Windowed rank functions.
Six variations on ranking functions, mimicking 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 = row_number(), n) min_rank(x) dense_rank(x) percent_rank(x) cume_dist(x)
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. |
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. The size of the buckets may differ by up to one,
larger buckets have lower rank.
x <- c(5, 1, 3, 2, 2, NA) row_number(x) min_rank(x) dense_rank(x) percent_rank(x) cume_dist(x) ntile(x, 2) ntile(1:8, 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) mtcars %>% filter(between(row_number(), 1, 10))
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