Calculating a weighted mean using data.table in R with weights in one of the table columns -


i have data.table shown below. i'm trying calculate weighted mean subsets of data. i've tried 2 approaches mwe below

    set.seed(12345)     dt = data.table(a =c(10,20,25,10,10),b=rnorm(5),c=rnorm(5),d=rnorm(5),e=rnorm(5))     dt$key = sample(toupper(letters[1:3]),5,replace=t)     setkey(dt, key)

first subsetting .sd , using lapply call, doesnt work (and wasn't expected to) 

dt[,lapply(.sd,function(x) weighted.mean(x,.sd[1])),by=key]

second trying define function apply .sd if using ddply.

this fails too.

wmn=function(x){   tmp = null   for(i in 2:ncol(x)){     tmp1 = weighted.mean(x[,i],x[,1])     tmp = c(tmp,tmp1)   }   return(tmp) }  dt[,wmn,by=key]

any thoughts on how best this?

thanks

edit

change error on wmn formula on columns selected.

second edit

weighted mean formula reversed , added set.seed

if want take weighted means of "b"..."e" using "a" weight, think trick:

dt[,lapply(.sd,weighted.mean,w=a),by=key,.sdcols=letters[1:5]]

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