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In R, I often can write a dozen line program that will do something incredible, but it might take an hour to hammer out those lines and cannot reconstruct them from memory because of inconsistencies in syntax. It sort of feels like a statistics DSL built on Lisp.

R is exactly like a statistics DSL built on Lisp. The first sentences of a talk by Ihaka [1] are:

> R began as an experiment in trying to use the methods of Lisp implementors to build a small testbed which could be used to trial some ideas on how a statistical environment might be built. Early on, the decision was made to use an S-like syntax. Once that decision was made, the move toward being more and more like S has been irresistible.

Since I find it basically impossible to remember how 'eval', 'quote', 'substitute', etc. work in R, I suspect that the Lispers are onto something when they say that the lack of syntax in Lisp is an important feature.

[1] http://www.stat.auckland.ac.nz/%7Eihaka/downloads/Interface9...

R makes the hard things easy and the easy things hard.

> I found R difficult to learn because it is seems inconsistent to me. Now one reason might be that I picked up bits of R here and there without taking the time to learn the syntax from a book (unlike what I did for other languages), but it took me quite some time to be able to make sense of the different structures (vector, list, matrix, dataframe), their differences, and in particular how functions operate differently on these structures.

If it makes you feel any better, I've read several R books and they normally present it in an inconsistent manner as well. I've written programs in some 30 languages over the last 13 years, and I've yet to encounter one that's as difficult to pick up as R. Materials that purport to teach R are, in my experience, almost always presented as a set of recipes for very specific statistical methods. That approach stands in stark contrast from traditional programming introductions that attempt to teach general concepts rather than specific how-tos.

I agree, though, that despite the learning curve, R is rather useful.

I'd appreciate any comments on https://github.com/hadley/devtools/wiki - it's my attempt to teach R like a programming language, focussing on cross-cutting concerns and general concepts. It's still a work i progress, so your feedback can help make it better.

I read it cover to cover (so to speak) about two weeks ago, and although still a work in progress, it is indeed the best written guide to R that I've read to date. It appealed to me because it insisted on the functional programming side of R, and explained some of the intricacies and advanced concepts in R. It's pretty dense though and will certainly require a number of re-reads before I internalize all of the material.

One thing I would find very useful is a case study on how to design more complex packages. In particular, I would love to have an executive summary of the inner-working of ggplot2 and/or ddply. In particular, how can I manage to pass formulas as arguments to my functions, how can I achieve the pseudo DSL effect of ggplot2, etc... I know, I could read the source on github, but ggplot2 is pretty big, so a summary would be helpful.

I think as I flush out the functional programming and computing on the language sections, those techniques underlying ggplot2 and plyr should become more obvious. I like the idea of case studies on more complex packages, but I don't know if I can bring myself to try and describe how they work :/ Part of the problem is that I didn't understand the techniques terribly well myself when I wrote ggplot2 and plyr, so they don't have particularly clean implementations.

I have to second the usefulness of devtools/wiki. Its wonderfully clear and concise, though it does take some getting one's head around. And I write R every day and have read all of Venables and Ripley (more than once) and R still trips me up quite a lot. I tend to understand the error messages now, which is certainly progress.

I've been working with R 90% of my time over the last two years and I agree it's a freakishly inconsistent language.

Me and the team I work with are still finding out weird completely illogical errors that effect the language but I think I've somehow started loving it despite all it's flaws and I did actually semi-enjoy the process of figuring out all those little wtf issues.

For those starting out with it reading this book + r inferno helps a lot with understanding the underlying weirdness and inconstancies of the language: http://www.amazon.co.uk/The-Art-Programming-Statistical-Soft...