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  • Connect to and load data from R’s range of powerful databases
  • Successfully fetch and parse structured and unstructured data
  • Transform and restructure your data with efficient R packages
  • Define and build complex statistical models with glm
  • Develop and train machine learning algorithms
  • Visualize social networks and graph data
  • Deploy supervised and unsupervised classification algorithms
  • Discover how to visualize spatial data with R

R is an essential language for sharp and successful data analysis. Its numerous features and ease of use make it a powerful way of mining, managing, and interpreting large sets of data. In a world where understanding big data has become key, by mastering R you will be able to deal with your data effectively and efficiently.

This book will give you the guidance you need to build and develop your knowledge and expertise. Bridging the gap between theory and practice, this book will help you to understand and use data for a competitive advantage.

Beginning with taking you through essential data mining and management tasks such as munging, fetching, cleaning, and restructuring, the book then explores different model designs and the core components of effective analysis. You will then discover how to optimize your use of machine learning algorithms for classification and recommendation systems beside the traditional and more recent statistical methods.

  • Handle your data with precision and care for optimal business intelligence
  • Restructure and transform your data to inform decision-making
  • Packed with practical advice and tips to help you get to grips with data mining
Page Count 396
Course Length 11 hours 52 minutes
ISBN 9781783982028
Date Of Publication 29 Sep 2015


Gergely Daróczi

Gergely Daróczi is a former assistant professor of statistics and an enthusiastic R user and package developer. He is the founder and CTO of an R-based reporting web application at and a PhD candidate in sociology. He is currently working as the lead R developer/research data scientist at in Los Angeles.

Besides maintaining around half a dozen R packages, mainly dealing with reporting, Gergely has coauthored the books Introduction to R for Quantitative Finance and Mastering R for Quantitative Finance (both by Packt Publishing) by providing and reviewing the R source code. He has contributed to a number of scientific journal articles, mainly in social sciences but in medical sciences as well.