You want cut and tapply, perhaps along these lines:
## Your data frame:
a <- data.frame(patient=1:6, charges=c(100,500,200,90,400,500),
age=c(0,3,5,7,10,16),
race=c("black","white","hispanic","asian","hispanic","black"))
## Add an age category:
a <- cbind(a, age_category=cut(a$age, breaks=c(-Inf,4,11,17)))
## Calculate average charges per age category and race
with(a, tapply(charges, list(age_category, race), mean))
# asian black hispanic white
# (-Inf,4] NA 100 NA 500
# (4,11] 90 NA 300 NA
# (11,17] NA 500 NA NA
Hope this helps.
Allan.
alamoboy wrote:
Newbie here. Many apologies in advance for using the incorrect lingo. I'm
new to statistics and VERY new to R.
I'm attempting to "group" or "bin" data together in order to analyze them as
a combined group rather than as discrete set. I'll provide a simple example
of the data for illustrative purposes.
Patient ID | Charges | Age | Race
1 | 100 | 0 | Black
2 | 500 | 3 | White
3 | 200 | 5 | Hispanic
4 | 90 | 7 | Asian
5 | 400 | 10 | Hispanic
6 | 500 | 16 | Black
I'm trying to create three age categories--"0 to 4", "5 to 11" and "12 to
17"--and analyze their "Charges" by their "Race." How do I go abouts to
doing this?
Thanks for any assistance!
Sam
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