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精通R语言对象导向编程

Object

Oriented

Programming

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"R Object-Oriented Programming(PACKT,2014) - Kelly Black" 在《R Object-Oriented Programming》这本书中，作者Kelly Black深入浅出地介绍了使用R语言进行面向对象编程的方法和技巧。这本书分为三个部分，旨在帮助读者逐步掌握R语言在数据分析和可视化中的应用。首先，书中详细讲解了R语言的基础数据类型，如向量、矩阵、列表和数据框，以及数据结构。理解这些基础是学习任何编程语言的关键，它们构成了R语言处理数据的基础框架。此外，书中还涵盖了常用的数据操作命令和工具，使读者能够高效地解决日常遇到的任务。其次，书中的第二部分深入探讨了R语言中的控制结构，如条件语句（if-else）和循环（for、while），这些都是实现算法的重要组成部分。同时，作者介绍了如何利用这些控制结构来编写更加复杂和逻辑清晰的代码。这部分内容对于提升代码的可读性和效率至关重要。接下来，书中特别关注了R语言的面向对象编程机制，包括S3和S4类系统。S3是R中最简单的面向对象系统，而S4则提供了更严格的类定义和方法分发机制。读者将了解到如何创建和使用这两种类，以及它们之间的差异，这对于构建可重用和模块化的代码来说极其重要。最后，通过三个详细的实战例子，读者将有机会将所学的知识融会贯通，解决实际问题。这些案例不仅展示了如何综合运用前面学到的概念，还能帮助读者了解如何在实际项目中应用面向对象编程。通过阅读本书，读者不仅可以理解R语言中面向对象编程的基本设计和方法，还能掌握一套实用的编程技巧，从而更好地利用R的强大功能进行数据处理和分析。无论你是R的新手还是经验丰富的用户，这本书都能提供宝贵的指导，帮助你提升在R中的编程能力。

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Preface

[ 5 ]

When we wish to draw your attention to a particular part of a code block, the

relevant lines or items are set in bold:

> v <- c(1,3,5,7,-10)

> v

[1] 1 3 5 7 -10

> v[4]

[1] 7

> v[2] <- v[1]-v[5]

> v

[1] 1 11 5 7 -10

New terms and important words are shown in bold.

Warnings or important notes appear in a box like this.

Tips and tricks appear like this.

Reader feedback

Feedback from our readers is always welcome. Let us know what you think about

this book—what you liked or may have disliked. Reader feedback is important for us

to develop titles that you really get the most out of.

To send us general feedback, simply send an e-mail to

feedback@packtpub.com,

and mention the book title via the subject of your message.

If there is a topic that you have expertise in and you are interested in either writing

or contributing to a book, see our author guide on

www.packtpub.com/authors.

Customer support

Now that you are the proud owner of a Packt book, we have a number of things to

help you to get the most from your purchase.

Preface

[ 6 ]

Downloading the example code

You can download the example code les for all Packt books you have purchased

from your account at http://www.packtpub.com. An additional source for the

examples in this book can be found at https://github.com/KellyBlack/R-

Object-Oriented-Programming

. If you purchased this book elsewhere, you can

visit http://www.packtpub.com/support and register to have the les e-mailed

directly to you.

Errata

Although we have taken every care to ensure the accuracy of our content, mistakes

do happen. If you nd a mistake in one of our books—maybe a mistake in the text or

the code—we would be grateful if you would report this to us. By doing so, you can

save other readers from frustration and help us improve subsequent versions of this

book. If you nd any errata, please report them by visiting http://www.packtpub.

com/submit-errata

, selecting your book, clicking on the errata submission form link,

and entering the details of your errata. Once your errata are veried, your submission

will be accepted and the errata will be uploaded on our website, or added to any list of

existing errata, under the Errata section of that title. Any existing errata can be viewed

by selecting your title from http://www.packtpub.com/support.

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Internet, please provide us with the location address or website name immediately

so that we can pursue a remedy.

Please contact us at

pirated material.

We appreciate your help in protecting our authors, and our ability to bring

you valuable content.

Questions

You can contact us at questions@packtpub.com if you are having a problem with

any aspect of the book, and we will do our best to address it.

Data Types

In this chapter, we provide a broad overview of the different data types available in

the R environment. This material is introductory in nature, and this chapter ensures

that important information on implementing algorithms is available to you. There

are roughly ve parts in this chapter:

• Working with variables in the R environment: This section gives you a

broad overview of interacting with the R shell, creating variables, deleting

variables, saving variables, and loading variables

• Discrete data types: This section gives you an overview of the principle data

types used to represent discrete data

• Continuous data types: This section gives you an overview of the principle

data types used to represent continuous data

• Introduction to vectors: This section gives you an introduction to vectors and

manipulating vectors in R

• Special data types: This section gives you a list of other data types that do

not t in the other categories or have other meanings

Assignment

The R environment is an interactive shell. Commands are entered using the

keyboard, and the environment should feel familiar to anyone used to MATLAB or

the Python interactive interpreter. To assign a value to a variable, you can usually

use the = symbol in the same way as these other interpreters. The difference with

R, however, is that there are other ways to assign a variable, and their behavior

depends on the context.

Data Types

[ 8 ]

Another way to assign a value to a variable is to use the <- symbols (sometimes

called operators). At rst glance, it seems odd to have different ways to assign a

value, but we will see that variables can be saved in different environments. The

same name may be used in different environments, and the name can be ambiguous.

We will adopt the use of the <- operator in this text because it is the most common

operator, and it is also the least likely to cause confusion in different contexts.

The R environment manages memory and variable names dynamically. To create

a new variable, simply assign a value to it, as follows:

> a <- 6

> a

[1] 6

A variable has a scope, and the meaning of a variable name can vary depending

on the context. For example, if you refer to a variable within a function (think

subroutine) or after attaching a dataset, then there may be multiple variables in

the workspace with the same name. The R environment maintains a search path

to determine which variable to use, and we will discuss these details as they arise.

The

<- operator for the assignment will work in any context while the = operator

only works for complete expressions. Another option is to use the <<- operator. The

advantage of the <<- operator is that it instructs the R environment to search parent

environments to see whether the variable already exists. In some contexts, within a

function for example, the <- operator will create a new variable; however, the <<-

operator will make use of an existing variable outside of the function if it is found.

Another way to assign variables is to use the

-> and ->> operators. These operators

are similar to those given previously. The only difference is that they reverse the

direction of assignment, as follows:

> 14.5 -> a

> 1/12.0 ->> b

> a

[1] 14.5

> b

[1] 0.08333333

Chapter 1

[ 9 ]

The workspace

The R environment keeps track of variables as well as allocates and manages

memory as it is requested. One command to list the currently dened variables is

the ls command. A variable can be deleted using the rm command. In the following

example, the

a and b variables have been changed, and the a variable is deleted:

> a <- 17.5

> b <- 99/4

> ls()

[1] "a" "b"

> objects()

[1] "a" "b"

> rm(a)

> ls()

[1] "b"

If you wish to delete all of the variables in the workspace, the list option in the rm

command can be combined with the ls command, as follows:

> ls()

[1] "b"

> rm(list=ls())

> ls()

character(0)

A wide variety of other options are available. For example, there are directory

options to show and set the current directory, as follows:

> getwd()

[1] "/home/black"

> setwd("/tmp")

> getwd()

[1] "/tmp"

> dir()

[1] "antActivity.R" "betterS3.R"

[3] "chiSquaredArea.R" "firstS3.R"

[5] "math100.csv" "opsTesting.R"

[7] "probabilityExampleOne.png" "s3.R"

[9] "s4Example.R"

剩余189页未读，继续阅读

Data Manipulation with R(PACKT,2014)

One of the most important aspects of computing with data is the ability to manipulate it to enable subsequent analysis and visualization. R offers a wide range of tools for this purpose. Data from any source, be it flat files or databases, can be loaded into R and this will allow you to manipulate data format into structures that support reproducible and convenient data analysis. This practical, example-oriented guide aims to discuss the split-apply-combine strategy in data manipulation, which is a faster data manipulation approach. After reading this book, you will not only be able to efficiently manage and check the validity of your datasets with the split-apply-combine strategy, but you will also learn to handle larger datasets.

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