add_prop_miss {naniar} | R Documentation |
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
|
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