svmiller / dragracer

An R package containing data for RuPaul's Drag Race, Seasons 1-14. The package includes data at the episode-level, contestant-level, and episode-contestant-level.
http://svmiller.com/blog/2019/02/dragracer-rupauls-drag-race-analysis/
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Data Update #4

Open will-ball opened 3 years ago

will-ball commented 3 years ago

Hi Steven,

Thanks for this package - it's great. I've been having a little play around with the data, creating some quick visualisations. I know the data is in there, but I thought it might be nice to add some stats to the 'contestants' file. For example, I added my own columns for number of maxi-challenge wins, times in the bottom and times 'safe' (i.e. neither winning, nor in the bottom/disqualified etc).

Would you be up for this sort of thing being added to the .rda files?

Thanks, Will

P.S. here's an example of some dataviz rpdr maxi winners

svmiller commented 3 years ago

Hi Will,

I actually had this in this earlier versions of the data. I took it out for the release to CRAN because I was worried as a relatively new CRAN author that the package maintainers would question the value of the package if I had done that for them. So, I took that out of the contestant-level data and put the underlying code as a vignette. Perhaps, in the broad scheme of things, that fear was unfounded.

I think one possible compromise here would be to code just the maxi-challenge wins, as you note. Before, I had calculated so many stats at the contestant-level from the episode-contestant-level. I'll add that for the next submission.

PythonCoderUnicorn commented 2 years ago

hello,

i was interested in making such a dataset myself.

what is minic, minicw1 etc ? why is there multiple bottoms instead of just 1?

also, if you could spend more time on your README file explaining more about what the pkg does, what data is has, and should have SPOILER ALERT.

Screen Shot 2021-12-21 at 3 50 59 PM
ericpb commented 2 years ago

I believe minic is for mini challenges and their topics and minicw1, minicw2, minicw3 are their winners. Also @PythonCoderUnicorn I just saw on twitter that you participate in the initiative of #TidyTuesday looks super interesting and fun. I might join in soon to practice some skills!