Re: [R] Using apply to get group means
That is precisely the reason for the existence of the ave function.
Using Wickham's example:
> x1 <- rep(c("A", "B", "C"), 3)
> x2 <- c(rep(1, 3), rep(2, 3), 1, 2, 1)
> x3 <- c(1, 2, 3, 4, 5, 6, 2, 6, 4)
> df <- data.frame(x1, x2, x3)
> df$grpx3 <- ave(df$x3, list(x1,x2))
> df
x1 x2 x3 grpx3
1 A 1 1 1.5
2 B 1 2 2.0
3 C 1 3 3.5
4 A 2 4 4.0
5 B 2 5 5.5
6 C 2 6 6.0
7 A 1 2 1.5
8 B 2 6 5.5
9 C 1 4 3.5
Note that the default function is mean() but other functions could be
specified.
--
David Winsemius
On Mar 31, 2009, at 12:09 PM, Alan Cohen wrote:
Hi all,
I'm trying to improve my R skills and make my programming more
efficient and succinct. I can solve the following question, but
wonder if there's a better way to do it:
I'm trying to calculate mean by several variables and then put this
back into the original data set as a new variable. For example, if
I were measuring weight, I might want to have each individual's
weight, and also the group mean by, say, race, sex, and geographic
region. The following code works:
x1<-rep(c("A","B","C"),3)
x2<-c(rep(1,3),rep(2,3),1,2,1)
x3<-c(1,2,3,4,5,6,2,6,4)
x<-as.data.frame(cbind(x1,x2,x3))
x3.mean<-rep(0,nrow(x))
for (i in 1:nrow(x)){
+ x3.mean[i]<-mean(as.numeric(x[,3][x[,1]==x[,1][i]&x[,2]==x[,2]
[i]]))
+ }
cbind(x,x3.mean)
x1 x2 x3 x3.mean
1 A 1 1 1.5
2 B 1 2 2.0
3 C 1 3 3.5
4 A 2 4 4.0
5 B 2 5 5.5
6 C 2 6 6.0
7 A 1 2 1.5
8 B 2 6 5.5
9 C 1 4 3.5
However, I'd love to be able to do this with "apply" rather than a
for-loop. Or is there a built-in function? Any suggestions?
Also, any way to avoid the hassles with having to convert to a data
frame and then again to numeric when one variable is character?
Cheers,
Alan Cohen
David Winsemius, MD
Heritage Laboratories
West Hartford, CT
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Re: [R] Using apply to get group means
Sorry, there was a mistake in the previous mail:
Domenico Vistocco wrote:
A different solution (using aggregate for the table of means and merge
for adding it to the dataframe):
x1<-rep(c("A","B","C"),3)
x2<-c(rep(1,3),rep(2,3),1,2,1)
x3<-c(1,2,3,4,5,6,2,6,4)
x<-data.frame(x1,x2,x3) #here using data.frame the x1 variable is
directly converted to factor
x3means <- aggregate(x$x3, by=list(x$x1), FUN="mean")
merge(x, x3means, by.x="x1", by.y="Group.1")
#I forgot the second variable in the by argument (both for aggregate and
by):
x3means <- aggregate(x$x3, by=list(x$x1, x$x2), FUN="mean")
merge(x, x3means, by.x=c("x1","x2"), by.y=c("Group.1", "Group.2"))
Ciao,
domenico
Alan Cohen wrote:
Hi all,
I'm trying to improve my R skills and make my programming more
efficient and succinct. I can solve the following question, but
wonder if there's a better way to do it:
I'm trying to calculate mean by several variables and then put this
back into the original data set as a new variable. For example, if I
were measuring weight, I might want to have each individual's weight,
and also the group mean by, say, race, sex, and geographic region.
The following code works:
x1<-rep(c("A","B","C"),3)
x2<-c(rep(1,3),rep(2,3),1,2,1)
x3<-c(1,2,3,4,5,6,2,6,4)
x<-as.data.frame(cbind(x1,x2,x3))
x3.mean<-rep(0,nrow(x))
for (i in 1:nrow(x)){
+ x3.mean[i]<-mean(as.numeric(x[,3][x[,1]==x[,1][i]&x[,2]==x[,2][i]]))
+ }
cbind(x,x3.mean)
x1 x2 x3 x3.mean
1 A 1 1 1.5
2 B 1 2 2.0
3 C 1 3 3.5
4 A 2 4 4.0
5 B 2 5 5.5
6 C 2 6 6.0
7 A 1 2 1.5
8 B 2 6 5.5
9 C 1 4 3.5
However, I'd love to be able to do this with "apply" rather than a
for-loop. Or is there a built-in function? Any suggestions?
Also, any way to avoid the hassles with having to convert to a data
frame and then again to numeric when one variable is character?
Cheers,
Alan Cohen
__
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PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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PLEASE do read the posting guide
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and provide commented, minimal, self-contained, reproducible code.
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] Using apply to get group means
A different solution (using aggregate for the table of means and merge
for adding it to the dataframe):
x1<-rep(c("A","B","C"),3)
x2<-c(rep(1,3),rep(2,3),1,2,1)
x3<-c(1,2,3,4,5,6,2,6,4)
x<-data.frame(x1,x2,x3) #here using data.frame the x1 variable is directly
converted to factor
x3means <- aggregate(x$x3, by=list(x$x1), FUN="mean")
merge(x, x3means, by.x="x1", by.y="Group.1")
Ciao,
domenico
Alan Cohen wrote:
Hi all,
I'm trying to improve my R skills and make my programming more efficient and
succinct. I can solve the following question, but wonder if there's a better
way to do it:
I'm trying to calculate mean by several variables and then put this back into
the original data set as a new variable. For example, if I were measuring
weight, I might want to have each individual's weight, and also the group mean
by, say, race, sex, and geographic region. The following code works:
x1<-rep(c("A","B","C"),3)
x2<-c(rep(1,3),rep(2,3),1,2,1)
x3<-c(1,2,3,4,5,6,2,6,4)
x<-as.data.frame(cbind(x1,x2,x3))
x3.mean<-rep(0,nrow(x))
for (i in 1:nrow(x)){
+ x3.mean[i]<-mean(as.numeric(x[,3][x[,1]==x[,1][i]&x[,2]==x[,2][i]]))
+ }
cbind(x,x3.mean)
x1 x2 x3 x3.mean
1 A 1 1 1.5
2 B 1 2 2.0
3 C 1 3 3.5
4 A 2 4 4.0
5 B 2 5 5.5
6 C 2 6 6.0
7 A 1 2 1.5
8 B 2 6 5.5
9 C 1 4 3.5
However, I'd love to be able to do this with "apply" rather than a for-loop.
Or is there a built-in function? Any suggestions?
Also, any way to avoid the hassles with having to convert to a data frame and
then again to numeric when one variable is character?
Cheers,
Alan Cohen
__
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https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
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and provide commented, minimal, self-contained, reproducible code.
Re: [R] Using apply to get group means
On Tue, Mar 31, 2009 at 11:31 AM, baptiste auguie wrote:
> Not exactly the output you asked for, but perhaps you can consider,
>
> library(doBy)
>> summaryBy(x3~x2+x1,data=x,FUN=mean)
>>
>> x2 x1 x3.mean
>> 1 1 A 1.5
>> 2 1 B 2.0
>> 3 1 C 3.5
>> 4 2 A 4.0
>> 5 2 B 5.5
>> 6 2 C 6.0
>
>
> the plyr package also provides similar functionality, as do the ?by, ?ave,
> and ?tapply base functions.
In plyr it would look like:
x1 <- rep(c("A", "B", "C"), 3)
x2 <- c(rep(1, 3), rep(2, 3), 1, 2, 1)
x3 <- c(1, 2, 3, 4, 5, 6, 2, 6, 4)
df <- data.frame(x1, x2, x3)
ddply(df, .(x1, x2), transform, x3.mean = mean(x3))
Note how I created the data frame - only use cbind if you want a
matrix (i.e. all the columns have the same type)
Hadley
--
http://had.co.nz/
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Re: [R] Using apply to get group means
Not exactly the output you asked for, but perhaps you can consider,
library(doBy)
> summaryBy(x3~x2+x1,data=x,FUN=mean)
x2 x1 x3.mean
1 1 A 1.5
2 1 B 2.0
3 1 C 3.5
4 2 A 4.0
5 2 B 5.5
6 2 C 6.0
the plyr package also provides similar functionality, as do the ?by, ?
ave, and ?tapply base functions.
HTH,
baptiste
On 31 Mar 2009, at 17:09, Alan Cohen wrote:
Hi all,
I'm trying to improve my R skills and make my programming more
efficient and succinct. I can solve the following question, but
wonder if there's a better way to do it:
I'm trying to calculate mean by several variables and then put this
back into the original data set as a new variable. For example, if
I were measuring weight, I might want to have each individual's
weight, and also the group mean by, say, race, sex, and geographic
region. The following code works:
x1<-rep(c("A","B","C"),3)
x2<-c(rep(1,3),rep(2,3),1,2,1)
x3<-c(1,2,3,4,5,6,2,6,4)
x<-as.data.frame(cbind(x1,x2,x3))
x3.mean<-rep(0,nrow(x))
for (i in 1:nrow(x)){
+ x3.mean[i]<-mean(as.numeric(x[,3][x[,1]==x[,1][i]&x[,2]==x[,2]
[i]]))
+ }
cbind(x,x3.mean)
x1 x2 x3 x3.mean
1 A 1 1 1.5
2 B 1 2 2.0
3 C 1 3 3.5
4 A 2 4 4.0
5 B 2 5 5.5
6 C 2 6 6.0
7 A 1 2 1.5
8 B 2 6 5.5
9 C 1 4 3.5
However, I'd love to be able to do this with "apply" rather than a
for-loop. Or is there a built-in function? Any suggestions?
Also, any way to avoid the hassles with having to convert to a data
frame and then again to numeric when one variable is character?
Cheers,
Alan Cohen
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
_
Baptiste Auguié
School of Physics
University of Exeter
Stocker Road,
Exeter, Devon,
EX4 4QL, UK
Phone: +44 1392 264187
http://newton.ex.ac.uk/research/emag
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and provide commented, minimal, self-contained, reproducible code.
[R] Using apply to get group means
Hi all,
I'm trying to improve my R skills and make my programming more efficient and
succinct. I can solve the following question, but wonder if there's a better
way to do it:
I'm trying to calculate mean by several variables and then put this back into
the original data set as a new variable. For example, if I were measuring
weight, I might want to have each individual's weight, and also the group mean
by, say, race, sex, and geographic region. The following code works:
> x1<-rep(c("A","B","C"),3)
> x2<-c(rep(1,3),rep(2,3),1,2,1)
> x3<-c(1,2,3,4,5,6,2,6,4)
> x<-as.data.frame(cbind(x1,x2,x3))
> x3.mean<-rep(0,nrow(x))
> for (i in 1:nrow(x)){
+ x3.mean[i]<-mean(as.numeric(x[,3][x[,1]==x[,1][i]&x[,2]==x[,2][i]]))
+ }
> cbind(x,x3.mean)
x1 x2 x3 x3.mean
1 A 1 1 1.5
2 B 1 2 2.0
3 C 1 3 3.5
4 A 2 4 4.0
5 B 2 5 5.5
6 C 2 6 6.0
7 A 1 2 1.5
8 B 2 6 5.5
9 C 1 4 3.5
However, I'd love to be able to do this with "apply" rather than a for-loop.
Or is there a built-in function? Any suggestions?
Also, any way to avoid the hassles with having to convert to a data frame and
then again to numeric when one variable is character?
Cheers,
Alan Cohen
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
