Take your typical Excel user and explain to them that R is significantly more powerful, and they will stop listening the first time they get the arrow wrong on a variable assignment. You probably won't even get that far because the idea of a typing a variable name is foreign even though they've been using "variables" hidden behind cell references. "Why am I assigning a variable... in Excel I just type my data where I want it and click when I want to use it".
I need to use R about once a year -- each time I've forgotten everything from last time, and use a combination of stackoverflow and swearing to do whatever I need to do.
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/
Not a month goes by that I don't try to switch some Excel sheet I have into R; I run into the problems with Excel every day. It's not like I don't know Excel isn't great. My point is that the solution to those problems isn't R. I don't know what is, but I know it's not R.
Like, the other day I tried to do a real estate investment analysis in R. I always do that in Excel; I build the model step by step, starting with some basics, then filling in the details of the case at hand as I go. All the time, I can focus on the numbers; adding an indicator or refinement is part of the natural workflow. In R, you always have to switch between 'code mode' and 'data mode'. In Excel, you don't have this difference. Which is at the same time also its weakness, of course.
Sharing R models is a pain in the ass. Others have to get the exact packages, you have to tell them what to look at out of all the variables, ... Shiny sucks for that, it's read-only. In Excel someone makes some changes and sends you back the sheet, boom done. In R you would set up a versioning repo, data is split over multiple files, packages need to be installed, scripts for reporting, ... Fine for big projects, not for the small analysis exercises that make up the bulk of the uses of Excel.
Sure you 'can' blog in R. Last week I didn't want to walk to the shed to get my hammer, so I used a brick to pound in a nail. Doesn't make it right. String manipulation in R is a joke. A function to concatenate strings? Please. Again, R is fine for statistics, but it's not a general purpose replacement of Excel.
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.