Cut data into intervals, separating out common values

Sometimes it's useful to separate out common elements of x. dissect() chops x, but puts common elements of x ("spikes") into separate categories.

Usage

dissect(
  x,
  breaks,
  ...,
  n = NULL,
  prop = NULL,
  spike_labels = "{{{l}}}",
  exclude_spikes = FALSE
)

tab_dissect(x, breaks, ..., n = NULL, prop = NULL)

Arguments

x, breaks, ...

Passed to chop().

n, prop

Scalar. Provide either n, a number of values, or prop, a proportion of length(x). Values of x which occur at least this often will get their own singleton break.

spike_labels

Glue string for spike labels. Use "{l}" for the spike value.

exclude_spikes

Logical. Exclude spikes before chopping x? This can affect the location of data-dependent breaks.

Value

dissect() returns the result of chop(), but with common values put into separate factor levels.

tab_dissect() returns a contingency table().

Details

Unlike chop_spikes(), dissect() doesn't break up intervals which contain a spike. As a result, unlike chop_* functions, dissect() does not chop x into disjoint intervals. See the examples.

If breaks are data-dependent, their labels may be misleading after common elements have been removed. See the example below. To get round this, set exclude_spikes to TRUE. Then breaks will be calculated after removing spikes from the data.

Levels of the result are ordered by the minimum element in each level. As a result, if drop = FALSE, empty levels will be placed last.

This function is .

See also

chop_spikes() for a different approach.

Examples

x <- c(2, 3, 3, 3, 4)
dissect(x, c(2, 4), n = 3)
#> [1] [2, 4] {3}    {3}    {3}    [2, 4]
#> Levels: [2, 4] {3}
dissect(x, brk_width(2), prop = 0.5)
#> [1] [2, 4] {3}    {3}    {3}    [2, 4]
#> Levels: [2, 4] {3}

set.seed(42)
x <- runif(40, 0, 10)
x <- sample(x, 200, replace = TRUE)
# Compare:
table(dissect(x, brk_width(2, 0), prop = 0.05))
#> 
#>  [0, 2)  [2, 4)  [4, 6)  [6, 8) [8, 10] {9.057} 
#>      30      24      36      40      59      11 
# Versus:
tab_spikes(x, brk_width(2, 0), prop = 0.05)
#>      [0, 2)      [2, 4)      [4, 6)      [6, 8)  [8, 9.057)     {9.057} 
#>          30          24          36          40          22          11 
#> (9.057, 10] 
#>          37 

# Potentially confusing data-dependent breaks:
set.seed(42)
x <- rnorm(99)
x[1:9] <- x[1]
tab_quantiles(x, 1:2/3)
#>     [0%, 33.33%) [33.33%, 66.67%)   [66.67%, 100%] 
#>               33               33               33 
tab_dissect(x, brk_quantiles(1:2/3), n = 9)
#>     [0%, 33.33%) [33.33%, 66.67%)   [66.67%, 100%]          {1.371} 
#>               33               33               24                9 
# Calculate quantiles excluding spikes:
tab_dissect(x, brk_quantiles(1:2/3), n = 9, exclude_spikes = TRUE)
#>     [0%, 33.33%) [33.33%, 66.67%)   [66.67%, 100%]          {1.371} 
#>               30               30               30                9