After I sent this, a colleague referred me to the GPT-4 interface on Bing. I
entered the exact email query below and it provided the following solution,
which worked for the toy example and was successfully adapted to my application:
# Define the cutoffs
cutoffs <- seq(0, 0.15, by = 0.01)
# Create a new column with cutoffs
dummydata$Cutoff <- cut(dummydata$Pct, breaks = cutoffs, labels =
cutoffs[-1])
# Aggregate Totpop by Cutoff
result <- aggregate(Totpop ~ Cutoff, data = dummydata, sum)
# Rename the columns
names(result) <- c("Cutoff", "Pop")
# Print the result
result
So thanks to all for considering this query?we're in a brave new world of
AI-generated coding.
Message: 3
Date: Fri, 13 Oct 2023 20:13:56 +0000
From: "Jason Stout, M.D." <jason.stout at duke.edu>
To: "r-help at r-project.org" <r-help at r-project.org>
Subject: [R] Create new data frame with conditional sums
Message-ID:
<BN8PR05MB60663B9D27B40E8C240A387981D2A at
BN8PR05MB6066.namprd05.prod.outlook.com>
Content-Type: text/plain; charset="utf-8"
This seems like it should be simple but I can't get it to work properly.
I'm starting with a data frame like this:
Tract Pct Totpop
1 0.05 4000
2 0.03 3500
3 0.01 4500
4 0.12 4100
5 0.21 3900
6 0.04 4250
7 0.07 5100
8 0.09 4700
9 0.06 4950
10 0.03 4800
And I want to end up with a data frame with two columns, a "Cutoff"
column that is a simple sequence of equally spaced cutoffs (let's say in
this case from 0-0.15 by 0.01) and a "Pop" column which equals the sum
of "Totpop" in the prior data frame in which "Pct" is
greater than or equal to "cutoff." So in this toy example, this is
what I want for a result:
Cutoff Pop
1 0.00 43800
2 0.01 43800
3 0.02 39300
4 0.03 39300
5 0.04 31000
6 0.05 26750
7 0.06 22750
8 0.07 17800
9 0.08 12700
10 0.09 12700
11 0.10 8000
12 0.11 8000
13 0.12 8000
14 0.13 3900
15 0.14 3900
16 0.15 3900
I can do this with a for loop but it seems there should be an easier, vectorized
way that would be more efficient. Here is a reproducible example:
dummydata<-data.frame(Tract=seq(1,10,by=1),Pct=c(0.05,0.03,0.01,0.12,0.21,0.04,0.07,0.09,0.06,0.03),Totpop=c(4000,3500,4500,4100,
3900,4250,5100,4700,
4950,4800))
dfrm<-data.frame(matrix(ncol=2,nrow=0,dimnames=list(NULL,c("Cutoff","Pop"))))
for (i in seq(0,0.15,by=0.01)) {
temp<-sum(dummydata[dummydata$Pct>=i,"Totpop"])
dfrm[nrow(dfrm)+1,]<-c(i,temp)
}
Jason Stout, MD, MHS
Box 102359-DUMC
Durham, NC 27710
FAX 919-681-7494
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