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by clebio
5135 days ago
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This is a really detailed comparison and seems balanced. Thanks for that. Do you not use both in conjunction (R for the stats libraries and Python for the more general computing)? Having done some background reading (but without yet getting down into the weeds with them), that was my take on the relationship. Edit: re-reading your last paragraph, I guess you're saying that Python can reach parity with R's libraries, at which point it's elegance and speed will win out. R's decades of lineage do seem to hobble it in terms of style and syntax, after all. |
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As an example, I like the ability to use expressions on the left side of an assignment (e.g. names(df) = "stuff"). But, it sounds like you are right that the python developers are getting to learn from R's mistakes and avoid getting locked into to legacy ideas.
As far as libraries... R has a lot. So I don't expect python to totally catch up soon. But, I only use 10 or 15 R libraries, and those are really popular libraries. So, unless you do an incredible range of stuff, python probably doesn't hvae to completely catch up.
One major advantage for R is the package management system (CRAN). The uniformity of the interface... the ability to search for stuff in it... that's been really useful for me. Not sure if anything like that is in the works for python.
Lastly, there are a lot of little helper functions that I've written for myself in python that are part of base R. The first example that comes to mind is head() to view the top few lines of a data structure. It seems strange that python would be missing these little helper functions, but I never found it.