You can install the development version from GitHub with:
Use chop() like cut() to cut your data up:
library(santoku)
x <- runif(10, 0, 10)
(chopped <- chop(x, breaks = 0:10))
#> [1] [4, 5) [8, 9) [3, 4) [4, 5) [7, 8) [9, 10] [6, 7) [8, 9) [1, 2)
#> [10] [4, 5)
#> Levels: [1, 2) [3, 4) [4, 5) [6, 7) [7, 8) [8, 9) [9, 10]
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 [4, 5)
#> 2 8.969989 [8, 9)
#> 3 3.391823 [3, 4)
#> 4 4.676785 [4, 5)
#> 5 7.057042 [7, 8)
#> 6 9.707687 [9, 10]
#> 7 6.713807 [6, 7)
#> 8 8.376589 [8, 9)
#> 9 1.086165 [1, 2)
#> 10 4.495479 [4, 5)chop() returns a factor.
If data is beyond the limits of breaks, they will be extended automatically:
chopped <- chop(x, breaks = 3:7)
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 [4, 5)
#> 2 8.969989 (7, 9.708]
#> 3 3.391823 [3, 4)
#> 4 4.676785 [4, 5)
#> 5 7.057042 (7, 9.708]
#> 6 9.707687 (7, 9.708]
#> 7 6.713807 [6, 7]
#> 8 8.376589 (7, 9.708]
#> 9 1.086165 [1.086, 3)
#> 10 4.495479 [4, 5)To chop a single number into a separate category, put the number twice in breaks:
x_fives <- x
x_fives[1:5] <- 5
chopped <- chop(x_fives, c(2, 5, 5, 8))
data.frame(x_fives, chopped)
#> x_fives chopped
#> 1 5.000000 {5}
#> 2 5.000000 {5}
#> 3 5.000000 {5}
#> 4 5.000000 {5}
#> 5 5.000000 {5}
#> 6 9.707687 (8, 9.708]
#> 7 6.713807 (5, 8]
#> 8 8.376589 (8, 9.708]
#> 9 1.086165 [1.086, 2)
#> 10 4.495479 [2, 5)To quickly produce a table of chopped data, use tab():
To chop into fixed-width intervals, starting at the minimum value, use chop_width():
chopped <- chop_width(x, 2)
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 [3.086, 5.086)
#> 2 8.969989 [7.086, 9.086)
#> 3 3.391823 [3.086, 5.086)
#> 4 4.676785 [3.086, 5.086)
#> 5 7.057042 [5.086, 7.086)
#> 6 9.707687 [9.086, 11.09]
#> 7 6.713807 [5.086, 7.086)
#> 8 8.376589 [7.086, 9.086)
#> 9 1.086165 [1.086, 3.086)
#> 10 4.495479 [3.086, 5.086)To chop into exactly groups fixed-with intervals, use chop_evenly():
chopped <- chop_evenly(x, groups = 3)
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 [3.96, 6.834)
#> 2 8.969989 [6.834, 9.708)
#> 3 3.391823 [1.086, 3.96)
#> 4 4.676785 [3.96, 6.834)
#> 5 7.057042 [6.834, 9.708)
#> 6 9.707687 [9.708, 12.58]
#> 7 6.713807 [3.96, 6.834)
#> 8 8.376589 [6.834, 9.708)
#> 9 1.086165 [1.086, 3.96)
#> 10 4.495479 [3.96, 6.834)To chop into groups with a fixed number of members, use chop_n():
chopped <- chop_n(x, 4)
table(chopped)
#> chopped
#> [1.086, 4.978) [4.978, 8.97) [8.97, 9.708]
#> 4 4 2To chop into a fixed number of equal-sized groups, use chop_equally():
chopped <- chop_equally(x, groups = 5)
table(chopped)
#> chopped
#> [0%, 20%) [20%, 40%) [40%, 60%) [60%, 80%) [80%, 100%]
#> 2 2 2 2 2To chop data up by quantiles, use chop_quantiles():
chopped <- chop_quantiles(x, c(0.25, 0.5, 0.75))
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 [25%, 50%)
#> 2 8.969989 (75%, 100%]
#> 3 3.391823 [0%, 25%)
#> 4 4.676785 [25%, 50%)
#> 5 7.057042 [50%, 75%]
#> 6 9.707687 (75%, 100%]
#> 7 6.713807 [50%, 75%]
#> 8 8.376589 (75%, 100%]
#> 9 1.086165 [0%, 25%)
#> 10 4.495479 [0%, 25%)To chop data by standard deviations around the mean, use chop_mean_sd():
chopped <- chop_mean_sd(x)
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 [-1 sd, 0 sd)
#> 2 8.969989 [1 sd, 2 sd)
#> 3 3.391823 [-1 sd, 0 sd)
#> 4 4.676785 [-1 sd, 0 sd)
#> 5 7.057042 [0 sd, 1 sd)
#> 6 9.707687 [1 sd, 2 sd)
#> 7 6.713807 [0 sd, 1 sd)
#> 8 8.376589 [0 sd, 1 sd)
#> 9 1.086165 [-2 sd, -1 sd)
#> 10 4.495479 [-1 sd, 0 sd)tab_n(), tab_width(), tab_evenly() and tab_mean_sd() act similarly to tab(), calling the related chop_ function and then table().
tab_n(x, 4)
#> x
#> [1.086, 4.978) [4.978, 8.97) [8.97, 9.708]
#> 4 4 2
tab_width(x, 2)
#> x
#> [1.086, 3.086) [3.086, 5.086) [5.086, 7.086) [7.086, 9.086) [9.086, 11.09]
#> 1 4 2 2 1
tab_evenly(x, 5)
#> x
#> [1.086, 2.81) [2.81, 4.535) [4.535, 6.259) [6.259, 7.983) [7.983, 9.708]
#> 1 2 2 2 3
tab_mean_sd(x)
#> x
#> [-2 sd, -1 sd) [-1 sd, 0 sd) [0 sd, 1 sd) [1 sd, 2 sd)
#> 1 4 3 2You can change factor labels with the labels argument:
chopped <- chop(x, c(2, 5, 8), labels = c("Lowest", "Low", "Higher", "Highest"))
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 Low
#> 2 8.969989 Highest
#> 3 3.391823 Low
#> 4 4.676785 Low
#> 5 7.057042 Higher
#> 6 9.707687 Highest
#> 7 6.713807 Higher
#> 8 8.376589 Highest
#> 9 1.086165 Lowest
#> 10 4.495479 LowYou need as many labels as there are intervals - one fewer than length(breaks) if your data doesn’t extend beyond breaks, one more than length(breaks) if it does.
To label intervals with a dash, use lbl_dash():
chopped <- chop(x, c(2, 5, 8), lbl_dash())
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 2 - 5
#> 2 8.969989 8 - 9.708
#> 3 3.391823 2 - 5
#> 4 4.676785 2 - 5
#> 5 7.057042 5 - 8
#> 6 9.707687 8 - 9.708
#> 7 6.713807 5 - 8
#> 8 8.376589 8 - 9.708
#> 9 1.086165 1.086 - 2
#> 10 4.495479 2 - 5For arbitrary formatting use lbl_format() and sprintf-style format strings:
chopped <- chop(x, c(2, 5, 8), lbl_format("%s to %s"))
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 2 to 5
#> 2 8.969989 8 to 9.708
#> 3 3.391823 2 to 5
#> 4 4.676785 2 to 5
#> 5 7.057042 5 to 8
#> 6 9.707687 8 to 9.708
#> 7 6.713807 5 to 8
#> 8 8.376589 8 to 9.708
#> 9 1.086165 1.086 to 2
#> 10 4.495479 2 to 5To label intervals in order use lbl_seq():
chopped <- chop(x, c(2, 5, 8), lbl_seq())
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 b
#> 2 8.969989 d
#> 3 3.391823 b
#> 4 4.676785 b
#> 5 7.057042 c
#> 6 9.707687 d
#> 7 6.713807 c
#> 8 8.376589 d
#> 9 1.086165 a
#> 10 4.495479 bYou can use numerals or even roman numerals:
chop(x, c(2, 5, 8), lbl_seq("(1)"))
#> [1] (2) (4) (2) (2) (3) (4) (3) (4) (1) (2)
#> Levels: (1) (2) (3) (4)
chop(x, c(2, 5, 8), lbl_seq("i."))
#> [1] ii. iv. ii. ii. iii. iv. iii. iv. i. ii.
#> Levels: i. ii. iii. iv.By default, chop() extends breaks if necessary. If you don’t want that, set extend = FALSE:
chopped <- chop(x, c(3, 5, 7), extend = FALSE)
data.frame(x, chopped)
#> x chopped
#> 1 4.978305 [3, 5)
#> 2 8.969989 <NA>
#> 3 3.391823 [3, 5)
#> 4 4.676785 [3, 5)
#> 5 7.057042 <NA>
#> 6 9.707687 <NA>
#> 7 6.713807 [5, 7]
#> 8 8.376589 <NA>
#> 9 1.086165 <NA>
#> 10 4.495479 [3, 5)Data outside the range of breaks will become NA.
By default, intervals are closed on the left, i.e. they include their left endpoints. If you want right-closed intervals, use brk_right():
y <- 1:5
data.frame(
y = y,
left_closed = chop(y, 1:5),
right_closed = chop(y, brk_right(1:5))
)
#> y left_closed right_closed
#> 1 1 [1, 2) [1, 2]
#> 2 2 [2, 3) [1, 2]
#> 3 3 [3, 4) (2, 3]
#> 4 4 [4, 5] (3, 4]
#> 5 5 [4, 5] (4, 5]The last finite interval is right-closed (or if you use brk_right, the first finite interval is left-closed). If you don’t want that, use brk_left() explicitly and set close_end = FALSE:
z <- 1:5
data.frame(
z = z,
rightmost_closed = chop(1:5, brk_left(1:5)),
rightmost_open = chop(1:5, brk_left(1:5, close_end = FALSE))
)
#> z rightmost_closed rightmost_open
#> 1 1 [1, 2) [1, 2)
#> 2 2 [2, 3) [2, 3)
#> 3 3 [3, 4) [3, 4)
#> 4 4 [4, 5] [4, 5)
#> 5 5 [4, 5] {5}If you want to chop repeatedly with the same arguments, create your own knife:
chop_by_quartiles <- knife(
breaks = brk_quantiles(c(0.25, 0.5, 0.75)),
labels = lbl_dash()
)
chop_by_quartiles(x)
#> [1] 25% - 50% 75% - 100% 0% - 25% 25% - 50% 50% - 75% 75% - 100%
#> [7] 50% - 75% 75% - 100% 0% - 25% 0% - 25%
#> Levels: 0% - 25% 25% - 50% 50% - 75% 75% - 100%
table(chop_by_quartiles(rnorm(50)))
#>
#> 0% - 25% 25% - 50% 50% - 75% 75% - 100%
#> 13 12 12 13