By visualizing information, we turn it into a landscape that you can explore with your eyes, a sort of information map. And when you’re lost in information, an information map is kind of useful.
- David McCandless (Journalist and Information Designer)
You’ve landed on one of the tens of thousands of datasets in Enigma Public, the world’s broadest repository of public data, and are interested in exploring it visually. Given the infinite range of visual forms that are possible to represent any dataset, the options may feel overwhelming. A good tool helps to constrain this vast design space, and gives the user a solid set of principles to build upon.
Vega-Lite is a layer of abstraction on top of d3.js. Developed at the University of Washington Interactive Data Lab, it is a web-based “grammar of graphics” that gives users the power to rapidly experiment with different visual encodings for their data. As a web based tool, it lets the user create both static and interactive data graphics, making it an excellent item to have in any data explorer’s toolbox.
Never heard of Observable Notebooks before? Read on!
Data scientists and journalists alike love using “notebook”-style tools such as Jupyter (in contrast to plain text editors) for many reasons, including
- Ability to present text, code, and graphics side-by-side
- Ability to run and iterate on code one section at a time through “cells”
- A better overall coding experience
Observablehq is a free, web-based notebook for data science, founded by a team of folks with roots in the open-source data visualization community (Mike Bostock, Tom MacWright). Unlike Jupyter, readers can view and run Observable notebooks without needing to install anything, making it an ideal tool for sharing reproducible and interactive analyses.
Interested in solving complex, unique problems? We’re hiring.
Originally published at www.enigma.com on April 2, 2018.