Big Book of R

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.

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.

This is, I think, the main reason R has lost a lot of market share to Pandas. As far as I know, there's no way to write even a rudimentary web interface (for example) in R, and if there is, I think the language doesn't suit the task very well. Pandas might be less ergonomic for statistical tasks, but when you want to do anything with the statistical results, you've got the entire Python ecosystem at your fingertips. I'd love to see some way of embedding R in Python (or some other language).

Plumber is a mature package for building an api in R.

https://www.rplumber.io/

For capital P Production use I would still rewrite it in rust (polars) or go (stats). But that’s only if it’s essential to either achieve high throughput with concurrency or measure performance in nanoseconds vs microseconds.

We tried plumber at work and ran into enough issues (memory leaks, difficulty wrangling JSON in R, poor performance) that I don't think I could recommend it.