If you are looking for a book that takes you all the way through the practical applications of advanced and effective analytics methodologies in R, then this is the book for you. A fundamental understanding of R and the basic concepts of data analysis are all you need to get started with this book.
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R Data Analysis Projects
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R Data Analysis Projects
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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)
Preface
Free Chapter
Association Rule Mining
Fuzzy Logic Induced Content-Based Recommendation
Collaborative Filtering
Taming Time Series Data Using Deep Neural Networks
Twitter Text Sentiment Classification Using Kernel Density Estimates
Record Linkage - Stochastic and Machine Learning Approaches
Streaming Data Clustering Analysis in R
Analyze and Understand Networks Using R
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