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<h1 class="title">Automating data analysis pipelines</h1>
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<div id="TOC">
<ul>
<li><a href="#overview">Overview</a></li>
<li><a href="#install-make">Install <code>make</code></a></li>
<li><a href="#test-drive-make-and-rstudio">Test drive <code>make</code> and RStudio</a></li>
<li><a href="#hands-on-activity">Hands-on activity</a></li>
<li><a href="#more-examples">More examples</a></li>
<li><a href="#resources">Resources</a></li>
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<p>Although we spend alot of time working with data interactively, this sort of hands-on babysitting is not always appropriate. We have a philosophy of “source is real” in this class and that philosophy can be implemented on a grander scale. Just as we save R code in a script so we can replay analytical steps, we can also record how a series of scripts and commands work together to produce a set of analytical results. This is what we mean by automating data analysis or building an analytical pipeline.</p>
<div id="overview" class="section level3">
<h3>Overview</h3>
<p><a href="automation01_slides/index.html" target="_blank">slides</a></p>
<p>Why and how we automate data analyses + examples.</p>
</div>
<div id="install-make" class="section level3">
<h3>Install <code>make</code></h3>
<p><a href="automation02_windows.html">Windows installation</a></p>
<p>(If you are running Mac OS or Linux, <code>make</code> should already be installed.)</p>
</div>
<div id="test-drive-make-and-rstudio" class="section level3">
<h3>Test drive <code>make</code> and RStudio</h3>
<p><a href="automation03_make-test-drive.html">Test drive of <code>make</code></a>.</p>
<p>Walk before you run! Prove that <code>make</code> is actually installed and that it can be found and executed from the <a href="git09_shell.html">shell</a> and from RStudio. It is also important to tell RStudio to NOT substitute spaces for tabs when editing a <code>Makefile</code> (applies to any text editor).</p>
</div>
<div id="hands-on-activity" class="section level3">
<h3>Hands-on activity</h3>
<p><a href="automation04_make-activity.html">This fully developed example</a> shows you</p>
<ul>
<li>How to run an R script non-interactively</li>
<li>How to use <code>make</code>
<ul>
<li>to record which files are inputs vs. intermediates vs. outputs</li>
<li>to capture how scripts and commands convert inputs to outputs</li>
<li>to re-run parts of an analysis that are out-of-date</li>
</ul></li>
<li>The intersection of R and <code>make</code>, i.e. how to
<ul>
<li>run snippets of R code</li>
<li>run an entire R script</li>
<li>render an R Markdown document (or R script)</li>
</ul></li>
<li>The interface between RStudio and <code>make</code></li>
<li>How to use <code>make</code> from the <a href="git09_shell.html">shell</a></li>
<li>How Git facilitates the process of building a pipeline</li>
</ul>
</div>
<div id="more-examples" class="section level3">
<h3>More examples</h3>
<p>There are three more toy pipelines, using the Lord of the Rings data, that reinforce:</p>
<ul>
<li><a href="https://github.com/STAT545-UBC/STAT545-UBC.github.io/tree/master/automation10_holding-area/01_automation-example_just-r">01_automation-example_just-r</a>: use of an R script as a pseudo-<code>Makefile</code></li>
<li><a href="https://github.com/STAT545-UBC/STAT545-UBC.github.io/tree/master/automation10_holding-area/02_automation-example_r-and-make">02_automation-example_r-and-make</a>: use of a simple <code>Makefile</code></li>
<li><a href="https://github.com/STAT545-UBC/STAT545-UBC.github.io/tree/master/automation10_holding-area/03_automation-example_render-without-rstudio">03_automation-example_render-without-rstudio</a>: use of <code>rmarkdown::render()</code> from a <code>Makefile</code>, as the default way of running an R script or an R Markdown document, leading to pretty HTML reports without any mouse clicks</li>
</ul>
</div>
<div id="resources" class="section level3">
<h3>Resources</h3>
<p><a href="http://xkcd.com/1319/">xkcd comic on automation</a>. ‘Automating’ comes from the roots ‘auto-’ meaning ‘self-’, and ‘mating’, meaning ‘screwing’.</p>
<p>Karl Broman covers GNU Make in his course <a href="http://kbroman.org/Tools4RR/pages/schedule.html">Tools for Reproducible Research</a> <em>(see first week)</em></p>
<p>Karl Broman also wrote <a href="http://kbroman.github.io/minimal_make/">An introduction to <code>Make</code></a>, aimed at stats / data science types</p>
<p><a href="http://www.bendmorris.com/2013/09/using-make-for-reproducible-scientific.html">Using Make for reproducible scientific analyses</a>, blog post by Ben Morris</p>
<p>Software Carpentry’s <a href="http://swcarpentry.github.io/v4/make/index.html">Slides on <code>Make</code></a></p>
<p>Zachary M. Jones wrote <a href="http://zmjones.com/make.html">GNU Make for Reproducible Data Analysis</a></p>
<p><a href="http://www.adamlaiacano.com/post/45356689519/keeping-tabs-on-your-data-analysis-workflow">Keeping tabs on your data analysis workflow</a>, blog post by Adam Laiacano, who works at Tumblr</p>
<p>Mike Bostock, of D3.js and New York Times fame, explains <a href="http://bost.ocks.org/mike/make/">Why Use Make</a>: “it’s about the benefits of capturing workflows via a file-based dependency-tracking build system”</p>
<p><a href="http://bitaesthetics.com/posts/make-for-data-scientists.html">Make for Data Scientists</a>, blog post by Paul Butler, who also made a <a href="https://www.facebook.com/notes/facebook-engineering/visualizing-friendships/469716398919">beautiful map of Facebook connections</a> using R</p>
<p>Other, more modern data-oriented alternatives to <code>make</code></p>
<ul>
<li><a href="https://github.com/Factual/drake">Drake</a>, a kind of “make for data”</li>
<li><a href="http://www.nextflow.io">Nextflow</a> for “data-driven computational pipelines”</li>
<li><a href="https://github.com/richfitz/maker"><code>maker</code></a>, “Make-like build management, re-imagined for R”</li>
</ul>
<p><a href="http://www.oreilly.com/openbook/make3/book/">Managing Projects with GNU Make, Third Edition By Robert Mecklenburg</a> is a fantastic book but, sadly, is very focused on compiling software</p>
<p>RStudio’s <a href="http://rmarkdown.rstudio.com">website documenting R Markdown</a> is generated from <a href="https://github.com/rstudio/rmarkdown/tree/gh-pages">this repo</a> using <a href="https://github.com/rstudio/rmarkdown/blob/gh-pages/Makefile">this 20 line Makefile</a>, which is sort of amazing. This is why we study regular expressions and follow filename conventions, people!</p>
<p><a href="http://dirk.eddelbuettel.com/code/littler.html">littler</a> is an R package maintained by Dirk Eddelbuettel that “provides the <code>r</code> program, a simplified command-line interface for GNU R.”</p>
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