book

The Book of R, 2nd Edition

Name: The Book of R, 2nd Edition
Author: Tilman M. Davies
ISBN: 9781718503687

by Tilman M. Davies

November 2025

Intermediate to advanced

936 pages

24h 27m

English

No Starch Press

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Cover Page
Title Page
Copyright Page
Dedication Page
About the Author
About the Technical Reviewer
BRIEF CONTENTS
CONTENTS IN DETAIL
PREFACE TO THE SECOND EDITION
PREFACE TO THE FIRST EDITION

ACKNOWLEDGMENTS
INTRODUCTION
A Brief History of RAbout This BookWhat’s New in the Second EditionConventions and StylesCodingMath and Equation ReferencesExercisesFor StudentsFor InstructorsWeb Resources
PART I THE LANGUAGE
1 GETTING STARTED
1.1 Obtaining and Installing R from CRAN1.2 Opening R for the First Time1.2.1 Base R GUI1.2.2 RStudio and Other Third-Party Editors1.3 Workflow Basics1.3.1 Console and Editor Panes1.3.2 Code Execution1.3.3 Comments1.3.4 Working Directory1.3.5 R Packages1.3.6 Help Files and Function Documentation1.4 Saving Work and Exiting R1.4.1 Workspaces1.4.2 Scripts
2 NUMERICS, ARITHMETIC, ASSIGNMENT, AND VECTORS
2.1 R for Basic Math2.1.1 Arithmetic2.1.2 Logarithms and Exponentials2.1.3 E-Notation2.2 Assigning Objects2.3 Vectors2.3.1 Vector Creation2.3.2 Sequences, Repetition, Sorting, and Lengths2.3.3 Subsets and Element Extraction2.3.4 Vector-Oriented Behavior
3 MATRICES AND ARRAYS
3.1 Two-Dimensional Matrix3.1.1 Matrix Creation3.1.2 Subsets3.1.3 Matrix Operations and Algebra3.2 Multidimensional Arrays3.2.1 Array Creation3.2.2 Subsets, Extractions, and Replacements
4 NON-NUMERIC VALUES
4.1 Logical Values4.1.1 TRUE or FALSE?4.1.2 Relational Operators4.1.3 Logical Operators4.1.4 Logicals Are Numbers!4.1.5 Logical Subsetting and Extraction4.2 Characters4.2.1 String Creation4.2.2 Concatenation4.2.3 Escape Sequences4.2.4 Substrings and Matching4.3 Factors4.3.1 Identifying Categories4.3.2 Defining and Ordering Levels4.3.3 Dropping Categories4.3.4 Working with the Numeric Representation of Factors4.3.5 Collapsing Factors4.3.6 Combining Factors4.3.7 Creating Factors from Continuous Values4.4 Dates and Times4.4.1 Creating and Formatting Dates4.4.2 Performing Simple Operations with Dates4.4.3 Incorporating Time
5 LISTS AND DATA FRAMES
5.1 Lists of Objects5.1.1 Creating Lists and Accessing Components5.1.2 Naming5.1.3 Nesting5.2 Data Frames5.2.1 Construction5.2.2 Basic Subsets5.2.3 Column Insertions and Data Frame Combinations5.2.4 Logical Record Subsets5.2.5 The Explicit subset Command
6 SPECIAL VALUES, CLASSES, AND COERCION
6.1 Special Values6.1.1 Infinity6.1.2 NaN: Not a Number6.1.3 NA: Not Available6.1.4 NULL6.2 Types, Classes, and Coercion6.2.1 Attributes6.2.2 Object Class6.2.3 Is-Dot Object-Checking Functions6.2.4 As-Dot Coercion Functions
7 BASIC PLOTTING
7.1 Using plot with Coordinate Vectors7.2 Visual Controls and Graphical Parameters7.2.1 Automatic Plot Types7.2.2 Title and Axis Labels7.2.3 Color7.2.4 Line and Point Appearances7.2.5 Custom Region Limits7.2.6 Aspect Ratio7.3 Adding Points, Lines, and Text to an Existing Plot7.4 The ggplot2 Package7.4.1 Plotting a Toy Example7.4.2 Setting Appearance Constants with Geoms7.4.3 Aesthetic Mapping with Geoms
8 READING AND WRITING FILES
8.1 R-Ready Datasets8.1.1 Built-In Datasets8.1.2 Contributed Datasets8.2 Reading in External Data Files8.2.1 The Table Format8.2.2 Spreadsheet Workbooks8.2.3 Web-Based Files8.2.4 Other File Formats8.3 Writing Out Data Files and Plots8.3.1 Datasets8.3.2 Plots and Graphics Files8.4 Ad Hoc Object Read/Write Operations8.4.1 save and load8.4.2 saveRDS and readRDS8.4.3 dput and dget8.4.4 Suitability
PART II PROGRAMMING
9 CALLING FUNCTIONS
9.1 Scoping9.1.1 Environments9.1.2 Search Path9.1.3 Reserved and Protected Names9.2 Argument Matching9.2.1 Exact9.2.2 Partial9.2.3 Positional9.2.4 Mixed9.2.5 Ellipses9.3 Pipes9.3.1 Syntax and Examples9.3.2 Should You Pipe?
10 CONDITIONS AND LOOPS
10.1 if Statements10.1.1 Stand-Alone Statement10.1.2 else Statements10.1.3 Element-Wise Checks with ifelse10.1.4 Nested and Stacked Statements10.1.5 The switch Function10.2 for Loops10.2.1 Definition and Basics10.2.2 Via Index or Value10.2.3 Nests10.3 while Loops10.3.1 Definition and Basics10.3.2 Condition Object Modification10.4 Implicit Looping10.4.1 The apply Function10.4.2 Some apply Variants10.4.3 When to Use Implicit Loops10.5 Other Control Flow Mechanisms10.5.1 break10.5.2 next10.5.3 repeat
11 WRITING FUNCTIONS
11.1 The function Command11.1.1 Function Creation11.1.2 The return Statement11.2 Arguments11.2.1 Considering Lazy Evaluation11.2.2 Setting Defaults11.2.3 Checking for Missing Arguments11.2.4 Dealing with Ellipses11.3 Specialized Functions11.3.1 Helper Functions11.3.2 Anonymous Functions11.3.3 Recursive Functions
12 EXCEPTIONS, TIMINGS, AND VISIBILITY
12.1 Exception Handling12.1.1 Errors and Warnings12.1.2 try Statements12.2 Progress and Timing12.2.1 Tracking Progress: Are We There Yet?12.2.2 Measuring Completion Time: How Long Did It Take?12.2.3 Profiling Your Code: Where’s the Bottleneck?12.3 Masking12.3.1 Function and Object Distinction12.3.2 Data Frame Variable Distinction
PART III STATISTICS AND PROBABILITY
13 ELEMENTARY STATISTICS
13.1 Describing Raw Data13.1.1 Numeric Variables13.1.2 Categorical Variables13.1.3 Examples13.1.4 Univariate and Multivariate Data13.1.5 Parameter or Statistic?13.2 Summary Statistics13.2.1 Measures of Centrality13.2.2 Counts, Percentages, and Proportions13.2.3 Rounding, Floors, Ceilings, and Absolute Values13.2.4 Quantiles, Percentiles, and the “Five-Number Summary”13.2.5 Measures of Spread13.2.6 Relationships Between Numeric Variables13.2.7 Outliers
14 BASIC DATA VISUALIZATION
14.1 Bar Plots and Pie Charts14.1.1 Bar Plot Creation14.1.2 In ggplot214.1.3 A Quick Pie Chart14.2 Histograms14.2.1 A Simple Example14.2.2 Bin Width and Customization14.2.3 In ggplot214.3 Density Plots14.3.1 Construction14.3.2 Bandwidths and Customization14.3.3 When Multiple Densities Overlap14.3.4 In ggplot214.4 Box Plots14.4.1 Stand-Alone14.4.2 Side-by-Side14.4.3 In ggplot214.5 Violin Plots14.5.1 Design and Interpretation14.5.2 Side-by-Side Violins14.5.3 In ggplot214.6 Scatterplots and Coordinate-Based Plots14.6.1 Single Plot14.6.2 Matrix of Plots14.6.3 In ggplot214.6.4 Plots of Spatial Coordinates
15 PROBABILITY
15.1 What Is a Probability?15.1.1 Events and Probability15.1.2 Conditional Probability15.1.3 Intersection15.1.4 Union15.1.5 Complement15.2 Random Variables and Probability Distributions15.2.1 Realizations15.2.2 Discrete Random Variables15.2.3 Continuous Random Variables15.2.4 Shape, Skew, and Modality
16 COMMON PROBABILITY DISTRIBUTIONS
16.1 Common Probability Mass Functions16.1.1 Bernoulli Distribution16.1.2 Binomial Distribution16.1.3 Poisson Distribution16.1.4 Other Mass Functions16.2 Common Probability Density Functions16.2.1 Uniform16.2.2 Normal16.2.3 Student’s t-Distribution16.2.4 Exponential16.2.5 Chi-Squared16.2.6 F-Distribution16.2.7 Other Density Functions
PART IV STATISTICAL TESTING AND MODELING
17 SAMPLING DISTRIBUTIONS AND CONFIDENCE
17.1 Sampling Distributions17.1.1 For a Sample Mean17.1.2 For a Sample Proportion17.1.3 For Other Statistics17.2 Confidence Intervals17.2.1 An Interval for a Mean17.2.2 An Interval for a Proportion17.2.3 Other Intervals17.2.4 Comments on the Interpretation of a CI
18 HYPOTHESIS TESTING
18.1 Components of a Hypothesis Test18.1.1 Hypotheses18.1.2 Test Statistic18.1.3 p-Value18.1.4 Significance Level18.1.5 Criticisms of Hypothesis Testing18.2 Testing Means18.2.1 Single Mean18.2.2 Two Means18.3 Testing Proportions18.3.1 Single Proportion18.3.2 Two Proportions18.4 Testing Categorical Variables18.4.1 Single Categorical Variable18.4.2 Two Categorical Variables18.5 Errors and Power18.5.1 Hypothesis Test Errors18.5.2 Type I Errors18.5.3 Type II Errors18.5.4 Statistical Power
19 ANALYSIS OF VARIANCE
19.1 One-Way ANOVA19.1.1 Hypotheses and Diagnostic Checking19.1.2 Table Construction19.1.3 ANOVA Table Construction with the aov Function19.2 Two-Way ANOVA19.2.1 A Suite of Hypotheses19.2.2 Main Effects and Interactions19.3 Kruskal–Wallis Test19.3.1 Hypotheses19.3.2 Example and Interpretation
20 SIMPLE LINEAR REGRESSION
20.1 An Example of a Linear Relationship20.2 General Concepts20.2.1 Defining the Model20.2.2 Estimating the Intercept and Slope Parameters20.2.3 Fitting Linear Models with lm20.2.4 Illustrating Residuals20.3 Statistical Inference20.3.1 The summary Command20.3.2 Regression Coefficient Significance Tests20.3.3 Coefficient of Determination20.3.4 Other summary Output20.4 Prediction20.4.1 Confidence Interval or Prediction Interval?20.4.2 Interval Interpretation20.4.3 Interval Plots20.4.4 Interpolation vs. Extrapolation20.5 Understanding Categorical Predictors20.5.1 Working with Binary Variables: k = 220.5.2 Working with Multilevel Variables: k > 220.5.3 Changing the Reference Level20.5.4 Treating Categorical Variables as Numeric20.5.5 Checking for Equivalence with One-Way ANOVA
21 MULTIPLE LINEAR REGRESSION
21.1 Terminology21.2 Theory21.2.1 Extending the Simple Model to a Multiple Model21.2.2 Representing in Matrix Notation21.2.3 Reviewing a Basic Example21.3 R Implementation and Interpretation21.3.1 Adding Predictors21.3.2 Interpreting Partial Effects21.3.3 Visualizing the Multiple Linear Model21.3.4 Finding Confidence Intervals21.3.5 Computing the Omnibus F-Test21.3.6 Predicting from a Multiple Linear Model21.4 Transforming Numeric Variables21.4.1 Polynomial21.4.2 Logarithmic21.4.3 Other Transformations21.5 Interactive Terms21.5.1 Concept and Motivation21.5.2 One Categorical, One Continuous21.5.3 Two Categorical21.5.4 Two Continuous21.5.5 Higher-Order Interactions
22 LINEAR MODEL SELECTION AND DIAGNOSTICS
22.1 Goodness-of-Fit vs. Complexity22.1.1 Principle of Parsimony22.1.2 General Guidelines22.2 Model Selection Algorithms22.2.1 Nested Comparisons: The Partial F-Test22.2.2 Forward Selection22.2.3 Backward Selection22.2.4 Stepwise AIC Selection22.2.5 Other Selection Algorithms22.3 Residual Diagnostics22.3.1 Inspecting and Interpreting Residuals22.3.2 Assessing Normality22.3.3 Illustrating Outliers, Leverage, and Influence22.3.4 Calculating Leverage22.3.5 Calculating Cook’s Distance22.3.6 Graphically Combining Residuals, Leverage, and Cook’s Distance22.4 Collinearity22.4.1 Potential Warning Signs22.4.2 Correlated Predictors: A Quick Example
PART V ADVANCED GRAPHICS
23 ADVANCED PLOT CUSTOMIZATION
23.1 Handling the Graphics Device23.1.1 Manually Opening a New Device23.1.2 Switching Between Devices23.1.3 Closing a Device23.1.4 Controlling Multiple Plots in One Device23.2 Plotting Regions and Margins23.2.1 Default Spacing23.2.2 Custom Spacing23.2.3 Clipping23.3 Point-and-Click Coordinate Interaction23.3.1 Retrieving Coordinates Silently23.3.2 Visualizing Selected Coordinates23.3.3 Placing Ad Hoc Annotations23.4 Customizing Traditional R Plots23.4.1 Graphical Parameters for Style and Suppression23.4.2 Box Customization23.4.3 Axis Customization23.5 Specialized Text and Label Notation23.5.1 Font23.5.2 Greek Symbols23.5.3 Mathematical Expressions23.6 Extended Example: A Custom Scatterplot
24 GOING FURTHER WITH THE GRAMMAR OF GRAPHICS
24.1 A Quick Recap24.2 Smoothing and Shading24.2.1 Adding LOESS Trends24.2.2 Constructing Smooth Density Estimates24.3 Multiple Plots and Variable-Mapped Facets24.3.1 Independent Plots24.3.2 Facets Mapped to a Categorical Variable24.4 Interactive Tools in ggvis
25 DEFINING COLORS AND PLOTTING IN HIGHER DIMENSIONS
25.1 Representing and Using Color25.1.1 Red-Green-Blue Hexadecimal Color Codes25.1.2 Built-in Palettes25.1.3 Custom Palettes25.1.4 Approaches to Color Indexing25.1.5 Color Legends25.1.6 Opacity25.1.7 RGB Alternatives and Further Functionality25.2 3D Scatterplots25.2.1 Basic Syntax25.2.2 Visual Enhancements25.3 Preparing a Surface for Plotting25.3.1 Constructing an Evaluation Grid25.3.2 Constructing the z-Matrix25.3.3 Conceptualizing the z-Matrix25.4 Contour Plots25.4.1 Contour Lines25.4.2 Color-Filled Contours25.5 Pixel Images25.5.1 One Grid Point Equals One Pixel25.5.2 Surface Truncation and Empty Pixels25.6 Perspective Plots25.6.1 Interpreting Basic Plots and Adjusting Angles25.6.2 Coloring Facets25.6.3 Rotating with Loops
26 INTERACTIVE 3D PLOTS
26.1 Point Clouds26.1.1 Basic 3D Cloud: Points and Spheres26.1.2 Color and Legends26.1.3 Alternate 3D Point Characters26.1.4 3D Text26.1.5 3D Lines, Axes, Grids, and Other Annotations26.1.6 Extended Example: A Custom, Interactive 3D Scatterplot26.2 Bivariate Surfaces26.2.1 Plotting Basic Perspective Surfaces26.2.2 Adding Components26.2.3 Coloring by z Value26.2.4 Dealing with the Aspect Ratio26.3 Trivariate Surfaces26.3.1 Evaluation Coordinates in 3D26.3.2 Isosurfaces26.3.3 Example: Nonparametric Trivariate Density26.4 Handling Parametric Equations26.4.1 Simple Loci26.4.2 Mathematical Abstractions
A INSTALLING R AND CONTRIBUTED PACKAGES
A.1 Downloading and Installing RA.2 Using PackagesA.2.1 Base PackagesA.2.2 Recommended PackagesA.2.3 Contributed PackagesA.3 Updating R and Installed PackagesA.4 Using Other Mirrors and RepositoriesA.4.1 Switching CRAN MirrorA.4.2 Reviewing Other Package RepositoriesA.5 Citing and Writing PackagesA.5.1 Citing R and Contributed PackagesA.5.2 Writing Your Own Packages
B WORKING WITH RSTUDIO
B.1 Basic Layout and UsageB.1.1 Editor Features and Appearance OptionsB.1.2 Pane CustomizationB.2 Auxiliary Tools and FeaturesB.2.1 ProjectsB.2.2 Package ManagerB.2.3 Support for ProfilingB.2.4 Support for DebuggingB.3 Markup, Document, and Graphics ToolsB.3.1 SweaveB.3.2 knitrB.3.3 R MarkdownB.3.4 QuartoB.3.5 Shiny
REFERENCES
INDEX

Content preview from The Book of R, 2nd Edition

BWORKING WITH RSTUDIO

A robot with hiking gear stands on a precipice, gazing at a “mountain range” made out of graphing data.

Although the base R application is all you typically need to unleash the full suite of available functionality, the bare-bones appearance of the console and code editor can be off-putting to some, especially beginners. One of the best integrated development environments (IDEs) designed specifically to enhance day-to-day use of the R language is RStudio, developed by Posit, a commercial software and data science company (Posit team 2024). This appendix provides an overview of RStudio and its most commonly used tools.

Like R itself, you can use the desktop version of RStudio on Windows, macOS, and Linux systems. It is free ...

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The Book of R, 2nd Edition

by Tilman M. Davies

BWORKING WITH RSTUDIO

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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