sjmisc (version 2.0.0)

set_na: Replace specific values in vector with NA

Description

This function replaces specific values of a variable, data frame or list of variables with missings (NA).

Usage

set_na(x, value)
set_na(x) <- value

Arguments

x

Variable (vector), data.frame or list of variables where new missing values should be defined. If x is a data.frame, each column is assumed to be a new variable, where missings should be defined.

value

Numeric vector with values that should be replaced with a tagged_na. Thus, for each variable in x, value are replaced by tagged NA values.

Value

x, where each value of value is replaced by an a tagged NA.

Details

set_na converts all values defined in value with a related tagged NA (see tagged_na). Tagged NAs work exactly like regular R missing values except that they store one additional byte of information: a tag, which is usually a letter ("a" to "z") or character number ("0" to "9"). Furthermore, see 'Details' in get_na.

See Also

replace_na to replace NA's with specific values, rec for general recoding of variables and recode_to for re-shifting value ranges. See get_na to get values of missing values in labelled vectors.

Examples

# create random variable
dummy <- sample(1:8, 100, replace = TRUE)
# show value distribution
table(dummy)
# set value 1 and 8 as missings
dummy <- set_na(dummy, c(1, 8))
# show value distribution, including missings
table(dummy, useNA = "always")

# add named vector as further missing value
set_na(dummy, c("Refused" = 5))
# see different missing types
library(haven)
print_tagged_na(set_na(dummy, c("Refused" = 5)))


# create sample data frame
dummy <- data.frame(var1 = sample(1:8, 100, replace = TRUE),
                    var2 = sample(1:10, 100, replace = TRUE),
                    var3 = sample(1:6, 100, replace = TRUE))
# set value 2 and 4 as missings
library(dplyr)
dummy %>% set_na(c(2, 4)) %>% head()
dummy %>% set_na(c(2, 4)) %>% get_na()
dummy %>% set_na(c(2, 4)) %>% get_values()

# create list of variables
data(efc)
dummy <- list(efc$c82cop1, efc$c83cop2, efc$c84cop3)
# check original distribution of categories
lapply(dummy, table, useNA = "always")
# set 3 to NA
lapply(set_na(dummy, 3), table, useNA = "always")