Network Analysis and Visualization in R: Quick Start Guide
Nov 2017 · STHDA
Ebook
39
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About this ebook
Social network analysis is used to investigate the inter-relationship between entities. Examples of network structures, include: social media networks, friendship networks and collaboration networks.
This book provides a quick start guide to network analysis and visualization in R.
You'll learn, how to:
- Create static and interactive network graphs using modern R packages.
- Change the layout of network graphs.
- Detect important or central entities in a network graph.
- Detect community (or cluster) in a network.
About the author
Alboukadel Kassambara is a PhD in Bioinformatics and Cancer Biology. He works since many years on genomic data analysis and visualization (read more: http://www.alboukadel.com/).
He has work experiences in statistical and computational methods to identify prognostic and predictive biomarker signatures through integrative analysis of large-scale genomic and clinical data sets.
He created a bioinformatics web-tool named GenomicScape (www.genomicscape.com) which is an easy-to-use web tool for gene expression data analysis and visualization.
He developed also a training website on data science, named STHDA (Statistical Tools for High-throughput Data Analysis, www.sthda.com/english), which contains many tutorials on data analysis and visualization using R software and packages.
He is the author of many popular R packages for:
- multivariate data analysis (factoextra, http://www.sthda.com/english/
- survival analysis (survminer, http://www.sthda.com/english/
- correlation analysis (ggcorrplot, http://www.sthda.com/english/
- creating publication ready plots in R (ggpubr, http://www.sthda.com/english/
Recently, he published several books on data analysis and visualization: https://www.
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