  An emerging generation of visualization authoring systems support expressive
information visualization without textual programming. As they vary in their
visualization models, system architectures, and user interfaces, it is
challenging to directly compare these systems using traditional evaluative
methods. Recognizing the value of contextualizing our decisions in the broader
design space, we present critical reflections on three systems we developed --
Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that
would have been daunting within the constituent papers of these three systems.
We compare and contrast their (previously unmentioned) limitations and
trade-offs between expressivity and learnability. We also reflect on common
assumptions that we made during the development of our systems, thereby
informing future research directions in visualization authoring systems.
