How to compute weighted means of a vector within factor levels?

Question

I am able to successfully get a simple mean of a given vector within factor levels, but in attempting to take it to the next step of weighting the observations, I can't get it to work. This works:

> tapply(exp.f,part.f.p.d,mean)
    1         2         3         4         5         6         7        8             9        10 
0.8535996 1.1256058 0.6968142 1.4346451 0.8136110 1.2006801 1.6112160 1.9168835     1.5135006 3.0312460 

But this doesn't:

> tapply(exp.f,part.f.p.d,weighted.mean,b.pct)
Error in weighted.mean.default(X[[1L]], ...) : 
  'x' and 'w' must have the same length
> 

In the code below, I am trying to find the weighted mean of exp.f, within levels of the factor part.f.p.d, weighted by the observations within b.pct that are in each level.

b.exp <- tapply(exp.f,part.f.p.d,weighted.mean,b.pct)

Error in weighted.mean.default(X[[1L]], ...) : 
  'x' and 'w' must have the same length

I am thinking I must be supplying the incorrect syntax, as all 3 of these vectors are the same length:

> length(b.pct)
[1] 978
> length(exp.f)
[1] 978
> length(part.f.p.d)
[1] 978

What is the correct way to do this? Thank you in advance.

Answer 1

Now I do it like this (thanks to Gavin):

sapply(split(Data,Data$part.f.p.d), function(x) weighted.mean(x$exp.f,x$b.pct)))

Others likely use ddply from the plyr package:

ddply(Data, "part.f.p.d", function(x) weighted.mean(x$exp.f, x$b.pct))