R语言实践：通过实例掌握统计与数据分析

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"《统计与数据：R语言应用实例教程》(Statistics and Data with R: An Applied Approach through Examples)是一本由耶西夫·科恩(Yosef Cohen)和杰瑞迈亚·科恩(Jeremiah Y. Cohen)合著的专业书籍，于2008年由约翰·威利父子出版公司(John Wiley & Sons)发行。该书的ISBN号为978-0-470-75805-0，旨在通过丰富的实例帮助读者深入了解统计学和数据分析在R语言中的实践应用。作者们分别来自美国明尼苏达大学和范德比尔特大学，他们的著作以其实用性和针对性而受到欢迎。本书强调的是将理论知识与实际操作相结合，适合那些希望掌握R语言进行统计分析和数据处理的读者，无论是初学者还是有一定经验的用户，都能从中找到适合自己的学习材料。版权方面，该书完全符合英国1988年的《版权、设计和专利法》规定，读者在引用或复制内容时必须遵守相关版权法规。书中详尽介绍了如何申请使用本书的版权材料，有兴趣的读者可以通过访问Wiley的官方网站获取更多详情。《统计与数据：R语言应用实例教程》涵盖了广泛的统计概念，如描述性统计、概率论、假设检验、回归分析、时间序列分析等，并配以R语言的具体代码示例，使得读者能够直观地理解并应用这些统计方法。此外，书中还探讨了数据清洗、可视化以及如何利用R进行大数据处理，这些都是当今数据科学领域的重要技能。通过阅读这本书，读者不仅能提升统计分析能力，还能熟悉R语言的工作流程，从而在实际工作中更有效地利用R进行复杂的数据分析项目。对于希望在这个快速发展的领域中保持竞争力的IT专业人士，这是一本不可多得的参考资料。"

Preface xvii

and ﬁrst-year graduate students in the sciences. The remaining Chapters, in a second

course. The book is accompanied by a solution manual which includes solutions to

most of the exercises. The solution manual is available through the book’s site.

• How should I study and use this book?

To study the book, read through each chapter. Each example provides enough

code to enable you to reproduce the results (statistical analysis and graphics) exactly

as shown. Scripts are explained in detail. Read through the explanation and try to

produce the results, as shown. This way, you ﬁrst see how a particular analysis is done

and how tables and graphics may be used to summarize it. Then when you study the

script, you will know what it does. Because of the adherence to the WYSIWYG

principle, some of the early examples might strike you as particularly complicated.

Be patient; as you progress, there are repetitions and you will soon learn the common

R idioms.

If you are familiar with both R and basic statistics, you may use the book as a

reference. Say you wish to run a simple logistic regression. You know how to do it

with R, but you wish to plot the regression on a probability scale. Then go to Chapter

16 and ﬂip through the pages until you hit Figure 16.5 in Example 16.5. Next, refer

to the example’s script and modify it to ﬁt your needs. There are so many R examples

and scripts that ﬂipping through the pages you are likely to ﬁnd one that is close to

what you need.

We include two indices, an R index and a general index. The R index is organized

such that functions are listed as ﬁrst entry and their arguments as sub-entries. The

page references point to applications of the functions by the various examples.

• Classical vs. Bayesian statistics

This topic addresses developments in statics in the last few decades. In recent

years and with advances in numerical computations, a trend among natural (and

other) scientists of moving away from classical statistics (where large data are needed

and no prior knowledge about it is assumed) to the so-called Bayesian statistics (where

prior knowledge about the data contributes to its analysis) has become fashionable.

Without getting into the sticky details, we make no judgment about the eﬃcacy of

one as opposed to the other approach. We prescribe to the following:

• With large data sets, statistical analyses using these two approaches often reach

the same conclusions. Developments in bootstrap methods make “small” data sets

“large”.

• One can hardly appreciate advances in Bayesian statistics without knowledge of

classical statistics.

• Certain situations require applications of Bayesian statistics (in particular when

real-time analysis is imperative). Others, do not.

• The analyses we present are suitable for both approaches and in most cases should

give identical results. Therefore, we pursue the classical statistics approach.

• A word about typography, data and scripts

We use monospaced characters to isolate our code work with R. Monospace lines

that begin with > indicate code as it is typed in an R session. Monospace lines that

Basic R

Here, we learn how to work with R. R is rooted in S, a statistical computing and

data visualization language originated at the Bell Laboratories (see Chambers and

Hastie, 1992). The S interpreter was written in the C programming language. R pro-

vides a wide variety of statistical and graphical techniques in a consistent computing

environment. It is also an interpreted programming environment. That is, as soon as

you enter a statement, or a group of statements and hit the Enter key, R computes

and responds with output when necessary. Furthermore, your whole session is kept

in memory until you exit R. There are a number of excellent introductions to and

tutorials for R (Becker et al., 1988; Chambers, 1998; Chambers and Hastie, 1992;

Dalgaard, 2002; Fox, 2002). Venables and Ripley (2000) was written by experts and

one can hardly improve upon it (see also The R Development Core Team, 2006b). An

excellent exposition of S and thereby of R, is given in Venables and Ripley (1994).

With minor diﬀerences, books, tutorials and applications written with S are rele-

vant in R. A good entry point to the vast resources on R is its website, http://

r-project.org.

R is not a panacea. If you deal with tremendously large data sets, with mil-

lions of observations, many variables and numerous related data tables, you will

need to rely on advanced database management systems. A particularly good one

is the open source PostgreSQL (http://www.postgresql.org). R deals eﬀectively—

and elegantly—with data that include several hundreds of thousands of observations

and scores of variables. The bulkier your system is in memory and hard disk space,

the larger the data set that R can handle. With all these caveats, R is a most ﬂexible

system for dealing with data manipulation and analysis. One often spends a large

amount of time preparing data for analysis and exploring statistical models. R makes

such tasks easy. All of this at no cost, for R is free!

This chapter introduces R. Learning R might seem like a daunting task, partic-

ularly if you do not have prior experience with programming. Steep as it may be,

the learning curve will yield much dividend later. You should develop tolerance

Statistics and Data with R: An applied approach through examples Y. Cohen and J.Y. Cohen

©2008 John Wiley & Sons, Ltd. ISBN: 978-0-470-75805

4 Basic R

ambiguity. Things might seem obscure at ﬁrst. Be patient and continue. With plenty

of repetitions and examples, the fog will clear. Being an introductory chapter, we are

occasionally compelled to make simple and sweeping statements about what R does

or does not do. As you become familiar with R, you will realize that some of our

statements may be slightly (but not grossly) inaccurate. In addition to R, we discuss

some abstract concepts such as classes and objects. The motivation for this seemingly

unrelated material is this: When you start with R, many of the commands (calls to

functions) and the results they produce may mystify you. With knowledge of the gen-

eral concepts introduced here, your R sessions will be clearer. To get the most out

of this chapter, follow this strategy: Read the whole chapter lightly and do whatever

exercises and examples you ﬁnd easy. Do not get stuck in a particular issue. Later, as

we introduce R code, revisit the relevant topics in this chapter for further clariﬁcation.

It makes little sense to read this chapter without actually implementing the code

and examples in a working R session. Some of the material in this chapter is based on

the excellent tutorial by Venables et al. (2003). Except for starting R, the instructions

and examples apply to all operating systems that R is implemented on. System speciﬁc

instructions refer to the Windows operating systems.

1.1 Preliminaries

In this section we learn how to work with R. If you have not yet done so, go to

http://r-project.org and download and install R. Next, start R. If you installed R

properly, click on its icon on the desktop. Otherwise, click on Start | Programs | R | R

x.y.z where x.y.z refers to the version number you installed. If all else fails, ﬁnd where

R is installed (usually in the Program Files directory). Then go to the bin directory

and click on the Rgui icon. Once you start R, you will receive a welcome statement

that ends with a prompt (usually >).

1.1.1 An R session

An R session consists of starting R, working with it and then quitting. You quit R

by typing q() or (in Windows) by selecting File | Exit.

R displays the prompt at the

beginning of the line as a cue for you. This is where you interact with the system

(you do not type the prompt). The vast majority of our work with R will consist of

executing functions. For now, we will say that a function is a small, single-purpose

executable program that R can be instructed to run. A function is identiﬁed by a

name followed by parentheses. Often the parentheses are separated by words. These

are called arguments. We say that we execute a function when we type the function’s

name, with the parentheses (and possibly arguments) and then hit the Enter key.

Let us go through a simple session. This will give you a feel for R. At any point,

if you wish to quit, type q() to end the session. To quit, you can also use the File |

Exit menu. At the prompt, type (recall that you do not type the prompt):

help.start()

As a rule, while working with R, we avoid menus and graphical interface and stick with

the command line interface.

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