Remove rows based on date and time where no update actually occurs but keep the first instance

Question

I've come up against a wall in trying to resolve this and hope somebody can help. I'm trying to implement a way to filter this dataset which reflects bike station occupancy data that is time stamped.

   ID  Time                   Bike.Availability
1  2   01/04/2020  04:31:16   11
2  2   01/04/2020  04:40:07   11
3  2   01/04/2020  04:50:15   10
4  2   01/04/2020  04:57:10   10
5  2   01/04/2020  05:07:19    9
6  2   01/04/2020  05:19:38   10
7  2   01/04/2020  05:29:47   10
8  2   01/04/2020  06:43:54   11

I want to remove the rows where there is no change in Bike.Availability and only keep the first instance. I would like the resulting dataset to look as follows:

   ID  Time                   Bike.Availability
1  2   01/04/2020  04:31:16   11
2  2   01/04/2020  04:50:15   10
3  2   01/04/2020  05:07:19    9
4  2   01/04/2020  05:19:38   10
5  2   01/04/2020  06:43:54   11

I've converted the timestamp:

bike_data$Time <- as.POSIXct(bike_data$Time,format="%Y-%m-%d %H:%M:%S")

And I've tried different variations of:

library(dplyr)
bike_data %>%
 group_by(Time) %>%
 arrange(Bike.Availability) %>%
 top_n(1)

Any help or feedback would be greatly appreciated.

Answer 1

We group by the 'ID' and run-length-id of 'Bike.Availability' ie it creates a grouping index based on the similarity of adjacent elements of 'Bike.Availability', then slice the first row with slice_head specifying n = 1

library(dplyr)
library(data.table)
bike_data %>%     
 group_by(ID, grp = rleid(Bike.Availability)) %>%
 slice_head(n = 1) %>%
 ungroup %>%
 select(-grp)

-output

# A tibble: 5 x 3
#     ID Time                 Bike.Availability
#  <int> <chr>                            <int>
#1     2 01/04/2020  04:31:16                11
#2     2 01/04/2020  04:50:15                10
#3     2 01/04/2020  05:07:19                 9
#4     2 01/04/2020  05:19:38                10
#5     2 01/04/2020  06:43:54                11

---

Grouping by 'Time' column would create groups with single observation per group (based on the values showed in 'Time'), thererefore top_n(1) returns the original dataset instead of subsetting

data

bike_data <- structure(list(ID = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
Time = c("01/04/2020  04:31:16", 
"01/04/2020  04:40:07", "01/04/2020  04:50:15", "01/04/2020  04:57:10", 
"01/04/2020  05:07:19", "01/04/2020  05:19:38", "01/04/2020  05:29:47", 
"01/04/2020  06:43:54"), Bike.Availability = c(11L, 11L, 10L, 
10L, 9L, 10L, 10L, 11L)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6", "7", "8"))

Answer 2

A dplyr solution alone. Checking if row above and below are same ifelse . Then NA to 0 and then filter.

library(dplyr)
bike_data %>% 
  mutate(same = ifelse(Bike.Availability == lag(Bike.Availability), 1, 0)) %>% 
  mutate(same = ifelse(is.na(same), 0, same)) %>% 
  filter(same=="NA" | same==0) %>% 
  select(-same)

Output:

  ID                 Time Bike.Availability
1  2 01/04/2020  04:31:16                11
3  2 01/04/2020  04:50:15                10
5  2 01/04/2020  05:07:19                 9
6  2 01/04/2020  05:19:38                10
8  2 01/04/2020  06:43:54                11