amd_uni: Amount of Missing Data - Univariate

In quest: Prepare Questionnaire Data for Analysis

Amount of Missing Data - Univariate

Description

amd_uni by default computes the proportion of missing data for variables in a data.frame, with arguments to allow for counts instead of proportions (i.e., prop) or observed data rather than missing data (i.e., ov). It is univariate in that each variable is treated in isolation. amd_uni is a simple wrapper for colNA.

Usage

amd_uni(data, vrb.nm, prop = TRUE, ov = FALSE)

Arguments

data	data.frame of data.
vrb.nm	character vector of the colnames from data specifying the variables.
prop	logical vector of length 1 specifying whether the frequency of missing values should be returned as a proportion (TRUE) or a count (FALSE).
ov	logical vector of length 1 specifying whether the frequency of observed values (TRUE) should be returned rather than the frequency of missing values (FALSE).

Value

numeric vector of length = length(vrb.nm) and names = vrb.nm providing the frequency of missing (or observed if ov = TRUE) values per variable. If prop = TRUE, the values will range from 0 to 1. If prop = FALSE, the values will range from 0 to nrow(data).

See Also

amd_bi amd_multi

Examples

amd_uni(data = airquality, vrb.nm = names(airquality)) # proportion of missing data
amd_uni(data = airquality, vrb.nm = names(airquality),
   ov = TRUE) # proportion of observed data
amd_uni(data = airquality, vrb.nm = names(airquality),
   prop = FALSE) # count of missing data
amd_uni(data = airquality, vrb.nm = names(airquality),
   prop = FALSE, ov = TRUE) # count of observed data

quest documentation built on May 29, 2024, 4:59 a.m.