At a Glance; Contents; About the Author; About the Technical Reviewer; Introduction; Chapter 1: Classes and Generic Functions; Generic Functions; Classes; Polymorphism in Action; Designing Interfaces; The Usefulness of Polymorphism; Polymorphism and Algorithmic Programming; Sorting Lists; General Comments on Flexible Implementations of Algorithms; More on UseMethod; Chapter 2: Class Hierarchies; Interfaces and Implementations; Polymorphism and Interfaces; Abstract and Concrete Classes; Implementing Abstract and Concrete Classes in R; Another Example: Graphical Objects
Class Hierarchies As Interfaces with RefinementsChapter 3: Implementation Reuse; Method Lookup in Class Hierarchies; Getting the Hierarchy Correct in the Constructors; NextMethod; Chapter 4: Statistical Models; Bayesian Linear Regression; Model Matrices; Constructing Fitted Model Objects; Coefficients and Confidence Intervals; Predicting Response Variables; Chapter 5: Operator Overloading; Functions and Operators; Defining Single Operators; Group Operators; Units Example; Chapter 6: S4 Classes; Defining S4 Classes; Generic Functions; Slot Prototypes; Object Validity
Generic Functions and Class HierarchiesRequiring Methods; Constructors; Dispatching on Type-Signatures; Operator Overloading; Combining S3 and S4 Classes; Chapter 7: R6 Classes; Defining Classes; Object Initialization; Private and Public Attributes; Active Bindings; Inheritance; References to Objects and Object Sharing; Interaction with S3 and Operator Overloading; Chapter 8: Conclusions; Index
Summary
Learn how to write object-oriented programs in R and how to construct classes and class hierarchies in the three object-oriented systems available in R. This book gives an introduction to object-oriented programming in the R programming language and shows you how to use and apply R in an object-oriented manner. You will then be able to use this powerful programming style in your own statistical programming projects to write flexible and extendable software. After reading Advanced Object-Oriented Programming in R, you'll come away with a practical project that you can reuse in your own analytics coding endeavors. You'll then be able to visualize your data as objects that have state and then manipulate those objects with polymorphic or generic methods. Your projects will benefit from the high degree of flexibility provided by polymorphism, where the choice of concrete method to execute depends on the type of data being manipulated. You will: Define and use classes and generic functions using R Work with the R class hierarchies Benefit from implementation reuse Handle operator overloading Apply the S4 and R6 classes
