Yes, having a decent sized central VM that everyone can use as their development area has really increased the productivity and consistency of using R at my company. I let users install packages into their own personal library, but also have a standardized and approved system library that makes deploying R code to production much simpler. I also find using package dependency tools like packrat + Docker makes deployment way more maintainable and less prone to various errors like package upgrades that deprecate functions.
I've made a few shell scripts to run weekly to clear up space on the shared VM so not really having problems with hanging sessions anymore, but I do see the VM usage be very inconsistent with people using all the resources causing problems with other users. I would be really awesome to have a more auto-scaling platform.
Jupyterhub is built on Docker and allows multiple user accounts on top of linux user accounts of the main VM, thus isolating resources and preventing any one user from taking over the entire machine. I'd like to eventually get my setup to something similar -- one central VM with each individual having their own RStudio instance as an isolated container tied to their linux user. Or maybe deploying on top of kubernetes and allowing it to decide when to add/remove pods as necessary.