  Large language models are able to generate code for visualisations in
response to user requests. This is a useful application, and an appealing one
for NLP research because plots of data provide grounding for language. However,
there are relatively few benchmarks, and it is unknown whether those that exist
are representative of what people do in practice. This paper aims to answer
that question through an empirical study comparing benchmark datasets and code
from public repositories. Our findings reveal a substantial gap in datasets,
with evaluations not testing the same distribution of chart types, attributes,
and the number of actions. The only representative dataset requires
modification to become an end-to-end and practical benchmark. This shows that
new, more benchmarks are needed to support the development of systems that
truly address users' visualisation needs. These observations will guide future
data creation, highlighting which features hold genuine significance for users.
