Dear R Users,
I have looked for a solution to the following problem and I have not been
able to find it on the archive, through Google or in the R documentation.
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.
Thank you very much for your help!
Rita
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