• vec_unique(): the unique values. Equivalent to unique().

  • vec_unique_loc(): the locations of the unique values.

  • vec_unique_count(): the number of unique values.

vec_unique(x)

vec_unique_loc(x)

vec_unique_count(x)

Arguments

x

A vector (including a data frame).

Value

  • vec_unique(): a vector the same type as x containing only unique values.

  • vec_unique_loc(): an integer vector, giving locations of unique values.

  • vec_unique_count(): an integer vector of length 1, giving the number of unique values.

Missing values

In most cases, missing values are not considered to be equal, i.e. NA == NA is not TRUE. This behaviour would be unappealing here, so these functions consider all NAs to be equal. (Similarly, all NaN are also considered to be equal.)

See also

vec_duplicate for functions that work with the dual of unique values: duplicated values.

Examples

x <- rpois(100, 8) vec_unique(x)
#> [1] 5 12 3 9 6 11 10 8 7 14 16 2 13 4 19 15
vec_unique_loc(x)
#> [1] 1 2 3 4 5 7 10 12 13 18 23 30 31 39 57 73
vec_unique_count(x)
#> [1] 16
# `vec_unique()` returns values in the order that encounters them # use sort = "location" to match to the result of `vec_count()` head(vec_unique(x))
#> [1] 5 12 3 9 6 11
head(vec_count(x, sort = "location"))
#> key count #> 1 5 6 #> 2 12 5 #> 3 3 4 #> 4 9 14 #> 5 6 15 #> 6 11 12
# Normally missing values are not considered to be equal NA == NA
#> [1] NA
# But they are for the purposes of considering uniqueness vec_unique(c(NA, NA, NA, NA, 1, 2, 1))
#> [1] NA 1 2