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(This article was first published on <strong><a href="http://www.win-vector.com/blog/2018/04/upcoming-speaking-engagments/"> R – Win-Vector Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>I have a couple of public appearances coming up soon.</p>
<ul>
<li><a rel="nofollow" href="https://www.meetup.com/r-enthusiasts/events/qvmgmpyxhbcb/" target="_blank">The East Bay R Language Beginners Group: Preparing Datasets – The Ugly Truth &amp; Some Solutions, Tuesday, May 1, 2018 at Robert Half Technologies, 1999 Harrison Street, Oakland, CA, 94612.</a></li>
<li><a rel="nofollow" href="https://www.meetup.com/R-Users/events/249752525/" target="_blank">Official May 2018 BARUG Meeting: rquery: a Query Generator for Working With SQL Data, Tuesday, May 8, 2018 at Intuit, Building 20<br>
2600 Marine Way · Mountain View, CA.</a></li>
</ul>
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<p>
<h2>vtreat</h2>
<p><a rel="nofollow" href="https://www.meetup.com/r-enthusiasts/events/qvmgmpyxhbcb/" target="_blank">Preparing Datasets – The Ugly Truth &amp; Some Solutions</a> is a great idea of Jim Porzak’s. Jim will speak on problems one is likely to encounter in trying to use real world data for predictive modeling and then I will speak on how the <a rel="nofollow" href="https://winvector.github.io/vtreat/" target="_blank"><code>vtreat</code> package</a> helps address these issues. <a rel="nofollow" href="https://winvector.github.io/vtreat/" target="_blank"><code>vtreat</code></a> systematizes a number of routine domain independent data repairs and preparations, leaving you more time to work on important domain specific issues (plus it has <a rel="nofollow" href="http://www.win-vector.com/blog/2018/03/r-tip-use-the-vtreat-package-for-data-preparation/" target="_blank">citable documentation</a>, helping make your methodology section smaller).</p>
<p><code>vtreat</code> is the best way to prepare messy real world data for predictive modeling.</p>
<h2>rquery</h2>
<p><a rel="nofollow" href="https://www.meetup.com/R-Users/events/249752525/" target="_blank">rquery: a Query Generator for Working With SQL Data</a>
<p> is an introduction to the <a rel="nofollow" href="https://winvector.github.io/rquery/" target="_blank"><code>rquery</code></a> query generator system. <a rel="nofollow" href="https://winvector.github.io/rquery/" target="_blank"><code>rquery</code></a> is a new <code>R</code> package that builds “pipe-able SQL” and includes a number of very powerful data operators and analyses. It includes a number of very neat features, including query pipeline diagrams.</p>
<p><a rel="nofollow" href="https://github.com/WinVector/rquery/blob/master/extras/DependencySorting.md" target="_blank"><img border="0" src="https://i1.wp.com/www.win-vector.com/blog/wp-content/uploads/2018/04/NewImage.png?w=660" alt="NewImage" title="NewImage.png" data-recalc-dims="1"></a></p>
<p>We think <code>rquery</code> (plus <a rel="nofollow" href="https://winvector.github.io/cdata/" target="_blank"><code>cdata</code></a>) is going to be the best way (easiest to learn, most expressive, easiest to maintain, and most performant) method to use <code>R</code> to manipulate data at scale (SQL databases and Spark).</p>
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<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/2018/04/upcoming-speaking-engagments/"> R – Win-Vector Blog</a></strong>.</div>
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