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

Summary


We started the chapter by introducing time series data and the traditional approaches to solving them. We gave you an overview of deep learning networks and information on how they learn. Furthermore, we introduced the MXNet R package. Then we prepared our stock market data so that our deep learning network could consume it. Finally, we built two deep learning networks, one for regression, where we predicted the actual closing price of the stock, and one for classification, where we predicted whether the stock price would move up or down.

In the next chapter, we will deal with sentiment mining. We will show how to extract tweets in R, process them and use a dictionary based method to find the sentiments of the tweets. Finally using those scored tweets as datasets we will build a Naive Bayes model based on Kernel density estimate.