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Table Of Contents

R Data Analysis Projects

By : Gopi Subramanian

5 (2)

Buy this Book

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

Collaborative filtering

Given a user rating matrix, where several users have rated several products, the goal of collaborative filtering is as follows:

Predict the ratings for all products unknown to the user
Produce the ratings for the top-n list of products unknown to the user

The underlying premise of the collaborative filtering algorithm is that if two users agree on ratings for a large set of items, they may tend to agree for other items too. Let us use a small R code snippet to explain this concept. Assume we have seven products (A, B, C, D, E, F, G) and two users (user.a and user.b). We also have the ratings provided by both of the users for some of the products. The ratings are range of numbers from 1 to 5, with 1 indicating a poor rating, 5 indicating a good rating, and 0 indicating no rating.

The following is an R snippet for demonstration purposes: