Hi,
The expected output is confusing.
dat1 <- read.table(text="date, user, items_bought
2013-01-01, x, 2
2013-01-02, x, 1
2013-01-03, x, 0
2013-01-04, x, 0
2013-01-05, x, 3
2013-01-06, x, 1
2013-01-01, y, 1
2013-01-02, y, 1
2013-01-03, y, 0
2013-01-04, y, 5
2013-01-05, y, 6
2013-01-06, y, 1",sep=",",header=TRUE,stringsAsFactors=FALSE)
##Assuming that the data is ordered by date and no gaps in date
res1 <- unsplit(lapply(split(dat1, dat1$user), function(x) {
indx <- (seq(nrow(x)) - 1)%/%3
x$cum_items_bought_3_days <- ave(x$items_bought, indx, FUN = cumsum)
x
}), dat1$user)
##expected output
res2 <- unsplit(lapply(split(dat1, dat1$user), function(x) {
indx <- (seq(nrow(x)) - 1)%/%3
x$cum_items_bought_3_days <- ave(x$items_bought, indx, FUN = cumsum)
indx2 <- seq(0, length(indx), by = 4)
x[indx2, 4] <- x[indx2, 4] + indx[indx2]
x
}), dat1$user)
A.K.
Say I have data that looks like
date, user, items_bought
2013-01-01, x, 2
2013-01-02, x, 1
2013-01-03, x, 0
2013-01-04, x, 0
2013-01-05, x, 3
2013-01-06, x, 1
2013-01-01, y, 1
2013-01-02, y, 1
2013-01-03, y, 0
2013-01-04, y, 5
2013-01-05, y, 6
2013-01-06, y, 1
to get the cumulative sum per user per data point I was doing
data.frame(cum_items_bought=unlist(tapply(as.numeric(data$items_bought),
data$user, FUN = cumsum)))
output from this looks like
date, user, items_bought
2013-01-01, x, 2
2013-01-02, x, 3
2013-01-03, x, 3
2013-01-04, x, 3
2013-01-05, x, 6
2013-01-06, x, 7
2013-01-01, y, 1
2013-01-02, y, 2
2013-01-03, y, 2
2013-01-04, y, 7
2013-01-05, y, 13
2013-01-06, y, 14
However I want to restrict my sum to only add up those that happened within 3
days of each row (relative to the user). i.e. the output needs to look like
this:
date, user, cum_items_bought_3_days
2013-01-01, x, 2
2013-01-02, x, 3
2013-01-03, x, 3
2013-01-04, x, 1
2013-01-05, x, 3
2013-01-06, x, 4
2013-01-01, y, 1
2013-01-02, y, 2
2013-01-03, y, 2
2013-01-04, y, 6
2013-01-05, y, 11
2013-01-06, y, 12
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