One week ago I was in Cape Town for the local satRday conference, where I had the honor to be one of the two keynote speakers, the other one being sports analytics extraordinaire Stephanie Kovalchik (You can read Stephanie Kovalchik’s account of the conference in this blog post). It was a fantastic experience! The event was very well organized, and 100% corresponds to its description as a “one day conference packed with R goodness”. You can watch all talks on Youtube. In my talk, I presented rOpenSci onboarding system of packages and… wore a hard hat!
It’d be a bit hard for me to really write a good recap of satRday that’d do it justice! Instead, I’ll use
rtweet and a bit of html hacking to storrrify it (like Storify, but in R) using my live tweets!
Do you know the Black Metal Cats Twitter account? As explained in this great introduction, it “combines kitties with heavy metal lyrics”. I know the account because I follow Scott Chamberlain who retweets them a lot, which I enjoy as far as one can enjoy such a dark mood. Speaking of which, I decided to try and transform Black Metal Cat tweets into something more positive… The Bubblegum Puppies were born!
This post requires some familiarity with the Harry Potter books but I’m committed to making this blog friendly to everyone, even Muggles/Nomajes.
Have you seen Mark Sellors’ blog post series about writing command line utilities in R? It’s a great one but I was a bit puzzled by his using randomness to assign houses in his sorting hat example (he added a new method based on name
digest-ing in the meantime).
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!
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.
Once again, a Twitter trend sent me to my R prompt… Here is a bit of context. My summary: Taylor Swift apparently plays the bad girl in her new album and a fan of hers asked a question…
The tweet was then quoted by many people mentioning badass women, and I decided to have a look at these heroes!
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
cld3 packages are similar and whether they’re similar to the language detection results from Twitter itself.
I’ll admit I didn’t really know who Bill Nye was before yesterday. His name sounds a bit like Bill Nighy’s, that’s all I knew. But well science is all around and quite often scientists on Twitter start interesting campaigns. Remember the #actuallylivingscientists whose animals I dedicated a blog post? This time, the Twitter campaign is the #BillMeetScienceTwitter hashtag with which scientists introduce themselves to the famous science TV host Bill Nye. Here is a nice article about the movement.
Since I like surfing on Twitter trends, I decided to download a few of these tweets and to use my own R interface to the Monkeylearn machine learning API,
monkeylearn (part of the rOpenSci project!), to classify the tweets in the hope of finding the most represented science fields. So, which science is all around?
This week I was impressed by this tweet where Daniel Pett, Digital Humanities Lead at the British Museum, presented a collage of Twitter profile pics of all his colleagues. He made this piece of art using R (for collecting the usernames) and Python. I’m a bit of an R fanatic (or a Python dummy…) so I decided to write a code in R only to make a collage of profile pics of Twitter #rstats users.
It’s the second time I write a post about the blog aggregator R-bloggers, probably because I’m all about R blogs now that I have one. My husband says my posts are so meta. My first post was about R blogs names, in this one I shall focus on the last 1,000 tweets from R-bloggers.
These last days a trending Twitter hashtag was “#actuallivingscientist”, whose origin can be find in this convo and whose original goal was to allow scientists to present themselves to everyone, a sort of #scicomm action. A great initiative, because we need science and we need everyone to know how it’s done, by actual human beings.
I didn’t tweet with the hashtag, but I consider myself a scientist with more or less experience in different fields – and my last post was about the scientist I married. In my timeline thanks to Auriel Fournier there were many tweets of ecologists studying animals. I’d like to say cute animals but some were carcasses… But still, it made me want to quantify which animals were the most present in the tweets. Any bet?
Note that this is a repost of my post on Monkeylearn’s blog.
Have you tweeted about your “#firstsevenjobs”? I did!