Reputation: 1008
The question is basically the samt as this: Aggregate and Weighted Mean in R.
But i want it to compute it on several columns, using data.table, as I have millions of rows. So something like this:
set.seed(42) # fix seed so that you get the same results
dat <- data.frame(assetclass=sample(LETTERS[1:5], 20, replace=TRUE),
tax=rnorm(20),tax2=rnorm(20), assets=1e7+1e7*runif(20), assets2=1e6+1e7*runif(20))
DT <- data.table(dat)
I can compute the weighted mean on one column, assets, like this:
DT[,list(wret = weighted.mean(tax,assets)),by=assetclass]
But how to do it on both assets and assets2?
What if there are several columns, like col=c("assets1", "assets2", "assets3", ... )
?
And is it also possible to do it for tax, tax1...
Upvotes: 2
Views: 1773
Reputation: 12559
So you can do it for several columns of weights
DT <- data.table(assetclass=sample(LETTERS[1:5], 20, replace=TRUE),
tax=rnorm(20), assets=1e7+1e7*runif(20), asets2=1e6+1e7*runif(20))
DT[, lapply(.SD, FUN=weighted.mean, x=tax), by=assetclass, .SDcols=3:4]
# assetclass assets asets2
# 1: D -0.14179882 -0.003717957
# 2: B 0.61146928 0.523913589
# 3: E -0.28037796 -0.147677384
# 4: C -0.09658125 -0.010338894
# 5: A 0.74954460 0.750190947
or you can exclude the non-weight columns from .SD
:
DT[, lapply(.SD, FUN=weighted.mean, x=tax), by=assetclass, .SDcols=-(1:2)]
Here is a variant using matrix multiplication:
DT[, as.list(crossprod(as.matrix(.SD), tax)/colSums(.SD)), by=assetclass, .SDcols=-(1:2)]
The matrix multiplication can do it also for several columns tax1
, tax2
, ...
DT <- data.table(assetclass=sample(LETTERS[1:5], 20, replace=TRUE),
tax1=rnorm(20), tax2=rnorm(20), assets=1e7+1e7*runif(20), asets2=1e6+1e7*runif(20))
DT[, as.list(crossprod(as.matrix(.SD), tax1)/colSums(.SD)), by=assetclass, .SDcols=-(1:2)]
DT[, as.list(crossprod(as.matrix(.SD), tax2)/colSums(.SD)), by=assetclass, .SDcols=-(1:2)]
DT[, as.list(crossprod(as.matrix(.SD), cbind(tax1, tax2))/colSums(.SD)), by=assetclass, .SDcols=-(1:2)]
Upvotes: 4