Advanced Object-Oriented Programming in R

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. What You'll LearnDefine and use classes and generic functions using R Work with the R class hierarchiesBenefit from implementation reuseHandle operator overloadingApply the S4 and R6 classes Who This Book Is ForExperienced programmers and for those with at least some prior experience with R programming language.

An up-to-date and practical R programming guide dealing with object-oriented aspects, classes and more Includes a case study project as a take away for readers Written by an expert in both programming and statistics

Autorentext
Thomas Mailund is an associate professor in bioinformatics at Aarhus University, Denmark. He has a background in math and computer science. For the last decade, his main focus has been on genetics and evolutionary studies, particularly comparative genomics, speciation, and gene flow between emerging species. He has published Beginning Data Science in R, Functional Programming in R and Metaprogramming in R with Apress as well as other books out there.

Inhalt

1. Classes and Generic Functions.- 2. Class Hierarchies.- 3. Implementation Reuse.- 4. Statistical Models.- 5. Operator Overloading.- 6. S4 Classes.- 7. R6 Classes.- 8. Conclusions.