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
About the Author
About the Reviewer
Customer Feedback

Introducing the stream package

Package stream is based on two major components:

  1. Data stream data is used to connect to data streams.
  2. Data stream task is to used to perform a data mining task on the data stream.

It's an extensible framework to work on data in motion.

Let us quickly look at the major components inside this framework:

Let us look at the individual boxes in the subsequent sections.

Data stream data

Data stream data (DSD) is an abstraction layer which connects to any streaming data source (of course, with some small hacks, which we will see as we progress). The stream package provides several DSD implementations.

Let us look at them:

DSD as a static simulator

As a simulator DSD can simulate static streams as well as streams with drift. In cases where we are developing algorithms to work on streaming data, we can use this simulator feature effectively.

Let us see how DSD can be leveraged as a data simulator:

> library(stream, quietly = TRUE)
> set.seed(100)
> &lt...