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My #rstats origin story: just about 20 years ago, I was working for a company that made turnkey Unix computer systems. I had written a collection of Perl scripts that ran in them and collected performance indicators, logging them as compressed CSV files.
When a customer reported performance issues, we'd download the CSV files and do statistical analysis, in Excel or Minitab. And we used the data to calibrate our capacity planning benchmarks.
Minitab has this visualization called a "six-pack" (support.minitab.com/en-us/minitab/…). Management loved these, so we had macros to make them and all that.
We were in the process of porting all our software, including the performance metric logging, to Linux. Someone, probably my boss, had this brilliant thought - Why don't we make the six-packs on the systems in the field every night?
Then when something goes wrong, instead of downloading a bunch of data and running a bunch of Minitab macros, we can just download a week's worth or a month's worth of six-packs and print them up.

So a plan was formed.
But how to do it? The constraints were that we had to use software that was in Red Hat Linux (6.2) and its add-on library. And we had to stay with Perl for the data collection - we had one programmer (me) and couldn't rewrite all of that.
The contenders were Perl Data Language (PDL), XLisp-Stat and R. PDL (pdl.perl.org) is an interesting but little known package. In looking at it now, I think it would have been the right choice, but it didn't have any heavy statistical graphics built in then.
XLisp-Stat (en.wikipedia.org/wiki/XLispStat) had all the statistical machinery we needed - more than we needed. But the programming language was Lisp, and I was the only programmer in the building that had ever written more than a class exercise in Lisp. (And not much more, frankly).
That left R. At the time (April 2000) R was at version 0.90 on the Red Hat Linux 6.2 Powertools CD (archive.download.redhat.com/pub/redhat/lin… if you don't believe me)!
I wish I could say I knew back then that R would grow up to be the #tidyverse and that I'd still be using it 20 years later, but frankly, I thought it would die off and I'd have to rewrite the whole thing from the ground up in Python. Which meant I'd have to learn Python.
So I ended up with about 50 lines of R code to plot a six-pack, and some ugly hacks to the Perl logging code to generate an input file for the R code and shell out to it with Perl's 'system'. Management got their six-packs and I learned just enough R to make them.
It's really interesting to go back and look at all the R packages that were on that Powertools CD (the ones that start with "R-" at archive.download.redhat.com/pub/redhat/lin…. R was very active back then; many of the packages had been ported from the large libraries for S.
Even ten years ago I was still getting my hands on books that came with code that would work with either S or R. Now, pretty much everything is developed on R and it can do everything we had to use Perl or Python for 20 years ago.

I never learned Python. And I'm not sorry.
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