On Aug 6, 2013, at 10:17 AM, Gavin Rudge wrote:
> #some sample data:
> library(Hmisc)
> set.seed(33)
> df<-data.frame(x=(sample(letters[1:10],1000,replace=TRUE)),y=rnorm(1000,mean=20,sd=15))
>
> x is a category from a to J, say a geographical area, into which an
> observation y falls, y being a score. Now if I want to put my score into
> quantiles (quintiles in this case) across the whole population of
> observations and then make a quintile variable I do the following:
>
> #make a quintile variable
> df<- within(df,z<-as.integer(cut2(y,quantile(y,probs=seq(0,1,0.2)))))
>
> I'm using cut2 here as I want the extremes of my ranges to be included in the
> upper and lower bins.
>
> So far so good, but I would also like another variable to indicate the
> quintile of the score within the areas indicated by the x variable, so all of
> the scores where x=a, binned into quintiles for area a, the same for scores
> in areas b, c and so on.
>
> I see that I could put my quintile variable code into a function and then
> split my data frame by x, apply the function in each of the ten groups and
> stitch the whole thing back together again (not sure I could write it
> though), but is there a much simpler solution?
>
Generally questions involving the distribution of a single variate grouped
within categories where the desired result is as long as the original variate
are well handled with th `ave` function:
> df$c2.grp <- ave(df$y, df$x, FUN=function(z) cut2(z,
> quantile(z,probs=seq(0,1,0.2)) ) )
> str(df)
'data.frame': 1000 obs. of 3 variables:
$ x : Factor w/ 10 levels "a","b","c","d",..: 5 4 5 10 9 6 5 4 1 2 ...
$ y : num 15 45.3 29.9 45.2 23.3 ...
$ c2.grp: num 2 5 4 5 3 4 2 4 3 2 ...
I was a bit surprised that the resulting column in df was numeric rather than
factor, but I suspect it was the fact that the levels of the intra-groups
splits could not be reconciled. You didn't apparently consider that issue in
your problem specification. The result could be "cleaned up" with:
> df$c2.grp <- factor(df$c2.grp, labels=paste0("Q", 1:5) )
> with(df, table(x, c2.grp))
c2.grp
x Q1 Q2 Q3 Q4 Q5
a 22 23 22 22 22
b 19 19 18 19 19
c 21 20 20 20 21
d 20 19 19 19 20
e 19 20 21 20 20
f 21 21 21 21 22
g 21 21 21 21 22
h 19 19 19 19 19
i 18 18 17 18 18
j 20 20 19 20 20
--
David Winsemius
Alameda, CA, USA
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