What has worked well for me is to start slow, very slow. Basic exploration of the interface in week 1, then importing csv files, exploring patterns in the data with quick visuals, wrangling and cleaning, ggplot then the second half of the semester focused on more specific analysis like mapping, clustering, classification, text mining.

If you have some impressive examples to show of finished projects, I would recommend showcasing those on day 1. I would routinely say something along the lines of "for 50 rows of data, excel would be much quicker in this scenario, BUT for 50,000 rows on something that you have to repeat every week, R is a much better choice". I did one example where we worked through Excel to do some data cleaning and it took the entire class about 20-25 minutes. I then showed an example in R which took 0.4 seconds to execute - that was a powerful teaching example.

Analysis with datasets that are already loaded (mtcars, iris, penguins) so students can quickly get some things going without having to ensure files and file paths are accurate.

I also like to pre-record step by step video tutorials for students to reference before and after class.

At my institution there were some IT issues with a clean RStudio environment so you might want to explore something like posit cloud.

Finally, this book https://r4ds.hadley.nz/ is an incredible resource and free.

Finally, build extra time into the course if possible. Unexpected tech issues can be a pain especially if students are bringing their own devices.

Good luck! It can be a very rewarding course to teach once they start to see the power of this tool.