labelled (version 2.7.0)

na_values: Get / Set SPSS missing values

Description

Get / Set SPSS missing values

Usage

na_values(x)

na_values(x) <- value

na_range(x)

na_range(x) <- value

set_na_values(.data, ..., .values = NA, .strict = TRUE)

set_na_range(.data, ..., .values = NA, .strict = TRUE)

user_na_to_na(x)

Arguments

x

A vector.

value

A vector of values that should also be considered as missing (for na_values) or a numeric vector of length two giving the (inclusive) extents of the range (for na_values, use -Inf and Inf if you want the range to be open ended).

.data

a data frame

...

name-value pairs of missing values (see examples)

.values

missing values to be applied to the data.frame, using the same syntax as value in na_values(df) <- value or na_range(df) <- value.

.strict

should an error be returned if some labels doesn't correspond to a column of x?

Value

na_values() will return a vector of values that should also be considered as missing. na_range() will return a numeric vector of length two giving the (inclusive) extents of the range.

set_na_values() and set_na_range() will return an updated copy of .data.

Details

See haven::labelled_spss() for a presentation of SPSS's user defined missing values. Note that base::is.na() will return TRUE for user defined misssing values. You can use user_na_to_na() to convert user defined missing values to NA.

See Also

haven::labelled_spss(), user_na_to_na()

Examples

Run this code
# NOT RUN {
v <- labelled(c(1,2,2,2,3,9,1,3,2,NA), c(yes = 1, no = 3, "don't know" = 9))
v
na_values(v) <- 9
na_values(v)
v
is.na(v)
user_na_to_na(v)
na_values(v) <- NULL
v
na_range(v) <- c(5, Inf)
na_range(v)
v
user_na_to_na(v)
if (require(dplyr)) {
  # setting value labels
  df <- tibble(s1 = c("M", "M", "F", "F"), s2 = c(1, 1, 2, 9)) %>%
    set_value_labels(s2 = c(yes = 1, no = 2)) %>%
    set_na_values(s2 = 9)
  na_values(df)

  # removing missing values
  df <- df %>% set_na_values(s2 = NULL)
  df$s2
}
# }

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