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(This article was first published on <strong><a href="https://blog.revolutionanalytics.com/2018/11/working-with-us-census-data-in-r.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>If you need data about the American populace, there's no source more canonical than the US Census Bureau. The bureau publishes a wide range of public sets, and not just from the main Census conducted every 10 years: there are more than <a rel="nofollow" href="https://www.census.gov/programs-surveys/surveys-programs.html" target="_blank">100 additional surveys and programs</a> published as well. To help R users access this rich source of data, Ari Lamstein and Logan Powell have published <a rel="nofollow" href="https://rconsortium.github.io/censusguide/" target="_blank">A Guide to Working with US Census Data in R</a>, a publication of the R Consortium Census Working Group.</p>
<p>The guide provides an overview of the data available from the US census bureau and various tools available in R to access and analyze it. The guide notes that there are <a rel="nofollow" href="https://rconsortium.github.io/censusguide/r-packages-all.html" target="_blank">22 R packages for working with census data</a>, and cites as being particularly useful:</p>
<ul>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/tigris/index.html" target="_blank">tigris</a>, for working with shape files of census regions (census data is may be aggregated to any of a number of levels as shown int the diagram below)</li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/acs/index.html" target="_blank">acs</a>, for downloading and managing data from the decennial census and the American Community Survey</li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/choroplethr/index.html" target="_blank">choroplethr</a> and <a rel="nofollow" href="https://cran.r-project.org/web/packages/choroplethrMaps/index.html" target="_blank">choroplethrMaps</a>, for mapping data (including census data) by region</li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/tidycensus/index.html" target="_blank">tidycensus</a>, to extract census data as tidy data frames</li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/censusapi/index.html" target="_blank">censusapi</a>, for extracting data using the Census API</li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/ipumsr/index.html" target="_blank">ipumsr</a>, to extract US census data in a form that can be compared with data from other countries </li>
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<div><a rel="nofollow" href="https://a6.typepad.com/6a0105360ba1c6970c022ad39e8d96200d-pi" target="_blank"><img border="0" alt="Census-geo" title="Census-geo" src="https://a6.typepad.com/6a0105360ba1c6970c022ad39e8d96200d-800wi"></a>
<div><a rel="nofollow" href="https://www2.census.gov/geo/pdfs/reference/geodiagram.pdf" target="_blank">Standard Hierarchy of Census Geographic Entities</a>, US Census Bureau.</div>
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<p>You can find the complete guide at the link below.</p>
<p>R Consortium: <a rel="nofollow" href="https://rconsortium.github.io/censusguide/" target="_blank">A Guide to Working with US Census Data in R</a></p>
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