No loop for break/next, jumping to top level

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I have a data frame that stores the amount someone spends per transaction for this month. I'm trying to create a loop that checks for repeated user IDs, then sums and stores the amount they spent in total in the first record that they appear. It should set the amount they spent in any other occurrences to 0.

I keep getting "Error: No loop for break/next, jumping to top level" when I stop it from running:

# Number of trips 
numTrips <- NROW(tripData)

# For each trip in data
for (i in 1:numTrips){
  
  # For each trip after i
  for (j in ((i+1): numTrips)){
    
    # If the user ID's match, sum prices 
    if (tripData[i,]$user_id == tripData[j,]$user_id){
      tripData[i,]$original_price <- tripData[i,]$original_price + tripData[j,]$original_price 
        tripData[j,]$original_price <- 0
    }
      
  }
}

Can someone please help?

error-handling r
1 Answers

I'll go with @MrFlick's comment and give you a sample:

set.seed(42)
dat <- tibble(
  id = rep(1:3, each=3),
  when = sort(Sys.Date() - sample(10, size=9)),
  amt = sample(1e4, size=9))
dat
# # A tibble: 9 x 3
#      id when         amt
#   <int> <date>     <int>
# 1     1 2020-06-19   356
# 2     1 2020-06-20  7700
# 3     1 2020-06-21  3954
# 4     2 2020-06-22  9091
# 5     2 2020-06-23  5403
# 6     2 2020-06-24   932
# 7     3 2020-06-25  9189
# 8     3 2020-06-27  5637
# 9     3 2020-06-28  4002

It sounds like you want to sum the amounts for each id, but preserve the individual rows with the rest of the amounts zeroed out.

dat %>%
  group_by(id) %>%
  mutate(amt2 = c(sum(amt), rep(0, n() - 1)))
# # A tibble: 9 x 4
# # Groups:   id [3]
#      id when         amt  amt2
#   <int> <date>     <int> <dbl>
# 1     1 2020-06-19   356 12010
# 2     1 2020-06-20  7700     0
# 3     1 2020-06-21  3954     0
# 4     2 2020-06-22  9091 15426
# 5     2 2020-06-23  5403     0
# 6     2 2020-06-24   932     0
# 7     3 2020-06-25  9189 18828
# 8     3 2020-06-27  5637     0
# 9     3 2020-06-28  4002     0

If instead you just want the summaries, you can use this:

dat %>%
  group_by(id) %>%
  summarize(amt = sum(amt))
# # A tibble: 3 x 2
#      id   amt
#   <int> <int>
# 1     1 12010
# 2     2 15426
# 3     3 18828

or if you want to preserve the date range, then

dat %>%
  group_by(id) %>%
  summarize(whenfrom = min(when), whento = max(when), amt = sum(amt))
# # A tibble: 3 x 4
#      id whenfrom   whento       amt
#   <int> <date>     <date>     <int>
# 1     1 2020-06-19 2020-06-21 12010
# 2     2 2020-06-22 2020-06-24 15426
# 3     3 2020-06-25 2020-06-28 18828
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