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

Introducing our use case


Our customer owns and operates water theme parks. Last year, he had induced a couple of small-time firms to solicit customers to purchase theme park tickets. The problem at hand now is with one of the small-time firms--let's call it Solit. Solit claims that in the last quarter, it was able to channel close to 15,000 customers to buy tickets at the various theme parks. Neither our customer nor Solit has an exact list of the customers. Both of them have their own database of customers. The problem at hand is how many exact customers were channeled by Solit? The data contains the first name, middle name, last name, and date of birth of the customers. Keep in mind the data fields can differ slightly, for example, two records referring to the same entity, say John, may have a small change in the last name or a slightly different date of birth:

Note

The preceding data is RLdata500 from the RecordLinkage package. We are using this data to demonstrate our use case.

The preceding...