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<h1 class="title toc-ignore">Introduction to dplyr</h1>
</div>
<div id="TOC">
<ul>
<li><a href="#intro">Intro</a><ul>
<li><a href="#load-dplyr-and-gapminder">Load <code>dplyr</code> and <code>gapminder</code></a></li>
<li><a href="#say-hello-to-the-gapminder-tibble">Say hello to the Gapminder tibble</a></li>
</ul></li>
<li><a href="#think-before-you-create-excerpts-of-your-data">Think before you create excerpts of your data …</a></li>
<li><a href="#use-filter-to-subset-data-row-wise.">Use <code>filter()</code> to subset data row-wise.</a></li>
<li><a href="#meet-the-new-pipe-operator">Meet the new pipe operator</a></li>
<li><a href="#use-select-to-subset-the-data-on-variables-or-columns.">Use <code>select()</code> to subset the data on variables or columns.</a></li>
<li><a href="#revel-in-the-convenience">Revel in the convenience</a></li>
<li><a href="#pure-predictable-pipeable">Pure, predictable, pipeable</a></li>
<li><a href="#resources">Resources</a></li>
</ul>
</div>
<div id="intro" class="section level3">
<h3>Intro</h3>
<p><code>dplyr</code> is a package for data manipulation, developed by Hadley Wickham and Romain Francois. It is built to be fast, highly expressive, and open-minded about how your data is stored. It is installed as part of the the <a href="https://github.com/hadley/tidyverse"><code>tidyverse</code></a> meta-package and, as a core package, it is among those loaded via <code>library(tidyverse)</code>.</p>
<p><code>dplyr</code>’s roots are in an earlier package called <a href="http://plyr.had.co.nz"><code>plyr</code></a>, which implements the <a href="https://www.jstatsoft.org/article/view/v040i01">“split-apply-combine” strategy for data analysis</a> (PDF). Where <code>plyr</code> covers a diverse set of inputs and outputs (e.g., arrays, data frames, lists), <code>dplyr</code> has a laser-like focus on data frames or, in the <code>tidyverse</code>, “tibbles”. <code>dplyr</code> is a package-level treament of the <code>ddply()</code> function from <code>plyr</code>, because “data frame in, data frame out” proved to be so incredibly important.</p>
<p>Have no idea what I’m talking about? Not sure if you care? If you use these base R functions: <code>subset()</code>, <code>apply()</code>, <code>[sl]apply()</code>, <code>tapply()</code>, <code>aggregate()</code>, <code>split()</code>, <code>do.call()</code>, <code>with()</code>, <code>within()</code>, then you should keep reading. Also, if you use <code>for()</code> loops alot, you might enjoy learning other ways to iterate over rows or groups of rows or variables in a data frame.</p>
<div id="load-dplyr-and-gapminder" class="section level4">
<h4>Load <code>dplyr</code> and <code>gapminder</code></h4>
<p>I choose to load the <code>tidyverse</code>, which will load <code>dplyr</code>, among other packages we use incidentally below. Also load <code>gapminder</code>.</p>
<pre class="r"><code>library(gapminder)
library(tidyverse)</code></pre>
<pre><code>## Loading tidyverse: ggplot2
## Loading tidyverse: tibble
## Loading tidyverse: tidyr
## Loading tidyverse: readr
## Loading tidyverse: purrr
## Loading tidyverse: dplyr</code></pre>
<pre><code>## Conflicts with tidy packages ----------------------------------------------</code></pre>
<pre><code>## filter(): dplyr, stats
## lag(): dplyr, stats</code></pre>
</div>
<div id="say-hello-to-the-gapminder-tibble" class="section level4">
<h4>Say hello to the Gapminder tibble</h4>
<p>The <code>gapminder</code> data frame is a special kind of data frame: a tibble.</p>
<pre class="r"><code>gapminder</code></pre>
<pre><code>## # A tibble: 1,704 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134
## 7 Afghanistan Asia 1982 39.854 12881816 978.0114
## 8 Afghanistan Asia 1987 40.822 13867957 852.3959
## 9 Afghanistan Asia 1992 41.674 16317921 649.3414
## 10 Afghanistan Asia 1997 41.763 22227415 635.3414
## # ... with 1,694 more rows</code></pre>
<p>It’s tibble-ness is why we get nice compact printing. For a reminder of the problems with base data frame printing, go type <code>iris</code> in the R Console or, better yet, print a data frame to screen that has lots of columns.</p>
<p>Note how gapminder’s <code>class()</code> includes <code>tbl_df</code>; the “tibble” terminology is a nod to this.</p>
<pre class="r"><code>class(gapminder)</code></pre>
<pre><code>## [1] "tbl_df" "tbl" "data.frame"</code></pre>
<p>There will be some functions, like <code>print()</code>, that know about tibbles and do something special. There will others that do not, like <code>summary()</code>. In which case the regular data frame treatment will happen, because every tibble is also a regular data frame.</p>
<p>To turn any data frame into a tibble use <code>as_tibble()</code>:</p>
<pre class="r"><code>as_tibble(iris)</code></pre>
<pre><code>## # A tibble: 150 × 5
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## <dbl> <dbl> <dbl> <dbl> <fctr>
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## # ... with 140 more rows</code></pre>
</div>
</div>
<div id="think-before-you-create-excerpts-of-your-data" class="section level3">
<h3>Think before you create excerpts of your data …</h3>
<p>If you feel the urge to store a little snippet of your data:</p>
<pre class="r"><code>(canada <- gapminder[241:252, ])</code></pre>
<pre><code>## # A tibble: 12 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Canada Americas 1952 68.750 14785584 11367.16
## 2 Canada Americas 1957 69.960 17010154 12489.95
## 3 Canada Americas 1962 71.300 18985849 13462.49
## 4 Canada Americas 1967 72.130 20819767 16076.59
## 5 Canada Americas 1972 72.880 22284500 18970.57
## 6 Canada Americas 1977 74.210 23796400 22090.88
## 7 Canada Americas 1982 75.760 25201900 22898.79
## 8 Canada Americas 1987 76.860 26549700 26626.52
## 9 Canada Americas 1992 77.950 28523502 26342.88
## 10 Canada Americas 1997 78.610 30305843 28954.93
## 11 Canada Americas 2002 79.770 31902268 33328.97
## 12 Canada Americas 2007 80.653 33390141 36319.24</code></pre>
<p>Stop and ask yourself …</p>
<blockquote>
<p>Do I want to create mini datasets for each level of some factor (or unique combination of several factors) … in order to compute or graph something?</p>
</blockquote>
<p>If YES, <strong>use proper data aggregation techniques</strong> or facetting in <code>ggplot2</code> – <strong>don’t subset the data</strong>. Or, more realistic, only subset the data as a temporary measure while you develop your elegant code for computing on or visualizing these data subsets.</p>
<p>If NO, then maybe you really do need to store a copy of a subset of the data. But seriously consider whether you can achieve your goals by simply using the <code>subset =</code> argument of, e.g., the <code>lm()</code> function, to limit computation to your excerpt of choice. Lots of functions offer a <code>subset =</code> argument!</p>
<p>Copies and excerpts of your data clutter your workspace, invite mistakes, and sow general confusion. Avoid whenever possible.</p>
<p>Reality can also lie somewhere in between. You will find the workflows presented below can help you accomplish your goals with minimal creation of temporary, intermediate objects.</p>
</div>
<div id="use-filter-to-subset-data-row-wise." class="section level3">
<h3>Use <code>filter()</code> to subset data row-wise.</h3>
<p><code>filter()</code> takes logical expressions and returns the rows for which all are <code>TRUE</code>.</p>
<pre class="r"><code>filter(gapminder, lifeExp < 29)</code></pre>
<pre><code>## # A tibble: 2 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Rwanda Africa 1992 23.599 7290203 737.0686</code></pre>
<pre class="r"><code>filter(gapminder, country == "Rwanda", year > 1979)</code></pre>
<pre><code>## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Rwanda Africa 1982 46.218 5507565 881.5706
## 2 Rwanda Africa 1987 44.020 6349365 847.9912
## 3 Rwanda Africa 1992 23.599 7290203 737.0686
## 4 Rwanda Africa 1997 36.087 7212583 589.9445
## 5 Rwanda Africa 2002 43.413 7852401 785.6538
## 6 Rwanda Africa 2007 46.242 8860588 863.0885</code></pre>
<pre class="r"><code>filter(gapminder, country %in% c("Rwanda", "Afghanistan"))</code></pre>
<pre><code>## # A tibble: 24 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134
## 7 Afghanistan Asia 1982 39.854 12881816 978.0114
## 8 Afghanistan Asia 1987 40.822 13867957 852.3959
## 9 Afghanistan Asia 1992 41.674 16317921 649.3414
## 10 Afghanistan Asia 1997 41.763 22227415 635.3414
## # ... with 14 more rows</code></pre>
<p>Compare with some base R code to accomplish the same things</p>
<pre class="r"><code>gapminder[gapminder$lifeExp < 29, ] ## repeat `gapminder`, [i, j] indexing is distracting
subset(gapminder, country == "Rwanda") ## almost same as filter; quite nice actually</code></pre>
<p>Under no circumstances should you subset your data the way I did at first:</p>
<pre class="r"><code>excerpt <- gapminder[241:252, ]</code></pre>
<p>Why is this a terrible idea?</p>
<ul>
<li>It is not self-documenting. What is so special about rows 241 through 252?</li>
<li>It is fragile. This line of code will produce different results if someone changes the row order of <code>gapminder</code>, e.g. sorts the data earlier in the script.</li>
</ul>
<pre class="r"><code>filter(gapminder, country == "Canada")</code></pre>
<p>This call explains itself and is fairly robust.</p>
</div>
<div id="meet-the-new-pipe-operator" class="section level3">
<h3>Meet the new pipe operator</h3>
<p>Before we go any further, we should exploit the new pipe operator that the tidyverse imports from the <a href="https://github.com/smbache/magrittr"><code>magrittr</code></a> package by Stefan Bache. This is going to change your data analytical life. You no longer need to enact multi-operation commands by nesting them inside each other, like so many <a href="http://blogue.us/wp-content/uploads/2009/07/Unknown-21.jpeg">Russian nesting dolls</a>. This new syntax leads to code that is much easier to write and to read.</p>
<p>Here’s what it looks like: <code>%>%</code>. The RStudio keyboard shortcut: Ctrl + Shift + M (Windows), Cmd + Shift + M (Mac).</p>
<p>Let’s demo then I’ll explain:</p>
<pre class="r"><code>gapminder %>% head()</code></pre>
<pre><code>## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007
## 4 Afghanistan Asia 1967 34.020 11537966 836.1971
## 5 Afghanistan Asia 1972 36.088 13079460 739.9811
## 6 Afghanistan Asia 1977 38.438 14880372 786.1134</code></pre>
<p>This is equivalent to <code>head(gapminder)</code>. The pipe operator takes the thing on the left-hand-side and <strong>pipes</strong> it into the function call on the right-hand-side – literally, drops it in as the first argument.</p>
<p>Never fear, you can still specify other arguments to this function! To see the first 3 rows of Gapminder, we could say <code>head(gapminder, 3)</code> or this:</p>
<pre class="r"><code>gapminder %>% head(3)</code></pre>
<pre><code>## # A tibble: 3 × 6
## country continent year lifeExp pop gdpPercap
## <fctr> <fctr> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.801 8425333 779.4453
## 2 Afghanistan Asia 1957 30.332 9240934 820.8530
## 3 Afghanistan Asia 1962 31.997 10267083 853.1007</code></pre>
<p>I’ve advised you to think “gets” whenever you see the assignment operator, <code><-</code>. Similary, you should think “then” whenever you see the pipe operator, <code>%>%</code>.</p>
<p>You are probably not impressed yet, but the magic will soon happen.</p>
</div>
<div id="use-select-to-subset-the-data-on-variables-or-columns." class="section level3">
<h3>Use <code>select()</code> to subset the data on variables or columns.</h3>
<p>Back to <code>dplyr</code> …</p>
<p>Use <code>select()</code> to subset the data on variables or columns. Here’s a conventional call:</p>
<pre class="r"><code>select(gapminder, year, lifeExp)</code></pre>
<pre><code>## # A tibble: 1,704 × 2
## year lifeExp
## <int> <dbl>
## 1 1952 28.801
## 2 1957 30.332
## 3 1962 31.997
## 4 1967 34.020
## 5 1972 36.088
## 6 1977 38.438
## 7 1982 39.854
## 8 1987 40.822
## 9 1992 41.674
## 10 1997 41.763
## # ... with 1,694 more rows</code></pre>
<p>And here’s the same operation, but written with the pipe operator and piped through <code>head()</code>:</p>
<pre class="r"><code>gapminder %>%
select(year, lifeExp) %>%
head(4)</code></pre>
<pre><code>## # A tibble: 4 × 2
## year lifeExp
## <int> <dbl>
## 1 1952 28.801
## 2 1957 30.332
## 3 1962 31.997
## 4 1967 34.020</code></pre>
<p>Think: “Take <code>gapminder</code>, then select the variables year and lifeExp, then show the first 4 rows.”</p>
</div>
<div id="revel-in-the-convenience" class="section level3">
<h3>Revel in the convenience</h3>
<p>Here’s the data for Cambodia, but only certain variables:</p>
<pre class="r"><code>gapminder %>%
filter(country == "Cambodia") %>%
select(year, lifeExp)</code></pre>
<pre><code>## # A tibble: 12 × 2
## year lifeExp
## <int> <dbl>
## 1 1952 39.417
## 2 1957 41.366
## 3 1962 43.415
## 4 1967 45.415
## 5 1972 40.317
## 6 1977 31.220
## 7 1982 50.957
## 8 1987 53.914
## 9 1992 55.803
## 10 1997 56.534
## 11 2002 56.752
## 12 2007 59.723</code></pre>
<p>and what a typical base R call would look like:</p>
<pre class="r"><code>gapminder[gapminder$country == "Cambodia", c("year", "lifeExp")]</code></pre>
<pre><code>## # A tibble: 12 × 2
## year lifeExp
## <int> <dbl>
## 1 1952 39.417
## 2 1957 41.366
## 3 1962 43.415
## 4 1967 45.415
## 5 1972 40.317
## 6 1977 31.220
## 7 1982 50.957
## 8 1987 53.914
## 9 1992 55.803
## 10 1997 56.534
## 11 2002 56.752
## 12 2007 59.723</code></pre>
</div>
<div id="pure-predictable-pipeable" class="section level3">
<h3>Pure, predictable, pipeable</h3>
<p>We’ve barely scratched the surface of <code>dplyr</code> but I want to point out key principles you may start to appreciate. If you’re new to R or “programming with data”, feel free skip this section and <a href="block010_dplyr-end-single-table.html">move on</a>.</p>
<p><code>dplyr</code>’s verbs, such as <code>filter()</code> and <code>select()</code>, are what’s called <a href="http://en.wikipedia.org/wiki/Pure_function">pure functions</a>. To quote from Wickham’s <a href="http://adv-r.had.co.nz/Functions.html">Advanced R Programming book</a>:</p>
<blockquote>
<p>The functions that are the easiest to understand and reason about are pure functions: functions that always map the same input to the same output and have no other impact on the workspace. In other words, pure functions have no side effects: they don’t affect the state of the world in any way apart from the value they return.</p>
</blockquote>
<p>In fact, these verbs are a special case of pure functions: they take the same flavor of object as input and output. Namely, a data frame or one of the other data receptacles <code>dplyr</code> supports.</p>
<p>And finally, the data is <strong>always</strong> the very first argument of the verb functions.</p>
<p>This set of deliberate design choices, together with the new pipe operator, produces a highly effective, low friction <a href="http://adv-r.had.co.nz/dsl.html">domain-specific language</a> for data analysis.</p>
<p>Go to the next block, <a href="block010_dplyr-end-single-table.html"><code>dplyr</code> functions for a single dataset</a>, for more <code>dplyr</code>!</p>
</div>
<div id="resources" class="section level3">
<h3>Resources</h3>
<p><code>dplyr</code> official stuff</p>
<ul>
<li>package home <a href="http://cran.r-project.org/web/packages/dplyr/index.html">on CRAN</a>
<ul>
<li>note there are several vignettes, with the <a href="http://cran.r-project.org/web/packages/dplyr/vignettes/introduction.html">introduction</a> being the most relevant right now</li>
<li>the <a href="http://cran.rstudio.com/web/packages/dplyr/vignettes/window-functions.html">one on window functions</a> will also be interesting to you now</li>
</ul></li>
<li>development home <a href="https://github.com/hadley/dplyr">on GitHub</a></li>
<li><a href="https://www.dropbox.com/sh/i8qnluwmuieicxc/AAAgt9tIKoIm7WZKIyK25lh6a">tutorial HW delivered</a> (note this links to a DropBox folder) at useR! 2014 conference</li>
</ul>
<p><a href="https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf">RStudio Data Wrangling cheatsheet</a>, covering <code>dplyr</code> and <code>tidyr</code>. Remember you can get to these via <em>Help > Cheatsheets.</em></p>
<p><a href="https://github.com/tjmahr/MadR_Pipelines">Excellent slides</a> on pipelines and <code>dplyr</code> by TJ Mahr, talk given to the Madison R Users Group.</p>
<p>Blog post <a href="http://www.dataschool.io/dplyr-tutorial-for-faster-data-manipulation-in-r/">Hands-on dplyr tutorial for faster data manipulation in R</a> by Data School, that includes a link to an R Markdown document and links to videos</p>
<p><a href="bit001_dplyr-cheatsheet.html">Cheatsheet</a> I made for <code>dplyr</code> join functions (not relevant yet but soon)</p>
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