README.md

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

README

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

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")
#> Joining, by = c("variable", "year")
#> # A tibble: 6,258 x 7
#>    GEOID   NAME           variable estimate   moe  year label              
#>    <chr>   <chr>          <chr>       <dbl> <dbl> <dbl> <chr>              
#>  1 550790… Census Tract … B03002_…     4476   437  2018 Estimate!!Total    
#>  2 550790… Census Tract … B03002_…     4090   478  2018 Estimate!!Total!!N…
#>  3 550790… Census Tract … B03002_…     1047   308  2018 Estimate!!Total!!N…
#>  4 550790… Census Tract … B03002_…     2879   504  2018 Estimate!!Total!!N…
#>  5 550790… Census Tract … B03002_…        0     9  2018 Estimate!!Total!!N…
#>  6 550790… Census Tract … B03002_…       64    58  2018 Estimate!!Total!!N…
#>  7 550790… Census Tract … B03002_…        0     9  2018 Estimate!!Total!!N…
#>  8 550790… Census Tract … B03002_…        0     9  2018 Estimate!!Total!!N…
#>  9 550790… Census Tract … B03002_…      100    96  2018 Estimate!!Total!!N…
#> 10 550790… Census Tract … B03002_…       28    46  2018 Estimate!!Total!!N…
#> # … with 6,248 more rows

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)
#> Joining, by = c("variable", "year")
#> # A tibble: 884 x 6
#>    GEOID NAME        variable    value  year label
#>    <chr> <chr>       <chr>       <dbl> <dbl> <chr>
#>  1 01    Alabama     P005001   4779736  2010 Total
#>  2 02    Alaska      P005001    710231  2010 Total
#>  3 04    Arizona     P005001   6392017  2010 Total
#>  4 05    Arkansas    P005001   2915918  2010 Total
#>  5 06    California  P005001  37253956  2010 Total
#>  6 22    Louisiana   P005001   4533372  2010 Total
#>  7 21    Kentucky    P005001   4339367  2010 Total
#>  8 08    Colorado    P005001   5029196  2010 Total
#>  9 09    Connecticut P005001   3574097  2010 Total
#> 10 10    Delaware    P005001    897934  2010 Total
#> # … with 874 more rows

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")
#> Joining, by = c("variable", "year")
#> # A tibble: 894 x 7
#>    GEOID   NAME                variable  estimate   moe  year label        
#>    <chr>   <chr>               <chr>        <dbl> <dbl> <dbl> <chr>        
#>  1 550790… Census Tract 1.01,… B01002_0…     30.7   3.6  2018 Estimate!!Me…
#>  2 550790… Census Tract 1.01,… B01002_0…     29.4   6.6  2018 Estimate!!Me…
#>  3 550790… Census Tract 1.01,… B01002_0…     32.8  11    2018 Estimate!!Me…
#>  4 550790… Census Tract 1.02,… B01002_0…     42.2   6.4  2018 Estimate!!Me…
#>  5 550790… Census Tract 1.02,… B01002_0…     32.6   3.7  2018 Estimate!!Me…
#>  6 550790… Census Tract 1.02,… B01002_0…     48     4.2  2018 Estimate!!Me…
#>  7 550790… Census Tract 2.01,… B01002_0…     25.3   3.3  2018 Estimate!!Me…
#>  8 550790… Census Tract 2.01,… B01002_0…     21.6   7.9  2018 Estimate!!Me…
#>  9 550790… Census Tract 2.01,… B01002_0…     31     7.3  2018 Estimate!!Me…
#> 10 550790… Census Tract 2.02,… B01002_0…     35.2   2.1  2018 Estimate!!Me…
#> # … with 884 more rows

jdjohn215/JDJcensus documentation built on Jan. 9, 2020, 12:12 a.m.