Inspired by Cameron Blevins' visualization (and using his data) I created this Tidy Tuesday entry, my first animated plot. Code available in my GitHub repository.
For this Tidy Tuesday entry, I’m looking at data from “Avatar: The Last Airbender”. I was curious if the central character of each episode (based on number of lines spoken) correlated with the episode’s IMDB rating. A first step was to determine the main character for each episode. If Aang speaks the most lines in nearly every episode then this exercise would be pretty pointless. It turns out that—unsurprisingly—Aang speaks more lines in more episodes than anyone else (21 episodes), followed by Sokka (17), Katara (10), Zuko (7), and Azula (2).
I have a new article out on the Rosa Luxemburg Stiftung’s website, As Goes Georgia: What Is at Stake in the Runoff Elections: We don’t know how these runoff elections will turn out, but we do know that whatever the outcome, it will determine the national political terrain at the beginning of the next presidential administration. In Georgia, win or lose, these elections are one more step in challenging Republican dominance and building real progressive power.
I missed last week’s Tidy Tuesday, but it’s about beer, so I wanted to come back to it. Many lists celebrate California as the best state for beer, and there is no doubt that some delicious beer comes out of California. Indeed, between 1987 and 2020, California brewers have won more medals than any other state at the Great American Beer Festival. But California is also the largest state by population, so it’s not surprising that there are many brewers there winning many medals.
This week’s Tidy Tuesday provides data on wind turbines across Canada. Seems like it would be fun (and perhaps informative) to make a map! Source code available on GitHub.
This week’s Tidy Tuesday speaks to the importance of visualization in data exploration. Alberto Cairo created this simulated data set in order to demonstrate how misleading summary statistics can be and to show how useful visualization is in uncovering patterns in data. In this spirit, let’s start exploring this data set to see what we find.
This week’s Tidy Tuesday deals with data about Himalayan climbing expeditions. Alex Cookson, who cleaned and shared this data from the Himalayan Database, wrote a two-part blog post exploring this data. The second part of this post looks at Everest expeditions and the dangers faced by climbers there. In the conclusion to the post, he notes a few other questions that could be explored using this data, namely: What is the composition of expeditions?
This week’s Tidy Tuesday takes a look the Urban Institute’s data on public spending on children from 1997 to 2016. I decided to narrow my focus to pre-k to 12 education spending and to look at the percentage change over this period in per-child spending, adjusted for inflation. This information would probably be best suited to a bar plot, but I wanted to learn how to make hex tile maps! Check out my source code on Github.
This week’s Tidy Tuesday presented a lot of data with many potential stories to tell. I decided to narrow in on grain production in the United States. I also decided to Americanize names and measurements because who really knows what tonnes of maize per hectare means? I used Commonwealth spellings in the code (which is available here) just to be inconsistent.
I have a new article out from the Rosa Luxemburg Stifgung, “Suppressing Democracy: The Attack on Voting Rights in Georgia": The State of Georgia set out to conduct a primary election during a global pandemic. June 9, 2020, Election Day, did not go well. From absentee ballots that were never delivered, to inoperable voting machines, to hours long waits—not to mention the risk of contracting a deadly virus—voters faced numerous obstacles, some of them insurmountable.