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naniar

naniar provides principled, tidy ways to summarise, visualise, and manipulate missing data with minimal deviations from the workflows in ggplot2 and tidy data. It does this by providing:

  • Shadow matrices, a tidy data structure for missing data:
    • bind_shadow() and nabular()
  • Shorthand summaries for missing data:
    • n_miss() and n_complete()
    • pct_miss()and pct_complete()
  • Numerical summaries of missing data in variables and cases:
    • miss_var_summary() and miss_var_table()
    • miss_case_summary(), miss_case_table()
  • Statistical tests of missingness:
  • Visualisation for missing data:
    • geom_miss_point()
    • gg_miss_var()
    • gg_miss_case()
    • gg_miss_fct()

For more details on the workflow and theory underpinning naniar, read the vignette Getting started with naniar.

For a short primer on the data visualisation available in naniar, read the vignette Gallery of Missing Data Visualisations.

For full details of the package, including

Installation

You can install naniar from CRAN:

install.packages("naniar")

Or you can install the development version on github using remotes:

# install.packages("remotes")
remotes::install_github("njtierney/naniar")

A short overview of naniar

Visualising missing data might sound a little strange - how do you visualise something that is not there? One approach to visualising missing data comes from ggobi and manet, which replaces NA values with values 10% lower than the minimum value in that variable. This visualisation is provided with the geom_miss_point() ggplot2 geom, which we illustrate by exploring the relationship between Ozone and Solar radiation from the airquality dataset.


library(ggplot2)

ggplot(data = airquality,
       aes(x = Ozone,
           y = Solar.R)) +
  geom_point()
#> Warning: Removed 42 rows containing missing values (`geom_point()`).

ggplot2 does not handle these missing values, and we get a warning message about the missing values.

We can instead use geom_miss_point() to display the missing data


library(naniar)

ggplot(data = airquality,
       aes(x = Ozone,
           y = Solar.R)) +
  geom_miss_point()

geom_miss_point() has shifted the missing values to now be 10% below the minimum value. The missing values are a different colour so that missingness becomes pre-attentive. As it is a ggplot2 geom, it supports features like faceting and other ggplot features.


p1 <-
ggplot(data = airquality,
       aes(x = Ozone,
           y = Solar.R)) + 
  geom_miss_point() + 
  facet_wrap(~Month, ncol = 2) + 
  theme(legend.position = "bottom")

p1

Data Structures

naniar provides a data structure for working with missing data, the shadow matrix (Swayne and Buja, 1998). The shadow matrix is the same dimension as the data, and consists of binary indicators of missingness of data values, where missing is represented as “NA”, and not missing is represented as “!NA”, and variable names are kep the same, with the added suffix “_NA” to the variables.


head(airquality)
#>   Ozone Solar.R Wind Temp Month Day
#> 1    41     190  7.4   67     5   1
#> 2    36     118  8.0   72     5   2
#> 3    12     149 12.6   74     5   3
#> 4    18     313 11.5   62     5   4
#> 5    NA      NA 14.3   56     5   5
#> 6    28      NA 14.9   66     5   6

as_shadow(airquality)
#> # A tibble: 153 × 6
#>    Ozone_NA Solar.R_NA Wind_NA Temp_NA Month_NA Day_NA
#>    <fct>    <fct>      <fct>   <fct>   <fct>    <fct> 
#>  1 !NA      !NA        !NA     !NA     !NA      !NA   
#>  2 !NA      !NA        !NA     !NA     !NA      !NA   
#>  3 !NA      !NA        !NA     !NA     !NA      !NA   
#>  4 !NA      !NA        !NA     !NA     !NA      !NA   
#>  5 NA       NA         !NA     !NA     !NA      !NA   
#>  6 !NA      NA         !NA     !NA     !NA      !NA   
#>  7 !NA      !NA        !NA     !NA     !NA      !NA   
#>  8 !NA      !NA        !NA     !NA     !NA      !NA   
#>  9 !NA      !NA        !NA     !NA     !NA      !NA   
#> 10 NA       !NA        !NA     !NA     !NA      !NA   
#> # … with 143 more rows

Binding the shadow data to the data you help keep better track of the missing values. This format is called “nabular”, a portmanteau of NA and tabular. You can bind the shadow to the data using bind_shadow or nabular:

bind_shadow(airquality)
#> # A tibble: 153 × 12
#>    Ozone Solar.R  Wind  Temp Month   Day Ozone…¹ Solar…² Wind_NA Temp_NA Month…³
#>    <int>   <int> <dbl> <int> <int> <int> <fct>   <fct>   <fct>   <fct>   <fct>  
#>  1    41     190   7.4    67     5     1 !NA     !NA     !NA     !NA     !NA    
#>  2    36     118   8      72     5     2 !NA     !NA     !NA     !NA     !NA    
#>  3    12     149  12.6    74     5     3 !NA     !NA     !NA     !NA     !NA    
#>  4    18     313  11.5    62     5     4 !NA     !NA     !NA     !NA     !NA    
#>  5    NA      NA  14.3    56     5     5 NA      NA      !NA     !NA     !NA    
#>  6    28      NA  14.9    66     5     6 !NA     NA      !NA     !NA     !NA    
#>  7    23     299   8.6    65     5     7 !NA     !NA     !NA     !NA     !NA    
#>  8    19      99  13.8    59     5     8 !NA     !NA     !NA     !NA     !NA    
#>  9     8      19  20.1    61     5     9 !NA     !NA     !NA     !NA     !NA    
#> 10    NA     194   8.6    69     5    10 NA      !NA     !NA     !NA     !NA    
#> # … with 143 more rows, 1 more variable: Day_NA <fct>, and abbreviated variable
#> #   names ¹​Ozone_NA, ²​Solar.R_NA, ³​Month_NA
nabular(airquality)
#> # A tibble: 153 × 12
#>    Ozone Solar.R  Wind  Temp Month   Day Ozone…¹ Solar…² Wind_NA Temp_NA Month…³
#>    <int>   <int> <dbl> <int> <int> <int> <fct>   <fct>   <fct>   <fct>   <fct>  
#>  1    41     190   7.4    67     5     1 !NA     !NA     !NA     !NA     !NA    
#>  2    36     118   8      72     5     2 !NA     !NA     !NA     !NA     !NA    
#>  3    12     149  12.6    74     5     3 !NA     !NA     !NA     !NA     !NA    
#>  4    18     313  11.5    62     5     4 !NA     !NA     !NA     !NA     !NA    
#>  5    NA      NA  14.3    56     5     5 NA      NA      !NA     !NA     !NA    
#>  6    28      NA  14.9    66     5     6 !NA     NA      !NA     !NA     !NA    
#>  7    23     299   8.6    65     5     7 !NA     !NA     !NA     !NA     !NA    
#>  8    19      99  13.8    59     5     8 !NA     !NA     !NA     !NA     !NA    
#>  9     8      19  20.1    61     5     9 !NA     !NA     !NA     !NA     !NA    
#> 10    NA     194   8.6    69     5    10 NA      !NA     !NA     !NA     !NA    
#> # … with 143 more rows, 1 more variable: Day_NA <fct>, and abbreviated variable
#> #   names ¹​Ozone_NA, ²​Solar.R_NA, ³​Month_NA

Using the nabular format helps you manage where missing values are in your dataset and make it easy to do visualisations where you split by missingness:


airquality %>%
  bind_shadow() %>%
  ggplot(aes(x = Temp,
             fill = Ozone_NA)) + 
  geom_density(alpha = 0.5)

And even visualise imputations


airquality %>%
  bind_shadow() %>%
  as.data.frame() %>% 
   simputation::impute_lm(Ozone ~ Temp + Solar.R) %>%
  ggplot(aes(x = Solar.R,
             y = Ozone,
             colour = Ozone_NA)) + 
  geom_point()
#> Warning: Removed 7 rows containing missing values (`geom_point()`).

Or perform an upset plot - to plot of the combinations of missingness across cases, using the gg_miss_upset function


gg_miss_upset(airquality)

naniar does this while following consistent principles that are easy to read, thanks to the tools of the tidyverse.

naniar also provides handy visualations for each variable:


gg_miss_var(airquality)

Or the number of missings in a given variable at a repeating span

gg_miss_span(pedestrian,
             var = hourly_counts,
             span_every = 1500)

You can read about all of the visualisations in naniar in the vignette Gallery of missing data visualisations using naniar.

naniar also provides handy helpers for calculating the number, proportion, and percentage of missing and complete observations:

n_miss(airquality)
#> [1] 44
n_complete(airquality)
#> [1] 874
prop_miss(airquality)
#> [1] 0.04793028
prop_complete(airquality)
#> [1] 0.9520697
pct_miss(airquality)
#> [1] 4.793028
pct_complete(airquality)
#> [1] 95.20697

Numerical summaries for missing data

naniar provides numerical summaries of missing data, that follow a consistent rule that uses a syntax begining with miss_. Summaries focussing on variables or a single selected variable, start with miss_var_, and summaries for cases (the initial collected row order of the data), they start with miss_case_. All of these functions that return dataframes also work with dplyr’s group_by().

For example, we can look at the number and percent of missings in each case and variable with miss_var_summary(), and miss_case_summary(), which both return output ordered by the number of missing values.


miss_var_summary(airquality)
#> # A tibble: 6 × 3
#>   variable n_miss pct_miss
#>   <chr>     <int>    <dbl>
#> 1 Ozone        37    24.2 
#> 2 Solar.R       7     4.58
#> 3 Wind          0     0   
#> 4 Temp          0     0   
#> 5 Month         0     0   
#> 6 Day           0     0
miss_case_summary(airquality)
#> # A tibble: 153 × 3
#>     case n_miss pct_miss
#>    <int>  <int>    <dbl>
#>  1     5      2     33.3
#>  2    27      2     33.3
#>  3     6      1     16.7
#>  4    10      1     16.7
#>  5    11      1     16.7
#>  6    25      1     16.7
#>  7    26      1     16.7
#>  8    32      1     16.7
#>  9    33      1     16.7
#> 10    34      1     16.7
#> # … with 143 more rows

You could also group_by() to work out the number of missings in each variable across the levels within it.


library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
airquality %>%
  group_by(Month) %>%
  miss_var_summary()
#> # A tibble: 25 × 4
#> # Groups:   Month [5]
#>    Month variable n_miss pct_miss
#>    <int> <chr>     <int>    <dbl>
#>  1     5 Ozone         5     16.1
#>  2     5 Solar.R       4     12.9
#>  3     5 Wind          0      0  
#>  4     5 Temp          0      0  
#>  5     5 Day           0      0  
#>  6     6 Ozone        21     70  
#>  7     6 Solar.R       0      0  
#>  8     6 Wind          0      0  
#>  9     6 Temp          0      0  
#> 10     6 Day           0      0  
#> # … with 15 more rows

You can read more about all of these functions in the vignette “Getting Started with naniar”.

Statistical tests of missingness

naniar provides mcar_test() for Little’s (1988) statistical test for missing completely at random (MCAR) data. The null hypothesis in this test is that the data is MCAR, and the test statistic is a chi-squared value. Given the high statistic value and low p-value, we can conclude that the airquality data is not missing completely at random:

mcar_test(airquality)
#> # A tibble: 1 × 4
#>   statistic    df p.value missing.patterns
#>       <dbl> <dbl>   <dbl>            <int>
#> 1      35.1    14 0.00142                4

Contributions

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

Future Work

  • Extend the geom_miss_* family to include categorical variables, Bivariate plots: scatterplots, density overlays
  • SQL translation for databases
  • Big Data tools (sparklyr, sparklingwater)
  • Work well with other imputation engines / processes
  • Provide tools for assessing goodness of fit for classical approaches of MCAR, MAR, and MNAR (graphical inference from nullabor package)

Acknowledgements

Firstly, thanks to Di Cook for giving the initial inspiration for the package and laying down the rich theory and literature that the work in naniar is built upon. Naming credit (once again!) goes to Miles McBain. Among various other things, Miles also worked out how to overload the missing data and make it work as a geom. Thanks also to Colin Fay for helping me understand tidy evaluation and for features such as replace_to_na, miss_*_cumsum, and more.

A note on the name

naniar was previously named ggmissing and initially provided a ggplot geom and some other visualisations. ggmissing was changed to naniar to reflect the fact that this package is going to be bigger in scope, and is not just related to ggplot2. Specifically, the package is designed to provide a suite of tools for generating visualisations of missing values and imputations, manipulate, and summarise missing data.

…But why naniar?

Well, I think it is useful to think of missing values in data being like this other dimension, perhaps like C.S. Lewis’s Narnia - a different world, hidden away. You go inside, and sometimes it seems like you’ve spent no time in there but time has passed very quickly, or the opposite. Also, NAniar = na in r, and if you so desire, naniar may sound like “noneoya” in an nz/aussie accent. Full credit to @MilesMcbain for the name, and @Hadley for the rearranged spelling.

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Install

install.packages('naniar')

Monthly Downloads

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Version

1.0.0

License

MIT + file LICENSE

Issues

63

Pull Requests

1

Stars

652

Forks

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Maintainer

Nicholas Tierney

Last Published

February 2nd, 2023

Functions in naniar (1.0.0)

as_shadow

Create shadows
cast_shadow

Add a shadow column to a dataset
gg_miss_span

Plot the number of missings in a given repeating span
gg_miss_upset

Plot the pattern of missingness using an upset plot.
gg_miss_var_cumsum

Plot of cumulative sum of missing value for each variable
gg_miss_var

Plot the number of missings for each variable
label_miss_1d

Label a missing from one column
label_miss_2d

label_miss_2d
draw_key

Key drawing functions
impute_below

Impute data with values shifted 10 percent below range.
gather_shadow

Long form representation of a shadow matrix
gg_miss_which

Plot which variables contain a missing value
miss_prop_summary

Proportions of missings in data, variables, and cases.
miss_var_cumsum

Cumulative sum of the number of missings in each variable
miss_scan_count

Search and present different kinds of missing values
miss_summary

Collate summary measures from naniar into one tibble
impute_below_all

Impute data with values shifted 10 percent below range.
miss_case_cumsum

Summarise the missingness in each case
impute_below_at

Scoped variants of impute_below
miss-pct-prop-defunct

Proportion of variables containing missings or complete values
label_missings

Is there a missing value in the row of a dataframe?
gg_miss_case_cumsum

Plot of cumulative sum of missing for cases
gg_miss_fct

Plot the number of missings for each variable, broken down by a factor
mcar_test

Little's missing completely at random (MCAR) test
impute_median

Impute the median value into a vector with missing values
is_shade

Detect if this is a shade
impute_mean

Impute the mean value into a vector with missing values
miss_case_summary

Summarise the missingness in each case
geom_miss_point

geom_miss_point
gg_miss_case

Plot the number of missings per case (row)
n_complete_row

Return a vector of the number of complete values in each row
n-var-case-complete

The number of variables with complete values
miss_var_which

Which variables contain missing values?
miss_var_summary

Summarise the missingness in each variable
miss_var_table

Tabulate the missings in the variables
impute_below_if

Scoped variants of impute_below
miss_case_table

Tabulate missings in cases.
GeomMissPoint

naniar-ggproto
n_complete

Return the number of complete values
n_miss

Return the number of missing values
pct-miss-complete-var

Percentage of variables containing missings or complete values
pct-miss-complete-case

Percentage of cases that contain a missing or complete values.
pct_miss

Return the percent of missing values
pedestrian

Pedestrian count information around Melbourne for 2016
naniar

naniar
n-var-case-miss

The number of variables or cases with missing values
pct_complete

Return the percent of complete values
prop-miss-complete-var

Proportion of variables containing missings or complete values
miss_var_run

Find the number of missing and complete values in a single run
oceanbuoys

West Pacific Tropical Atmosphere Ocean Data, 1993 & 1997.
miss_var_span

Summarise the number of missings for a given repeating span on a variable
prop_complete_row

Return a vector of the proportion of missing values in each row
prop_miss

Return the proportion of missing values
scoped-impute_median

Scoped variants of impute_median
prop_complete

Return the proportion of complete values
replace_with_na_at

Replace specified variables with NA where a certain condition is met
replace_with_na_if

Replace values with NA based on some condition, for variables that meet some predicate
shadow_long

Reshape shadow data into a long format
shade

Create new levels of missing
where

Split a call into two components with a useful verb name
plotly_helpers

Plotly helpers (Convert a geom to a "basic" geom.)
nabular

Convert data into nabular form by binding shade to it
where_na

Which rows and cols contain missings?
replace_to_na

Replace values with missings
prop-miss-complete-case

Proportion of cases that contain a missing or complete values.
unbinders

Unbind (remove) shadow from data, and vice versa
prop_miss_row

Return a vector of the proportion of missing values in each row
replace_with_na

Replace values with missings
stat_miss_point

stat_miss_point
n_miss_row

Return a vector of the number of missing values in each row
replace_with_na_all

Replace all values with NA where a certain condition is met
recode_shadow

Add special missing values to the shadow matrix
riskfactors

The Behavioral Risk Factor Surveillance System (BRFSS) Survey Data, 2009.
set-prop-n-miss

Set a proportion or number of missing values
reexports

Objects exported from other packages
which_are_shade

Which variables are shades?
scoped-impute_mean

Scoped variants of impute_mean
which_na

Which elements contain missings?
shadow_shift.numeric

Shift (impute) numeric values for graphical exploration
shadow_shift

Shift missing values to facilitate missing data exploration/visualisation
add_label_missings

Add a column describing if there are any missings in the dataset
add_span_counter

Add a counter variable for a span of dataframe
add_miss_cluster

Add a column that tells us which "missingness cluster" a row belongs to
all-is-miss-complete

Identify if all values are missing or complete
add_prop_miss

Add column containing proportion of missing data values
add_shadow_shift

Add a shadow shifted column to a dataset
add_any_miss

Add a column describing presence of any missing values
add_shadow

Add a shadow column to dataframe
add_label_shadow

Add a column describing whether there is a shadow
add_n_miss

Add column containing number of missing data values
any_row_miss

Helper function to determine whether there are any missings
bind_shadow

Bind a shadow dataframe to original data
as_shadow_upset

Convert data into shadow format for doing an upset plot
any-na

Identify if there are any missing or complete values
common_na_numbers

Common number values for NA
cast_shadow_shift

Add a shadow and a shadow_shift column to a dataset
common_na_strings

Common string values for NA
cast_shadow_shift_label

Add a shadow column and a shadow shifted column to a dataset