Makes dealing with U.S. ZIP codes painless.
{zipcodeR} is an R package that makes working with ZIP codes in Reasier. It provides data on all U.S. ZIP codes using multiple open datasources, making it easier for social science researchers and datascientists to work with ZIP code-level data in data science projectsusing R.
See Also
An R package that makes working with US ZIP codes painless | Gavin RozziParis, Île-de-France (code postal 75...The latest update to {zipcodeR} includes new functions for searchingZIP codes at various geographic levels &geocoding.
Installation
You can install the released version of zipcodeR fromCRAN with:
install.packages("zipcodeR")And the development version from GitHub with:
# install.packages("devtools")devtools::install_github("gavinrozzi/zipcodeR")Citing {zipcodeR} in Publications
If you use {zipcodeR} in a publication, please cite the followingjournalarticle.
A BibTeX entry for LaTeX users is:
@article{ROZZI2021100099,title = {zipcodeR: Advancing the analysis of spatial data at the ZIP code level in R},journal = {Software Impacts},volume = {9},pages = {100099},year = {2021},issn = {2665-9638},doi = {https://doi.org/10.1016/j.simpa.2021.100099},url = {https://www.sciencedirect.com/science/article/pii/S2665963821000373},author = {Gavin C. Rozzi},keywords = {ZIP code, R, ZCTA, ZIP code tabulation area, zipcodeR},abstract = {The United States Postal Service (USPS) assigns unique identifiers for postal service areas known as ZIP codes which are commonly used to identify cities and regions throughout the United States in datasets. Despite the widespread use of ZIP codes, there are challenges in using them for geospatial analysis in the social sciences. This paper presents zipcodeR, an R package that facilitates analysis of ZIP code-level data by providing an offline database of ZIP codes and functions for geocoding, normalizing and retrieving data about ZIP codes and relating them to other geographies in R without depending on any external services.}}Examples
# Load zipcodeR into Rlibrary(zipcodeR)Find all ZIP codes for a state
search_state('NJ')#> # A tibble: 732 × 24#> zipcode zipcode…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵#> <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr> #> 1 07001 Standard Avenel Avenel… <raw 18 B> Middl… NJ 40.6 -74.3 Eastern#> 2 07002 Standard Bayonne Bayonn… <raw 19 B> Hudso… NJ 40.7 -74.1 Eastern#> 3 07003 Standard Bloomf… Bloomf… <raw 22 B> Essex… NJ 40.8 -74.2 Eastern#> 4 07004 Standard Fairfi… Fairfi… <raw 21 B> Essex… NJ 40.9 -74.3 Eastern#> 5 07005 Standard Boonton Boonto… <raw 36 B> Morri… NJ 40.9 -74.4 Eastern#> 6 07006 Standard Caldwe… Caldwe… <raw 39 B> Essex… NJ 40.8 -74.3 Eastern#> 7 07007 PO Box Caldwe… <NA> <raw 30 B> Essex… NJ NA NA <NA> #> 8 07008 Standard Carter… Carter… <raw 20 B> Middl… NJ 40.6 -74.2 Eastern#> 9 07009 Standard Cedar … Cedar … <raw 23 B> Essex… NJ 40.9 -74.2 Eastern#> 10 07010 Standard Cliffs… Cliffs… <raw 32 B> Berge… NJ 40.8 -74.0 Eastern#> # … with 722 more rows, 14 more variables: radius_in_miles <dbl>,#> # area_code_list <blob>, population <int>, population_density <dbl>,#> # land_area_in_sqmi <dbl>, water_area_in_sqmi <dbl>, housing_units <int>,#> # occupied_housing_units <int>, median_home_value <int>,#> # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,#> # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names#> # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …Calculate the distance between two ZIP codes in miles
zip_distance('08901','08731')#> zipcode_a zipcode_b distance#> 1 08901 08731 40.7Calculate the distance between vectors of ZIP codes
zip_codes <- tribble(~zip_a, ~zip_b,"08731", "08901","08734", "08005")zip_distance(zip_codes$zip_a,zip_codes$zip_b)#> zipcode_a zipcode_b distance#> 1 08731 08901 40.70#> 2 08734 08005 8.06Geocode a ZIP code to get its centroid
geocode_zip('08901')#> # A tibble: 1 × 3#> zipcode lat lng#> <chr> <dbl> <dbl>#> 1 08901 40.5 -74.4Get data about a ZIP code
reverse_zipcode('08901')#> # A tibble: 1 × 24#> zipcode zipcode_…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵#> <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr> #> 1 08901 Standard New Br… New Br… <raw 25 B> Middl… NJ 40.5 -74.4 Eastern#> # … with 14 more variables: radius_in_miles <dbl>, area_code_list <blob>,#> # population <int>, population_density <dbl>, land_area_in_sqmi <dbl>,#> # water_area_in_sqmi <dbl>, housing_units <int>,#> # occupied_housing_units <int>, median_home_value <int>,#> # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,#> # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names#> # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …Find all ZIP codes for a county
search_county('Ocean','NJ')#> # A tibble: 32 × 24#> zipcode zipcode…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵#> <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr> #> 1 08005 Standard Barneg… Barneg… <raw 20 B> Ocean… NJ 39.8 -74.3 Eastern#> 2 08006 PO Box Barneg… Barneg… <raw 33 B> Ocean… NJ 39.8 -74.1 Eastern#> 3 08008 Standard Beach … Beach … <raw 61 B> Ocean… NJ 39.6 -74.2 Eastern#> 4 08050 Standard Manaha… Manaha… <raw 47 B> Ocean… NJ 39.7 -74.3 Eastern#> 5 08087 Standard Tucker… Tucker… <raw 51 B> Ocean… NJ 39.6 -74.4 Eastern#> 6 08092 Standard West C… West C… <raw 22 B> Ocean… NJ 39.7 -74.3 Eastern#> 7 08527 Standard Jackson Jackso… <raw 19 B> Ocean… NJ 40.1 -74.4 Eastern#> 8 08533 Standard New Eg… New Eg… <raw 21 B> Ocean… NJ 40.0 -74.5 Eastern#> 9 08701 Standard Lakewo… Lakewo… <raw 20 B> Ocean… NJ 40.1 -74.2 Eastern#> 10 08721 Standard Bayvil… Bayvil… <raw 20 B> Ocean… NJ 39.9 -74.2 Eastern#> # … with 22 more rows, 14 more variables: radius_in_miles <dbl>,#> # area_code_list <blob>, population <int>, population_density <dbl>,#> # land_area_in_sqmi <dbl>, water_area_in_sqmi <dbl>, housing_units <int>,#> # occupied_housing_units <int>, median_home_value <int>,#> # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,#> # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names#> # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …Find all ZIP codes for a city
search_city('Jersey City','NJ')#> # A tibble: 13 × 24#> zipcode zipcode…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵#> <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr> #> 1 07097 Unique Jersey… <NA> <raw 23 B> Hudso… NJ NA NA <NA> #> 2 07302 Standard Jersey… Jersey… <raw 23 B> Hudso… NJ 40.7 -74.0 Eastern#> 3 07303 PO Box Jersey… <NA> <raw 23 B> Hudso… NJ NA NA <NA> #> 4 07304 Standard Jersey… Jersey… <raw 23 B> Hudso… NJ 40.7 -74.1 Eastern#> 5 07305 Standard Jersey… Jersey… <raw 23 B> Hudso… NJ 40.7 -74.1 Eastern#> 6 07306 Standard Jersey… Jersey… <raw 23 B> Hudso… NJ 40.7 -74.1 Eastern#> 7 07307 Standard Jersey… Jersey… <raw 23 B> Hudso… NJ 40.8 -74.0 Eastern#> 8 07308 PO Box Jersey… <NA> <raw 23 B> Hudso… NJ NA NA <NA> #> 9 07309 Standard Jersey… <NA> <raw 23 B> Hudso… NJ NA NA <NA> #> 10 07310 Standard Jersey… Jersey… <raw 23 B> Hudso… NJ 40.7 -74.0 Eastern#> 11 07311 Standard Jersey… Jersey… <raw 23 B> Hudso… NJ 40.7 -74.0 Eastern#> 12 07395 Unique Jersey… <NA> <raw 23 B> Hudso… NJ NA NA <NA> #> 13 07399 Unique Jersey… <NA> <raw 23 B> Hudso… NJ NA NA <NA> #> # … with 14 more variables: radius_in_miles <dbl>, area_code_list <blob>,#> # population <int>, population_density <dbl>, land_area_in_sqmi <dbl>,#> # water_area_in_sqmi <dbl>, housing_units <int>,#> # occupied_housing_units <int>, median_home_value <int>,#> # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,#> # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names#> # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …Find all ZIP codes for a timezone
search_tz('Eastern')#> # A tibble: 14,025 × 24#> zipcode zipcode…¹ major…² post_…³ common_c…⁴ county state lat lng timez…⁵#> <chr> <chr> <chr> <chr> <blob> <chr> <chr> <dbl> <dbl> <chr> #> 1 06001 Standard Avon Avon, … <raw 16 B> Hartf… CT 41.8 -72.9 Eastern#> 2 06002 Standard Bloomf… Bloomf… <raw 22 B> Hartf… CT 41.8 -72.7 Eastern#> 3 06010 Standard Bristol Bristo… <raw 19 B> Hartf… CT 41.7 -72.9 Eastern#> 4 06013 Standard Burlin… Burlin… <raw 36 B> Hartf… CT 41.8 -73.0 Eastern#> 5 06016 Standard Broad … Broad … <raw 46 B> Hartf… CT 41.9 -72.6 Eastern#> 6 06018 Standard Canaan Canaan… <raw 18 B> Litch… CT 42.0 -73.3 Eastern#> 7 06019 Standard Canton Canton… <raw 34 B> Hartf… CT 41.9 -72.9 Eastern#> 8 06020 Standard Canton… Canton… <raw 25 B> Hartf… CT 41.8 -72.9 Eastern#> 9 06021 Standard Colebr… Colebr… <raw 21 B> Litch… CT 42.0 -73.1 Eastern#> 10 06022 Standard Collin… Collin… <raw 24 B> Hartf… CT 41.8 -72.9 Eastern#> # … with 14,015 more rows, 14 more variables: radius_in_miles <dbl>,#> # area_code_list <blob>, population <int>, population_density <dbl>,#> # land_area_in_sqmi <dbl>, water_area_in_sqmi <dbl>, housing_units <int>,#> # occupied_housing_units <int>, median_home_value <int>,#> # median_household_income <int>, bounds_west <dbl>, bounds_east <dbl>,#> # bounds_north <dbl>, bounds_south <dbl>, and abbreviated variable names#> # ¹zipcode_type, ²major_city, ³post_office_city, ⁴common_city_list, …Get all Census tracts for a given ZIP code
get_tracts('08731')#> # A tibble: 6 × 3#> ZCTA5 TRACT GEOID#> <chr> <chr> <dbl>#> 1 08731 732001 34029732001#> 2 08731 732002 34029732002#> 3 08731 732101 34029732101#> 4 08731 732103 34029732103#> 5 08731 732104 34029732104#> 6 08731 733000 34029733000Documentation
Documentation for the current release is availablehere. See the referencesection for fulldetails on how to use each of the functions provided by zipcodeR.
Data Sources
This project was inspired by the excellentuszipcode library forPython and utilizes the same backend database released by its authorunder the MIT license. This project also incorporates open data from theU.S. Census Bureau and Department of Housing & Urban Development.
