Yeah. I think Wes McKinney liked the data frames in R, but preferred the programming language of Python. I've heard somewhere that he also got a lot of inspiration from APL.
R is literally designed to do statistics and has first class support and language feature support for many specialized tasks in statistics and closely related fields.
Python is literally designed to be easy to program with in general.
Well, it turns out when you’re dealing with terabytes of data and TFLOPS, the programming becomes more
important than the math. Not all R devs are happy about this and they are very loud about it.
But it shouldn’t really surprise anyone. That is literally how those languages are designed.
Most of the R devs I know like this are just butthurt they are paid less and refuse to switch because they’re obstinate, or they’re a little scared they’re being left behind. first group is all over the place, but the second group tends to skew older of course
R is heavily influenced by Scheme. Not only is it heavily functional, but it has metaprogramming capabilities allowing a high level of flexibility and expressiveness. The tidyverse libraries use this heavily to produce very nice composable APIs that aren't really practically possible in Python.
R is fine. The issue is more in the ecosystem (with the aforementioned exception of the tidyverse).
> Most of the R devs I know like this are just butthurt they are paid less and refuse to switch because they’re obstinate, or they’re a little scared they’re being left behind. first group is all over the place, but the second group tends to skew older of course
Look, I started with R and use mostly Python these days, but this is not really a fair take.
R is (still) much, much, much better for analytics and graphing (the only decent plotting library in python is a ggplot clone). The big change (and why Python ended up winning) is that integrating R with other tools (like web stuff, for example) is harder than just using Python.
pandas (for instance) is like an unholy clone of the worst features from both R and Python. Polars is pretty rocking, though (mostly because it clones from Spark/dplyr/linc).
It's another example of Python being the second best language for everything winning out in the marketplace.
That being said, if I was starting a data focused company and needed to pick a language, I'd almost certainly build all the DS focused stuff in R as it would be many many times quicker, as long as I didn't need to hire too many people.
