<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Hadleyverse on DataScience.LA</title><link>https://datasciencela.github.io/tag/hadleyverse/</link><description>Recent content in Hadleyverse on DataScience.LA</description><generator>Hugo -- gohugo.io</generator><language>en-us</language><copyright>DataScience.LA, 2014–2021 · Read-only archive: comments are closed.</copyright><lastBuildDate>Wed, 03 Dec 2014 10:25:55 -0800</lastBuildDate><atom:link href="https://datasciencela.github.io/tag/hadleyverse/index.xml" rel="self" type="application/rss+xml"/><item><title>dplyr - some more reflections</title><link>https://datasciencela.github.io/dplyr-some-more-reflections/</link><pubDate>Thu, 04 Dec 2014 09:30:00 -0800</pubDate><guid>https://datasciencela.github.io/dplyr-some-more-reflections/</guid><description>&lt;p&gt;Yesterday, I published a post &lt;a href="https://datasciencela.github.io/dplyr-and-a-very-basic-benchmark/"&gt;here&lt;/a&gt; on DataScience.LA with a simple/basic benchmark for dplyr. It was the main part of a short talk at our last Los Angeles R meetup. Below, I continue my thoughts with some reflections on dplyr&amp;rsquo;s API and philosophy, AKA the other part of my talk.&lt;/p&gt;</description><media:content xmlns:media="http://search.yahoo.com/mrss/" url="https://datasciencela.github.io/dplyr-some-more-reflections/feature.jpg"/></item></channel></rss>