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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
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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

Chapter 4. Calculating Probabilities and Random Numbers

In this chapter, we provide a broad overview of the functions related to probability distributions. This includes functions associated with probability distributions, Random Number Generation, and issues associated with sampling. The chapter is divided into five parts:

  • Distribution functions: This section gives you a brief overview of the ideas and concepts behind random variables and approximating the height of a probability mass function of a probability density function for a given distribution
  • The cumulative distribution function: This section gives you an overview of how to approximate the cumulative distribution for a given distribution
  • The inverse cumulative distribution function: This section gives you an overview of how to approximate the inverse cumulative distribution function for a given distribution
  • Random Number Generation (RNG): This section gives you an overview of how R generates pseudorandom numbers with examples...

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