Book Image

Simulation for Data Science with R

By : Matthias Templ
Book Image

Simulation for Data Science with R

By: Matthias Templ

Overview of this book

Data Science with R aims to teach you how to begin performing data science tasks by taking advantage of Rs powerful ecosystem of packages. R being the most widely used programming language when used with data science can be a powerful combination to solve complexities involved with varied data sets in the real world. The book will provide a computational and methodological framework for statistical simulation to the users. Through this book, you will get in grips with the software environment R. After getting to know the background of popular methods in the area of computational statistics, you will see some applications in R to better understand the methods as well as gaining experience of working with real-world data and real-world problems. This book helps uncover the large-scale patterns in complex systems where interdependencies and variation are critical. An effective simulation is driven by data generating processes that accurately reflect real physical populations. You will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. By the end of this book, you reader will get in touch with the software environment R. After getting background on popular methods in the area, you will see applications in R to better understand the methods as well as to gain experience when working on real-world data and real-world problems.
Table of Contents (18 chapters)
Simulation for Data Science with R
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Generic functions, methods, and classes


R has different class systems, the most important ones are S3 and S4 classes. Programming with S3 classes is easy living, it's easier than S4. However, S4 is clean and the use of S4 can make packages very user-friendly.

In any case, in R each object is assigned to a class (the attribute class). Classes allow object-oriented programming and overloading of generic functions. Generic functions produce different output for objects of different classes as soon as methods are written for such classes.

This sounds complex, but with the following example it should get clearer.

As an example of a generic function, we will use the function summary. summary is a generic function used to produce result summaries. The function invokes particular methods that depend on the class of the first argument:

## how often summary is overloaded with methods
## on summary for certain classes
(the number depends on loaded packages)
length(methods(summary))
## [1] 137
class(Cars93...