acs-class {acs} | R Documentation |

`"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

- acs.colnames
`signature(object = "acs")`

: Standard accessor function; returns character vector- acs.units
`signature(object = "acs")`

: Standard accessor function; returns factor vector- currency.year
`signature(object = "acs")`

: Standard accessor function; returns integer- endyear
`signature(object = "acs")`

: Standard accessor function; returns integer- estimate
`signature(object = "acs")`

: Standard accessor function; returns matrix- geography
`signature(object = "acs")`

: Standard accessor function; returns data.frame- modified
`signature(object = "acs")`

: Standard accessor function; return logical- span
`signature(object = "acs")`

: Standard accessor function; returns integer- standard.error
`signature(object = "acs")`

: Standard accessor function; returns matrix- sum
`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- summary
`signature(object = "acs")`

: Prints standard summary data on both estimates and standard errors- confint
`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.

Ezra Haber Glenn eglenn@mit.edu

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]

[Package *acs* version 2.1.4 Index]