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  • Book Overview & Buying R Object-oriented Programming
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R Object-oriented Programming

R Object-oriented Programming

By : Black
4.4 (9)
Buy this Book
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R Object-oriented Programming

R Object-oriented Programming

4.4 (9)
By: Black
Buy this Book

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.
Table of Contents (14 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Data Types
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1. Data Types
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Assignment
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The workspace
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Discrete data types
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Summary
2
2. Organizing Data
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2. Organizing Data
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Basic data structures
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Operations on data structures
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Summary
3
3. Saving Data and Printing Results
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3. Saving Data and Printing Results
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File and directory information
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Entering data
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Printing results and saving data
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Primitive input/output
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Network options
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Summary
4
4. Calculating Probabilities and Random Numbers
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4. Calculating Probabilities and Random Numbers
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Overview
5
5. Character and String Operations
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5. Character and String Operations
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Basic string operations
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Six focused tasks
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Regular expressions
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Summary
6
6. Converting and Defining Time Variables
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6. Converting and Defining Time Variables
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Introduction and assumptions
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Converting strings to time data types
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Summary
7
7. Basic Programming
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7. Basic Programming
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Conditional execution
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Summary
8
8. S3 Classes
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8. S3 Classes
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Defining classes and methods
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Defining objects and inheritance
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Encapsulation
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A final note
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Summary
9
9. S4 Classes
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9. S4 Classes
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Introducing the Ant class
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Defining an S4 class
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Defining methods for an S4 class
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Inheritance
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Miscellaneous notes
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Summary
10
10. Case Study – Course Grades
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10. Case Study – Course Grades
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Overview
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The Course class
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The assignment classes
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Defining indexing operations
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Redefining existing functions
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Redefining arithmetic operations
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Summary
11
11. Case Study – Simulation
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11. Case Study – Simulation
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The simulation classes
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The Monte-Carlo class
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Examples
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Summary
12
A. Package Management
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A. Package Management
13
Index
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Index

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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Tech Concepts
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Programming languages
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R Object-oriented Programming
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