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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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Overview

The base R environment has options for approximating many of the properties associated with probability distributions. This is not a complete discussion, and a more complete list can be found within the R environment using the help(Distributions) command. In this chapter, we will discuss how R can be used to approximate distribution functions, how to approximate cumulative distribution functions, how to approximate inverse cumulative distribution functions, Random Number Generation (RNG), and sampling.

The commands used for the first set of topics have a common format, and each command has the form of a prefix and a suffix. The suffix specifies the distribution by name. For example, the norm suffix refers to the normal distribution. A list of the distributions available in the base R installation is given in Table 1. The prefix is one of the following:

d: This determines the value of the distribution function, for example, dnorm is the height of the normal's probability distribution...

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