Extract all subsets in vector where elements are above a given threshold-CodePudding

I would like to know if there is an R way (one liner) to extract the coordinates of all subsets of a vector that are above a given threshold. Suppose I have the following data:

v =  c(3.48, 2.59, 1.73, 0.91, 0.13, -0.63, -1.34, -2.03, -2.67, -3.28, -3.04, -2.15, -1.20, -0.19, 0.84, 1.86, 2.84, 3.77, 4.60, 5.31, 4.16, 2.87, 1.89, 0.51, 0.23, 0.78, 1.34, 2.63, 1.72, 0.62, 0.98, 1.45)

and let's say I have threshold = 0.7. The desired output would be:

left    right
1       4
15      23
26      29
31      32

I can in principle write a while loop or some sort, subsetting v and juggling with left and right coordinates of these regions, something like:

left = which(subset >= threshold)[1]   right
right = which(subset[left:length(subset)] < threshold)[1] - 1 # -1 to get the last element above the threshold

subset = v[(right   1):length(v)]

(not tested), but I am sure there is an R way that i can't seem to remember.

I had a look here but it's not really what I am after. Any help is appreciated.

CodePudding user response：

You can use rle() to find the runs of values that exceed your threshhold. When you can turn that into your desired format

rle(v>.7) |>
  with(
    data.frame(start=1, end=cumsum(lengths)) |> 
      transform(start=c(1, head(end, -1)   1)) |> 
      subset(values)
  )

And that returns

  start end
1     1   4
3    15  23
5    26  29
7    31  32

This is nearly identical to this existing question with the main difference of using rle() on your Boolean condition and then subsetting to only the TRUE values.

CodePudding user response：

Same solution but using data.table

v =  c(3.48, 2.59, 1.73, 0.91, 0.13, -0.63, -1.34, -2.03, -2.67, -3.28, -3.04, -2.15, -1.20, -0.19, 0.84, 1.86, 2.84, 3.77, 4.60, 5.31, 4.16, 2.87, 1.89, 0.51, 0.23, 0.78, 1.34, 2.63, 1.72, 0.62, 0.98, 1.45)

data.table(v)[, .(start = .I[1], end = .I[.N], keep = unique(v > 0.7)), by = rleid(v > 0.7)][keep == T, .(start, end)]

#    start end
# 1:     1   4
# 2:    15  23
# 3:    26  29
# 4:    31  32