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

Nicholas Tierney () and Dianne Cook ()

No 14/18, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: Despite the large body of research on missing value distributions and imputation, there is comparatively little literature 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 a goal to integrating missing value handling as an integral part of data analysis workflows. New data structures are defined along with new functions (verbs) to perform common operations. Together these provide a cohesive framework for handling, exploring, and imputing missing values. These methods have been made available in the R package naniar.

Keywords: workflow; statistical computing; data science; data visualization; tidyverse; data pipeline. (search for similar items in EconPapers)
JEL-codes: C10 C14 C22 (search for similar items in EconPapers)
Pages: 41
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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