Andrew,

Interesting issue.  My tack would be to define an age key that 
incorporates all of the different cut-points that are used in your data 
tables.  Then, with the use of some simple functions, you can test which 
factors are "nested" within other factors, and you can broaden those 
categories as needed.  For example:

# define a key to the age categories
age <- 1:30
agef1 <- cut(age, breaks=seq(0, 120, 10))
agef2 <- cut(age, breaks=seq(0, 120, 20))
agef3 <- cut(age, breaks=seq(0, 120, 25))
age.key <- data.frame(age, agef1, agef2, agef3)
rm(age, agef1, agef2, agef3)

# some raw data
mydat <- structure(list(agef1 = structure(c(3L, 2L, 1L, 2L, 2L, 2L, 3L, 
        2L, 1L, 3L, 1L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 3L, 1L), .Label = 
c("(0,10]", 
        "(10,20]", "(20,30]", "(30,40]", "(40,50]", "(50,60]", "(60,70]", 
        "(70,80]", "(80,90]", "(90,100]", "(100,110]", "(110,120]"), class 
= "factor"), 
        x = c(92, 49, 17, 77, 4, 76, 70, 30, 37, 2, 66, 72, 32, 66, 
        1, 57, 28, 6, 1, 21)), .Names = c("agef1", "x"), class = 
"data.frame", row.names = c("1", 
        "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", 
        "14", "15", "16", "17", "18", "19", "20"))

# a function to test if one factor is "nested" in another
# that is, is each value of f1 associated with only one unique value in 
f2?
is.nested <- function(f1, f2) {
        t12 <- table(f1, f2)
        max(apply(t12>0, 1, sum))==1
        }

# a function to broaden the categories of x from those used in f1 to those 
used in f2
broaden <- function(x, f1, f2) {
        if(!is.nested(f1, f2)) stop("f1 not nested within f2")
        f2[match(x, f1)]
        }

is.nested(age.key$agef1, age.key$agef2)
is.nested(age.key$agef1, age.key$agef3)

broaden(mydat$agef1, age.key$agef1, age.key$agef2)
broaden(mydat$agef1, age.key$agef1, age.key$agef3)

Jean



andrewH <ahoerner@rprogress.org> wrote on 11/27/2012 05:40:53 PM:
> 
> ear folks ?
> 
> I have a question, though it is more of a logic- or a good
> practices-question than a programming question per se. I am working with
> data from the American Community Survey summary file.  It is mainly
> categorical count data. Currently I am working with about 40 tables 
covering
> about 35 variables, mainly in two-way tables, with some 3-way and a 
handful
> of four-way tables. I am going to be doing a lot of analysis on these
> tables, and hope to make them available in zipped format to other R 
users. 
> Right now I am keeping this data in single-state data frames, but I will
> probably have to shift over to a database if I add many more variables.
> 
> Here is my problem: of my 35 variables, five of them are different 
versions
> of age. Different tables cover different age ranges, and have different
> levels of disaggregation for the age ranges they cover.
> 
> Currently I just have a factor for each with the cut-points in the 
labels.
> But I feel uncomfortable with this. It seems to throw away a lot of
> information. There is a ?natural? mapping from the different age ranges 
to
> one another, at least within universes (e.g. individuals vs. heads of
> household), and my current approach does not encode that mapping in any 
way
> that R can notice (unless I write special functions that read the 
labels) 
> 
> One of the first things I am doing with this data is using all the
> cross-tabs to produce some basic estimates of higher-dimensional 
tabulations
> ? some 10-way tables covering age, race, sex, age, rent/own, income, 
etc.
> that are consistent with all the lower-dimensional margins, using a
> multi-dimensional analogue of the RAS balancing (biproportional matrix
> balancing) algorithm often used to update Leontief input-output tables. 
> Right now the approach I am using is to sum the age variables into four
> categories the let me use four of my five age variables, and throw the 
fifth
> (which has inconsistent breakpoints and is used in only one table) away. 
But
> this seems wasteful to me ? not only of one table, but of a lot of
> information on finer age sub-structure which is shared by two or more
> tables. 
> 
> I am guessing that this is a fairly common problem in dealing with large
> data sets of count objects. Is there a ?standard? approach to is, or a 
set
> of commonly used approaches, that anyone could suggest or point me to? 
I?d
> be happy with either coding suggestions or pointers to the methodology
> literature if there is one.
> 
> Any help or suggestions would be greatly appreciated. Thanks! 
> 
> andrewH

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