Learn and understand the programming techniques necessary to solve specific problems and speed up development processes for statistical models and applications
Explore the fundamentals of building objects and how they program individual aspects of larger data designs
Step-by-step guide to understand how OOP can be applied to application and data models within R

Who This Book Is For

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.

What You Will Learn

Understand the fundamental data types and data structures in R
Explore the basic commands and tools to aid in addressing common tasks
Use the primary control structures in R to implement algorithms
Use and develop S3 and S4 classes
Discover the differences between S3 and S4 classes
Bring different ideas together to solve common problems
Understand the fundamental design and approach to object-oriented programming in R

In Detail

R is best suited to produce data and visual analytics through customizable scripts and commands, instead of typical statistical tools that provide tick boxes and drop-down menus for users. The book is divided into three parts to help you perform these steps. It starts by providing you with an overview of the basic data types, data structures, and tools available in R that are used to solve common tasks. It then moves on to offer insights and examples on object-oriented programming with R; this includes an introduction to the basic control structures available in R with examples. It also includes details on how to implement S3 and S4 classes. Finally, the book provides three detailed examples that demonstrate how to bring all of these ideas together.

About the Author:

Kelly Black

Kelly Black is a faculty member in the Department of Mathematics at Clarkson University. His background is in numerical analysis with a focus on the use of spectral methods and stochastic differential equations. He makes extensive use of R in the analysis of the results of Monte-Carlo simulations. In addition to using R for his research interests, Kelly also uses the R environment for his statistics classes. He has extensive experience sharing his experiences with R in the classroom. The use of R to explore datasets is an important part of the curriculum.

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