Dplyr Summarize: Combining values for certain groups - r

Question

I have data on hospital admissions per patients. I am trying add up the price of care for patients that were re-admitted to hospital within 5 days.
This is an example dataset:
(
dt <- data.frame(
id = c(1, 1, 2, 2, 3, 4),
admit_date = c(1, 9, 5, 9, 10, 20),
price = c(10, 20, 20, 30, 15, 16)
)
)
# id admit_date price
# 1 1 1 10
# 2 1 9 20
# 3 2 5 20
# 4 2 9 30
# 5 3 10 15
# 6 4 20 16
And this is what I have tried so far:
library(dplyr)
# 5-day readmission:
dt %>%
group_by(id) %>%
arrange(id, admit_date)%>%
mutate(
duration = admit_date - lag(admit_date),
readmit = ifelse(duration < 6, 1, 0)
) %>%
group_by(id, readmit) %>% # this is where i get stuck
summarize(sumprice = sum(price))
# # A tibble: 6 × 3
# # Groups: id [4]
# id readmit sumprice
# <dbl> <dbl> <dbl>
# 1 1 0 20
# 2 1 NA 10
# 3 2 1 30
# 4 2 NA 20
# 5 3 NA 15
# 6 4 NA 16
And this is what I would like to have:
# id sum_price
# 1 1 10
# 2 1 20
# 3 2 50
# 4 3 15
# 5 4 16

Answer 1

If the difference in days, between adjacent visits is greater than 5 - return TRUE if not - return FALSE (-Inf > 5 is FALSE for the first day, thus lags default is Inf). After that, for each individual we take a cumulative sum to label the groups. We finally summarize within each individual, using this cumsum as a grouping variable for by:
dt |>
group_by(id) |>
arrange(id, admit_date) |>
summarise(
sum_price = by(
price,
cumsum((admit_date - lag(admit_date, , Inf)) > 5),
sum
)
) |>
ungroup()
# # A tibble: 5 × 2
# id sum_price
# <dbl> <by>
# 1 1 10
# 2 1 20
# 3 2 50
# 4 3 15
# 5 4 16

Answer 2

So, you want (at most) one row per patient in the final dataframe, so you should group on just id.
Then, for each patient, you should calculate if that patient has any row with readmit==).
Finally, you filter out any patient that wasn't readmitted from your summarized dataframe.
Putting it all together, it might look like:
dt %>%
group_by(id) %>%
arrange(id, admit_date) %>%
mutate(duration = admit_date - lag(admit_date),
readmit = ifelse(duration < 6, 1, 0)) %>%
group_by(id) %>% # group by just 'id' to get one row per patient
summarize(sumprice = sum(price, na.rm = T),
is_readmit = any(readmit == 1)) %>% # If patient has any 'readmit' rows, count the patient as a readmit patient
filter(is_readmit) %>% # Filter out any non-readmit patients
select(-is_readmit) # get rid of the `is_readmit` column
Which should result in:
# A tibble: 1 x 3
id sumprice is_readmit
<dbl> <dbl> <lgl>
1 2 50 TRUE