Set the start point for time intervals in R - r

Question

I have different sets of data with the following format
Time Value1 Value2 ....
11/04/2015 15:12:22 1 2 ....
11/04/2015 15:13:46 1 2 ....
And I want to group them in intervals of 15 minutes. I can do this with the following code
data$time = cut(data$time, breaks = "15 min")
data.grouped <- aggregate(data[,c(-1)], by = list(time = datos$time), median)
The problem is that the time field in the output has the following values
12/04/2015 16:12
12/04/2015 16:27
12/04/2015 16:42
12/04/2015 16:57
And I want the times to be :00 :15 :30 or :45. Is there any way of forcing the intervals to be like this or a different approach to merge the data that allows it?
A sample data from dput:
structure(list(time = structure(list(sec = c(49, 5, 21, 37, 54,
10, 38), min = c(12L, 13L, 13L, 13L, 13L, 14L, 22L), hour = c(15L,
15L, 15L, 15L, 15L, 15L, 16L), mday = c(11L, 11L, 11L, 11L, 11L,
11L, 12L), mon = c(3L, 3L, 3L, 3L, 3L, 3L, 3L), year = c(116L,
116L, 116L, 116L, 116L, 116L, 116L), wday = c(1L, 1L, 1L, 1L,
1L, 1L, 2L), yday = c(101L, 101L, 101L, 101L, 101L, 101L, 102L
), isdst = c(1L, 1L, 1L, 1L, 1L, 1L, 1L), zone = c("CEST", "CEST",
"CEST", "CEST", "CEST", "CEST", "CEST"), gmtoff = c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_,
NA_integer_)), .Names = c("sec", "min", "hour", "mday", "mon",
"year", "wday", "yday", "isdst", "zone", "gmtoff"), class = c("POSIXlt",
"POSIXt")), value1 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("time",
"value1"), row.names = c(NA, -7L), class = "data.frame")

Answer 1

Starting with your dput, calling it df, first we'll convert your factor to a POSIXct class, then we will floor it to closest 15 minutes below. (use round instead of floor if you want the closest 15 minutes in general):
df$time = as.POSIXct(df$time)
df$time15 = lubridate::floor_date(df$time, unit = "15 min")
df
# time value1 time15
# 1 2016-04-11 15:12:49 0 2016-04-11 15:00:00
# 2 2016-04-11 15:13:05 0 2016-04-11 15:00:00
# 3 2016-04-11 15:13:21 0 2016-04-11 15:00:00
# 4 2016-04-11 15:13:37 0 2016-04-11 15:00:00
# 5 2016-04-11 15:13:54 0 2016-04-11 15:00:00
# 6 2016-04-11 15:14:10 0 2016-04-11 15:00:00
# 7 2016-04-12 16:22:38 0 2016-04-12 16:15:00
You can then aggregate using the time15 column as the grouper.

Answer 2

I provide an example you can replicate with your data frame. First, I create a dummy time series (ts) as.POSIXct by 5 min intervals and then group them by 15 min intervals using dplyr.
ts <- seq.POSIXt(as.POSIXct("2017-01-01", tz = "UTC"),
as.POSIXct("2017-02-01", tz = "UTC"),
by = "5 min")
ts <- as.data.frame(ts)
library(dplyr)
ts %>%
group_by(interval = cut(ts, breaks = "15 min")) %>%
summarise(count= n())
Output
# A tibble: 2,977 x 2
interval sumvalue
<fct> <int>
1 2017-01-01 00:00:00 3
2 2017-01-01 00:15:00 3
3 2017-01-01 00:30:00 3
4 2017-01-01 00:45:00 3
5 2017-01-01 01:00:00 3
6 2017-01-01 01:15:00 3
7 2017-01-01 01:30:00 3
8 2017-01-01 01:45:00 3
9 2017-01-01 02:00:00 3
10 2017-01-01 02:15:00 3
# ... with 2,967 more rows