Instant Heat Maps in R How-to

by Sebastian Raschka

In Detail

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.

Approach

Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. Heat Maps in R: How-to is an easy to understand book that starts with a simple heat map and takes you all the way through to advanced heat maps with graphics and data manipulation.

Who this book is for

Heat Maps in R: How-to is the book for you if you want to make use of this free and open source software to get the most out of your data analysis. You need to have at least some experience in using R and know how to run basic scripts from the command line. However, knowledge of other statistical scripting languages such as Octave, S-Plus, or MATLAB will suffice to follow along with the recipes. You need not be from a statistics background.

Genres: Mathematics

74 pages, Kindle Edition

First published January 1, 2013

About the author

Some of my greatest passions are "Data Science" and machine learning. I enjoy everything that involves working with data: The discovery of interesting patterns and coming up with insightful conclusions using techniques from the fields of data mining and machine learning for predictive modeling.

I am a big advocate of working in teams and the concept of "open source." In my opinion, it is a positive feedback loop: Sharing ideas and tools that are useful to others and getting constructive feedback that helps us learn!

A little bit more about myself: Currently, I am sharpening my analytical skills as a PhD candidate at Michigan State University where I am currently working on a highly efficient virtual screening software for computer-aided drug-discovery and a novel approach to protein ligand docking (among other projects). Basically, it is about the screening of a database of millions of 3-dimensional structures of chemical compounds in order to identifiy the ones that could potentially bind to specific protein receptors in order to trigger a biological response.

In my free-time I am also really fond of sports: Either playing soccer or tennis in the open air or building models for predictions. I always enjoy creative discussions, and I am happy to connect with people. Please feel free to contact me by email or in one of those many other networks!