No loop for break/next, jumping to top level [duplicate] - r

Question

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R group by aggregate
(3 answers)
Closed 2 years ago.
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?

Answer 1

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