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