Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations

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.

Citation:
Tierney N, Cook D. Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations. J Stat Software. 2023;105(7):1-31.

Keywords:
Data pipeline; data science; data visualization; R; statistical computing; statistical graphics; tidy-verse

Abstract:
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.