CRAN/E | naniar

naniar

Data Structures, Summaries, and Visualisations for Missing Data

Installation

About

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. The work is fully discussed at Tierney & Cook (2023) doi:10.18637/jss.v105.i07.

Citation naniar citation info
github.com/njtierney/naniar
naniar.njtierney.com/
Bug report File report

Key Metrics

Version 1.1.0
R ≥ 3.1.2
Published 2024-03-05 55 days ago
Needs compilation? no
License MIT
License File
CRAN checks naniar results
Language en-US

Downloads

Yesterday 385 0%
Last 7 days 4.783 -16%
Last 30 days 20.170 +0%
Last 90 days 55.688 +33%
Last 365 days 184.628 -53%

Maintainer

Maintainer

Nicholas Tierney

nicholas.tierney@gmail.com

Authors

Nicholas Tierney

aut / cre

Di Cook

aut

Miles McBain

aut

Colin Fay

aut

Mitchell O'Hara-Wild

ctb

Jim Hester

ctb

Luke Smith

ctb

Andrew Heiss

ctb

Material

README
NEWS
Reference manual
Package source

In Views

MissingData

Vignettes

Exploring Imputed Values
Getting Started with naniar
Gallery of Missing Data Visualisations
Replacing values with NA
Special Missing Values

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

naniar archive

Depends

R ≥ 3.1.2

Imports

dplyr
ggplot2
purrr
tidyr
tibble ≥ 2.0.0
norm
magrittr
stats
visdat
rlang ≥ 1.1.0
forcats
viridis
glue
UpSetR
cli
vctrs
lifecycle

Suggests

knitr
rmarkdown
testthat ≥ 3.0.0
rpart
rpart.plot
covr
gridExtra
wakefield
vdiffr
here
simputation
imputeTS
Hmisc
spelling

Reverse Imports

dbGaPCheckup
smdi
suddengains
tLOH

Reverse Suggests

causalCmprsk