In jdjohn215/JDJcensus: Some custom tools for quickly accessing census data

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(JDJcensus)
library(tidyverse)

This package includes tools to make accessing census data faster. It wraps commands from the excellent tidycensus package, which pulls from the Census API. If you haven't already, you'll need to install a census API key. Sign up for an API key here. Then follow these instructions to install it in your .Renviron file for repeated use.

JDJcensus builds on tidycensus by providing a built-in dataframe with all variable names from different data sources combined. It also returns census data with the data source, year, and text label appended.

Getting started

Install the package this way.

devtools::install_github("jdjohn215/JDJcensus")

JDJcensus includes a built in file showing all the available variables from 2010-2018 American Community Surveys (ACS) and the 1990, 2000, 2010 SF1 decennial census products. You can access it by running View(AllVariables). It looks like this

reactable::reactable(AllVariables, filterable = TRUE, searchable = TRUE, defaultPageSize = 5,
                     striped = TRUE)

Downloading data

You can identify the table you want to download either with "table" code or the "concept" label. For example, the SEX BY AGE concept is table code B01001.

Use either the table code or concept label in the table_download function.

This example downloads the selected table for census tracts in Milwaukee County, Wisconsin from the 2018 5YR ACS.

table_download("HISPANIC OR LATINO ORIGIN BY RACE",
               geography = "tract", source = "acs", year = 2018, 
               state = "WI", county = "MILWAUKEE")

This example downloads the same table for each state in the 2010 decennial census.

table_download("HISPANIC OR LATINO ORIGIN BY RACE",
               geography = "state", source = "sf1", year = 2010)

Commonly, I find myself downloading data for the same area over and over again. Here's an example of a simple helper function that can speed up that process.

quick_acs <- function(table){
  table_download(table = table, geography = "tract", source = "acs", year = 2018,
                 state = "WI", county = "MILWAUKEE")
}

quick_acs("MEDIAN AGE BY SEX")