I couldn't agree with more OP. I got my graduate degree in Statistics and, after working for several years in such a role, made a similar vow to Never Again™ touch R (or SAS). This effectively forced a career change. (I'm now "officially" a software developer and couldn't be happier.) My distaste for Statistics stems directly from the commonly used tools.
I appreciate your point. In most contexts, such as your comparison between Japanese and English, I agree with it. However, paradigms like OO, inheritance, scoping, etc. are hard-won, intellectual accomplishments; they're not arbitrary. They're purposefully designed to solve specific problems. My experience with R showed it to be rife with problems that have been avoidable for decades. AFAICT, it boils down to the tragic view of "I'm a (statistician|engineer|mathematician) not a coder so I don't need to care". The unfortunate truth is that despite such a view, doing analysis with R makes the analyst a programmer by definition. And so the language, ecosystem, and, consequently, users suffer from half-baked, poorly designed workarounds to problems which have long been solved (and abundantly documented in the software literature). To reduce it to a matter of perspective feels to me like a tautology: it's easy once you get it.
Clearly, I'm triggered by this. I hope I've expressed myself respectfully. My point is, I don't feel it's arbitrary. The design and complexity of R has real consequences in terms of cost and reproducibility.
I appreciate your point. In most contexts, such as your comparison between Japanese and English, I agree with it. However, paradigms like OO, inheritance, scoping, etc. are hard-won, intellectual accomplishments; they're not arbitrary. They're purposefully designed to solve specific problems. My experience with R showed it to be rife with problems that have been avoidable for decades. AFAICT, it boils down to the tragic view of "I'm a (statistician|engineer|mathematician) not a coder so I don't need to care". The unfortunate truth is that despite such a view, doing analysis with R makes the analyst a programmer by definition. And so the language, ecosystem, and, consequently, users suffer from half-baked, poorly designed workarounds to problems which have long been solved (and abundantly documented in the software literature). To reduce it to a matter of perspective feels to me like a tautology: it's easy once you get it.
Clearly, I'm triggered by this. I hope I've expressed myself respectfully. My point is, I don't feel it's arbitrary. The design and complexity of R has real consequences in terms of cost and reproducibility.