More Information
Learn
  • Use different heat map functions in R
  • Customize the layout of your heat maps
  • Read the data and apply it to the heat map
  • Export your heat maps into high-quality picture formats
  • Display geographical data on choropleth maps and contour plots
  • Add interactive hover effects to your heat maps
About

R has grown rapidly over the years to become one of the most versatile and valuable tools for data analysis and graphing. One of its many useful features is the heat map representation of numerical data, which is an invaluable tool to discover patterns in data quickly and efficiently.

Instant Heat Maps in R How-to provides you with practical recipes to create heat maps of all difficulty levels by yourself right from the start. At the end of each recipe, you will find an in-depth analysis that will equip you with everything you need to know to frame the code to your own needs.

Instant Heat Maps in R will present you with all the different heat map plotting functions that exist in R. You will start by creating simple heat maps before moving on to learn how to add more features to them. While you advance step-by-step through the well-connected recipes, you will find out which tool suits the given situation best. You will learn how to read data from popular file formats and how to format the data to create heat maps as well as the ways to export them for presentation.

Features
  • Learn something new in an Instant! A short, fast, focused guide delivering immediate results
  • Create heat maps in R using different file formats
  • Learn how to make choropleth maps and contour plots
  • Generate your own customized heat maps and add interactivity for displaying on the web
Page Count 72
Course Length 2 hours 9 minutes
ISBN 9781782165651
Date Of Publication 23 Jun 2013

Authors

Sebastian Raschka

Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. Some of his recent research methods have been applied to solving problems in the field of biometrics for imparting privacy to face images. Other research focus areas include the development of methods related to model evaluation in machine learning, deep learning for ordinal targets, and applications of machine learning to computational biology.