Order and sort vectors

Usage

vec_order(
  x,
  ...,
  direction = c("asc", "desc"),
  na_value = c("largest", "smallest")
)

vec_sort(
  x,
  ...,
  direction = c("asc", "desc"),
  na_value = c("largest", "smallest")
)

Arguments

x: A vector
...: These dots are for future extensions and must be empty.
direction: Direction to sort in. Defaults to ascending.
na_value: Should NAs be treated as the largest or smallest values?

Value

vec_order() an integer vector the same size as x.
vec_sort() a vector with the same size and type as x.

Differences with `order()`

Unlike the na.last argument of order() which decides the positions of missing values irrespective of the decreasing argument, the na_value argument of vec_order() interacts with direction. If missing values are considered the largest value, they will appear last in ascending order, and first in descending order.

Dependencies of `vec_order()`

vec_proxy_order()

Dependencies of `vec_sort()`

vec_proxy_order()
vec_order()
vec_slice()

Examples

x <- round(c(runif(9), NA), 3)
vec_order(x)
#>  [1]  2  1  8  4  9  7  5  6  3 10
vec_sort(x)
#>  [1] 0.274 0.384 0.440 0.449 0.754 0.794 0.810 0.812 0.815    NA
vec_sort(x, direction = "desc")
#>  [1]    NA 0.815 0.812 0.810 0.794 0.754 0.449 0.440 0.384 0.274

# Can also handle data frames
df <- data.frame(g = sample(2, 10, replace = TRUE), x = x)
vec_order(df)
#>  [1]  2  1  4  7  3  8  9  5  6 10
vec_sort(df)
#>    g     x
#> 1  1 0.274
#> 2  1 0.384
#> 3  1 0.449
#> 4  1 0.794
#> 5  1 0.815
#> 6  2 0.440
#> 7  2 0.754
#> 8  2 0.810
#> 9  2 0.812
#> 10 2    NA
vec_sort(df, direction = "desc")
#>    g     x
#> 1  2    NA
#> 2  2 0.812
#> 3  2 0.810
#> 4  2 0.754
#> 5  2 0.440
#> 6  1 0.815
#> 7  1 0.794
#> 8  1 0.449
#> 9  1 0.384
#> 10 1 0.274

# Missing values interpreted as largest values are last when
# in increasing order:
vec_order(c(1, NA), na_value = "largest", direction = "asc")
#> [1] 1 2
vec_order(c(1, NA), na_value = "largest", direction = "desc")
#> [1] 2 1