Vega-Lite – A Grammar of Interactive Graphics

Vega-Lite is a high-level grammar of interactive graphics. It provides a concise JSON syntax for rapidly generating visualizations to support analysis. Vega-Lite specifications can be compiled to Vega specifications.

Vega-Lite specifications describe visualizations as mappings from data to properties of graphical marks (e.g., points or bars). The Vega-Lite compiler automatically produces visualization components including axes, legends, and scales. It then determines properties of these components based on a set of carefully designed rules. This approach allows specifications to be succinct and expressive, but also provide user control. As Vega-Lite is designed for analysis, it supports data transformations such as aggregation, binning, filtering, sorting, and visual transformations including stacking and faceting. Moreover, Vega-Lite specifications can be composed into layered and multi-view displays, and made interactive with selections. Get started
Latest Version: 4.0.0
Try online

Read our introduction article to Vega-Lite v2 on Medium, watch our OpenVis Conf talk about the new features in Vega-Lite v2, check out the documentation and take a look at our example gallery. Follow us on Twitter at @vega_vis to stay informed about updates.

Example

Users

Vega-Lite is used by thousands of data enthusiasts, developers, journalists, data scientists, teachers, and researchers across many organizations. Here are some of them. Learn about integrations on our ecosystem page.

  • Apple
  • Google
  • Microsoft
  • Tableau
  • Airbnb
  • JupyterLab
  • LA Times
  • CERN
  • Massachusetts Institute of Technology
  • University of Washington
  • Carnegie Mellon University
  • Berkeley

Team

The development of Vega-Lite is led by the alumni and members of the University of Washington Interactive Data Lab (UW IDL), including Kanit “Ham” Wongsuphasawat (now at Apple), Dominik Moritz (now at CMU / Apple), Arvind Satyanarayan (now at MIT), and Jeffrey Heer (UW IDL).

Vega-Lite gets significant contributions from its community. In particular Will Strimling, Yuhan (Zoe) Lu, Souvik Sen, Chanwut Kittivorawong, Matthew Chun, Akshat Shrivastava, Saba Noorassa, and Sira Horradarn. Please see the contributors page for the full list of contributors.