When I want to find out the background and policy positions of politicians, I usually start by checking their Wikipedia pages. I’d bet that most people do, too.

I recently learned that the Wikimedia Foundation provides a ton of data for research purposes, and it’s totally free! One of the first things I stumbled upon was hourly page view data for every article on Wikipedia (over 800 million as of September 2013). The datasets are large, though, so using them was a bit cumbersome on my Macbook Air (mostly because I had to download them).

I could connect to the shared server and run queries, but, fortunately, Wikimedia provides an API and a Python wrapper. The API only provides access to data going back through mid-2015, but it’s still awesome for a free service.

Since we just had a presidential election, I was curious how the election might have affected the candidates’ popularity on Wikipedia. The results are pretty dramatic.

All the candidates’ pages were visited more on the day after the election, but Trump had a staggering 5.5 million more views the day after.

The raw hourly page views datasets are updated every hour, which is pretty cool. I bet there are signals in the Wikipedia usage data that would be pretty useful for short-term forecasting a ton of different phenomena.

For those interested, the Jupyter Notebook with Python code to get the data and the R code to make the visualization can be found in the Github repository for this post.

Additionally, all analyses and conclusions presented on this website reflect my views and do not indicate concurrence by the Board of Governors or the Federal Reserve System.