Limiting the time that a function processes in an R for loop

Question

I want to apply a function ("foo" for this explanation) to tranform a vector of data into another value. This function takes the data as an input, and needs submit forms to webpages. Sometimes, this goes quickly, and other times, it can a long time. I would like to run the for loop (or equivalent apply function) in a way that skips over the items that take too long. I have tried to limit the time the loop runs before skipping to the next to 5 seconds using the following:

pb <- txtProgressBar(min = 1, max = 100, style = 3)
storage <- matrix(nrow = sample.length, ncol = 2)

for(i in 1:100){  
     s <- Sys.time()  
     storage[i,] <- try(foo(data.vec[i]), TRUE)  
     if (Sys.time() - s >5) {next}  
     # update progress bar  
     setTxtProgressBar(pb, i)  
}  
close(pb)  

I think that I must not be understanding how to apply the 'next' condition in a for loop. have searched to find a clearer explanation, but not getting any luck here.

Answer 1

withTimeout() from package R.utils, in concert with tryCatch(), might provide a cleaner solution.

For example:

require(R.utils)

for(i in 1:5) {
    tryCatch(
        expr = {
            withTimeout({Sys.sleep(i); cat(i, "\n")}, 
                         timeout = 3.1)
            }, 
        TimeoutException = function(ex) cat("Timeout. Skipping.\n")
    )
}

# 1 
# 2 
# 3 
# Timeout. Skipping.
# Timeout. Skipping.

In the artificial example above:

• The first argument to withTimeout() contains the code to be evaluated within each loop.

• The timeout argument to withTimeout() sets the time limit in seconds.

• The TimeoutException argument to tryCatch() takes a function that is to be executed when an iteration of the loop is timed out.