Garrett is the author of Hands-On Programming with R and R for Data Science from O'Reilly Media. He is a Data Scientist at RStudio and holds a Ph.D. in Statistics, but specializes in teaching. He's taught people how to use R at over 50 government agencies, small businesses, and multi-billion dollar global companies; and he's designed RStudio's training materials for R, Shiny, dplyr and more and is a frequent contributor to the RStudio blog. He wrote the popular lubridate package for R.
In our ggvis data visualization course, you will learn how to create static and interactive graphs to display distributions, relationships, model fits, and more.
The first part of the course will focus on how to think conceptually about data visualizations using the theory behind the grammar of graphics.
Next, you will dive into the syntax of the R graphics package ggvis. Step-by-step, you'll learn how to create clear R data visualizations with sliders, widgets and text fields, and you’ll become familiar with the ggvis layering scheme to create multi-layered graphs.
Once you complete this data visualization class, you will be able to create meaningful static and interactive graphs to display distributions, relationships, and more!
Introduction to the ggvis package and the grammar of graphics. Learn the philosophy that guides ggvis and discover a clear, logical way to think about data visualization.
Examine each part of the grammar of graphics, and learn the ggvis syntax to make it easier to think about plots.
Discover how to build statistical transformations with the ggvis compute functions, as well as how to visualize the results. Learn shortcuts for visualizing transformations, such as smoothed lines, binned counts, and model predictions.
Practice creating graphs that can be controlled through sliders, text fields, and other widgets. You'll also practice building sophisticated, multi-layered graphs with the ggvis layering scheme.
Change the appearance of axes and legends in your plots, and use the ggvis scale system to zoom in and out, to change the color scheme, and to control how your plot maps data values to visual properties.