Back in the mist of time that would eventually coalesce into my memories of the 1990s, I met a fellow PhD-er at a summer school. She had just returned to the fatherland after doing an MA in the UK and found re-integration into German Political Science rather difficult.
The toughest bit, according to her, was the collective obsession with Weberian concepts. She eventually solved that problem by assigning a set of (essentially random) choice MW quotes to the function keys of her keyboard. Or so she said.
I would not know, because this turned into reject after review and some substantial revisions. But after all these years, the meme still rings true.
Back in the mist of time, when I should have been was working on my PhD, I found a blue book on the shelf that a previous occupant of the office had left there. As I learned later, it was The Blue Book that introduced the S language, the predecessor of R. I got sidetracked (as you do) and taught myself how to produce beautiful graphs in what is now known as base R, and how to run poorly understood time series analyses (impossible in SPSS at this point).
A little later, I got hooked on Stata, and to the present day, I refuse to be Stata-shamed, as Ben Stanley put it. 95 per cent of the time, it does the job, and quickly so. Also, the documentation is simply excellent.
But every now and then, I came back to R because I needed something specific. And it was mostly fun. Having access to all these APIs (in fact, concurrently having more than one data set in memory) was exciting. Having a real, reasonably straightforward scripting /programming language at my disposal instead of Stata’s hodgepodge of three (four if you count the graph language) half-baked syntaxes was exhilarating. Having a go at the latest methods on the basis of nothing more than skimming a working paper (skipping every non-trivial equation) was… I guess a little bit like trimming your hair with a chainsaw.
But finding, installing, updating and then loading three packages, just to make recoding a little more intuitive? Seriously, R? Not so cool. In fact, finding a variable (whose name and data set must be given in full) was usually enough to reduce me to tears. Attach() somehow never does what I think it should do. And so, I would return to Stata once more, like <insert awkward metaphor>.
Then, during one of my last forays, I began playing with the tidyverse. And as the young ones are prone to say: my mind was blown. Tibbles! Pipelines! Lots of yummie helper functions! Going from long to wide format and back (in various different ways)! Grouping, summarising, and even some pythonesque list traversing. This was no longer the fascinating but slightly stroppy R I used to know.
Compared to the handful of letters and abbreviations that I use in Stata to get things done, recoding-wise, this is still quite verbose, and I have to look up just about everything. But I really like it. Like, really like it. And so doing more stuff in R is firmly on the endless List Of Things I Want To Look Into. To end on the most positive note possible, here is a gratuitous picture of a cat.
On the day of the umpteenth Not-So-Special Brexit Council, the stunning image of a super-massive black hole was revealed. In unrelated news, I live with two teenagers. So when idly trying to catch up on the less-than-stellar proceedings in Brussels, the headlines collided in my poor head. For posterity, here is some exclusive coverage of the incident.
Germany’s carnival is supposed to be funny and political. Usually it is neither. But sometimes, there is a glimpse.
Mocked at Mainz
This is a picture I took at the Rosenmontagszug in Mainz, a major parade that attracts hundreds of thousands of revellers. The front of the float shows a pretty realistic AfD election poster (I did not get my phone out in time to take a snap). This is the float’s backside. The sign reads “it’s difficult to conceal”.