This week, Ellis Hughes and I go over strip plots and dotplots using the code provided by Catriona Cunningham. Catriona used these two data visualization approaches to create some really nice looking plots detailing African American Achievements from a data set provided by the Tidy Tuesday Project.

To watch our screen cast, CLICK HERE.

If you would like to check out Catriona’s code, CLICK HERE.

This week on TidyX, Ellis Hughes and I discuss bump plots built by Cedric Scherer using data on Jelle’s Marbe Race results, provided by TidyTuesday. If you’ve never seen Cedric’s work, he is a great follow on Twitter as he makes some very interesting data visualizations.

We follow up Cedric’s code by building a bump plot of our own using NFL Combine 40yd Sprint Times. We go over different approaches for scraping and organizing the data from both a base R and tidyverse approach.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.

This week, Ellis Hughes and I welcome our first guest to the episode, TidyTuesday creator, Thomas Mock!

Collectively, we go over a code submission by Joshua de la Bruere, who shares a code that provides a good lesson in data cleaning for a rather messy data set. We then have a discussion with Thomas regarding all things R, TidyTuesday, and a bit about his PhD in Neuroscience!

To watch the screen cast, CLICK HERE.

To obtain the code, CLICK HERE.

This week, Ellis Hughes and I use pro beach volleyball data, provided by TidyTuesday, to visualize winning percentage for different teams over time. We then show a simple Bayes adjustment for winning percentage when teams have small sample sizes (similar to what I discussed in an older blog article). Finally, we plot distributions of winning percentage to compare different teams. But before that, we go over code from Eric Ekholm, who produced a really cool plot of the performance metrics of Kerri Walsh-Jennings and Misty May-Treanor across their respective careers.

To watch the screen cast, CLICK HERE.

For our code, CLICK HERE.

This week, instead of working through someone else’s code, Ellis Hughes and I decided to use the TidyTuesday data set on volcano eruptions and walk through how we might start to approach looking at the data from an exploratory data analysis perspective.

Along the way, we not only show how we approach cleaning the data and creating new features but also touch on the value and ease of using the ggplotly() to quickly construct interactive visualizations from the ggplot2 visuals you’ve already built. These types of visuals provide a useful and interesting way to exploring your data are can be valuable when sharing information with decision makers to assist discussions around how to proceed with further analysis.

To watch the screen cast, CLICK HERE.

To view our code, CLICK HERE.