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Six variations on ranking functions, mimicking the ranking functions described in SQL2003. They are currently implemented using the built in rank() function. All ranking functions map smallest inputs to smallest outputs. Use desc() to reverse the direction.

Usage

cume_dist(x)

dense_rank(x)

min_rank(x)

ntile(x = row_number(), n)

percent_rank(x)

row_number(x)

Arguments

x

A vector of values to rank. Missing values are left as is. If you want to treat them as the smallest or largest values, replace with Inf or -Inf before ranking.

n

integer(1). The number of groups to split up into.

Details

  • cume_dist(): a cumulative distribution function. Proportion of all values less than or equal to the current rank.

  • dense_rank(): like min_rank(), but with no gaps between ranks

  • min_rank(): equivalent to rank(ties.method = "min")

  • ntile(): a rough rank, which breaks the input vector into n buckets. The size of the buckets may differ by up to one, larger buckets have lower rank.

  • percent_rank(): a number between 0 and 1 computed by rescaling min_rank to [0, 1]

  • row_number(): equivalent to rank(ties.method = "first")

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
percent_rank(x)
#> [1] 1.00 0.00 0.75 0.25 0.25   NA
cume_dist(x)
#> [1] 1.0 0.2 0.8 0.6 0.6  NA

ntile(x, 2)
#> [1]  2  1  2  1  1 NA
ntile(1:8, 3)
#> [1] 1 1 1 2 2 2 3 3

# row_number can be used with single table verbs without specifying x
# (for data frames and databases that support windowing)
mutate(mtcars, row_number() == 1L)
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
#> Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
#> Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
#> Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
#> Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
#> Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
#> AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
#> Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
#> Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
#> Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
#> Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
#> Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
#> Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
#> Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
#>                     row_number() == 1L
#> Mazda RX4                         TRUE
#> Mazda RX4 Wag                    FALSE
#> Datsun 710                       FALSE
#> Hornet 4 Drive                   FALSE
#> Hornet Sportabout                FALSE
#> Valiant                          FALSE
#> Duster 360                       FALSE
#> Merc 240D                        FALSE
#> Merc 230                         FALSE
#> Merc 280                         FALSE
#> Merc 280C                        FALSE
#> Merc 450SE                       FALSE
#> Merc 450SL                       FALSE
#> Merc 450SLC                      FALSE
#> Cadillac Fleetwood               FALSE
#> Lincoln Continental              FALSE
#> Chrysler Imperial                FALSE
#> Fiat 128                         FALSE
#> Honda Civic                      FALSE
#> Toyota Corolla                   FALSE
#> Toyota Corona                    FALSE
#> Dodge Challenger                 FALSE
#> AMC Javelin                      FALSE
#> Camaro Z28                       FALSE
#> Pontiac Firebird                 FALSE
#> Fiat X1-9                        FALSE
#> Porsche 914-2                    FALSE
#> Lotus Europa                     FALSE
#> Ford Pantera L                   FALSE
#> Ferrari Dino                     FALSE
#> Maserati Bora                    FALSE
#> Volvo 142E                       FALSE
mtcars %>% filter(between(row_number(), 1, 10))
#>                    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive    21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Valiant           18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Duster 360        14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Merc 240D         24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Merc 230          22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
#> Merc 280          19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4