Check previous row in datetime, if time is greater than a certain value, place in a group and take its duration in seconds (R, dplyr, lubridate)

I have a dataset, df: (the dataset contains over 4000 rows)

  DATEB

  9/9/2019 7:51:58 PM
  9/9/2019 7:51:59 PM
  9/9/2019 7:51:59 PM
  9/9/2019 7:52:00 PM
  9/9/2019 7:52:01 PM
  9/9/2019 7:52:01 PM
  9/9/2019 7:52:02 PM
  9/9/2019 7:52:03 PM
  9/9/2019 7:54:00 PM
  9/9/2019 7:54:02 PM
  9/10/2019 8:00:00PM

I wish to place in groups (if the times are not within 10 seconds of the previous row) and then take the duration of the newly formed group.

Desired output:

Group   Duration

 a       5 sec
 b       2 sec
 c       0 sec




 dput:


  structure(list(DATEB = structure(c(2L, 3L, 3L, 4L, 5L, 5L, 6L, 
  7L, 8L, 9L, 1L), .Label = c("      9/10/2019 8:00:00 PM", "      9/9/2019 7:51:58 PM", 
  "      9/9/2019 7:51:59 PM", "      9/9/2019 7:52:00 PM", "      9/9/2019 7:52:01 PM", 
  "      9/9/2019 7:52:02 PM", "      9/9/2019 7:52:03 PM", "      9/9/2019 7:54:00 PM", 
  "      9/9/2019 7:54:02 PM"), class = "factor")), class = "data.frame", row.names = c(NA, 
  -11L))

I have tried the code below, which works well, except, I am wanting the units in seconds only. The code below gives units of minutes and seconds.

  library(dplyr)
  library(lubridate)


  df2 <- mutate(df,
          DATEB = lubridate::mdy_hms(DATEB))

 df2$time_since_last_row <- df2$DATEB - lag(df2$DATEB)
 df2$time_since_last_row[[1]] <- 0 # replace the first NA
 df2$group_10s <- 0

 for ( i in 2:nrow(df2))
   {
 if(df2$time_since_last_row[[i]]>seconds(10))
 df2$group_10s[[i]] <-  df2$group_10s[[i-1]] +1 
 else 
 df2$group_10s[[i]] <-  df2$group_10s[[i-1]]
      }





 df3 <- group_by(df2,
            group_10s) %>%
   summarise(volume_in_group=n(),
   min_DATEB=min(DATEB),
   max_DATEB=max(DATEB),
   group_duration = max_DATEB - min_DATEB)



   #nirgrahamuk-R community

Any suggestion is appreciated.