gg_miss_upset: Plot the pattern of missingness using an upset plot.

In njtierney/naniar: Data Structures, Summaries, and Visualisations for Missing Data

Plot the pattern of missingness using an upset plot.

Description

Upset plots are a way of visualising common sets, gg_miss_upset shows the number of missing values for each of the sets of data. The default option of gg_miss_upset is taken from UpSetR::upset - which is to use up to 5 sets and up to 40 interactions. We also set the ordering to by the frequency of the intersections. Setting nsets = 5 means to look at 5 variables and their combinations. The number of combinations or rather intersections is controlled by nintersects. If there are 40 intersections, there will be 40 combinations of variables explored. The number of sets and intersections can be changed by passing arguments nsets = 10 to look at 10 sets of variables, and nintersects = 50 to look at 50 intersections.

Usage

gg_miss_upset(data, order.by = "freq", ...)

Arguments

data	data.frame
order.by	(from UpSetR::upset) How the intersections in the matrix should be ordered by. Options include frequency (entered as "freq"), degree, or both in any order. See ?UpSetR::upset for more options
...	arguments to pass to upset plot - see ?UpSetR::upset

Value

a ggplot visualisation of missing data

Examples

## Not run: 
gg_miss_upset(airquality)
gg_miss_upset(riskfactors)
gg_miss_upset(riskfactors, nsets = 10)
gg_miss_upset(riskfactors, nsets = 10, nintersects = 10)

## End(Not run)

njtierney/naniar documentation built on March 19, 2024, 9:48 p.m.