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With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis examples using the most up-to-date data, such as the newly discovered COVID-19.
List of contents
1 Introduction 2 Data Wrangling 3 Data Visualization with R Package "ggplot2" 4 Interactive Visualization 5 R Shiny 6 Interactive Geospatial Visualization 7 Epidemic Modeling 8 Compartment Models 9 Time Series Analysis of Infectious Disease Data 10 Regression Methods 11 Neural Networks 12 Hybrid Models
About the author
Dr. Lily Wang is a tenured professor of statistics at George Mason University. She earned her PhD in statistics from Michigan State University in 2007. Before joining Mason in 2021, she was on the faculty of Iowa State University (2014-2021) and the University of Georgia (2007-2014). Her primary research areas include non/semi-parametric modeling and inference, statistical learning of data objects with complex features, methodologies for functional data, spatiotemporal data, imaging, and general issues related to data science and big data analytics. Dr. Wang is a fellow of both the Institute of Mathematical Statistics and the American Statistical Association and an Elected Member of the International Statistical Institute. She is currently serving on the editorial board of
Journal of the Royal Statistical Society,
Series B, Journal of Nonparametric Statistics and
Statistical Analysis and Data Mining.
Summary
With a quick start guide to epidemiological data visualization and analysis in R, this book spans the gulf between academia and practices providing many lively, instructive data analysis examples using the most up-to-date data, such as the newly discovered COVID-19.
