ggplot2: Elegant Graphics for Data Analysis (Use R)

Wickham, Hadley

ISBN 10: 331924275X / ISBN 13: 9783319242750

Edité par Springer, 2016

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Titre : ggplot2: Elegant Graphics for Data Analysis ...

Éditeur : Springer

Date d'édition : 2016

Reliure : paperback

Etat : New

Edition : 2nd ed. 2016.

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Présentation de l'éditeur:

This update to the classic book by ggplot2 creator Hadley Wickham upgrades the book to match the many upgrades to ggplot2 that have happened in the last five years. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to : superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales ; add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression ; save any ggplot2 plot (or part thereof) for later modification or reuse ; create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots ; approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot. This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page. New to this edition : brings the book up-to-date with ggplot2 2.1.0, including major updates to the theme system ; new scales, stats and geoms added throughout ; additional practice exercises ; a revised introduction that focuses on ggplot() instead of qplot() ; updated chapters on data and modeling using tidyr, dplyr and broom.