A Probabilistic Grammar of Graphics

Honorable Mention
Recommender System UXUncertainty VisualizationData Scientists & AnalystsHCI Researchers

Visualizations depicting probabilities and uncertainty are used everywhere from medical risk communication to machine learning, yet these probabilistic visualizations are difficult to specify, prone to error, and their designs are cumbersome to explore. We propose a Probabilistic Grammar of Graphics (PGoG), an extension to Wilkinson's original framework. Inspired by the success of probabilistic programming languages, PGoG makes probability expressions, such as P(A|B), a first-class citizen in the language. PGoG abstractions also reflect the distinction between probability and frequency framing, a concept from the uncertainty communication literature. It is expressive, encompassing product plots, density plots, icon arrays, and dotplots, among other visualizations. Its coherent syntax ensures correctness (that the proportions of visual elements and their spatial placement reflect the underlying probability distribution) and reduces edit distance between probabilistic visualization specifications, potentially supporting more design exploration. We provide a proof-of-concept implementation of PGoG in R.

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https://hci.top/en/papers/chi/32317/2020

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DOI: https://doi.org/10.1145/3313831.3376466
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Source
CHI
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Year
2020
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Honorable Mention
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Authors
2 authors
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Subtopics
Recommender System UX, Uncertainty Visualization
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Professions
Data Scientists & Analysts, HCI Researchers
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Content Status
Abstract only
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Related Papers
1 related papers