TY - GEN
T1 - How Data Analysts Use a Visualization Grammar in Practice
AU - Pu, Xiaoying
AU - Kay, Matthew
N1 - Funding Information:
We thank Eytan Adar, Mark Guzdial, Cyrus Omar, Priti Shah, Arvind Satyanarayan, and the organizers of VIS 2021 Doctoral Colloquium for general feedback, and Gabi Marcu for advice on qualitative methods. This project is funded by the National Science Foundation, Award Number 1910431.
Publisher Copyright:
© 2023 ACM.
PY - 2023/4/19
Y1 - 2023/4/19
N2 - 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.
AB - 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.
KW - computational notebook
KW - TidyTuesday
KW - Visualization grammar
UR - http://www.scopus.com/inward/record.url?scp=85160013835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85160013835&partnerID=8YFLogxK
U2 - 10.1145/3544548.3580837
DO - 10.1145/3544548.3580837
M3 - Conference contribution
AN - SCOPUS:85160013835
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
T2 - 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Y2 - 23 April 2023 through 28 April 2023
ER -