We’re talking about an order of magnitude difference in number of packages (82096 on PyPI vs. 8551 on CRAN) and their maturity, and such a naïve metric probably undersells the difference in variety of use cases.

If you picked 100 random production python projects out of a hat, no more than a small handful of them would be remotely appropriate to build using R.

And that's entirely fine. R is great at being a quick and dirty statistical analysis language for people doing data exploration.

I suspect that the utility of more packages increases only logarithmically. Having 10x more packages doesn't mean it's 10 times more useful. If your obscure need isn't in the first 8,000 packages, it probably won't be in the next 80,000. That's just how power laws work. And any common task you can think of will probably be in the top 8,000.

I totally buy that argument, but there's probably not a huge overlap in the set of "common tasks" for R and python.

Pandas for Python is almost re-imaging of R (Base 0 instead of base 1 UGH) very closely and the "common task" match closely, but I still use R over Python.

Well then all the more reason to use R, if those are the set of tasks you want to do.

how about quick and friendly?

Wickham has done a ton but but R had great popularity even before those projects, especially with projects like bioconductor.

Dplyr is not for databases at all. Its a piping operator that simplifies complex syntax a lot.

Yes, you can use dplyr to connect and query data from a database. https://cran.r-project.org/web/packages/dplyr/vignettes/data...

Yes and no - the dplyr example code heavily used pipes but that's really a feature of Magrittr package. You can used dplyr without pipes.

Plus dplyr essentially allows you to treat tables in a database as if they are data.frames.