如果行在特定时间内按 R 中的组值发生，则删除行

Question

My data df looks like the following:

Row    Timestamp            ID
1    0020-06-29 12:14:00     B 
2    0020-06-29 12:27:00     A 
3    0020-06-29 12:27:22     B  
4    0020-06-29 12:28:30     A 
5    0020-06-29 12:43:00     B 
6    0020-06-29 12:44:00     C 
7    0020-06-29 12:45:00     B 
8    0020-06-29 12:55:00     A 
9    0020-06-29 12:57:00     C 
10   0020-06-29 13:04:00     B 


   
   

The Timestamp indicates the date and time of a reading, and ID the tag identification code.

What I am trying to do is remove any Timestamp by the same ID that occurs within 5 minutes of the previous Timestamp. So, although ID A is seen in Row 2 and Row 4, since the two rows of the dataframe occur within 5 minutes of each other, we would remove Row 4 but keep Row 2 and Row 8, which for ID A occurs 18 minutes later.

Update: The first timestamp should take precedent and all subsequent timestamps should be either kept or removed from then on. So, if we have 3 timestamps corresponding to the same ID and with a time interval of 4.5 minutes and 2 minutes, respectively, between timestamp 1 and 2 and timestamp 2 and 3, I would like remove timestamp 2 and keep 1 and 3. This way the next timestamp we keep would be the one that occurs at least 5 minutes after timestamp 3, and so on.

I have tried the following:

first_date <- df$Timestamp[1:(length(df$Timestamp)-1)]
second_date <- df$Timestamp[2:length(df$Timestamp)]
second_gap <- difftime(second_date, first_date, units="mins")

dup_index <- second_gap>5 # set this as a 5-minute threshold
dup_index <- c(TRUE, dup_index)
df_cleaned <- df[dup_index, ]

But this deletes all observations within 5-minutes of each other and does not take into account the ID . I would usually just subset but I am working with around 180 unique ID s.

Answer 1

Supposing that what I comment above does not occur, a possible solution is the following:

library(tidyverse)
library(lubridate)

elapsed <- function(x)
{
  y <- abs(as.duration(x[2:length(x)] %--% x[1:(length(x)-1)]))
  y >= 5*60
} 

df %>% 
  group_split(ID) %>% 
  map_dfr(~ .[c(T, if (nrow(.) > 1) elapsed(.$Timestamp)),]) %>% 
  arrange(Row)

The output:

# A tibble: 8 × 3
    Row Timestamp           ID   
  <int> <chr>               <chr>
1     1 0020-06-29 12:14:00 B    
2     2 0020-06-29 12:27:00 A    
3     3 0020-06-29 12:27:22 B    
4     5 0020-06-29 12:43:00 B    
5     6 0020-06-29 12:44:00 C    
6     8 0020-06-29 12:55:00 A    
7     9 0020-06-29 12:57:00 C    
8    10 0020-06-29 13:04:00 B