• 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.)

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

## Examples

x <- rpois(100, 8)
vec_unique(x)#>    5 12  3  9  6 11 10  8  7 14 16  2 13  4 19 15vec_unique_loc(x)#>    1  2  3  4  5  7 10 12 13 18 23 30 31 39 57 73vec_unique_count(x)#>  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))#>   5 12  3  9  6 11head(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#>  NA
# But they are for the purposes of considering uniqueness
vec_unique(c(NA, NA, NA, NA, 1, 2, 1))#>  NA  1  2