TL;DR

I wanted something that goes some way to automating my own time-consuming process of scrolling twitter for cool things to read. I thought a few other people might feel somewhat similar, so I decided to build a feed.

R posts you might have missed is an semi-automated twitter account posting recent R-related content. The goal is to make it easier to keep up with the most important packages and news from the community. Links to relevant and popular resources are gathered from twitter and the R blogosphere before being processed and lightly curated.

Read on to learn the origin story of the account, how it works and what comes next!

News overload!

Keeping track of new developments in the data science, open source and R communities is hard. The number of active developers, application areas and R packages is exploding. Ever since I started writing R code I’ve found it hard to avoid reinventing solutions to problems that are already solved by other developers, usually through ignorance of those developments. Being up-to-date with recent developments equips you with options that can change the way you approach a new problem.

This is more or less the reason I still use twitter, because it’s still the place where a majority of R developers hang out and share their projects and ideas. The problem is that the volume of new stuff is just too large – and I could easily spend endless hours per week scrolling twitter, discovering and re-discovering new stuff (and getting very distracted in the process). This is compounded by twitter’s news feed algorithm which I think has made it even harder to develop a tailored feed. So what can you do?

Well you’ve got options of course. R Bloggers has been around for some time and aggregates the feeds of several hundred well-known R blogs. I’ve never found this solves my problem: blog articles are one type of content, but there are many other types of content that I’d like to see in the same place, and most of them do not have RSS feeds. The site itself carries a lot of banner ads and doesn’t render articles very nicely – although those may be minor considerations if you still use an RSS reader to access the posts.

Ok so what else? R-Weekly is a terrific resource. The team gather links to posts, packages, community news and tweets into a single weekly digest. I think R Weekly is a wonderful resource, and I still read it every week – it does a particularly good job of creating a nicely formatted list broken into content types and topics that were active in the last week. However, this doesn’t scratch my itch completely. One issue is that it’s not totally automated (AFAIK, please correct me if that’s false), and there’s always the risk that something gets excluded. Additionally – any news oriented resource focusses on what’s occurred most recently (of course, yeah I know) and by definition excludes older useful resources that keep resurfacing. I think it’s good for those things to continue to get air-time – particularly because if I’m not working on a specific topic at the time of the initial news announcement, I’ll probably forget about it. Or more likely I just missed the announcement to begin with. I think repeated exposure and reminders can be important.

Long story short, I wanted something that goes some way to automating my own time-consuming process of scrolling twitter for cool things to read. I thought a few other people might feel somewhat similar, so I decided to build a feed.

R posts you might have missed

R posts you might have missed is a twitter feed with the following attributes:

• Publishes about 10 posts per day
• Posts are usually blog posts, repos and tutorials containing R code
• Emphasis on non-commercial content that is free to access
• Lightly curated with a lean towards more recent posts and repos
• Ensure the author is directly credited in each post

How does it work?

The recipe underpinning the feed takes the following steps:

1. Gather links from #rstats twitter

• Use Michael Kearney’s rtweet package to gather recent #rstats tagged tweets from twitter (last 9 days)
• Also use rtweet to gather tweets from a subset of highly active users – not all of these are necessarily #rstats tagged
• Extract the urls embedded inside the tweets

2. Gather new post urls from RSS feeds

• Use Robert Myles McDonnell’s tidyRSS package to read a large number of RSS feeds.
• Extract the urls of posts published in the last week that include code chunks

3. Read and filter urls based on content

• Steps 1. and 2. usually result in around 2000 urls per week. Use htmldf to download page content from the urls.
• Filter out any pages that don’t have code tags in the source and that haven’t already been tweeted by R posts you might have missed recently.
• Filter out any commercial content, anything that looks spammy. This uses some simple lists of sites to exclude. Medium posts are also completely filtered out – Medium paywalls it’s content, and also tends to have lower quality content in general.
• Extract page titles from <meta>, <title> or <h1> tags. For github repos, extract repo descriptions. These are used for composing tweets.
• After reading and filtering, we’re usually down to about 300 possible resources and urls we could tweet.
• For each of the 300 pages, extract image urls on each page (images are selected manually in the next step). Download and convert any images that are base64 or SVG encoded to png (twitter doesn’t accept these file types in tweets).

4. Find the author’s twitter username

• Usually, bloggers declare their social media profile information on their blogs. If this is the case, htmldf does a reasonable job of finding these automatically in the html.
• Author credentials are a bit trickier for github repos. Sometimes, this is directly embedded on the user’s GH profile – so all we need to do is visit the profile associated with the repo, and fetch the credentials from there. Sometimes twitter credentials aren’t provided here, but a personal website is declared on the GH profile where twitter profiles can be found. 80% of the time it seems that about 80% of R users twitter details can be gathered this way from their GH profile.

5. Compose tweets using an interactive shiny app

Everything until this point is totally automatic and carried out using a batch process on a cheap Google VM. Now the tweets are composed from various ingredients that have been gathered. To do this, a simple GUI built using R shiny, provides a simple editing environment to choose the correct author credentials, choose an image to show with the post and to check for any errors or formatting issues. For each tweet:

• Check the authorship from a list of options gathered in the previous scraping steps.
• Check the title, check emoji and choose a display image.
• Filter out tweets that aren’t relevant.
• Save the tweets to .csv: this includes columns for scheduled time (a randomly generated time in the week following Sys.time()), tweet text and image url.

6. Post!

• Bulk upload the processed tweets to a scheduling service – I use OneUpApp who are particularly flexible with bulk uploads and cross-posting to other social networks.

What next?

There’s a lot to do. In the short-term the intention is to

• Reduce the effort involved in manual curation. The curation process takes about an hour for a week’s worth of R tweets, most of that time is checking author credentials are correct and that the urls contain high-quality content. A bit more NLP could help with both of these tasks.
• Improve cross-posted author tagging for LinkedIn and Facebook posts. At present, full user credentials only appear on the twitter posts. It doesn’t seem to be possible/easy to schedule posts to LinkedIn with profile tags, where the author’s LinkedIn profile is known.
• Incorporate R-adjacent content. All of the candidate posts either contain code tags in the html, or are github repositories. Posts that are about R and data science but don’t include any code (like this one) are automatically excluded. It would be a big step to automatically identify and include these pages too.

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Comments? Suggestions? Issues?

Feedback is very welcome! Do you find R posts you might have missed useful? What do you like? How would you improve it? Find me on twitter at rushworth_a or write a github issue here.