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Comment by zaptheimpaler | Hacker News Reader

It would be better to let it die and port the libraries over to Python instead.

People have tried to port core R data science packages like dplyr to Python many times now and all that's happened is Python has fallen further behind.

It's not just a matter of manpower and funding, which Python has more of. Python is actually a less capable language in some important ways. It's not that expressive, and has basically no affordances for domain-specific languages.

I'll be happy when we have a successor language for R, but it won't be 2024 Python.

Could not agree more

> I'll be happy when we have a successor language for R, but it won't be 2024 Python.

When it comes to statistics, and especially having to program stats analysis instead of just calling libraries, R is a much better language than Python or Julia. When it comes to other things, they are better than R. I use all 3 daily.

And with Quarto being available, why wouldn't you switch to whatever language is best for the current task? I'm throwing ObservableJS in there now too with the Quarto compatibility. You can do your data cleanup in R/Python, then use native web libraries for displaying pretty plots in the browser.

It is clear that R is not doing as well as Python. It is not clear that it is not doing well overall. It is still the best in statistical analysis and interactive usage (sklearn, statmodels, etc are not good enough). Maybe there is a healthy community that can thrive. For the moment being it seems that they are trying to eat SAS userbase.

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.

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.

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

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

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.

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.

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

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

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