R Data Analysis Projects

Get valuable insights from your data by building data analysis systems from scratch with R.
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R Data Analysis Projects

Gopi Subramanian

Get valuable insights from your data by building data analysis systems from scratch with R.

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Book Details

ISBN 139781788621878
Paperback366 pages

Book Description

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

Chapter 1: 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
Chapter 2: 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
Chapter 3: Collaborative Filtering
Collaborative filtering
Recommenderlab package
Use case and data
Designing and implementing collaborative filtering
Complete R Code
Summary
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: 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
Chapter 8: 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

What You Will Learn

  • Build end-to-end predictive analytics systems in R
  • Build an experimental design to gather your own data and conduct analysis
  • Build a recommender system from scratch using different approaches
  • Use and leverage RShiny to build reactive programming applications
  • Build systems for varied domains including market research, network analysis, social media analysis, and more
  • Explore various R Packages such as RShiny, ggplot, recommenderlab, dplyr, and find out how to use them effectively
  • Communicate modeling results using Shiny Dashboards
  • Perform multi-variate time-series analysis prediction, supplemented with sensitivity analysis and risk modeling

Authors

Table of Contents

Chapter 1: 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
Chapter 2: 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
Chapter 3: Collaborative Filtering
Collaborative filtering
Recommenderlab package
Use case and data
Designing and implementing collaborative filtering
Complete R Code
Summary
Chapter 4: 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
Chapter 5: 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
Chapter 6: 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
Chapter 7: 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
Chapter 8: 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

Book Details

ISBN 139781788621878
Paperback366 pages
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