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I have this function in R:

Minimum <- function(data) {
    answer <- numeric(length(data))

    diference <- c(0, diff(data, lag = 1, differences = 1)) #Padded initially =0

    answer[1]=data[1]
    for (i in 2:length(diference)) {
        if (diference[i]==0) {
            answer[i]=answer[i-1]
        } else {
            answer[i]=data[i]-diference[i]/2
        }
    }
    return(answer)
}

Its purpose is to find the minimum value which "data" could had before it was rounded.

The minimum possible value is the average of the values which "data" had at the last change of value in "data"

This code works, but since for loops are inefficient in R, it is advised to vectorize the function.

The problem is that the "answer" vector depends on the former values in "answer", so I cannot use a lambda function.

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  • \$\begingroup\$ I don't understand what this has to do with rounding, or the meaning of The minimum possible value is the average of the values which "data" had at the last change of value in "data". Could you elaborate? Also diference[i]==0 will be subject to floating point errors, so not reliable if you are dealing with numeric (non integer) vectors. \$\endgroup\$ – flodel Apr 16 '18 at 23:01
  • \$\begingroup\$ For example, the numbers [1.14 1.23 1.28 1.35] could be rounded to [1.1 1.2 1.3 1.3]. The minimum possible value would be [? 1.15 1.25 1.25], otherwise they would not had been rounded that way. Even worse, on this example I know the module/precision of the rounding, but in real life is unknown how the values were rounded. They could had been rounded to multiples of pi, or who knows what number. \$\endgroup\$ – yoxota Apr 17 '18 at 14:26
  • \$\begingroup\$ Thanks for the explanation. If I understand correctly, it is assuming your input data is increasing. If so, you might want to check that assumption in your function, something like stopifnot(all(diff(data) >= 0)). \$\endgroup\$ – flodel Apr 17 '18 at 23:53
  • \$\begingroup\$ I gave it some thought. Should you not look for the smallest value in diff(data) that is not exactly zero and make that your (estimated) rounded precision for all values? Minimum <- function(data) { d <- diff(data); p <- min(d[d > 0]); data - p/2 }. It's all vectorized, faster, and provides a better (larger) minimum bound on your pre-rounded data. \$\endgroup\$ – flodel Apr 18 '18 at 0:01
  • \$\begingroup\$ @ flodel Sorry for the delay. Looking for the smaller difference is complicated, because small values are noisy, so the smaller values correspond to noise around difference=0. It looks like the rounding scales up with the value. \$\endgroup\$ – yoxota Apr 25 '18 at 13:57
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You can firstly change by difference != 0 and then use na.locf to replace NAs by last available value recursively.

minimum_new <- function(data) {
  answer <- rep(NA, length(data))
  difference <- c(0, diff(data, lag = 1, differences = 1)) / 2
  answer[1] <- data[1]
  answer[difference != 0] <- data[difference != 0] - difference[difference != 0]
  answer <- zoo::na.locf(answer, na.rm = FALSE)
  answer
}

This version is faster for me by at least 2 times.

> data <- sample(10, 10000, replace = TRUE)
> check <- function(values) all(sapply(values[-1], function(x) identical(values[[1]], x)))
> bench <- microbenchmark::microbenchmark(loop = Minimum(data), vectorised = minimum_new(data), check=check)
Unit: microseconds
       expr      min       lq     mean   median       uq      max neval cld
       loop 1401.959 1415.552 1665.816 1457.274 1586.407 4620.835   100   b
 vectorised  742.325  758.183 1111.202  796.507 1383.268 2587.940   100  a 

With check it's also checks the equality of output.

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  • \$\begingroup\$ I would never had found na.locf function by myself. I wonder how I could had found it. Thank you. have a reward youtube.com/watch?v=fD7ji3YOwcM \$\endgroup\$ – yoxota Apr 17 '18 at 14:20

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