How to do a three-way weighted table in R - similar to wtd.table - frequency

Question

I have found MANY questions similar to mine, but either they don't want weighted tables or only want two-ways tables. I am trying to do both.
Using wtd.table, I have the following line of code:
wtd.table(fulldata2$income, fulldata2$WIHH, fulldata2$hhsize, weights = fulldata2$WGTP)
This output only provides incomes and WIHH weighted. It does not also include hhsize.
Using regular table, I get the correct output in a three-way format, but not weighted.
tab <- table(fulldata2$income, fulldata2$WIHH, fulldata2$hhsize)
tab2 <- prop.table(tab)
What function can do both three-way and weighted frequency tables? Ideally, also give it in a proportion like prop.table does.
Thanks!

Answer 1

First, here are some sample data (try to include these in your questions, even if it requires creating a sample data set like this). Note that I am using the tidyverse packages here:
test <-
tibble(
var1 = "A"
, var2 = "b"
, var3 = "alpha") %>%
complete(
var1 = c("A", "B")
, var2 = c("a", "b")
, var3 = c("alpha", "beta")) %>%
mutate(wt = 1:n())
So, the data are:
# A tibble: 8 x 4
var1 var2 var3 wt
<chr> <chr> <chr> <int>
1 A a alpha 1
2 A a beta 2
3 A b alpha 3
4 A b beta 4
5 B a alpha 5
6 B a beta 6
7 B b alpha 7
8 B b beta 8
The function you are looking for then is xtabs:
xtabs(wt ~ var1 + var2 + var3
, data = test)
gives:
, , var3 = alpha
var2
var1 a b
A 1 3
B 5 7
, , var3 = beta
var2
var1 a b
A 2 4
B 6 8
If you don't need the result to have the table class, you can also do this by just using count from dplyr (part of the tidyverse):
test %>%
count(var1, var2, var3
, wt = wt)
gives a tibble (a modified data.frame) with your results:
# A tibble: 8 x 4
var1 var2 var3 n
<chr> <chr> <chr> <int>
1 A a alpha 1
2 A a beta 2
3 A b alpha 3
4 A b beta 4
5 B a alpha 5
6 B a beta 6
7 B b alpha 7
8 B b beta 8
And you can then perform whatever calculations you want on it, e.g. the percent within each var3:
test %>%
count(var1, var2, var3
, wt = wt) %>%
group_by(var3) %>%
mutate(prop_in_var3 = n / sum(n))
gives:
# A tibble: 8 x 5
# Groups: var3 [2]
var1 var2 var3 n prop_in_var3
<chr> <chr> <chr> <int> <dbl>
1 A a alpha 1 0.0625
2 A a beta 2 0.1
3 A b alpha 3 0.188
4 A b beta 4 0.2
5 B a alpha 5 0.312
6 B a beta 6 0.3
7 B b alpha 7 0.438
8 B b beta 8 0.4