JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1221 10.6339/26-JDS1221 Statistical Data Science Clusters, Trends, and Choices: Feature Selection in Interactive Statistical Graphics LeDylan1 RogersRachel2 RobinsonEmily1 California Polytechnic State University - San Luis Obispo, United States University of Technology Sydney, Austrailia Corresponding author. Email: erobin17@calpoly.edu. 2026263202600121 Supplementary Material

Shiny App Code: The code used to replicate the study Shiny app can be accessed at https://github.com/earobinson95/interactive-lineup-study-applet.

accuracy_clean.csv: De-identified participant accuracy data collected in the study and used for accuracy analyses.

toggles_clean.csv: De-identified participant toggle moves data collected in the study and used for understanding toggle workflow analyses.

analysis.qmd The code used to replicate the analyses presented in this paper.

15820252122026 2026 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2026 Open access article under the CC BY license.

This study investigates how user ability to manipulate plot features affects graphical perception, by extending a previous graphical study (Vanderplas and Hofmann, 2017) with an interactive framework. Similar to the original study, statistical lineups included two target patterns (a linear trend and a clustering pattern), as well as eighteen null plots generated from three different mixture proportions of the combined cluster and trend models. Participants were asked to select two plots that they perceived as ‘most different’, and were able to interact with the graphics by toggling aesthetic features such as cluster coloring, cluster ellipses, linear trendlines, and regression error bands.

We found that toggle workflow varied across participants, revealing a divide between “maximalists,” who enabled all features, and “minimalists,” who used few or none, with most toggling occurring before the first selection. Starting features aesthetics did not have a significant effect on target choice. A generalized linear mixed model identified mixture proportion as the strongest predictor of target selection, with additional interactions involving the enabled ending features. These findings contribute to understanding how users engage with interactive graphical tools and how such tools support data interpretation in exploratory data analysis.

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