[YWall39]

The R code I run daily has at least 10 stats libraries that don't exist in Python, an important new one dropped a few days ago.

And as someone who uses both languages daily, Python is much better at some things, R in others. R is much better in coding stats analysis.

Competition is great, may both thrive.

[kuhewa]

And I don't think younger statisticians, those that will have 30+ years in the career tank, are now favouring Python over R (a few, Julia perhaps). So I don't imagine new stats functionality dropping in R first is going to change any time even close to soon.

[YWall39]

yes, what I see. R is so dominant in stats, and the momentum against it around me is more towards Julia not Python.

[williamcotton]

I’m curious and new to R. What are those R libraries you’re referring to?

[YWall39]

most are specialised in my field. for the more general, I cannot work without dynlm and plm. Just last week I needed to compare the empirical distribution of something to a known reference, with either the empirical or QQ, and searched Python, Julia and R. The R libraries were by far the best. More subjectively, I like data.table better than any alternative in any language for the type of work I do.

[bonadrag]

There is nothing like data.table in Julia, Python or JavaScript (if you want to stick to high level programming languages). It's the best combo you can get for speed + syntax.

[jmount]

Syntactically, no. But functionally Polars is new in the space.

[bonadrag]

Yes, Polars does extremely well in popular data manipulation benchmarks.