ok, i have it - match()
10x all again! :)
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hi guys,
many many thanks for all the solutions! :-D they are working great!
i have another "little" question:
i would like to save these groups in a new column with serial number like
the solution from David, but wit integer values: 1,2,3...
i do this allready but with my 1. solution and there
Hi:
Here's another way:
c1<-c(1,2,3,2,2,3,1,2,2,2)
c2<-c(5,6,7,7,5,7,5,7,6,6)
c3<-rnorm(10)
x <- data.frame(c1 = factor(c1), c2 = factor(c2), c3)
x <- transform(x, mean = ave(c3, c1, c2, FUN = mean))
Yet another with function ddply() in package plyr:
ddply(x, .(c1, c2), transform, mean = mean(c3
On Feb 25, 2011, at 10:14 AM, zem wrote:
Yeah, you are right
i want to post an short example what i want to do .. and in the
meantime i
solved the problem ...
but here is:
i have something like this dataframe:
c1<-c(1,2,3,2,2,3,1,2,2,2)
c2<-c(5,6,7,7,5,7,5,7,6,6)
c3<-rnorm(10)
x<-cbind(c1,c
Ok, now I think I've understood, but I'm not sure since I think that my
ave() solution does work. Although, I though you have several numerical
variables and 1 factor; it is the opposite but it is still possible:
c3_mean <- ave(x[,3], list(x[,1],x[,2]), FUN=mean) #note that values
are differe
Yeah, you are right
i want to post an short example what i want to do .. and in the meantime i
solved the problem ...
but here is:
i have something like this dataframe:
c1<-c(1,2,3,2,2,3,1,2,2,2)
c2<-c(5,6,7,7,5,7,5,7,6,6)
c3<-rnorm(10)
x<-cbind(c1,c2,c3)
> x
c1 c2 c3
[1,] 1 5
10x i solved it ... mein problem was that i had 2 column by them i have to
group, i just "pasted" the values together so that at the end i have one
column to group and then was easy ...
here is the script that i used:
http://tolstoy.newcastle.edu.au/R/help/06/07/30184.html
Ivan thanks for the hel
Hi Ivan,
thanks for your replay!
but the problem is there that the dataframe has 2 rows and ca. 2000
groups, but i dont have the column with the groupnames, because the groups
are depending on 2 onother columns ...
any other idea or i didnt understand waht are you posted ... :(
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View th
Hi,
I think ave() might do what you want:
df <- data.frame(a=rep(c("this","that"),5), b1=rnorm(10), b2=rnorm(10))
ave(df[,2], df[,1], FUN=mean)
For all columns, you could do that:
d <- lapply(df[,2:3], FUN=function(x)ave(x,df[,1],FUN=mean))
df2 <- cbind(df, d)
HTH,
Ivan
Le 2/25/2011 12:11, zem
Hi all,
i have a little problem, and i think it is really simple to solve, but i
dont know exactly how to.
here is the "challange":
i have a data.frame with n colum, i have to group 2 of them and calculate
the mean value of the 3. one. so far so good, that was easy - i used
aggregate function to
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