Recently Ted Unangst wrote about his tool, watc, to extract line count and file size statistics to support some of his work. Chris Wellons followed up with his take on watc. Inspired by both posts, I thought it would be an interesting tool to add to my own toolbox. It pairs nicely with some of my current work on extracting useful information from code repositories. This feels like a good way to put together a quick tool using F#. I’ll also use this as an opportunity to show some F# along the way.
Data in Motion - Earthquakes Map
Read Time: 5 minutesToday’s “data in motion” post is a visualization of earthquakes over time. I’ll use seismic data from the National Science Foundation. Keeping with the theme, I’ll use F# and FFmpeg to convert the raw data into a video of the data over time.
Data in Motion - Population Map
Read Time: 4 minutesTaking Stock of More Anomalies with F# and ML.NET
Read Time: 10 minutesDiscriminated Unions and Dapper
Read Time: 12 minutesPersisting data can be a subtle art. Today I am taking a look at how F#’s Discriminated Unions can interact with Dapper. I’ve discussed Dapper before, but never really discussed its facilities for interacting with Discriminated Unions. It is a useful bit of knowledge when determining project data structures.