D4 Tutorial: Reproducibly Accessing Census Data in R & Python

Authored By:

Tyler Fricker, Assistant Professor - Geography, University of Louisiana-Monroe

Jessica Godwin, Statistical Demographer - Center for Studies in Demography & Ecology, University of Washington

June Yang, Computational Demographer - Center for Studies in Demography & Ecology, University of Washington

Contents

R tutorial contained in Census-data-in-R.Rmd and Census-data-in-R.html files.

• Dependencies
  • Data/ contains file Shreveport.json necessary for the Areal Interpolation section.
  • Figures/ contains some .png files necessary to knit the .Rmd into an .html.
  • CensusAPIKey.R is a file necessary for running or knitting the R tutorial, but is included in the .gitignore file. Read the R tutorial's section titled Getting and managing your Census API Key for more information on how to set up your own CensusAPIKey.R file.

Python tutorial contained in Census-data-in-Python.ipynb and Census-data-in-Python.html files.

• Dependencies
  • Figures/ contains some .png files necessary to knit the .ipynb into an .html.
  • CensusAPIKey.py is a file necessary for running or knitting the Python tutorial, but is included in the .gitignore file. Read the Python tutorial's section titled Getting and managing your Census API Key for more information on how to set up your own CensusAPIKey.py file.

Tutorial Outline

• Introduction
  • Why use U.S. Census data in climate research?
  • Census geographies and uncertainty
  • Why access Census data with R or Python?
  • Tutorial Outline
• Accessing Census Bureau Data with R Packages
  • The Census API and censusapi
  • IPUMS, NHGIS, and ipums
  • tidycensus and tigris
  • Getting and managing your Census API key
• Using tidycensus
  • Selecting variables and tables
    • Datasets
    • Variable & table names
    • Data Year
    • Geographic scale
    • Temporal scale
  • Querying the Census API
    • get_decennial()
    • get_acs()
  • Dealing with variable labels
• Getting results
  • Basic tables
  • Multi-year results
  • Aggregating estimates across labels
• Census Geographic Data
  • Plotting Geographic Data in R
  • Working with Census Geometries
  • Mapping Census and ACS Estimates in R
    • Map-Making with ggplot2
    • Map-Making with tmap
• Areal Interpolation