I'd second that. R and Python both have the same pre-LLVM performance issues.

I don't expect either R or Python to go away either time soon, nor would I want them to, but I would like to see people moving to things like Julia and Nim, which have the same level of expressivity, but are much more performant. I have difficulty imagining many people saying "I love programming in R and Python, but don't like Julia or Nim."

I like Python but at least with stats/numerics there isn't a big reason to move away from R except for specific libraries (especially DL stuff) or front-end integration with web-land (and even then things like Jupyter mitigate against that).

I would also add two good reasons to stick with R: RStudio and Hadley Wickham.

In theory, there are Python and Julia equivalents to RStudio (JupyterLab, Spyder, PyCharm, Juno, whatever) but RStudio is just so, so, so good. A truly great piece of software.

And of course if you have a data pipeline type workflow, and it fits into the Hadleyverse paradigm and isn't too performance intensive, there's nothing better.

Julia has it's own "*verse" type data pipeline framework, with an even greater variety of backends and plotting solutions than R.

It's still in development (mutate and select have PRs) but it's almost there.

https://github.com/queryverse/Query.jl

I've tried it while it was unstable and it was excellent (I remember some random forest training that went from a few days down to half an hour). Since everything was unstable at the time, one update was all it took to break everything. Now that 1.0 is out, I'd definitely like to pick it up again. Unfortunately for my current use cases, the ecosystem just doesn't exist like it does with R or Python.