trjhtr

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.

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.