Getting Started With R

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Getting Started With SAS Graphics

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Getting Started With R

The R programming language is the most advanced statistical computing environment available. Anyone wishing to: use state of the art statistical packages, produce state of the art statistical graphics, or have a flexibile computing environment in which to develop state of the art statistical software will want to use R.

Here are some R resources:

(1)  An overview of the R programming language is provided at The R Project for Statistical Computing.

(2)  Download R from The Comprehensive R Archive Network (CRAN).  Versions are available for Linux, Mac OSX, and Windows in both 32-bit and 64-bit installations.

(3)  R comes with a default GUI.  However if you would like to have a nicer interface that is more like an IDE, then try RStudio.  Note that RStudio requires that you already have R installed.

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 goodness-of-fit, 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.