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.

Tangentially, R can help produce living Markdown documents (.Rmd files). A couple of ways include pandoc with knitr[0] or my FOSS text editor, KeenWrite[1]. I've kept the R syntax in KeenWrite compatible with knitr. Living documents as part of a build process can produce PDFs that are always up-to-date with respect to external data sources[2], which includes source code.

[0]: https://yihui.org/knitr/

[1]: https://keenwrite.com/

[2]: https://youtu.be/XSbTF3E5p7Q?list=PLB-WIt1cZYLm1MMx2FBG9KWzP...

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.

Any Julians comment?

Having seen Julia proposed as the nemesis of R (not python, that too political, non-lispy)

>the creator of the R programming language, Ross Ihaka, who provided benchmarks demonstrating that Lisp’s optional type declaration and machine-code compiler allow for code that is 380 times faster than R and 150 times faster than Python

(Would especially love an overview of the controversies in graphics/rendering)

https://news.ycombinator.com/item?id=42785785

I also like this fun though dated handbook, full of gotchas common among new R programmers:

https://www.burns-stat.com/pages/Tutor/R_inferno.pdf