MultiCat is an interactive visualisation technique for analysing multidimensional categorical data. Our prototype has been implemented in Svelte and can handle up to 20 nominal and ordinal variables. The starting point for this project was a template created by Connor Rothschild for his newline course, Better Data Visualizations with Svelte.
A demo instance of the prototype, featuring the Titanic data, is available at https://dgt12.github.io/multicat/.
You can load your own categorical data into MultiCat by following these instructions:
- Install the following development tools on your machine: Node.js, Visual Studio Code and Python, including the Pandas library.
- Download or clone the entire multicat GitHub repository.
- Prepare a CSV file of the data you wish to analyse that contains only nominal and/or ordinal variables (one row per data item, one column per variable). Save this in the
multicat/src/data
folder. - Open the Python script
convert_to_js.py
in the samedata
folder. Update the filename on line 9 to match the name of your CSV file, minus the.csv
file extension. - Run
convert_to_js.py
. This will create a file in thedata
folder with the same name but with a.js
extension. This is the format that MultiCat accepts as input. - Open the entire
multicat
folder in Visual Studio Code. Update line 11 ofApp.svelte
to reference your new.js
file (import data from "$data/<filename>.js";
). - Open a terminal within Visual Studio Code and enter the command
npm run dev
. - Open the
localhost
link in Google Chrome. - Start exploring the data!
If you have any questions, feel free to contact David Trye.