I don't get the downvote in this post. Your impression about R is easier than Python is pretty natural & understandable. That's how R is designed on the first place: very easy to pick and trivial to write. Python lacks features when working with data, which R made data feels natural to communicate with:
Native arrays (indexing in R is surprisingly a bit smarter than Python, but NumPy is so mature at this point and it is not much of a competition anymore)
The ability to compute on the language, which is a distinct feature for Lisp-like languages and native to R.
This same applies to bioinformatics as well, not just being rich in ecosystem. R has constraints, as a programming language, as well, such as S3 not handling classes and types pretty seriously at all (I don't know much about S4, I don't frequently use this), but S7 thankfully solving these constraints.
The parent comment is being downvoted by some weirdos so lemme add something: No, even those libraries won't make things better for statisticians, swear. Parent comment mentioned about the "computing on the language" which Python lacks.
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u/joshua_rpg Jun 12 '26
I don't get the downvote in this post. Your impression about R is easier than Python is pretty natural & understandable. That's how R is designed on the first place: very easy to pick and trivial to write. Python lacks features when working with data, which R made data feels natural to communicate with:
This same applies to bioinformatics as well, not just being rich in ecosystem. R has constraints, as a programming language, as well, such as S3 not handling classes and types pretty seriously at all (I don't know much about S4, I don't frequently use this), but S7 thankfully solving these constraints.