Six variations on ranking functions, mimicing the ranking functions described in SQL2003. They are currently implemented using the built in rank function, and are provided mainly as a convenience when converting between R and SQL. All ranking functions map smallest inputs to smallest outputs. Use desc() to reverse the direction.

row_number(x)

ntile(x, n)

min_rank(x)

dense_rank(x)

percent_rank(x)

cume_dist(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

number of groups to split up into.

Details

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

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

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

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

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

  • ntile(): a rough rank, which breaks the input vector into n buckets.

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(runif(100), 10)
#> [1] 9 1 4 8 8 9 5 8 10 9 9 3 2 4 7 9 10 5 4 5 4 1 2 8 9 #> [26] 4 3 6 7 6 10 9 8 4 10 7 10 4 6 7 1 4 6 8 10 8 6 5 1 5 #> [51] 2 2 10 1 5 10 9 7 8 7 6 2 10 9 9 6 2 1 3 1 10 3 2 3 7 #> [76] 3 5 7 6 1 1 8 3 5 1 4 7 3 3 3 5 6 8 2 5 2 6 4 2 7
# 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 row_number() == 1L #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 TRUE #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 FALSE #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 FALSE #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 FALSE #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 FALSE #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 FALSE #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 FALSE #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 FALSE #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 FALSE #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 FALSE #> 11 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 FALSE #> 12 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 FALSE #> 13 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 FALSE #> 14 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 FALSE #> 15 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 FALSE #> 16 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 FALSE #> 17 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 FALSE #> 18 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 FALSE #> 19 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 FALSE #> 20 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 FALSE #> 21 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 FALSE #> 22 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 FALSE #> 23 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 FALSE #> 24 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 FALSE #> 25 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 FALSE #> 26 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 FALSE #> 27 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 FALSE #> 28 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 FALSE #> 29 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 FALSE #> 30 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 FALSE #> 31 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 FALSE #> 32 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 FALSE
mtcars %>% filter(between(row_number(), 1, 10))
#> mpg cyl disp hp drat wt qsec vs am gear carb #> 1 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 #> 2 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 #> 3 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 #> 4 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 #> 5 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 #> 6 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 #> 7 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 #> 8 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 #> 9 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 #> 10 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4