Chop by quantiles

chop_quantiles() chops data by quantiles. chop_deciles() is a convenience function which chops into deciles.

Usage

chop_quantiles(
  x,
  probs,
  ...,
  labels = if (raw) lbl_intervals() else lbl_intervals(single = NULL),
  left = is.numeric(x),
  raw = FALSE,
  weights = NULL,
  recalc_probs = FALSE
)

chop_deciles(x, ...)

brk_quantiles(probs, ..., weights = NULL, recalc_probs = FALSE)

tab_quantiles(x, probs, ..., left = is.numeric(x), raw = FALSE)

tab_deciles(x, ...)

Arguments

x

A vector.

probs

A vector of probabilities for the quantiles. If probs has names, these will be used for labels.

...

For chop_quantiles, passed to chop(). For brk_quantiles(), passed to stats::quantile() or Hmisc::wtd.quantile().

labels

A character vector of labels or a function to create labels.

left

Logical. Left-closed or right-closed breaks?

raw

Logical. Use raw values in labels?

weights

NULL or numeric vector of same length as x. If not NULL, Hmisc::wtd.quantile() is used to calculate weighted quantiles.

recalc_probs

Logical. Recalculate probabilities of quantiles using ecdf(x)? See below.

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

For non-numeric x, left is set to FALSE by default. This works better for calculating "type 1" quantiles, since they round down. See stats::quantile().

By default, chop_quantiles() shows the requested probabilities in the labels. To show the numeric quantiles themselves, set raw = TRUE.

When x contains duplicates, consecutive quantiles may be the same number. If so, interval labels may be misleading, and if recalc_probs = FALSE a warning is emitted. Set recalc_probs = TRUE to recalculate the probabilities of the quantiles using the empirical cumulative distribution function of x. Doing so may give you different labels from what you expect, and will remove any names from probs, but it never changes the actual quantiles used for breaks. At present, recalc_probs = TRUE is incompatible with non-null weights. See the example below.

See also

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

Examples

chop_quantiles(1:10, 1:3/4)
#>  [1] [0%, 25%)   [0%, 25%)   [0%, 25%)   [25%, 50%)  [25%, 50%)  [50%, 75%) 
#>  [7] [50%, 75%)  [75%, 100%] [75%, 100%] [75%, 100%]
#> Levels: [0%, 25%) [25%, 50%) [50%, 75%) [75%, 100%]

chop_quantiles(1:10, c(Q1 = 0, Q2 = 0.25, Q3 = 0.5, Q4 = 0.75))
#>  [1] Q1 Q1 Q1 Q2 Q2 Q3 Q3 Q4 Q4 Q4
#> Levels: Q1 Q2 Q3 Q4

chop(1:10, brk_quantiles(1:3/4))
#>  [1] [0%, 25%)   [0%, 25%)   [0%, 25%)   [25%, 50%)  [25%, 50%)  [50%, 75%) 
#>  [7] [50%, 75%)  [75%, 100%] [75%, 100%] [75%, 100%]
#> Levels: [0%, 25%) [25%, 50%) [50%, 75%) [75%, 100%]

chop_deciles(1:10)
#>  [1] [0%, 10%)   [10%, 20%)  [20%, 30%)  [30%, 40%)  [40%, 50%)  [50%, 60%) 
#>  [7] [60%, 70%)  [70%, 80%)  [80%, 90%)  [90%, 100%]
#> 10 Levels: [0%, 10%) [10%, 20%) [20%, 30%) [30%, 40%) [40%, 50%) ... [90%, 100%]

# to label by the quantiles themselves:
chop_quantiles(1:10, 1:3/4, raw = TRUE)
#>  [1] [1, 3.25)   [1, 3.25)   [1, 3.25)   [3.25, 5.5) [3.25, 5.5) [5.5, 7.75)
#>  [7] [5.5, 7.75) [7.75, 10]  [7.75, 10]  [7.75, 10] 
#> Levels: [1, 3.25) [3.25, 5.5) [5.5, 7.75) [7.75, 10]

# duplicate quantiles:
x <- c(1, 1, 1, 2, 3)
quantile(x, 1:5/5)
#>  20%  40%  60%  80% 100% 
#>  1.0  1.0  1.4  2.2  3.0 
tab_quantiles(x, 1:5/5)
#> Warning: `x` has duplicate quantiles: break labels may be misleading
#>  [20%, 40%]  [60%, 80%) [80%, 100%] 
#>           3           1           1 
tab_quantiles(x, 1:5/5, recalc_probs = TRUE)
#>   [0%, 60%]  [60%, 80%) [80%, 100%] 
#>           3           1           1 
set.seed(42)
tab_quantiles(rnorm(100), probs = 1:3/4, raw = TRUE)
#> [-2.993, -0.6167) [-0.6167, 0.0898)  [0.0898, 0.6616)   [0.6616, 2.287] 
#>                25                25                25                25