The Book of R: A First Course in Programming and Statistics

Brief Contents
Contents in Detail
Preface
Acknowledgments
Introduction
	A Brief History of R
	About This Book
		Part I: The Language
		Part II: Programming
		Part III: Statistics and Probability
		Part IV: Statistical Testing and Modeling
		Part V: Advanced Graphics
	For Students
	For Instructors
Part I: The Language
	Chapter 1: Getting Started
		1.1 Obtaining and Installing R from CRAN
		1.2 Opening R for the First Time
			1.2.1 Console and Editor Panes
			1.2.2 Comments
			1.2.3 Working Directory
			1.2.4 Installing and Loading R Packages
			1.2.5 Help Files and Function Documentation
			1.2.6 Third-Party Editors
		1.3 Saving Work and Exiting R
			1.3.1 Workspaces
			1.3.2 Scripts
		1.4 Conventions
			1.4.1 Coding
			1.4.2 Math and Equation References
			1.4.3 Exercises
	Chapter 2: Numerics, Arithmetic, Assignment, and Vectors
		2.1 R for Basic Math
			2.1.1 Arithmetic
			2.1.2 Logarithms and Exponentials
			2.1.3 E-Notation
		2.2 Assigning Objects
		2.3 Vectors
			2.3.1 Creating a Vector
			2.3.2 Sequences, Repetition, Sorting, and Lengths
			2.3.3 Subsetting and Element Extraction
			2.3.4 Vector-Oriented Behavior
	Chapter 3: Matrices and Arrays
		3.1 Defining a Matrix
			3.1.1 Filling Direction
			3.1.2 Row and Column Bindings
			3.1.3 Matrix Dimensions
		3.2 Subsetting
			3.2.1 Row, Column, and Diagonal Extractions
			3.2.2 Omitting and Overwriting
		3.3 Matrix Operations and Algebra
			3.3.1 Matrix Transp
ose
			3.3.2 Identity Matrix
			3.3.3 Scalar Multiple of a Matrix
			3.3.4 Matrix Addition and Subtraction
			3.3.5 Matrix Multiplication
			3.3.6 Matrix Inversion
		3.4 Multidimensional Arrays
			3.4.1 Definition
			3.4.2 Subsets, Extractions, and Replacements
	Chapter 4: Non-Numeric Values
		4.1 Logical Values
			4.1.1 TRUE or FALSE?
			4.1.2 A Logical Outcome: Relational Operators
			4.1.3 Multiple Comparisons: Logical Operators
			4.1.4 Logicals Are Numbers!
			4.1.5 Logical Subsetting and Extraction
		4.2 Characters
			4.2.1 Creating a String
			4.2.2 Concatenation
			4.2.3 Escape Sequences
			4.2.4 Substrings and Matching
		4.3 Factors
			4.3.1 Identifying Categories
			4.3.2 Defining and Ordering Levels
			4.3.3 Combining and Cutting
	Chapter 5: Lists and Data Frames
		5.1 Lists of Objects
			5.1.1 Definition and Component Access
			5.1.2 Naming
			5.1.3 Nesting
		5.2 Data Frames
			5.2.1 Construction
			5.2.2 Adding Data Columns and Combining Data Frames
			5.2.3 Logical Record Subsets
	Chapter 6: Special Values, Classes, and Coercion
		6.1 Some Special Values
			6.1.1 Infinity
			6.1.2 NaN
			6.1.3 NA
			6.1.4 NULL
		6.2 Understanding Types, Classes, and Coercion
			6.2.1 Attributes
			6.2.2 Object Class
			6.2.3 Is-Dot Object-Checking Functions
			6.2.4 As-Dot Coercion Functions
	Chapter 7: Basic Plotting
		7.1 Using plot with Coordinate Vectors
		7.2 Graphical Parameters
			7.2.1 Automatic Plot Types
			7.2.2 Title and Axis Labels
			7.2.3 Color
			7.2.4 Line and Point Appearances
			7.2.5 Plotting Region Limits
		7.3 Adding Points, Lines, and Text to an Existing Plot
		7.4 The ggplot2 Package
			7.4.1 A Quick Plot with qplot
			7.4.2 Setting Appearance Constants with Geoms
			7.4.3 Aesthetic Mapping with Geoms
	Chapter 8: Reading and Writing Files
		8.1 R-Ready Data Sets
			8.1.1 Built-in Data Sets
			8.1.2 Contributed Data Sets
		8.2 Reading in External Data Files
			8.2.1 The Table Format
			8.2.2 Spreadsheet Workbooks
			8.2.3 Web-Based Files
			8.2.4 Other File Formats
		8.3 Writing Out Data Files and Plots
			8.3.1 Data Sets
			8.3.2 Plots and Graphics Files
		8.4 Ad Hoc Object Read/Write Operations
Part II: Programming
	Chapter 9: Calling Functions
		9.1 Scoping
			9.1.1 Environments
			9.1.2 Search Path
			9.1.3 Reserved and Protected Names
		9.2 Argument Matching
			9.2.1 Exact
			9.2.2 Partial
			9.2.3 Positional
			9.2.4 Mixed
			9.2.5 Dot-Dot-Dot: Use of Ellipses
	Chapter 10: Conditions and Loops
		10.1 if Statements
			10.1.1 Stand-Alone Statement
			10.1.2 else Statements
			10.1.3 Using ifelse for Element-wise Checks
			10.1.4 Nesting and Stacking Statements
			10.1.5 The switch Function
		10.2 Coding Loops
			10.2.1 for Loops
			10.2.2 while Loops
			10.2.3 Implicit Looping with apply
		10.3 Other Control Flow Mechanisms
			10.3.1 Declaring break or next
			10.3.2 The repeat Statement
	Chapter 11: Writing Functions
		11.1 The function Command
			11.1.1 Function Creation
			11.1.2 Using return
		11.2 Arguments
			11.2.1 Lazy Evaluation
			11.2.2 Setting Defaults
			11.2.3 Checking for Missing Arguments
			11.2.4 Dealing with Ellipses
		11.3 Specialized Functions
			11.3.1 Helper Functions
			11.3.2 Disposable Functions
			11.3.3 Recursive Functions
	Chapter 12: Exceptions, Timings, and Visibility
		12.1 Exception Handling
			12.1.1 Formal Notifications: Errors and Warnings
			12.1.2 Catching Errors with try Statements
		12.2 Progress Timing
			12.2.1 Textual Progress Bars: Are We There Yet?
			12.2.2 Measuring Completion Time: How Long Did It Take?
		12.3 Masking
			12.3.1 Function and Object Distinction
			12.3.2 Data Frame Variable Distinction
Part III: Statistics and Probability
	Chapter 13: Elementary Statistics
		13.1 Describing Raw Data
			13.1.1 Numeric Variables
			13.1.2 Categorical Variables
			13.1.3 Univariate and Multivariate Data
			13.1.4 Parameter or Statistic?
		13.2 Summary Statistics
			13.2.1 Centrality: Mean, Median, Mode
			13.2.2 Counts, Percentages, and Proportions
			13.2.3 Quantiles, Percentiles, and the Five-Number Summary
			13.2.4 Spread: Variance, Standard Deviation, and the Interquartile Range
			13.2.5 Covariance and Correlation
			13.2.6 Outliers
	Chapter 14: Basic Data Visualization
		14.1 Barplots and Pie Charts
			14.1.1 Building a Barplot
			14.1.2 A Quick Pie Chart
		14.2 Histograms
		14.3 Box-and-Whisker Plots
			14.3.1 Stand-Alone Boxplots
			14.3.2 Side-by-Side Boxplots
		14.4 Scatterplots
			14.4.1 Single Plot
			14.4.2 Matrix of Plots
	Chapter 15: Probability
		15.1 What Is a Probability?
			15.1.1 Events and Probability
			15.1.2 Conditional Probability
			15.1.3 Intersection
			15.1.4 Union
			15.1.5 Complement
		15.2 Random Variables and Probability Distributions
			15.2.1 Realizations
			15.2.2 Discrete Random Variables
			15.2.3 Continuous Random Variables
			15.2.4 Shape, Skew, and Modality
	Chapter 16: Common Probability Distributions
		16.1 Common Probability Mass Functions
			16.1.1 Bernoulli Distribution
			16.1.2 Binomial Distribution
			16.1.3 Poisson Distribution
			16.1.4 Other Mass Functions
		16.2 Common Probability Density Functions
			16.2.1 Uniform
			16.2.2 Normal
			16.2.3 Student's t-distribution
			16.2.4 Exponential
			16.2.5 Other Density Functions
Part IV: Statistical Testing and Modeling
	Chapter 17: Sampling Distributions and Confidence
		17.1 Sampling Distributions
			17.1.1 Distribution for a Sample Mean
			17.1.2 Distribution for a Sample Proportion
			17.1.3 Sampling Distributions for Other Statistics
		17.2 Confidence Intervals
			17.2.1 An Interval for a Mean
			17.2.2 An Interval for a Proportion
			17.2.3 Other Intervals
			17.2.4 Comments on Interpretation of a CI
	Chapter 18: Hypothesis Testing
		18.1 Components of a Hypothesis Test
			18.1.1 Hypotheses
			18.1.2 Test Statistic
			18.1.3 p-value
			18.1.4 Significance Level
			18.1.5 Criticisms of Hypothesis Testing
		18.2 Testing Means
			18.2.1 Single Mean
			18.2.2 Two Means
		18.3 Testing Proportions
			18.3.1 Single Proportion
			18.3.2 Two Proportions
		18.4 Testing Categorical Variables
			18.4.1 Single Categorical Variable
			18.4.2 Two Categorical Variables
		18.5 Errors and Power
			18.5.1 Hypothesis Test Errors
			18.5.2 Type I Errors
			18.5.3 Type II Errors
			18.5.4 Statistical Power
	Chapter 19: Analysis of Variance
		19.1 One-Way ANOVA
			19.1.1 Hypotheses and Diagnostic Checking
			19.1.2 One-Way ANOVA Table Construction
			19.1.3 Building ANOVA Tables with the aov Function
		19.2 Two-Way ANOVA
			19.2.1 A Suite of Hypotheses
			19.2.2 Main Effects and Interactions
		19.3 Kruskal-Wallis Test
	Chapter 20: Simple Linear Regression
		20.1 An Example of a Linear Relationship
		20.2 General Concepts
			20.2.1 Definition of the Model
			20.2.2 Estimating the Intercept and Slope Parameters
			20.2.3 Fitting Linear Models with lm
			20.2.4 Illustrating Residuals
		20.3 Statistical Inference
			20.3.1 Summarizing the Fitted Model
			20.3.2 Regression Coefficient Significance Tests
			20.3.3 Coefficient of Determination
			20.3.4 Other summary Output
		20.4 Prediction
			20.4.1 Confidence Interval or Prediction Interval?
			20.4.2 Interpreting Intervals
			20.4.3 Plotting Intervals
			20.4.4 Interpolation vs. Extrapolation
		20.5 Understanding Categorical Predictors
			20.5.1 Binary Variables: k=2
			20.5.2 Multilevel Variables: k>2
			20.5.3 Changing the Reference Level
			20.5.4 Treating Categorical Variables as Numeric
			20.5.5 Equivalence with One-Way ANOVA
	Chapter 21: Multiple Linear Regression
		21.1 Terminology
		21.2 Theory
			21.2.1 Extending the Simple Model to a Multiple Model
			21.2.2 Estimating in Matrix Form
			21.2.3 A Basic Example
		21.3 Implementing in R and Interpreting
			21.3.1 Additional Predictors
			21.3.2 Interpreting Marginal Effects
			21.3.3 Visualizing the Multiple Linear Model
			21.3.4 Finding Confidence Intervals
			21.3.5 Omnibus F-Test
			21.3.6 Predicting from a Multiple Linear Model
		21.4 Transforming Numeric Variables
			21.4.1 Polynomial
			21.4.2 Logarithmic
			21.4.3 Other Transformations
		21.5 Interactive Terms
			21.5.1 Concept and Motivation
			21.5.2 One Categorical, One Continuous
			21.5.3 Two Categorical
			21.5.4 Two Continuous
			21.5.5 Higher-Order Interactions
	Chapter 22: Linear Model Selection and Diagnostics
		22.1 Goodness-of-Fit vs. Complexity
			22.1.1 Principle of Parsimony
			22.1.2 General Guidelines
		22.2 Model Selection Algorithms
			22.2.1 Nested Comparisons: The Partial F-Test
			22.2.2 Forward Selection
			22.2.3 Backward Selection
			22.2.4 Stepwise AIC Selection
			22.2.5 Other Selection Algorithms
		22.3 Residual Diagnostics
			22.3.1 Inspecting and Interpreting Residuals
			22.3.2 Assessing Normality
			22.3.3 Illustrating Outliers, Leverage, and Influence
			22.3.4 Calculating Leverage
			22.3.5 Cook's Distance
			22.3.6 Graphically Combining Residuals, Leverage, and Cook's Distance
		22.4 Collinearity
			22.4.1 Potential Warning Signs
			22.4.2 Correlated Predictors: A Quick Example
Part V: Advanced Graphics
	Chapter 23: Advanced Plot Customization
		23.1 Handling the Graphics Device
			23.1.1 Manually Opening a New Device
			23.1.2 Switching Between Devices
			23.1.3 Closing a Device
			23.1.4 Multiple Plots in One Device
		23.2 Plotting Regions and Margins
			23.2.1 Default Spacing
			23.2.2 Custom Spacing
			23.2.3 Clipping
		23.3 Point-and-Click Coordinate Interaction
			23.3.1 Retrieving Coordinates Silently
			23.3.2 Visualizing Selected Coordinates
			23.3.3 Ad Hoc Annotation
		23.4 Customizing Traditional R Plots
			23.4.1 Graphical Parameters for Style and Suppression
			23.4.2 Customizing Boxes
			23.4.3 Customizing Axes
		23.5 Specialized Text and Label Notation
			23.5.1 Font
			23.5.2 Greek Symbols
			23.5.3 Mathematical Expressions
		23.6 A Fully Annotated Scatterplot
	Chapter 24: Going Further with the Grammar of Graphics
		24.1 ggplot or qplot?
		24.2 Smoothing and Shading
			24.2.1 Adding LOESS Trends
			24.2.2 Constructing Smooth Density Estimates
		24.3 Multiple Plots and Variable-Mapped Facets
			24.3.1 Independent Plots
			24.3.2 Facets Mapped to a Categorical Variable
		24.4 Interactive Tools in ggvis
	Chapter 25: Defining Colors and Plotting in Higher Dimensions
		25.1 Representing and Using Color
			25.1.1 Red-Green-Blue Hexadecimal Color Codes
			25.1.2 Built-in Palettes
			25.1.3 Custom Palettes
			25.1.4 Using Color Palettes to Index a Continuum
			25.1.5 Including a Color Legend
			25.1.6 Opacity
			25.1.7 RGB Alternatives and Further Functionality
		25.2 3D Scatterplots
			25.2.1 Basic Syntax
			25.2.2 Visual Enhancements
		25.3 Preparing a Surface for Plotting
			25.3.1 Constructing an Evaluation Grid
			25.3.2 Constructing the z-Matrix
			25.3.3 Conceptualizing the z-Matrix
		25.4 Contour Plots
			25.4.1 Drawing Contour Lines
			25.4.2 Color-Filled Contours
		25.5 Pixel Images
			25.5.1 One Grid Point = One Pixel
			25.5.2 Surface Truncation and Empty Pixels
		25.6 Perspective Plots
			25.6.1 Basic Plots and Angle Adjustment
			25.6.2 Coloring Facets
			25.6.3 Rotating with Loops
	Chapter 26: Interactive 3D Plots
		26.1 Point Clouds
			26.1.1 Basic 3D Cloud
			26.1.2 Visual Enhancements and Legends
			26.1.3 Adding Further 3D Components
		26.2 Bivariate Surfaces
			26.2.1 Basic Perspective Surface
			26.2.2 Additional Components
			26.2.3 Coloring by z Value
			26.2.4 Dealing with the Aspect Ratio
		26.3 Trivariate Surfaces
			26.3.1 Evaluation Coordinates in 3D
			26.3.2 Isosurfaces
			26.3.3 Example: Nonparametric Trivariate Density
		26.4 Handling Parametric Equations
			26.4.1 Simple Loci
			26.4.2 Mathematical Abstractions
Appendix A: Installing R and Contributed Packages
	A.1 Dowloading and Installing R
	A.2 Using Packages
		A.2.1 Base Packages
		A.2.2 Recommended Packages
		A.2.3 Contributed Packages
	A.3 Updating R and Installed Packages
	A.4 Using Other Mirrors and Repositories
		A.4.1 Switching CRAN Mirror
		A.4.2 Other Package Repositories
	A.5 Citing and Writing Packages
		A.5.1 Citing R Contributed Packages
		A.5.2 Writing Your Own Packages
Appendix B: Working with RStudio
	B.1 Basic Layout and Usage
		B.1.1 Editor Features and Appearance Options
		B.1.2 Customizing Panes
	B.2 Auxiliary Tools
		B.2.1 Projects
		B.2.2 Package Installer and Updater
		B.2.3 Support for Debugging
		B.2.4 Markup, Document, and Graphics Tools
Reference List
Index
About the Author