If you really want them to write "well-tested and modular algorithmic code" then you should teach them how to write packages in RStudio. Then require everything submitted to be in RMarkdown.

I mentor learning data scientists and my advice is always to start using RMarkdown as soon they're remotely comfortable with RStudio. Not only does it avoid issues with an easily polluted global namespace, but more importantly encourages literate programming from the early stages. In stats/data science literate programming is vital to having any idea what you were working on a few months ago. It also makes writing reports much, much easier.

RStudio makes it pretty easy to put together R packages, and the package structure for R does a great job of enforcing proper documentation and testing. Sourcing R files should primarily be used to quickly play around with ideas, or for exploratory data analysis that doesn't fit well inside an RMarkdown document. Any code you intended on reusing between projects should end up in a local package.

I do think it's a problem that R has no intermediate method of organizing code like simple modules in Python. But this means if you're serious about writing clean R, you just have to bite the bullet and teach students to write packages.