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

Description

R is the world's most popular programming language for data analysis and statistical modeling. The Book of R provides an in-depth, beginner-friendly guide to the R language, and teaches you how to use R for common statistical analyses. In The Book of R you'll learn key programming techniques, including how to manipulate data structures, read in data from a file, and author functions. You'll also be introduced to statistics topics like probability distributions, hypothesis testing, regression analysis, and statistical plots as you learn how to wield R to analyze data. Helpful exercises throughout each chapter will test your knowledge and practice using the language. The Book of R assumes no prior experience in statistics or programming. It's a great first book for students, researchers, and professionals who want to learn the tools they need to enter the growing world of data analysis. 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