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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2017/09/practical-data-science-for-stats.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>PeerJ Preprints has recently published a collection of articles that focus on the practical side of statistical analysis: <a rel="nofollow" href="https://peerj.com/collections/50-practicaldatascistats/" target="_blank">Practical Data Science for Stats</a>. While the articles are not peer-reviewed, they have been selected and edited by Jennifer Bryan and Hadley Wickham, both well-respected members of the R community. And while the articles provide great advice for any data scientist, the content does heavily feature the use of R, so it's particularly useful to R users.</p>
<p><a rel="nofollow" href="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01b7c91bd222970b-pi" target="_blank"><img border="0" alt="Practical-data-science" title="Practical-data-science" src="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01b7c91bd222970b-800wi"></a></p>
<p>There are 16 articles in the collection (with possibly more to come?). Here are just a few examples that caught my eye:</p>
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<li>Bryan: &quot;<a rel="nofollow" href="https://peerj.com/preprints/3159/" target="_blank">Excuse me, do you have a moment to talk about version control?</a>&quot;. Practical advice on using Git, Github and Markdown for data science projects, with a focus on R.</li>
<li>Marwick, Boettiger and Mullen: <a rel="nofollow" href="https://peerj.com/preprints/3192/" target="_blank">Packaging data analytical work reproducibly using R (and friends)</a>. On using R packages as a vehicle for sharing research in a reproducible manner.</li>
<li>Taylor and Letham: <a rel="nofollow" href="https://peerj.com/preprints/3190/" target="_blank">Forecasting at Scale</a>. On using the Prophet package for production-scale forecasting of time series.</li>
<li><span>Ross, Wickham, and Robinson:</span> <a rel="nofollow" href="https://peerj.com/preprints/3180/" target="_blank"><span>Declutter your R workflow with tidy tools</span></a>. On using the tidyverse to make data analysis in R as smooth as possible.</li>
<li><span>Eddelbuettel and Balamuta: </span><a rel="nofollow" href="https://peerj.com/preprints/3188/" target="_blank"><span>Extending <em>R</em> with <em>C++</em>: A Brief Introduction to <em>Rcpp</em></span></a>. An introduction to the Rcpp package for R.</li>
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<p>There's lots more to explore in the collection as well, including case studies on using R at the likes of <a rel="nofollow" href="https://peerj.com/preprints/3182/" target="_blank">AirBnB</a> and the <a rel="nofollow" href="https://peerj.com/collections/50-practicaldatascistats/" target="_blank">New York Mets</a>. Check out the entire collection at the link below.</p>
<p>PeerJ Collections: <a rel="nofollow" href="https://peerj.com/collections/50-practicaldatascistats/" target="_blank">Practical Data Science for Stats</a> (<a rel="nofollow" href="https://twitter.com/JennyBryan/status/903252711365152772" target="_blank">via</a> Jenny Bryan)</p>
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