Value mapping by condition in R - r

Question

I have a raw data frame that looks like this:
test
id class time
1 1 start 2019-06-20 00:00:00
2 1 end 2019-06-20 00:05:00
3 1 start 2019-06-20 00:10:00
4 1 end 2019-06-20 00:15:00
5 2 end 2019-06-20 00:20:00
6 2 start 2019-06-20 00:25:00
7 2 end 2019-06-20 00:30:00
8 2 start 2019-06-20 00:35:00
9 3 end 2019-06-20 00:40:00
10 3 start 2019-06-20 00:45:00
11 3 end 2019-06-20 00:50:00
12 3 start 2019-06-20 00:55:00
My goal is to map the values to an output table for each id only where there is a start and an end in consecutive order (time). Therefore, the output would look like:
output
id start end
1 1 2019-06-20 00:00:00 2019-06-20 00:05:00
2 1 2019-06-20 00:10:00 2019-06-20 00:15:00
3 2 2019-06-20 00:25:00 2019-06-20 00:30:00
4 3 2019-06-20 00:45:00 2019-06-20 00:50:00
I have tried with the dplyr package, but
test %>% group_by(id) %>% arrange(time) %>% starts_with("start")
Error in starts_with(., "start") : is_string(match) is not TRUE
starts_with always throws an error. I would like to avoid writing a for loop because I am sure this can be handled by a few chain operations. Any ideas for a workaround in dplyr or data.table?

Answer 1

One possible approach:
test[, {
si <- which(class=="start" & shift(class, -1L)=="end")
.(id, start=time[si], end=time[si + 1L])
}, by=.(id)]
output:
id start end
1: 1 1 2019-06-20 00:00:00 2019-06-20 00:05:00
2: 1 1 2019-06-20 00:10:00 2019-06-20 00:15:00
3: 2 2 2019-06-20 00:25:00 2019-06-20 00:30:00
4: 3 3 2019-06-20 00:45:00 2019-06-20 00:50:00
data:
library(data.table)
test <- fread("id,class,time
1,start,2019-06-20 00:00:00
1,end,2019-06-20 00:05:00
1,start,2019-06-20 00:10:00
1,end,2019-06-20 00:15:00
2,end,2019-06-20 00:20:00
2,start,2019-06-20 00:25:00
2,end,2019-06-20 00:30:00
2,start,2019-06-20 00:35:00
3,end,2019-06-20 00:40:00
3,start,2019-06-20 00:45:00
3,end,2019-06-20 00:50:00
3,start,2019-06-20 00:55:00")

Answer 2

I usually use cumsum() is these cases
test %>%
group_by(id) %>%
arrange(time, .by_group = TRUE) %>% # should use .by_group arg
mutate(flag = cumsum(class == "start")) %>%
group_by(id, flag) %>%
filter(n() == 2L) %>%
ungroup() %>%
spread(class, time) %>%
select(-flag)

Answer 3

Using dplyr and tidyr, we can first filter the rows which follow the "start" and "end" pattern, create groups of 2 rows and spread to long format.
library(dplyr)
library(tidyr)
test %>%
group_by(id) %>%
filter(class == "start" & lead(class) == "end" |
class == "end" & lag(class) == "start") %>%
group_by(group = gl(n()/2, 2)) %>%
spread(class, time) %>%
ungroup() %>%
select(-group) %>%
select(id, start, end)
# id start end
# <int> <dttm> <dttm>
#1 1 2019-06-20 00:00:00 2019-06-20 00:05:00
#2 1 2019-06-20 00:10:00 2019-06-20 00:15:00
#3 2 2019-06-20 00:25:00 2019-06-20 00:30:00
#4 3 2019-06-20 00:45:00 2019-06-20 00:50:00

Answer 4

You can keep each start row plus the end immediately after it (if any), then use dcast to switch from long to wide form:
test[,
if (.N >= 2) head(.SD, 2)
, by=.(g = rleid(id, cumsum(class=="start"))), .SDcols=names(test)][,
dcast(.SD, id + g ~ factor(class, levels=c("start", "end")), value.var="time")
]
id g start end
1: 1 1 2019-06-20 00:00:00 2019-06-20 00:05:00
2: 1 2 2019-06-20 00:10:00 2019-06-20 00:15:00
3: 2 4 2019-06-20 00:25:00 2019-06-20 00:30:00
4: 3 7 2019-06-20 00:45:00 2019-06-20 00:50:00
rleid and cumsum are used to find the sequences; and factor is needed to tell dcast the column order.
Side note: This is essentially the same as #cheetahfly's answer (I didn't realize when I posted): since the cumsum is increasing, it is sufficient to group by id + cumsum and there's no need to use rleid (which is for tracking runs of values). The only difference is that my approach woudl keep a run like start, end, end; while the other answer would filter it out with the n() == 2 check.