<?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>Knitr on The Final Artefact</title><link>https://www.thefinalartefact.xyz/tags/knitr/</link><description>Recent content in Knitr on The Final Artefact</description><generator>Hugo</generator><language>en-gb</language><lastBuildDate>Tue, 04 Mar 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://www.thefinalartefact.xyz/tags/knitr/index.xml" rel="self" type="application/rss+xml"/><item><title>Using Swift for Data Science Workflows</title><link>https://www.thefinalartefact.xyz/post/swift-data-science/</link><pubDate>Tue, 04 Mar 2025 00:00:00 +0000</pubDate><guid>https://www.thefinalartefact.xyz/post/swift-data-science/</guid><description>&lt;h2 id="why-swift"&gt;Why Swift?&lt;/h2&gt;
&lt;p&gt;Data science is dominated by Python and R, with some usage of Julia, Scala, Java, and C++. While Swift may not be the most popular choice, it offers several notable benefits—especially for developers already invested in the Apple ecosystem.&lt;/p&gt;
&lt;h2 id="key-advantages"&gt;Key Advantages&lt;/h2&gt;
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&lt;p&gt;&lt;strong&gt;Performance Considerations&lt;/strong&gt;&lt;br&gt;
As a compiled language, Swift often runs faster than languages like Python or R. This can be especially beneficial when handling large datasets or complex computations.&lt;/p&gt;</description></item></channel></rss>