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

2018/09/06 by Nicholas Tierney, Tierney, Nicholas J, Dianne H Cook +1 · 3 citations
Mathematics · #Computation (stat.CO) #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.1809.02264

openalex publication_date 2018/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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

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