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Confusion/Contingency Table Bubble Plot

Source: R/gg_conf.R
gg_conf.Rd

The function automatically computes frequency counts for each unique combination of the x and y categorical variables using table(). Bubble sizes are scaled proportionally to represent counts, with the range controlled by point_size_range. Useful for visualizing cross-tabulations, confusion matrices, or any bivariate categorical data.

Usage

gg_conf(
  data,
  x,
  y,
  fill = "skyblue",
  text_size = 4,
  text_color = "black",
  point_size_range = c(3, 15),
  border_color = "black",
  show_grid = TRUE,
  expand = 0.15,
  facet_x = NULL,
  facet_y = NULL
)

Arguments

data

A data frame containing the categorical variables.

x

Character string specifying the column name in data for the x-axis categorical variable.

y

Character string specifying the column name in data for the y-axis categorical variable.

fill

Character string specifying the fill color for bubbles. Default is "skyblue".

text_size

Numeric value specifying the size of count labels. Default is 4.

text_color

Character string specifying the color of count labels. Default is "black".

point_size_range

Numeric vector of length 2 specifying the minimum and maximum bubble sizes. Default is c(3, 15).

border_color

Character string specifying the color of bubble borders. Default is "black".

show_grid

Logical indicating whether to show major grid lines. Default is TRUE.

expand

Numeric value specifying the expansion multiplier for both axes. Default is 0.15.

facet_x

Character string specifying an optional column name in data for horizontal faceting. Default is NULL (no faceting).

facet_y

Character string specifying an optional column name in data for vertical faceting. Default is NULL (no faceting).

Value

A ggplot2 object showing the confusion table as a bubble plot. The plot displays:

  • Bubbles at each x-y combination, sized by frequency count

  • Count labels displayed in the center of each bubble

  • Optional faceting for additional categorical variables

  • Customizable colors, sizes, and grid visibility

Examples

data(mtcars)
# Create synthetic categorical variables
# Bin horsepower & fuel efficiency into ordered categories
mtcars$horsepower <- 
  cut(mtcars$hp, breaks = 5, 
      labels = c("Very Low", "Low", "Medium", "High", "Very High"))
mtcars$`miles per gallon` <- 
  cut(mtcars$mpg, breaks = 5,
      labels = c("Very Low", "Low", "Medium", "High", "Very High"))

# Base plot
gg_conf(data = mtcars, x = "horsepower", y = "miles per gallon")


# Custom styling
gg_conf(data = mtcars, x = "horsepower", y = "miles per gallon",
        fill = "lightcoral", point_size_range = c(5, 20),
        show_grid = FALSE)


# With faceting by "vs" column
gg_conf(data = mtcars, x = "horsepower", y = "miles per gallon",
        fill = "lightcoral", point_size_range = c(5, 20),
        facet_x = "vs",
        show_grid = FALSE)