At the same time there are companies in which there's R code running in production to serve ML results in real time. No idea how common it is, but I've worked for such a company. My point is that R isn't just for scripting and interactive use even if that's its best use case.
A plumber in front of your ML serving function running in a docker container. There, simple, fast enough. We have multiple R containers in production, it is working fine and the data scientist are happy that they can keep working in RStudio.
