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Despite writing most of my procedural code in Python, I've always preferred doing my data analysis in R. For all of R's warts, the ergonomics of the dplyr + ggp
by wsowens 2mo ago
Despite writing most of my procedural code in Python, I've always preferred doing my data analysis in R. For all of R's warts, the ergonomics of the dplyr + ggplot + the rest of the tidyverse are very tough to beat. My few attempts to use Pandas and matplotlib/seaborne have always proved frustrating. Based on this cheatsheet though, it seems like Polars addresses some of the friction of Pandas. Looking forward to trying it!
- 220hertz 2mo agoLess friction, considerably faster. I have a statistician friend who's recently made the jump away from R. I think he would agree with you.
- holub008 2mo agoAgreed, as an R and polars user. The fundamental advantage R holds over other languages/libraries is expressions. The ability to reference columns directly AND interoperate with vectorized base ops in R is unfair. Of course, this super power is equally confusing to learners, fraught for production code, etc.
- rtpg 2mo agodo you have a snippet of what this looks like in R?
- latent-person 2mo agoNot OP, but here is an example using tidyverse (I leave the meaning of it to you; should be clear without any R knowledge): purchases |> group_by(country) |> filter(amount <= median(amount) * 10) |> summarize(total = sum(amount - discount))