Same issue as Excel, really. Easy to use, so you get a lot of users with very thin engineering skills.
The solution is for production engineers to understand just enough R to set standards for data scientist code that enable reliable translation of the models to the production language. As with JS, you can complain about the yucky parts, or you can accept that it's the best tool for some jobs and make an effort to work around the yucky parts, or use the tools of those who are doing that (e.g. tidyverse and Wickham).
If you want data scientists to produce production-ready results, you have to hold them to the standards of production engineering.
Huh?
While I totally agree with your quote, I'd think it applied a lot more to python than to R. Especially given that python seems to be the dominant "first language for people to learn when they get into programming" because it is "easy".
Python isn't much better in this regard, thanks to the GIL.
What I actually found most baffling when I delved into R is the fact that it doesn't support 64bit integers (lack of proper native UTF-8 support coming a close second).
Take some standard ML model built with Caret or LME4 and try serve predictions with Plumber in R. It’s significantly more painful than using sklearn + FastAPI. You either need to use future::promise (which still sucks because it’s forking new R runtimes) or forgo this and go K8s or something similar.
I don’t get the love for RStudio either. It crashes frequently for me, or locks up randomly. The debugging experience is abysmal compared to PyCharm. Getting reproducible R builds are a pain, slightly alleviated by Renv. But not really if you want separate dependencies for dev and production.
Python and R tooling are not comparable. You will have serious issues operationalising R. Skills that most statisticians are simply not equipped to deal with, and serious software engineers will hate about R.
Unintentional and unnecessary creation of huge, memory-hogging objects is a closely related footgun. Packages are often not built with large data in mind and make choices that scale terribly, such as storing multiple copies of the data in the model object, or creating enormous nonsparse matrices to represent the model term structure. It's a legacy of the academic statistics culture R grew out of. Most researchers test their fancy new method on a tiny dataset, write a paper, and call it a day.
No argument about the debugging experience. I find it very slow, especially with large datasets, and try to avoid it. Not much experience with reproducible R builds but I wouldn't be surprised if it was a pain.
I hear a lot of "R is bad, Python is Enterprise Production Quality (TM)" blather at my work. It's always because the people involved don't understand computers, don't read documentation, don't debug, don't do root cause analysis, and want to quickly pass off responsibility for their laziness and incompetence. Meanwhile I and my team are happily chugging away, producing millions of dollars of reliable value for my company in R year after year.
Python lags far behind R in wide swaths of data science. Pandas is inferior to both dplyr and data.table, and R's modeling capabilities blow Python's out of the water in breadth and depth. You only use Python when you have to, e.g. for unstructured data and deep learning type stuff.
If your colleagues make you deal with their bad R code, that's too bad, but don't blame the language. It's designed to be easy to use, so a lot of bad coders use it. Go train your bad coders or hire better ones.