R for Data Science: Visualize, Model, Transform, Tidy, and Import Data

R for Data Science: Visualize, Model, Transform, Tidy, and Import Data

This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it.

Publication date: 25 Dec 2016

ISBN-10: 1491910399

ISBN-13: 9781491910399

Paperback: 518 pages

Views: 9,577

Type: Book

Publisher: O’Reilly Media, Inc.

License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States

Post time: 16 Dec 2016 04:00:00

R for Data Science: Visualize, Model, Transform, Tidy, and Import Data

R for Data Science: Visualize, Model, Transform, Tidy, and Import Data This book will teach you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it.
Tag(s): Data Science R
Publication date: 25 Dec 2016
ISBN-10: 1491910399
ISBN-13: 9781491910399
Paperback: 518 pages
Views: 9,577
Document Type: Book
Publisher: O’Reilly Media, Inc.
License: Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States
Post time: 16 Dec 2016 04:00:00
Summary/Excerpts of (and not a substitute for) the Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States:
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From the Book Description:
Grolemund and Wickham wrote:What exactly is data science? With this book, you’ll gain a clear understanding of this discipline for discovering natural laws in the structure of data. Along the way, you’ll learn how to use the versatile R programming language for data analysis.

Whenever you measure the same thing twice, you get two results—as long as you measure precisely enough. This phenomenon creates uncertainty and opportunity. Author Garrett Grolemund, Master Instructor at RStudio, shows you how data science can help you work with the uncertainty and capture the opportunities. You’ll learn about:

- Data Wrangling—how to manipulate datasets to reveal new information
- Data Visualization—how to create graphs and other visualizations
- Exploratory Data Analysis—how to find evidence of relationships in your measurements
- Modelling—how to derive insights and predictions from your data
- Inference—how to avoid being fooled by data analyses that cannot provide foolproof results

Through the course of the book, you’ll also learn about the statistical worldview, a way of seeing the world that permits understanding in the face of uncertainty, and simplicity in the face of complexity.




About The Author(s)


Garrett Grolemund is a statistician, teacher and R developer who currently works for RStudio. He sees data analysis as a largely untapped fountain of value for both industry and science. Garrett received his Ph.D at Rice University in Hadley Wickham's lab, where his research traced the origins of data analysis as a cognitive process and identified how attentional and epistemological concerns guide every data analysis.

Garrett Grolemund

Garrett Grolemund is a statistician, teacher and R developer who currently works for RStudio. He sees data analysis as a largely untapped fountain of value for both industry and science. Garrett received his Ph.D at Rice University in Hadley Wickham's lab, where his research traced the origins of data analysis as a cognitive process and identified how attentional and epistemological concerns guide every data analysis.


Hadley Wickham is an Assistant Professor and the Dobelman FamilyJunior Chair in Statistics at Rice University. He is an active memberof the R community, has written and contributed to over 30 R packages, and won the John Chambers Award for Statistical Computing for his work developing tools for data reshaping and visualization. His research focuses on how to make data analysis better, faster and easier, with a particular emphasis on the use of visualization to better understand data and models.

Hadley Wickham

Hadley Wickham is an Assistant Professor and the Dobelman FamilyJunior Chair in Statistics at Rice University. He is an active memberof the R community, has written and contributed to over 30 R packages, and won the John Chambers Award for Statistical Computing for his work developing tools for data reshaping and visualization. His research focuses on how to make data analysis better, faster and easier, with a particular emphasis on the use of visualization to better understand data and models.


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