Learn R Through Examples (2020)

Everything? I'm just wondering what you've been doing exactly. I know that Tensorflow in R is just a wrapper on top of Python, not sure what else. If you're doing any deep learning, then going straight to Python is certainly much better than using R. For most other things it's not quite as clear of a decision.

Certainly R has its quirks, but most of this comes from being one of the oldest continuing existing programming languages there is. It derives from S which was written 46 years ago. Because of this it has multiple object/class systems reflecting the changing standards for OOP. It's most dominant one, S3, predates Java and therefore uses the Generic Function paradigm of OOP similar to Common Lisp's ClOS. If you're experienced but have never worked with non-Java style OOP you're going to be a bit confused.

R's most important feature, which is well worth studying and mastering for any serious programmer, is that it is a completely vectorized programming language. It borrows this style from APL (though is a million times more readable). Every value in R is a vector and for the vast majority of operations the best approach to solve your problem is by thinking in vector operations. This makes simple things like string formatting with `paste` seem like a confusing nightmare, but there is a real logic there. Functions like `ifelse` can seem strange, and writing C-style code in R, while possible will result in horrible performance.

Once you do learn to think in vectors you realize that R isn't just popular in the stats world because most statisticians haven't seen a "real" programming language, but because you can very rapidly iterate on models. Translating mathematical notation into R, for the experience R programmer, is easier than any other language I've worked with by a long shot.

My advice to any experienced programmer approaching R is to have some respect for the language. Most of the frustrations you'll have aren't because R is a bad language, but because you have less experience than you think and learning R well can expand your programming views in a similar way to Haskell.

And R excels at doing statistics and data science. If you keep that mindset, I believe many will find that its an excellent programming language.

We were essentially told "Install R Studio, then just copy and paste these library imports and you're good to go". Your description of a vectorized language makes total sense, and that single paragraph is more of an intro to R than we ever got in class.

That said, I think the reason this happened with this class in particular is the viewpoint of (what I perceive) as the majority user's of R. Mathematics focused researchers who never learned the language, they just have done enough to get by and don't really appreciate the underpinnings, or the nuances of running an environment on a machine that isn't theres.

I can't entirely absolve myself of blame though, once I realized what was happening I should have gone and done some more foundational R learning, but at that point I just wanted to be done with the class.