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The Book of R

A Fi r s t C o u r s e i n Programming and Statistics

by Tilman M. Davies

San Francisco

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The Book of R. Copyright © 2016 by Tilman M. Davies.

All rights reserved. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage or retrieval system, without the prior written permission of the copyright owner and the publisher.

Printed in USA

First printing

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ISBN-10: 1-59327-651-6 ISBN-13: 978-1-59327-651-5

Publisher: William Pollock Production Editor: Riley Hoffman Cover Illustration: Josh Ellingson Interior Design: Octopod Studios Developmental Editor: Liz Chadwick Technical Reviewer: Debbie Leader

Copyeditor: Kim Wimpsett Compositor: Riley Hoffman Proofreader: Paula Fleming Indexer: BIM Creatives, LLC

For information on distribution, translations, or bulk sales, please contact No Starch Press, Inc. directly:

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Library of Congress Cataloging-in-Publication Data

Names: Davies, Tilman M., author. Title: The book of R : a first course in programming and statistics / by Tilman M. Davies. Description: San Francisco : No Starch Press, [2016] | Includes bibliographical references and index. Identifiers: LCCN 2015035305| ISBN 9781593276515 | ISBN 1593276516 Subjects: LCSH: R (Computer program language) | Computer programming. | Statistics–Data processing. Classification: LCC QA76.73.R3 D38 2016 | DDC 519.50285–dc23 LC record available at http://lccn.loc.gov/2015035305

No Starch Press and the No Starch Press logo are registered trademarks of No Starch Press, Inc. Other product and company names mentioned herein may be the trademarks of their respective owners. Rather than use a trademark symbol with every occurrence of a trademarked name, we are using the names only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark.

The information in this book is distributed on an “As Is” basis, without warranty. While every precaution has been taken in the preparation of this work, neither the author nor No Starch Press, Inc. shall have any liability to any person or entity with respect to any loss or damage caused or alleged to be caused directly or indirectly by the information contained in it.

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Prof. Dr. Dr. Jochen Weniger, 1925–2015 Ein Wissenschaftler. Mein Opa.

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B R I E F C O N T E N T S

Preface xxiii
Acknowledgments xxv
Introduction xxvii

PART I: THE LANGUAGE
Chapter 1: Getting Started	3
Chapter 2: Numerics, Arithmetic, Assignment, and Vectors 17
Chapter 3: Matrices and Arrays 39
Chapter 4: Non-numeric Values 59
Chapter 5: Lists and Data Frames 89
Chapter 6: Special Values, Classes, and Coercion 103
Chapter 7: Basic Plotting 127
Chapter 8: Reading and Writing Files 147

PART II: PROGRAMMING
Chapter 9: Calling Functions 165
Chapter 10: Conditions and Loops 179
Chapter 11: Writing Functions 215
Chapter 12: Exceptions, Timings, and Visibility 241

PART III: STATISTICS AND PROBABILITY
Chapter 13: Elementary Statistics 261
Chapter 14: Basic Data Visualization 289
Chapter 15: Probability 309
Chapter 16: Common Probability Distributions 331

PART IV: STATISTICAL TESTING AND MODELING
Chapter 17: Sampling Distributions and Confidence 367
Chapter 18: Hypothesis Testing 385
Chapter 19: Analysis of Variance 435
Chapter 20: Simple Linear Regression 451
Chapter 21: Multiple Linear Regression 485
Chapter 22: Linear Model Selection and Diagnostics 527
PART V: ADVANCED GRAPHICS
Chapter 23: Advanced Plot Customization 575
Chapter 24: Going Further with the Grammar of Graphics 609
Chapter 25: Defining Colors and Plotting in Higher Dimensions 631
Chapter 26: Interactive 3D Plots 691
Appendix A: Installing R and Contributed Packages 737
Appendix B: Working with RStudio 751
Reference List 761
Index 767

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C O N T E N T S I N D E T A I L

PREFACE			xxiii
ACKNOWLEDGMENTS			xxv
INTRODUCTION			xxvii
	A Brief History of R		xxvii
	About This Book		xxviii
		Part I: The Language	xxviii
		Part II: Programming	xxix
		Part III: Statistics and Probability	xxix
		Part IV: Statistical Testing and Modeling	xxx
		Part V: Advanced Graphics	xxx
	For Students		xxx
	For Instructors		xxxi
PART I
THE LANGUAGE
1 GETTING STARTED
1.1	Obtaining and Installing R from CRAN		3
1.2	Opening R for the First Time		4
	1.2.1	Console and Editor Panes	5
	1.2.2	Comments	6
	1.2.3	Working Directory	7
	1.2.4	Installing and Loading R Packages	7
	1.2.5	Help Files and Function Documentation	8
	1.2.6	Third-Party Editors	11
1.3	Saving Work and Exiting R		11
	1.3.1	Workspaces	11
	1.3.2	Scripts	12
1.4	Conventions		12
	1.4.1	Coding	13
	1.4.2	Math and Equation References	14
	1.4.3	Exercises	14
		Exercise 1.1	14
2	NUMERICS, ARITHMETIC, ASSIGNMENT, AND VECTORS	17
2.1	R for Basic Math	17
	2.1.1 Arithmetic	18
	2.1.2 Logarithms and Exponentials	19
	2.1.3 E-Notation	20
	Exercise 2.1	21
2.2	Assigning Objects	21
	Exercise 2.2	22
2.3	Vectors	23
	2.3.1 Creating a Vector	23
	2.3.2 Sequences, Repetition, Sorting, and Lengths	24
	Exercise 2.3	27
	2.3.3 Subsetting and Element Extraction	28
	Exercise 2.4	32
	2.3.4 Vector-Oriented Behavior	33
	Exercise 2.5	36
3	MATRICES AND ARRAYS	39
3.1	Defining a Matrix	39
	3.1.1 Filling Direction	40
	3.1.2 Row and Column Bindings	41
	3.1.3 Matrix Dimensions	42
3.2	Subsetting	42
	3.2.1 Row, Column, and Diagonal Extractions	43
	3.2.2 Omitting and Overwriting	44
	Exercise 3.1	47
3.3	Matrix Operations and Algebra	47
	3.3.1 Matrix Transpose	47
	3.3.2 Identity Matrix	48
	3.3.3 Scalar Multiple of a Matrix	49
	3.3.4 Matrix Addition and Subtraction	49
	3.3.5 Matrix Multiplication	50
	3.3.6 Matrix Inversion	51
	Exercise 3.2	52
3.4	Multidimensional Arrays	52
	3.4.1 Definition	53
	3.4.2 Subsets, Extractions, and Replacements	55
	Exercise 3.3	58
4	NON-NUMERIC VALUES	59
4.1	Logical Values	59
	4.1.1 TRUE or FALSE?	60
	4.1.2 A Logical Outcome: Relational Operators	60
	Exercise 4.1	64
4.1.3	Multiple Comparisons: Logical Operators	64
	Exercise 4.2	67
4.1.4	Logicals Are Numbers!	67
4.1.5	Logical Subsetting and Extraction	68
	Exercise 4.3	72
4.2	Characters	72
4.2.1	Creating a String	73
4.2.2	Concatenation	74
4.2.3	Escape Sequences	76
4.2.4	Substrings and Matching	77
	Exercise 4.4	78
4.3	Factors	79
4.3.1	Identifying Categories	79
4.3.2	Defining and Ordering Levels	82
4.3.3	Combining and Cutting	83
	Exercise 4.5	86
5	LISTS AND DATA FRAMES	89
5.1	Lists of Objects	89
5.1.1	Definition and Component Access	89
5.1.2	Naming	91
5.1.3	Nesting	93
	Exercise 5.1	94
5.2	Data Frames	95
5.2.1	Construction	96
5.2.2	Adding Data Columns and Combining Data Frames	98
5.2.3	Logical Record Subsets	100
	Exercise 5.2	101
6	SPECIAL VALUES, CLASSES, AND COERCION	103
6.1	Some Special Values	103
6.1.1	Infinity	104
6.1.2	NaN	106
	Exercise 6.1	107
6.1.3	NA	108
6.1.4	NULL	110
	Exercise 6.2	113
6.2	Understanding Types, Classes, and Coercion	114
6.2.1	Attributes	114
6.2.2	Object Class	116
6.2.3	Is-Dot Object-Checking Functions	119
6.2.4	As-Dot Coercion Functions	120
	Exercise 6.3	125
7	BASIC PLOTTING	127
7.1	Using plot with Coordinate Vectors	127
7.2	Graphical Parameters	129
	7.2.1 Automatic Plot Types	129
	7.2.2 Title and Axis Labels	130
	7.2.3 Color	131
	7.2.4 Line and Point Appearances	133
	7.2.5 Plotting Region Limits	133
7.3	Adding Points, Lines, and Text to an Existing Plot	134
	Exercise 7.1	139
7.4	The ggplot2 Package	139
	7.4.1 A Quick Plot with qplot	140
	7.4.2 Setting Appearance Constants with Geoms	141
	7.4.3 Aesthetic Mapping with Geoms	143
	Exercise 7.2	146
8	READING AND WRITING FILES	147
8.1	R-Ready Data Sets	147
	8.1.1 Built-in Data Sets	148
	8.1.2 Contributed Data Sets	149
8.2	Reading in External Data Files	150
	8.2.1 The Table Format	150
	8.2.2 Spreadsheet Workbooks	153
	8.2.3 Web-Based Files	154
	8.2.4 Other File Formats	155
8.3	Writing Out Data Files and Plots	156
	8.3.1 Data Sets	156
	8.3.2 Plots and Graphics Files	157
8.4	Ad Hoc Object Read/Write Operations	160
	Exercise 8.1	161
PART II PROGRAMMING
9	CALLING FUNCTIONS	165
9.1	Scoping	165
	9.1.1 Environments	166
	9.1.2 Search Path	168
	9.1.3 Reserved and Protected Names	170
	Exercise 9.1	172
9.2	Argument Matching	172
	9.2.1	Exact	172
	9.2.2	Partial	173
	9.2.3	Positional	174
	9.2.4	Mixed	175
	9.2.5	Dot-Dot-Dot: Use of Ellipses	176
		Exercise 9.2	177
10	CONDITIONS AND LOOPS	179
10.1	if Statements	179
	10.1.1	Stand-Alone Statement	180
	10.1.2	else Statements	183
	10.1.3	Using ifelse for Element-wise Checks	184
		Exercise 10.1	185
	10.1.4	Nesting and Stacking Statements	186
	10.1.5	The switch Function	189
		Exercise 10.2	191
10.2	Coding Loops	193
	10.2.1	for Loops	193
		Exercise 10.3	199
	10.2.2	while Loops	200
		Exercise 10.4	203
	10.2.3	Implicit Looping with apply	204
		Exercise 10.5	208
10.3	Other Control Flow Mechanisms	209
	10.3.1	Declaring break or next	209
	10.3.2	The repeat Statement	211
		Exercise 10.6	213
11	WRITING FUNCTIONS	215
11.1	The function Command	215
	11.1.1	Function Creation	216
	11.1.2	Using return	220
		Exercise 11.1	221
11.2	Arguments	222
	11.2.1	Lazy Evaluation	222
	11.2.2	Setting Defaults	225
	11.2.3	Checking for Missing Arguments	227
	11.2.4	Dealing with Ellipses	228
		Exercise 11.2	231
11.3	Specialized Functions	233
	11.3.1	Helper Functions	233
	11.3.2	Disposable Functions	236
	11.3.3	Recursive Functions	237
		Exercise 11.3	238

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12 EXCEPTIONS, TIMINGS, AND VISIBILITY	241
12.1 Exception Handling	241
12.1.1 Formal Notifications: Errors and Warnings	242
12.1.2 Catching Errors with try Statements	244
Exercise 12.1	247
12.2 Progress and Timing	249
12.2.1 Textual Progress Bars: Are We There Yet?	249
12.2.2 Measuring Completion Time: How Long Did It Take?	250
Exercise 12.2	251
12.3 Masking	252
12.3.1 Function and Object Distinction	252
12.3.2 Data Frame Variable Distinction	255
PART III STATISTICS AND PROBABILITY
13 ELEMENTARY STATISTICS	261
13.1 Describing Raw Data	261
13.1.1 Numeric Variables	262
13.1.2 Categorical Variables	262
13.1.3 Univariate and Multivariate Data	264
13.1.4 Parameter or Statistic?	265
Exercise 13.1	266
13.2 Summary Statistics	267
13.2.1 Centrality: Mean, Median, Mode	267
13.2.2 Counts, Percentages, and Proportions	271
Exercise 13.2	273
13.2.3 Quantiles, Percentiles, and the Five-Number Summary	274
13.2.4 Spread: Variance, Standard Deviation, and the Interquartile Range	275
Exercise 13.3	279
13.2.5 Covariance and Correlation	280
13.2.6 Outliers	285
Exercise 13.4	287
14 BASIC DATA VISUALIZATION	289
14.1 Barplots and Pie Charts	289
14.1.1 Building a Barplot	289
14.1.2 A Quick Pie Chart	293
14.2 Histograms	294
14.3	Box-and-Whisker Plots	298
	14.3.1 Stand-Alone Boxplots	298
	14.3.2 Side-by-Side Boxplots	299
14.4	Scatterplots	300
	14.4.1 Single Plot	301
	14.4.2 Matrix of Plots	302
	Exercise 14.1	306
15	PROBABILITY	309
15.1	What Is a Probability?	309
	15.1.1 Events and Probability	310
	15.1.2 Conditional Probability	311
	15.1.3 Intersection	311
	15.1.4 Union	312
	15.1.5 Complement	312
	Exercise 15.1	313
15.2	Random Variables and Probability Distributions	314
	15.2.1 Realizations	314
	15.2.2 Discrete Random Variables	315
	15.2.3 Continuous Random Variables	318
	15.2.4 Shape, Skew, and Modality	326
	Exercise 15.2	327
16	COMMON PROBABILITY DISTRIBUTIONS	331
16.1	Common Probability Mass Functions	332
	16.1.1 Bernoulli Distribution	332
	16.1.2 Binomial Distribution	333
	Exercise 16.1	338
	16.1.3 Poisson Distribution	338
	Exercise 16.2	341
	16.1.4 Other Mass Functions	342
16.2	Common Probability Density Functions	342
	16.2.1 Uniform	343
	Exercise 16.3	347
	16.2.2 Normal	348
	Exercise 16.4	356
	16.2.3 Student’s $t$ -distribution	357
	16.2.4 Exponential	359
	Exercise 16.5	362
	16.2.5 Other Density Functions	362
17	SAMPLING DISTRIBUTIONS AND CONFIDENCE	367
17.1	Sampling Distributions	367
	17.1.1 Distribution for a Sample Mean	368
	17.1.2 Distribution for a Sample Proportion	373
	Exercise 17.1	376
	17.1.3 Sampling Distributions for Other Statistics	377
17.2	Confidence Intervals	378
	17.2.1 An Interval for a Mean	378
	17.2.2 An Interval for a Proportion	381
	17.2.3 Other Intervals	382
	17.2.4 Comments on Interpretation of a CI	382
	Exercise 17.2	383
18	HYPOTHESIS TESTING	385
18.1	Components of a Hypothesis Test	385
	18.1.1 Hypotheses	386
	18.1.2 Test Statistic	387
	18.1.3 p-value	387
	18.1.4 Significance Level	387
	18.1.5 Criticisms of Hypothesis Testing	388
18.2	Testing Means	388
	18.2.1 Single Mean	389
	Exercise 18.1	392
	18.2.2 Two Means	392
	Exercise 18.2	401
18.3	Testing Proportions	402
	18.3.1 Single Proportion	402
	18.3.2 Two Proportions	405
	Exercise 18.3	408
18.4	Testing Categorical Variables	410
	18.4.1 Single Categorical Variable	410
	18.4.2 Two Categorical Variables	415
	Exercise 18.4	419
18.5	Errors and Power	420
	18.5.1 Hypothesis Test Errors	420
	18.5.2 Type I Errors	421
	18.5.3 Type II Errors	424
	Exercise 18.5	427
	18.5.4 Statistical Power	428
	Exercise 18.6	433
19		ANALYSIS OF VARIANCE	435
19.1	One-Way ANOVA		435
	19.1.1	Hypotheses and Diagnostic Checking	436
	19.1.2	One-Way ANOVA Table Construction	439
	19.1.3	Building ANOVA Tables with the aov Function	440
	Exercise 19.1		442
19.2	Two-Way ANOVA		443
	19.2.1	A Suite of Hypotheses	443
	19.2.2	Main Effects and Interactions	444
19.3	Kruskal-Wallis Test		447
	Exercise 19.2		449
20		SIMPLE LINEAR REGRESSION	451
20.1		An Example of a Linear Relationship	451
20.2	General Concepts		453
	20.2.1	Definition of the Model	453
	20.2.2	Estimating the Intercept and Slope Parameters	454
	20.2.3	Fitting Linear Models with lm	455
	20.2.4	Illustrating Residuals	456
20.3	Statistical Inference		458
	20.3.1	Summarizing the Fitted Model	458
	20.3.2	Regression Coefficient Significance Tests	459
	20.3.3	Coefficient of Determination	460
	20.3.4	Other summary Output	460
20.4	Prediction		461
	20.4.1	Confidence Interval or Prediction Interval?	461
	20.4.2	Interpreting Intervals	462
	20.4.3	Plotting Intervals	464
	20.4.4	Interpolation vs. Extrapolation	466
	Exercise 20.1		467
20.5	Understanding Categorical Predictors		468
	20.5.1	Binary Variables: k = 2	468
	20.5.2	Multilevel Variables: k > 2	472
	20.5.3	Changing the Reference Level	477
	20.5.4	Treating Categorical Variables as Numeric	478
	20.5.5	Equivalence with One-Way ANOVA	481
	Exercise 20.2		482
21		MULTIPLE LINEAR REGRESSION	485
21.1	Terminology		486
21.2		Theory	486
	21.2.1	Extending the Simple Model to a Multiple Model	487
	21.2.2	Estimating in Matrix Form	487
	21.2.3	A Basic Example	488
21.3		Implementing in R and Interpreting	490
	21.3.1	Additional Predictors	490
	21.3.2	Interpreting Marginal Effects	493
	21.3.3	Visualizing the Multiple Linear Model	494
	21.3.4	Finding Confidence Intervals	495
	21.3.5	Omnibus F-Test	496
	21.3.6	Predicting from a Multiple Linear Model	498
		Exercise 21.1	499
21.4		Transforming Numeric Variables	501
	21.4.1	Polynomial	502
	21.4.2	Logarithmic	508
	21.4.3	Other Transformations	512
		Exercise 21.2	512
21.5		Interactive Terms	514
	21.5.1	Concept and Motivation	514
	21.5.2	One Categorical, One Continuous	515
	21.5.3	Two Categorical	519
	21.5.4	Two Continuous	521
	21.5.5	Higher-Order Interactions	523
		Exercise 21.3	525

22	LINEAR MODEL SELECTION AND DIAGNOSTICS		527
22.1		Goodness-of-Fit vs. Complexity	527
	22.1.1	Principle of Parsimony	528
	22.1.2	General Guidelines	528
22.2		Model Selection Algorithms	529
	22.2.1	Nested Comparisons: The Partial F-Test	529
	22.2.2	Forward Selection	533
	22.2.3	Backward Selection	537
	22.2.4	Stepwise AIC Selection	541
		Exercise 22.1	546
	22.2.5	Other Selection Algorithms	548
22.3		Residual Diagnostics	548
	22.3.1	Inspecting and Interpreting Residuals	549
	22.3.2	Assessing Normality	554
	22.3.3	Illustrating Outliers, Leverage, and Influence	555
	22.3.4	Calculating Leverage	558
	22.3.5	Cook’s Distance	559
	22.3.6	Graphically Combining Residuals, Leverage, and Cook’s Distance	563
		Exercise 22.2	567
22.4		Collinearity	569
	22.4.1	Potential Warning Signs	569
23		ADVANCED PLOT CUSTOMIZATION	575
23.1		Handling the Graphics Device	576
	23.1.1	Manually Opening a New Device	576
	23.1.2	Switching Between Devices	577
	23.1.3	Closing a Device	578
	23.1.4	Multiple Plots in One Device	578
23.2		Plotting Regions and Margins	582
	23.2.1	Default Spacing	582
	23.2.2	Custom Spacing	583
	23.2.3	Clipping	584
23.3		Point-and-Click Coordinate Interaction	586
	23.3.1	Retrieving Coordinates Silently	586
	23.3.2	Visualizing Selected Coordinates	587
	23.3.3	Ad Hoc Annotation	588
		Exercise 23.1	589
23.4		Customizing Traditional R Plots	591
	23.4.1	Graphical Parameters for Style and Suppression	592
	23.4.2	Customizing Boxes	593
	23.4.3	Customizing Axes	594
23.5		Specialized Text and Label Notation	597
	23.5.1	Font	597
	23.5.2	Greek Symbols	598
	23.5.3	Mathematical Expressions	599
23.6		A Fully Annotated Scatterplot	601
		Exercise 23.2	606
24		GOING FURTHER WITH THE GRAMMAR OF GRAPHICS	609
24.1		ggplot or qplot?	609
24.2		Smoothing and Shading	611
	24.2.1	Adding LOESS Trends	611
	24.2.2	Constructing Smooth Density Estimates	614
24.3		Multiple Plots and Variable-Mapped Facets	616
	24.3.1	Independent Plots	616
	24.3.2	Facets Mapped to a Categorical Variable	619
		Exercise 24.1	622
24.4		Interactive Tools in ggvis	623
		Exercise 24.2	628
25 DEFINING COLORS AND PLOTTING IN HIGHER DIMENSIONS			631
25.1		Representing and Using Color	631
	25.1.1	Red-Green-Blue Hexadecimal Color Codes	632
	25.1.2	Built-in Palettes	633
	25.1.3	Custom Palettes	636
	25.1.4	Using Color Palettes to Index a Continuum	637
	25.1.5	Including a Color Legend	641
	25.1.6	Opacity	643
	25.1.7	RGB Alternatives and Further Functionality	645
	Exercise 25.1		646
25.2		3D Scatterplots	649
	25.2.1	Basic Syntax	649
	25.2.2	Visual Enhancements	650
	Exercise 25.2		652
25.3		Preparing a Surface for Plotting	653
	25.3.1	Constructing an Evaluation Grid	654
	25.3.2	Constructing the z-Matrix	655
	25.3.3	Conceptualizing the z-Matrix	656
25.4		Contour Plots	657
	25.4.1	Drawing Contour Lines	657
	25.4.2	Color-Filled Contours	663
	Exercise 25.3		666
25.5		Pixel Images	668
	25.5.1	One Grid Point = One Pixel	668
	25.5.2	Surface Truncation and Empty Pixels	671
	Exercise 25.4		676
25.6		Perspective Plots	679
	25.6.1	Basic Plots and Angle Adjustment	679
	25.6.2	Coloring Facets	682
	25.6.3	Rotating with Loops	686
	Exercise 25.5		687
26 INTERACTIVE 3D PLOTS			691
26.1		Point Clouds	691
	26.1.1	Basic 3D Cloud	692
	26.1.2	Visual Enhancements and Legends	693
	26.1.3	Adding Further 3D Components	694
	Exercise 26.1		697
26.2		Bivariate Surfaces	699
	26.2.1	Basic Perspective Surface	700
	26.2.2	Additional Components	701
	26.2.3	Coloring by z Value	703
	26.2.4	Dealing with the Aspect Ratio	704
	Exercise 26.2		706
26.3	Trivariate Surfaces	709
	26.3.1 Evaluation Coordinates in 3D	709
	26.3.2 Isosurfaces	710
	26.3.3 Example: Nonparametric Trivariate Density	715
26.4	Handling Parametric Equations	720
	26.4.1 Simple Loci	720
	26.4.2 Mathematical Abstractions	725
	Exercise 26.3	732

A	INSTALLING R AND CONTRIBUTED PACKAGES	737
A.1	Downloading and Installing R	737
A.2	Using Packages	739
	A.2.1 Base Packages	739
	A.2.2 Recommended Packages	740
	A.2.3 Contributed Packages	740
A.3	Updating R and Installed Packages	746
A.4	Using Other Mirrors and Repositories	747
	A.4.1 Switching CRAN Mirror	747
	A.4.2 Other Package Repositories	748
A.5	Citing and Writing Packages	748
	A.5.1 Citing R and Contributed Packages	748
	A.5.2 Writing Your Own Packages	749
B
	WORKING WITH RSTUDIO	751
B.1	Basic Layout and Usage	752
	B.1.1 Editor Features and Appearance Options	752
	Customizing Panes B.1.2	753
B.2	Auxiliary Tools	754
	B.2.1 Projects	754
	B.2.2 Package Installer and Updater	755
	B.2.3 Support for Debugging	756
	B.2.4 Markup, Document, and Graphics Tools	756
	REFERENCE LIST	761

INDEX		767

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PART IV STATISTICAL TESTING AND MODELING

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PART V ADVANCED GRAPHICS

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PREFACE

The aim of The Book of R: A First Course in Programming and Statistics is to provide a relatively gentle yet informative exposure to the statistical software environment R, alongside some common statistical analyses, so that readers may have a solid foundation from which to eventually become experts in their own right. Learning to use and program in a computing language is much the same as learning a new spoken language. At the beginning, it is often difficult and may even be daunting—but total immersion in and active use of the language is the best and most effective way to become fluent.

Many beginner-style texts that focus on R can generally be allocated to one of two categories: those concerned with computational aspects (that is, syntax and general programming tools) and those with statistical modeling and analysis in mind, often one particular type. In my experience, these texts are extremely well written and contain a wealth of useful information but better suit those individuals wanting to pursue fairly specific goals from the outset. This text seeks to combine the best of both worlds, by first focusing on only an appreciation and understanding of the language and its style and subsequently using these skills to fully introduce, conduct, and interpret some common statistical practices. The target audience is, quite simply, anyone who wants to gain a foothold in R as a first computing language,

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perhaps with the ultimate goal of completing their own statistical analyses. This includes but is certainly not limited to undergraduates, postgraduates, academic researchers, and practitioners in the applied sciences with little or no experience in programming or statistics in general. A basic understanding of elementary mathematical behavior (for example, the order of operations) and associated operators (for example, the summation symbol Σ) is desirable, however.

In view of this, The Book of R can be used purely as a programming text to learn the language or as an introductory statistical methods book with accompanying instruction in R. Though it is not intended to represent an exhaustive dictionary of the language, the aim is to provide readers with a comfortable learning tool that eliminates the kind of foreboding many have voiced to me when they have considered learning R from scratch. The fact remains that there are usually many different ways to go about any given task—something that holds true for most so-called high-level computer languages. What this text presents reflects my own way of thinking about learning and programming in R, which I approach less as a computer scientist and more as an applied data analyst.

In part, I aim to provide a precursor and supplement to the work in The Art of R Programming: A Tour of Statistical Software Design, the other R text published by No Starch Press (2011), written by Professor Norman Matloff (University of California, Davis). In his detailed and well-received book, Professor Matloff comes at R from a computer science angle, that is, treating it as a programming language in its own right. As such, The Art of R Programming provides some of the best descriptions of R’s computational features I’ve yet to come across (for example, running external code such as C from R programs, handling and manipulating R’s memory allocations, and formal debugging strategies). Noteworthy, however, is the fact that some previous experience and knowledge of programming in general goes a long way to appreciating some of these more advanced features. It is my hope that my text will not only provide this experience but do so in R itself at a comfortable pace, with statistical analyses as the supplementary motivation.

This text, which serves as a “traveler’s guide” as we backpack our way through R country, was born out of a three-day introductory R workshop I began teaching at the University of Otago in New Zealand. The emphasis is on active use of the software, with each chapter containing a number of code examples and practice exercises to encourage interaction. For those readers not part of a workshop, just fire up your computer, grab a drink and a comfy chair, and start with Chapter 1.

Tilman M. Davies Dunedin, New Zealand

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ACKNOWLEDGMENTS

First, I must of course acknowledge the creators and maintainers of R, both past and present. Whether or not any prominent contributor ever picks up this book (perhaps as a handy stabilizer for a wonky piece of furniture), they should be reminded of the appreciation that many, including myself, have for the incredible role R plays as a data analysis tool worldwide and a glowing example of what can be achieved with free software. R has transformed how academic and industry statisticians, and many others, make contributions to research and data analysis. This doesn’t refer just to their respective pursuits but also to the ability to make computational functionality readily available so that others may perform the same analyses. I’m just one voice of many to echo this, and The Book of R essentially represents my own professional love letter to all things R. The best way to profess this sentiment is to encourage and educate both students and professionals in the analytic freedom R provides and hence perpetuate the enthusiasm many already hold for the software.

The team at No Starch Press is undoubtedly an absolute pleasure to work with. If “informal professionalism” is actually a thing, it’s exactly how I like to work and exactly how they guided the progress of what turned out to be a large, time-consuming project. Thanks to Bill Pollock and Tyler Ortman for taking on this project. Thanks especially to those at the coalface

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of my drafts, specifically Liz Chadwick and Seph Kramer for picking through and streamlining the language and exposition in numerous TEX files, and to those who turned the collection of these files into a cohesive, polished book, headed by Riley Hoffman. Dr. Debbie Leader, a senior university tutor in statistics at Massey University (Palmerston North, New Zealand), can’t escape my appreciation for the scrutiny she applied to the R code and statistical content, improving the clarity and interpretation of the core focus of the book. Comments from other reviewers and early readers were also instrumental in shaping the finished product.

A special mention must be made of Professor Martin Hazelton at Massey University. Once my undergraduate lecturer during my freshman year at the University of Western Australia; then my graduate and doctoral supervisor in New Zealand, where I followed him; and now my ongoing collaborator, mentor, and friend, Martin is pretty much solely responsible for my interest and career in statistics. Although my scribblings mightn’t include all the big fancy Oxford words he’d use, it’s only with this education that I’m now in a position to write this book. With it, I hope I can inspire my own students and any others wanting to learn more about statistical programming in the same way he inspired me.

Last, but certainly not least, my immediate family in Perth and my extended family in Germany must be thanked for their ongoing and unwavering love and support through many years. My New Zealand family must also be thanked, especially Andrea, who would often put up with a glowing Apple logo instead of my face on the other end of the couch as I continued tapping away on the manuscript after work, only pausing to reach for a beer (or if something juicy was happening on Shortland Street—Google it). The cat, Sigma, was always nearby at these moments too, ensuring her insertion in a couple of exercises in this book.

I suppose all this could be summarized in one word, though:

Cheers.

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