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.
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.
Example
With Vega-Lite, we can start with a bar chart of the average monthly precipitation in Seattle, overlay a rule for the overall yearly average, and have it represent an interactive moving average for a dragged region.
Additional Links
- Award winning research paper and video of our OpenVis Conf talk on the design of Vega-Lite
- Listen to a Data Stories episode about Declarative Visualization with Vega-Lite and Altair
- JSON schema specification for Vega-Lite (latest)
- Ask questions about Vega-Lite on Stack Overflow or Slack
- Fork our Vega-Lite Block, or Observable Notebook.
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 (UW IDL), 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.