Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction, that enables rapid specification of interactive data visualizations.
Abstract:
We present Vega-Lite, a high-level grammar that enables rapid specification of interactive data visualizations. Vega-Lite combines a traditional grammar of graphics, providing visual encoding rules and a composition algebra for layered and multi-view displays, with a novel grammar of interaction. Users specify interactive semantics by composing selections. In Vega-Lite, a selection is an abstraction that defines input event processing, points of interest, and a predicate function for inclusion testing. Selections parameterize visual encodings by serving as input data, defining scale extents, or by driving conditional logic. The Vega-Lite compiler automatically synthesizes requisite data flow and event handling logic, which users can override for further customization. In contrast to existing reactive specifications, Vega-Lite selections decompose an interaction design into concise, enumerable semantic units. We evaluate Vega-Lite through a range of examples, demonstrating succinct specification of both customized interaction methods and common techniques such as panning, zooming, and linked selection.
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Q1. What are the contributions in "Vega-lite: a grammar of interactive graphics" ?
The authors present Vega-Lite, a high-level grammar that enables rapid specification of interactive data visualizations. The Vega-Lite compiler automatically synthesizes requisite data flow and event handling logic, which users can override for further customization.
Q2. What are the future works in "Vega-lite: a grammar of interactive graphics" ?
One promising avenue for future work is to develop models and techniques to analogously recommend suitable interaction methods for given visualizations and underlying data types.
Q3. What is the function that applies the selection against the backing datasets?
The filterWith data transform applies the selection against the backing datasets such that only data values that fall within the selection are displayed.
Q4. What are the primary features of a low-level grammar?
Low-level grammars such as Protovis [3], D3 [4], and Vega [22] are useful for explanatory data visualization or as a basis for customized analysis tools, as their primitives offer fine-grained control.
Q5. What is the function that offsets the spatial properties of the backing points?
by): Offsets the spatial properties (or corresponding data fields) of backing points by an amount determined by the coordinates of the sequenced events.
Q6. What is the process of merging components?
Once the necessary components have been built, the compiler performs a bottom-up traversal of the model tree to merge redundant components.
Q7. How does Vega-Lite support expressive interaction methods?
To support expressive interaction methods, the authors first contribute an algebra to compose singleview Vega-Lite specifications into multi-view displays using layer, concatenate, facet and repeat operators.
Q8. What is the function that augments the selection’s event processing?
nearest(): Computes a Voronoi decomposition, and augments the selection’s event processing, such that the data value or visual elementnearest the selection’s triggering event is selected (approximating a Bubble Cursor [11]).
Q9. What is the syntax for creating a composite view?
Their formal definitions are instantiated in a JSON (JavaScript Object Notation) syntax, as shown in Fig. 2.Given multiple unit specifications, composite views can be created using a set of composition operators.
Q10. How can you adapt techniques to a different design?
Specifying common techniques can be time-consuming, requiring tens of lines of JSON, and it is difficult to know how to adapt techniques in pursuit of alternative designs.