Plus, if you use a tool like RStudio or Jupyter, it's pretty easy to see the data as you manipulate it, which is a commonly cited advantage of Excel, with none of the awkwardness of trying to look at cell formulas.

That would depend on what you want to do. If by statistical analysis you mean regression or similar tasks, then sure R is the way to go.

However there are other family of analytics tasks which can be summed up as "decision analysis". Taking regression models (for example, maybe even built in R) and simulating them under different inputs, getting quantiles, sensitivity, etc. This goes much further with multiple sets, multiple outcomes with decision trees (NOT regression trees/CART) and even further with solvers. Excel is the best option in those tasks most of the time because of quick data entry and already built output reports and interfaces.

For people used to code, yes. But many excel user won't leave the ui they're familiar with. Including R will build a bridge between Excel user and modern development features (testing, vc ...)