Comment by isoprophlex
Comment by isoprophlex 2 days ago
A bit of a tangent but this might be somewhat illuminating: I worked for a big utility company. They needed some economic analysis done on where to best put some transport pipes into the ground, macro granularity (eg smallest unit was a neighborhood).
The team that did the analysis apparently did a great job, so additional requirements were thrown their way: a frontend and an interactive planning module on designing the pipe networks on micro level (smallest unit was a single house)
A year later the absolute maniacs delivered the application. They only knew R and some html/js, so that's what they used.
Cursed as fuck of course, and because they were external they left short after. This was pretty bad for the people left holding the bag; I mostly found it awe inspiring in the "doom running on an electric toothbrush" kinda sense.
R is cursed beyond reason, but traditional software engineers are sleeping on it, IMO. It's very easy for quantitative people that are not software developers to get something done quick. The downside is exactly what you described, most projects are not just the model, they eventually tend to incorporate generic data wrangling, UI/web code, etc, and a general purpose language tends to work better overall.
I have a similar anecdote: I was brought in on a project where a group of terrorists implemented a solution for a TSP-like problem directly in R. We eventually replaced that thing with OR-Tools.