# [R] Weighted Average on More than One Variable in Data Frame

StellathePug ritacarreira at hotmail.com
Thu Sep 22 00:19:53 CEST 2011

```Thanks Jean, that worked perfectly!
Try this

sapply(split(df, df\$g), function(x) apply(x[, 1:2], 2, weighted.mean,
x\$w))

Jean

StellathePug wrote on 09/21/2011 01:15:33 PM:
>
> I have a data frame, say df, which has 4 variables, one of which I would
> like to use as a grouping variable (g), another one that I would like to
> use
> for my weights (w) The other two variables are variables (x1 and x2) for
> which I would like to compute the weighted average by group.
>
> df <- data.frame(x1 = c(15, 12,  3, 10, 10, 14, 12),
>                        x2 = c(10, 11, 16,  9,   7, 17, 18),
>                         g = c(  1,   1,  1,  2,   2,   3,  3),
>                         w = c( 2,   3,  1,  5,   5,   2,  5))
>
> wx1 <- sapply(split(df, df\$g), function(x){weighted.mean(x\$x1, x\$w)})
> wx2 <- sapply(split(df, df\$g), function(x){weighted.mean(x\$x2, x\$w)})
>
> The above code works, the result is:
> > wx1
>        1        2        3
> 11.50000 10.00000 12.57143
> > wx2
>        1        2        3
> 11.50000  8.00000 17.71429
>
> But is there not a more elegant way of acting on x1 and x2
simultaneously?
> Something along the lines of
>
> wdf <- sapply(split(df, df\$g), function(x){weighted.mean(df, x\$w)})
>
> which is wrong since df has two columns, while w only has one. I
suppose, one could write a loop but that strikes me as being highly
inefficient.

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