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

Expanding tidy data principles to facilitate missing data exploration, visualization and assessment of imputations