Book Description
Hugely successful and popular text presenting an extensive and comprehensive guide for all R users
The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.
This edition:
Features full colour text and extensive graphics throughout.
Introduces a clear structure with numbered section headings to help readers locate information more efficiently.
Looks at the evolution of R over the past five years.
Features a new chapter on Bayesian Analysis and MetaAnalysis.
Presents a fully revised and updated bibliography and reference section.
Is supported by an accompanying website allowing examples from the text to be run by the user.
Praise for the first edition:
'...if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.' (The American Statistician, August 2008)
'The Highlevel software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book...' (Professional Pensions, July 2007)
Table of Contents
 Cover
 Title Page
 Copyright
 Preface
 Chapter 1: Getting Started

Chapter 2: Essentials of the R Language
 2.1 Calculations
 2.2 Logical operations
 2.3 Generating sequences
 2.4 Membership: Testing and coercing in R
 2.5 Missing values, infinity and things that are not numbers
 2.6 Vectors and subscripts
 2.7 Vector functions
 2.8 Matrices and arrays
 2.9 Random numbers, sampling and shuffling
 2.10 Loops and repeats
 2.11 Lists
 2.12 Text, character strings and pattern matching
 2.13 Dates and times in R
 2.14 Environments
 2.15 Writing R functions
 2.16 Writing from R to file
 2.17 Programming tips

Chapter 3: Data Input
 3.1 Data input from the keyboard
 3.2 Data input from files
 3.3 Input from files using scan
 3.4 Reading data from a file using readLines
 3.5 Warnings when you attach the dataframe
 3.6 Masking
 3.7 Input and output formats
 3.8 Checking files from the command line
 3.9 Reading dates and times from files
 3.10 Builtin data files
 3.11 File paths
 3.12 Connections
 3.13 Reading data from an external database

Chapter 4: Dataframes
 4.1 Subscripts and indices
 4.2 Selecting rows from the dataframe at random
 4.3 Sorting dataframes
 4.4 Using logical conditions to select rows from the dataframe
 4.5 Omitting rows containing missing values, NA
 4.6 Using order and !duplicated to eliminate pseudoreplication
 4.7 Complex ordering with mixed directions
 4.8 A dataframe with row names instead of row numbers
 4.9 Creating a dataframe from another kind of object
 4.10 Eliminating duplicate rows from a dataframe
 4.11 Dates in dataframes
 4.12 Using the match function in dataframes
 4.13 Merging two dataframes
 4.14 Adding margins to a dataframe
 4.15 Summarizing the contents of dataframes

Chapter 5: Graphics
 5.1 Plots with two variables
 5.2 Plotting with two continuous explanatory variables: Scatterplots
 5.3 Adding other shapes to a plot
 5.4 Drawing mathematical functions
 5.5 Shape and size of the graphics window
 5.6 Plotting with a categorical explanatory variable
 5.7 Plots for single samples
 5.8 Plots with multiple variables
 5.9 Special plots
 5.10 Saving graphics to file
 5.11 Summary
 Chapter 6: Tables
 Chapter 7: Mathematics

Chapter 8: Classical Tests
 8.1 Single samples
 8.2 Bootstrap in hypothesis testing
 8.3 Skew and kurtosis
 8.4 Two samples
 8.5 Tests on paired samples
 8.6 The sign test
 8.7 Binomial test to compare two proportions
 8.8 Chisquared contingency tables
 8.9 Correlation and covariance
 8.10 Kolmogorov–Smirnov test
 8.11 Power analysis
 8.12 Bootstrap

Chapter 9: Statistical Modelling
 9.1 First things first
 9.2 Maximum likelihood
 9.3 The principle of parsimony (Occam's razor)
 9.4 Types of statistical model
 9.5 Steps involved in model simplification
 9.6 Model formulae in R
 9.7 Multiple error terms
 9.8 The intercept as parameter 1
 9.9 The update function in model simplification
 9.10 Model formulae for regression
 9.11 Box–Cox transformations
 9.12 Model criticism
 9.13 Model checking
 9.14 Influence
 9.15 Summary of statistical models in R
 9.16 Optional arguments in modelfitting functions
 9.17 Akaike's information criterion
 9.18 Leverage
 9.19 Misspecified model
 9.20 Model checking in R
 9.21 Extracting information from model objects
 9.22 The summary tables for continuous and categorical explanatory variables
 9.23 Contrasts
 9.24 Model simplification by stepwise deletion
 9.25 Comparison of the three kinds of contrasts
 9.26 Aliasing
 9.27 Orthogonal polynomial contrasts: contr.poly
 9.28 Summary of statistical modelling

Chapter 10: Regression
 10.1 Linear regression
 10.2 Polynomial approximations to elementary functions
 10.3 Polynomial regression
 10.4 Fitting a mechanistic model to data
 10.5 Linear regression after transformation
 10.6 Prediction following regression
 10.7 Testing for lack of fit in a regression
 10.8 Bootstrap with regression
 10.9 Jackknife with regression
 10.10 Jackknife after bootstrap
 10.11 Serial correlation in the residuals
 10.12 Piecewise regression
 10.13 Multiple regression
 Chapter 11: Analysis of Variance
 Chapter 12: Analysis of Covariance

Chapter 13: Generalized Linear Models
 13.1 Error structure
 13.2 Linear predictor
 13.3 Link function
 13.4 Proportion data and binomial errors
 13.5 Count data and Poisson errors
 13.6 Deviance: Measuring the goodness of fit of a GLM
 13.7 Quasilikelihood
 13.8 The quasi family of models
 13.9 Generalized additive models
 13.10 Offsets
 13.11 Residuals
 13.12 Overdispersion
 13.13 Bootstrapping a GLM
 13.14 Binomial GLM with ordered categorical variables
 Chapter 14: Count Data

Chapter 15: Count Data in Tables
 15.1 A twoclass table of counts
 15.2 Sample size for count data
 15.3 A fourclass table of counts
 15.4 Twobytwo contingency tables
 15.5 Using loglinear models for simple contingency tables
 15.6 The danger of contingency tables
 15.7 QuasiPoisson and negative binomial models compared
 15.8 A contingency table of intermediate complexity
 15.9 Schoener's lizards: A complex contingency table
 15.10 Plot methods for contingency tables
 15.11 Graphics for count data: Spine plots and spinograms

Chapter 16: Proportion Data
 16.1 Analyses of data on one and two proportions
 16.2 Count data on proportions
 16.3 Odds
 16.4 Overdispersion and hypothesis testing
 16.5 Applications
 16.6 Averaging proportions
 16.7 Summary of modelling with proportion count data
 16.8 Analysis of covariance with binomial data
 16.9 Converting complex contingency tables to proportions
 Chapter 17: Binary Response Variables
 Chapter 18: Generalized Additive Models

Chapter 19: MixedEffects Models
 19.1 Replication and pseudoreplication
 19.2 The lme and lmer functions
 19.3 Best linear unbiased predictors
 19.4 Designed experiments with different spatial scales: Split plots
 19.5 Hierarchical sampling and variance components analysis
 19.6 Mixedeffects models with temporal pseudoreplication
 19.7 Time series analysis in mixedeffects models
 19.8 Random effects in designed experiments
 19.9 Regression in mixedeffects models
 19.10 Generalized linear mixed models
 Chapter 20: NonLinear Regression
 Chapter 21: MetaAnalysis

Chapter 22: Bayesian Statistics
 22.1 Background
 22.2 A continuous response variable
 22.3 Normal prior and normal likelihood
 22.4 Priors
 22.5 Bayesian statistics for realistically complicated models
 22.6 Practical considerations
 22.7 Writing BUGS models
 22.8 Packages in R for carrying out Bayesian analysis
 22.9 Installing JAGS on your computer
 22.10 Running JAGS in R
 22.11 MCMC for a simple linear regression
 22.12 MCMC for a model with temporal pseudoreplication
 22.13 MCMC for a model with binomial errors
 Chapter 23: Tree Models
 Chapter 24: Time Series Analysis
 Chapter 25: Multivariate Statistics
 Chapter 26: Spatial Statistics

Chapter 27: Survival Analysis
 27.1 A Monte Carlo experiment
 27.2 Background
 27.3 The survivor function
 27.4 The density function
 27.5 The hazard function
 27.6 The exponential distribution
 27.7 Kaplan–Meier survival distributions
 27.8 Agespecific hazard models
 27.9 Survival analysis in R
 27.10 Parametric analysis
 27.11 Cox's proportional hazards
 27.12 Models with censoring
 Chapter 28: Simulation Models

Chapter 29: Changing the Look of Graphics
 29.1 Graphs for publication
 29.2 Colour
 29.3 Crosshatching
 29.4 Grey scale
 29.5 Coloured convex hulls and other polygons
 29.6 Logarithmic axes
 29.7 Different font families for text
 29.8 Mathematical and other symbols on plots
 29.9 Phase planes
 29.10 Fat arrows
 29.11 Threedimensional plots
 29.12 Complex 3D plots with wireframe
 29.13 An alphabetical tour of the graphics parameters
 29.14 Trellis graphics
 References and Further Reading
 Index
Product Information
 Title: The R Book, 2nd Edition
 Author(s):
 Release date: December 2012
 Publisher(s): Wiley
 ISBN: 9781118448960