[fjuerfilis]

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).

[currymj]

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.

[byt143]

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