I've used R since it was in beta, as well as many other languages: python, perl, C/C++, Fortran, lisp, Julia, ... others I'm forgetting.
I think you're right about fads and the appeal of a new sexy language. No dispute on that point.
However, some of what constitutes fads are really more like a coincidental convergence of advantages. So for example, something gets picked up in field X because it's more convenient and all the people learned that in college, and then the same thing happens in another field, and then when libraries in the two fields interact, it's like multiplying the reasons. These network effects happen everywhere in tech. It's not necessarily good, but it happens.
With R in particular there's a long arc to reasons why it might fade. I've heard about R fading before and then it picked up again, so who knows, but it will probably fade and there are reasons why.
If what you're doing involves mathematical or computational fundamentals, all that wrapping around C and Fortran gets annoying really fast. Not everything involving heavy lifting has been coded in fortran or C already, and sometimes passing back and forth between those heavy lifting routines becomes a huge bottleneck.
R is slow as hell, and yes you can write things in C or Fortran (or Rust etc?), but it turns something that should be fairly straightforward into a library project on its own almost. It's just easier to be able to write all the underlying stuff and IO/API stuff all in the same language and have it perform optimally. In fact, I'd probably rather just write it all in C or Fortran than write parts in one language and then wrap it in R — the R wrapping would mostly be to make it accessible to others (which is important, but there's the library bit).
R too has become horribly fragmented in my opinion. A lot of things like ggplot and tidyverse are great, but it's led to this kind of fracturing of syntax in what was already a kind of fuzzily defined syntax in some ways.
For what it's worth, I'm not sure I greatly prefer python. It's more general-purpose than R so has that advantage, but also has the same performance issues as R, and doesn't seem quite as well suited to statistical and numerical computing to me. Maybe it's just the object-heavy structure of python or something — maybe I prefer something closer to either lisp or C in the end — but python has never felt quite right to me. I'm looking forward to seeing what happens with Mojo, because that could be a real game changer, but am not holding my breath.
Julia is appealing to me and seems to check all the right boxes, has a lot of the fun I had with R early on, but I agree with some of the criticism it's gotten for library interdependence problems due to type flexibility and conversion-type issues. I also think error handling needs a lot of work. It's great when it works, but sometimes just becomes intractable to debug.
For me, statistical/mathematical computing is exciting in the last several years in a way it hasn't been in a long time, but also feels not quite "there" yet. I could still see a lot of currently used languages be superceded pretty dramatically by something new.