Classification and Scoring of U.S. County and ZIP Code Rurality

Provides USDA Rural-Urban Continuum Codes (RUCC 2023), Rural-Urban Commuting Area codes (RUCA 2020), and a composite rurality score for all U.S. counties. Functions enable lookup by FIPS code, ZIP code, or county name, and easy merging with existing datasets. Data sources include the USDA Economic Research Service, U.S. Census Bureau American Community Survey, and Census TIGER/Line shapefiles.

rurality

Documentation: https://cwimpy.github.io/rurality/

Rurality classification and scoring for U.S. counties and ZIP codes.

Provides USDA Rural-Urban Continuum Codes (RUCC 2023), Rural-Urban Commuting Area codes (RUCA 2020), and a composite rurality score for all 3,235 U.S. counties. Built to make rurality data easy to use in research without manually downloading and merging USDA spreadsheets.

Web app: rurality.app

Install

install.packages("rurality")

Or install the development version from GitHub:

# install.packages("devtools")
devtools::install_github("cwimpy/rurality")

Usage

library(rurality)

# Look up a county by FIPS
get_rurality("05031")
#> Craighead County, AR — Score: 40 (Mixed), RUCC: 3

# Just the score
rurality_score("05031")
#> 40

# Just the RUCC code
get_rucc("05031")
#> 3

# RUCA code for a ZIP
get_ruca("72401")
#> Primary RUCA: 1 (Metropolitan core)

# Merge onto your own data
my_data <- data.frame(
  fips = c("05031", "06037", "48453"),
  outcome = c(0.7, 0.4, 0.6)
)
my_data |> add_rurality()

# Add all available variables
my_data |> add_rurality(vars = "all")

Data

The package ships two datasets:

`county_rurality`

All 3,235 U.S. counties with 24 variables including:

Variable	Description
`fips`	5-digit county FIPS code
`rurality_score`	Composite score (0-100)
`rurality_classification`	Urban, Suburban, Mixed, Rural, Very Rural
`rucc_2023`	USDA Rural-Urban Continuum Code (1-9)
`pop_density`	Population per square mile
`dist_large_metro`	Distance to nearest large metro (miles)
`median_income`	ACS 2022 median household income
`median_age`	ACS 2022 median age

# Browse the full dataset
county_rurality

# Filter to a state
county_rurality |> dplyr::filter(state_abbr == "AR")

# Distribution
table(county_rurality$rurality_classification)
#> Mixed    Rural Suburban    Urban Very Rural
#>   663      921      781       87        783

`ruca_codes`

USDA RUCA codes (2020) for 41,146 ZIP code tabulation areas.

ruca_codes |> dplyr::filter(state == "AR")

Methodology

The composite rurality score is a weighted average of three components:

Component	Weight	Source
RUCC score	55%	USDA Economic Research Service, 2023
Population density	28%	Census ACS 2022 5-year estimates
Distance to metro	17%	Haversine distance to nearest metro area

Scores range from 0 (most urban) to 100 (most rural). See the full methodology for details.

Citation

If you use this package in published research, please cite:

Wimpy, Cameron (2026). rurality: Rurality Classification and Scoring
  for U.S. Counties and ZIP Codes. R package version 0.1.0.
  https://github.com/cwimpy/rurality