掌握R语言：统计编程入门(第二版)

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"Beginning R (Apress, 2ed, 2015)" 是一本面向初学者的R语言教程，作者是Dr. Joshua F. Wiley和Larry A. Pace。这本书采用实践操作的方式，教授如何使用R语言进行数据处理、脚本编写、自定义统计函数以及利用内置函数。第二版针对R版本3进行了全面更新，涵盖了最新的包和特性。书中通过具体的案例，如一元方差分析、线性回归、逻辑回归、数据可视化、并行处理和Bootstrap等，深入浅出地介绍了统计学概念。R语言作为强大的开源统计编程环境，已成为计算统计的标准工具，其易学易用且免费的特点，以及庞大的用户社区和丰富的包生态系统，使其在数据分析领域广受欢迎。本书旨在帮助读者掌握R语言的基础，包括： 1. **R语言基础**：学习R的基本语法、对象类型（如向量、列表、数据框和矩阵）以及控制流结构（如循环和条件语句）。 2. **脚本编写**：了解如何编写和保存R脚本，这对于重复性任务和代码组织至关重要。 3. **数据输入与输出**：学习如何导入各种数据格式（如CSV、Excel或数据库）到R中，并导出分析结果。 4. **统计分析**：介绍一元方差分析(ANOVA)、线性回归和逻辑回归的基本概念和实现方法。 5. **数据可视化**：使用R中的ggplot2或其他可视化包创建有效的数据图表。 6. **自定义统计函数**：学习如何根据需求编写自己的统计函数，增强R的灵活性。 7. **并行处理**：利用R的并行计算功能加速大数据分析。 8. **Bootstrap方法**：理解Bootstrap抽样及其在估计参数和构建置信区间的应用。 9. **最佳实践**：提供统计分析的最佳实践，确保数据处理的准确性和可靠性。此外，书中强调了对统计概念的清晰解释，鼓励读者在实际案例中应用R，从而加深对统计学的理解。同时，读者还可以通过社区和不断更新的R包来扩展其数据分析能力。这本书适合希望学习R语言用于数据分析的初学者，以及需要进一步巩固R语言基础知识的统计学或数据科学学生。无论是在学术研究还是商业分析中，掌握R语言都能极大地提升数据处理和统计建模的能力。

xix

About the Technical Reviewer

Sarah Stowell is a contract statistician based in the UK. Previously, she

has worked with Mitsubishi Pharma Europe, MDSL International, and

GlaxoSmithKline. She holds a master of science degree in statistics.

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xxiii

Introduction

This book is about the R programming language. Maybe more important, this book is for you.

These days, R is an impressively robust language for solving problems that lend themselves to statistical

programming methods. There is a large community of users and developers of this language, and together

we are able to accomplish things that were not possible before we virtually met.

Of course, to leverage this collective knowledge, we have to start somewhere. Chapters 1 through 5

focus on gaining familiarity with the R language itself. If you have prior experience in programming, these

chapters will be very easy for you. If you have no prior programming experience, that is perfectly fine.

We build from the ground up, and let us suggest you spend some thoughtful time here. Thinking like a

programmer has some very great advantages. It is a skill we would want you to have, and this book is,

after all, for you.

Chapters 6 through 10 focus on what might be termed elementary statistical methods in R. We did not

have the space to introduce those methods in their entirety—we are supposing some knowledge of statistics.

An introductory or elementary course for nonmajors would be more than enough. If you are already familiar

with programming and statistics, we suggest you travel through these chapters only briefly.

With Chapter 11, we break into the last part of the book. For someone with both a fair grasp of traditional

statistics and some programming experience, this may well be a good place to start. For our readers who

read through from the first pages, this is where it starts to get very exciting. From bootstrapping to logistic

regression to data visualization to high-performance computing, these last chapters have hands-on examples

that work through some much applied and very interesting examples.

One final note: While we wrote this text from Chapter 1 to Chapter 19 in order, the chapters are fairly

independent of each other. Don't be shy about skipping to the chapter you're most interested in learning.

We show all our code, and you may well be able to modify what we have to work with what you have.

Happy reading!

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Chapter 1

Getting Star ted

There are compelling reasons to use R. Enthusiastic users, programmers, and contributors support R and its

development. A dedicated core team of R experts maintains the language. R is accurate, produces excellent

graphics, has a variety of built-in functions, and is both a functional language and an object-oriented one.

There are (literally) thousands of contributed packages available to R users for specialized data analyses.

Developing from a novice into a more competent user of R may take as little as three months by only

using R on a part-time basis (disclaimer: n = 1). Realistically, depending on background, your development

may take days, weeks, months, or even a few years, depending on how often you use R and how quickly

you can learn its many intricacies. R users often develop into R programmers who write R functions, and R

programmers sometimes want to develop into R contributors, who write packages that help others with

their data analysis needs. You can stop anywhere on that journey you like, but if you finish this book and

follow good advice, you will be a competent R user who is ready to develop into a serious R programmer if

you want to do it. We wish you the best of luck!

1.1 What is R, Anyway?

R is an open-source implementation of the S language created and developed at Bell Labs. S is also the basis

of the commercial statistics program S-PLUS, but R has eclipsed S-PLUS in popularity. If you do not already

have R on your system, the quickest way to get it is to visit the CRAN (Comprehensive R Network Archive)

website and download and install the precompiled binary files for your operating system. R works on

Windows, Mac OS, and Linux systems. If you use Linux, you may already have R with your Linux distribution.

Open your terminal and type $ R --version. If you do not already have R, the CRAN website is located at

the following URL:

http://cran.r-project.org/

Download and install the R binaries for your operating system, accepting all the defaults. At this writing,

the current version of R is 3.2.0, and in this book, you will see screenshots of R working in both Windows 7

and Windows 8.1. Your authors run on 64-bit operating systems, so you will see that information displayed

in the screen captures in this book. Because not everything R does in Unix-based systems can be done in

Windows, I often switch to Ubuntu to do those things, but we will discuss only the Windows applications

here, and leave you to experiment with Ubuntu or other flavors of Unix. One author runs Ubuntu on the

Amazon Cloud, but that is way beyond our current needs.

Go ahead and download Rstudio (current version as of this writing is 0.98.1103) now too, again,

accepting all defaults from the following URL:

http://www.rstudio.com/products/rstudio/download/

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Chapter 1 ■ GettinG Star ted

Rstudio is a very forgiving environment for the novice user, and code written in here will work just as

well in R itself.

Launch Rstudio and examine the resulting interface. Make sure that you can identify the following

parts of the R interface shown in Figure1-1: the menu bar, the script editing area, the R console, and the

R command prompt, which is >.

Before we continue our first R session, let’s have a brief discussion of how R works. R is a high-level

vectorized computer language and statistical computing environment. You can write your own R code, use

R code written by others, and use R packages you write and those written by you or by others. You can use R

in batch mode, terminal mode, in the R graphical user interface (RGui), or in Rstudio, which is what we will

do in this book. As you learn more about R and how to use it effectively, you will find that you can integrate R

with other languages such as Python or C++, and even with other statistical programs such as SPSS.

In some computer languages, for instance, C++, you have to declare a data type before you assign a

value to a new variable, but that is not true in R. In R, you simply assign a value to the object, and you can

change the value or the data type by assigning a new one. There are two basic assignment operators in R.

The first is < −, a left-pointing assignment operator produced by a less than sign followed by a “minus” sign,

which is really a hyphen. You can also use an equals sign = for assignments in R. I prefer the < − assignment

operator, and will use it throughout this book.

You must use the = sign to assign the parameters in R functions, as you will learn. R is not sensitive to

white space the way some languages are, and the readability of R code is benefited from extra spacing and

indentation, although these are not mandatory. R is, however, case-sensitive, so to R, the variables x and X

are two different things. There are some reserved names in R, which I will tell you about in Chapter 5.

The best way to learn R is to use R, and there are many books, web-based tutorials, R blog sites, and

videos to help you with virtually any question you might have. We will begin with the basics in this book but

will quickly progress to the point that you are ready to become a purposeful R programmer, as mentioned

earlier.

Figure 1-1. The R console running in Rstudio

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