Big Book of R

(www.bigbookofr.com)

288 points sebg | 1 comments | 10 Apr 25 17:34 UTC | HN request time: 0s | source

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uptownfunk ◴[10 Apr 25 21:33 UTC] No.43648229[source]▶

I will say, now after 15 years messing with this. With LLM I just do it all in Python. But, I still miss the elegance and simplicity of R for data manipulation and analysis. Especially the dplyr semantics. They really nailed it. I think they got crushed by the namespace / import system. There’s something about R that makes you so fluid and intuitive. But the engineering, the efficiency, I get with Python now, I can’t go back.

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dkga ◴[11 Apr 25 05:14 UTC] No.43650701[source]▶

>>43648229 #

I agree with all your comment… except the very last bit. Do you really find python to be more efficient at engineering stuff than R? And especially speed, which in my experience at least is broadly the same if not faster with R because it interages easier with Rust and C++?

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1. claytonjy ◴[11 Apr 25 14:17 UTC] No.43654080[source]▶

>>43650701 #

Not OP, but i think python is very far above R for engineering stuff. I built my early career on R and ran R user groups. R is great for one-off analyses, or low-volume controlled repetition like running the same report with new inputs.

For engineering stuff i want strong static analysis (type hints, pydantic, mypy), observability (logfire, structlog), and support (can i upload a package to my cloud package registry?).

For ML stuff, i want the libraries everyone else uses (pytorch, huggingface) because popularity brings a lot of development and documentation and obscure github issues the R clones lack.

Userbase matters. In R, hardly any users are doing any engineering; most R code only needs to run successfully one time. The ecosystem reflects that. The python-based ML world has the same problem, but the broader sea of python engineers helps counterbalance.

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