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R Data Analysis Projects

By : Gopi Subramanian

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R Data Analysis Projects

5 (2)

By: Gopi Subramanian

Buy this Book

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.

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

Association Rule Mining

Understanding the recommender systems

Retailer use case and data

Association rule mining

The cross-selling campaign

Weighted association rule mining

Hyperlink-induced topic search (HITS)

Negative association rules

Rules visualization

Wrapping up

Summary

Fuzzy Logic Induced Content-Based Recommendation

Introducing content-based recommendation

News aggregator use case and data

Designing the content-based recommendation engine

Complete R Code

Summary

Collaborative Filtering

Collaborative filtering

Recommenderlab package

Use case and data

Designing and implementing collaborative filtering

Complete R Code

Summary

Taming Time Series Data Using Deep Neural Networks

Time series data

Deep neural networks

Introduction to the MXNet R package

Symbolic programming in MXNet

Training test split

Complete R code

Summary

Twitter Text Sentiment Classification Using Kernel Density Estimates

Kernel density estimation

Twitter text

Sentiment classification

Dictionary based scoring

Text pre-processing

Building a sentiment classifier

Assembling an RShiny application

Complete R code

Summary

Record Linkage - Stochastic and Machine Learning Approaches

Introducing our use case

Demonstrating the use of RecordLinkage package

Stochastic record linkage

Machine learning-based record linkage

Building an RShiny application

Complete R code

Summary

Streaming Data Clustering Analysis in R

Streaming data and its challenges

Introducing stream clustering

Introducing the stream package

Use case and data

Complete R code

Summary

Analyze and Understand Networks Using R

Graphs in R

Use case and data

Data preparation

Product network analysis

Building a RShiny application

The complete R script

Summary

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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...