Using a rolling time interval to count rows in R and dplyr - r

Question

Let's say I have a dataframe of timestamps with the corresponding number of tickets sold at that time.
Timestamp ticket_count
(time) (int)
1 2016-01-01 05:30:00 1
2 2016-01-01 05:32:00 1
3 2016-01-01 05:38:00 1
4 2016-01-01 05:46:00 1
5 2016-01-01 05:47:00 1
6 2016-01-01 06:07:00 1
7 2016-01-01 06:13:00 2
8 2016-01-01 06:21:00 1
9 2016-01-01 06:22:00 1
10 2016-01-01 06:25:00 1
I want to know how to calculate the number of tickets sold within a certain time frame of all tickets. For example, I want to calculate the number of tickets sold up to 15 minutes after all tickets. In this case, the first row would have three tickets, the second row would have four tickets, etc.
Ideally, I'm looking for a dplyr solution, as I want to do this for multiple stores with a group_by() function. However, I'm having a little trouble figuring out how to hold each Timestamp fixed for a given row while simultaneously searching through all Timestamps via dplyr syntax.

Answer 1

In the current development version of data.table, v1.9.7, non-equi joins are implemented. Assuming your data.frame is called df and the Timestamp column is POSIXct type:
require(data.table) # v1.9.7+
window = 15L # minutes
(counts = setDT(df)[.(t=Timestamp+window*60L), on=.(Timestamp<t),
.(counts=sum(ticket_count)), by=.EACHI]$counts)
# [1] 3 4 5 5 5 9 11 11 11 11
# add that as a column to original data.table by reference
df[, counts := counts]
For each row in t, all rows where df$Timestamp < that_row is fetched. And by=.EACHI instructs the expression sum(ticket_count) to run for each row in t. That gives your desired result.
Hope this helps.

Answer 2

This is a simpler version of the ugly one I wrote earlier..
# install.packages('dplyr')
library(dplyr)
your_data %>%
mutate(timestamp = as.POSIXct(timestamp, format = '%m/%d/%Y %H:%M'),
ticket_count = as.numeric(ticket_count)) %>%
mutate(window = cut(timestamp, '15 min')) %>%
group_by(window) %>%
dplyr::summarise(tickets = sum(ticket_count))
window tickets
(fctr) (dbl)
1 2016-01-01 05:30:00 3
2 2016-01-01 05:45:00 2
3 2016-01-01 06:00:00 3
4 2016-01-01 06:15:00 3

Answer 3

Here is a solution using data.table. Also incorporating different stores.
Example data:
library(data.table)
dt <- data.table(Timestamp = as.POSIXct("2016-01-01 05:30:00")+seq(60,120000,by=60),
ticket_count = sample(1:9, 2000, T),
store = c(rep(c("A","B","C","D"), 500)))
Now apply the following:
ts <- dt$Timestamp
for(x in ts) {
end <- x+900
dt[Timestamp <= end & Timestamp >= x ,CS := sum(ticket_count),by=store]
}
This gives you
Timestamp ticket_count store CS
1: 2016-01-01 05:31:00 3 A 13
2: 2016-01-01 05:32:00 5 B 20
3: 2016-01-01 05:33:00 3 C 19
4: 2016-01-01 05:34:00 7 D 12
5: 2016-01-01 05:35:00 1 A 15
---
1996: 2016-01-02 14:46:00 4 D 10
1997: 2016-01-02 14:47:00 9 A 9
1998: 2016-01-02 14:48:00 2 B 2
1999: 2016-01-02 14:49:00 2 C 2
2000: 2016-01-02 14:50:00 6 D 6