Vega-Lite – A Grammar of Interactive Graphics

Vega-Lite is a high-level grammar of interactive graphics. It provides a concise, declarative JSON syntax to create an expressive range of visualizations for data analysis and presentation.

Vega-Lite specifications describe visualizations as encoding 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 determines default properties of these components based on a set of carefully designed rules. This approach allows Vega-Lite specifications to be concise for quick visualization authoring, while giving user control to override defaults and customize various parts of a visualization. As we also designed Vega-Lite to support data analysis, Vega-Lite supports both data transformations (e.g., aggregation, binning, filtering, sorting) and visual transformations (e.g., stacking and faceting). Moreover, Vega-Lite specifications can be composed into layered and multi-view displays, and made interactive with selections. Get started
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Compared to Vega, Vega-Lite provides a more concise and convenient form to author common visualizations. As Vega-Lite can compile its specifications to Vega specifications, users may use Vega-Lite as the primary visualization tool and, if needed, transition to use the lower-level Vega for advanced use cases.

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



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.

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


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 Databricks), 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, Sira Horradarn, Donghao Ren, and Halden Lin. Please see the contributors page for the full list of contributors.