Get / Set SPSS missing values
na_values(x)
na_values(x) <- value
na_range(x)
na_range(x) <- value
get_na_values(x)
get_na_range(x)
set_na_values(.data, ..., .values = NA, .strict = TRUE)
set_na_range(.data, ..., .values = NA, .strict = TRUE)
is_user_na(x)
is_regular_na(x)
user_na_to_na(x)
user_na_to_regular_na(x)
user_na_to_tagged_na(x)A vector (or a data frame).
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).
a data frame or a vector
name-value pairs of missing values (see examples)
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.
should an error be returned if some labels
doesn't correspond to a column of x?
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.
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 missing values.
It will also return TRUE for regular NA values. If you want to test if a
specific value is a user NA but not a regular NA, use is_user_na().
If you want to test if a value is a regular NA but not a user NA, not a
tagged NA, use is_regular_na().
You can use user_na_to_na() to convert user defined missing values to
regular NA. Note that any value label attached to a user defined missing
value will be lost.
user_na_to_regular_na() is a synonym of user_na_to_na().
The method user_na_to_tagged_na() will convert user defined missing values
into haven::tagged_na(), preserving value labels. Please note that
haven::tagged_na() are defined only for double vectors. Therefore, integer
haven_labelled_spss vectors will be converted into double haven_labelled
vectors; and user_na_to_tagged_na() cannot be applied to a character
haven_labelled_spss vector.
tagged_na_to_user_na() is the opposite of user_na_to_tagged_na() and
convert tagged NA into user defined missing values.
get_na_values() is identical to na_values() and get_na_range()
to na_range().
set_na_values() and set_na_range() could be used with dplyr
syntax.
haven::labelled_spss(), user_na_to_na()
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>[10]>
#> [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>[10]>
#> [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) # TRUE for the 6th and 10th values
#> [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE TRUE
is_user_na(v) # TRUE only for the 6th value
#> [1] FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
user_na_to_na(v)
#> <labelled<double>[10]>
#> [1] 1 2 2 2 3 NA 1 3 2 NA
#>
#> Labels:
#> value label
#> 1 yes
#> 3 no
na_values(v) <- NULL
v
#> <labelled<double>[10]>
#> [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>[10]>
#> [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>[10]>
#> [1] 1 2 2 2 3 NA 1 3 2 NA
#>
#> Labels:
#> value label
#> 1 yes
#> 3 no
user_na_to_tagged_na(v)
#> <labelled<double>[10]>
#> [1] 1 2 2 2 3 NA(a) 1 3 2 NA
#>
#> Labels:
#> value label
#> 1 yes
#> 3 no
#> NA(a) don't know
# it is not recommended to mix user NAs and tagged NAs
x <- c(NA, 9, tagged_na("a"))
na_values(x) <- 9
x
#> <labelled_spss<double>[3]>
#> [1] NA 9 NA(a)
#> Missing values: 9
is.na(x)
#> [1] TRUE TRUE TRUE
is_user_na(x)
#> [1] FALSE TRUE FALSE
is_tagged_na(x)
#> [1] FALSE FALSE TRUE
is_regular_na(x)
#> [1] TRUE FALSE FALSE
if (require(dplyr)) {
# setting value label and user NAs
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
# example with a vector
v <- 1:10
v <- v %>% set_na_values(5, 6, 7)
v
v %>% set_na_range(8, 10)
v %>% set_na_range(.values = c(9, 10))
v %>% set_na_values(NULL)
}
#> <labelled<integer>[10]>
#> [1] 1 2 3 4 5 6 7 8 9 10