Fill NAs in R for certain columns

Question

I'm running a linear regression, but many of my observations can be used because some of the values have an NA in the row. I know that if one of a set of variables is entered, then and NA is actually 0. However, if all the values are NA, then the columns do not change. I will include and example because I know this might be confusing.

What I have is something that looks likes this:

df <- data.frame(outcome = c(1, 0, 1, 1, 0),
                 Var1 = c(1, 0, 1, NA, NA),
                 Var2 = c(NA, 1, 0, 0, NA),
                 Var3 = c(0, 1, NA, 1, NA))

For Vars 1-3, the first 4 rows have an NA, but have other entries in other vars. In the last row, however, all values are NA. I know that everything in the last row is NA, but I want the NAs in those first 4 rows to be filled with 0. The desired outcome would look like this:

desired - data.frame(outcome = c(1, 0, 1, 1, 0),
                     Var1 = c(1, 0, 1, 0, NA),
                     Var2 = c(0, 1, 0, 0, NA),
                     Var3 = c(0, 1, 0, 1, NA))

I know there are messy ways I could go about this, but I was wondering what would be the most streamlined process for this?

I hope this makes sense, I know the question is confusing. I can clarify anything if needed.

Answer 1

We can create a logical vector with rowSums , use that to subset the rows before changing the NA to 0

i1 <- rowSums(!is.na(df[-1])) > 0
df[i1, -1][is.na(df[i1, -1])] <- 0

-checking with desired

identical(df, desired)
#[1] TRUE

Answer 2

You can use apply to conditionally replace NA in certain rows:

data.frame(t(apply(df, 1, function(x) if (all(is.na(x[-1]))) x else replace(x, is.na(x), 0))))

Output

 outcome Var1 Var2 Var3
1       1    1    0    0
2       0    0    1    1
3       1    1    0    0
4       1    0    0    1
5       0   NA   NA   NA