<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/win-vector-llc-announces-new-big-data-in-r-tools/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://www.win-vector.com/blog/2017/11/win-vector-llc-announces-new-big-data-in-r-tools/"> R – Win-Vector Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>Win-Vector LLC is proud to introduce two important new tool families (with documentation) in <a rel="nofollow" href="https://winvector.github.io/seplyr/" target="_blank">the <code>0.5.0</code> version of <code>seplyr</code></a> (also now <a rel="nofollow" href="https://cran.r-project.org/package=seplyr" target="_blank">available on CRAN)</a>:</p>
<ul>
<li><a rel="nofollow" href="https://winvector.github.io/seplyr/reference/partition_mutate_se.html" target="_blank"><code>partition_mutate_se()</code></a> / <a rel="nofollow" href="https://winvector.github.io/seplyr/reference/partition_mutate_qt.html" target="_blank"><code>partition_mutate_qt()</code></a>: these are query planners/optimizers that work over <code><a rel="nofollow" href="http://dplyr.tidyverse.org/" target="_blank">dplyr</a>::<a rel="nofollow" href="http://dplyr.tidyverse.org/reference/mutate.html" target="_blank">mutate()</a></code> assignments. When using big-data systems through <a rel="nofollow" href="https://www.r-project.org/" target="_blank">R</a> (such as PostgreSQL or Apache Spark) these planners can make your code faster and sequence steps to avoid <em>critical</em> issues (the complementary problems of too long in-mutate dependence chains, of too many mutate steps, and <a rel="nofollow" href="https://github.com/WinVector/Examples/blob/master/dplyr/Dependencies.md" target="_blank">incidental bugs</a>; all explained in the linked tutorials).</li>
<li><a rel="nofollow" href="https://winvector.github.io/seplyr/reference/if_else_device.html" target="_blank"><code>if_else_device()</code></a>: provides a <code>dplyr::mutate()</code> based simulation of per-row conditional blocks (including conditional assignment). This allows powerful imperative code (such as often seen in porting from <a rel="nofollow" href="https://www.sas.com/en_us/software/stat.html" target="_blank">SAS</a>) to be directly and legibly translated into performant <code>dplyr::mutate()</code> data flow code that works on Spark (via <a rel="nofollow" href="https://spark.rstudio.com/" target="_blank">Sparklyr</a>) and databases.</li>
</ul>
<p><center><br>
<img border="0" src="https://i2.wp.com/www.win-vector.com/blog/wp-content/uploads/2017/11/Blacksmith_working.jpg?resize=598%2C398" alt="Blacksmith working" title="Blacksmith_working.jpg" data-recalc-dims="1">
<p>Image by <a rel="nofollow" href="http://www.flickr.com/people/95118988@N00" target="_blank">Jeff Kubina</a> from Columbia, Maryland – <a rel="nofollow" href="http://www.flickr.com/photos/95118988@N00/2750824192/" target="_blank">[1]</a>, <a rel="nofollow" href="https://creativecommons.org/licenses/by-sa/2.0" title="Creative Commons Attribution-Share Alike 2.0" target="_blank">CC BY-SA 2.0</a>, <a rel="nofollow" href="https://commons.wikimedia.org/w/index.php?curid=7860357" target="_blank">Link</a><br>
</center><span></span></p>
<p>
<p>For <a rel="nofollow" href="http://www.win-vector.com/blog/tag/r-and-big-data/" target="_blank">“big data in R”</a> users these two function families (plus the included support functions and examples) are simple, yet game changing. These tools were developed by <a rel="nofollow" href="http://www.win-vector.com/" target="_blank">Win-Vector LLC</a> to fill gaps identified by Win-Vector and our partners when standing-up production scale R plus Apache Spark projects. </p>
<p>We are happy to share these tools as open source, and very interested in consulting with your teams on developing R/Spark solutions (including porting existing SAS code). For more information please <a rel="nofollow" href="mailto:contact@win-vector.com" target="_blank">reach out to Win-Vector</a>.</p>
<p>To teams get started we are supplying the following initial documentation, discussion, and examples:</p>
<ul>
<li><a rel="nofollow" href="https://winvector.github.io/seplyr/articles/MutatePartitioner.html" target="_blank">Mutate Partitioner package vignette</a></li>
<li><a rel="nofollow" href="https://winvector.github.io/seplyr/reference/if_else_device.html" target="_blank"><code>if_else_device</code> reference</a></li>
<li><a rel="nofollow" href="http://winvector.github.io/FluidData/partition_mutate.html" target="_blank">“Partition Mutate” article</a></li>
<li><a rel="nofollow" href="http://winvector.github.io/FluidData/partition_mutate_ex2.html" target="_blank">“Partitioning Mutate, Example 2” (includes <code>if_else_device</code>) article.</a></li>
</ul>
	<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://www.win-vector.com/blog/2017/11/win-vector-llc-announces-new-big-data-in-r-tools/"> R – Win-Vector Blog</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/cDmwzUROkac" height="1"></a></div>