  Despite the large body of research on missing value distributions and
imputation, there is comparatively little literature with a focus on how to
make it easy to handle, explore, and impute missing values in data. This paper
addresses this gap. The new methodology builds upon tidy data principles, with
the goal of integrating missing value handling as a key part of data analysis
workflows. We define a new data structure, and a suite of new operations.
Together, these provide a connected framework for handling, exploring, and
imputing missing values. These methods are available in the R package `naniar`.
