Calculating weight ratios in the presence of empty cells - r

Question

I have a sample which needs to weighed in order to represent the population.
library(data.table)
sample <- fread("
1,0,2,2
3,4,3,0
")
V1 V2 V3 V4
1: 1 0 2 2
2: 3 4 3 0
population <- fread("
10,20,20,10
30,40,20,10
")
This weight would simply be:
weights <- population/sample
V1 V2 V3 V4
1: 10 Inf 10.000000 5
2: 10 10 6.666667 Inf
However, because V2 in row 1 of the sample has no observations, it receives an infinite weight (Note that also V4 in row 2 receives an Inf, but this is easier to solve, because the weight is irrelevant, as there are no observations in either the sample or the population).
A solution to the problem, would be to count V1 and V2 together in the sample and the population.
EDIT:
After some thought I realised that, for the weights to be correct, only the population values have to be adapted. If V1 and V2 in row 1 of population are added together in V1 of population, this will already lead to the correct weight for the sample observation of V1 row . The value of V2 becomes irrelevant because there is no observation in the sample to receive that weight.
End of EDIT
The observation would then get a weight of:
(population[1,1]+population[1,2])/(sample[1,1]+sample[1,2])
(10+20)/(1+0)=30
In my actual data, there however many more rows, with hero and there a 0 in the sample. I am trying to figure out if there is a way to write my code, so that I do not have to do this manually..
Desired outcome (notice that the weight of V1 row 1 is now 30):
weights
V1 V2 V3 V4
1: 30 0 10.000000 5
2: 10 10 6.666667 0
Attempt
I was think of doing something like:
for (i in seq_along(ncol(sample))) {
lapply(population, (ifelse(sample[i]==0), population[i]<-population[i+1], population[i])
}
Where the values in the population of the cell to right will be added when the value in the sample is zero. However I am having trouble getting the syntax right, and even if it did, it does not solve the case where V4 is 0.

Answer 1

Here is a rather verbose solution. In case there are more columns that should be aggregated in case of zeros in sample, I would have proposed a more flexible approach but this seems sufficient for your example
library(data.table)
sample <- fread("
1,0,2,2
3,4,3,0
")
population <- fread("
10,20,20,10
30,40,20,10
")
# aggregate Values if sample is zero
population[sample$V1 == 0, `:=`(V1 = 0,
V2 = V1 + V2)]
population[sample$V2 == 0, `:=`(V1 = V1 + V2,
V2 = 0)]
weights <- population/sample
# Fix NaNs
weights[is.na(weights), ] <- 0
weights
#> V1 V2 V3 V4
#> 1: 30 0 10.000000 5
#> 2: 10 10 6.666667 Inf