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R Data Analysis Projects: Build end to end analytics systems to get deeper insights from your data


English | 17 Nov. 2017 | ISBN: 1788621875 | 366 Pages | PDF/EPUB/MOBI/Code files | 94.18 MB

Key Features
A handy guide to take your understanding of data analysis with R to the next level
Real-world projects that focus on problems in finance, network analysis, social media, and more
From data manipulation to analysis to visualization in R, this book will teach you everything you need to know about building end-to-end data analysis pipelines using R

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.

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


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