Discretise numeric data into categorical

cut_interval makes n groups with equal range, cut_number makes n groups with (approximately) equal numbers of observations; cut_width makes groups of width width.

cut_interval(x, n = NULL, length = NULL, ...)

cut_number(x, n = NULL, ...)

cut_width(x, width, center = NULL, boundary = NULL,
  closed = c("right", "left"), ...)

Arguments

x	numeric vector
n	number of intervals to create, OR
length	length of each interval
...	Arguments passed on to base::cut.default breaks either a numeric vector of two or more unique cut points or a single number (greater than or equal to 2) giving the number of intervals into which x is to be cut. labels labels for the levels of the resulting category. By default, labels are constructed using "(a,b]" interval notation. If labels = FALSE, simple integer codes are returned instead of a factor. right logical, indicating if the intervals should be closed on the right (and open on the left) or vice versa. dig.lab integer which is used when labels are not given. It determines the number of digits used in formatting the break numbers. ordered_result logical: should the result be an ordered factor?
width	The bin width.
center, boundary	Specify either the position of edge or the center of a bin. Since all bins are aligned, specifying the position of a single bin (which doesn't need to be in the range of the data) affects the location of all bins. If not specified, uses the "tile layers algorithm", and sets the boundary to half of the binwidth. To center on integers, width = 1 and center = 0. boundary = 0.5.
closed	One of "right" or "left" indicating whether right or left edges of bins are included in the bin.

Examples

table(cut_interval(1:100, 10))
#> 
#>    [1,10.9] (10.9,20.8] (20.8,30.7] (30.7,40.6] (40.6,50.5] (50.5,60.4] 
#>          10          10          10          10          10          10 
#> (60.4,70.3] (70.3,80.2] (80.2,90.1]  (90.1,100] 
#>          10          10          10          10 
table(cut_interval(1:100, 11))
#> 
#>   [1,10]  (10,19]  (19,28]  (28,37]  (37,46]  (46,55]  (55,64]  (64,73] 
#>       10        9        9        9        9        9        9        9 
#>  (73,82]  (82,91] (91,100] 
#>        9        9        9 

table(cut_number(runif(1000), 10))
#> 
#> [0.000382,0.0947]    (0.0947,0.181]     (0.181,0.302]     (0.302,0.407] 
#>               100               100               100               100 
#>     (0.407,0.498]     (0.498,0.604]     (0.604,0.728]     (0.728,0.814] 
#>               100               100               100               100 
#>       (0.814,0.9]           (0.9,1] 
#>               100               100 

table(cut_width(runif(1000), 0.1))
#> 
#> [-0.05,0.05]  (0.05,0.15]  (0.15,0.25]  (0.25,0.35]  (0.35,0.45]  (0.45,0.55] 
#>           48          110           98           96          110           99 
#>  (0.55,0.65]  (0.65,0.75]  (0.75,0.85]  (0.85,0.95]  (0.95,1.05] 
#>          100           90          111           93           45 
table(cut_width(runif(1000), 0.1, boundary = 0))
#> 
#>   [0,0.1] (0.1,0.2] (0.2,0.3] (0.3,0.4] (0.4,0.5] (0.5,0.6] (0.6,0.7] (0.7,0.8] 
#>        89       110       102        92       103        97       101       112 
#> (0.8,0.9]   (0.9,1] 
#>        88       106 
table(cut_width(runif(1000), 0.1, center = 0))
#> 
#> [-0.05,0.05]  (0.05,0.15]  (0.15,0.25]  (0.25,0.35]  (0.35,0.45]  (0.45,0.55] 
#>           48           95          113          117           92           90 
#>  (0.55,0.65]  (0.65,0.75]  (0.75,0.85]  (0.85,0.95]  (0.95,1.05] 
#>           97          108          107           83           50 
table(cut_width(runif(1000), 0.1, labels = FALSE))
#> 
#>   1   2   3   4   5   6   7   8   9  10  11 
#>  54 107  90 104 114 107  91  99  86  95  53