Remember my blog post about automatic tools for improving R packages? One of these tools is Jim Hester’s lintr, a package that performs static code analysis. In my experience it mostly helps identifying too long code lines and missing space, although it’s a bit more involved than that. In any case, lintr helps you maintain good code style, and as mentioned in that now old post of mine, you can add a lintr unit test to your package which will ensure you don’t get lazy over time.
Now say your package has a lintr unit test and lives on GitHub. What happens if someone makes a pull request and writes looong code lines? Continuous integration builds will fail but not only that… The contributor will get to know Lintr Bot, lintr’s Hester (Easter) egg!
This prompted a reply by David Hood who later came up with R code to assign you to a Hogwarts house based on your Twitter activity!
It should be possible to assign House on the basis of Twitter analysis (among R using tweeters). Quatitatively: Original posts - opinionated - Gryffindor Replies - social - Slytherin posts links out of Twitter - homework - Ravenclaw Retweets- keeping it all working - Hufflepuff
I was thrilled to see David Hood’s sorting hat Github repo and thought it’d be the perfect occasion to answer that fascinating question: are #python users more likely to get into Slytherin than #rstats users?
Another note: I do not care about any Python vs. R fights except for Quidditch games, so go away trolls.
On my pretty and up-to-date CV, one of the first things one sees is my Github username, linking to my Github profile. What does a potential employer look at there? Hopefully not my non informative commit messages… My imitating a red Ampelmann, my being part of several organizations, my pinned repositories described with emojis… But how would they know where&how I’ve mostly been active without too much effort?
A considerable part of my Github work happens in organizations: I’m a co-editor at rOpenSci onboarding of packages, I contribute content to the R Weekly newsletter, etc. Although my profile shows the organizations I belong to, one would need to dig into them for a while before seeing how much or how little I’ve done. Which is fine most of the time but less so when trying to profile myself for jobs, right? Let’s try and fetch some Github data to create a custom profile.
Note: yep I’m looking for a job and ResearchGate’s suggestions are not helpful! Do you need an enthusiastic remote data scientist or research software engineer for your team? I’m available up to 24 hours a week! I care a lot about science, health, open source and community. Ideally I’d like to keep working in something close to public research but we can talk!
My husband and I recently started watching the wonderful series “Parks and recreation” which was recommended to me by my fellow R-Lady Jennifer Thompson in this very convincing thread. The serie was even endorsed by other R-Ladies. Jennifer told me the first two seasons are not as good as the following ones, but that it was worth it to make it through them. We actually started enjoying the humor and characters right away!
Then, this week while watching the show, one of the characters did a very basic text analysis that made me feel like imitating him for a blog post – my husband told me it was very Leslie of me to plan something while doing something else which made me very proud. I tested my idea on other Leslie fans, and they seemed to think it was a great idea… and that this post should be the beginning of a series of R-Ladies blog posts about Parks and recreation!
In this two-short-part blog post, I’ll therefore inaugurate this series, what an honor!
I’ve recently been binge-reading The Guardian Experience columns. I’m a big fan of The Guardian life and style section regulars: the blind dates to which I dedicated a blog post, Oliver Burkeman’s This column will change your life, etc. Experience is another regular that I enjoy a lot. In each of the column, someone tells something remarkable that happened to them. It can really be anything.
I was thinking of maybe scraping the titles and get a sense of most common topics. The final push was my husband’s telling me about this article of
Gabriella Paiella’s about the best Guardian Experience columns. She wrote “the “Experience” column does often touch on heavier topics”. Can one know what is the most prevalent “weight” of Experience columns scraping all their titles?
I don’t think rOpenSci’s Jeroen Ooms can ever top the coolness of his magick package but I have to admit other things he’s developped are not bad at all. He’s recently been working on interfaces to Google compact language detectors 2 and 3 (the latter being more experimental). I saw this cool use case and started thinking about other possible applications of the packages.
I was very sad when I realized it was too late to try and download tweets about the Eurovision song context but then I also remembered there’s this famous tennis tournament going on right now, about which people probably tweet in various languages. I don’t follow the French Open myself, but it seemed interesting to find out which languages were the most prevalent, and whether the results from the cld2 and cld3 packages are similar and whether they’re similar to the language detection results from Twitter itself.
Last week I published a post about scraping Radio Swiss Classic program. After that, Bob Rudis wrote an extremely useful post improving my code a lot and teaching me cool stuff. I don’t know why I forgot to add pauses between requests… Really bad behaviour! I will use his code today for re-scraping the data.
Why re-scrape the data? I mentioned broken links in my post. In fact, each time I hit a broken page, Radio Swiss Classic webmaster received an email. That person received a lot of emails because of me. They repaired the bug explaining these broken pages and contacted me because someone had turned me in (I feel super famous or spied on now), very kindly mentioning they had fixed all pages, and not holding any grudge against me. So let’s scrape everything again!
I am not a classical music expert at all, but I happen to have friends who are, and am even married to someone who plays the cello (and the ukulele!). I appreciate listening to such music from time to time, in particular Baroque music. A friend made me discover Radio Swiss classic, an online radio playing classical music all day and all night long, with a quite nice variety, and very little speaking between pieces, with no ads (thank you, funders of the radio!). Besides, the voices telling me which piece has just been played are really soothing, so Radio Swiss classic is a good one in my opinion.
Today, instead of anxiously waiting for the results of the French presidential elections, I decided to download the program of the radio in the last years and have a quick look at it, since after all, the website says that the radio aims at relaxing people.
Recently a reader left a comment on this blog mentioning his cool blog post in which he mapped the spread of a migratory bird using Twitter. His data source was the Waxwings UK account which reports sightings of Bohemian waxwings in the UK. I decided to try reproducing and extending his work using the rOpenSci spocc package that interfaces different sources of species occurrence data.
One of my more or less guilty pleasures is reading The Guardian blind date each week. I think I started doing this when living in Cambridge, England for five months. I would buy i every weekday and The Guardian week-end every week-end. I wasn’t even dating at the time I discovered The Guardian blind dates but I’ve always liked their format.
I get so much into each date report that seeing both participants say they want to meet again makes me ridiculously happy. I like wondering how matches were made, but today I just want to look into the contents of post-date interviews.