Trying to create a for loop based on a sum condition, can't get it to work

Question

I'm trying to go through a column and create a secondary column called status. Status is based on a condition of times. If times is >250 then status should be assigned a "good", if not then the current times row should be summed (similar to cumsum) to rows below until the point where the running_sum is >250. At this point the status of the current row should be changed to good and everything starts afresh.

I've tried the for loop below but I can't get it to work (for instance 3rd row status should be good in the example). Can someone provide an example of the above and explain how it works please? Thank you.

set.seed(1234)
test = data.frame(times = round(abs(rnorm(20,100,100)),0))

test
#>    times
#> 1     21
#> 2    128
#> 3    208
#> 4    135
#> 5    143
#> 6    151
#> 7     43
#> 8     45
#> 9     44
#> 10    11
#> 11    52
#> 12     0
#> 13    22
#> 14   106
#> 15   196
#> 16    89
#> 17    49
#> 18     9
#> 19    16
#> 20   342

test$status   <- 'bad'
running_sum <- 0


for (i in 1:length(test$times)) {
  if (test$times[i] >= 250 | running_sum > 250) { 

    test$status[i] <- "good"
    running_sum <- 0

  } else {
    running_sum <- running_sum + test$times[i]
  }
  print(running_sum)
}
#> [1] 21
#> [1] 149
#> [1] 357
#> [1] 0
#> [1] 143
#> [1] 294
#> [1] 0
#> [1] 45
#> [1] 89
#> [1] 100
#> [1] 152
#> [1] 152
#> [1] 174
#> [1] 280
#> [1] 0
#> [1] 89
#> [1] 138
#> [1] 147
#> [1] 163
#> [1] 0

test
#>    times status
#> 1     21    bad
#> 2    128    bad
#> 3    208    bad
#> 4    135   good
#> 5    143    bad
#> 6    151    bad
#> 7     43   good
#> 8     45    bad
#> 9     44    bad
#> 10    11    bad
#> 11    52    bad
#> 12     0    bad
#> 13    22    bad
#> 14   106    bad
#> 15   196   good
#> 16    89    bad
#> 17    49    bad
#> 18     9    bad
#> 19    16    bad
#> 20   342   good

Answer 1

You just need to change the order of your loop operations: increment first, then test.

set.seed(1234)
test = data.frame(times = round(abs(rnorm(20,100,100)),0))

test$status   <- 'bad'
running_sum <- 0

for (i in 1:length(test$times)) {
  running_sum <- running_sum + test$times[i]

  print(running_sum)

  if (test$times[i] >= 250 | running_sum > 250) { 
    test$status[i] <- "good"
    running_sum <- 0
  }
}

Result:

   times status
1     21    bad
2    128    bad
3    208   good
4    135    bad
5    143   good
6    151    bad
7     43    bad
8     45    bad
9     44   good
10    11    bad
11    52    bad
12     0    bad
13    22    bad
14   106    bad
15   196   good
16    89    bad
17    49    bad
18     9    bad
19    16    bad
20   342   good

Answer 2

using this nice answer from @MrFlick,

set.seed(1234)
test = data.frame(times = round(abs(rnorm(20,100,100)),0))

sum_reset_at <- function(thresh) {
  function(x) {
    accumulate(x, ~if_else(.x>=thresh, .y, .x+.y))
  }  
}

library(tidyverse)
test %>% mutate(temp = ifelse(sum_reset_at(250)(times) < 250, "bad", "good"))
#    times temp
# 1     21  bad
# 2    128  bad
# 3    208 good
# 4    135  bad
# 5    143 good
# 6    151  bad
# 7     43  bad
# 8     45  bad
# 9     44 good
# 10    11  bad
# 11    52  bad
# 12     0  bad
# 13    22  bad
# 14   106  bad
# 15   196 good
# 16    89  bad
# 17    49  bad
# 18     9  bad
# 19    16  bad
# 20   342 good