Changing values in dataframe iteraring over all rows and multiple columns - r

Question

I need to change some values in my dataframe iterating over rows. For each row, if there is a 1 in some column I need to change 0 values in other columns to NA.
I have a code that works, but is super slow when using a bigger dataset.
data = data.frame(id=c("A","B","C"),V1=c(1,0,0),V2=c(0,0,0),V3=c(1,0,1))
cols = names(data)[2:4]
for (i in 1:nrow(data)){
if(any(data[i,cols]==1)){
data[i,cols][data[i,cols]==0]=NA
}
}
I have an example data set
data
id V1 V2 V3
1 A 1 0 1
2 B 0 0 0
3 C 0 0 1
and the expected (and the actual) result is
data
id V1 V2 V3
1 A 1 NA 1
2 B 0 0 0
3 C NA NA 1
How can I write this in a more optimal way?

Answer 1

A one-liner can be,
data[rowSums(data[-1]) > 0,] <- replace(data[rowSums(data[-1]) > 0,],
data[rowSums(data[-1]) > 0,] == 0,
NA)
data
# id V1 V2 V3
#1 A 1 NA 1
#2 B 0 0 0
#3 C NA NA 1
To avoid evaluating the same expression over and over again, we can define it first, i.e.
v1 <- rowSums(data[-1]) > 0
data[v1,] <- replace(data[v1,],
data[v1,] == 0,
NA)

Answer 2

It is easy with dplyr assuming you want to change values for V1 and V2 column based on values in V3. We can specify columns for whom we want to change values in mutate_at and in funs argument specify the condition for which you want to change values.
library(dplyr)
data %>% mutate_at(vars(V1:V2), funs(replace(., V3 == 1 & . == 0, NA)))
# id V1 V2 V3
#1 A 1 NA 1
#2 B 0 0 0
#3 C NA NA 1

Answer 3

We can do this in base R, by creating a logical vector with rowSums and then update the numeric columns based on this index
i1 <- rowSums(data[-1] == 1) > 0
data[-1][i1,] <- NA^ !data[-1][i1,]
data
# id V1 V2 V3
#1 A 1 NA 1
#2 B 0 0 0
#3 C NA NA 1
If the index needs to be based on a single column, say 'V3', change the 'i1' to
i1 <- data$V3 == 1
and update the other numeric columns after subsetting the rows with 'i1', create a logical matrix with negation (! - returns TRUE for 0 values and all others FALSE). Then, using NA^ on logical matrix returns NA for TRUE and 1 for other values. As there are only binary values, this can be updated
data[i1, 2:3] <- NA^!data[i1, 2:3]