Weighted Average in R using NA weights

Question

 a=c(1,2,NA,4)
 b=c(10,NA,30,40)
 weighted.mean(a,b,na.rm = T)

The above code gives me NA as the answer, I think na.rm only ignores the NA values in vector a and not b. How can I ignore the NA in vector b or weights to be specific. I just cannot change the NA to 0, I know that would do the trick but looking for a tweak in the formula itself.

Answer 1

This is the function I ended up writing to solve this problem:

weighted_mean <- function(x, w, ..., na.rm = FALSE){

  if(na.rm){

    df_omit <- na.omit(data.frame(x, w))

    return(weighted.mean(df_omit$x, df_omit$w, ...))

  } 

  weighted.mean(x, w, ...)
}

Answer 2

I adapted Mhairi's code to not use data.frame nor na.omit:

weighted_mean = function(x, w, ..., na.rm=F){
  if(na.rm){
    keep = !is.na(x)&!is.na(w)
    w = w[keep]
    x = x[keep]
  }
  weighted.mean(x, w, ..., na.rm=F)
}

It's really surprising that R builtin weighted.mean na.rm=T doesn't handle NA weights. Just wasted a few hours discovering that.

Answer 3

I made a simple modification to the weight w in weighted.mean by coalesce as follows:

a = c(1,2,NA,4)
b = c(10,NA,30,40)
weighted.mean(a, dplyr::coalesce(b,0), na.rm = T)

The idea is I replaced missing weights by zeros, so it fix the error. It returns the result as 3.4, :)).