I am definitely inexperienced with R. The intention of this post was to highlight a key point of friction I encountered throughout my introduction to the language. Namely, I was introduced to R (by someone more senior than myself) as "data science in a runtime/IDE", but there quite finite boundaries where the conveniences of R stop and other tools begin.

What motivated me to write this post was the lack of discussion of R in this use case. I actually stumbled into using "Rscript -e" one liners while looking to do basic stats in a Linux CLI.

That being said, I still stand by my point. Taking data from "out in the wild" (log files, tarballs of images, unstructured text) and making use of it can be frustrating in R because cleaning up edge cases, removing unwanted data, and getting everything into the correct container/type often involved unintuitive chaining of function calls. This is coming from the perspective of someone who worked with Python/awk/sed prior to being exposed to R.

If you have good counterarguments, I'd be more than happy to hear them and addresss them in an end note in the post.