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.

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Table of Contents (15 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Association Rule Mining
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
2
Fuzzy Logic Induced Content-Based Recommendation
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
3
Collaborative Filtering
Collaborative Filtering
Collaborative filtering
Recommenderlab package
Use case and data
Designing and implementing collaborative filtering
Complete R Code
Summary
4
Taming Time Series Data Using Deep Neural Networks
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
5
Twitter Text Sentiment Classification Using Kernel Density Estimates
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
6
Record Linkage - Stochastic and Machine Learning Approaches
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
7
Streaming Data Clustering Analysis in R
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
8
Analyze and Understand Networks Using R
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

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)
> gaussian.stream &lt...
Previous Section
End of Section 4
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