[rjdj377dhabsn]

I disagree. R is just not a very nice language.

It has some really great statistical and data science packages that were well ahead of the competition 10-15 years ago. The web frameworks were good enough for dashboards and what most people were using R for.

But if you wanted to write fast and elegant nom-vectorized code, R is really lacking. I left it for Julia for that reason.

[mvieira38]

How is Julia in terms of data science dev experience? Nothing ever felt as good as the R+tidyverse combo to me, at least in Python.

[rjdj377dhabsn]

Julia is pretty good at basic data science. Working with dataframes is comparable to R's data.tables with the benefit that I don't need to switch languages if I want to run a fast loop over some data as part of a calculation or use a custom data structure.

I'm not a fan of pandas, so I'd say Julia and R beat python at basic dataframe manipulation. Nothing beats kdb+/q at dataframes though imo.

[mvieira38]

Have you tried Polars in Python? When you get going it's pretty similar to tidyverse, except you're chaining methods instead of piping, and it's lazily evaluated + parallel because of the underlying Rust engine. IME it's tidyverse > polars > pandas > data.table in terms of ergonomics

[mhogers]

I agree somewhat with you - nonetheless a FastAPI + Alembic + SQLAlchemy alternative in R would make it possible to use it as a general purpose language