Book Image

R Data Analysis Projects

Book Image

R Data Analysis Projects

Overview of this book

R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, it’s one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. This book will demonstrate how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. You’ll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. You’ll implement time-series modeling for anomaly detection, and understand cluster analysis of streaming data. You’ll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow codes. With the help of these real-world projects, you’ll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The book covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively. By the end of this book, you’ll have a better understanding of data analysis with R, and be able to put your knowledge to practical use without any hassle.
Table of Contents (15 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Use case and data


Category management is analyzing a discrete set of similar or related items sold by a retailer, grouped together, as a strategic business unit. This allows the retailer to then evaluate these units by their turnover and profitability. Brain F. Harris is the inventor of the study of category management. His eight-step process, famously called the Brain Harris model, is used widely today. For more information about category management, refer to http://www.nielsen.com/tw/en/insights/reports/2014/category-management-the-win-win-platform-for-manufacturers-and-retailers.html.

The Nielsen definition of a category is based on product features. Products that exhibit the following features are put under the same category:

  • They should meet similar end-consumer needs
  • Products should be interrelated, for example, substitutable
  • We should be able to place the products together on a retailer shelf

When analyzing purchasing behavior, several patterns emerge; some products are  sold together...