All Times ET
Keywords: data science, data visualization, data wrangling, R, tidyverse, ggplot2
I have been gardening for nearly twenty years and teaching data science for three. I realized that I could combine the two by collecting data from my garden and using it in the introductory data science course I teach. I created four datasets that are now in a GitHub R package called gardenR (https://github.com/llendway/gardenR). I use the data in two ways: 1. to introduce new concepts and functions and 2. for an in-depth data visualization assignment I call the "Perfect Garden Graph" assignment. As often as possible, I use the garden data to introduce new topics to decrease cognitive load. Because the students become familiar with it early on, they can concentrate more on the functions without having to think about the data. The course I teach focuses on tidyverse tools and I have used these datasets to introduce functions from nearly all the tidyverse packages. The in-depth graphing assignment has students working on improving the same graph, using the garden data, over the entire semester. This gives them time to work on all the details that can be easily forgotten in assignments where they produce many graphs. They get into details like color schemes, appropriate labels and legends, and customized theming. One of the surprising outcomes of using the garden data was the personal connection it created. For example, as we were discussing the data, I could show pictures of the vegetables and I made a video tour of the garden so they got to know the data more intimately. They could also ask detailed questions about the data since they had access to the person who collected and created it. In this lightning talk, I will show some of my teaching materials and examples of the progress students make on their graphs throughout the semester. I hope that you will leave wanting to use the data in your classes.