Book Image

RStudio for R Statistical Computing Cookbook

By : Andrea Cirillo
Book Image

RStudio for R Statistical Computing Cookbook

By: Andrea Cirillo

Overview of this book

The requirement of handling complex datasets, performing unprecedented statistical analysis, and providing real-time visualizations to businesses has concerned statisticians and analysts across the globe. RStudio is a useful and powerful tool for statistical analysis that harnesses the power of R for computational statistics, visualization, and data science, in an integrated development environment. This book is a collection of recipes that will help you learn and understand RStudio features so that you can effectively perform statistical analysis and reporting, code editing, and R development. The first few chapters will teach you how to set up your own data analysis project in RStudio, acquire data from different data sources, and manipulate and clean data for analysis and visualization purposes. You'll get hands-on with various data visualization methods using ggplot2, and you will create interactive and multidimensional visualizations with D3.js. Additional recipes will help you optimize your code; implement various statistical models to manage large datasets; perform text analysis and predictive analysis; and master time series analysis, machine learning, forecasting; and so on. In the final few chapters, you'll learn how to create reports from your analytical application with the full range of static and dynamic reporting tools that are available in RStudio so that you can effectively communicate results and even transform them into interactive web applications.
Table of Contents (15 chapters)
RStudio for R Statistical Computing Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Creating custom objects and methods in R using the S3 system


When dealing with a programming task pertaining to a specific business domain, developing custom objects with their related methods can enhance your work quality and usability.

Consider, for instance, a programmer dealing with a logistics project. Imagine him facing a choice of different types of means of transport packed with different attributes, cost functions, and time availability.

Defining different classes for these means of transport and adding proper attributes and methods to these classes will let you build a specific domain language that is able to represent, in a convenient way, the real problem you are trying to solve with your code. This concept is further explained in The Pragmatic Programmer by Andrew Hunt and David Thomas.

R comes packed with three different systems for object-oriented programming, which are as follows:

  • The S3 system

  • The S4 system

  • Reference classes

In the following example, we are going to use the S3...