Return when last event occurred in dataframe by group

My data frame:

set.seed(1)
minimalsample <- data.frame(ID=rep(1:5, each= 20), round_number= rep(1:20, 5), event_occurred=rbinom(100, size=1, prob=0.2))

I want a new column lagEventOccurred which tells me how many rounds ago the event has last occurred, i.e.:

lagEventOccurred <- c(Inf, Inf, Inf, Inf, Inf, 1,  2, 1, 1, 2, 3, 4, 5, 6, 7)

for the first few entries and so on. Note that if the event occurred in the same round, it is not considered. I only want to look at previous rounds. All of this should be grouped by ID.

I tried multiple things, including something like

minimalsample %>% dplyr::mutate(firststep = ifelse(event_occurred==1, round_number, Inf) )  %>%
  dplyr::mutate(secondstep = ifelse((lag(firststep) < round_number), lag(firststep), firststep )) %>%
  dplyr::mutate(thirdstep = ifelse((lag(firststep) < round_number), round_number- lag(firststep), round_number-firststep ))

but it seems that I would have to iterate over the lag() procedure 20 times to get the desired results. How to return how many lag()s ago a condition was satisfied?

I have assumed that if the beginning of each "ID" group is a sequence of zeros, it should be treated as Inf. Using the dplyr and tidyr packages, the workflow is:

1. Create copy of "event_occurred" called "lagEventOccurred"
2. Within each "ID" group, create a new 'id' value for every event using cumsum()
3. Use fill() to define start and end of each event 'group'
4. Lag the "lagEventOccurred" column and use seq_along() to return lagged length since last event occurred

library(dplyr)
library(tidyr)

set.seed(1)
minimalsample <- data.frame(ID=rep(1:5, each= 20), 
                            round_number= rep(1:20, 5), 
                            event_occurred=rbinom(100, size=1, prob=0.2))


minimalsample <- minimalsample %>%
  mutate(lagEventOccurred = ifelse(event_occurred == 0, NA, 1)) %>%
  group_by(ID, lagEventOccurred) %>%
  mutate(lagEventOccurred = cumsum(lagEventOccurred)) %>%
  ungroup() %>%
  group_by(ID) %>%
  fill(lagEventOccurred) %>%
  mutate(lagEventOccurred = lag(lagEventOccurred)) %>%
  group_by(ID, lagEventOccurred) %>%
  mutate(lagEventOccurred = ifelse(is.na(lagEventOccurred), 
                                   Inf, seq_along(lagEventOccurred))) %>%
  ungroup()


head(minimalsample, 10)
# A tibble: 10 × 4
     ID round_number event_occurred lagEventOccurred
  <int>        <int>          <int>            <dbl>
1     1            1              0              Inf
2     1            2              0              Inf
3     1            3              0              Inf
4     1            4              1              Inf
5     1            5              0                1
6     1            6              1                2
7     1            7              1                1
8     1            8              0                1
9     1            9              0                2
10    1           10              0                3

tail(minimalsample, 10)
# A tibble: 10 × 4
     ID round_number event_occurred lagEventOccurred
  <int>        <int>          <int>            <dbl>
1     5           11              0              Inf
2     5           12              0              Inf
3     5           13              0              Inf
4     5           14              1              Inf
5     5           15              0                1
6     5           16              0                2
7     5           17              0                3
8     5           18              0                4
9     5           19              1                5
10    5           20              0                1