Assign variable based on time-CodePudding

I have a time series and I have a specific requirement for adding a new variable.

Here is some data

dput(df)
structure(list(Time = structure(c(1567423339.229, 1567423399.018, 
1567424218.867, 1567425478.666, 1567425498.883, 1567426519.008, 
1567429378.848, 1567429398.979, 1567429978.723, 1567431218.909
), tzone = "", class = c("POSIXct", "POSIXt")), RaceNum = c("1", 
"1", "1", "1", "1", "1", "2", "2", "2", "2")), class = "data.frame", row.names = c(NA, 
-10L))

What I have tried to do unseussesfully using case_when or ifelse is to assign d on a 1:nrow basis unless the next time series event is within 1 minute then it takes the previous variable and adds a b to it. As you can see the numbering starts again whne RaceNum changes. I was splitting the df by RaceNum then cbind back together once I had established d.

Here is the expected result

dput(df2)
structure(list(Time = structure(c(1567423339.229, 1567423399.018, 
1567424218.867, 1567425478.666, 1567425498.883, 1567426519.008, 
1567429378.848, 1567429398.979, 1567429978.723, 1567431218.909
), tzone = "", class = c("POSIXct", "POSIXt")), RaceNum = c("1", 
"1", "1", "1", "1", "1", "2", "2", "2", "2"), d = c("1", "1b", 
"2", "3", "3b", "4", "1", "1b", "2", "3")), class = "data.frame", row.names = c(NA, 
-10L))

CodePudding user response：

For each RaceNum create a variable which increments when the difference between consecutive records is greater than 1 minute.
For each group (d) paste letters to group number.

library(dplyr)

df %>%
  group_by(RaceNum) %>%
  mutate(d = cumsum(difftime(Time, lag(Time, default = first(Time)),
                    units = 'min') > 1)   1) %>%
  group_by(d, .add = TRUE) %>%
  mutate(d = paste0(d, letters[row_number()]), 
         #For 1st row remove a from 1a, 2a etc. 
         d = ifelse(row_number() == 1, sub('a', '', d), d))  %>%
  ungroup

#   Time                RaceNum d    
#   <dttm>              <chr>   <chr>
# 1 2019-09-02 19:22:19 1       1    
# 2 2019-09-02 19:23:19 1       1b   
# 3 2019-09-02 19:36:58 1       2    
# 4 2019-09-02 19:57:58 1       3    
# 5 2019-09-02 19:58:18 1       3b   
# 6 2019-09-02 20:15:19 1       4    
# 7 2019-09-02 21:02:58 2       1    
# 8 2019-09-02 21:03:18 2       1b   
# 9 2019-09-02 21:12:58 2       2    
#10 2019-09-02 21:33:38 2       3