naniar: Data Structures, Summaries, and Visualisations for Missing Data

Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data.

Depends: R (≥ 3.1.2)
Imports: dplyr, ggplot2, purrr, tidyr, tibble, magrittr, stats, visdat, rlang, forcats, viridis, glue, UpSetR
Suggests: knitr, rmarkdown, testthat, rpart, rpart.plot, covr, gridExtra, wakefield, vdiffr, here, simputation, imputeTS, gdtools, Hmisc
Published: 2018-09-10
Author: Nicholas Tierney ORCID iD [aut, cre], Di Cook ORCID iD [aut], Miles McBain ORCID iD [aut], Colin Fay ORCID iD [aut], Mitchell O'Hara-Wild [ctb], Jim Hester [ctb], Luke Smith [ctb]
Maintainer: Nicholas Tierney <nicholas.tierney at>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: naniar results


Reference manual: naniar.pdf
Vignettes: Exploring Imputed Values
Getting Started with naniar
Gallery of Missing Data Visualisations
Replacing values with NA
Special Missing Values
Package source: naniar_0.4.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: naniar_0.4.0.0.tgz, r-oldrel: naniar_0.4.0.0.tgz
Old sources: naniar archive


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