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<h1 class="title toc-ignore">Introduce Yourself</h1>
<h4 class="author"><em>Alison Hill</em></h4>
</div>
<blockquote>
<p>Read all the way through step 6, and note that there is a file that needs to be turned in to Sakai before Wednesday at noon!</p>
</blockquote>
<div id="overview" class="section level1">
<h1><span class="header-section-number">1</span> Overview</h1>
<p>This guide will lead you through the steps to install and use <a href="https://www.r-project.org">R, a free and open-source software environment for statistical computing and graphics.</a></p>
<p>What is R?</p>
<ul>
<li><strong>R</strong> is the name of the programming language itself, based off of S from Bell Labs, which users access through a command-line interpreter (<code>></code>)</li>
</ul>
<p>What is RStudio?</p>
<ul>
<li><strong>RStudio</strong> is a powerful and convenient user interface that allows you to access the R programming language along with a lot of other bells and whistles that enhance functionality (and sanity).</li>
</ul>
<p>Our end goal is to get you looking at a screen like this:</p>
<div class="figure">
<img src="images/00_final-screenshot.png" />
</div>
</div>
<div id="install-r" class="section level1">
<h1><span class="header-section-number">2</span> Install R</h1>
<p>Install R from <a href="https://cran.rstudio.com">CRAN, the Comprehensive R Archive Network</a>. Please choose a <strong>precompiled binary distribution</strong> for your operating system.</p>
<ul>
<li>If you need more help, check out one of the following videos (courtesy of Roger Peng at Johns Hopkins Biostatistics):
<ul>
<li><a href="https://www.youtube.com/watch?v=Icawuhf0Yqo&feature=youtu.be">Installing R on a mac</a></li>
<li><a href="https://www.youtube.com/watch?v=mfGFv-iB724&feature=youtu.be">Installing R on windows</a></li>
</ul></li>
<li>If you need even more help, read this <a href="https://beckmw.files.wordpress.com/2014/09/r_install_guide.pdf">step-by-step guide</a>, including screenshots.</li>
</ul>
<div id="check-in" class="section level2">
<h2><span class="header-section-number">2.1</span> Check in</h2>
<p>Launch R. You should see one console with a command line interpreter (<code>></code>). Close R.</p>
</div>
</div>
<div id="install-rstudio" class="section level1">
<h1><span class="header-section-number">3</span> Install RStudio</h1>
<p>Install the free, open-source edition of RStudio: <a href="http://www.rstudio.com/products/rstudio/download/" class="uri">http://www.rstudio.com/products/rstudio/download/</a></p>
<p>RStudio provides a powerful user interface for R, called an <em>integrated development environment</em>. RStudio includes:</p>
<ul>
<li>a console (the standard command line interface: <code>></code>),</li>
<li>a syntax-highlighting editor that supports direct code execution, and</li>
<li>tools for plotting, history, debugging and workspace management.</li>
</ul>
<div id="check-in-1" class="section level2">
<h2><span class="header-section-number">3.1</span> Check in</h2>
<p>Launch RStudio. You should get a window similar to the screenshot you see <a href="https://www.rstudio.com/wp-content/uploads/2014/04/rstudio-workbench.png">here</a>, but yours will be empty. Look at the bottom left pane: this is the same console window you saw when you opened R in step 1.15.</p>
<ul>
<li>Place your cursor where you see <code>></code> and type <code>x <- 2 + 2</code>, hit enter or return, then type <code>x</code>, and hit enter/return again.</li>
<li>If <code>[1] 4</code> prints to the screen, you have successfully installed R and RStudio, and you can move onto installing packages.</li>
</ul>
</div>
</div>
<div id="install-packages" class="section level1">
<h1><span class="header-section-number">4</span> Install packages</h1>
<p>The version of R that you just downloaded is considered base R, which provides you with good but basic statistical computing and graphics powers. For analytical and graphical super-powers, you’ll need to install add-on packages, which are user-written, to extend/expand your R capabilities. Packages can live in one of two places:</p>
<ul>
<li>They may be carefully curated by CRAN (which involves a thorough submission and review process), and thus are easy install using <code>install.packages("name_of_package", dependencies = TRUE)</code>.</li>
<li>Alternatively, they may be available via GitHub. To download these packages, you first need to install the <code>devtools</code> package.</li>
</ul>
<pre class="r"><code>install.packages("devtools")
library(devtools)
install_github("name_of_package")</code></pre>
<p>Place your cursor in the console again (where you last typed <code>x</code> and <code>[4]</code> printed on the screen). You can use the first method to install the following packages directly from CRAN, all of which we will use:</p>
<ul>
<li><a href="http://dplyr.tidyverse.org"><code>dplyr</code>,</a></li>
<li><a href="http://ggplot2.tidyverse.org"><code>ggplot2</code>,</a></li>
<li><a href="https://github.com/hadley/babynames"><code>babynames</code></a></li>
</ul>
<p>You can download all of these at once, too:</p>
<pre class="r"><code>install.packages(c("dplyr", "ggplot2", "babynames"),
dependencies = TRUE)</code></pre>
<p class="text-info">
<strong>Heads up:</strong> We should formally introduce the combine command, <a href="http://stat.ethz.ch/R-manual/R-patched/library/base/html/c.html"><code>c()</code></a>, used above. You will use this often- any time you want to combine things into a vector.
</p>
<pre class="r"><code>c("hello", "my", "name", "is", "alison")</code></pre>
<pre><code>[1] "hello" "my" "name" "is" "alison"</code></pre>
<pre class="r"><code>c(1:3, 20, 50)</code></pre>
<pre><code>[1] 1 2 3 20 50</code></pre>
<p>Mind your use of quotes carefully with packages.</p>
<ul>
<li>To <em>install</em> a package, you put the name of the package in quotes as in <code>install.packages("name_of_package")</code>.</li>
<li>To <em>use</em> an already installed package, you must load it first, as in <code>library(name_of_package)</code>, leaving the name of the package bare. You only need to do this once per RStudio session.</li>
<li>If you want <em>help</em>, no quotes are needed: <code>help(name_of_package)</code> or <code>?name_of_package</code>.</li>
<li>If you want the <em>citation</em> for a package (and you should give credit where credit is due), ask R as in <code>citation("name_of_package")</code>.</li>
</ul>
<pre class="r"><code>install.packages("dplyr", dependencies = TRUE)
library(dplyr)
help("dplyr")</code></pre>
<pre class="r"><code>citation("ggplot2")</code></pre>
<pre><code>
To cite ggplot2 in publications, please use:
H. Wickham. ggplot2: Elegant Graphics for Data Analysis.
Springer-Verlag New York, 2016.
A BibTeX entry for LaTeX users is
@Book{,
author = {Hadley Wickham},
title = {ggplot2: Elegant Graphics for Data Analysis},
publisher = {Springer-Verlag New York},
year = {2016},
isbn = {978-3-319-24277-4},
url = {http://ggplot2.org},
}</code></pre>
<p class="text-info">
<strong>Heads up:</strong> R is <strong>case-sensitive</strong>, so <code>?dplyr</code> works but <code>?Dplyr</code> will not. Likewise, a variable called <code>A</code> is different from <code>a</code>.
</p>
</div>
<div id="make-a-name-plot" class="section level1">
<h1><span class="header-section-number">5</span> Make a name plot</h1>
<p>Open a new R script in RStudio by going to <code>File --> New File --> R Script</code>. For this first foray into R, I’ll give you the code, so sit back and relax and feel free to copy and paste my code with some small tweaks.</p>
<p>First load the packages:</p>
<pre class="r"><code>library(babynames) # contains the actual data
library(dplyr) # for manipulating data
library(ggplot2) # for plotting data</code></pre>
<p>Next, we’ll follow <a href="https://cran.r-project.org/doc/contrib/de_Jonge+van_der_Loo-Introduction_to_data_cleaning_with_R.pdf">best practices for inspecting a freshly read dataset</a>. Also, see <a href="http://simplystatistics.org/2014/06/13/what-i-do-when-i-get-a-new-data-set-as-told-through-tweets/">“What I do when I get a new data set as told through tweets”</a> for more ideas about exploring a new dataset. Here are some critical commands to obtain a high-level overview (HLO) of your freshly read dataset in R. We’ll call it saying hello to your dataset:</p>
<pre class="r"><code>glimpse(babynames) # dplyr</code></pre>
<pre><code>Observations: 1,858,689
Variables: 5
$ year <dbl> 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 1880, 188...
$ sex <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F...
$ name <chr> "Mary", "Anna", "Emma", "Elizabeth", "Minnie", "Margaret"...
$ n <int> 7065, 2604, 2003, 1939, 1746, 1578, 1472, 1414, 1320, 128...
$ prop <dbl> 0.072384329, 0.026679234, 0.020521700, 0.019865989, 0.017...</code></pre>
<pre class="r"><code>head(babynames) # base R</code></pre>
<pre><code># A tibble: 6 x 5
year sex name n prop
<dbl> <chr> <chr> <int> <dbl>
1 1880. F Mary 7065 0.0724
2 1880. F Anna 2604 0.0267
3 1880. F Emma 2003 0.0205
4 1880. F Elizabeth 1939 0.0199
5 1880. F Minnie 1746 0.0179
6 1880. F Margaret 1578 0.0162</code></pre>
<pre class="r"><code>tail(babynames) # same</code></pre>
<pre><code># A tibble: 6 x 5
year sex name n prop
<dbl> <chr> <chr> <int> <dbl>
1 2015. M Zyah 5 0.00000247
2 2015. M Zykell 5 0.00000247
3 2015. M Zyking 5 0.00000247
4 2015. M Zykir 5 0.00000247
5 2015. M Zyrus 5 0.00000247
6 2015. M Zyus 5 0.00000247</code></pre>
<pre class="r"><code>names(babynames) # same</code></pre>
<pre><code>[1] "year" "sex" "name" "n" "prop"</code></pre>
<p>If you have done the above and produced sane-looking output, you are ready for the next bit. Use the code below to create a new data frame called <code>alison</code>.</p>
<pre class="r"><code>alison <- babynames %>%
filter(name == "Alison" | name == "Allison") %>%
filter(sex == "F") </code></pre>
<ul>
<li><p>The first bit makes a new dataset called <code>alison</code> that is a copy of the <code>babynames</code> dataset- the <code>%>%</code> tells you we are doing some other stuff to it later.</p></li>
<li><p>The second bit <code>filters</code> our <code>babynames</code> to only keep rows where the <code>name</code> is either Alison or Allison (read <code>|</code> as <em>“or”</em>.)</p></li>
<li><p>The third bit applies another <code>filter</code> to keep only those where sex is female.</p></li>
</ul>
<p>Let’s check out the data.</p>
<pre class="r"><code>alison</code></pre>
<pre><code># A tibble: 214 x 5
year sex name n prop
<dbl> <chr> <chr> <int> <dbl>
1 1905. F Alison 7 0.0000226
2 1907. F Alison 5 0.0000148
3 1908. F Allison 6 0.0000169
4 1910. F Alison 5 0.0000119
5 1910. F Allison 5 0.0000119
6 1911. F Allison 9 0.0000204
7 1912. F Allison 12 0.0000205
8 1912. F Alison 9 0.0000153
9 1913. F Alison 12 0.0000183
10 1913. F Allison 7 0.0000107
# ... with 204 more rows</code></pre>
<pre class="r"><code>glimpse(alison)</code></pre>
<pre><code>Observations: 214
Variables: 5
$ year <dbl> 1905, 1907, 1908, 1910, 1910, 1911, 1912, 1912, 1913, 191...
$ sex <chr> "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F", "F...
$ name <chr> "Alison", "Alison", "Allison", "Alison", "Allison", "Alli...
$ n <int> 7, 5, 6, 5, 5, 9, 12, 9, 12, 7, 22, 11, 16, 13, 24, 15, 2...
$ prop <dbl> 2.259012e-05, 1.481776e-05, 1.692367e-05, 1.191821e-05, 1...</code></pre>
<p>Again, if you have sane-looking output here, move along to plotting the data!</p>
<pre class="r"><code>plot <- ggplot(alison, aes(x = year,
y = prop,
group = name,
color = name)) +
geom_line() </code></pre>
<p>Now if you did this right, you will not see your plot! Because we saved the <code>ggplot</code> with a name (<code>plot</code>), R just saved the object for you. But check out the top right pane in RStudio again: under <code>Values</code> you should see <code>plot</code>, so it is there, you just have to ask for it. Here’s how:</p>
<pre class="r"><code>plot </code></pre>
<p><img src="00-install_name_plot_files/figure-html/unnamed-chunk-2-1.png" width="672" /></p>
</div>
<div id="make-a-new-name-plot" class="section level1">
<h1><span class="header-section-number">6</span> Make a new name plot</h1>
<p>Edit my code above to create a new dataset. Pick 2 names to compare how popular they each are (these could be different spellings of your own name, like I did, but you can choose any 2 names that are present in the dataset). Make the new plot, changing the name of the first argument <code>alison</code> in <code>ggplot()</code> to the name of your new dataset.</p>
<div id="save-and-share" class="section level2">
<h2><span class="header-section-number">6.1</span> Save and share</h2>
<p>Save your work so you can share your favorite plot with us. You will not like the looks of your plot if you mouse over to <code>Export</code> and save it. Instead, use <code>ggplot2</code>’s command for saving a plot with sensible defaults:</p>
<pre class="r"><code>help(ggsave)</code></pre>
<pre class="r"><code>ggsave("alison_hill.pdf", plot) # please make the filename unique!</code></pre>
<p>Upload this exported plot to Sakai before Wednesday at noon.</p>
</div>
</div>
<div id="other-cool-babynames-projects" class="section level1">
<h1><span class="header-section-number">7</span> Other cool <code>babynames</code> projects</h1>
<ul>
<li>Julia Silge <em>‘My Baby Boomer Name Might Have Been “Debbie”’</em>: <a href="https://juliasilge.com/blog/my-baby-boomer-name/" class="uri">https://juliasilge.com/blog/my-baby-boomer-name/</a>
<ul>
<li>Use Julia’s Shiny app: <a href="https://juliasilge.shinyapps.io/PredictNamesApp/" class="uri">https://juliasilge.shinyapps.io/PredictNamesApp/</a> <img src="https://juliasilge.com/figs/2016-02-29-My-Baby-Boomer-Name/unnamed-chunk-8-1.png" /></li>
</ul></li>
<li>Hilary Parker: Hilary: The Most Poisoned Baby Name in US History: <a href="https://hilaryparker.com/2013/01/30/hilary-the-most-poisoned-baby-name-in-us-history/" class="uri">https://hilaryparker.com/2013/01/30/hilary-the-most-poisoned-baby-name-in-us-history/</a></li>
</ul>
<div class="figure">
<img src="https://hilaryparker.files.wordpress.com/2013/01/more_names_trimmed2.png" />
</div>
</div>
<div id="resources" class="section level1">
<h1><span class="header-section-number">8</span> Resources</h1>
<ul>
<li><p><a href="https://alison.rbind.io/html/jamboree_heart_ggplot.html" class="uri">https://alison.rbind.io/html/jamboree_heart_ggplot.html</a></p></li>
<li><p><a href="http://moderndive.com/2-getting-started.html" class="uri">http://moderndive.com/2-getting-started.html</a></p></li>
<li><p><a href="http://r4ds.had.co.nz" class="uri">http://r4ds.had.co.nz</a></p></li>
<li><p><a href="https://www.rstudio.com/resources/cheatsheets/" class="uri">https://www.rstudio.com/resources/cheatsheets/</a></p></li>
<li><p><a href="https://rweekly.org" class="uri">https://rweekly.org</a></p></li>
</ul>
</div>
<p>
<a rel="license" href="http://creativecommons.org/licenses/by-nc/4.0/">
<img alt="Creative Commons License" style="border-width:0" src="by-nc.png" height="400" width="65"/></a>
</p>
</div>
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