compute means of a group by factor

Question

Is there a way that this can be improved, or done more simply?

means.by<-function(data,INDEX){
  b<-by(data,INDEX,function(d)apply(d,2,mean))
  return(         


        

Answer 1

You want tapply or ave, depending on how you want your output:

> Data <- data.frame(grp=sample(letters[1:3],20,TRUE),x=rnorm(20))
> ave(Data$x, Data$grp)
 [1] -0.3258590 -0.5009832 -0.5009832 -0.2136670 -0.3258590 -0.5009832
 [7] -0.3258590 -0.2136670 -0.3258590 -0.2136670 -0.3258590 -0.3258590
[13] -0.3258590 -0.5009832 -0.2136670 -0.5009832 -0.3258590 -0.2136670
[19] -0.5009832 -0.2136670
> tapply(Data$x, Data$grp, mean)
         a          b          c 
-0.5009832 -0.2136670 -0.3258590 

# Example with more than one column:
> Data <- data.frame(grp=sample(letters[1:3],20,TRUE),x=rnorm(20),y=runif(20))
> do.call(rbind,lapply(split(Data[,-1], Data[,1]), mean))
             x         y
a -0.675195494 0.4772696
b  0.270891403 0.5091359
c  0.002756666 0.4053922

Answer 2

Use only the generic function in R.

>d=data.frame(type=as.factor(rep(c("A","B","C"),each=3)),
x=rnorm(9),y=rgamma(9,2,1))
> d
type           x         y
1    A -1.18077326 3.1428680
2    A -0.91930418 4.4606603
3    A  0.88345422 1.0979301
4    B  0.06964133 1.1429911
5    B -1.15380345 2.7609049
6    B  1.13637202 0.6668986
7    C -1.12052765 1.7352306
8    C -1.34803630 2.3099202
9    C -2.23135374 0.7244689
>
> cbind(lm(x~-1+type,data=d)$coef,lm(y~-1+type,data=d)$coef)
         [,1]     [,2]
typeA -0.4055411 2.900486
typeB  0.0174033 1.523598
typeC -1.5666392 1.589873

Answer 3

Does the aggregate function do what you want?

If not, look at the plyr package, it gives several options for taking things apart, doing computations on the pieces, then putting it back together again.

You may also be able to do this using the reshape package.

Answer 4

With plyr

library(plyr)
df <- ddply(x, .(id),function(x) data.frame(
mean=mean(x$var)
))
print(df)

Update:

data<-data.frame(I=as.factor(rep(letters[1:10],each=3)),x=rnorm(30),y=rbinom(30,5,.5))
ddply(data,.(I), function(x) data.frame(x=mean(x$x), y=mean(x$y)))

See, plyr is smart :)

Update 2:

In response to your comment, I believe cast and melt from the reshape package are much simpler for your purpose.

cast(melt(data),I ~ variable, mean)