Calculate a series of weighted funds in R for groups with different weights

Question

I have the following data set (a simple version of my actual data), “data” and would like to calculate the weighted means for the variables x1 and x2, using the weights w1 and w2, respectively, divided into two groups (groups defined by the variable n).

data <- data.frame(n = c(1,1,1,2,2,2), x1 = c(4,5,4,7,5,5), x2 = c(7,10,9,NaN,11,12), w1 = c(0,1,1,1,1,1), w2 = c(1,1,1,0,0,1))

I try to do this with (), but I get an error message at startup:

with(data, aggregate(x = list(x1=x1, x2=x2), by = list(n = n), FUN = weighted.mean, w = list(w1 = w1,w2 = w2)))

On the other hand, if no scales are specified, it works, but in this case the default weights are used (for example, using FUN = mean).

with(data, aggregate(x = list(x1=x1, x2=x2), by = list(n = n), FUN = weighted.mean))

This question is similar to weighted means by group and column , except that my question includes different weight values ​​for different columns. I tried using a data table, but it came up with the same weighting errors as with (). Thanks in advance for any help.

Answer 1

Try

library(data.table)
setDT(data)[, .(x1=weighted.mean(x1, w1), x2=weighted.mean(x2, w2)) , by = n]

Or, as @thelatemail comments, we can use Mapto iterate over the "x", the corresponding "w" columns and call with oneweighted.mean

setDT(data)[, Map(weighted.mean, list(x1,x2), list(w1,w2)), by = n]

If there are many columns "x" and "w", we can use grepto get the column names mget, to return the values ​​insideMap

setDT(data)[,  Map(weighted.mean, mget(grep('x', names(data), 
    value=TRUE)), mget(grep('w', names(data), value=TRUE))), by = n]

Answer 2

Try:

library(dplyr)
data %>% 
  group_by(n) %>% 
  summarise(x1 = weighted.mean(x1, w1), x2 = weighted.mean(x2, w2))