R语言统计编程基础教程（第2版）

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"A First Course in Statistical Programming with R (2nd Edition)" 是一本面向初学者的R语言教程，特别针对金融领域的数据处理，提供了英文版的学习材料。本书结合了R Studio平台的使用，并更新了图形系统的内容，深入探讨了马尔科夫链蒙特卡洛方法，增加了对常见错误消息的解析，以及矩阵分解在实际应用中的动机和大量新的示例与练习。本书是学习R语言编程的全面指南，是数据分析领域计算标准的入门选择。由R核心团队成员和知名R语言作者共同编写，书中包含符合R语言标准的真实代码。与其他介绍R系统的书籍不同，这本书的重点在于编程，涵盖了适用于大多数编程语言的原则，以及用于开发更复杂项目的技术。读者可以在书的官方网站上找到解决方案、数据集和任何错误信息。书中众多源自真实应用的例子，使其成为从事实际数据分析工作的人员的宝贵资源。作者W. John Braun是加拿大统计科学研究所的副所长，同时也是教授和系主任，他在统计学和R语言教育方面有着丰富的经验。在这本书中，读者可以学习到以下关键知识点： 1. **R语言基础**：包括R语言的基本语法、数据类型、数据结构如向量、列表、矩阵和数据框，以及如何读取和写入数据文件。 2. **R Studio平台**：了解如何利用R Studio进行代码编辑、调试、项目管理以及与R交互的高效方式。 3. **数据处理**：学习如何清洗、整理和探索数据，包括使用dplyr包进行数据操作，以及tidyverse的概念。 4. **统计图形**：涵盖ggplot2系统，用于创建高质量的统计图形，理解图形层的概念和如何定制图形元素。 5. **马尔科夫链蒙特卡洛（MCMC）**：深入理解模拟方法，如贝叶斯统计中的Metropolis-Hastings算法和Gibbs采样。 6. **矩阵分解**：学习如何进行主成分分析（PCA）、奇异值分解（SVD）等矩阵运算，以及它们在降维和预测中的应用。 7. **编程原则**：涵盖控制流（循环、条件语句）、函数定义、错误处理和调试技巧，以及面向对象编程的基础。 8. **实战应用**：通过真实的案例研究，学习如何将这些理论知识应用于金融数据分析，例如时间序列分析、回归模型、假设检验等。 9. **练习与解答**：书中的大量练习题可以帮助巩固学习，提高解决问题的能力。 "A First Course in Statistical Programming with R (2nd Edition)" 是一本适合初学者的综合教程，它不仅教授R语言的基础知识，还强调了编程实践和统计分析的结合，为读者在金融数据分析领域打下坚实的基础。

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2 GETTING STARTED

Since graphs play an important role in statistical analysis, drawing

graphics of one-, two-, or higher-dimensional data is an aspect of statis-

tical programming.

An important part of statistical programming is stochastic simulation.

Digital computers are naturally very good at exact, reproducible computa-

tions, but the real world is full of randomness. In stochastic simulation we

program a computer to act as though it is producing random results, even

though, if we knew enough, the results would be exactly predictable.

Statistical programming is closely related to other forms of numerical

programming. It involves optimization, and approximation of mathematical

functions. Computational linear algebra plays a central role. There is less

emphasis on differential equations than in physics or applied mathematics

(though this is slowly changing). We tend to place more of an emphasis

on the results and less on the analysis of the algorithms than in computer

science.

1.2 Outline of this book

This book is an introduction to statistical programming. We will start with

basic programming: how to tell a computer what to do. We do this using the

open source R statistical package, so we will teach you R, but we will try

not to just teach you R. We will emphasize those things that are common

to many computing platforms.

Statisticians need to display data. We will show you how to construct

statistical graphics. In doing this, we will learn a little bit about human

vision, and how it motivates our choice of display.

In our introduction to programming, we will show how to control the

ow of execution of a program. For example, we might wish to do repeated

calculations as long as the input consists of positive integers, but then stop

when an input value hits 0. Programming a computer requires basic logic,

and we will touch on Boolean algebra, a formal way to manipulate logical

statements. The best programs are thought through carefully before being

implemented, and we will discuss how to break down complex problems

into simple parts. When we are discussing programming, we will spend

quite a lot of time discussing how to get it right: how to be sure that the

computer program is calculating what you want it to calculate.

One distinguishing characteristic of statistical programming is that it is

concerned with randomness: random errors in data, and models that include

stochastic components. We will discuss methods for simulating random

values with specied characteristics, and show how random simulations

are useful in a variety of problems.

Many statistical procedures are based on linear models. While discus-

sion of linear regression and other linear models is beyond the scope of

this book, we do discuss some of the background linear algebra, and how

the computations it involves can be carried out. We also discuss the gen-

eral problem of numerical optimization: nding the values which make a

function as large or as small as possible.

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03:48:57,

1.4 WHY USE A COMMAND LINE? 3

Each chapter has a number of exercises which are at varying degrees

of difculty. Solutions to selected exercises can be found on the web at

www.statprogr.science.

1.3 The R package

This book uses R, which is an open source package for statistical comput-

ing. “Open source” has a number of different meanings; here the important

one is that R is freely available, and its users are free to see how it is writ-

ten, and to improve it. R is based on the computer language S, developed

by John Chambers and others at Bell Laboratories in 1976. In 1993 Robert

Gentleman and Ross Ihaka at the University of Auckland wanted to exper-

iment with the language, so they developed an implementation, and named

it R. They made it open source in 1995, and thousands of people around

the world have contributed to its development.

1.4 Why use a command line?

The R system is mainly command-driven, with the user typing in text and

asking R to execute it. Nowadays most programs use interactive graphical

user interfaces (menus, touchscreens, etc.) instead. So why did we choose

such an old-fashioned way of doing things?

Menu-based interfaces are very convenient when applied to a limited set

of commands, from a few to one or two hundred. However, a command-

line interface is open ended. As we will show in this book, if you want

to program a computer to do something that no one has done before, you

can easily do it by breaking down the task into the parts that make it up,

and then building up a program to carry it out. This may be possible in

some menu-driven interfaces, but it is much easier in a command-driven

interface.

Moreover, learning how to use one command-line interface will give

you skills that carry over to others, and may even give you some insight

into how a menu-driven interface is implemented. As statisticians, it is our

belief that your goal should be understanding, and learning how to program

at a command line will give you that at a fundamental level. Learning to use

a menu-based program makes you dependent on the particular organization

of that program.

There is no question that command-line interfaces require greater

knowledge on the part of the user – you need to remember what to type

to achieve a particular outcome. Fortunately, there is help. We recommend

that you use the RStudio integrated development environment (IDE). IDEs

were rst developed in the 1970s to help programmers: they allow you to

edit your program, to search for help, and to run it; when your rst attempt

doesn’t work, they offer support for diagnosing and xing errors. RStudio

is an IDE for R programming, rst released in 2011. It is produced by a

Boston company named RStudio, and is available for free use.

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03:48:57,

4 GETTING STARTED

1.5 Font conventions

This book describes how to do computations in R. As we will see in the next

chapter, this requires that the user types input, and R responds with text or

graphs as output. To indicate the difference, we have typeset the user input

and R output in a gray box. The output is prexed with

##. For example

This was typed by the user

## This is a response from R

In most cases other than this one and certain exercises, we will show

the actual response from R corresponding to the preceding input.

We have used the knitr package so

that R itself is computing the output.

The computations in the text were done

with R version 3.2.2 (2015-08-14).

There are also situations where the code is purely illustrative and is not

meant to be executed. (Many of those are not correct R code at all; others

illustrate the syntax of R code in a general way.) In these situations we have

typeset the code examples in an upright typewriter font. For example,

f( some arguments )

1.6 Installation of R and RStudio

R can be http://cloud.r-project.org.Most

users should download and install a binary version. This is a version that

has been translated (by compilers) into machine language for execution

on a particular type of computer with a particular operating system. R is

designed to be very portable: it will run on Microsoft Windows, Linux,

Solaris, Mac OSX, and other operating systems, but different binary ver-

sions are required for each. In this book most of what we do would be the

same on any system, but when we write system-specic instructions, we

will assume that readers are using Microsoft Windows.

Installation on Microsoft Windows is straightforward. A binary ver-

sion is available for Windows Vista or above from the web page

http://cloud.r-project.org/bin/windows/base. Download

the “setup program,” a le with a name like

R-3.2.5-win.exe. Click-

ing on this le will start an almost automatic installation of the R system.

Though it is possible to customize the installation, the default responses

will lead to a satisfactory installation in most situations, particularly for

beginning users.

One of the default settings of the installation procedure is to create an

R icon on your computer’s desktop.

You should also install RStudio, after you have installed R. As with R,

there are separate versions for different computing platforms, but they all

look and act similarly. You should download the “Open Source Edition” of

“RStudio Desktop” from

www.rstudio.com/, and follow the instruc-

tions to install it on your computer.

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03:48:57,

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R语言统计编程基础教程（第2版）

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