ggplot2: Elegant Graphics for Data Analysis Use R!
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Produce handsome, publication-quality plots with automatic legends
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- New edition by ggplot2 creator Hadley Wickham with compatibility with knitr and RStudio highlighted
- A data visualization package for R that allows users to create multi-layered data graphics with ease
- Enables production of handsome, publication-quality plots with automatic legends
- Allows superimposition of multiple layers from different data sources with automatically adjusted common scales
- Features customizable smoothers using powerful modeling capabilities of R
- Enables creation of custom themes and provides a visual approach to graph creation
| Publisher | Springer |
| Publication date | June 16, 2016 |
| Edition | 2nd ed. 2016 |
| Language | English |
| Print length | 276 pages |
| ISBN-10 | 331924275X |
| ISBN-13 | 978-3319242750 |
| Item Weight | 4.22 Kilograms |
| Dimensions | 6.1 x 0.63 x 9.25 inches (15.5 x 1.6 x 23.5 cm) |
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English Edition Hadley Wickham Format: Paperback Editorial Review
ggplot2: Elegant Graphics for Data Analysis Use R! is a comprehensive guide published by Springer on June 16, 2016, featuring 276 pages of detailed content in English. This second edition explores the ggplot2 package, perfect for those looking to create advanced data visualizations in R. The book, with its focus on elegance in graphics, is particularly well-suited for data analysts and researchers wishing to enhance their visual communication. Readers will appreciate the clear explanations and practical examples throughout, making it an essential resource for mastering data visualization techniques in R.
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Pros
- Comprehensive guide for data visualization in R
- Second edition offers updated content
- Easy-to-understand explanations and examples
- Published by a reputable publisher
- Includes advanced ggplot2 features
Cons
- Slightly dense for complete beginners
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Features & Benefits
- Compatible with knitr and RStudio
- Create multi-layered data graphics with ease
- Superimpose multiple layers from different data sources
- Add customizable smoothers using powerful modeling capabilities of R
- Save plots for later modification or reuse
- Create custom themes for in-house or journal style requirements
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