Make an HCL legend for an unordered set of distributions

Source: R/legend.R

This function creates a legend to accompany a map describing an unordered set of distributions.

legend_set(
  palette,
  specificity = TRUE,
  group_labels = NULL,
  label_i = "Maximum\nintensity",
  label_s = "Specificity",
  axis_i = c("low", "high"),
  axis_s = c("low", "high"),
  return_df = FALSE
)

Arguments

palette

data frame containing a color palette generated by palette_set.
specificity

logical indicating whether to visualize intensity and layer information for the full range of potential specificity values (i.e., 0-100) or for a single specificity value (i.e., 100). Typically, a single specificity value is appropriate for map_multiples visualizations.
group_labels

(axis_l) character vector with labels for each distribution.
label_i

character vector with a single element describing the meaning of specificity.
label_s

character vector with a single element describing the meaning of intensity values.
axis_i

character vector with two elements describing the meaning of low and high intensity values.
axis_s

character vector with two elements describing the meaning of low and high specificity values.
return_df

logical indicating whether to return the legend as a ggplot2 object or return a data frame containing the necessary data to build the legend.

Value

A ggplot2 plot object of the legend. Alternatively, return_df = TRUE will return a data frame containing a data frame containing the data needed to build the legend. The data frame columns are:

  • specificity: the degree to which intensity values are unevenly distributed across layers; mapped to chroma.

  • layer_id: integer identifying the layer containing the maximum intensity value; mapped to hue.

  • color: the hexadecimal color associated with the given layer and specificity values.

  • intensity: maximum cell value across layers divided by the maximum value across all layers and cells; mapped to alpha level.

See also

legend_timecycle for cyclical sequences of distributions and legend_timeline for linear sequences of distributions.

Other legend: legend_timecycle(), legend_timeline()

Examples

# load elephant data
data(elephant_ud)

# generate hcl palette
pal <- palette_set(elephant_ud)

# create legend for palettes
legend_set(pal)