R Graphics Essentials for Great Data Visualization: 200 Practical Examples You Want to Know for Data Science

Alboukadel Kassambara

Nov. 2017 · STHDA

Über dieses E-Book

Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics.

With this book, you 'll learn:
    
-   How to quickly create beautiful graphics using ggplot2 packages
- How to properly customize and annotate the plots
- Type of graphics for visualizing categorical and continuous variables
- How to add automatically p-values to box plots, bar plots and alternatives
- How to add marginal density plots and correlation coefficients to scatter plots
- Key methods for analyzing and visualizing multivariate data
- R functions and packages for plotting time series data 
-  How to combine multiple plots on one page to create production-quality figures.

Autoren-Profil

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/rpkgs/factoextra),
- survival analysis (survminer, http://www.sthda.com/english/rpkgs/survminer/),
- correlation analysis (ggcorrplot, http://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2),
- creating publication ready plots in R (ggpubr, http://www.sthda.com/english/rpkgs/ggpubr).
    
Recently, he published three books on data analysis and visualization:
  
1. Practical Guide to Cluster Analysis in R (https://goo.gl/yhhpXh)
2. Practical Guide To Principal Component Methods in R (https://goo.gl/d4Doz9)