Hi everyone! I have been using SPSS but have heard that R has superior graphic capabilities and so I have downloaded it but I am in desperate need of a manual more comprehensive than the included help file. BTW, I am a first-year grad student and have limited stats experience, just one undergrad & 1 (2-semester length) grad course.

I am considering:

Introductory Statistics with R (Peter Dalgaard)

Data Analysis & Graphics Using R (John Maindonald)

Using R for Introductory Statistics (John Veranzi)

Does anyone have experience with any of these texts? I would love to hear your opinions about the usefulness of them, both as a basic introduction to using R and as a stats reference.

Thanks!

I would go for the second (Data Analysis & Graphics Using R).

> http://www.maths.anu.edu.au/~johnm/r-book.html

Check this too

> http://talkstats.com/showpost.php?p=9404&postcount=5

thanks so much!

I bought Dalgaard book last winter. I doubt its what your looking for; though I have flipped through it a couple times. It really concentrates on the intro statistics to regression type stuff and doesn't go all that deep into statistical topics either.


I just got a new book by Andrew Gelman called "Data Analysis using regression and Multilevel/Hierarchical Models"

He doesn't tack on the words "Using R" but its definatly "Using R with integrated Bugs" as R code and output is basically on every page. He is a reputable author with a successful Bayesian book.

The paper back version is cheaper than Dalgaard. It cover most all of the non-basic statistics that Dalgaard covers (starts with regression analysis and time series). It is more conversational than dalgaard (if you like an author that talks to you instead of spews facts this isn't the book). And it mixes in professional R code with almost ~every~ example which is basically every page. His chapter on causal inference is one of the most conversational I have ever read in a statistics book. I like that personally.

If you are willing to give up stuff like how to do a pairwise t-test and how to analyze a two-way table, but gain stuff like how to do inference with simulation in R, then I have no trouble saying this is a better buy. And heck I got it for 20 bucks. Though I must say I have only owned it one day(24 hours =)~


I just got "Modern Applied Statistics with S" a week and a half ago. I would advise any grad student that has a future of using R to get it and keep it around. It doesn't hit to much on the statistics, but it does cover a ton of ground--certainly fitting the bill "R Reference Manual". It seems like a great language reference, and I have used it already. Think of it as a comprehensive guide to core R. Personally I think its probably more important to get than the Gelman book I recommended over Dalgard, but its really aimed at people who want the statistics refreshed so they understand exactly what R is doing rather than being a statistics text.



So thats my contribution to the list:
Modern Applied Statistics with S (MASS)
Data Analysis Using Regression and Multilevel/Hierachical Models (Its applied statistics with heavy R focus in the text) by Andrew Gelman