R apply weighting operation by multiple groups - r

Question

Hi I have a dataset like this:
City = c(1,1,1,1,1,2,2,2,2,2,3,3,3,3)
Area=c("A","B","A","B","A","A","B","B","B","A","A","B","A","A")
Weights=c(2.4,1.9,0.51,0.7,2.2,1.5,1.86,1.66,1.09,2.57,2.4,0.9,3.4,3.7)
Tax=c(16,93,96,44,67,73,12,65,81,22,39,94,41,30)
z = data.frame(City,Area,Weights,Tax)
Which looks like this:
What I want to do is to obtain the weighted tax for each City and each area.
For eg. for row 1 above the computed value is:
2.4*16/(2.40+0.51+2.20) and so on.
I can do that using this function:
cit_data=list()
weighted_tax=function(z){
for (cit in unique(z$City)){
city_data=z[z$City==cit,]
area_new=list()
for (ar in unique(z$Area)){
area_data=city_data[city_data$Area==ar,]
area_data$area_dat_n = (area_data$Weight*area_data$Tax)/sum(area_data$Weights)
area_new=rbind(area_new,area_data)
}
cit_data=rbind(cit_data,area_new)
}
return(cit_data)
}
tax=weighted_tax(z)
Is there a easier/cleaner way to implement this? Thanks in advance.

Answer 1

Using dplyr :
library(dplyr)
z %>%
group_by(City, Area) %>%
mutate(Weighted_tax = Tax*Weights/sum(Weights))
Output:
# A tibble: 14 x 5
# Groups: City, Area [6]
City Area Weights Tax Weighted_tax
<dbl> <fct> <dbl> <dbl> <dbl>
1 1 A 2.4 16 7.51
2 1 B 1.9 93 68.0
3 1 A 0.51 96 9.58
4 1 B 0.7 44 11.8
5 1 A 2.2 67 28.8
6 2 A 1.5 73 26.9
7 2 B 1.86 12 4.84
8 2 B 1.66 65 23.4
9 2 B 1.09 81 19.2
10 2 A 2.57 22 13.9
11 3 A 2.4 39 9.85
12 3 B 0.9 94 94.
13 3 A 3.4 41 14.7
14 3 A 3.7 30 11.7

Answer 2

We also could do this in base R with by,
do.call(rbind, by(z, z[c("City", "Area")], function(x)
cbind(x, area.dat.n=with(x, Weights * Tax / sum(Weights)))))
# City Area Weights Tax area.dat.n
# 1 1 A 2.40 16 7.514677
# 3 1 A 0.51 96 9.581213
# 5 1 A 2.20 67 28.845401
# 6 2 A 1.50 73 26.904177
# 10 2 A 2.57 22 13.891892
# 11 3 A 2.40 39 9.852632
# 13 3 A 3.40 41 14.673684
# 14 3 A 3.70 30 11.684211
# 2 1 B 1.90 93 67.961538
# 4 1 B 0.70 44 11.846154
# 7 2 B 1.86 12 4.841649
# 8 2 B 1.66 65 23.405640
# 9 2 B 1.09 81 19.151844
# 12 3 B 0.90 94 94.000000
or with ave.
cbind(z,
area.dat.n=
apply(cbind(z, w=with(z, ave(Weights, City, Area, FUN=sum))), 1, function(x)
x[3] * x[4] / x[5]))
# City Area Weights Tax area.dat.n
# 1 1 1 2.40 16 7.514677
# 2 1 2 1.90 93 67.961538
# 3 1 1 0.51 96 9.581213
# 4 1 2 0.70 44 11.846154
# 5 1 1 2.20 67 28.845401
# 6 2 1 1.50 73 26.904177
# 7 2 2 1.86 12 4.841649
# 8 2 2 1.66 65 23.405640
# 9 2 2 1.09 81 19.151844
# 10 2 1 2.57 22 13.891892
# 11 3 1 2.40 39 9.852632
# 12 3 2 0.90 94 94.000000
# 13 3 1 3.40 41 14.673684
# 14 3 1 3.70 30 11.684211
Data
z <- structure(list(City = c(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 3,
3), Area = structure(c(1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 1L), .Label = c("A", "B"), class = "factor"), Weights = c(2.4,
1.9, 0.51, 0.7, 2.2, 1.5, 1.86, 1.66, 1.09, 2.57, 2.4, 0.9, 3.4,
3.7), Tax = c(16, 93, 96, 44, 67, 73, 12, 65, 81, 22, 39, 94,
41, 30)), class = "data.frame", row.names = c(NA, -14L))