zippeR

Motivation

zippeR provides a set of functions for working with ZCTAs and building spatial and demographic data for three-digit ZCTAs. These three-digit ZCTAs have limitations (they are large regions), but they are also used within American health care to protect patient confidentiality. zippeR therefore offers researchers who must use three-digit ZCTAs the capability to download geometric data and also aggregate demographic data from five-digit ZCTAs to three-digit ZCTAs. In addition, zippeR includes functions for validating and formatting vectors of ZIP Codes or ZCTAs as well as tools for cross-walking ZIP codes with ZCTAs.

Installation

The development version of zippeR can be accessed from GitHub with remotes:

# install.packages("remotes")
remotes::install_github("pfizer-opensource/zippeR")

Usage

zippeR contains functions that support the following tasks: * Labeling five-digit and three-digit ZIP Codes * Converting ZIP Codes to ZCTAs, counties, and other Census geographies * Downloading ZCTA geometries for both five-digit and three-digit areas * Aggregating demographic data from five-digit ZCTAs to three-digit ZCTAs

While a quick overview of the the core functionality is below, see the vignettes and our package website for more information on how to use these functions.

Converting ZIP Codes to ZCTAs

The zi_load_crosswalk() function provides access to the former UDS Mapper project’s ZIP Code to ZCTA crosswalk. This function returns a data frame with ZIP Codes and their corresponding ZCTAs.

> zi_load_crosswalk(year = 2020)
# A tibble: 41,096 × 6                                                                                                                                                                                 
   ZIP   PO_NAME    STATE ZIP_TYPE                             ZCTA  zip_join_type       
   <chr> <chr>      <chr> <chr>                                <chr> <chr>               
 1 00501 Holtsville NY    Post Office or large volume customer 11742 Spatial join to ZCTA
 2 00544 Holtsville NY    Post Office or large volume customer 11742 Spatial join to ZCTA
 3 00601 Adjuntas   PR    ZIP Code Area                        00601 ZIP Matches ZCTA    
 4 00602 Aguada     PR    ZIP Code Area                        00602 ZIP Matches ZCTA    
 5 00603 Aguadilla  PR    ZIP Code Area                        00603 ZIP Matches ZCTA    
 6 00604 Aguadilla  PR    Post Office or large volume customer 00603 Spatial join to ZCTA
 7 00605 Aguadilla  PR    Post Office or large volume customer 00603 Spatial join to ZCTA
 8 00606 Maricao    PR    ZIP Code Area                        00606 ZIP Matches ZCTA    
 9 00610 Anasco     PR    ZIP Code Area                        00610 ZIP Matches ZCTA    
10 00611 Angeles    PR    Post Office or large volume customer 00641 Spatial join to ZCTA
# … with 41,086 more rows

Likewise, users can use the zip_load_crosswalk() function combined with a HUD API key to access the HUD USPS ZIP Code Crosswalk. This function returns a data frame with ZIP Codes and their corresponding Census geographies:

zi_load_crosswalk(zip_source = "HUD", year = 2023, qtr = 1, target = "COUNTY",
  query = "63139", key = "<PASTE KEY>")
    ZIP GEOID RES_RATIO BUS_RATIO OTH_RATIO TOT_RATIO        CITY STATE
1 63139 29510         1         1         1         1 SAINT LOUIS    MO

Downloading ZCTA Geometries

The zi_get_geometry() function provides access to both five and three-digit ZCTA geometries. This function returns a sf object with the geometries of the requested ZCTAs:

mo_zcta5 <- zi_get_geometry(year = 2012, state = "MO", method = "centroid", 
    includes = c("51640", "52542", "52573", "52626"))

Downloading ZCTA Demographic Data

The zi_get_demographics() function provides access to demographic data for five-digit ZCTAs. This function returns a data frame with demographic data for the requested ZCTAs:

mo_gini12 <- zi_get_demographics(year = 2012, table = "B19083", 
  survey = "acs5", zcta = mo_zcta5$GEOID)

Aggregating Demographic Data to Three-digit ZCTAs

These same functions can be combined with zi_aggregate() to download geometric and demographic data, and then aggregate the demographic data to the three-digit ZCTA level:

## download Missouri geometric data
mo_zcta3 <- zi_get_geometry(year = 2020, style = "zcta3", state = "MO",
  territory = NULL, method = "intersect")

## download nationwide demographic data
mo_pop20 <- zi_get_demographics(year = 2020, variables = "B01003_001", 
  survey = "acs5")

## aggregate demographic data to three-digit ZCTAs
mo_pop20 <- zi_aggregate(mo_pop20, year = 2020, extensive = "B01003_001", 
  survey = "acs5", zcta = mo_zcta3$ZCTA3)

Gratitude

zippeR would not be possible without Kyle Walker’s packages tigris and tidycensus, which provide access to the underlying data U.S. Census Bureau data this package leverages.

Feedback and Code of Conduct

If you have feedback on zippeR, please open an issue on GitHub after checking the contribution guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.