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.


Reference manual

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0.1.0 by Nicholas Tierney, a month ago

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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