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.


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install.packages("naniar")

0.1.0 by Nicholas Tierney, 3 months ago


https://github.com/njtierney/naniar


Report a bug at https://github.com/njtierney/naniar/issues


Browse source code at https://github.com/cran/naniar


Authors: Nicholas Tierney [aut, cre], Di Cook [aut], Miles McBain [aut], Colin Fay [aut]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports dplyr, ggplot2, purrr, tidyr, tibble, magrittr, stats, visdat, purrrlyr, rlang, forcats, viridis

Suggests knitr, rmarkdown, testthat, rpart, rpart.plot, covr, gridExtra, wakefield, vdiffr, here, simputation, imputeTS


See at CRAN