
R for Everyone by Lander
This book is a basic introduction to R for the R beginner. It
provides a basic understaning of working with data in R,
programming with R, and an overview of several useful R
packages and how they are used.


The R Book by Michael J. Crawley
This book is a massive (~1000 pages) comprehensive reference to help
you perform the most common statistical analyses. The book is a
must have for the beginner who wishes to become a sophisticated R user.


Data Mining and Business
Analytics with R by Johanes Ledolter
This book is a nice self contained instroduction to a broad spectrum
of analytical techniques from statistics and machine
learning by example in R. This book would be a
very good starting point for learning about the
different modeling capabilities of R.


Data Manipulation with R by Phil
Spector
This book is a must have for the industry R user. As the title
suggests the book focuses on data manipulation in R, including
interacting with a SQL data base using R, manipulating dates and
strings in R, and rehaping data in R.


A Modern Approach to Regression with
R by Simon Sheather
If you want to begin to understand how statistician's are trained to
think about statistical modeling through the entire modeling process:
explorartory data analysis, model building, and assessment of the model
goodnessoffit, then you will need a modeling book based on R.
You should note that R is developed and maintained by research
statisticians, hence it is the only statistical software that is
developed with the tools to develop statistical models from a
statistical point of view.
Note that the book itself does not contain R code. For data sets
and R code see the book web site: www.stat.tamu.edu/~sheather/book.


Linear Models with R by Julian J.
Faraway
This book is a very task oriented modeling book. In this book
Faraway illustrates how to perform each step of a regression analysis
in R with direct reference to specific R code within the context of an
example. The book is very light on the underlying statistical
concepts, but very heavy on the R implementation. Typically this
book works very well in conjunction with another text such as
Sheather's book.


Applied Regression Analysis and
Generalized Linear Models by John Fox
This book provides a thorough introduction to Ordinary Least Squares
regression analysis and some more advanced topics from
Generalized Lienar Models and Robust Regression. The
book is written at the same level as Sheather.


Modern Regression Techniques
Using R by Wright and London
Nice introduction to the more advanced topics in regression models
with a focus on using these methods in R.


ggplot2: Elegant Graphics for Data
Analysis by Hadley Wickham
R is capable of producing very sophisticated statistical
graphics. However, producing these graphics takes an
understanding of how R builds the components of a plot and how to
reference and define those parameters. The easiest way for the
beginner to produce high quality graphics is to use the ggplot2
graphics package.


Lattice: Multivariate Data
Visualization with R by Deepayan Sarkar
The Lattice package can be used to simplify the production of views of
multivariate data using a functional
notation and without the need to subset your
data frame. The package can be extremely
useful if you like to use statistical
graphics in your Exploratory Data Analysis.


Software for Data Analysis:
Programming with R by John Chambers
When you have reached the point that you understand the basics of R and
you have decided that you want to become a very serious R programmer,
then this is the book for you. R is a derivative of the S
Programming Language, winner of the 1998 ACM Software System Award,
which was created at Bell Laboratories by John Chambers. The
books on S (and R) written by Chambers are considered to be definitive
sources on the R programming language.


R Graphics by Paul Murrell
If you want to be able to develop your own sophisticated R graphics
from scratch, then you will need to read and understand this
book. This book outlines the building blocks of a R plot and how
to modify those building blocks.
