  In the 1990s, statisticians began thinking in a principled way about how
computation could better support the learning and doing of statistics. Since
then, the pace of software development has accelerated, advancements in
computing and data science have moved the goalposts, and it is time to
reassess. Software continues to be developed to help do and learn statistics,
but there is little critical evaluation of the resulting tools, and no accepted
framework with which to critique them. This paper presents a set of attributes
necessary for a modern statistical computing tool. The framework was designed
to be broadly applicable to both novice and expert users, with a particular
focus on making more supportive statistical computing environments. A modern
statistical computing tool should be accessible, provide easy entry, privilege
data as a first-order object, support exploratory and confirmatory analysis,
allow for flexible plot creation, support randomization, be interactive,
include inherent documentation, support narrative, publishing, and
reproducibility, and be flexible to extensions. Ideally, all these attributes
could be incorporated into one tool, supporting users at all levels, but a more
reasonable goal is for tools designed for novices and professionals to `reach
across the gap,' taking inspiration from each others' strengths.
