Workshop: Tidy data and visualization in ggplot2 for ecologists

The tidyverse is a group of packages with a common design philosophy that uses a concise syntax to help you clean, organize, analyze, and visualize large data sets with ease. The syntax was popularized by “R for Data Science” by Hadley Wickham and Garrett Grolemund, but its rooted in the idea that workflows should be both readable and reproducible. Tidyverse packages help your code read more like a sentence: something like this h(g(f(x))) is coded and read like this x %>% f %>% g %>% h.

The tidyverse relies upon the package ggplot2 for data visualization. The package, based on “The Grammar of Graphics”, embodies a deep philosophy of visualization to declaratively create graphics. After providing the data, you tell ggplot2 how to map variables to aesthetics, then add layers, scales, faceting specifications, or coordinate systems. Not only is ggplot more concise than base graphics, it also allows you more creative freedom and greater control over your visualizations. Additionally, ggplot2 and its associated packages provide unique methods for presenting and distributing your data. From spatial analysis and mapping with ggmap to animations within Shiny apps, there are many avenues to share data with collaborators, stakeholders, and everyone in between.

This content of this workshop is somewhat subject to the attendees’ goals (survey link below), but the primary goal is to familiarize attendees with the ggplot2 syntax and provide a great starting point for tidy analysis. The first content chapter in R for Data Science is “Data visualization” because visualization is a great place to start learning this syntax.

The workshop is free and will be held on Thursday, August 29, 2019 from 15:30 to 18:00. The location/room will be identified shortly. Space will be limited to 25 participants, so please contact Lindsay Carlson at to reserve your spot and get workshop materials. 

If you’re interested, please fill out the survey linked below to help me better cater the workshop to everyone’s needs, interests, and experience level. 

Find more information about the workshop and download materials (coming soon) to the workshop’s GitHub repository:

“The simple graph has brought more information to the data analyst’s mind than any other device.” — John Tukey