Get / Set SPSS missing values

na_values(x)

na_values(x) <- value

na_range(x)

na_range(x) <- value

set_na_values(.data, ...)

set_na_range(.data, ...)

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)

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 labelled_spss for a presentation of SPSS's user defined missing values. Note that 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.

Note

set_na_values and set_na_range could be used with dplyr.

Examples

v <- labelled(c(1,2,2,2,3,9,1,3,2,NA), c(yes = 1, no = 3, "don't know" = 9))
v
#> <Labelled double>
#>  [1]  1  2  2  2  3  9  1  3  2 NA
#> 
#> Labels:
#>  value      label
#>      1        yes
#>      3         no
#>      9 don't know
na_values(v) <- 9
na_values(v)
#> [1] 9
v
#> <Labelled SPSS double>
#>  [1]  1  2  2  2  3  9  1  3  2 NA
#> Missing values: 9
#> 
#> Labels:
#>  value      label
#>      1        yes
#>      3         no
#>      9 don't know
is.na(v)
#>  [1] FALSE FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
user_na_to_na(v)
#> <Labelled double>
#>  [1]  1  2  2  2  3 NA  1  3  2 NA
#> 
#> Labels:
#>  value      label
#>      1        yes
#>      3         no
#>      9 don't know
na_values(v) <- NULL
v
#> <Labelled double>
#>  [1]  1  2  2  2  3  9  1  3  2 NA
#> 
#> Labels:
#>  value      label
#>      1        yes
#>      3         no
#>      9 don't know
na_range(v) <- c(5, Inf)
na_range(v)
#> [1]   5 Inf
v
#> <Labelled SPSS double>
#>  [1]  1  2  2  2  3  9  1  3  2 NA
#> Missing range:  [5, Inf]
#> 
#> Labels:
#>  value      label
#>      1        yes
#>      3         no
#>      9 don't know
user_na_to_na(v)
#> <Labelled double>
#>  [1]  1  2  2  2  3 NA  1  3  2 NA
#> 
#> Labels:
#>  value      label
#>      1        yes
#>      3         no
#>      9 don't know
if (require(dplyr)) {
  # setting value labels
  df <- data_frame(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
}
#> Warning: `data_frame()` is deprecated, use `tibble()`.
#> This warning is displayed once per session.
#> <Labelled double>
#> [1] 1 1 2 9
#> 
#> Labels:
#>  value label
#>      1   yes
#>      2    no