R: Recipes for Analysis, Visualization and Machine Learning

Get savvy with R language and actualize projects aimed at analysis, visualization and machine learning

Key Features:

• Proficiently analyze data and apply machine learning techniques
• Generate visualizations, develop interactive visualizations and applications to understand various data exploratory functions in R
• Construct a predictive model by using a variety of machine learning packages

Book Description:

The R language is a powerful, open source, functional programming language. At its core, R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This Learning Path is chock-full of recipes. Literally! It aims to excite you with awesome projects focused on analysis, visualization, and machine learning. We'll start off with data analysis - this will show you ways to use R to generate professional analysis reports. We'll then move on to visualizing our data - this provides you with all the guidance needed to get comfortable with data visualization with R. Finally, we'll move into the world of machine learning - this introduces you to data classification, regression, clustering, association rule mining, and dimension reduction.

This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:

R Data Analysis Cookbook by Viswa Viswanathan and Shanthi Viswanathan

R Data Visualization Cookbook by Atmajitsinh Gohil

Machine Learning with R Cookbook by Yu-Wei, Chiu (David Chiu)

What You Will Learn:

• Get data into your R environment and prepare it for analysis
• Perform exploratory data analyses and generate meaningful visualizations of the data
• Generate various plots in R using the basic R plotting techniques
• Create presentations and learn the basics of creating apps in R for your audience
• Create and inspect the transaction dataset, performing association analysis with the Apriori algorithm
• Visualize associations in various graph formats and find frequent itemset using the ECLAT algorithm
• Build, tune, and evaluate predictive models with different machine learning packages
• Incorporate R and Hadoop to solve machine learning problems on big data

Who this book is for:

This Learning Path is ideal for those who have been exposed to R, but have not used it extensively yet. It covers the basics of using R and is written for new and intermediate R users interested in learning. This Learning Path also provides in-depth insights into professional techniques for analysis, visualization, and machine learning with R - it will help you increase your R expertise, regardless of your level of experience.

iswa Viswanathan is an associate professor of Computing and Decision Sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence, Viswa spent a decade in academia and then switched to a leadership position in the software industry for another decade during which he worked for Infosys, Igate, and Starbase. He embraced academia once again in 2001. Viswa has taught extensively in fields ranging from operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to university teaching, Viswa has conducted training programs for industry professionals and has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education. He has authored a book titled Data Analytics with R:A hands-on approach. Viswa thoroughly enjoys hands-on software development and has single-handedly conceived, architected, developed, and deployed several web-based applications. Apart from his deep interest in technical fields such as data analytics, artificial intelligence, computer science, and software engineering, Viswa harbors a deep interest in education with special emphasis on the roots of learning and methods to foster deeper learning. He has done research in this area and hopes to pursue the subject further. Viswa would like to express deep gratitude to professors Amitava Bagchi and Anup Sen, who were inspirational forces during his early research career. He is also grateful to several extremely intelligent colleagues, notable among them being Rajesh Venkatesh, Dan Richner, and Sriram Bala, who significantly shaped his thinking. His aunt, Analdavalli; his sister, Sankari; and his wife, Shanthi, taught him much about hard work, and even the little he has absorbed has helped him immensely. His sons, Nitin and Siddarth, have helped with numerous insightful comments on various topics