Consider an irregular time series data frame with time and value columns.

I want to add another column where values are lead by delay seconds.

So, if we consider the second column to be value_2, then value_2 at time t must be equal to value at time t + delay or right after that.

ts=data.frame(
time=c(1,2,3,5,8,10,11,15,20,23),
value=c(1,2,3,4,5,6,7,8,9,10)
)
ts_with_delayed_value <- add_delayed_value(ts, "value", 2, "time")
> ts_with_delayed_value
time value value_2
1 1 1 3
2 2 2 4
3 3 3 4
4 5 4 5
5 8 5 6
6 10 6 8
7 11 7 8
8 15 8 9
9 20 9 10
10 23 10 10

I have my own version of this function add_delayed_value, here it is:

add_delayed_value <- function(data, colname, delay, colname_time) {
colname_delayed <- paste(colname, sprintf("%d", delay), sep="_")
data[colname_delayed] <- NaN
for (i in 1:nrow(data)) {
time_delayed <- data[i, colname_time] + delay
value_delayed <- data[data[colname_time] >= time_delayed, colname][1]
if (is.na(value_delayed)) {
value_delayed <- data[i, colname]
}
data[i, colname_delayed] <- value_delayed
}
return(data)
}

Is there a way to vectorize this routine to avoid the slow loop?

I'm quite new to R, so this code probably has lots of issues. What can be improved about it?