# Letterboxd 2022 Visualisation
- What: Graph visualisation of all the films I watched in the year of our lord 2022.
- When: 2022
- Who: Me
I only really had one aspiration for 2022 and that was to watch one film every day. As a paid-up subscriber at Letterboxd, I already receive a detailed summary of my activities (opens new window) but thought this dataset would also lend itself nicely to network visualisation.
- Wrote a simple web-scraping script in python to extract information about the films I watched this year.
- Converted the output to a graph data structure that included films, cast, genres and country. So that the network did not grow too large I limited the cast to the top 10 highest billed actors from each film.
- Imported data into Gephi (opens new window). I explored a few different options for rendering the graph including Cosmograph (opens new window) and D3 (opens new window) but decided Gephi was the simplest option for a one-off visualisation.
- Used the ForceAtlas2 algorithm to spacialise the nodes and edges in my graph. According to the documentation, ForceAtlast2 "simulates a physical system in order to spatialize a network. Nodes repulse each other like charged particles, while edges attract their nodes, like springs. These forces create a movement that converges to a balanced state."
- Exported the visualisation using SigmaJS and embedded below as an iframe.