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R Object-oriented Programming

By : Black

4.4 (9)

R Object-oriented Programming

4.4 (9)

By: Black

Overview of this book

This book is designed for people with some experience in basic programming practices. It is also assumed that they have some basic experience using R and are familiar using the command line in an R environment. Our primary goal is to raise a beginner to a more advanced level to make him/her more comfortable creating programs and extending R to solve common problems.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

1. Data Types

1. Data Types

Assignment

The workspace

Discrete data types

Summary

2. Organizing Data

2. Organizing Data

Basic data structures

Operations on data structures

Summary

3. Saving Data and Printing Results

3. Saving Data and Printing Results

File and directory information

Entering data

Printing results and saving data

Primitive input/output

Network options

Summary

4. Calculating Probabilities and Random Numbers

4. Calculating Probabilities and Random Numbers

Overview

5. Character and String Operations

5. Character and String Operations

Basic string operations

Six focused tasks

Regular expressions

Summary

6. Converting and Defining Time Variables

6. Converting and Defining Time Variables

Introduction and assumptions

Converting strings to time data types

Summary

7. Basic Programming

7. Basic Programming

Conditional execution

Summary

8. S3 Classes

8. S3 Classes

Defining classes and methods

Defining objects and inheritance

Encapsulation

A final note

Summary

9. S4 Classes

9. S4 Classes

Introducing the Ant class

Defining an S4 class

Defining methods for an S4 class

Inheritance

Miscellaneous notes

Summary

10. Case Study – Course Grades

10. Case Study – Course Grades

Overview

The Course class

The assignment classes

Defining indexing operations

Redefining existing functions

Redefining arithmetic operations

Summary

11. Case Study – Simulation

11. Case Study – Simulation

The simulation classes

The Monte-Carlo class

Examples

Summary

A. Package Management

A. Package Management

Index

Index

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Basic data structures

The basic data structures used to organize data within the R environment include vectors, lists, data frames, tables, and matrices. Here, we provide details for each of these data structures and demonstrate how to create them. This chapter does not include information about how to read data from a file, and the focus is on the data structures themselves. More information about reading from a file can be found in Chapter 3, Saving Data and Printing Results.

Vectors

The default data structure in R is the vector. For example, if you define a variable as a single number, R will treat it as a vector of length one:

> a <- 5
> a[1]
[1] 5

Vectors represent a convenient and straightforward way to store a long list of numbers. Please see Chapter 1, Data Types, to see more examples of creating vectors. One useful and common way to define a vector is to use the c command. The c command concatenates a set of arguments to form a single vector:

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

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