for loop - passing positive results from multiple columns into a single new column in r -

i trying work out way create single column multiple columns in r. want r go through rows multiple columns , if finds positive result in 1 of columns, pass result 'amalgam' column (sorry don't know better word it).

see toy dataset below

x <- c(na, na, na, na, na, 1) y <- c(na, na, 1, na, na, na) z <- c(na, 1, na, na, na, na)  df <- data.frame(cbind(x, y, z))  df[, "compcol"] <- na  df     x  y  z compcol 1 na na na      na 2 na na  1      na 3 na  1 na      na 4 na na na      na 5 na na na      na 6  1 na na      na 

i need pass positive results each of columns compcol column while changing negative results 0. looks this.

   x  y  z compcol 1 na na na       0 2 na na  1       3 3 na  1 na       2 4 na na na       0 5 na na na       0 6  1 na na       1  

i know if requires if else statement nested inside loop ways have tried result in errors don't understand.

i tried following single column

 (i in 1:length(x)) {     if (df$x[i] == 1) {     df$compcol[i] <- df$x[i]     }     }  

but didn't work @ all.

i got message 'error in if (df$x[i] == 1) { : missing value true/false needed'

and makes sense can't see put true/false statement

we can use max.col. create logical matrix checking whether selected columns greater 0 , not na ('ind'). use max.col column index each row , multiply rowsums of 'ind' if there 0 true values row, 0.

 ind <- df > 0 & !is.na(df)  df$compcol <- max.col(ind) *rowsums(ind)  df$compcol  #[1] 0 3 2 0 0 1 

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or option pmax after multiplying col(df)

do.call(pmax,col(df)*replace(df, is.na(df), 0)) #[1] 0 3 2 0 0 1 

note: used dataset before creating 'compcol' in op's post.