I was going to sit down and learn the {plotnine} python library today, but turns out there's nothing really to learn—it's literally {ggplot2} code wrapped in parentheses. If you know {ggplot2} in R, turns out you also know how to make plots on Python!
Eric R. Scott
mastodon 4.7.3Scientific Programmer & Educator at University of Arizona (@cct_datascience@fosstodon.org) | Mentor for @Posit@fosstodon.org Academy | Ecologist | Tea geek | whimsy enthusiast
Posts are mine and do not represent my employers in any way.
If you can't help but do a lil dance when you eat something tasty, I think we'll be friends.
There are now so many "backends" for {dplyr}—duckdb with {duckplyr}, polars with {tidypolars}, various database engines with {dbplyr}, {data.table} with {dtplyr}. Is there a blog post or flow chart somewhere with pros and cons of each? Like, comparisons of memory requirements, speed, and how likely they are to "just work"?
How do I politely explain to someone that you can't increase your sample size by just linearly interpolating values?
Can I do anything with tulsi besides dry it for herbal tea? I have so much right now!