Over 70 recipes for building and customizing publication-quality visualizations of powerful and stunning R graphsAbout This Book
- Create a wide range of powerful R graphs
- Leverage lattice and ggplot2 to create high-quality graphs.
- Develop well-structured maps for efficient data visualization
Who This Book Is For
Targeted at those with an existing familiarity with R programming, this practical guide will appeal directly to programmers interested in learning effective data visualization techniques with R and a wide-range of its associated libraries.
What You Will Learn
- Create diverse types of bar charts using the default R functions
- Produce and customize density plots and histograms with lattice and ggplot2
- Visualize frequency tabulated data with interactive and beautiful graphs
- Inspect large datasets by simultaneously visualizing numeric and categorical variables in a single plot
- Construct multiple graph matrix layouts
- Annotate graphs using ggplot2
- Construct various types of three-dimensional plots using three-dimensional visualizations
In Detail
Data visualization is one of the most important tasks in the data science track. Through effective visualization, we can easily uncover underlying patterns among variables without doing any sophisticated statistical analysis.
Starting with a high-level overview of the R graphics system and then moving through this practical cookbook, you will leverage powerful default R graphics and utilize advanced graphics systems such as lattice and ggplot2, the grammar of graphics. Through inspecting large datasets using tableplot and stunning three-dimensional visualizations, you will know how to produce, customize, and publish advanced visualizations using this popular, and powerful, framework.
Jaynal Abedin
Jaynal Abedin currently holds the position of Senior Statistician at the Centre for Communicable Diseases (CCD) at icddr, b (www.icddrb.org). He attained his Bachelor's and Master's degrees in Statistics from University of Rajshahi, Rajshahi, Bangladesh. He has vast experience in R programming and Stata and has efficient leadership qualities. He has written an R package named edeR: Email Data Extraction Using R, which is available at CRAN (http://cran.r-project.org/web/packages/edeR/index.html). He is currently leading a team of statisticians. He has hands-on experience in developing training material and facilitating training in R programming and Stata along with statistical aspects in public health research. He has authored Data Manipulation with R, Packt Publishing, which got good reviews. His primary area of interest in research includes causal inference and machine learning. He is currently involved in several ongoing public health research projects and is a co-author of seven peer-reviewed scientific papers. Moreover, he engages in several work-in-progress manuscripts. He is also one of the reviewers of the following two journals: · Journal of Applied Statistics(JAS) · Journal of Health Population and Nutrition(JHPN)
