One can see that in the JVM world with java vs scala: people attracted to scala tend to like "cute" DSL, java people tend to be more careful with shiny new features. (This is an oversimplification, of course)

Specifically for dplyr: it looks cute and tends to be easier to use in a REPL setting (you can build your pipeline step by step by running your command, looking at the output, get the command from history, add a step, run again; and at the end you get a single line to copy paste in your script). But if you want to wrap it in a function, it tends to create issues.

It also provides guardrails and encourages best practices which I find a bit to paternalistic and annoying but again I can see the value.

I think most R users would be surprised and just how much tidyverse functionality is hidden in base R but majority of the dplyr versions of functions have at least some intended improvement over the base R versions, and some are a massive improvement in functionality.

For example in a typical script the only tidyverse package I may load besides ggplot2 is tidyr, because the pivot_ wider/longer() functions really do solve a problem that was not fun in base R.