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README.Rmd
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---
output: github_document
editor_options:
chunk_output_type: console
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
[](https://travis-ci.org/jjchern/zcta)
# About
This R data package intends to store 2010 Census ZIP Code Tabulation Area (ZCTA) Relationship files. So far it includes:
- official US Census "2010 ZCTA to County Relationship File" (`zcta_county_rel_10.rda`)
- official US Census "2010 ZCTA to Tract Relationship File" (`zcta_tract_rel_10.rda`)
- ZIP Code to ZCTA crosswalk table developed by John Snow, Inc. (`zipzcta.rda`)
# Motivation
Linking the USPS's ZIP codes to US counties is tedious:
- ZIP codes do not resemble spatial entities; they are created for delivering mails. ZIP codes also change over time.
- To get a truly spatial representations of ZIP codes, the US Census Bureau develops the concept of ZIP Code tabulation areas (ZCTAs), which approximates ZIP codes. But
- Census does not release an official crosswalk between ZIP codes and ZCTAs.
- Census does release relationship files between ZCTAs and counties, but at least 25% of the ZCTAs cannot be uniquely linked to counties.
A proposed solution: ZIP codes -> ZCTAs -> counties. This package contains data for connecting these links using official US Census relationship files and ZIP-to-ZCTA crosswalk files created by John Snow, Inc.
# Useful Links
- [Census File Data Source](https://www.census.gov/geographies/reference-files/time-series/geo/relationship-files.2010.html#par_textimage_674173622)
- [Explanation of the 2010 ZCTA to County Relationship File](http://www2.census.gov/geo/pdfs/maps-data/data/rel/explanation_zcta_county_rel_10.pdf)
- [Census ZIP Code Tabulation Areas page](https://www.census.gov/programs-surveys/geography/guidance/geo-areas/zctas.html)
- [2010 ZIP Code Tabulation Area (ZCTA) Relationship File Layouts and Contents](https://www.census.gov/programs-surveys/geography/technical-documentation/records-layout/2010-zcta-record-layout.html)
- [UDS Mapper](https://udsmapper.org/) hosts the ZCTA-ZIP crosswalk file, which should be able to link ZIP codes to 2010 Census ZCTAs. Newer crosswalks are also available from UDS Mapper.
- [The ZIP Code Resources Page](http://mcdc.missouri.edu/allabout/zipcodes.html)
- [All About ZIP Codes: 2010 Supplement](http://mcdc.missouri.edu/allabout/zipcodes_2010supplement.shtml)
# Installation
```r
# install.packages("devtools")
devtools::install_github("jjchern/zcta")
```
# Usage
```{r, message=FALSE, warning=FALSE}
# devtools::install_github("jjchern/gaze")
library(tidyverse)
# ZCTA to counties
zcta::zcta_county_rel_10
# ZIP codes to ZCTAs
zcta::zipzcta
# Show variable labels, and whether value label exists for certain variables
# devtools::install_github("larmarange/labelled")
labelled::var_label(zcta::zcta_county_rel_10)
# Total number of zcta records
nrow(zcta::zcta_county_rel_10)
# Number of distinct zcta
zcta::zcta_county_rel_10 %>% distinct(zcta5) %>% nrow()
# In most instances the ZCTA code is the same as the ZIP Code for an area
# But some zctas fall in more than one county
# For example, there're 7060 zctas fall in 2 counties
zcta::zcta_county_rel_10 %>%
group_by(zcta5) %>%
summarise(`Num of counties` = n()) %>%
group_by(`Num of counties`) %>%
summarise(`Num of zctas` = n())
# To get an one-to-one relationship between zcta and county, assign county to
# a zcta if the zcta has the most population. For Example:
# Before: zcta 601 fall in county 72001 and 72141
zcta::zcta_county_rel_10 %>%
select(zcta5, state, county, geoid, poppt, zpoppct)
# After: relate zcta 601 only to county 72001 as it accounts for 99.43% of the population
one_to_one_pop <- zcta::zcta_county_rel_10 %>%
select(zcta5, state, county, geoid, poppt, zpoppct) %>%
group_by(zcta5) %>%
slice(which.max(zpoppct)) %>%
ungroup()
one_to_one_pop
# Or assign county to a zcta if the zcta accounts for most of the area.
one_to_one_area <- zcta::zcta_county_rel_10 %>%
select(zcta5, state, county, geoid, poppt, zpoppct, zareapct) %>%
group_by(zcta5) %>%
slice(which.max(zareapct)) %>%
ungroup()
one_to_one_area
# Using either of these ZCTA-to-county tables, you can go from ZIP codes to ZCTAs to county
zipcounty <- zcta::zipzcta %>%
left_join(one_to_one_area, by = c("zcta" = "zcta5")) %>%
select(zip, zcta, state = state.x, countygeoid = geoid) %>%
arrange(zip)
zipcounty
# Merge the two 1 to 1 relationship datasets and identify zctas that have different county match
one_to_one_pop %>%
left_join(one_to_one_area, by = "zcta5") %>%
select(zcta5,
county.x, geoid.x, zpoppct.x,
county.y, geoid.y, zpoppct.y) %>%
filter(geoid.x != geoid.y)
# Get county names for the 1 to 1 relationship dataset
# Also keep just states and DC
one_to_one_pop %>%
mutate(geoid = as.integer(geoid)) %>%
left_join(gaze::county10, by = "geoid") %>%
select(zcta5, state, usps, county, geoid, name) %>%
filter(state <= 56)
```