mapgl (version 0.3)

step_classification: Step expressions with automatic classification

Description

These functions create step expressions using different classification methods, similar to choropleth mapping in GIS software. They automatically calculate break points and generate appropriate step expressions for styling map layers.

Usage

step_equal_interval(
  data = NULL,
  column,
  data_values = NULL,
  n = 5,
  palette = NULL,
  colors = NULL,
  na_color = "grey"
)
step_quantile(
  data = NULL,
  column,
  data_values = NULL,
  n = 5,
  palette = NULL,
  colors = NULL,
  na_color = "grey"
)
step_jenks(
  data = NULL,
  column,
  data_values = NULL,
  n = 5,
  palette = NULL,
  colors = NULL,
  na_color = "grey"
)

Value

A list of class "mapgl_classification" containing the step expression and metadata.

Arguments

data

A data frame or sf object containing the data. If provided, data_values will be extracted from data[[column]]. Either data or data_values must be provided.

column

The name of the column to use for the step expression.

data_values

A numeric vector of the actual data values used to calculate breaks. If NULL and data is provided, will be extracted from data[[column]].

n

The number of classes/intervals to create. Defaults to 5.

palette

A function that takes n and returns a character vector of colors. If NULL and colors is also NULL, defaults to viridisLite::viridis.

colors

A character vector of colors to use. Must have exactly n colors for step classification functions. Either palette or colors should be provided, but not both.

na_color

The color to use for missing values. Defaults to "grey".

Details

step_equal_interval()

Creates equal interval breaks by dividing the data range into equal parts

step_quantile()

Creates quantile breaks ensuring approximately equal numbers of observations in each class

step_jenks()

Creates Jenks natural breaks using Fisher-Jenks optimization to minimize within-class variance

See Also

interpolate_palette() for continuous color scales

Examples

if (FALSE) {
# Texas county income data
library(tidycensus)
tx <- get_acs(geography = "county", variables = "B19013_001",
              state = "TX", geometry = TRUE)

# Using palette function (recommended)
eq_class <- step_equal_interval(data = tx, column = "estimate", n = 5,
                                palette = viridisLite::plasma)
# Or with piping
eq_class <- tx |> step_equal_interval("estimate", n = 5)

# Using specific colors
qt_class <- step_quantile(data = tx, column = "estimate", n = 3,
                         colors = c("red", "yellow", "blue"))

# Jenks natural breaks with default viridis
jk_class <- step_jenks(data = tx, column = "estimate", n = 5)

# Use in a map with formatted legend
maplibre() |>
  add_fill_layer(source = tx, fill_color = eq_class$expression) |>
  add_legend(
    legend_title = "Median Income",
    values = get_legend_labels(eq_class, format = "currency"),
    colors = get_legend_colors(eq_class),
    type = "categorical"
  )

# Compare different methods
print(eq_class, format = "currency")
print(qt_class, format = "compact", prefix = "$")
}