My objective is to use the rle() function to find out how long bike stations have been empty. Using the test data below, rle(test$bikes) will return the lengths of recurring values in test$bikes.
> rle(test$bikes)
Run Length Encoding
lengths: int [1:9] 3 2 3 1 5 1 7 1 1
values : num [1:9] 0 1 2 1 0 1 0 1 0
> test
station_id time bikes
1 1 2017-12-25 00:00:02 0
2 1 2017-12-25 00:01:02 0
3 1 2017-12-25 00:02:02 0
4 1 2017-12-25 00:03:02 1
5 2 2017-12-25 00:04:02 1
6 2 2017-12-25 00:05:02 2
7 2 2017-12-25 00:06:02 2
8 2 2017-12-25 00:07:02 2
9 2 2017-12-25 00:08:02 1
10 3 2017-12-25 00:09:02 0
11 3 2017-12-25 00:10:02 0
12 3 2017-12-25 00:11:02 0
13 3 2017-12-25 00:12:02 0
14 3 2017-12-25 00:13:02 0
15 4 2017-12-25 00:14:03 1
16 4 2017-12-25 00:15:02 0
17 4 2017-12-25 00:16:02 0
18 4 2017-12-25 00:17:02 0
19 4 2017-12-25 00:18:02 0
20 5 2017-12-25 00:19:02 0
21 5 2017-12-25 00:20:02 0
22 5 2017-12-25 00:21:02 0
23 5 2017-12-25 00:22:02 1
24 5 2017-12-25 00:23:02 0
My goal is to go one step further and to produce an output that groups by staiton_id, and returns the difference in time (in minutes) only when test$bikes has recurring zeros. This can occur multiple times for each station (for exmaple, for station 5 in the test data). In the end, the above dataset would produce the following output:
> output
station_id diff_time interval
1 1 2 00:00 - 00:02
2 3 4 00:09 - 00:13
3 4 3 00:15 - 00:18
4 5 2 00:19 -00:21
5 5 0 00:23 - 00:23
Any suggestions on how to do this with dplyr and rle would be appreciated!
Here is the test data:
> dput(test)
structure(list(station_id = c(1, 1, 1, 1, 2, 2, 2, 2, 2, 3, 3,
3, 3, 3, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5), time = structure(c(1514178002.88487,
1514178062.99145, 1514178122.88463, 1514178182.63461, 1514178242.71401,
1514178302.20358, 1514178362.13263, 1514178422.88907, 1514178482.6502,
1514178542.59171, 1514178602.51222, 1514178662.23203, 1514178722.04015,
1514178782.87382, 1514178843.02124, 1514178902.71852, 1514178962.6987,
1514179022.42077, 1514179082.19535, 1514179142.97175, 1514179202.81556,
1514179262.85187, 1514179322.66264, 1514179382.50223), class = c("POSIXct",
"POSIXt"), tzone = ""), bikes = c(0, 0, 0, 1, 1, 2, 2, 2, 1,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0)), .Names = c("station_id",
"time", "bikes"), row.names = c(NA, 24L), class = "data.frame")
We can use rleid from data.table. Create a grouping variable ('grp') based on the run-length-id of 'bikes', then grouped by 'station_id' and 'grp', specify the i where 'bikes' are 0, summarise the output by taking the difftime of the last and first observation of 'time' and also paste the formattted corresponding 'time' elements
library(data.table)
setDT(test)[, grp:= rleid(bikes)][bikes==0,
.(diff_time = as.numeric(round(difftime(time[.N], time[1], unit = "min"))),
interval = paste(format(time[1], "%M:%S"), format(time[.N], "%M:%S"), sep=" - ")),
.(station_id, grp)]