Author: Hadley Wickham
Edition: 1
Binding: Kindle Edition
ISBN: B0041KLFRW
Publisher: Springer
Edition: 1
Binding: Kindle Edition
ISBN: B0041KLFRW
Publisher: Springer
ggplot2: Elegant Graphics for Data Analysis
This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkison's Grammar of Graphics to create a powerful and flexible system for creating data graphics. ggplot2
ggplot2: Elegant Graphics for Data Analysis will be useful to everyone who has struggled with displaying their data in an informative and attractive way. You will need some basic knowledge of R (ie you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in ggplot2: Elegant Graphics for Data Analysis. After reading ggplot2: Elegant Graphics for Data Analysis 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. isbn13: 9780387981406
With ggplot2, it's easy to:produce handsome, publication-quality plots, with automatic legends created from the plot specificationsuperpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scalesadd customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regressionsave any ggplot2 plot (or part thereof) for later modification or reusecreate custom themes that capture Download free ggplot2: Elegant Graphics for Data Analysis (Use R!) from rapidshare, mediafire, 4shared
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ith ggplot2, it's easy to:produce handsome, publication-quality plots, with automatic legends created from the plot specificationsuperpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scalesadd customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised additive models and robust regressionsave any ggplot2 plot (or part thereof) for later modification or reusecreate custom themes that capture
