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 (9 chapters)

Recommenderlab package


It will be very useful for the subsequent sections to get an overview of the recommenderlab package. Let us quickly look at the S4 objects inside the package and see how we can use them to build collaborative filtering projects.

A high-level overview is shown in the following figure:

The ratingMatrix is an abstract object, used to store and manipulate the ratings matrix. The term abstract is from object-oriented programming. ratingMatrix defines the interfaces to develop a user ratingsMatrix, but does not implement them. There are two concrete implementations of this object one for the real-valued matrix and the other one for the binary matrix. The Recommender class is used to store the recommendation models. It takes as an input a ratingsMatrix object and other parameters and builds the required recommender model.

The predict function can produce recommendations for unseen/unknown data (where we don't know the recommendation) using the Recommender model.

Proceed to install...