I agree. I'm glad I took time to take quite a few statistics courses using R though. I composed the sentence deliberately: Just a course "learning R" as we have

I agree. I'm glad I took time to take quite a few statistics courses using R though. I composed the sentence deliberately: Just a course "learning R" as we have for all other languages IMHO is pretty useless, unless it is for people already statistics-savvy. What I learned from the experience was a focus on the data, not the code. I can't explain it, but to me it seems that when I code(d) in regular programming languages my focus is different, much more on the code. When doing something in R my focus was much more on the data at hand. That's probably also because when I wrote/write normal software I do it generically, when working on an R problem I already had the concrete data set. Maybe that's different for people writing more advanced code in R like packages, then it may be closer to "normal programming", don't know. I assume most people using R are not doing it to program, but to get something from concrete data, and only a minority of R users do much programming. Clarification - I mean code like this: https://rpubs.com/Noseshine/74191 https://rpubs.com/Noseshine/74191 It's code, sure, but I just wanted to get something from the data, no thoughts of writing a general purpose program. R programming felt very "hackish" to me, but I also feel it's fine for such concrete-data-oriented purposes as in my example link. In that case it's like Excel but more editable, processable and reproducible since you've got linear source code, and not a path of clicks through lots of complicated dialogues.