Animated Vega-Lite: Unifying Animation with a Grammar of Interactive Graphics

Zong, Jonathan; Wootton, Dylan; Satyanarayan, Arvind; Pollock, Josh

doi:10.1109/tvcg.2022.3209369

Cited by 12 publications

(2 citation statements)

References 39 publications

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“…If our survey prompts any prediction, it is that new visualization languages will continue to be developed -some of which may be in the JSON-style. Since the final update to our survey in June 2022, we have observed a variety of additional JSON DSLs [199,326,420], suggesting there will long be many more.…”

Section: Discussionmentioning

confidence: 99%

Understanding and Enhancing JSON-based DSL Interfaces for Visualization

McNutt¹

2023

Preprint

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Domain-specific languages represented in data serialization formats (such as Javascript Object Notation or JSON) are an increasingly common means to control numerous systems.These range from database queries to application configuration, narrative generation, Twitter bots, data visualization, and many other areas.These languages allow potentially unsophisticated human users to concisely specify their intent through logic and notation that is relevant to task domain.Further, they provide a means for computational agents to easily manipulate that form, allowing for powerful recommendation engines and systems of automated analyses.In this thesis, we consider how end-user agency might be enhanced and maintained through the design of tools that support these domain-specific languages, as well as through the study of the design of the languages themselves.In support of this goal, we conducted four interconnected projects which variously study how JSON-based DSLs are designed, how abstraction can be integrated into those languages, how interfaces can be designed to specifically facilitate their manipulation, as well as how those programs might be automatically validated.Through these projects, we demonstrate that giving primacy to these textual interfaces as design elements can be valuable for end users.We find that this style of interventions are useful for helping end users learn, use, and re-use programs written in these languages.We primarily consider languages focused on data visualization tasks, as there has been substantial work in the visualization research community on this form of interface—although the lessons learned could be applied to any relevant domain.

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Section: Discussionmentioning

confidence: 99%

Understanding and Enhancing JSON-based DSL Interfaces for Visualization

McNutt¹

2023

Preprint

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“…For example, gg-plot2 [53] is a charting library in R based on visual encodings. Vega-Lite [44] and its Python-wrapper Altair [49] extend the traditional grammar of graphics design with rules for layered and multi-view displays, as well as interactions, and Animated Vega-Lite [63] further extends it to support animations. These grammars hide low-level implementation details and are concise.…”

Section: Related Workmentioning

confidence: 99%

Data Formulator: AI-powered Concept-driven Visualization Authoring

Wang,

Thompson,

Lee

2023

IEEE Trans. Visual. Comput. Graphics

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With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.

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