<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>R on Pi Stack</title>
    <link>https://www.pistack.xyz/tags/r/</link>
    <description>Recent content in R on Pi Stack</description>
    <generator>Hugo</generator>
    <language>en-us</language>
    <lastBuildDate>Thu, 15 Oct 2026 00:00:00 +0000</lastBuildDate>
    <atom:link href="https://www.pistack.xyz/tags/r/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>data.table vs dplyr vs tidytable in 2026: Which R Data Library Should You Actually Use?</title>
      <link>https://www.pistack.xyz/posts/2026-10-15-r-data-manipulation-data-table-dplyr-tidytable-comparison/</link>
      <pubDate>Thu, 15 Oct 2026 00:00:00 +0000</pubDate>
      <guid>https://www.pistack.xyz/posts/2026-10-15-r-data-manipulation-data-table-dplyr-tidytable-comparison/</guid>
      <description>&lt;p&gt;An R script that reads a 4 GB CSV, groups by three columns and writes a summary should not take longer than your coffee break — but the same job can run in seconds or in minutes depending on which data library you load. That difference is not academic. It decides whether an analyst iterates five times an hour or twice a day, and it decides whether a laptop with 16 GB of RAM can hold the working set at all.&lt;/p&gt;</description>
    </item>
  </channel>
</rss>
