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.
In this talk, I will provide an overview of what ggvis can do, and how to use it to explore data. One of the central goals of ggvis is to not only make it possible to create interactive graphics, but to make it simple for R users who are not experts in programming or data visualization.
With ggvis, data manipulation and transformation is performed in R, while the presentation and interaction occur in a web browser. The communication between the two sides is handled by Shiny, which also provides the basis for the reactive programming model of interaction in ggvis.
This talk builds on the work of Winston Chang and Hadley Wickham, the developers of ggvis.
This talk builds on ‘ggvis: Interactive graphics in R’, the original ggvis presentation delivered by Winston Chang at useR! 2014.
I specialize in teaching people how to use R – and especially Hadley Wickham’s R packages – to do insightful, reliable data analysis. I’ve worked with Hadley for five years. He 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 analysis. 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 doctorial research on developing pragmatic principles that guide data analysis. 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`.
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