">
<pre class="text"><code> diamonds %>%
# - select: carat and price
select(carat, price) %>%
# - filter: only where carat is > 0.5
filter(carat > 0.5) %>%
# - rename: rename price as cost
rename(cost = price) %>%
# - mutate: create a variable 'cheap_expensive' with 'expensive' if greater than mean of cost and 'cheap' otherwise
mutate(cheap_expensive = ifelse(
cost > mean(cost),
"expensive", "cheap")) %>%
# - group_by: split into cheap and expensive
group_by(cheap_expensive) %>%
# - summarize: give some summary statistics of your choice
summarize(mean(cost), mean(carat))</code></pre>
</div>
</div>
</div>
<div id="section-data-visualization" class="section level2">
<h2>Data Visualization</h2>
<p><img src="images/Circos.png" style="width:1.5in" /> <img
src="images/ggplot2.png" style="width:1.1in" /></p>
<p>The video below offers an overview of the content in covered in this
section of the tutorial. Keep in mind that I did not yet have your data
when I recorded this so the results of my scripts may be slightly
different than yours. Feel free to watch the video and follow along or
simply work through the tutorial. All the <a
href="http://htmlpreview.github.io/?https://github.com/CWWhitney/teaching_R/blob/master/Data_Visualization_Rpres/Data_Visualization.html#/">Data
Visualization slides</a> are also available as a web page.</p>
<!--  -->
<iframe width="560" height="315" src="https://www.youtube.com/embed/y4BPnwyxU-E" frameborder="0" allowfullscreen>
</iframe>
<div id="section-getting-stuck" class="section level3">
<h3>Getting stuck</h3>
<p>If you ever get stuck</p>
<ul>
<li><strong>Open RStudio</strong></li>
</ul>
<p><img src="images/rstudio-hex.png" style="width:1in" /></p>
<ul>
<li>Type <code>?</code> in R console with function, package or data
name</li>
<li>Add <code>R</code> to an internet search query with a copy of any
error message you get from R</li>
<li>In RStudio on your machine find <strong>Help > Cheatsheets >
Data Visualization with ggplot2</strong></li>
</ul>
<p>For getting help</p>
<ul>
<li>Many talented programmers</li>
<li>Some scan the web and answer issues</li>
</ul>
<p><img src="images/stack-overflow.png" style="width:4in" /></p>
<p><a href="https://stackoverflow.com"
class="uri">https://stackoverflow.com</a></p>
<p><img src="images/hadley_wickham.png" style="width:1in" /></p>
<p>Hadley Wickham</p>
<p><img src="images/Yihui.png" style="width:1in" /></p>
<p>Yihui Xie <a href="https://yihui.name/en/2017/08/so-gh-email"
class="uri">https://yihui.name/en/2017/08/so-gh-email</a></p>
<p><a href="https://rmarkdown.rstudio.com/"
class="uri">https://rmarkdown.rstudio.com/</a></p>
</div>
<div id="section-getting-your-data-in-r-1" class="section level3">
<h3>Getting your data in R</h3>
<p><img src="images/R_logo.png" style="width:1in" /></p>
<ul>
<li>Load the data with the <code>read.csv</code> function</li>
</ul>
<pre><code>participants_data <- read.csv("participants_data.csv")</code></pre>
<ul>
<li>Keep your data in the same folder structure as the .RProj file</li>
<li>at or below the level of the .RProj file</li>
</ul>
</div>
<div id="section-creating-a-barplot-in-base-r" class="section level3">
<h3>Creating a barplot in base R</h3>
<p><strong>R has several systems for making graphs</strong></p>
<ul>
<li><strong>Base R</strong></li>
<li>Create a barplot with the <code>table()</code> and
<code>barplot()</code> functions</li>
</ul>
<div class="tutorial-exercise" data-label="vis-base-barplot"
data-completion="1" data-diagnostics="1" data-startover="1"