Replace specific values in vector with NA

Description

This function replaces specific values of variables with NA. set_na_if() is a scoped variant of set_na(), where values will be replaced only with NA's for those variables that match the logical condition of predicate.

Usage

set_na(x, ..., na, drop.levels = TRUE, as.tag = FALSE)

set_na_if(x, predicate, na, drop.levels = TRUE, as.tag = FALSE)

Arguments

Details

set_na() converts all values defined in na with a related NA or tagged NA value (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 also 'Details' in get_na.

Value

x, with all values in na being replaced by NA. If x is a data frame, the complete data frame x will be returned, with NA's set for variables specified in ...; if ... is not specified, applies to all variables in the data frame.

Note

Labels from values that are replaced with NA and no longer used will be removed from x, however, other value and variable label attributes are preserved. For more details on labelled data, see vignette Labelled Data and the sjlabelled-Package.

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, na = c(1, 8))
# show value distribution, including missings
table(dummy, useNA = "always")

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


# 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
dummy %>% set_na(na = c(2, 4)) %>% head()
dummy %>% set_na(na = c(2, 4), as.tag = TRUE) %>% get_na()
dummy %>% set_na(na = c(2, 4), as.tag = TRUE) %>% get_values()

data(efc)
dummy <- data.frame(
  var1 = efc$c82cop1,
  var2 = efc$c83cop2,
  var3 = efc$c84cop3
)
# check original distribution of categories
lapply(dummy, table, useNA = "always")
# set 3 to NA for two variables
lapply(set_na(dummy, var1, var3, na = 3), table, useNA = "always")

# drop unused factor levels when being set to NA
x <- factor(c("a", "b", "c"))
x
set_na(x, na = "b", as.tag = TRUE)
set_na(x, na = "b", drop.levels = FALSE, as.tag = TRUE)

# set_na() can also remove a missing by defining the value label
# of the value that should be replaced with NA. This is in particular
# helpful if a certain category should be set as NA, however, this category
# is assigned with different values accross variables
x1 <- sample(1:4, 20, replace = TRUE)
x2 <- sample(1:7, 20, replace = TRUE)
x1 <- set_labels(x1, labels = c("Refused" = 3, "No answer" = 4))
x2 <- set_labels(x2, labels = c("Refused" = 6, "No answer" = 7))

tmp <- data.frame(x1, x2)
get_labels(tmp)
table(tmp, useNA = "always")

get_labels(set_na(tmp, na = "No answer"))
table(set_na(tmp, na = "No answer"), useNA = "always")

# show values
tmp
set_na(tmp, na = c("Refused", "No answer"))