Aggregate and Weighted Mean in R

This is also easily done with aggregate. It helps to remember alternate equations for a weighted mean.

rw <- dat$return * dat$assets
dat1 <- aggregate(rw ~ assetclass, data = dat, sum)
datw <- aggregate(assets ~ assetclass, data = dat, sum)
dat1$weighted.return <- dat1$rw / datw$assets

A data.table solution, will be faster than plyr

library(data.table)
DT <- data.table(dat)
DT[,list(wret = weighted.mean(return,assets)),by=assetclass]
##    assetclass        wret
## 1:          A -0.05445455
## 2:          E -0.56614312
## 3:          D -0.43007547
## 4:          B  0.69799701
## 5:          C  0.08850954

For starters, w=(dat$return, dat$assets)) is a syntax error.

And plyr makes this a little easier:

> set.seed(42)   # fix seed so that you get the same results
> dat <- data.frame(assetclass=sample(LETTERS[1:5], 20, replace=TRUE), 
+                   return=rnorm(20), assets=1e7+1e7*runif(20))
> library(plyr)
> ddply(dat, .(assetclass),   # so by asset class invoke following function
+       function(x) data.frame(wret=weighted.mean(x$return, x$assets)))
  assetclass     wret
1          A -2.27292
2          B -0.19969
3          C  0.46448
4          D -0.71354
5          E  0.55354
> 

The recently released collapse package provides a fast solution to this and similar problems (using weighted median, mode etc.) by providing a full set of Fast Statistical Functions performing grouped and weighted computations internally in C++:

library(collapse)
dat <- data.frame(assetclass = sample(LETTERS[1:5], 20, replace = TRUE), 
                  return = rnorm(20), assets = 1e7+1e7*runif(20))

# Using collap() function with fmean, which supports weights: (by default weights are aggregated using the sum, which is prevented using keep.w = FALSE)
collap(dat, return ~ assetclass, fmean, w = ~ assets, keep.w = FALSE)
##   assetclass     return
## 1          A -0.4667822
## 2          B  0.5417719
## 3          C -0.8810705
## 4          D  0.6301396
## 5          E  0.3101673

# Can also use a dplyr-like workflow: (use keep.w = FALSE to omit sum.assets)
library(magrittr)
dat %>% fgroup_by(assetclass) %>% fmean(assets)
##   assetclass sum.assets     return
## 1          A   80683025 -0.4667822
## 2          B   27411156  0.5417719
## 3          C   22627377 -0.8810705
## 4          D  146355734  0.6301396
## 5          E   25463042  0.3101673

# Or simply a direct computation yielding a vector:
dat %$% fmean(return, assetclass, assets)
##          A          B          C          D          E 
## -0.4667822  0.5417719 -0.8810705  0.6301396  0.3101673