add_prop_miss: Add column containing proportion of missing data values

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

Add column containing proportion of missing data values

Description

It can be useful when doing data analysis to add the proportion of missing data values into your dataframe. add_prop_miss adds a column named "prop_miss", which contains the proportion of missing values in that row. You can specify the variables that you would like to show the missingness for.

Usage

add_prop_miss(data, ..., label = "prop_miss")

Arguments

data	a dataframe
...	Variable names to use instead of the whole dataset. By default this looks at the whole dataset. Otherwise, this is one or more unquoted expressions separated by commas. These also respect the dplyr verbs starts_with, contains, ends_with, etc. By default will add "_all" to the label if left blank, otherwise will add "_vars" to distinguish that it has not been used on all of the variables.
label	character string of what you need to name variable

Value

a dataframe

See Also

bind_shadow() add_any_miss() add_label_missings() add_label_shadow() add_miss_cluster() add_prop_miss() add_shadow_shift() cast_shadow()

Examples

airquality %>% add_prop_miss()
airquality %>% add_prop_miss(Solar.R, Ozone)
airquality %>% add_prop_miss(Solar.R, Ozone, label = "testing")

# this can be applied to model the proportion of missing data
# as in Tierney et al \doi{10.1136/bmjopen-2014-007450}
# see "Modelling missingness" in vignette "Getting Started with naniar"
# for details

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