I think a lot of it depends on what you use SAS for, and why people are interested in R - what are the motivations for your user group?
SAS and R are both great for basic analyses, but people often find SAS can become very unwieldy when they try to go beyond the basics and do more advanced analyses.
I work for Mango Solutions, and we see lots of organisations moving from SAS to R. I can't speak for academia, but in industry, the main motivations seem to be the cost of SAS licenses, matching skills of new graduates (SAS is being taught less and less, with R being taught more), and availability of the latest analysis techniques - typically things take a while to make it into SAS, compared to R where anyone can release a package on CRAN quickly. Certain R packages like shiny and ggplot2 are also motivators.
In response to question 1 in your second set of questions, I'm not aware of any statistical analyses that can be done in SAS that cannot be done in R. The opposite is more likely, if anything!
2a is a tricky one. I think SAS is easier to read than R for someone who doesn't already know the language, and is certainly easier to pick up. However, it's a trade-off against the greater flexibilty - and with the tidyverse packages and associated learning resources, R is a lot easier to learn than it has been in the past.
2b - I can't think of any stats reasons to believe SAS over R. That said, we sometimes see hesitance over the fact that anyone can submit a package to CRAN, which can be a bit of a worry to people more used to proprietary software than open source. I think here it's a bit of a mindset shift; typically the most widely used and well established packages have had so many people testing and using them, this isn't an issue.
All of the above said, I wouldn't say necessarily "R is better than SAS" or "SAS is better than R" - it all depends on what you need it for, and why you're interested in moving.
Hope that helps!