I specialize in teaching people how to use R – and especially Hadley Wickham’s R packages – to do insightful, reliable data science. Hadley was my dissertation advisor at Rice University, where I gained a first-hand understanding of his R libraries. While at Rice, I taught (and helped developed) the courses “Statistics 405: Introduction to Data Analysis,” and “Visualization in R with ggplot2”. Before that, I taught introductory statistics as a Teaching Fellow at Harvard University.
I’m very passionate about helping people analyze data better. I have travelled as far as New Zealand, where R was born, to learn new ways to teach data science. I worked alongside some of the original developers of R to hone my programming skills, and I collaborated with the New Zealand government in a nationwide project to improve how New Zealand teaches data analysis to new statisticians.
Back in the states, I focused my doctoral research on developing pragmatic principles that guide data science. These principles create a foundation for learning R, which is a bit of a layer cake. R is a set of tools for implementing statistical methods, and statistical methods are themselves a set of tools for learning from data. Like all toolkits, R gives its best results to those who use it wisely.
Outside of teaching, I have spent time doing clinical trials research, legal research, and financial analysis. I also develop R software. I co-authored the `lubridate` R package, which provides methods to parse, manipulate, and do arithmetic with date-times, and I wrote the `ggsubplot` package, which extends `ggplot2`. I’m also the Editor-in-chief of RStudio’s Shiny Development Center (shiny.rstudio.com), the official resource for learning to use the shiny package to make interactive web apps with R.
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