Polars+DuckDB beats Pandas.
Tidyverse (R) is superior for data exploration but R is not fun to deploy and make complex multi job data pipelines.
Tidyverse (R) is superior for data exploration but R is not fun to deploy and make complex multi job data pipelines.
Are folks who say this not using containers? R has been at least as easy to dockerize as Python since whenever Rocker started, only easier with more recent package management options. Once dockerized, my only R complaints are around logging inconsistencies.
I used to think the culture around R meant that productionizing arbitrary code was harder on average than in Python...but years of suffering with the pandas API has me thinking the opposite these days.
I can trust a junior R dev to write reusable pure functions but can't trust a senior Python dev to do the same!
This means you don't just have to pin your R package versions, you have to pin all the build dependencies.
And you have to have a different image for different sets of R packages because they might have different build dependencies.
I do sympathize with long from-source build times; as a Linux user I don't think I had binaries available until I stopped using R, so I've spent days, perhaps weeks, waiting for dplyr to install over the course of my R usage.
> different image for different sets of R packages because they might have different build dependencies
Is this not true of all software in all languages?