It can be challenging for sure.
I will say, having struggled to use R and finally prevailing, that I will NEVER use anything else for generating figures for academic publications or internal reports (ggplot2 - same as the author of course).
Nothing comes close to the composable, functional way that it "just works" -- I don't use it every day - but it is my "go to" for data exploration etc. -- more than pandas / ipython.
You might enjoy <http://adv-r.had.co.nz/>, which discusses R from more of a programming language perspective (albeit a programming language that is chiefly used for data analysis). There are a lot of misunderstanding about R the language.
I looked at several tutorials and what worked for me the best so far are the official manuals https://cran.r-project.org/manuals.html (esp. the language definition and the "introduction to R").
Moreover, for the programming languages enthusiasts, the following article is pretty interesting:
Evaluating the Design of the R Language (Morandat, Hill, Osvald, Vitek).
"R is a dynamic language for statistical computing that combines lazy functional features and object-oriented programming. This rather unlikely linguistic cocktail would probably never have been prepared by computer scientists, yet the language has become surprisingly popular. With millions of lines of R code available in repositories, we have an opportunity to evaluate the fundamental choices underlying the R language design. Using a combination of static and dynamic program analysis we can assess the impact and success of different language features."