naniar: add_prop_miss – R documentation

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add_prop_miss

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

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

naniar

Data Structures, Summaries, and Visualisations for Missing Data

v0.6.0

MIT + file LICENSE

Authors

Nicholas Tierney [aut, cre] (<https://orcid.org/0000-0003-1460-8722>), Di Cook [aut] (<https://orcid.org/0000-0002-3813-7155>), Miles McBain [aut] (<https://orcid.org/0000-0003-2865-2548>), Colin Fay [aut] (<https://orcid.org/0000-0001-7343-1846>), Mitchell O'Hara-Wild [ctb], Jim Hester [ctb], Luke Smith [ctb]

Initial release