## Latest R bookmarks

This note describes how I compile(d) my R-related bookmarks on Pinboard.

## R-Bloggers

Most of my early R bookmarks come from R-Bloggers, at a time where this website was the best place to find tutorials that used base R and popular packages like ggplot2. Those were my formative years, which predated things like the tidyverse.

## R Weekly

The R user community has greatly expanded since, and following R-Bloggers has become unwieldy. I now follow the more selective and slower-paced R Weekly instead, as well as a few selected R blogs that I discovered thanks to R-Bloggers, R Weekly or Twitter, such as Bob Rudis' blog.

## GitHub

As of today, most of my R bookmarks come from following people on GitHub. Building a list of GitHub users with interests similar to mine has allowed me to find very specific R packages, but also R-based teaching material and non-R resources that are relevant to statistical computing and scientific research.

## Elsewhere

I also follow a handful of other people via their GitHub Gists, via their activity on other code repositories, such as GitLab, or via their answers on Cross Validated.

Last, as mentioned earlier and in another note, I check Twitter a few times per week, although I am not that much into it and have stopped automatically adding my liked tweets to my Pinboard bookmarks. As for other social media platforms, such as Facebook, Reddit or Slack, I either ignore them or use them for different purposes.

My notes on components of the R ecosystem and on asking R questions have more links to resources that might be useful to R users.

Update (February 3, 2019): this note used to display my most recent R bookmarks, but changes in cross-origin resource sharing policies now make this impossible, as Pinboard does not send an Access-Control-Allow-Origin header in its response. The code that I use to do something similar on my academic homepage is available as a Gist.

• First published on December 14th, 2015