<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>StackOverflow on The Final Artefact</title><link>https://www.thefinalartefact.xyz/tags/stackoverflow/</link><description>Recent content in StackOverflow on The Final Artefact</description><generator>Hugo</generator><language>en-gb</language><lastBuildDate>Tue, 29 Jun 2021 00:00:00 +0000</lastBuildDate><atom:link href="https://www.thefinalartefact.xyz/tags/stackoverflow/index.xml" rel="self" type="application/rss+xml"/><item><title>Why regex is not fuzzy matching</title><link>https://www.thefinalartefact.xyz/post/why-regex-is-not-fuzzy-matching/</link><pubDate>Tue, 29 Jun 2021 00:00:00 +0000</pubDate><guid>https://www.thefinalartefact.xyz/post/why-regex-is-not-fuzzy-matching/</guid><description>&lt;p&gt;Recently, I cam across an interesting discussion on StackOverflow^[SO discussion on: &lt;a href="https://stackoverflow.com/a/68182330/1655567"&gt;&lt;em&gt;Fuzzy Join with Partial String Match in R&lt;/em&gt;&lt;/a&gt;] pertaining to approach to fuzzy matching tables in R. Good answer contributed by one of the most resilient and excellent contributors to whom I owe a lot of thanks for help suggested relying on regular expression, combining this with basic string removal and transformations like &lt;code&gt;toupper&lt;/code&gt; to deterministically match the tables. The solution solved the problem and was accepted.&lt;/p&gt;</description></item></channel></rss>