This example will show you how to setup the tidycensus package in R

citation(package = "tidycensus")
## 
## To cite package 'tidycensus' in publications use:
## 
##   Kyle Walker (2020). tidycensus: Load US Census Boundary and Attribute
##   Data as 'tidyverse' and 'sf'-Ready Data Frames. R package version
##   0.9.5. https://CRAN.R-project.org/package=tidycensus
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {tidycensus: Load US Census Boundary and Attribute Data as 'tidyverse' and
## 'sf'-Ready Data Frames},
##     author = {Kyle Walker},
##     year = {2020},
##     note = {R package version 0.9.5},
##     url = {https://CRAN.R-project.org/package=tidycensus},
##   }

Steps

  1. Go to the US Census Developer API and request a key, it will be emailed to you.
  2. Follow the directions in the email to activate your key
  3. In R install tidycensus using install.packages("tidycensus", dep=T)
  4. Wait for it to install
  5. Once it is finished, load the library, library(tidycensus)
  6. Save your API key to your working directory. Type tidycensus::census_api_key(key = "yourkeyhere", install = T)
  7. Restart R
  8. Test it, below I load the variables for the 2017 5 year ACS Data Profile tables, and search for a variable that includes the text “Built 2000 to 2009”, and display the “name” and “label” fields from the table.
library(tidycensus)
v15_Profile <- load_variables(2017 , "acs5/profile", cache = TRUE) #demographic profile tables
v15_Profile[grep(x = v15_Profile$label, "Built 2000 to 2009"), c("name", "label")]
ABCDEFGHIJ0123456789
name
<chr>
DP04_0019
DP04_0019P
2 rows | 1-1 of 2 columns

I see that the variable DP04_0019P is the Percentage estimate for the % of houses built between 2000 and 2009. Alternatively, you can go to the Census’s list of variables for the ACS Data Profile tables here and search.

I can extract the variable, in this case for Texas counties and their associated geographies using:

Make a quick interactive map

Below, I use the mapview library to quikly map the data. You must have mapview installed for this to work

library(mapview)
mapview(mydat["DP04_0019PE"], legend = T, layer.name = "% Housing built 2000 to 2009")
+−
% Housing built 2000 to 2009
510152025303540

200 km
100 mi
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