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

Nicholas Tierney, Dianne Cook

Research output: Contribution to journalArticleResearchpeer-review

21 Citations (Scopus)


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.

Original languageEnglish
Number of pages31
JournalJournal of Statistical Software
Issue number7
Publication statusPublished - 2023


  • data pipeline
  • data science
  • data visualization
  • R
  • statistical computing
  • statistical graphics
  • tidy-verse

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