Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations
Dublin Core
Title
Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations
Subject
statistical computing, statistical graphics, data science, data visualization, tidyverse, data pipeline, R.
Description
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.
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.
Creator
Nicholas Tierney
Source
https://www.jstatsoft.org/article/view/v105i07
Publisher
Monash University,
Telethon Kids Institute
Telethon Kids Institute
Date
January 2023
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Nicholas Tierney, “Expanding Tidy Data Principles to Facilitate Missing Data Exploration, Visualization and Assessment of Imputations,” Repository Horizon University Indonesia, accessed April 5, 2025, https://repository.horizon.ac.id/items/show/8288.