What makes R infuriating is most of the complexity and gotchas aren't inherent to the problem.
So you learn Clojure and you inevitably meet the collections. And it takes like 5 minutes to tell you how to map and how to reduce and then the lecture ends with "and it just works". And in fact it does just work.
Then you learn functional R and the first five minutes are the same as the Clojure experience. Then the slow motion train wreck starts and "And R likes mapping so much, we have nine microscopically different apply statements for list and tables and they input some things and output other things and if you pick the wrong one the failure looks like the Trinity nuclear test but more impressive". Every R language lecture is like that, five minutes of how real languages do it, then the rest of the 45 minutes is endless pitfalls and accidents. Its like a 45 minute long fever dream or nightmare "... and if you accidentally tapply, table apply, to a list, then it coerces the input to ..." and drift back to Cthulhu, or maybe away from, whatever.
Pragmatically if you teach R as a statistical analysis language what looks weird often enough turns out to be super convenient. But if you try to teach and learn R as a general purpose computational language, you wonder if its a joke and nobody would actually use Intercal or BF to run analysis, would they?
Its a very powerful system in spite of the language. Think of PC hardware architecture going back to the old XT days, its sinfully ugly, but its quite capable. R is no PDP-11 or VAX, thats for sure.
So you learn Clojure and you inevitably meet the collections. And it takes like 5 minutes to tell you how to map and how to reduce and then the lecture ends with "and it just works". And in fact it does just work.
Then you learn functional R and the first five minutes are the same as the Clojure experience. Then the slow motion train wreck starts and "And R likes mapping so much, we have nine microscopically different apply statements for list and tables and they input some things and output other things and if you pick the wrong one the failure looks like the Trinity nuclear test but more impressive". Every R language lecture is like that, five minutes of how real languages do it, then the rest of the 45 minutes is endless pitfalls and accidents. Its like a 45 minute long fever dream or nightmare "... and if you accidentally tapply, table apply, to a list, then it coerces the input to ..." and drift back to Cthulhu, or maybe away from, whatever.
Pragmatically if you teach R as a statistical analysis language what looks weird often enough turns out to be super convenient. But if you try to teach and learn R as a general purpose computational language, you wonder if its a joke and nobody would actually use Intercal or BF to run analysis, would they?
Its a very powerful system in spite of the language. Think of PC hardware architecture going back to the old XT days, its sinfully ugly, but its quite capable. R is no PDP-11 or VAX, thats for sure.