Posts

I remember noticing somewhere that it was possible to embed a Shiny application into a webpage created via the R blogdown package. I thought it would be interesting to attempt to do this for a blog post.

In this post I’m going to explore the tigris and leaflet packages to make some maps. I live in Cook County, Illinois, and I thought it would be interesting to create a relatively granular map of the county.

While looking through the census data website I found an interesting measure that the Census Bureau has been tracking since 1790. Based on the results of each 10 year census, they track the county that is the population ‘center’ of the United States.

The freely available Statsbomb event level soccer data includes 520 Barcelona La Liga games during the period from 2004 to 2021. Barcelona are famous for their ability to out-possess nearly every team they play against, so I thought it would be interesting to dig into the data and produce a few charts to demonstrate Barcelona’s possession dominance.

On TV, post-game soccer possession statistics are measured by the number of passess for each team during the game. In this post I explore possession using the freely available Statsbomb event level data from the 2019 Champions League final between Liverpool and Tottenham Hotspur.

This post uses freely available Statsbomb event level data to visualize the frequency of possession changes in the 2019 Champions League final between Liverpool and Tottenham Hotspur. Liverpool won the game 2-0, but they possessed the ball for less than half the time Spurs had it.

This post shows how I used the ‘StatsBombR’ R package to get freely available soccer data provided by StatsBomb. StatsBomb is a sports data company and they provide free online samples of some of their professional level data.

A number of my posts source data via the ‘Tidy Tuesday’ project, so I thought it would make sense for me to provide some further information on this project. Every Tuesday a new dataset is provided and people are encouraged to wrangle the data and create a visualisation using the R tidyverse (although other code based methodologies are also welcome).

I recently read an interesting blog post by Julia Silge about creating a supervised machine learning classification model to distinguish text between two different novels. I thought it would be good exercise to create a similar model using the wine dataset I previously used in other blog posts.

I thought it would be interesting to plot some data using box plots and then to plot the same data again using a tile grid. Box plots are great to get a quick visual overview of some standard statistical properties of the data.