Creating missingness in repeated measurement data

Dear R users,
 
I have the following problems. My dataset (dat) is as follows: 

a <- c(1,2,3) 
id <- rep(a, c(3,2,3))
stat <- c(1,1,0,1,0,1,1,1)
g <- c(0,0,0,0,0,0,1,0)
stop <- c(1,2,4,2,4,1,1.5,3)
dat <- data.frame(id,stat,g,stop)
 
I want to creat a new dataset (dat2) with missing values 
such that when either g = =1 or stat = =0, the remaining rows for an 
individual subject is set to NA by using a new variable d (that states 
the exact time this 
happened from the stop variable). By this I mean dat2 that looks like,
 
id <- rep(a, c(3,2,3))
sta2<- c(1,1,NA,1,NA,1,NA,NA)
g2<- c(0,0,NA,0,NA,0,NA,NA)
stop2 <- c(1,2,NA,2,NA,1,NA,NA)
d <- c(4,4,NA,4,NA,1.5,NA,NA)
 
dat2 <- data.frame(id=id, stat2=sta2, g2=g2,stop2=stop2,d=d).

suppressidx <- ave(dat$stat==0 | dat$g==1, dat$id, FUN=cumsum)
suppress <- function(col) { ifelse( suppressidx, NA, col)}
cbind(dat[1], sapply( dat[-1], function(x) suppress(x) ) )