Garrett Grolemund

Garrett Grolemund
Curriculum Developer, RStudio

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.

Sessions

Data Science
Location: 122-123
Garrett Grolemund (RStudio)
Average rating: ****.
(4.21, 14 ratings)
This tutorial will teach you how to streamline your code and your thinking when doing data science. Analysts often spend over 80% of their time preparing and exploring data sets before they begin more formal analysis work. In this tutorial, I will introduce a set of principles -- and R packages -- that make this work easier and faster. Read more.
Data Science, Design
Location: 115
Garrett Grolemund (RStudio)
Average rating: ****.
(4.78, 18 ratings)
The ggvis package makes it easy to create interactive data graphics with R, with a declarative syntax similar to that of ggplot2. Like ggplot2, ggvis uses concepts from the grammar of graphics, but it also adds the ability to create interactive graphics and deliver them over the web. Read more.