Instant Heat Maps in R: How-to
Sebastian Raschka
Sold by THE SAINT BOOKSTORE, Southport, United Kingdom
AbeBooks Seller since 14 June 2006
New - Soft cover
Condition: New
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Add to basketSold by THE SAINT BOOKSTORE, Southport, United Kingdom
AbeBooks Seller since 14 June 2006
Condition: New
Quantity: Over 20 available
Add to basketThis item is printed on demand. New copy - Usually dispatched within 5-9 working days.
Seller Inventory # C9781782165644
Learn how to design heat maps in R to enhance your data analysis
Overview
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.
What you will learn from this book
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 written 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.
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