R loop until condition matched, then go on-CodePudding

I have a dataframe with numerical values in one row. Now I want to calculate the cumsum of those rows, until >= 1. If this point is reached -> print for all those rows a counter, write in every row the cumsum for its counter, then look for the cumsum of the next rows.

Should look somewhow like this:

value    counter   cumsum
0.3      1         0.9
0.3      1         0.9
0.3      1         0.9
0.3      2         0.4
0.1      2         0.4
2        3         2

My problem is how to tell R to stop the cumsum, if >= than 1. Any ideas? Thank you in advance.

CodePudding user response：

I don't know if I understood your problem correctly, but maybe this one here helps:

value = round(runif(20, min = 0.1, max = 0.5), 1)

csumVec = numeric(length(value))
counterVec = numeric(length(value))
startIndex = 1
csum = 0
counter = 1

for(i in 1:length(value)) {
  csum = csum   value[i]
  if(csum > 1) {
    counterVec[startIndex:i] = counter
    csumVec[startIndex:i] = csum-value[i]
    startIndex = i
    counter = counter 1
    csum = value[i]
  }
  if(i == length(value)) {
    counterVec[startIndex:i] = counter
    csumVec[startIndex:i] = csum
  }
}

cbind(value, counterVec, csumVec)

CodePudding user response：

It seems like you can calculate the cumulative sum, divide by 1, and take the floor() (round down)

floor(cumsum(value) / 1)
## [1] 0 0 0 1 1 3

This is correct, except that it is 0-based and the counter does not increment by 1. Fix these by matching the result above with their unique values

counter0 = floor(cumsum(value) / 1)
counter = match(counter0, unique(counter0))
counter
## [1] 1 1 1 2 2 3

Having got the 'tricky' part, I'd use dplyr (library(dplyr)) for the rest

## library(dplyr)
tibble(value, counter) |>
    mutate(cum_sum = cumsum(value)) |>
    group_by(counter) |>
    mutate(cumsum = max(cumsum(value)))
## # A tibble: 6 × 3
## # Groups:   counter [3]
##   value counter cumsum
##   <dbl>   <int>  <dbl>
## 1   0.3       1    0.9
## 2   0.3       1    0.9
## 3   0.3       1    0.9
## 4   0.3       2    0.4
## 5   0.1       2    0.4
## 6   2         3    2

or perhaps capturing the tricky part in a (more general) function

cumgroup <- function(x, upper = 1) {
    counter0 = floor(cumsum(x) / upper)
    match(counter0, unique(counter0))
}

and incorporating into the dplyr solution

tibble(value) |>
    mutate(counter = cumgroup(value)) |>
    group_by(counter) |>
    mutate(cumsum = max(cumsum(value)))

or depending on what precisely you want

tibble(value) |>
    mutate(
        cumsum = cumsum(value) %% 1,
        counter = cumgroup(value)
    ) |>
    group_by(counter) |> 
    mutate(cumsum = max(cumsum)) |>
    select(value, counter, cumsum)