How test if an NA value is equal to zero; replace if so, leave as NA if not

Question

Edited as hadn't included the full set of factors in the example dataset, which is causing the original solutions to break.

I'm trying to clean a dataset by determining if an NA should be replaced with a 0, or if left as NA.

The below is a sample data set. 'Dom.Supply' should equal the sum of the remaining factors. For example, the NAs that appear in rows 3:5 for 'Feed', 'Waste', 'Processing' and 'Other.Uses' could each be replaced with 0 as the sum of factors with values (ie. 'Food' and 'Seed') is equal to the value of 'Dom.Supply. However, in rows 1 & 2, the NAs would have to remain as the sum of 'Food' and 'Seed' is not equal to 'Dom.Supply'.

Region  Country Group   Item    Year    Production  Imports Stock.Var   Exports Dom.Supply  Feed    Seed    Waste   Processing  Other.Uses  Food
NAm.Oceania Australia   Cereals Rye 1961    11  0   0   2   9   NA  1   NA  NA  NA  7
NAm.Oceania Australia   Cereals Rye 1962    10  0   0   3   7   NA  1   NA  NA  NA  5
NAm.Oceania Australia   Cereals Rye 1963    10  0   0   1   9   NA  2   NA  NA  NA  7
NAm.Oceania Australia   Cereals Rye 1964    14  0   -5  0   9   NA  2   NA  NA  NA  7
NAm.Oceania Australia   Cereals Rye 1965    11  0   5   0   16  NA  2   NA  NA  NA  14

I had though to use the replace function as follows (as an example, but not the operation I wish to use), but this is a simplistic replacement of NAs with 0s rather than a test that NA=0.

data$AF2 <- 1-((replace(data$Feed, is.na(data$Feed), 0) + (replace(data$Seed,
    is.na(data$Seed), 0)) / data$Dom.Supply))

Thanks!

Answer 1

CathG answer deserves to be in a real answer, not only as a comment (posting here as community wiki):

DF <- read.table(text = "Dom.Supply  Feed    Seed    Waste   Processing  Other.Uses  Food
9   NA  1   NA  NA  NA  7
7   NA  1   NA  NA  NA  5
9   NA  2   NA  NA  NA  7
9   NA  2   NA  NA  NA  7
16  NA  2   NA  NA  NA  14", header = TRUE)

ix <- rowSums(DF[, -1], na.rm = TRUE) == DF[, 1]
DF[ix, ][is.na(DF[ix, ])] <- 0 
DF
#  Dom.Supply Feed Seed Waste Processing Other.Uses Food
#1          9   NA    1    NA         NA         NA    7
#2          7   NA    1    NA         NA         NA    5
#3          9    0    2     0          0          0    7
#4          9    0    2     0          0          0    7
#5         16    0    2     0          0          0   14

EDIT
In case you have NAs in the column Dom.Supply, ix will have a NA for the corresponding row. You can change the NA into FALSE (as you want to leave the NAs in the other columns as NAs):

ix <- rowSums(DF[, -1], na.rm = TRUE) == DF[, 1]
ix[is.na(ix)] <- FALSE
DF[ix, ][is.na(DF[ix, ])] <- 0 

Example, with the same original DF:

DF[2, 1] <- NA
ix <- rowSums(DF[, -1], na.rm = TRUE) == DF[, 1]
ix[is.na(ix)] <- FALSE
DF[ix, ][is.na(DF[ix, ])] <- 0 
DF
#  Dom.Supply Feed Seed Waste Processing Other.Uses Food
#1          9   NA    1    NA         NA         NA    7
#2         NA   NA    1    NA         NA         NA    5
#3          9    0    2     0          0          0    7
#4          9    0    2     0          0          0    7
#5         16    0    2     0          0          0   14

Answer 2

I'd use this:

DF <- read.table(text = "Dom.Supply  Feed    Seed    Waste   Processing  Other.Uses  Food
9   NA  1   NA  NA  NA  7
7   NA  1   NA  NA  NA  5
9   NA  2   NA  NA  NA  7
9   NA  2   NA  NA  NA  7
16  NA  2   NA  NA  NA  14", header = TRUE)

ix <- rowSums(DF[, -1], na.rm = TRUE) == DF[, 1]
DF[ix,] <- lapply(DF[ix,], function(x) {
  x[is.na(x)] <- 0
  x
})
#  Dom.Supply Feed Seed Waste Processing Other.Uses Food
#1          9   NA    1    NA         NA         NA    7
#2          7   NA    1    NA         NA         NA    5
#3          9    0    2     0          0          0    7
#4          9    0    2     0          0          0    7
#5         16    0    2     0          0          0   14