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Statistical Application Development with R and Python - Second Edition

You're reading from  Statistical Application Development with R and Python - Second Edition

Product type Book
Published in Aug 2017
Publisher
ISBN-13 9781788621199
Pages 432 pages
Edition 2nd Edition
Languages
Toc

Table of Contents (19) Chapters close

Statistical Application Development with R and Python - Second Edition
Credits
About the Author
Acknowledgment
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Data Characteristics 2. Import/Export Data 3. Data Visualization 4. Exploratory Analysis 5. Statistical Inference 6. Linear Regression Analysis 7. Logistic Regression Model 8. Regression Models with Regularization 9. Classification and Regression Trees 10. CART and Beyond Index

A brief peek at ggplot2


Tufte (2001) and Wilkinson (2005) emphasize the aesthetics of graphics a lot. There is indeed more to graphics than mere mathematics, and the subtle changes/corrections in a display may lead to an improved, enhanced, and pleasing feeling on the eye diagrams. Wilkinson emphasizes on what he calls grammar of graphics, and an R adaptation of it is given by Wickham (2009).

Thus far, we have used various functions, such as barchart, dotchart, spineplot, fourfoldplot, boxplot, plot, and so on. The grammar of graphics emphasizes that a statistical graphic is a mapping from data to aesthetic attributes of geometric objects. The aesthetics aspect consist of color, shape, and size, while the geometric objects are composed of points, lines, and bars. A detailed discussion of these aspects is unfortunately not feasible in this book, and we will satisfy ourselves with a quick introduction to the grammar of graphics. To begin with, we will simply consider the qplot function from...

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