1. Integrating the development environment on their host PC (for example connecting RStudio in R's case, or connecting their web browser back to a server running in the VM in the case of Jupyter) is another set of skills to master.
2. Many data analyses are memory hungry unless you want to resort to coding practices that optimize for memory consumption. The overhead of running a VM is a bummer for some scientists.
3. Many scientists are not using Linux top-to-bottom, and therefore don't have a great way of virtualizing a platform that they are familiar with (e.g. Windows, macOS)
Can people think of others? I'm sure I'm missing some.
(EDIT: To be clear, I think VMs are a great path, but I do think there are some practical reasons why some scientists don't use them)
Touching that PC, in anyway would be considered harmful to everybody using that specific piece of equipment.
Therefore, from the beginning of your acquisition, you are basically using a machine you don't control.
From the scientist’s perspective, a lot of this can start to feel like yak shaving. The opportunity costs are real.
There's never a need to mess with root access -- not even to install headers/dev packages. Dev tools and compilers can also be made to look to custom locations. I mean, there's never a need aside from self-imposed limitations our OSes and tooling places upon us.
Nothing inherently complex: just tons of accidental complexity.
Those things are only hard because we never did any coordinated effort to fix them.