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(This article was first published on <strong><a href="http://www.win-vector.com/blog/2017/11/update-on-coordinatized-or-fluid-data/"> R – Win-Vector Blog</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>We have just released a major update of the <a rel="nofollow" href="https://github.com/WinVector/cdata" target="_blank"><code>cdata</code></a> <a rel="nofollow" href="https://www.r-project.org/" target="_blank">R</a> package to <a rel="nofollow" href="https://cran.r-project.org/" target="_blank">CRAN</a>.</p>
<p><center><a rel="nofollow" href="https://winvector.github.io/cdata/" target="_blank"><img border="0" src="https://i2.wp.com/www.win-vector.com/blog/wp-content/uploads/2017/11/cdata.png?w=660" alt="Cdata" title="cdata.png" data-recalc-dims="1"></a></center></p>
<p>If you work with <code>R</code> and data, now is the time to check out the <code>cdata</code> package.<span></span></p>
<p>
Among the changes in the <code>0.5.*</code> version of <code>cdata</code> package:
<ul>
<li>All coordinatized data or fluid data operations are now in the <code>cdata</code> package (no longer split between the <code>cdata</code> and <a rel="nofollow" href="https://github.com/WinVector/replyr/" target="_blank"><code>replyr</code></a> packages).</li>
<li>The transforms are now centered on the more general table driven <a rel="nofollow" href="https://winvector.github.io/cdata/reference/moveValuesToRowsN.html" target="_blank"><code>moveValuesToRowsN()</code></a> and <a rel="nofollow" href="https://winvector.github.io/cdata/reference/moveValuesToColumnsN.html" target="_blank"><code>moveValuesToColumnsN()</code></a> operators (though pivot and un-pivot are now made available as convenient special cases).</li>
<li>All the transforms are now implemented in <code>SQL</code> through <code>DBI</code> (no longer using <code>tidyr</code> or <code>dplyr</code>, though we do include examples of using <code>cdata</code> with <code>dplyr</code>).</li>
<li>This is (unfortunately) a user visible API change, however adapting to the changed API is deliberately straightforward.</li>
</ul>
<p><code>cdata</code> now supplies very general data transforms on <em>both</em> in-memory <code>data.frame</code>s and remote or large data systems (<code>PostgreSQL</code>, <code>Spark/Hive</code>, and so on). These transforms include operators such as pivot/un-pivot that were previously not conveniently available for these data sources (for example <code>tidyr</code> does not operate on such data, despite <code>dplyr</code> doing so).</p>
<p>To help transition we have updated the existing documentation:</p>
<ul>
<li><a rel="nofollow" href="https://github.com/WinVector/cdata/blob/master/extras/RowsAndColumns.md" target="_blank">“Coordinatized Data” Theory.</a></li>
<li><a rel="nofollow" href="https://github.com/WinVector/cdata/blob/master/extras/FluidData.md" target="_blank">The “fluid data” methodology.</a></li>
</ul>
<p>The fluid data document is a bit long, as it covers a lot of concepts quickly. We hope to develop more targeted training material going forward.</p>
<p>In summary: <code>cdata</code> theory and package now allow very concise and powerful transformations of big data using <code>R</code>.</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/2017/11/update-on-coordinatized-or-fluid-data/"> R – Win-Vector Blog</a></strong>.</div>
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