I adore R.
It's the language I'm most proficient in. Tidyverse is the most human-friendly way for explorational data analysis. Data.Table is blazing fast. RStudio is the best IDE I've ever seen and so tightly coupled with the mostly amazing documentation that it's a pure delight. CRAN's quality control next to none.
That being said, I prefer Python for my production use cases.
Why? What's missing in R, imho, is:
a) a decent interface to the web.
b) a decent way to use async processing / utilise the CPU fully.
I've found ways around both, but compare `Shiny` to `FastApi` + `Jinja2` and `future` to `asyncio`.
Shiny just feels clunky, no matter what you do. And R `future` (which saved my butt in 2019 when I was processing millions of geospatial data points for a project, so not throwing any shade) can be a total mindfuck and bug out (at least back then).
(Man, I miss working more in R, though.)
It's the language I'm most proficient in. Tidyverse is the most human-friendly way for explorational data analysis. Data.Table is blazing fast. RStudio is the best IDE I've ever seen and so tightly coupled with the mostly amazing documentation that it's a pure delight. CRAN's quality control next to none.
That being said, I prefer Python for my production use cases.
Why? What's missing in R, imho, is:
a) a decent interface to the web.
b) a decent way to use async processing / utilise the CPU fully.
I've found ways around both, but compare `Shiny` to `FastApi` + `Jinja2` and `future` to `asyncio`.
Shiny just feels clunky, no matter what you do. And R `future` (which saved my butt in 2019 when I was processing millions of geospatial data points for a project, so not throwing any shade) can be a total mindfuck and bug out (at least back then).
(Man, I miss working more in R, though.)