rsegregation

A shortcut package with formulas for several different indices of segregation. rsegregation is designed to fit into the tidyverse framework, particularly dplyr.

Installation

The development version from GitHub can be installed with:

  # install.packages("devtools")
  devtools::install_github("arthurgailes/rsegregation")

Usage

rsegregation depends upon dplyr (>1.0.0), and can be used with it. To return a single divergence score for Bay Area County:

Divergence and Entropy

Calculate the divergence score for the entire dataset

rsegregation can work with base r, or within several dplyr verbs:

library(rsegregation)
library(dplyr)
## included dataset of Bay Area Census tracts
# Using dplyr
bay_divergence <- bay_race %>% 
  summarize(bay_divergence = divergence(white,black,asian, hispanic, all_other,
    population=total_pop, summed = T))

# Using base r
bay_divergence <- divergence(bay_race[c('white','black','asian', 'hispanic', 'all_other')], 
  population=bay_race$total_pop, summed = T)
# or
bay_divergence <- divergence(bay_race$white,bay_race$black,bay_race$asian, 
  bay_race$hispanic, bay_race$all_other, population=bay_race$total_pop, summed = T)
# all return the same result:
bay_divergence

Calculate divergence by group

Using the included Bay Area dataset of 2010 racial groups, divergence can be calculated by county using dplyr::group_by().

#library(dplyr)
group_by(bay_race, county) %>% 
  summarize(bay_divergence = divergence(white,black,asian, hispanic, all_other, 
    population=total_pop, summed = T))

county	bay_divergence
Alameda County, California, 2010	0.2450583
Contra Costa County, California, 2010	0.2129913
Marin County, California, 2010	0.1304815
Napa County, California, 2010	0.1459522
San Francisco County, California, 2010	0.2056087
San Mateo County, California, 2010	0.2387524
Santa Clara County, California, 2010	0.2093378
Solano County, California, 2010	0.1333189
Sonoma County, California, 2010	0.0756877

By-observation divergence scores

Divergence and entropy are both calculated rowwise by default (summed = FALSE).

bay_entropy <- bay_race
bay_entropy$entropy <- entropy(bay_race[c('white','black','asian',
  'hispanic','all_other')], population=bay_race$total_pop, summed = F)
head(bay_entropy)

fips	total_pop	hispanic	white	black	asian	all_other	county	entropy
06001400100	2937	0.0398366	0.7075247	0.0476677	0.1552605	0.0497106	Alameda County, California, 2010	0.9566644
06001400200	1974	0.0764944	0.7831814	0.0157042	0.0739615	0.0506586	Alameda County, California, 2010	0.7969746
06001400300	4865	0.0820144	0.6692703	0.1052415	0.0861254	0.0573484	Alameda County, California, 2010	1.0859266
06001400400	3703	0.0896570	0.6546044	0.1209830	0.0729139	0.0618417	Alameda County, California, 2010	1.1121719
06001400500	3517	0.0966733	0.5055445	0.2652829	0.0591413	0.0733580	Alameda County, California, 2010	1.2816122
06001400600	1571	0.0802037	0.4271165	0.3914704	0.0509230	0.0502864	Alameda County, California, 2010	1.2348325

Miscellaneous

Dataframes should be formatted as long on geographic observations (e.g. tracts), but wide on group observations (e.g. races), as in the included dataset of the San Francisco Bay Area.

head(bay_race)

fips	total_pop	hispanic	white	black	asian	all_other	county
06001400100	2937	0.0398366	0.7075247	0.0476677	0.1552605	0.0497106	Alameda County, California, 2010
06001400200	1974	0.0764944	0.7831814	0.0157042	0.0739615	0.0506586	Alameda County, California, 2010
06001400300	4865	0.0820144	0.6692703	0.1052415	0.0861254	0.0573484	Alameda County, California, 2010
06001400400	3703	0.0896570	0.6546044	0.1209830	0.0729139	0.0618417	Alameda County, California, 2010
06001400500	3517	0.0966733	0.5055445	0.2652829	0.0591413	0.0733580	Alameda County, California, 2010
06001400600	1571	0.0802037	0.4271165	0.3914704	0.0509230	0.0502864	Alameda County, California, 2010

Future development:

• decomposition of entropy index
• more measures of segregation

License

This package is free and open source software, licensed under GPL-3.