- Häftad (Paperback / softback)
- Antal sidor
- 2nd ed. 2016
- Springer International Publishing AG
- Bibliographie 100 schwarz-weiße und 100 farbige Abbildungen
- 5 Tables, color; 140 Illustrations, color; 92 Illustrations, black and white; XVI, 260 p. 232 illus.
- 235 x 155 x 15 mm
- Antal komponenter
- 1 Paperback / softback
- 430 g
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Elegant Graphics for Data Analysis
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"The versatility and efficiency of ggplot have led to the development of ggplot2 and this book which overviews the standard use and presentation secrets of functions developed in the last 5 years. ... The book is written in an accessible manner and it is suitable for undergraduates, postgraduates and researchers with some R experience. All theoretical concepts are accompanied by code making it easy to learn by reproducing the examples." (Irina Ioana Mohorianu, zbMATH 1397.62006, 2018) "The book is an excellent and very comprehensive manual of ... one of the most popular R packages. It is currently the only book describing ggplot2 in such depth. The book contains many examples and is very nicely illustrated, demonstrating the strength of the package." (Klaus Galensa, Computing Reviews, May, 2017)
Bloggat om ggplot2
Hadley Wickham is Chief Scientist at RStudio and Assistant Professor of Statistics at Rice University. Hadley is interested in developing computational and cognitive tools for making data preparation, visualization, and analysis easier. He has developed 15 R packages and in 2006 won the John Chambers Award for Statistical Computing for his work on the ggplot and reshape R packages. Carson Sievert is a PhD student in the Department of Statistics at Iowa State University. His work includes R packages for acquiring data from the Web (pitchRx, bbscrapeR, XML2R), designing interactive Web graphics (animint, plotly), and visualizations for exploring statistical models (LDAvis).
Introduction.- Getting Started with ggplot2.- Toolbox.- Mastering the Grammar.- Building a Plot Layer by Layer.- Scales, Axes and Legends.- Positioning.- Themes.- Data Analysis.- Data Transformation.- Modelling for Visualisation.- Programming with ggplot2.- Index.- R Code Index.