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Calculate frequencies.

Source: R/calculate.R
prodcalc.Rd

Calculate frequencies.

Usage

prodcalc(
  data,
  formula,
  divider = mosaic(),
  cascade = 0,
  scale_max = TRUE,
  na.rm = FALSE,
  offset = 0.01,
  expected = NULL,
  variable_labels = NULL
)

Arguments

data

input data frame

formula

formula specifying display of plot

divider

divider function

cascade

cascading amount, per nested layer

scale_max

Logical vector of length 1. If TRUE maximum values within each nested layer will be scaled to take up all available space. If FALSE, areas will be comparable between nested layers.

na.rm

Logical vector of length 1 - should missing levels be silently removed?

offset

Numeric value specifying the fixed gap at the deepest split (default: 0.01). Gaps increase by a factor of 1.5 toward the outermost split.

expected

Optional. Specification for loglinear model to calculate residuals. Can be:

  • NULL (default): No model fitting

  • Formula: Custom model specification (e.g., ~ A + B for independence)

  • Character: Shortcut - "independence", "saturated", or "conditional"

When specified, adds .expected and .residual columns to output.

variable_labels

Optional named character vector mapping internal variable names to the expressions shown to users. Used internally by the ggplot2 layer wrappers.

Value

A data frame giving rectangle boundaries (l, r, b, t) and computed frequencies for each partition/cell, plus .expected/.residual columns when expected is supplied.

Examples

data(happy)
prodcalc(happy, ~ happy, "hbar", offset = 0.005)
#>           happy   .wt       l       r b         t level    .n
#> 1 not too happy  7668 0.00000 0.24625 0 0.2284659     1  7668
#> 2  pretty happy 33563 0.25125 0.49750 0 1.0000000     1 33563
#> 3    very happy 18823 0.50250 0.74875 0 0.5608259     1 18823
#> 4          <NA>  4760 0.75375 1.00000 0 0.1418228     1  4760
prodcalc(happy, ~ happy, "hspine", offset = 0.01)
#>           happy   .wt         l         r b t level    .n
#> 1 not too happy  7668 0.0000000 0.1147585 0 1     1  7668
#> 2  pretty happy 33563 0.1247585 0.6270591 0 1     1 33563
#> 3    very happy 18823 0.6370591 0.9187623 0 1     1 18823
#> 4          <NA>  4760 0.9287623 1.0000000 0 1     1  4760