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(This article was first published on <strong><a href="https://rviews.rstudio.com/2017/12/29/down-time-reading/"> R Views</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Not everyone has the luxury of taking some downtime at the end the year, but if you do have some free time, you may enjoy something on my short list of downtime reading. The books and articles here are not exactly “light reading”, nor are they literature for cuddling by the fire. Nevertheless, you may find something that catches your eye.</p>
<p>The <a rel="nofollow" href="https://www.syncfusion.com/resources/techportal/ebooks" target="_blank">Syncfusion series</a> of free eBooks contains more than a few gems on a variety of programming subjects, including James McCaffrey’s <a rel="nofollow" href="https://www.syncfusion.com/resources/techportal/details/ebooks/R-Programming_Succinctly" target="_blank">R Programming Succinctly</a> and Barton Poulson’s <a rel="nofollow" href="https://www.syncfusion.com/resources/techportal/details/ebooks/rsuccinctly" target="_blank">R Succinctly</a>.</p>
<p><img alt="" src="https://i1.wp.com/rviews.rstudio.com/post/2017-12-28-Rickert-Reading_files/succinctly.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p>For a more ambitious read, mine the rich vein of <a rel="nofollow" href="https://textbooks.opensuny.org/open-source-textbooks/" target="_blank">SUNY Open Textbooks</a>. My pick is Hiroki Sayama’s <a rel="nofollow" href="https://textbooks.opensuny.org/introduction-to-the-modeling-and-analysis-of-complex-systems/" target="_blank">Introduction to the Modeling and Analysis of Complex Systems</a>.</p>
<p><img alt="" src="https://i0.wp.com/rviews.rstudio.com/post/2017-12-28-Rickert-Reading_files/complex.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p>If you just can’t get enough of data science, then a few articles that caught my attention are:</p>
<ul>
<li>Christopher Olah’s brief but mind-stretching post on <a rel="nofollow" href="http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/" target="_blank">Neural Networks, Manifolds, and Topology</a>, which is good preparation for the Fujitsu Laboratories paper on <a rel="nofollow" href="https://www.jstage.jst.go.jp/article/tjsai/32/3/32_D-G72/_pdf" target="_blank">Time Series Classification via Topological Data Analysis</a></li>
<li>The paper by Nguyen and Holmes on their <a rel="nofollow" href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-017-1790-x" target="_blank">Bayesian Unidimensional Scaling (BUDS)</a> method for detecting patterns in high-dimensional data</li>
<li>Bou-Hamad et. al’s <a rel="nofollow" href="https://projecteuclid.org/download/pdfview_1/euclid.ssu/1315833185" target="_blank">A review of survival trees</a>, a valuable introduction to the literature on the subject</li>
<li>Rob Hyndman’s recent post on <a rel="nofollow" href="https://robjhyndman.com/hyndsight/tspackages/" target="_blank">Some new time series packages</a></li>
<li>Mike Bostock’s beautiful and mind-altering post on <a rel="nofollow" href="https://bost.ocks.org/mike/algorithms/?t=1&cn=ZmxleGlibGVfcmVjcw%3D%3D&refsrc=email&iid=90e204098ee84319b825887ae4c1f757&uid=765311247189291008&nid=244+281088008" target="_blank">Visualizing Algorithms</a>.</li>
</ul>
<p><img alt="Starry Night through 6,667 uniform random samples" src="https://i2.wp.com/rviews.rstudio.com/post/2017-12-28-Rickert-Reading_files/starry.png?w=456&ssl=1" data-recalc-dims="1"></p>
<p>Finally, if you really have some time on your hands, try searching through the 318M+ papers on <a rel="nofollow" href="https://www.pdfdrive.net/" target="_blank">PDFDRIVE</a>.</p>
<p>Happy reading, and have a <em>Happy and Prosperous New Year</em> from all of us at RStudio!!</p>
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