[dm319]

Yes, there is a lot of R-bashing by people used to imperative languages designed for efficiency in repetitive tasks, not a functional language designed for numerical analysis. The complaints fall into these categories:

1. It's not zero-indexed (even though most numerical languages aren't)

2. Loops are slow (though if you're looping in R you're probably doing it wrong)

3. It's inconsistent

4. The syntax is weird.

But people don't talk about the somewhat beautiful functional ability of the language to wrangle data almost magically. Its basis in lisp allows for the tidyverse and data.table to exist[1], and ggplot is a formidable analysis/plotting platform that Python doesn't come close to.

[1] https://news.ycombinator.com/item?id=15869039

[throwawayboise]

I attended an intro to R workshop and found it very confusing. Being "functional" had nothing to do with it. Inconsistent, yes very much so in my opinion. It felt like a lot of little separately developed tools thrown together into a bundle. But I think mostly my difficulty with R is that I'm not a researcher or statistician. My exposure to and experience with those domains was an undergrad class or two many decades ago. If you don't deeply understand the problem space for which R is intended, you will be lost and confused trying to learn it.

[dm319]

It's a very different language to imperative languages out there, so it's not surprising that an introductory course would be confusing. There are several ways to do things in R (for example subsetting data, or pulling out elements of structured data), but that doesn't mean it's inconsistent - they are convenience functions. As you say, you have to do some statistics 'in anger' to really get why R is so good. When I've taught introductory sessions on R I focus more on a very short analysis to demonstrate what it is good at.