Category Archives: TidyX Screen Cast

TidyX 37: Parsing JSON & Code Review

This week, Ellis Hughes and I deviate from our typical format and instead work on some code that Ben Baldwin shared with us. Ben is an analyst would does a lot of public facing NFL analysis, writes for The Athletic, and is co-creator of  nflfastR, an R package for NFL play-by-play data.

Ben had some code that he shared on twitter where he was parsing an NFL play-by-play data from the data provider Sportradar. As he shared the code he lamented about it being a bit messy. We all have code that we wrote at one time that looks messy to us! Thus, we asked Ben if we could take the code and attempt to build a function that could process these files for any number of games.

We tackle this one totally live, having not looked at or discussed the code prior to hitting “record”. So, you get to watch us make mistakes and fumble around and learn along with us as we try to understand the data format and work up a solution in about an hour.

To watch the screen cast, CLICK HERE.

For our code, CLICK HERE.

TidyX 35: Visualizing dimensions & Washington State Hikes

This week, Ellis Hughes and I discuss code from Henry Wakefield, who made an interesting plot of height and weight data of Ikea furniture (data provided by Tidy Tuesday).

This was a unique way of showing the relationship between two variables. Thus, we scraped several thousand hikes in the state of Washington and plotted the length relative to the elevation gain for each. We took it a step further and used {plotly} to give the visualization an interactive component.

To watch the screen cast, CLICK HERE.
To access our code, CLICK HERE.

TidyX 34: Map Visualizations for Garmin Running Data

Following up last week’s screen cast on maps, Ellis Hughes and I continue looking at visualizations for maps by discussing an R script from Florcence Dubois. Florence built a really cool plot using data from TidyTuesday about Canadian wind turbines. She also pulled in some political data from a research article to help give the plot more context.

We then follow this up by discussing how to use Garmin running data to build your own running report and track your progress.

To view the screen cast, CLICK HERE.

To access our code, CLICK HERE.

TidyX 33: Beer, Web Scraping, and State Maps

This week, Ellis Hughes and I discuss a TidyTuesday submission from Richard Bamattre. Richard visualized craft beers by state using the {statebins} package. This approach produces a very clean method of displaying data across the USA.

We follow up our discussion of Richard’s R code by giving a short web scraping tutorial to acquire more data about beer production across the USA and then create our own state map visualization.

To watch our screen cast, CLICK HERE.

To access our code, CLICK HERE.