Visualize CLOSURE data in a histogram

Call closure_plot_bar() to get a barplot of CLOSURE results.

For each scale value, the bars show how often this value appears in the full list of possible raw data samples found by the CLOSURE algorithm.

Usage

closure_plot_bar(
  data,
  frequency = c("absolute-percent", "absolute", "relative", "percent"),
  samples = c("mean", "all"),
  bar_alpha = 0.75,
  bar_color = "#5D3FD3",
  show_text = TRUE,
  text_color = bar_color,
  text_size = 12,
  text_offset = 0.05,
  mark_thousand = ",",
  mark_decimal = "."
)

Arguments

data

List returned by closure_generate().

frequency

String (length 1). What should the bars display? The default, "absolute-percent", displays the count of each scale value and its percentage of all values. Other options are "absolute", "relative", and "percent".

samples

String (length 1). How to aggregate the samples? Either take the average sample ("mean", the default) or the sum of all samples ("all"). This only matters if absolute frequencies are shown.

bar_alpha

Numeric (length 1). Opacity of the bars. Default is 0.75.

bar_color

String (length 1). Color of the bars. Default is "#5D3FD3", a purple color.

show_text

Logical (length 1). Should the bars be labeled with the corresponding frequencies? Default is TRUE.

text_color

String (length 1). Color of the frequency labels. By default, the same as bar_color.

text_size

Numeric. Base font size in pt. Default is 12.

text_offset

Numeric (length 1). Distance between the text labels and the bars. Default is 0.05.

mark_thousand, mark_decimal

Strings (length 1 each). Delimiters between groups of digits in text labels. Defaults are "," for mark_thousand (e.g., "20,000") and "." for mark_decimal (e.g., "0.15").

Value

A ggplot object.

See also

closure_plot_ecdf(), an alternative visualization.

Examples

# Create CLOSURE data first:
data <- closure_generate(
  mean = "3.5",
  sd = "2",
  n = 52,
  scale_min = 1,
  scale_max = 5
)

# Visualize:
closure_plot_bar(data)