MuTable: Composable and Reusable Table Transformations for In-Situ Data Exploration
TL;DR
MuTable is presented, a prototype that reifies transformations as persistent, composable, and reusable modifiers to support in-situ data exploration and can support coordination between representations, rapid exploration, and greater user agency in constructing visualizations.
Abstract
Tables are central to data work to support precise lookup and full detail, but they can be limiting for overview and pattern-finding tasks. Visualizations are then created to gain richer perceptual support. In practice, moving between tables and charts often requires maintaining parallel representations, introducing context switching, and extra coordination work. Building on prior hybrid table-visualization systems, we present MuTable, a prototype that reifies transformations as persistent, composable, and reusable modifiers to support in-situ data exploration. Users can reshape the table while retaining and adapting intermediate forms as their questions evolve. An expert interview with eight data workers suggests that MuTable can support coordination between representations, rapid exploration, and greater user agency in constructing visualizations, as a low-commitment exploration space.
