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 grpx
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 usi
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 <-
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.
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 t
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
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