I wonder how much of this is just a feedback loop; were people taught both tools and then chose the one that works best, or was one more heavily promoted than the other, so people went with what was easiest to get started?

Once you are using the tidy paradigm, it lends itself to efficient plotting with ggplot2. Plotting with base R would require reshaping your data. So I think insofar as dplyr becomes a popular default it makes sense ggplot2 would be in lock step

Its definitely a feedback loop. Every time you look up an R question on stackoverflow people give you a ggplot or dpylr answer and usually not a base package implementation. Its almost as bad as Ole Tang spamming gnu parallel on every xargs thread.

Im sure that’s part of it. But you could say the same for using python or R over another language. Besides, someone who knows R well enough to write DplyR thought the situation was dire enough to write it. And there’s also data.table but that is inscrutable to most folks and I have only ever used it for fread - which is 10x faster than any other method of loading csvs into R.

Hardly. Hand holding tools are popular but that doesn't mean they aren't hand holding tools that don't give you any new function you didn't have otherwise. Jupyter notebooks are probably more popular to write than python scripts for new data scientists too, doesn't mean anything though or take away some of the advantages you get writing properly packaged scripts instead of a big old notebook you iterate a pipeline in line by line and figure by figure.