Grouping every n minutes with dplyr

Question

I have a dataset containing 10 events occuring at a certain time on a given day, with corresponding value for each event:

d1 <- data.frame(date = as.POSIXct(c("21/05/2010 19:59:37", "21/05/2010 08:40:30", 
                            "21/05/2010 09:21:00", "21/05/2010 22:29:50", "21/05/2010 11:27:34", 
                            "21/05/2010 18:25:14", "21/05/2010 15:16:01", "21/05/2010 09:41:53", 
                            "21/05/2010 15:01:29", "21/05/2010 09:02:06"), format ="%d/%m/%Y %H:%M:%S"),
                 value = c(11313,42423,64645,643426,1313313,1313,3535,6476,11313,9875))

I want to aggregate the results every 3 minutes, in a standard dataframe format (from "21/05/2010 00:00:00" to "21/05/2010 23:57:00", so that the dataframe has 480 bins of 3 minutes each)

First, I create a dataframe containing bins of 3 minutes each:

d2 <- data.frame(date = seq(as.POSIXct("2010-05-21 00:00:00"), 
                            by="3 min", length.out=(1440/3)))

Then, I merge the two dataframes together and remove NAs:

library(dplyr)
m <- merge(d1, d2, all=TRUE) %>% mutate(value = ifelse(is.na(value),0,value))

Finally, I use period.apply() from the xts package to sum the values for each bin:

library(xts)
a <- period.apply(m$value, endpoints(m$date, "minutes", 3), sum)

Is there a more efficient way to do this ? It does not feel optimal.

Update #1

I adjusted my code after Joshua's answer:

library(xts)
startpoints <- function (x, on = "months", k = 1) { 
  head(endpoints(x, on, k) + 1, -1) 
}

m <- seq(as.POSIXct("2010-05-21 00:00:00"), by="3 min", length.out=1440/3)
x <- merge(value=xts(d1$value, d1$date), xts(,m))
y <- period.apply(x, c(0,startpoints(x, "minutes", 3)), sum, na.rm=TRUE)

I wasn't aware that na.rm=TRUE could be used with period.apply(), which now allows me to skip mutate(value = ifelse(is.na(value),0,value)). It's a step forward and I'm actually pleased with the xts approach here but I would like to know if there is a pure dplyr solution I could use in such a situation.

Update #2

After trying Khashaa's answer, I had an error because my timezone was not specified. So I had:

> tail(d4)
               interval sumvalue
476 2010-05-21 23:45:00       NA
477 2010-05-21 23:48:00       NA
478 2010-05-21 23:51:00       NA
479 2010-05-21 23:54:00       NA
480 2010-05-21 23:57:00    11313
481 2010-05-22 02:27:00   643426
> d4[450,]
               interval sumvalue
450 2010-05-21 22:27:00       NA

Now, after Sys.setenv(TZ="UTC"), it all works fine.

Answer 1

I'm not sure about a dplyr solution, but here's an xts solution:

startpoints <- function (x, on = "months", k = 1) {
  head(endpoints(x, on, k) + 1, -1)
}
m3 <- seq(as.POSIXct("2010-05-21 00:00:00"),
  by="3 min", length.out=1440/3)
x <- merge(value=xts(d1$value, d1$date), xts(,m3))
y <- period.apply(x, c(0,startpoints(x, "minutes", 3)), sum, na.rm=TRUE)

Update: Here's another xts solution that is a bit more careful about correctly aligning the aggregated values. Not to suggest the prior solution was wrong, but this solution is easier to follow and repeat in other analysis.

m3 <- seq(as.POSIXct("2010-05-20 23:59:59.999"),
  by="3 min", length.out=1440/3)
x <- merge(value=xts(d1$value, d1$date), xts(,m3))
y <- period.apply(x, endpoints(x, "minutes", 3), sum, na.rm=TRUE)
y <- align.time(y, 60*3)

Answer 2

Recently, the padr package has been developed which can also solve this in a clean way.

library(lubridate)
library(dplyr)
library(padr)

d1 <- data.frame(date = as.POSIXct(c("21/05/2010 19:59:37", "21/05/2010 08:40:30", 
                                     "21/05/2010 09:21:00", "21/05/2010 22:29:50", "21/05/2010 11:27:34", 
                                     "21/05/2010 18:25:14", "21/05/2010 15:16:01", "21/05/2010 09:41:53", 
                                     "21/05/2010 15:01:29", "21/05/2010 09:02:06"), format ="%d/%m/%Y %H:%M:%S"),
                 value = c(11313,42423,64645,643426,1313313,1313,3535,6476,11313,9875))

res <- d1 %>% 
  as_tibble() %>%
  arrange(date) %>%

  # Thicken the results to fall in 3 minute buckets
  thicken(
    interval  = '3 min', 
    start_val = as.POSIXct('2010-05-21 00:00:00'),
    colname   = "date_pad") %>% 

  # Pad the results to fill in the rest of the 3 minute buckets
  pad(
    interval  = '3 min', 
    by        = 'date_pad', 
    start_val = as.POSIXct('2010-05-21 00:00:00'),
    end_val   = as.POSIXct('2010-05-21 23:57:00')) %>%

  select(date_pad, value)

res
#> # A tibble: 480 x 2
#>    date_pad            value
#>    <dttm>              <dbl>
#>  1 2010-05-21 00:00:00    NA
#>  2 2010-05-21 00:03:00    NA
#>  3 2010-05-21 00:06:00    NA
#>  4 2010-05-21 00:09:00    NA
#>  5 2010-05-21 00:12:00    NA
#>  6 2010-05-21 00:15:00    NA
#>  7 2010-05-21 00:18:00    NA
#>  8 2010-05-21 00:21:00    NA
#>  9 2010-05-21 00:24:00    NA
#> 10 2010-05-21 00:27:00    NA
#> # ... with 470 more rows

res[450,]
#> # A tibble: 1 x 2
#>   date_pad             value
#>   <dttm>               <dbl>
#> 1 2010-05-21 22:27:00 643426

Answer 3

If you need to group data into n minute bins, the floor_date function can allow multiple units to be specified within the unit argument of the function. For example:

library(lubridate)
x <- ymd_hms("2009-08-03 12:25:59.23")
floor_date(x, unit = "3minutes")

"2009-08-03 12:24:00 UTC"

Using your example:

library(lubridate)
library(tidyverse)

# make complete time sequence
d2 <- data.frame(timePeriod = seq(as.POSIXct("2010-05-21 00:00:00"), 
                        by="3 min", length.out=(1440/3)))

d1 %>%
  mutate(timePeriod = floor_date(date, "3minutes")) %>%
  group_by(timePeriod) %>%
  summarise(sum = sum(value)) %>%
  right_join(d2)

Answer 4

lubridate-dplyr-esque solution.

library(lubridate)
library(dplyr)
d2 <- data.frame(interval = seq(ymd_hms('2010-05-21 00:00:00'), by = '3 min',length.out=(1440/3)))
d3 <- d1 %>% 
  mutate(interval = floor_date(date, unit="hour")+minutes(floor(minute(date)/3)*3)) %>% 
  group_by(interval) %>% 
  mutate(sumvalue=sum(value))  %>% 
  select(interval,sumvalue) 
d4 <- merge(d2,d3, all=TRUE) # better if left_join is used
tail(d4)
#               interval sumvalue
#475 2010-05-21 23:42:00       NA
#476 2010-05-21 23:45:00       NA
#477 2010-05-21 23:48:00       NA
#478 2010-05-21 23:51:00       NA
#479 2010-05-21 23:54:00       NA
#480 2010-05-21 23:57:00       NA
d4[450,]
#               interval sumvalue
#450 2010-05-21 22:27:00   643426

If you are comfortable working with Date (I am not), you can dispense with lubridate, and replace the final merge with left_join.