探索数据之美：R语言实战第二版

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"R in Action 2nd Edition by Robert I. Kabacoff" 本书"R in Action 2nd Edition"是关于使用R语言进行数据分析和图形化的深入指南，由作者Robert I. Kabacoff撰写。书中的描述引用了《爱丽丝梦游仙境》的名言，以此来体现统计分析的本质——一个互动的探索、可视化和解释过程。同时，书中也提到了R语言普遍被认为学习难度较高的观点，但作者旨在证明，通过指导和说明，无论是初学者还是经验丰富的用户，都能驾驭R语言的强大功能。 R语言是一种高度灵活的平台和语言，拥有超过50,000个分析和图形函数，这使得它在数据科学领域中占有重要地位。然而，如此庞大的资源库可能会对新用户造成困扰，但Kabacoff在书中提供了一套方法，帮助读者理解其中的逻辑，选择合适的工具以优雅、高效且具创新性的方式完成数据分析任务。该书的标签包括"R-Language"和"statistics"，表明内容专注于R语言及其在统计分析中的应用。书中可能涵盖了基础的R编程概念，如数据类型、控制结构和函数，以及更高级的主题，如数据清洗、统计建模、可视化技巧（例如使用ggplot2包）和预测分析。此外，考虑到R在现代数据分析中的重要性，书中可能还会讨论到如何将R与其他工具（如Python或SQL）集成，以及如何利用R进行大数据处理和机器学习。书的前言部分还提到了出版商Manning Publications的相关信息，包括购买和批量订购图书的渠道，以及版权声明。作者强调，尽管制造商和销售商的产品标识可能是商标，但书中已尽可能尊重并正确表示这些商标。 "R in Action 2nd Edition"是一本旨在降低R语言学习门槛，帮助读者有效利用其进行数据分析和图形化展示的实用书籍。无论你是初涉数据分析的新手，还是希望提升R技能的专业人士，这本书都将为你提供宝贵的指导。

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preface

What is the use of a book, without pictures or conversations?

—

Alice, Alice’s Adventures in Wonderland

It’s wondrous, with treasures to satiate desires both subtle and gross; but it’s not

for the timid.

—Q, “Q Who?” Stark T

rek: The Next Generation

When I began writing this book, I spent quite a bit of time searching for a good quote

to start things off. I ended up with two. R is a wonderfully flexible platform and lan-

guage for exploring, visualizing, and understanding data. I chose the quote from

Alice’s Adventures in Wonderland to capture the flavor of statistical analysis today—an

interactive process of exploration, visualization, and interpretation.

The second quote reflects the generally held notion that R is difficult to learn.

What I hope to show you is that is doesn’t have to be. R is broad and powerful, with so

many analytic and graphic functions available (more than 50,000 at last count) that it

easily intimidates both novice and experienced users alike. But there is rhyme and rea-

son to the apparent madness. With guidelines and instructions, you can navigate the

tremendous resources available, selecting the tools you need to accomplish your work

with style, elegance, efficiency—and more than a little coolness.

I first encountered R several years ago, when applying for a new statistical consult-

ing position. The prospective employer asked in the pre-interview material if I was

conversant in R. Following the standard advice of recruiters, I immediately said yes,

PREFACE

xviii

and set off to learn it. I was an experienced statistician and researcher, had 25 years

experience as an

SAS and SPSS programmer, and was fluent in a half dozen program-

ming languages. How hard could it be? Famous last words.

As I tried to learn the language (as fast as possible, with an interview looming), I

found either tomes on the underlying structure of the language or dense treatises on

specific advanced statistical methods, written by and for subject-matter experts. The

online help was written in a spartan style that was more reference than tutorial. Every

time I thought I had a handle on the overall organization and capabilities of R, I

found something new that made me feel ignorant and small.

To make sense of it all, I approached R as a data scientist. I thought about what it

takes to successfully process, analyze, and understand data, including

■

Accessing the data (getting the data into the application from multiple sources)

■

Cleaning the data (coding missing data, fixing or deleting miscoded data, trans-

forming variables into more useful formats)

■

Annotating the data (in order to remember what each piece represents)

■

Summarizing the data (getting descriptive statistics to help characterize the

data)

■

Visualizing the data (because a picture really is worth a thousand words)

■

Modeling the data (uncovering relationships and testing hypotheses)

■

Preparing the results (creating publication-quality tables and graphs)

Then I tried to understand how I could use R to accomplish each of these tasks.

Because I learn best by teaching, I eventually created a website (www.statmethods.net)

to document what I had learned.

Then, about a year later, Marjan Bace, Manning’s publisher, called and asked if I

would like to write a book on R. I had already written 50 journal articles, 4 technical

manuals, numerous book chapters, and a book on research methodology, so how

hard could it be? At the risk of sounding repetitive—famous last words.

A year after the first edition came out in 2011, I started working on the second edi-

tion. The R platform is evolving, and I wanted to describe these new developments. I

also wanted to expand the coverage of predictive analytics and data mining—impor-

tant topics in the world of big data. Finally, I wanted to add chapters on advanced data

visualization, software development, and dynamic report writing.

The book you’re holding is the one that I wished I had so many years ago. I have

tried to provide you with a guide to R that will allow you to quickly access the power of

this great open source endeavor, without all the frustration and angst. I hope you

enjoy it.

P.S. I was offered the job but didn’t take it. But learning R has taken my career in

directions that I could never have anticipated. Life can be funny.

ABOUT THIS BOOK

xxii

you can go to learn more. You’ll be able to apply a variety of techniques for visualizing

data, and you’ll have the skills to tackle both basic and advanced data analytic

problems.

What’s new in the second edition

If you want to delve into the use of R more deeply, the second edition offers more

than 200 pages of new material. Concentrated in the second half of the book are new

chapters on data mining, predictive analytics, and advanced programming. In particu-

lar, chapters 15 (time series), 16 (cluster analysis), 17 (classification), 19 (

ggplot2

graphics), 20 (advanced programming), 21 (creating a package), and 22 (creating

dynamic reports) are new. In addition, chapter 2 (creating a dataset) has more

detailed information on importing data from text and SAS files, and appendix F

(working with large datasets) has been expanded to include new tools for working

with big data problems. Finally, numerous updates and corrections have been made

throughout the text.

Who should read this book

R in Action, Second Edition should appeal to anyone who deals with data. No back-

ground in statistical programming or the R language is assumed. Although the book is

accessible to novices, there should be enough new and practical material to satisfy

even experienced R mavens.

Users without a statistical background who want to use R to manipulate, summa-

rize, and graph data should find chapters 1–6, 11, and 19 easily accessible. Chapters 7

and 10 assume a one-semester course in statistics; and readers of chapters 8, 9, and

12–18 will benefit from two semesters of statistics. Chapters 20–22 offer a deeper dive

into the R language and have no statistical prerequisites. I’ve tried to write each chap-

ter in such a way that both beginning and expert data analysts will find something

interesting and useful.

Roadmap

This book is designed to give you a guided tour of the R platform, with a focus on

those methods most immediately applicable for manipulating, visualizing, and under-

standing data. The book has 22 chapters and is divided into 5 parts: “Getting Started,”

“Basic Methods,” “Intermediate Methods,” “Advanced Methods,” and “Expanding

Your Skills." Additional topics are covered in seven appendices.

Chapter 1 begins with an introduction to R and the features that make it so useful

as a data-analysis platform. The chapter covers how to obtain the program and how to

enhance the basic installation with extensions that are available online. The remain-

der of the chapter is spent exploring the user interface and learning how to run pro-

grams interactively and in batch.

Chapter 2 covers the many methods available for getting data into R. The first half

of the chapter introduces the data structures R uses to hold data, and how to enter

ABOUT THIS BOOK

xxiii

data from the keyboard. The second half discusses methods for importing data into R

from text files, web pages, spreadsheets, statistical packages, and databases.

Many users initially approach R because they want to create graphs, so we jump

right into that topic in chapter 3. No waiting required. We review methods of creating

graphs, modifying them, and saving them in a variety of formats.

Chapter 4 covers basic data management, including sorting, merging, and subset-

ting datasets, and transforming, recoding, and deleting variables.

Building on the material in chapter 4, chapter 5 covers the use of functions (math-

ematical, statistical, character) and control structures (looping, conditional execu-

tion) for data management. I then discuss how to write your own R functions and how

to aggregate data in various ways.

Chapter 6 demonstrates methods for creating common univariate graphs, such as

bar plots, pie charts, histograms, density plots, box plots, and dot plots. Each is useful

for understanding the distribution of a single variable.

Chapter 7 starts by showing how to summarize data, including the use of descrip-

tive statistics and cross-tabulations. We then look at basic methods for understanding

relationships between two variables, including correlations, t-tests, chi-square tests,

and nonparametric methods.

Chapter 8 introduces regression methods for modeling the relationship between a

numeric outcome variable and a set of one or more numeric predictor variables.

Methods for fitting these models, evaluating their appropriateness, and interpreting

their meaning are discussed in detail.

Chapter 9 considers the analysis of basic experimental designs through the analysis

of variance and its variants. Here we’re usually interested in how treatment combina-

tions or conditions affect a numerical outcome. Methods for assessing the appropri-

ateness of the analyses and visualizing the results are also covered.

Chapter 10 provides a detailed treatment of power analysis. Starting with a discus-

sion of hypothesis testing, the chapter focuses on how to determine the sample size

necessary to detect a treatment effect of a given size with a given degree of confi-

dence. This can help you to plan experimental and quasi-experimental studies that

are likely to yield useful results.

Chapter 11 expands on the material in chapter 6, covering the creation of graphs

that help you to visualize relationships among two or more variables. These include

various types of 2D and 3D scatter plots, scatter-plot matrices, line plots, correlograms,

and mosaic plots.

Chapter 12 presents analytic methods that work well in cases where data are sam-

pled from unknown or mixed distributions, where sample sizes are small, where outli-

ers are a problem, or where devising an appropriate test based on a theoretical

distribution is too complex and mathematically intractable. They include both resam-

pling and bootstrapping approaches—computer-intensive methods that are easily

implemented in R.

Chapter 13 expands on the regression methods in chapter 8 to cover data that are

not normally distributed. The chapter starts with a discussion of generalized linear

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