I'm not sure what that last example is meant to be other than an anti-Python caricature. If you're implementing calculating things like standard deviations by hand, that's not real-world coding, that's the undergraduate harassment package which should end with a STEM bachelor's.

Of course there's a bunch of loops and things; you're exposing what has to happen in both R and Python under the hood of all those packages.

> that's not real-world coding

It's pretty clear the post is focused on the context of work being done in an academic research lab. In that context I think most of the points are pretty valid, but most of the real world benefit I've experience from using Python is being able to work more closely with engineering (even on non-Python teams).

I shipped R code to a production environment once over my career and it felt incredibly fragile.

R is great for EDA, but really doesn't work well for iteratively building larger software projects. R is has a great package system, but it's not so great when you need abstraction in between.