Provides a general toolkit for downloading, managing, analyzing, and presenting data from the U.S. Census, including SF1 (Decennial short-form), SF3 (Decennial long-form), and the American Community Survey (ACS). Confidence intervals provided with ACS data are converted to standard errors to be bundled with estimates in complex acs objects. Package provides new methods to conduct standard operations on acs objects and present/plot data in statistically appropriate ways.
Ezra Haber Glenn email@example.com
The current version of the package is 2.1.3, released in March, 2018. This extremely minor update corrects a problem related to acs.lookup for acs tables for 2016.
Please note: this fix is more of a “workaround” than a true fix: starting with the 2016 release, the Census Bureau changed the format for the XML variable lookup tables and calls to acs.lookup (and acs.fetch) were failing; the quick solution was to simply use the 2015 lookup tables for these requests, which should be safe in most situations, since table numbers and variable codes generally do not change from year to year. (In some situations this assumption is not true: see https://www.census.gov/programs-surveys/acs/technical-documentation/table-and-geography-changes/2016/5-year.html for details.)
No other aspects of the package were changed with this release.
The previous versions (2.1.2, released in September 2017; 2.1.0 and 2.1.1, both released in July 2017) were minor updates to replace the package’s dependency on RCurl (specifically, RCurl::urlGet and RCurl::url_exists) with similar httr functions and address some https/TLS incompatability issues between RCurl and users with Windows environments, all necessary to accommodate changes in the Census API format, including a shift to https transfer. Other changes included removing plyr from a “dependency” and simply importing the required “rbind.fill” function, and updating cbind/rbind options to be consistent with S3 methods.
In March, 2016, acs version 2.0 was released, considered a substantial update over the previous version 1.2 due to (1) a major expansion in the number of datasets available and (2) a modification to the acs.fetch and acs.lookup options, which now require a user to explicitly specify “endyear=” for all calls.
As of this version, the package provides full support for all ACS, SF1, and SF3 data currently available via the Census API, including ACS data from 2005-2015 and Decennial data from 1990, 2000, and 2010.
You can track development of the
acs package at http://eglenn.scripts.mit.edu/citystate/.
To install the updated version, simply fire up an R session and type:
The package maintainer recommends two additional (optional) steps to improve performance:
To use the package to download data via the American Community Survey application program interface (API), users need to request an API key from the Census. See http://www.census.gov/developers/tos/key_request.html.
The package includes a function, api.key.install, to allow users to save their key in the package data directory, where it can be found and used automatically for future sessions:
> # do this once, you never need to do it again > api.key.install(key="592bc14cnotarealkey686552b17fda3c89dd389")
If a user has previously installed a key, it may be lost during the update process. If the “clean” option has been set as part of the update, the package configure scripts will attempt to migrate the key to a new location. Failing this, the install script will suggest that users run api.key.migrate() after installation, which might resolve the issue.
At worst, if both migration methods fail, you can simply re-run api.key.install() with your original key and be good to go.
To obtain variable codes and other metadata needed to access the Census API, both “acs.fetch” and “acs.lookup” must consult various XML lookup files, which are provided by the Census with each data release. As of version 2.0 these files are accessed online at run-time for each query (a change made to keep the package-size small to conform with CRAN policies). As an alternative to package-time installation of lookup tables, users may run “acs.tables.install()” after installation to download and archive all current tables (approximately 10MB, as of version 2.0 release).
Use of this function is completely optional and the package should work fine without it (assuming the computer is online and is able to access the lookup tables), but running it once will result in faster searches and quicker downloads for all subsequent sessions. (The results are saved and archived, so once a user has run the function, it is unnecessary to run again, unless the acs package is re-installed or updated.)
If you’ve previously installed the package, you can upgrade with:
Remember to re-run acs.tables.install() after upgrading (see above).
The package includes a number of functions with advanced options, to allow users to work with data from the Census in any number of different ways. That said, the general workflow is fairly simple:
install and load the package, and (optionally) install an API key;
create a geo.set using the geo.make() function;
optionally, use the acs.lookup() function to explore the variables you may want to download;
use the acs.fetch() function to download data for your new geography; and then
use the existing functions in the package to work with your data.
To learn more, consult the following:
the printed manual pages for the package;
[The CityState webpage] (http://eglenn.scripts.mit.edu/citystate/ “CityState”)
endyear is now required: under the old package, acs.fetch and acs.lookup would default to endyear=2011 when no endyear was provided. This seemed smart at the time – 2011 was the most recent data available – but it is becoming increasingly absurd. One solution would have been to change the default to be whatever data is most recent, but that would have the unintended result of making the same script run differently from one year to the next: bad mojo. So the new preferred “version 1.3 solution” is to require users to explicitly indicate the endyear that they want to fetch each time.
New ACS Data: the package now provides on-board support for all endyears and spans currently available through the API, including:
American Community Survey 5-Year Data (2005-2009 through 2010-2015)
American Community Survey 3 Year Data (2013, 2012)
American Community Survey 1 Year Data (2015, 2014, 2013, 2012, 2011)
See http://www.census.gov/data/developers/data-sets.html for more info, including guidance about which geographies are provided for each dataset.
Decennial Census Data: the package now includes the ability to download Decennial Data from the SF1 and SF3, using the same acs.fetch() function used for ACS data. Data includes:
SF1/Short-Form (1990, 2000, 2010)
SF3/Long-Form (1990, 2000)
CPI tables: the CPI tables used for currency.year() and currency.convert() have been updated to include data up through 2015.
acs.fetching with saved acs.lookup results: the results of acs.lookup can still be saved and passed to acs.fetch via the “variable=” option, with a slight change: under v. 1.2, the passed acs.lookup results would overrule any explicit endyear or span; with v. 1.3, the opposite is true (the endyear and span in the acs.lookup results are ignored by acs.fetch). This may seem insignificant, but it will eventually be important, when users want to fetch data from years that are more recent than the version of the package, and need to use old lookup results to do so.
divide.acs fixes: the package includes a more robust divide.acs() function, which handles zero denominators better and takes full advantage of the potential for reduced standard errors when dividing proportions.
treatment of SF1/SF3 data: When fetched via acs.fetch(), this data is downloaded and converted to acs-class objects. (Note: standard errors for Decennial data will always be zero, which is technically not correct for SF3 survey data, but no margins of error are reported by the API.) See http://www.census.gov/data/developers/data-sets.html for more info.
1990 table names and numbers: Census support for the 1990 data has been a bit inconsistent – the variable lookup tables were not in the same format as others, and far less descriptive information has been provided about table and variable names. This can make it tricky to find and fetch data, but if you know what you want, you can probably find it; looking in the files in package’s extdata directory might help give you a sense of what the variable codes and table numbers look like.