Complete Guide to 3D Plots in R: Static and interactive 3-dimension graphs - Tapa blanda

Kassambara, Mr. Alboukadel

9781517651596: Complete Guide to 3D Plots in R: Static and interactive 3-dimension graphs

Tapa blanda

ISBN 10: 151765159X ISBN 13: 9781517651596

Editorial: CreateSpace Independent Publishing Platform, 2015

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This book provides a complete guide for visualizing a data in 3 dimensions (3D) using R software. It contains 2 main parts and 7 chapters describing how to draw static and interactive 3D plots. The chapter 1 is about data preparation for 3D plot. In chapter 2, we describe how to create and customize easily basic static 3D scatter plots. Chapter 3 presents how to create advanced static 3D plots including 3D scatter plots with confidence interval, 3D line plots, 3D texts, 3D bar plots, 3D histograms and 3D arrows. Chapter 4 describes the required package for drawing interactive 3D plots. In chapter 5, we show how to transform easily an existing static 3D plot into an interactive 3D plot. Chapter 6 provides many examples of R codes for creating interactive 3D scatter plots with 3D regression surfaces and concentration ellipsoids. We describe also how to export these graphs as PNG or PDF files. Chapter 7 presents a complete guide to RGL 3D visualization device system. We provide also R codes for creating a movie from RGL 3D scene and for exporting plot into an interactive HTML web file.

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Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization. He created a bioinformatics tool called GenomicScape (www.genomicscape.com) which is an easy-to-use web tool for gene expression data analysis. He developed also a website called STHDA (Statistical Tools for High-throughput Data Analysis, www.sthda.com), which contains many tutorials on data analysis and visualization using R software and packages. He is the author of the R package factoextra which can be used to easily extract and visualize the results of multivariate analysis such PCA, CA, MCA and clustering.

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