Integer ranking functions

Three ranking functions inspired by SQL2003. They differ primarily in how they handle ties:

• row_number() gives every input a unique rank, so that c(10, 20, 20, 30) would get ranks c(1, 2, 3, 4). It's equivalent to rank(ties.method = "first").

• min_rank() gives every tie the same (smallest) value so that c(10, 20, 20, 30) gets ranks c(1, 2, 2, 4). It's the way that ranks are usually computed in sports and is equivalent to rank(ties.method = "min").

• dense_rank() works like min_rank(), but doesn't leave any gaps, so that c(10, 20, 20, 30) gets ranks c(1, 2, 2, 3).

Usage

row_number(x)

min_rank(x)

dense_rank(x)

Arguments

x

A vector to rank

By default, the smallest values will get the smallest ranks. Use desc() to reverse the direction so the largest values get the smallest ranks.

Missing values will be given rank NA. Use coalesce(x, Inf) or coalesce(x, -Inf) if you want to treat them as the largest or smallest values respectively.

To rank by multiple columns at once, supply a data frame.

Value

An integer vector.

See also

Other ranking functions: ntile(), percent_rank()

Examples

x <- c(5, 1, 3, 2, 2, NA)
row_number(x)
#> [1]  5  1  4  2  3 NA
min_rank(x)
#> [1]  5  1  4  2  2 NA
dense_rank(x)
#> [1]  4  1  3  2  2 NA

# Ranking functions can be used in `filter()` to select top/bottom rows
df <- data.frame(
  grp = c(1, 1, 1, 2, 2, 2, 3, 3, 3),
  x = c(3, 2, 1, 1, 2, 2, 1, 1, 1),
  y = c(1, 3, 2, 3, 2, 2, 4, 1, 2),
  id = 1:9
)
# Always gives exactly 1 row per group
df %>% group_by(grp) %>% filter(row_number(x) == 1)
#> # A tibble: 3 × 4
#> # Groups:   grp [3]
#>     grp     x     y    id
#>   <dbl> <dbl> <dbl> <int>
#> 1     1     1     2     3
#> 2     2     1     3     4
#> 3     3     1     4     7
# May give more than 1 row if ties
df %>% group_by(grp) %>% filter(min_rank(x) == 1)
#> # A tibble: 5 × 4
#> # Groups:   grp [3]
#>     grp     x     y    id
#>   <dbl> <dbl> <dbl> <int>
#> 1     1     1     2     3
#> 2     2     1     3     4
#> 3     3     1     4     7
#> 4     3     1     1     8
#> 5     3     1     2     9
# Rank by multiple columns (to break ties) by selecting them with `pick()`
df %>% group_by(grp) %>% filter(min_rank(pick(x, y)) == 1)
#> # A tibble: 3 × 4
#> # Groups:   grp [3]
#>     grp     x     y    id
#>   <dbl> <dbl> <dbl> <int>
#> 1     1     1     2     3
#> 2     2     1     3     4
#> 3     3     1     1     8
# See slice_min() and slice_max() for another way to tackle the same problem

# You can use row_number() without an argument to refer to the "current"
# row number.
df %>% group_by(grp) %>% filter(row_number() == 1)
#> # A tibble: 3 × 4
#> # Groups:   grp [3]
#>     grp     x     y    id
#>   <dbl> <dbl> <dbl> <int>
#> 1     1     3     1     1
#> 2     2     1     3     4
#> 3     3     1     4     7

# It's easiest to see what this does with mutate():
df %>% group_by(grp) %>% mutate(grp_id = row_number())
#> # A tibble: 9 × 5
#> # Groups:   grp [3]
#>     grp     x     y    id grp_id
#>   <dbl> <dbl> <dbl> <int>  <int>
#> 1     1     3     1     1      1
#> 2     1     2     3     2      2
#> 3     1     1     2     3      3
#> 4     2     1     3     4      1
#> 5     2     2     2     5      2
#> 6     2     2     2     6      3
#> 7     3     1     4     7      1
#> 8     3     1     1     8      2
#> 9     3     1     2     9      3