How to count the days after rain events in r - r

Question

I have a data frame 'test' like below,
day Rain SWC_11 SWC_12 SWC_13 SWC_14 SWC_21
01/01/2019 00:00:00 0.2 51 60 63 60 64
02/01/2019 00:00:00 0.2 51.5 60.3 63.4 60.8 64.4
03/01/2019 00:00:00 0.0 51.3 60.3 63.3 60.6 64.1
04/01/2019 00:00:00 0.4 51.5 60.3 63.4 60.8 64.4
15/01/2019 00:00:00 0.0 NA NA NA NA NA
16/01/2019 00:00:00 0.0 NA NA NA NA NA
17/01/2019 00:00:00 0.0 51.5 60.3 63.4 60.8 64.4
Now I want to count the days after each rain event, once it comes to the next rain events, it restarts again. My ideal outputs gonna be like below.
day Rain SWC_11 SWC_12 SWC_13 SWC_14 SWC_21 events
01/01/2019 00:00:00 0.2 51 60 63 60 64 1
02/01/2019 00:00:00 0.2 51.5 60.3 63.4 60.8 64.4 1
03/01/2019 00:00:00 0.0 51.3 60.3 63.3 60.6 64.1 2
04/01/2019 00:00:00 0.4 51.5 60.3 63.4 60.8 64.4 1
15/01/2019 00:00:00 0.0 NA NA NA NA NA 12
16/01/2019 00:00:00 0.0 NA NA NA NA NA 13
17/01/2019 00:00:00 0.0 51.5 60.3 63.4 60.8 64.4 14
my code is
test$day<- as.numeric(as.Date(test$day))
for(i in 1:(nrow(test)-1))
if (test$Rain[[i]] != 0){
test$event[i] <- 1
test$event[i+nrow(test)] <-test$day[i+nrow(test)]- test$day[i] +1
}else{
test$event <-0
}
but the results looks wired and the warning message is as below,
Error in `$<-.data.frame`(`*tmp*`, "event", value = c(0, 1, 0, 0, 0, 0, :
replacement has 12 rows, data has 10
Hope someone gonna help.

Answer 1

Instead of the rle-derivative I suggested earlier, I think cumulative-sum logic can be used here.
dat %>%
mutate(
day = as.Date(day, format="%d/%m/%Y"),
daylag = dplyr::lag(day, default = first(day) - 1)
) %>%
group_by(grp = cumsum(Rain > 0)) %>%
mutate(event = day - daylag[1]) %>%
ungroup()
# # A tibble: 7 x 11
# day Rain SWC_11 SWC_12 SWC_13 SWC_14 SWC_21 events daylag grp event
# <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <date> <int> <drtn>
# 1 2019-01-01 0.2 51 60 63 60 64 1 2018-12-31 1 1 days
# 2 2019-01-02 0.2 51.5 60.3 63.4 60.8 64.4 1 2019-01-01 2 1 days
# 3 2019-01-03 0 51.3 60.3 63.3 60.6 64.1 2 2019-01-02 2 2 days
# 4 2019-01-04 0.4 51.5 60.3 63.4 60.8 64.4 1 2019-01-03 3 1 days
# 5 2019-01-15 0 NA NA NA NA NA 12 2019-01-04 3 12 days
# 6 2019-01-16 0 NA NA NA NA NA 13 2019-01-15 3 13 days
# 7 2019-01-17 0 51.5 60.3 63.4 60.8 64.4 14 2019-01-16 3 14 days
Data:
dat <- read.table(header = TRUE, text = "
day Rain SWC_11 SWC_12 SWC_13 SWC_14 SWC_21 events
01/01/2019 0.2 51 60 63 60 64 1
02/01/2019 0.2 51.5 60.3 63.4 60.8 64.4 1
03/01/2019 0.0 51.3 60.3 63.3 60.6 64.1 2
04/01/2019 0.4 51.5 60.3 63.4 60.8 64.4 1
15/01/2019 0.0 NA NA NA NA NA 12
16/01/2019 0.0 NA NA NA NA NA 13
17/01/2019 0.0 51.5 60.3 63.4 60.8 64.4 14")

Answer 2

I have a solution which works just with base R, but it is not as short as the one above.
# imagine if you have to do this manually, how would you achieve it step-by-step?
# then just use base R to realize every single step.
# create the "events" column and "day2" column
test$events <- NA
test$day2 <- as.Date(test$day,format="%d/%m/%Y")
# test if the first day, rains or not, and assign the value for event
for (i in 1:1){
if(test$Rain[[i]] !=0){
test$events[[i]] <- 1
}
else {
test$events[[i]] <- 0
}
}
# then starting from the 2nd row, go down one by one
# assign the value for "events" column based on your criteria
for (i in 2:(nrow(test))){
if(test$Rain[[i-1]] !=0 &
test$Rain[[i]] != 0){
test$events[[i]] <- 1
}
if (test$Rain[[i-1]] != 0 &
test$Rain[[i]] == 0){
test$events[[i]] <- test$events[[i-1]] + 1*(as.Date(as.character(test$day2[[i]]), format="%d/%m/%Y") -
as.Date(as.character(test$day2[[i-1]]), format="%d/%m/%Y"))
}
if (test$Rain[[i-1]] == 0 &
test$Rain[[i]] !=0){
test$events[[i]] <-1
}
if (test$Rain[[i-1]] == 0 &
test$Rain[[i]] ==0){
test$events[[i]] <- test$events[[i-1]] + + 1*(as.Date(as.character(test$day2[[i]]), format="%d/%m/%Y") -
as.Date(as.character(test$day2[[i-1]]), format="%d/%m/%Y"))
}
}
Now you will have the desired results. My solution is not very smart, but this is my thinking when the codes seem to be difficult.