conferences

acs.R version 1.1: PUMAs and Zip Codes and MSAs, Oh My!

Posted by Ezra Glenn on July 14, 2013
Census, Code, Self-promotion / No Comments

Development continues on the acs package for R, with the latest update (version 1.1) now officially available on the CRAN repository. If you’ve already installed the package in the past, you can easily update with the update.packages() command; if you’ve never installed it, you can just as easily install it for the first time, by simply typing install.packages(“acs”). In either case, be sure to load the library after installing by typing library(acs), and install (or re-install) an API key with api.key.install() — see the documentation and the latest version of the acs user guide (which still references version 1.0).

Beyond improvements described in a previous post about version 1.0, the most significant change in the latest version is support for many more different combinations of census geography via the geo.make function. As described in the manual and on-line help, users can now specify options to create user-defined geographies composed of combinations of states, counties, county subdivisions, tracts, places, blockgroups (all available in the previous version), plus many more: public use microdata areas (PUMAs), metropolitan statistical areas (MSAs), combined statistical areas (CSAs), zip code tabulation areas, census regions and divisions, congressional district and state legislative districts (both upper and lower chambers), American Indian Areas, state school districts (of various types), New England County and Town Areas (NECTAs), and census urban areas. These geographies can be combined to create 25 different census summary levels, which can then even be bundled together to make even more complex geo.sets.

Once created and saved, these new user-defined geo.sets can be fed into the existing acs.fetch function to immediately download data from the ACS for these areas, combining them as desired in the process (and handling all those pesky estimates and margins of error in statistically-appropriate ways.)

We encourage you to update to the latest version and begin to explore the full power of the census data now available through the Census American Community Survey API. (And be sure to subscribe to the acs.R user group mailing list to be informed of future improvements.

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Now on CRAN: acs.R version 1.0

Posted by Ezra Glenn on June 25, 2013
Census, Code, Self-promotion / No Comments

We are pleased to announce that the acs.R package is now ready for prime-time: version 1.0 was officially released last week and is now available on CRAN.1 This version, developed in partnership with the Puget Sound Regional Council, includes all the enhancements described in this post, plus additional tweaks, and lots of documentation.

Just to recap, as of version 1.0:

  • The package is now capable of downloading data directly from the new Census American Community Survey API and importing into R (with proper statistical treatment of estimates and error, variable and geographic relabeling, and more), all through a single “acs.fetch()” function;
  • The package includes a new “geo.make()” function to allow users to create their own custom geographies for organize and download data; and
  • The package provides two special “lookup” tools to help filter through all the existing Census geographies (with the “geo.lookup()” function) and tables (with the “acs.lookup()” function) to find exactly what they want. The acs.lookup function return new “acs.lookup” objects which can be saved, manipulated, and passed to acs.fetch() for downloading data.

I’ve also updated the user guide (version 1.0), which includes step-by-step instructions for working with the package, plus an extended example in the appendix on using blockgroup-level ACS data to create your own neighborhood geographies. (You can also view the complete package manual from the CRAN site.)

Finally, if you’re interested in staying in touch with the ongoing development of the package, be sure to sign up for the acs.R user group mailing list: to register, visit http://mailman.mit.edu/mailman/listinfo/acs-r.

Footnotes:

1 Note: the latest version of this package is actually 1.01, which includes a few additional big-fixes.

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acs-r Mailing List: keep in the loop

Posted by Ezra Glenn on April 24, 2013
Census, Code, Self-promotion / No Comments

We’re pleased to announce the creation of a new mailing list for the acs.R package. The “acs” package allows users to download, manipulate, analyze, and visualize data from the American Community Survey in R; the “acs-r” e-mail list allows members to keep in touch and share information about the package, including updates from the development team concerning improvements, user questions and help requests, worked examples, and more. To register, visit http://mailman.mit.edu/mailman/listinfo/acs-r.

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acs Package at Upcoming Conference: UseR! 2012

Posted by Ezra Glenn on April 09, 2012
Census, Code, Self-promotion / No Comments

I’m happy to report that I’ll be giving a paper on my acs package at the 8th annual useR! conference, Coming June 12-15th to Vanderbilt University in Nashville, TN. The paper is titled “Estimates with Errors and Errors with Estimates: Using the R acs Package for Analysis of American Community Survey Data.” Here’s the abstract:


"Estimates with Errors and Errors with Estimates: Using the R acs
Package for Analysis of American Community Survey Data"
Ezra Haber Glenn

Over the past decade, the U.S. Census Bureau has implemented the
American Community Survey (ACS) as a replacement for its traditional
decennial ``long-form'' survey.  Last year—for the first time
ever—ACS data was made available at the census tract and block group
level for the entire nation, representing geographies small enough to
be useful to local planners; in the future these estimates will be
updated on a yearly basis, providing much more current data than was
ever available in the past.  Although the ACS represents a bold
strategy with great promise for government planners, policy-makers,
and other advocates working at the neighborhood scale, it will require
them to become comfortable with statistical techniques and concerns
that they have traditionally been able to avoid.

To help with this challenge the author has been working with
local-level planners to determine the most common problems associated
with using ACS data, and has implemented these functions as a package
in R.  The package—currently hosted on CRAN in version 0.8—defines
a new ``acs'' class object (containing estimates, standard errors, and
metadata for tables from the ACS), with methods to deal appropriately
with common tasks (e.g., combining subgroups or geographies,
mathematical operations on estimates, tests of significance, plots of
confidence intervals, etc.).

This paper will present both the use and the internal structure of the
package, with discussion of additional lines of development.

Hope to see you all there!

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