chop_n() creates intervals containing a fixed number of elements.

## Usage

chop_n(x, n, ..., close_end = TRUE, tail = "split")

brk_n(n, tail = "split")

tab_n(x, n, ..., tail = "split")

## Arguments

x

A vector.

n

Integer. Number of elements in each interval.

...

Passed to chop().

close_end

Logical. Close last break at right? (If left is FALSE, close first break at left?)

tail

String. What to do if the final interval has fewer than n elements? "split" to keep it separate. "merge" to merge it with the neighbouring interval.

## Value

chop_* functions return a factor of the same length as x.

brk_* functions return a function to create breaks.

tab_* functions return a contingency table().

## Details

The algorithm guarantees that intervals contain no more than n elements, so long as there are no duplicates in x and tail = "split". It also guarantees that intervals contain no fewer than n elements, except possibly the last interval (or first interval if left is FALSE).

To ensure that all intervals contain at least n elements (so long as there are at least n elements in x!) set tail = "merge".

If tail = "split" and there are intervals containing duplicates with more than n elements, a warning is given.

Other chopping functions: chop_equally(), chop_evenly(), chop_fn(), chop_mean_sd(), chop_proportions(), chop_quantiles(), chop_width(), chop(), fillet()

## Examples

chop_n(1:10, 5)
#>  [1] [1, 6)  [1, 6)  [1, 6)  [1, 6)  [1, 6)  [6, 10] [6, 10] [6, 10] [6, 10]
#> [10] [6, 10]
#> Levels: [1, 6) [6, 10]

chop_n(1:5, 2)
#> [1] [1, 3) [1, 3) [3, 5) [3, 5) {5}
#> Levels: [1, 3) [3, 5) {5}
chop_n(1:5, 2, tail = "merge")
#> [1] [1, 3) [1, 3) [3, 5] [3, 5] [3, 5]
#> Levels: [1, 3) [3, 5]

# too many duplicates
x <- rep(1:2, each = 3)
chop_n(x, 2)
#> Warning: Some intervals contain more than 2 elements
#> [1] [1, 2) [1, 2) [1, 2) {2}    {2}    {2}
#> Levels: [1, 2) {2}

tab_n(1:10, 5)
#>  [1, 6) [6, 10]
#>       5       5

# fewer elements in one group
tab_n(1:10, 4)
#>  [1, 5)  [5, 9) [9, 10]
#>       4       4       2