Title:	Calculate Diversity and Segregation Indices
Version:	0.1.0
Description:	Implements common measures of diversity and spatial segregation. This package has tools to compute the majority of measures are reviewed in Massey and Denton (1988) <doi:10.2307/2579183>. Multiple common measures of within-geography diversity are implemented as well. All functions operate on data frames with a 'tidyselect' based workflow.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
BugReports:	https://github.com/christopherkenny/divseg/issues
URL:	https://github.com/christopherkenny/divseg/, https://christophertkenny.com/divseg/
Suggests:	roxygen2, testthat (≥ 3.0.0)
Imports:	sf (≥ 1.0.0), rlang (≥ 0.4.11), dplyr, tidyselect, tibble, units
Depends:	R (≥ 4.1.0)
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-09-02 21:35:27 UTC; chris
Author:	Christopher T. Kenny [aut, cre]
Maintainer:	Christopher T. Kenny <ctkenny@proton.me>
Repository:	CRAN
Date/Publication:	2025-09-02 23:20:02 UTC

de_county

Description

This data contains 2010 Census data for each of the three counties in DE.

Usage

data("de_county")

Format

An sf dataframe with 3 observations

Examples

data('de_county')

de_tract

Description

This data contains 2010 Census data for each of the 218 tracts in DE.

Usage

data("de_tract")

Format

An sf dataframe with 218 observations

Examples

data('de_tract')

Compute Absolute Centralization

Description

Compute Absolute Centralization

Usage

ds_abs_cent(.data, .cols, .name)

abs_cent(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_abs_cent(de_county, c(pop_white, starts_with('pop_')))
ds_abs_cent(de_county, c(pop_white, starts_with('pop_')), 'abs_cent')

Compute Absolute Clustering

Description

Compute Absolute Clustering

Usage

ds_abs_clust(.data, .cols, .name)

abs_clust(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_abs_clust(de_county, c(pop_white, starts_with('pop_')))
ds_abs_clust(de_county, c(pop_white, starts_with('pop_')), 'abs_clust')

Compute Absolute Concentration

Description

Compute Absolute Concentration

Usage

ds_abs_conc(.data, .cols, .name)

abs_conc(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_abs_conc(de_county, c(pop_black, starts_with('pop_')))
ds_abs_conc(de_county, c(pop_black, starts_with('pop_')), 'abs_conc')

Compute Atkinson b Index

Description

Compute Atkinson b Index

Usage

ds_atkinson(.data, .cols, .name, b = 0.5)

atkinson(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_atkinson(de_county, c(pop_white, starts_with('pop_')))
ds_atkinson(de_county, starts_with('pop_'), 'atkinson')

Compute Blau's Index

Description

Compute Blau's Index

Usage

ds_blau(.data, .cols, .name)

blau(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_blau(de_county, starts_with('pop_'))
ds_blau(de_county, starts_with('pop_'), 'blau')

Compute Correlation Index

Description

Compute Correlation Index

Usage

ds_correlation(.data, .cols, .name)

correlation(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_correlation(de_county, c(pop_white, starts_with('pop_')))
ds_correlation(de_county, starts_with('pop_'), 'correlation')

Compute Distance Decay Interaction

Description

Compute Distance Decay Interaction

Usage

ds_dd_interaction(.data, .cols, .name, .comp = FALSE)

dd_interaction(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_dd_interaction(de_county, c(pop_black, starts_with('pop_')))
ds_dd_interaction(de_county, c(pop_black, starts_with('pop_')), 'dd_interaction')

Compute Distance Decay Isolation

Description

Compute Distance Decay Isolation

Usage

ds_dd_isolation(.data, .cols, .name, .comp = FALSE)

dd_isolation(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_dd_isolation(de_county, c(pop_black, starts_with('pop_')))
ds_dd_isolation(de_county, c(pop_black, starts_with('pop_')), 'dd_isolation')

Compute Delta Index

Description

Compute Delta Index

Usage

ds_delta(.data, .cols, .name, .comp = FALSE)

delta(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_delta(de_county, c(pop_white, starts_with('pop_')))
ds_delta(de_county, starts_with('pop_'), 'delta')

Compute Dissimilarity Index

Description

Compute Dissimilarity Index

Usage

ds_dissim(.data, .cols, .name, .comp = FALSE)

dissim(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_dissim(de_county, c(pop_white, starts_with('pop_')))
ds_dissim(de_county, c(pop_white, starts_with('pop_')), .comp = TRUE)
ds_dissim(de_county, starts_with('pop_'), 'dissim')

Compute Diversity

Description

This is equivalent to perplexity.

Usage

ds_diversity(.data, .cols, .name, q = 1)

diversity(..., .data = dplyr::across(everything()))

ds_perplexity(.data, .cols, .name, q = 1)

perplexity(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_diversity(de_county, starts_with('pop_'))
ds_diversity(de_county, starts_with('pop_'), 'diversity')

Compute Entropy Index

Description

Compute Entropy Index

Usage

ds_entropy(.data, .cols, .name, .comp = FALSE)

entropy(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_entropy(de_county, c(pop_white, starts_with('pop_')))
ds_entropy(de_county, starts_with('pop_'), 'entropy')

Compute Gini Index

Description

Compute Gini Index

Usage

ds_gini(.data, .cols, .name, .comp = FALSE)

gini(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_gini(de_county, c(pop_white, starts_with('pop_')))
ds_gini(de_county, starts_with('pop_'), 'gini')

Compute Herfindahl-Hirshman Index

Description

This is equivalent to the Simpson Index.

Usage

ds_hhi(.data, .cols, .name)

hhi(..., .data = dplyr::across(everything()))

ds_simpson(.data, .cols, .name)

simpson(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_hhi(de_county, starts_with('pop_'))
ds_hhi(de_county, starts_with('pop_'), 'blau')

Compute Interaction Index

Description

Compute Interaction Index

Usage

ds_interaction(.data, .cols, .name, .comp = FALSE)

interaction(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_interaction(de_county, c(pop_white, starts_with('pop_')))
ds_interaction(de_county, starts_with('pop_'), 'interaction')

Compute Simpson Index

Description

Compute Simpson Index

Usage

ds_inv_simpson(.data, .cols, .name)

inv_simpson(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_inv_simpson(de_county, starts_with('pop_'))
ds_inv_simpson(de_county, starts_with('pop_'), 'blau')

Compute Isolation Index

Description

Compute Isolation Index

Usage

ds_isolation(.data, .cols, .name, .comp = FALSE)

isolation(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_isolation(de_county, c(pop_white, starts_with('pop_')))
ds_isolation(de_county, starts_with('pop_'), 'isolation')

Compute Relative Centralization

Description

Compute Relative Centralization

Usage

ds_rel_cent(.data, .cols, .name)

rel_cent(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_rel_cent(de_county, c(pop_white, starts_with('pop_')))
ds_rel_cent(de_county, c(pop_white, starts_with('pop_')), 'rel_cent')

Compute Relative Clustering

Description

Compute Relative Clustering

Usage

ds_rel_clust(.data, .cols, .name)

rel_clust(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_rel_clust(de_county, c(pop_black, starts_with('pop_')))
ds_rel_clust(de_county, c(pop_black, starts_with('pop_')), 'rel_clust')

Compute Relative Concentration

Description

Compute Relative Concentration

Usage

ds_rel_conc(.data, .cols, .name)

rel_conc(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_rel_conc(de_county, c(pop_black, starts_with('pop_')))
ds_rel_conc(de_county, c(pop_black, starts_with('pop_')), 'rel_conc')

Compute Reyni Entropy

Description

Compute Reyni Entropy

Usage

ds_reyni(.data, .cols, .name, q = 0)

reyni(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_reyni(de_county, starts_with('pop_'))
ds_reyni(de_county, starts_with('pop_'), 'reyni')

Compute Shannon Index

Description

Compute Shannon Index

Usage

ds_shannon(.data, .cols, .name)

shannon(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_shannon(de_county, starts_with('pop_'))
ds_shannon(de_county, starts_with('pop_'), 'shannon')

Compute Spatial Proximity

Description

Compute Spatial Proximity

Usage

ds_spat_prox(.data, .cols, .name)

spat_prox(..., .data = dplyr::across(everything()))

Arguments

Value

a tibble or numeric vector if .name missing

Examples

data('de_county')
ds_spat_prox(de_county, c(pop_black, starts_with('pop_')))
ds_spat_prox(de_county, c(pop_black, starts_with('pop_')), 'spat_prox')

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

tidyselect

all_of, any_of, contains, ends_with, everything, last_col, matches, num_range, one_of, starts_with

Tidy eval helpers

Description

This page lists the tidy eval tools reexported in this package from rlang. To learn about using tidy eval in scripts and packages at a high level, see the dplyr programming vignette and the ggplot2 in packages vignette. The Metaprogramming section of Advanced R may also be useful for a deeper dive.

• The tidy eval operators ⁠{{⁠, ⁠!!⁠, and ⁠!!!⁠ are syntactic constructs which are specially interpreted by tidy eval functions. You will mostly need ⁠{{⁠, as ⁠!!⁠ and ⁠!!!⁠ are more advanced operators which you should not have to use in simple cases.

  The curly-curly operator ⁠{{⁠ allows you to tunnel data-variables passed from function arguments inside other tidy eval functions. ⁠{{⁠ is designed for individual arguments. To pass multiple arguments contained in dots, use ... in the normal way.

  my_function <- function(data, var, ...) {
    data |>
      group_by(...) |>
      summarise(mean = mean({{ var }}))
  }
  

• enquo() and enquos() delay the execution of one or several function arguments. The former returns a single expression, the latter returns a list of expressions. Once defused, expressions will no longer evaluate on their own. They must be injected back into an evaluation context with ⁠!!⁠ (for a single expression) and ⁠!!!⁠ (for a list of expressions).

  my_function <- function(data, var, ...) {
    # Defuse
    var <- enquo(var)
    dots <- enquos(...)
  
    # Inject
    data |>
      group_by(!!!dots) |>
      summarise(mean = mean(!!var))
  }
  

  In this simple case, the code is equivalent to the usage of ⁠{{⁠ and ... above. Defusing with enquo() or enquos() is only needed in more complex cases, for instance if you need to inspect or modify the expressions in some way.

• The .data pronoun is an object that represents the current slice of data. If you have a variable name in a string, use the .data pronoun to subset that variable with [[.

  my_var <- "disp"
  mtcars |> summarise(mean = mean(.data[[my_var]]))
  

• Another tidy eval operator is ⁠:=⁠. It makes it possible to use glue and curly-curly syntax on the LHS of =. For technical reasons, the R language doesn't support complex expressions on the left of =, so we use ⁠:=⁠ as a workaround.

  my_function <- function(data, var, suffix = "foo") {
    # Use `{{` to tunnel function arguments and the usual glue
    # operator `{` to interpolate plain strings.
    data |>
      summarise("{{ var }}_mean_{suffix}" := mean({{ var }}))
  }
  

• Many tidy eval functions like dplyr::mutate() or dplyr::summarise() give an automatic name to unnamed inputs. If you need to create the same sort of automatic names by yourself, use as_label(). For instance, the glue-tunnelling syntax above can be reproduced manually with:

  my_function <- function(data, var, suffix = "foo") {
    var <- enquo(var)
    prefix <- as_label(var)
    data |>
      summarise("{prefix}_mean_{suffix}" := mean(!!var))
  }
  

  Expressions defused with enquo() (or tunnelled with ⁠{{⁠) need not be simple column names, they can be arbitrarily complex. as_label() handles those cases gracefully. If your code assumes a simple column name, use as_name() instead. This is safer because it throws an error if the input is not a name as expected.