Cut time series into specific bins and label each chunk - r

Question

I have some experimental data on CO2 values over a few days in a room which are time and date-stamped. I would like to break it up into a series of "experiments" based on an experiment list of when each experiment happened.
e.g.
Data
df<-data.frame(CO2.ppm.=runif(10), Date.time.=as.POSIXct(" 2019-2-08 07:00:00") + runif(n=10, min=0, max=3600))
List of experiments with start and stop times:
ExpertimentList<- data.frame(StartTime=c("2019-2-08 07:10:00", "2019-2-08 07:15:00", "2019-2-08 08:30:00"), StopTime=c("2019-2-08 07:12:00","2019-2-08 07:16:00","2019-2-08 08:15:00"),ExptID=c(1,2,3))
Note there is time when CO2 is measured but no experiment is happening. E.g. between 07:12:00 and 07:15:00.
I would like to split df$Date.time. by ExperimentList's StartTime and StopTime
So far I've converted everything to integers
df$Date.time.<-as.integer(df$Date.time.)
ExperimentList$StartTime<-as.integer(ExperimentList$StartTime
ExperimentList$StopTime<-as.integer(ExperimentList$StopTime)
Then looking at cut
breakz<-dplyr::arrange(paste(Experiment_List$StartTime,Experiment_List$StopTime)%>%as_tibble())
cut(df$Dev.Date.Time,breaks=unique(breakz$value))
But I can't filter out the data when no experiment was taking place. Any thoughts are much appreciated.
Expected output:
set.seed(143)
data.frame(CO2.ppm.=runif(10), Date.time.=sort(as.POSIXct(" 2019-2-08 07:00:00") + runif(n=10, min=0, max=3600)),ExptID=c(NA,NA,NA,1,NA,NA,NA,NA,NA,NA))
ANSWER:
I found that I would run out of memory with #Ronak's answer so I chunked the data.frame into 10000 row segments:
df<-split(df, (as.numeric(rownames(df))-1) %/% 10000)
Then based on #Ronak's answer, I popped the code into a function and used mclapply from the parallel package.
#Do a left join to remove any rows not belonging to an experiment
fuzzyJoinFunction<-function(a){
a<-fuzzy_left_join(a, Experiment_List,
by = c('Dev.Date.Time' = 'StartTime', 'Dev.Date.Time'= 'StopTime'),
match_fun = c(`>=`, `<=`))
a
}
df<-rbindlist(mclapply(X=df,FUN=fuzzyJoinFunction,mc.cores=4))

Answer 1

We can use fuzzyjoin::fuzzy_inner_join to keep only the rows which are in range.
library(dplyr)
library(fuzzyjoin)
#All the datetime values should be of type POSIXct.
ExpertimentList %>%
mutate(across(c(StartTime, StopTime), lubridate::ymd_hms)) -> ExpertimentList
fuzzy_inner_join(df, ExpertimentList,
by = c('Date.time.' = 'StartTime', 'Date.time.'= 'StopTime'),
match_fun = c(`>=`, `<=`))
To get all df values in the final output with NA for ExptID use fuzzy_left_join.