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Instant Heat Maps in R How-to
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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.
ApproachFilled 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 forHeat 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.
LanguageEnglish
PublisherPackt Publishing
Release dateJun 24, 2013
ISBN9781782165651
Author
Sebastian Raschka
Sebastian Raschka is an Assistant Professor of Statistics at the University of Wisconsin-Madison focusing on machine learning and deep learning research. As Lead AI Educator at Grid AI, Sebastian plans to continue following his passion for helping people get into machine learning and artificial intelligence.
Read more from Sebastian Raschka
Python Machine Learning - Third Edition: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
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Instant Heat Maps in R How-to - Sebastian Raschka
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Table of Contents
Instant Heat Maps in R How-to
Credits
About the Author
About the Reviewers
www.PacktPub.com
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Why Subscribe?
Free Access for Packt account holders
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Errata
Piracy
Questions
1. Instant Heat Maps in R How-to
Creating your first heat map in R (Simple)
Getting ready
How to do it...
How it works...
There's more...
More information on dendrograms and clustering
Reading data from different file formats (Intermediate)
Getting ready
How to do it...
How it works...
There's more...
More information on decimal marks
Customizing heat maps (Intermediate)
Getting ready
How to do it...
How it works...
Drawing choropleth maps and contour plots (Intermediate)
Getting ready
How to do it...
How it works...
Exporting for presentation (Simple)
Getting ready
How to do it...
How it works...
Bringing your data to life (Advanced)
Getting ready
How to do it...
How it works...
Instant Heat Maps in R How-to
Instant Heat Maps in R How-to
Copyright © 2013 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: June 2013
Production Reference: 1180613
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78216-564-4
www.packtpub.com
Credits
Author
Sebastian Raschka
Reviewers
John B. Johnston
Kristopher Opron
Acquisition Editor
Martin Bell
Commissioning Editor
Yogesh Dalvi
Technical Editor
Ankita R. Meshram
Project Coordinator
Akash Poojary
Proofreader
Paul Hindle
Production Coordinator
Nitesh Thakur
Cover Work
Nitesh Thakur
Cover Image
Aditi Gajjar
About the Author
Sebastian Raschka is a PhD student at Michigan State University and is pursuing a doctorate in Biochemistry and Computer Science. He works in the field of protein structure modeling and is focused on the specificity of protein-ligand interactions. His research involves the development of a protein-ligand docking software based on a novel approach, where he combines the fields of machine learning, pattern recognition, and data mining.
In his free time, Sebastian works on web development and uses JavaScript among other technologies to develop web applications that are used by Bioinformaticians and Computational Biologists.
About the Reviewers
John Johnston is the Bioinformatics Domain Specialist for the Institute for Cyber-enabled Research at Michigan State University. He specializes in scientific analysis in a high-performance computing environment, and the development of software for the interpretation of biological data. He is an experienced Linux systems administrator and scientific consultant. He previously worked for 18 years as a Senior Groundwater Scientist for several prominent engineering firms, where he specialized in the delineation and mitigation of environmental contamination and site restoration.
Kristopher Opron is a PhD student at Michigan State University studying Computational and Mathematical Biology. For his undergraduate research, he worked under Professor Zachary Burton on molecular dynamics simulations of RNA polymerase II. He is currently working on the development of new computational tools for scientists under Professor Guowei Wei.
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