I discovered "How To Design Programs" somewhere late in my first year of using R. Like most beginning R coders with nominal experience in other languages, I wrote a lot of monolithic scripts in a very imperative style. HtDP gave me a mental framework for decomposing larger problems into bite-sized chunks. The lispy roots of R lent itself particularly well to the model of thinking presented in that book.
Ever since then, I've pined for the graphing calculator parts in a more modern Scheme. When ggplot and then the tidyverse (neé hadleyverse) came on the scene, I was even more convinced that Scheme, especially Racket, was the ideal future for data science. If R could support a large ecosystem like tidyverse, just imagine what the metaprogramming facilities of Racket could do!
But I think those graphing calculator parts are hard to reproduce. Attempts to clone ggplot2 fall short year after year, because most other languages don't have grid graphics to build on top of. R is a deep ecosystem on "an OK scheme," which is damned hard to beat.
Aside: my first year with R, was in an urban planning masters program and I was terrified of my first big kid statistics course (taught in SPSS). I decided I'd give myself bonus work by learning R. While it was absurd to be doing my stats homework in SPSS, then R, then reviewing HtDP on top of the rest of my course load, I did ace that stats course. :-)