"acs"The acs class provides a convenient wrapper for demographic data from the U.S. Census, especially the American Community Survey. Estimates and standard errors are kept together, along with geographic information and metadata necessary to manipulate and analyze data in this form.
acs objects can be created by calls of the form new("acs", ...),
or through helper functions provided by the package (currently
read.acs and acs.fetch), or from the output of
subsetting or other calls on existing acs objects. Once created, acs
objects can be manipulated through new methods to deal appropriately
with common analytical tasks such as combining subgroups or
geographies, mathematical operations on estimates, and computing (and
plotting) confidence intervals.
endyear:Object of class "integer" indicating
the last year included in the dataset (e.g., 2012 for data from the
2008--2012 ACS)
span:Object of class "integer" representing the
number of years the dataset spans (e.g., 3 for data from the
2011--2013 ACS); for decennial census datasets (SF1 and SF3), span =
0.
geography:Object of class "data.frame" containing
columns extracted from the data's geographic header: typically
includes geographic place names, census summary level values, and
unique numeric identifiers, but can contain any geographic names or
labels desired. When acs objects are created or modified, the first
geography column will be used to label estimates and standard
errors.
acs.colnames:Object of class "character" giving
the variable names for each column
modified:Object of class "logical" to indicate
whether the object has been modified since construction
acs.units:Object of class "factor" designating the
type of units in each column (e.g., count or percentage or dollars);
only used minimally, to check appropriateness of some operations;
mostly reserved for future functionality
currency.year:Object of class "integer" indicating
the year that all currency values have been adjusted to (by default
the same as endyear, but able to be modified by the user for
comparisons with other years; see currency.convert.)
estimate:Object of class "matrix" holding
the reported ACS estimates
standard.error:Object of class "matrix" holding
the calculated values of the standard errors for each estimate,
derived from the reported 90% confidence intervals
signature(object = "acs"): Standard
accessor function; returns character vector
signature(object = "acs"): Standard
accessor function; returns factor vector
signature(object = "acs"): Standard accessor function; returns integer
signature(object = "acs"): Standard accessor function; returns integer
signature(object = "acs"): Standard accessor
function; returns matrix
signature(object = "acs"): Standard accessor
function; returns data.frame
signature(object = "acs"): Standard accessor
function; return logical
signature(object = "acs"): Standard accessor function; returns integer
signature(object = "acs"): Standard
accessor function; returns matrix
signature(object = "acs"): Aggregates (adds) all
estimates in the object, and adds the corresponding standard
errors in a statistically appropriate way; returns new acs object
signature(object = "acs"): Prints standard summary data on both estimates
and standard errors
signature(object = "acs"): Prints estimates
with confidence intervals
signature(x = "acs"): subsetting works for acs objects
using standard [i,j] square bracket notation, similar to
two-dimensional matrices; returns a new acs object with estimates,
standard errors, and associated metadata for "i" rows (geographies) and
"j" columns (variable columns); essentially, subsetting for this class is
structured to mirror standard operations on matrix objects
signature(x = "acs"): new values may be
replaced/assigned to an existing acs object using standard
[i,j] bracket notation. The assignment can accept a number
of different forms: a valid acs object (including a subsetted one),
a list of two matrices (ideally named "estimate" and "error" or
"standard.error"), or a numeric object which may be coerced into a
matrix (to be used as estimates, with zero-values assigned to
corresponding standard errors).
In addition to these methods, new methods for basic arithmetic functions (+, -, *, /) have been provided to deal appropriately with combining estimates and standard errors.
# NOT RUN {
showClass("acs")
# load some data from the ACS
data(kansas09)
str(kansas09)
# access slots
endyear(kansas09)
span(kansas09)
estimate(kansas09)[1:5,1:5]
standard.error(kansas09[1:5,1:5])
# subset
kansas09[1:4,6:9]
# more complicated subsets
kansas09[c("Linn County, Kansas", "Wilson County, Kansas") ,
grep(pattern="21.years", x=acs.colnames(kansas09))]
# addition on estimates and errors
kansas09[1:4,25]+kansas09[1:4,49]
# can even multiply and divide
# males per female, by county
kansas09[1:4,2]/kansas09[1:4,26]
# (males<5 plus females<5) * 12
(kansas09[7,3]+kansas09[7,27]) * 12
# some replacement: males<5 as a percentage of all males
kansas09[,3]=kansas09[,3]/kansas09[,2]
# }
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