Other packages > Find by keyword >

naniar  

Data Structures, Summaries, and Visualisations for Missing Data
View on CRAN: Click here


Download and install naniar package within the R console
Install from CRAN:
install.packages("naniar")

Install from Github:
library("remotes")
install_github("cran/naniar")

Install by package version:
library("remotes")
install_version("naniar", "1.1.0")



Attach the package and use:
library("naniar")
Maintained by
Nicholas Tierney
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2017-08-09
Latest Update: 2024-03-05
Description:
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) .
How to cite:
Nicholas Tierney (2017). naniar: Data Structures, Summaries, and Visualisations for Missing Data. R package version 1.1.0, https://cran.r-project.org/web/packages/naniar. Accessed 05 Aug. 2026.
Previous versions and publish date:
(2026-07-09 06:24), 0.1.0 (2017-08-09 06:06), 0.2.0 (2018-02-09 07:01), 0.3.0 (2018-06-07 18:18), 0.3.1 (2018-06-08 10:03), 0.4.0.0 (2018-09-10 14:40), 0.4.1 (2018-11-20 09:20), 0.4.2 (2019-02-15 15:30), 0.5.0 (2020-02-28 08:20), 0.5.1 (2020-05-01 00:00), 0.5.2 (2020-06-29 10:00), 0.6.0 (2020-09-02 11:50), 0.6.1 (2021-05-14 12:20), 1.0.0 (2023-02-02 10:50)
Other packages that cited naniar R package
View naniar citation profile
Other R packages that naniar depends, imports, suggests or enhances
Complete documentation for naniar
Functions, R codes and Examples using the naniar R package
Some associated functions: add_any_miss . add_label_missings . add_label_shadow . add_miss_cluster . add_n_miss . add_prop_miss . add_shadow . add_shadow_shift . add_span_counter . all-is-miss-complete . any-na . any_row_miss . as_shadow . as_shadow_upset . bind_shadow . cast_shadow . cast_shadow_shift . cast_shadow_shift_label . common_na_numbers . common_na_strings . draw_key . gather_shadow . geom_miss_point . gg_miss_case . gg_miss_case_cumsum . gg_miss_fct . gg_miss_span . gg_miss_upset . gg_miss_var . gg_miss_var_cumsum . gg_miss_which . impute_below . impute_below_all . impute_below_at . impute_below_if . impute_mean . impute_median . is_shade . label_miss_1d . label_miss_2d . label_missings . mcar_test . miss-pct-prop-defunct . miss_case_cumsum . miss_case_summary . miss_case_table . miss_prop_summary . miss_scan_count . miss_summary . miss_var_cumsum . miss_var_run . miss_var_span . miss_var_summary . miss_var_table . miss_var_which . n-var-case-complete . n-var-case-miss . n_complete . n_complete_row . n_miss . n_miss_row . nabular . naniar-ggproto . naniar . oceanbuoys . pct-miss-complete-case . pct-miss-complete-var . pct_complete . pct_miss . pedestrian . plotly_helpers . prop-miss-complete-case . prop-miss-complete-var . prop_complete . prop_complete_row . prop_miss . prop_miss_row . recode_shadow . reexports . replace_to_na . replace_with_na . replace_with_na_all . replace_with_na_at . replace_with_na_if . riskfactors . scoped-impute_mean . scoped-impute_median . set-prop-n-miss . shade . shadow_long . shadow_shift.numeric . shadow_shift . stat_miss_point . unbinders . where . where_na . which_are_shade . which_na . 
Some associated R codes: add-cols.R . add-n-prop-miss.R . cast-shadows.R . data-common-na-numbers.R . data-common-na-strings.R . data-oceanbuoys.R . data-pedestrian.R . data-riskfactors.R . geom-miss-point.R . geom2plotly.R . gg-miss-case-cumsum.R . gg-miss-case.R . gg-miss-fct.R . gg-miss-span.R . gg-miss-upset.R . gg-miss-var-cumsum.R . gg-miss-var.R . gg-miss-which.R . helpers.R . impute-median.R . impute_below.R . impute_mean.R . label-miss.R . legend-draw.R . mcar-test.R . miss-complete-x-pct-prop.R . miss-prop-pct-summary.R . miss-scan-count.R . miss-x-cumsum.R . miss-x-run.R . miss-x-span.R . miss-x-summary.R . miss-x-table.R . n-prop-miss-complete-rows.R . n-prop-miss-complete.R . n-var-miss.R . nabular.R . naniar-ggproto.R . naniar-package.R . prop-pct-var-case-miss-complete.R . replace-to-na.R . replace-with-na.R . scoped-replace-with-na.R . set-n-prop-miss.R . shade.R . shadow-recode.R . shadow-shifters.R . shadows.R . stat-miss-point.R . utils.R . where-na.R .  Full naniar package functions and examples
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

kernelPSI  
Post-Selection Inference for Nonlinear Variable Selection
Different post-selection inference strategies for kernelselection as described in kernelPSI a Post-S ...
Download / Learn more Package Citations See dependency  
noisyr  
Noise Quantification in High Throughput Sequencing Output
Quantifies and removes technical noise from high-throughput sequencing data. Two approaches are use ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  
dhReg  
Dynamic Harmonic Regression
Building and forecasting time series data with multiple seasonality using Dynamic Harmonic Regressio ...
Download / Learn more Package Citations See dependency  
dcov  
A Fast Implementation of Distance Covariance
Efficient methods for computing distance covariance and relevant statistics. See Sz ...
Download / Learn more Package Citations See dependency  

28,083

R Packages

239,283

Dependencies

74,457

Author Associations

28,084

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA