Skip to contents

Two-variable mosaic analysis (chi-square test + flat mosaic)

Source: R/mosaic_analysis.R
mosaic_analysis.Rd

Analyses the association between two categorical columns of a data.frame. Builds the contingency table, drops sparse categories below min_count, runs a Pearson chi-square test (or Fisher's exact test), computes Cramer's V with a df-adjusted effect-size label, and draws a flat ggplot2 mosaic whose tile area encodes counts and whose fill encodes the standardized Pearson residual (Nestimate diverging palette). All tabular output is a tidy one-row-per-cell data.frame.

Usage

mosaic_analysis(
  data,
  var1,
  var2,
  min_count = 10L,
  test = c("chisq", "fisher"),
  percentage_base = c("total", "row", "column"),
  tile_label = c("count", "percent", "residual", "category", "none"),
  title = "",
  ...
)

Arguments

data

A data.frame containing the two variables.

var1

Character. Name of the first variable (mosaic columns).

var2

Character. Name of the second variable (stacked within columns).

min_count

Integer. Minimum marginal count for a category to be kept. Categories of either variable below this are dropped before testing. Default 10.

test

Character. "chisq" (default) Pearson chi-square, or "fisher" Fisher's exact test (simulated p-value). Cramer's V and the residual fill are always derived from the chi-square statistic.

percentage_base

Character. Base for the "percent" tile label and the pct column: "total" (default), "row" (within var1), or "column" (within var2).

tile_label

Character. What to print inside each tile: "count" (default), "percent", "residual", "category" (var2 level), or "none".

title

Character. Plot title. Default "".

...

Further flat-mosaic styling arguments passed to the renderer (e.g. col_label_side, row_label_side, legend_position, legend_size, label_size, palette). Tile fill uses the ColorBrewer RdBu ramp by default (override with palette). Column labels auto-rotate to vertical when there are more than 6 columns; pass col_label_angle to force an angle.

Value

An object of class "mosaic_analysis": a list with

plot

The flat mosaic ggplot object.

counts

Tidy data.frame, one row per (var1, var2) cell, with observed, expected, residual (standardized), and pct (on percentage_base).

stats

One-row data.frame: test, statistic, df, p_value, cramers_v, effect_size, n.

test

The raw htest object.

cramers_v, effect_size

Effect size value and label.

table

The filtered contingency table.

removed

List of dropped var1 / var2 categories.

n_original, n_filtered

Row counts before/after filtering.

See also

mosaic_plot for the network/table mosaic (which also accepts style = "flat").

Examples

df <- data.frame(
  gender = sample(c("F", "M"), 200, replace = TRUE),
  level  = sample(c("Low", "Mid", "High"), 200, replace = TRUE)
)
res <- mosaic_analysis(df, "gender", "level", min_count = 5)
res$stats
#>         test statistic df p_value cramers_v effect_size   n
#> 1 Chi-square     0.848  2  0.6545     0.065  negligible 200
res$counts
#>   gender level observed expected residual  pct
#> 1      F  High       33   30.030    0.897 16.5
#> 2      M  High       33   35.970   -0.897 16.5
#> 3      F   Low       31   33.215   -0.653 15.5
#> 4      M   Low       42   39.785    0.653 21.0
#> 5      F   Mid       27   27.755   -0.233 13.5
#> 6      M   Mid       34   33.245    0.233 17.0
# \donttest{
plot(res, tile_label = "percent")

# }