From the tone of your post I'm thinking that you've made up your mind on this point, but there really is a lot of richness and extensibility in R which isn't available in Excel. For instance my blog is written in an R library called Blogdown, you can process 5 GB datasets on your laptop, send a reproducible analysis to a colleague, stuff like that.
I guess the thing I would ask you to consider when thinking about this stuff is the amount of time you've spent learning Excel. At the time I was learning R I probably had several thousand hours of focused Excel practice under my belt, and I could do a lot with the tool. So Excel was a way better tool for me than R because I was an Excel expert and an R novice. After now putting in about that same amount of time working with R I can say it's a much more powerful and extensible tool. But if you mostly work in areas where Excel...uh...excels, then there's no real reason to make the switch.
That's a good point, I'm not exactly sure what the best practice is for using NSE vs. SE functions within functions. My practice is to just use the NSE version unless I have a good reason not to. Generally my goal with teaching beginners to use R is to just not be afraid of functions as the barrier tends to be feeling like they can use them at all rather than using them in the safest way.
For PML() kinds of functions, I often just google something like "Excel PML() function in R" and something usually turns up: - https://cran.r-project.org/web/packages/optiRum/optiRum.pdf - https://gist.github.com/econ-r/dcd503815bbb271484ff
Another good tactic is to follow some of the R quants on twitter. A really popular package for this is http://www.quantmod.com/