[mrtranscendence]

Just continually saying Python is the wrong tool for the job doesn't cut it. I've gotten a lot of mileage out of type hints in Python and have caught my share of bugs statically, so I disagree with your contention that it's merely a "bandaid for huge shortcomings". It's a helpful tool that serves a purpose.

You could pick at every tool, every language. What's better than Python at (since you brought it up) data science? Julia? That's hardly any more type safe, if that's an issue for you. Haskell? Good luck getting non-CS types to buy in to the restrictions, and good luck porting everything you need. R? Slower than Python, even! I mean, I dunno, if you have something in mind that's better in every way than Python then I'd love to hear it.

[Mikeb85]

R is way better than Python for most (all?) data science things. It's better at data munging, has more packages, it's array based so way more terse while being easy to reason about, has super easy C++ FFI, etc...

And no environment really beats RStudio.

[mrtranscendence]

I dispute at least some of that. R has more statistical packages, perhaps, but can't compete with Python in terms of sheer array of packages and developer mindshare in general. This is no different for data science, where major tools are either Python-only or are accessed in R only through Python (like Tensorflow).

R is also hardly more terse in my experience, though perhaps that depends on style; I'm a tidyverse fan but it's not particularly concise.

Finally ... RStudio. It's just okay. If you're willing to use a language-specific editor that may not get keybindings right (I hope you're not an emacs user), it works fine. I like the RMarkdown integration. I don't use it, though, and I don't feel like I'm missing that much.

BTW, you don't mention what actually does make R better than Python: lazy evaluation allowing something close to syntactical macros. You'll never get a magrittr or a dplyr in Python.