biscale (version 1.1.0)

bi_class: Create Classes for Bivariate Maps

Description

Creates mapping classes for a bivariate map. These data will be stored in a new variable named bi_class, which will be added to the given data object.

Usage

bi_class(.data, x, y, style, dim = 3, keep_factors = FALSE, dig_lab = 3, na_rm = FALSE)

Value

A copy of .data with a new variable bi_class that contains combinations of values that correspond to an observations values for x

and y. This is the basis for applying a bivariate color palette.

Arguments

.data

A data frame, tibble, or sf object

x

The x variable, either a numeric (including double and integer classes) or factor

y

The y variable, either a numeric (including double and integer classes) or factor

style

A string identifying the style used to calculate breaks. Currently supported styles are "quantile", "equal", "fisher", and "jenks". If both x and y are factors, this argument can be omitted.

Note that older versions of biscale used "quantile" as the default for this argument. Now that bi_class accepts factors, this argument no longer as a default and older code will error.

dim

The dimensions of the palette. To use the built-in palettes, this value must be either 2, 3, or 4. A value of 3, for example, would be used to create a three-by-three bivariate map with a total of 9 classes.

If you are using a custom palette, this value may be larger (though these maps can be very hard to interpret).

If you are using pre-made factors, both factors must have the same number of levels as this value.

keep_factors

A logical scalar; if TRUE, the intermediate factor variables created as part of the calculation of bi_class will be retained. If FALSE (default), they will not be returned.

dig_lab

An integer that is passed to base::cut()

na_rm

A logical scalar that is passed to classInt::classIntervals(); if TRUE, NA values will be removed prior to calculating breaks. If FALSE (default), they will be included.

Examples

# quantile breaks, 2x2
data <- bi_class(stl_race_income, x = pctWhite, y = medInc, style = "quantile", dim = 2)

# summarize quantile breaks, 2x2
table(data$bi_class)

# quantile breaks, 3x3
data <- bi_class(stl_race_income, x = pctWhite, y = medInc, style = "quantile", dim = 3)

# summarize quantile breaks, 3x3
table(data$bi_class)