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1. DIEL: Interactive Visualization Beyond the Here and Now

   Wu, Yifan; Chang, Remco; Hellerstein, Joseph M; Satyanarayan, Arvind; Wu, Eugene. (April 2022, IEEE transactions on visualization and computer graphics)

   null (Ed.)

   Interactive visualization design and research have primarily focused on local data and synchronous events. However, for more complex use cases—e.g., remote database access and streaming data sources—developers must grapple with distributed data and asynchronous events. Currently, constructing these use cases is difficult and time-consuming; developers are forced to operationally program low-level details like asynchronous database querying and reactive event handling. This approach is in stark contrast to modern methods for browser-based interactive visualization, which feature high-level declarative specifications. In response, we present DIEL, a declarative framework that supports asynchronous events over distributed data. As in many declarative languages, DIEL developers specify only what data they want, rather than procedural steps for how to assemble it. Uniquely, DIEL models asynchronous events (e.g., user interactions, server responses) as streams of data that are captured in event logs. To specify the state of a visualization at any time, developers write declarative queries over the data and event logs; DIEL compiles and optimizes a corresponding dataflow graph, and automatically generates necessary low-level distributed systems details. We demonstrate DIEL's performance and expressivity through example interactive visualizations that make diverse use of remote data and asynchronous events. We further evaluate DIEL's usability using the Cognitive Dimensions of Notations framework, revealing wins such as ease of change, and compromises such as premature commitments. 

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2. How Data Analysts Use a Visualization Grammar in Practice

   https://doi.org/10.1145/3544548.3580837  

   Pu, Xiaoying; Kay, Matthew (April 2023, CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems)

   Visualization grammars, often based on the Grammar of Graphics (GoG), have much potential for augmenting data analysis in a programming environment. However, we do not know how analysts conceptualize grammar abstractions, or how a visualization grammar works with data analysis in practice. Therefore, we qualitatively analyzed how experienced analysts (N = 6) from TidyTuesday, a social data project, wrangled and visualized data using GoG-based ggplot2 without given tasks in R Markdown. Though participants’ analysis and customization needs could mismatch with GoG component design, their analysis processes aligned with the goal of GoG to expedite visualization iteration. We also found a feedback loop and tight coupling between visualization and data transformation code, explaining both participants’ productivity and their errors. From these results, we discuss how future visualization grammars can become more practical for analysts and how visualization grammar and analysis tools can better integrate within a programming (i.e., computational notebook) environment. 

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3. Data Theater: A Live Programming Environment for Prototyping Data-Driven Explorable Explanations

   Lau, Sam; Guo, Philip J. (November 2020, Workshop on Live Programming (LIVE))

   null (Ed.)

   Explorable explanations (a.k.a. 'explorables') enable readers to learn concepts in domains such as math, physics, and the social sciences by interacting with live visualizations. Despite their popularity, there is currently a high barrier to creating explorables since one must be adept at UI and visualization programming. To learn about these challenges, we interviewed 6 educators who were interested in explorables but lacked the skills to create them from scratch. These interviews gave us design insights to lower some of these implementation barriers. We used these insights to create a live programming system called Data Theater that enables programmers to prototype explorables by writing their simulation logic in Python and mapping Python values to visualization elements using a declarative JSON grammar. To demonstrate the capabilities of Data Theater, we used it to recreate two of Bret Victor's original physics simulation explorables and found that our approach can lower the barriers to prototyping explorables. 

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4. Computer-Assisted Heuristic Evaluation of Data Visualization

   https://doi.org/10.1007/978-3-030-90436-4_33  

   Zhu, Ying; Gumieniak, Julia A (January 2021, Proceedings of the International Symposium on Visual Computing)

   Bebis, George (Ed.)

   Heuristic evaluation has been an important part of data visualization. Many heuristic rules and guidelines for evaluating data visualization have been proposed and reviewed. However, applying heuristic evaluation in practice is not trivial. First, the heuristic rules are discussed in different publications across different disciplines. There is no central repository of heuristic rules for data visualization. There are no consistent guidelines on how to apply them. Second, it is difficult to find multiple experts who are knowledgeable about the heuristic rules, their pitfalls, and counterpoints. To address this issue, we present a computer-assisted heuristic evaluation method for data visualization. Based on this method, we developed a Python-based tool for evaluating plots created by the visualization tool Plotly. Recent advances in declarative data visualization libraries have made it feasible to create such a tool. By providing advice, critiques, and recommendations, this tool serves as a knowledgeable virtual assistant to help data visualization developers evaluate their visualizations as they code. 

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5. A Probabilistic Grammar of Graphics

   https://doi.org/10.1145/3313831.3376466  

   Pu, Xiaoying; Kay, Matthew (April 2020, CHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems)

   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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