←back to thread

Big Book of R

(www.bigbookofr.com)
288 points sebg | 1 comments | 10 Apr 25 17:34 UTC | HN request time: 0s | source
Show context
cye131 ◴[10 Apr 25 23:51 UTC] No.43649039[source]▶
R especially dplyr/tidyverse is so underrated. Working in ML engineering, I see a lot of my coworkers suffering through pandas (or occasionally polars or even base Python without dataframes) to do basic analytics or debugging, it takes eons and gets complex so quickly that only the most rudimentary checks get done. Anyone working in data-adjacent engineering work would benefit from R/dplyr in their toolkit.
replies(6): >>43649143 #>>43649208 #>>43649881 #>>43650319 #>>43650677 #>>43683325 #
wwweston ◴[11 Apr 25 00:20 UTC] No.43649208[source]▶
what’s the story integrating R code into larger software systems (say, a saas product)?

I’m sure part of Python’s success is sheer mindshare momentum from being a common computing denominator, but I’d guess the integration story is part of the margins. Your back end may well already be in python or have interop, reducing stack investment and systems tax.

replies(4): >>43649470 #>>43650307 #>>43650696 #>>43650785 #
1. vhhn ◴[11 Apr 25 05:34 UTC] No.43650785[source]▶
There are so many options to emded R in any kind of system. Thanks to the C API, there are connectors for any if the traditional language. There is also RServe and plumber for inter-process interaction. Managing dependencies is also super easy.

My employer is using R to crunch numbers enbeded in a large system based on microservices.

The only thing to keep in mind is that most people writing R are not programmers by trade so it is good to have one person on the project who can refactor their code from time to time.