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

Product network analysis


There are two steps in product network analysis. The first step is to transform the point-of-sale data into product pairs and their transaction frequency. The second step is to create a graph using data from the first step and run a clustering algorithm on the graph. The subgraphs or the clusters formed are presented as the micro-categories. Also, some products in the graph play key roles. Clustering and visualizing these product subgraphs will also help us identify those key products. According to the white paper by Corte Consultancy, a product fitting any of the following definitions is considered as key to the network:

  • The core product: In a subgraph or a cluster group, the product that is most commonly purchased in the group is termed as the core product of that group.
  • The connectors: These are products that connect two subgraphs or clusters together. They are the ones that are typically bought first, if a customer starts shopping for products in that subgraph...