I'm trying to calculate asset-weighted returns by asset class. For the life of me, I can't figure out how to do it using the aggregate command.
My data frame looks like this
dat <- data.frame(company, fundname, assetclass, return, assets)
I'm trying to do something like (don't copy this, it's wrong):
aggregate(dat, list(dat$assetclass), weighted.mean, w=(dat$return, dat$assets))
The recently released collapse package provides a fast solution to this and similar problems (using weighted median, mode etc.) by providing a full set of Fast Statistical Functions performing grouped and weighted computations internally in C++:
library(collapse)
dat <- data.frame(assetclass = sample(LETTERS[1:5], 20, replace = TRUE),
return = rnorm(20), assets = 1e7+1e7*runif(20))
# Using collap() function with fmean, which supports weights: (by default weights are aggregated using the sum, which is prevented using keep.w = FALSE)
collap(dat, return ~ assetclass, fmean, w = ~ assets, keep.w = FALSE)
## assetclass return
## 1 A -0.4667822
## 2 B 0.5417719
## 3 C -0.8810705
## 4 D 0.6301396
## 5 E 0.3101673
# Can also use a dplyr-like workflow: (use keep.w = FALSE to omit sum.assets)
library(magrittr)
dat %>% fgroup_by(assetclass) %>% fmean(assets)
## assetclass sum.assets return
## 1 A 80683025 -0.4667822
## 2 B 27411156 0.5417719
## 3 C 22627377 -0.8810705
## 4 D 146355734 0.6301396
## 5 E 25463042 0.3101673
# Or simply a direct computation yielding a vector:
dat %$% fmean(return, assetclass, assets)
## A B C D E
## -0.4667822 0.5417719 -0.8810705 0.6301396 0.3101673