Aggregating Results from Unreliable Functions in R

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I posted this as a response to a question on R-help. I think the idea of a “collect” function could be useful both in the context of unreliable functions that sometimes error out and also in filtering contexts where currently one creates a list containing good elements and some sort of sentinel, usually NULL, which has itself to be filtered out in a separate subsetting operation after the main filtering loop.

Here’s an example:

    d <- runif(20, min=-2, max=8) # test data

    aFunc <- function(x) {  # gives error occasionally
        if (x > 0)
          stop("encountered bad x")

collect <- function(x, FUN, skip_error=TRUE, args_list=NULL)
    if (!is.vector(x))
      stop("arg x must be a vector")
    fname <- deparse(substitute(FUN))
    xvar <- deparse(substitute(x))
    i <- 1
    j <- 1
    result <- vector(mode=mode(x), length=length(x))
    while (i <= length(x)) {
            args <- list(x[i])
            if (length(args_list))
              args <- c(args, args_list)
            ans <-, args)
            result[j] <- ans
            j <- j + 1
        }, error=function(e) {
            if (!skip_error) {
                msg <- paste("collect\n",
                             "call to", fname, "failed at",
                             paste(xvar, "[",  i, "]\n", sep=""),
                             "Message:\n", conditionMessage(e))
                stop(msg, call.=FALSE)
                 finally={i <- i + 1})
    if (j > 1)
      vector(mode=mode(x), length=0)

## Example

collect(d, aFunc, skip_error=FALSE)
Error: collect
 call to aFunc failed at d[2]
 encountered bad x

collect(d, aFunc, skip_error=TRUE)
 [1] 7.7380303 0.7554328 1.8352623 0.5136118 4.4231091 2.5368103 1.8656615
 [8] 2.9244200 2.1364120 7.6711189 0.2141325 7.8216620 5.8347576 5.3939892

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