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ggplot2: Elegant Graphics for Data Analysis
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TLDR
This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.Abstract:
This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics. With ggplot2, its easy to: produce handsome, publication-quality plots, with automatic legends created from the plot specification superpose multiple layers (points, lines, maps, tiles, box plots to name a few) from different data sources, with automatically adjusted common scales add customisable smoothers that use the powerful modelling capabilities of R, such as loess, linear models, generalised 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 your 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 their data in an informative and attractive way. You will need some basic knowledge of R (i.e. you should be able to get your data into R), but ggplot2 is a mini-language specifically tailored for producing graphics, and youll learn everything you need in the book. After reading this book youll be able to produce graphics customized precisely for your problems,and youll find it easy to get graphics out of your head and on to the screen or page.read more
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Journal ArticleDOI
A Serum Protein Biomarker Panel Improves Outcome Prediction in Human Traumatic Brain Injury
Eric Peter Thelin,Eric Peter Thelin,Eric Peter Thelin,Faiez Al Nimer,Faiez Al Nimer,Arvid Frostell,Henrik Zetterberg,Kaj Blennow,Kaj Blennow,Harriet Nyström,Harriet Nyström,Mikael Svensson,Mikael Svensson,Bo-Michael Bellander,Bo-Michael Bellander,Fredrik Piehl,Fredrik Piehl,David W. Nelson +17 more
TL;DR: A panel of several different protein biomarkers, all associated with injury severity, with different cellular origin and temporal trajectories, improve outcome prediction models.
Journal ArticleDOI
Tackling uncertainty in multi-criteria decision analysis - An application to water supply infrastructure planning
TL;DR: A novel approach for practically tackling uncertainty in preference elicitation and predictive modeling to support complex multi-criteria decisions based on multi-attribute utility theory (MAUT), and suggests GSA to focus elicitation on most sensitive preference parameters.
Journal ArticleDOI
A Pan-cancer Transcriptome Analysis Reveals Pervasive Regulation through Alternative Promoters.
Deniz Demircioğlu,Engin Cukuroglu,Martin Kindermans,Tannistha Nandi,Claudia Calabrese,Nuno A. Fonseca,André Kahles,Kjong-Van Lehmann,Oliver Stegle,Alvis Brazma,Angela N. Brooks,Gunnar Rätsch,Patrick Tan,Jonathan Göke +13 more
TL;DR: It is found that promoters are deregulated across tissues, cancer types, and patients, affecting known cancer genes and novel candidates, and it is demonstrated that promoter activity provides a more accurate predictor of patient survival than gene expression.
Journal ArticleDOI
An Algebraic Process for Visualization Design
TL;DR: A model of visualization design based on algebraic considerations of the visualization process, which helps characterize visual encodings, guide their design, evaluate their effectiveness, and highlight their shortcomings is presented.
Journal ArticleDOI
Visualization of Brain Statistics With R Packages ggseg and ggseg3d
TL;DR: Two packages for the statistical software R that integrate the spatial dimension that is inherent in neuroimaging data are presented and facilitate parcellation-based visualizations in R, improve and facilitate the dissemination of results, and increase the efficiency of workflows.