The `collapse` package uses pipe tidyverse-like functions that was written in C, like data.table. So, in R you can code with same intuitive syntax than dplyr but with code performance as good as (or even better than) data.table.
Collapse and other very fast packages are gathered in a meta-package called "fastverse".
"This is to say that it has gone mainstream. And when something goes mainstream, people don’t necessarily use it because it’s the best tool for the job; more often than not they use it because everyone else is using it."
This is a big reason why I use Python, along with the fact that productionizing things is comparatively easier with Python. For any work where I don’t expect my teammates to collaborate with me, I use R.
The `collapse` package uses pipe tidyverse-like functions that was written in C, like data.table. So, in R you can code with same intuitive syntax than dplyr but with code performance as good as (or even better than) data.table.
Collapse and other very fast packages are gathered in a meta-package called "fastverse".
According to current benckmarks, collapse is faster than Polars in most of data manipulation operations: https://github.com/AdrianAntico/Benchmarks
"This is to say that it has gone mainstream. And when something goes mainstream, people don’t necessarily use it because it’s the best tool for the job; more often than not they use it because everyone else is using it."
This is a big reason why I use Python, along with the fact that productionizing things is comparatively easier with Python. For any work where I don’t expect my teammates to collaborate with me, I use R.