Hadley brings an in-depth analysis of the R statistical language in this book and opens up more questions as it enlightens the reader to possibilities with R.

This book is only for the nerds of the nerds as it explores the R programming language, a language used mainly by non-programmers for statistical analysis. As such, the readership for this book is presumably small. Nonetheless, it fills a need as it supplies R users with a textbook for advanced programming.

R is a functional programming language, which means that functions can be passed as arguments in calls to other functions. Hadley shows us, as only a true scientist can, how to take full advantage of this property. He also explores R's underpinnings (R written to implement R functions); R's foundation in another programming language, C; and R's extensions into C , a third programming language. All this in the name of making R more usable and have better performance. ( )

I think I would've enjoyed this book a lot more if I had little more experience in writing R and as the title suggests, this book does talk about a lot of advanced concepts of R. Books like Beginning R by Mark Gardener and The Art of R Programming by Norman Matloff are definite stepping stones.

The internals of R are detailed very well in this book like - three properties of vector, four environments, the difference between [[ and $ and so on. I did have to lookup on help for a few functions since the book doesn't talk about what most of these functions do, only the 'how' of it.

Lexical scoping and method dispatch are so excellently explained, I read it twice just because I enjoyed it so much. Useful packages are mentioned across the chapters. The author is humble about what makes R difficult to learn as well.

As mentioned above, it'll take me some more time to get a grip on R and appreciate this book thoroughly, but nevertheless it did up my knowledge about R - 5 stars for that. ( )