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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2018/04/r-bloggers.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>Tal Galili's <a rel="nofollow" href="http://www.r-bloggers.com/" target="_blank">R-bloggers.com</a> has been syndicating blog posts about R for quite a while — from memory I'd say about 8 years, but I couldn't find the exact date it started aggregating. Anyway, it contains a wealth of information about activity in the R ecosystem, but without any easy way to access that information other than the blog post feed. Bob Rudis figured it out though — <a rel="nofollow" href="https://rud.is/b/2018/04/04/exploring-r-bloggers-posts-with-the-feedly-api/" target="_blank">by using the Feedly API</a> and the RSS feeds of R-bloggers, you can extract quite a bit of data about the posts of the 750+ bloggers syndicated on the site. Among the insights:</p>
<ul>
<li>Since 2014, there have been more than 160 posts per month (and up to as many as 250 in one month) aggregated on R-bloggers</li>
<li>Most posts appear on Mondays and Tuesdays. Sunday is the quietest day for R bloggers.</li>
<li>More than 1,000 authors have been published on the site.</li>
<li>And (with more than a modicum of personal pride here), the top 10 authors on the site, measured by total engagement, are:</li>
</ul>
<p><a rel="nofollow" href="http://a4.typepad.com/6a0105360ba1c6970c01b8d2e9d7dc970c-pi" target="_blank"><img alt="R-bloggers" title="R-bloggers" src="http://a4.typepad.com/6a0105360ba1c6970c01b8d2e9d7dc970c-320wi"></a></p>
<p>You can find more analysis, including charts and the detailed R code used to produce them <em>and</em> a link to the extracted data file, in Bob Rudis's blog post at the link below:</p>
<p>rud.is: <a rel="nofollow" href="https://rud.is/b/2018/04/04/exploring-r-bloggers-posts-with-the-feedly-api/" target="_blank">Exploring R-Bloggers Posts with the Feedly API</a></p>
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