How to replace all columns in matrix with NA based on variable in another dataframe?

Question

I have a large adjacency matrix and a dataframe with a variable 'missing'. I need to replace all rows in the matrix with NA, for which 'missing' = TRUE (rows match between matrix and dataframe).

Example: I have: matrix

     [,1] [,2] [,3]
[1,]    0    0    1
[2,]    1    0    1
[3,]    0    1    0

and dataframe

  ID missing
1  1   FALSE
2  2   FALSE
3  3    TRUE

I need: matrix

     [,1] [,2] [,3]
[1,]    0    0    1
[2,]    1    0    1
[3,]   NA    NA    NA

Reproducible data:

m1.data <- c(0, 1, 0, 0, 0, 1, 1, 1, 0 )
m1 <- matrix(m1.data, nrow = 3)

df <- data.frame(ID = c(1, 2, 3),
                missing = c(FALSE, FALSE, TRUE))

Hope that someone can help! Thank you so much in advance!

Answer 1

We can extract the missing column and use that as the row index in m1 and assign it to NA

m1[df$missing,] <- NA

-output

> m1
     [,1] [,2] [,3]
[1,]    0    0    1
[2,]    1    0    1
[3,]   NA   NA   NA

---

Or we may do

> NA^(df$missing) * m1
     [,1] [,2] [,3]
[1,]    0    0    1
[2,]    1    0    1
[3,]   NA   NA   NA

Answer 2

With which we could get the Id for the missing and assing a NA to m1 it is similar to akrun's approach m1[df$missing,] <- NA

m1[which(df$missing==TRUE),] <- NA

     [,1] [,2] [,3]
[1,]    0    0    1
[2,]    1    0    1
[3,]   NA   NA   NA