R Cookbook Proven Recipes for Data Analysis, Statistics, and Graphics (J. D. Long, Paul Teetor) (Z-Library)

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J.D. Long & Paul Teetor R Cookbook Proven Recipes for Data Analysis, Statistics & Graphics Second Edition

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J.D. Long and Paul Teetor R Cookbook Proven Recipes for Data Analysis, Statistics, and Graphics SECOND EDITION

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978-1-492-04068-2 [LSI] R Cookbook by J.D. Long and Paul Teetor Copyright © 2019 J.D. Long and Paul Teetor. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com. Editors: Nicole Tache and Melissa Potter Production Editor: Kristen Brown Copyeditor: Rachel Monaghan Proofreader: Rachel Head Indexer: Ellen Troutman-Zaig Interior Designer: David Futato Cover Designer: Karen Montgomery Illustrator: Rebecca Demarest March 2011: First Edition July 2019: Second Edition Revision History for the Second Edition 2019-06-21: First Release 2019-11-15: Second Release See http://oreilly.com/catalog/errata.csp?isbn=9781492040682 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. R Cookbook, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. The views expressed in this work are those of the authors, and do not represent the publisher’s views. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

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Table of Contents Welcome to the R Cookbook, 2nd Edition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1. Getting Started and Getting Help. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Downloading and Installing R 2 1.2 Installing RStudio 4 1.3 Starting RStudio 5 1.4 Entering Commands 7 1.5 Exiting from RStudio 9 1.6 Interrupting R 11 1.7 Viewing the Supplied Documentation 12 1.8 Getting Help on a Function 14 1.9 Searching the Supplied Documentation 16 1.10 Getting Help on a Package 17 1.11 Searching the Web for Help 18 1.12 Finding Relevant Functions and Packages 21 1.13 Searching the Mailing Lists 22 1.14 Submitting Questions to Stack Overflow or Elsewhere in the Community 23 2. Some Basics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.1 Printing Something to the Screen 27 2.2 Setting Variables 29 2.3 Listing Variables 31 2.4 Deleting Variables 32 2.5 Creating a Vector 34 2.6 Computing Basic Statistics 35 2.7 Creating Sequences 38 2.8 Comparing Vectors 39 2.9 Selecting Vector Elements 41 iii

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2.10 Performing Vector Arithmetic 44 2.11 Getting Operator Precedence Right 46 2.12 Typing Less and Accomplishing More 47 2.13 Creating a Pipeline of Function Calls 49 2.14 Avoiding Some Common Mistakes 52 3. Navigating the Software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.1 Getting and Setting the Working Directory 59 3.2 Creating a New RStudio Project 60 3.3 Saving Your Workspace 63 3.4 Viewing Your Command History 64 3.5 Saving the Result of the Previous Command 65 3.6 Displaying Loaded Packages via the Search Path 66 3.7 Viewing the List of Installed Packages 68 3.8 Accessing the Functions in a Package 69 3.9 Accessing Built-in Datasets 70 3.10 Installing Packages from CRAN 72 3.11 Installing a Package from GitHub 73 3.12 Setting or Changing a Default CRAN Mirror 74 3.13 Running a Script 76 3.14 Running a Batch Script 77 3.15 Locating the R Home Directory 79 3.16 Customizing R Startup 81 3.17 Using R and RStudio in the Cloud 84 4. Input and Output. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 4.1 Entering Data from the Keyboard 87 4.2 Printing Fewer Digits (or More Digits) 88 4.3 Redirecting Output to a File 90 4.4 Listing Files 91 4.5 Dealing with “Cannot Open File” in Windows 94 4.6 Reading Fixed-Width Records 94 4.7 Reading Tabular Data Files 97 4.8 Reading from CSV Files 101 4.9 Writing to CSV Files 103 4.10 Reading Tabular or CSV Data from the Web 104 4.11 Reading Data from Excel 105 4.12 Writing a Data Frame to Excel 107 4.13 Reading Data from a SAS File 109 4.14 Reading Data from HTML Tables 111 4.15 Reading Files with a Complex Structure 113 4.16 Reading from MySQL Databases 118 iv | Table of Contents

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4.17 Accessing a Database with dbplyr 120 4.18 Saving and Transporting Objects 122 5. Data Structures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 5.1 Appending Data to a Vector 135 5.2 Inserting Data into a Vector 136 5.3 Understanding the Recycling Rule 137 5.4 Creating a Factor (Categorical Variable) 139 5.5 Combining Multiple Vectors into One Vector and a Factor 140 5.6 Creating a List 142 5.7 Selecting List Elements by Position 144 5.8 Selecting List Elements by Name 145 5.9 Building a Name/Value Association List 147 5.10 Removing an Element from a List 149 5.11 Flattening a List into a Vector 150 5.12 Removing NULL Elements from a List 151 5.13 Removing List Elements Using a Condition 152 5.14 Initializing a Matrix 153 5.15 Performing Matrix Operations 155 5.16 Giving Descriptive Names to the Rows and Columns of a Matrix 156 5.17 Selecting One Row or Column from a Matrix 157 5.18 Initializing a Data Frame from Column Data 158 5.19 Initializing a Data Frame from Row Data 160 5.20 Appending Rows to a Data Frame 162 5.21 Selecting Data Frame Columns by Position 165 5.22 Selecting Data Frame Columns by Name 168 5.23 Changing the Names of Data Frame Columns 170 5.24 Removing NAs from a Data Frame 171 5.25 Excluding Columns by Name 172 5.26 Combining Two Data Frames 173 5.27 Merging Data Frames by Common Column 174 5.28 Converting One Atomic Value into Another 177 5.29 Converting One Structured Data Type into Another 178 6. Data Transformations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 6.1 Applying a Function to Each List Element 182 6.2 Applying a Function to Every Row of a Data Frame 184 6.3 Applying a Function to Every Row of a Matrix 185 6.4 Applying a Function to Every Column 186 6.5 Applying a Function to Parallel Vectors or Lists 188 6.6 Applying a Function to Groups of Data 191 6.7 Creating a New Column Based on Some Condition 192 Table of Contents | v

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7. Strings and Dates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 7.1 Getting the Length of a String 197 7.2 Concatenating Strings 198 7.3 Extracting Substrings 199 7.4 Splitting a String According to a Delimiter 200 7.5 Replacing Substrings 201 7.6 Generating All Pairwise Combinations of Strings 202 7.7 Getting the Current Date 204 7.8 Converting a String into a Date 204 7.9 Converting a Date into a String 205 7.10 Converting Year, Month, and Day into a Date 206 7.11 Getting the Julian Date 208 7.12 Extracting the Parts of a Date 208 7.13 Creating a Sequence of Dates 210 8. Probability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 8.1 Counting the Number of Combinations 215 8.2 Generating Combinations 216 8.3 Generating Random Numbers 217 8.4 Generating Reproducible Random Numbers 219 8.5 Generating a Random Sample 220 8.6 Generating Random Sequences 221 8.7 Randomly Permuting a Vector 222 8.8 Calculating Probabilities for Discrete Distributions 223 8.9 Calculating Probabilities for Continuous Distributions 225 8.10 Converting Probabilities to Quantiles 227 8.11 Plotting a Density Function 228 9. General Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233 9.1 Summarizing Your Data 235 9.2 Calculating Relative Frequencies 237 9.3 Tabulating Factors and Creating Contingency Tables 238 9.4 Testing Categorical Variables for Independence 239 9.5 Calculating Quantiles (and Quartiles) of a Dataset 240 9.6 Inverting a Quantile 241 9.7 Converting Data to z-Scores 242 9.8 Testing the Mean of a Sample (t-Test) 243 9.9 Forming a Confidence Interval for a Mean 244 9.10 Forming a Confidence Interval for a Median 246 9.11 Testing a Sample Proportion 247 9.12 Forming a Confidence Interval for a Proportion 248 9.13 Testing for Normality 249 vi | Table of Contents

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9.14 Testing for Runs 250 9.15 Comparing the Means of Two Samples 252 9.16 Comparing the Locations of Two Samples Nonparametrically 254 9.17 Testing a Correlation for Significance 255 9.18 Testing Groups for Equal Proportions 257 9.19 Performing Pairwise Comparisons Between Group Means 258 9.20 Testing Two Samples for the Same Distribution 260 10. Graphics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 10.1 Creating a Scatter Plot 267 10.2 Adding a Title and Labels 268 10.3 Adding (or Removing) a Grid 270 10.4 Applying a Theme to a ggplot Figure 274 10.5 Creating a Scatter Plot of Multiple Groups 278 10.6 Adding (or Removing) a Legend 280 10.7 Plotting the Regression Line of a Scatter Plot 284 10.8 Plotting All Variables Against All Other Variables 288 10.9 Creating One Scatter Plot for Each Group 290 10.10 Creating a Bar Chart 292 10.11 Adding Confidence Intervals to a Bar Chart 295 10.12 Coloring a Bar Chart 298 10.13 Plotting a Line from x and y Points 300 10.14 Changing the Type, Width, or Color of a Line 302 10.15 Plotting Multiple Datasets 305 10.16 Adding Vertical or Horizontal Lines 307 10.17 Creating a Boxplot 309 10.18 Creating One Boxplot for Each Factor Level 311 10.19 Creating a Histogram 313 10.20 Adding a Density Estimate to a Histogram 315 10.21 Creating a Normal Quantile–Quantile Plot 317 10.22 Creating Other Quantile–Quantile Plots 319 10.23 Plotting a Variable in Multiple Colors 322 10.24 Graphing a Function 325 10.25 Displaying Several Figures on One Page 328 10.26 Writing Your Plot to a File 331 11. Linear Regression and ANOVA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333 11.1 Performing Simple Linear Regression 335 11.2 Performing Multiple Linear Regression 337 11.3 Getting Regression Statistics 339 11.4 Understanding the Regression Summary 342 11.5 Performing Linear Regression Without an Intercept 345 Table of Contents | vii

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11.6 Regressing Only Variables That Highly Correlate with Your Dependent Variable 347 11.7 Performing Linear Regression with Interaction Terms 350 11.8 Selecting the Best Regression Variables 352 11.9 Regressing on a Subset of Your Data 357 11.10 Using an Expression Inside a Regression Formula 358 11.11 Regressing on a Polynomial 360 11.12 Regressing on Transformed Data 361 11.13 Finding the Best Power Transformation (Box–Cox Procedure) 363 11.14 Forming Confidence Intervals for Regression Coefficients 368 11.15 Plotting Regression Residuals 369 11.16 Diagnosing a Linear Regression 371 11.17 Identifying Influential Observations 375 11.18 Testing Residuals for Autocorrelation (Durbin–Watson Test) 376 11.19 Predicting New Values 378 11.20 Forming Prediction Intervals 379 11.21 Performing One-Way ANOVA 380 11.22 Creating an Interaction Plot 382 11.23 Finding Differences Between Means of Groups 383 11.24 Performing Robust ANOVA (Kruskal–Wallis Test) 386 11.25 Comparing Models by Using ANOVA 388 12. Useful Tricks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391 12.1 Peeking at Your Data 391 12.2 Printing the Result of an Assignment 394 12.3 Summing Rows and Columns 394 12.4 Printing Data in Columns 395 12.5 Binning Your Data 396 12.6 Finding the Position of a Particular Value 397 12.7 Selecting Every nth Element of a Vector 398 12.8 Finding Minimums or Maximums 399 12.9 Generating All Combinations of Several Variables 401 12.10 Flattening a Data Frame 402 12.11 Sorting a Data Frame 403 12.12 Stripping Attributes from a Variable 404 12.13 Revealing the Structure of an Object 405 12.14 Timing Your Code 408 12.15 Suppressing Warnings and Error Messages 410 12.16 Taking Function Arguments from a List 411 12.17 Defining Your Own Binary Operators 413 12.18 Suppressing the Startup Message 414 12.19 Getting and Setting Environment Variables 415 viii | Table of Contents

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12.20 Use Code Sections 416 12.21 Executing R in Parallel Locally 417 12.22 Executing R in Parallel Remotely 420 13. Beyond Basic Numerics and Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425 13.1 Minimizing or Maximizing a Single-Parameter Function 425 13.2 Minimizing or Maximizing a Multiparameter Function 426 13.3 Calculating Eigenvalues and Eigenvectors 428 13.4 Performing Principal Component Analysis 429 13.5 Performing Simple Orthogonal Regression 430 13.6 Finding Clusters in Your Data 433 13.7 Predicting a Binary-Valued Variable (Logistic Regression) 436 13.8 Bootstrapping a Statistic 438 13.9 Factor Analysis 441 14. Time Series Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 14.1 Representing Time Series Data 449 14.2 Plotting Time Series Data 452 14.3 Extracting the Oldest or Newest Observations 454 14.4 Subsetting a Time Series 456 14.5 Merging Several Time Series 458 14.6 Filling or Padding a Time Series 460 14.7 Lagging a Time Series 463 14.8 Computing Successive Differences 464 14.9 Performing Calculations on Time Series 466 14.10 Computing a Moving Average 467 14.11 Applying a Function by Calendar Period 469 14.12 Applying a Rolling Function 471 14.13 Plotting the Autocorrelation Function 473 14.14 Testing a Time Series for Autocorrelation 475 14.15 Plotting the Partial Autocorrelation Function 476 14.16 Finding Lagged Correlations Between Two Time Series 478 14.17 Detrending a Time Series 479 14.18 Fitting an ARIMA Model 482 14.19 Removing Insignificant ARIMA Coefficients 486 14.20 Running Diagnostics on an ARIMA Model 487 14.21 Making Forecasts from an ARIMA Model 490 14.22 Plotting a Forecast 491 14.23 Testing for Mean Reversion 492 14.24 Smoothing a Time Series 495 Table of Contents | ix

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15. Simple Programming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 15.1 Choosing Between Two Alternatives: if/else 500 15.2 Iterating with a Loop 502 15.3 Defining a Function 503 15.4 Creating a Local Variable 505 15.5 Choosing Between Multiple Alternatives: switch 506 15.6 Defining Defaults for Function Parameters 507 15.7 Signaling Errors 508 15.8 Protecting Against Errors 509 15.9 Creating an Anonymous Function 510 15.10 Creating a Collection of Reusable Functions 511 15.11 Automatically Reindenting Code 513 16. R Markdown and Publishing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 16.1 Creating a New Document 516 16.2 Adding a Title, Author, or Date 518 16.3 Formatting Document Text 520 16.4 Inserting Document Headings 521 16.5 Inserting a List 521 16.6 Showing Output from R Code 523 16.7 Controlling Which Code and Results Are Shown 525 16.8 Inserting a Plot 526 16.9 Inserting a Table 530 16.10 Inserting a Table of Data 531 16.11 Inserting Math Equations 534 16.12 Generating HTML Output 535 16.13 Generating PDF Output 536 16.14 Generating Microsoft Word Output 539 16.15 Generating Presentation Output 546 16.16 Creating a Parameterized Report 548 16.17 Organizing Your R Markdown Workflow 552 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 x | Table of Contents

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Welcome to the R Cookbook, 2nd Edition R is a powerful tool for statistics, graphics, and statistical programming. It is used by tens of thousands of people daily to perform serious statistical analyses. It is a free, open source system whose implementation is the collective accomplishment of many intelligent, hard-working people. There are more than 10,000 available add-on pack‐ ages, and R is a serious rival to all commercial statistical packages. But R can be frustrating. It’s not obvious how to accomplish many tasks, even simple ones. The simple tasks are easy once you know how, yet figuring out that “how” can be maddening. This book is full of how-to recipes, each of which solves a specific problem. Each recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works. We know these recipes are useful and we know they work, because we use them ourselves. The range of recipes is broad. It starts with basic tasks before moving on to input and output, general statistics, graphics, and linear regression. Any significant work with R will involve most or all of these areas. If you are a beginner, then this book will get you started faster. If you are an inter‐ mediate user, this book will be useful for expanding your horizons and jogging your memory (“How do I do that Kolmogorov–Smirnov test again?”). The book is not a tutorial on R, although you will learn something by studying the recipes. It is not a reference manual, but it does contain a lot of useful information. It is not a book on programming in R, although many recipes are useful inside R scripts. Finally, this book is not an introduction to statistics. Many recipes assume that you are familiar with the underlying statistical procedure, if any, and just want to know how it’s done in R. xi

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The Recipes Most recipes use one or two R functions to solve a specific problem. It’s important to remember that we do not describe the functions in detail; rather, we describe just enough to solve the immediate problem. Nearly every such function has additional capabilities beyond those described here, and some have amazing capabilities. We strongly urge you to read the functions’ help pages. You will likely learn something valuable. Each recipe presents one way to solve a particular problem. Of course, there are likely several reasonable solutions to each problem. When we knew of multiple solutions, we generally selected the simplest one. For any given task, you can probably discover several alternative solutions yourself. This is a cookbook, not a bible. In particular, R has literally thousands of downloadable add-on packages, many of which implement alternative algorithms and statistical methods. This book concen‐ trates on the core functionality available through the basic distribution combined with several important packages known collectively as the tidyverse. The most concise definition of the tidyverse comes from Hadley Wickham, its origi‐ nator and one of its core maintainers: The tidyverse is a set of packages that work in harmony because they share common data representations and API design. The tidyverse package is designed to make it easy to install and load core packages from the tidyverse in a single command. The best place to learn about all the packages in the tidyverse and how they fit together is R for Data Science. A Note on Terminology The goal of every recipe is to solve a problem and solve it quickly. Rather than labo‐ ring in tedious prose, we occasionally streamline the description with terminology that is correct but not precise. A good example is the term generic function. We refer to print(x) and plot(x) as generic functions because they work for many kinds of x, handling each kind appropriately. A computer scientist would wince at our terminol‐ ogy because, strictly speaking, these are not simply “functions”; they are polymorphic methods with dynamic dispatching. But if we carefully unpacked every such technical detail, the essential solutions would be buried in the technicalities. So we just call them functions, which we think is more readable. Another example, taken from statistics, is the complexity surrounding the semantics of statistical hypothesis testing. Using the strict language of probability theory would obscure the practical application of some tests, so we use more colloquial language when describing each statistical test. See the introduction to Chapter 9 for more about how hypothesis tests are presented in the recipes. xii | Welcome to the R Cookbook, 2nd Edition

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Our goal is to make the power of R available to a wide audience by writing readably, not formally. We hope that experts in their respective fields will understand if our ter‐ minology is occasionally informal. Software and Platform Notes The base distribution of R has frequent and planned releases, but the language defini‐ tion and core implementation are stable. The recipes in this book should work with any recent release of the base distribution. Some recipes have platform-specific considerations, and we have carefully noted them. Those recipes mostly deal with software issues, such as installation and config‐ uration. As far as we know, all other recipes will work on all three major platforms for R: Windows, macOS, and Linux/Unix. Other Resources Here are a few suggestions for further reading, if oyu’d like to dig a little deeper: On the web The mother ship for all things R is the R project site. From there you can down‐ load R for your platform, add-on packages, documentation, and source code as well as many other resources. Beyond the R project site, we recommend using an R-specific search engine, such as RSeek, created by Sasha Goodman. You can use a generic search engine, such as Google, but the “R” search term brings up too much extraneous stuff. See Recipe 1.11 for more about searching the web. Reading blogs is a great way to learn about R and stay abreast of leading-edge developments. There are surprisingly many such blogs, so we recommend fol‐ lowing two blogs-of-blogs: R-bloggers, created by Tal Galili, and PlanetR. By sub‐ scribing to their RSS feeds, you will be notified of interesting and useful articles from dozens of websites. R books There are many, many books about learning and using R. Listed here are a few that we have found useful. Note that the R project site contains an extensive bib‐ liography of books related to R. R for Data Science, by Hadley Wickham and Garrett Grolemund (O’Reilly), is an excellent introduction to the tidyverse packages, especially for using them in data analysis and statistics. It is also available online. We find the R Graphics Cookbook, 2nd ed., by Winston Chang (O’Reilly), indis‐ pensible for creating graphics. The book ggplot2: Elegant Graphics for Data Welcome to the R Cookbook, 2nd Edition | xiii

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Analysis by Hadley Wickham (Springer) is the definitive reference for the graph‐ ics package ggplot2, which we use in this book. Anyone doing serious graphics work in R will want R Graphics by Paul Murrell (Chapman & Hall/CRC). R in a Nutshell, by Joseph Adler (O’Reilly), is the quick tutorial and reference you’ll keep by your side. It covers many more topics than this cookbook. New books on programming in R appear regularly. We suggest Hands On Pro‐ gramming with R by Garrett Grolemund (O’Reilly) for an introduction, or The Art of R Programming by Normal Matloff (No Starch Press). Hadley Wickham’s Advanced R (Chapman & Hall/CRC) is available either as a printed book or free online and is a great deeper dive into advanced R topics. Efficient R Programming, by Colin Gillespie and Robin Lovelace (O’Reilly), is another good guide to learn‐ ing the deeper concepts about R programming. Modern Applied Statistics with S, 4th ed., by William Venables and Brian Ripley (Springer), uses R to illustrate many advanced statistical techniques. The book’s functions and datasets are available in the MASS package, which is included in the standard distribution of R. Serious geeks can download the R Language Definition from the R Core Team. The Definition is a work in progress, but it can answer many of your detailed questions regarding R as a programming language. Statistics books For learning statistics, a great choice is Using R for Introductory Statistics by John Verzani (Chapman & Hall/CRC). It teaches statistics and R together, giving you the necessary computer skills to apply the statistical methods. You will need a good statistics textbook or reference book to accurately interpret the statistical tests performed in R. There are many such fine books—far too many for us to recommend any one above the others. Increasingly, statistics authors are using R to illustrate their methods. If you work in a specialized field, then you will likely find a useful and relevant book in the R project bibliography. Conventions Used in This Book The following typographical conventions are used in this book: Italic Indicates new terms, URLs, email addresses, filenames, and file extensions. xiv | Welcome to the R Cookbook, 2nd Edition

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Constant width Used for program listings, as well as within paragraphs to refer to program ele‐ ments such as variable or function names, databases, packages, data types, envi‐ ronment variables, statements, and keywords. Constant width bold Shows commands or other text that should be typed literally by the user. Constant width italic Shows text that should be replaced with user-supplied values or by values deter‐ mined by context. This element signifies a tip or suggestion. This element signifies a general note. This element indicates a warning or caution. Using Code Examples Supplemental material (code examples, source code for the book, exercises, etc.) is available for download at http://rc2e.com. The Twitter account for content associated with this book is @R_cookbook. This book is here to help you get your job done. In general, you may use the code in this book in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission. Welcome to the R Cookbook, 2nd Edition | xv

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We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “R Cookbook, 2nd ed., by J.D. Long and Paul Teetor. Copyright 2019 J.D. Long and Paul Teetor, 978-1-492-04068-2.” If you feel your use of code examples falls outside fair use or the permission just described, feel free to contact us at permissions@oreilly.com. O’Reilly Online Learning For almost 40 years, O’Reilly Media has provided technology and business training, knowledge, and insight to help compa‐ nies succeed. Our unique network of experts and innovators share their knowledge and expertise through books, articles, conferences, and our online learning platform. O’Reilly’s online learning platform gives you on-demand access to live training courses, in- depth learning paths, interactive coding environments, and a vast collection of text and video from O’Reilly and 200+ other publishers. For more information, please visit http://oreilly.com. How to Contact Us Please address comments and questions concerning this book to the publisher: O’Reilly Media, Inc. 1005 Gravenstein Highway North Sebastopol, CA 95472 800-998-9938 (in the United States or Canada) 707-829-0515 (international or local) 707-829-0104 (fax) We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://bit.ly/RCookbook_2e. To comment or ask technical questions about this book, send email to bookques‐ tions@oreilly.com. For more information about our books, courses, conferences, and news, see our web‐ site at http://www.oreilly.com. Find us on Facebook: http://facebook.com/oreilly Follow us on Twitter: http://twitter.com/oreillymedia Watch us on YouTube: http://www.youtube.com/oreillymedia xvi | Welcome to the R Cookbook, 2nd Edition

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Acknowledgments With gratitude we thank the R community in general and the R Core Team in partic‐ ular. Their selfless contributions are enormous. The world of statistics is benefiting tremendously from their work. The R Studio Community Discussion participants were very helpful in workshopping ideas around how to explain many things. And the staff and leadership of R Studio were supportive in so many little and big ways. We owe them a debt of gratitude for all they have given back to the R community. We wish to thank the book’s technical reviewers: David Curran, Justin Shea, and MAJ Dusty Turner. Their feedback was critical for improving the quality, accuracy, and usefulness of this book. Our editors, Melissa Potter and Rachel Monaghan, were help‐ ful beyond imagination and they frequently prevented us from publicly demonstrat‐ ing our ignorance. Our production editor, Kristen Brown, is the envy of all technical authors because of her speed and her proficiency with both Markdown and Git. Paul would like to thank his family for their support and patience during the creation of this book. J.D. would like to thank his wife Mary Beth and daughter Ada for their patience with all the early mornings and weekends that he spent with his face in the laptop working on this book. Welcome to the R Cookbook, 2nd Edition | xvii

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