问题
this question is a modification of a problem I posted here where I have occurrences of a specific type on different days, but this time they are assigned to multiple users, for example:
df = data.frame(user_id = c(rep(1:2, each=5)),
cancelled_order = c(rep(c(0,1,1,0,0), 2)),
order_date = as.Date(c('2015-01-28', '2015-01-31', '2015-02-08', '2015-02-23', '2015-03-23',
'2015-01-25', '2015-01-28', '2015-02-06', '2015-02-21', '2015-03-26')))
user_id cancelled_order order_date
1 0 2015-01-28
1 1 2015-01-31
1 1 2015-02-08
1 0 2015-02-23
1 0 2015-03-23
2 0 2015-01-25
2 1 2015-01-28
2 1 2015-02-06
2 0 2015-02-21
2 0 2015-03-26
I'd like to calculate
1) the number of cancelled orders that each customer is going to have in the next x days (e.g. 7, 14), excluding the current one and
1) the number of cancelled orders that each customer had in the past x days (e.g. 7, 14) , excluding the current one.
The desired output would look like this:
solution
user_id cancelled_order order_date plus14 minus14
1 0 2015-01-28 2 0
1 1 2015-01-31 1 0
1 1 2015-02-08 0 1
1 0 2015-02-23 0 0
1 0 2015-03-23 0 0
2 0 2015-01-25 2 0
2 1 2015-01-28 1 0
2 1 2015-02-06 0 1
2 0 2015-02-21 0 0
2 0 2015-03-26 0 0
The solution that is perfectly fit for this purpose was presented by @joel.wilson using data.table
library(data.table)
vec <- c(14, 30) # Specify desired ranges
setDT(df)[, paste0("x", vec) :=
lapply(vec, function(i) sum(df$cancelled_order[between(df$order_date,
order_date,
order_date + i, # this part can be changed to reflect the past date ranges
incbounds = FALSE)])),
by = order_date]
However, it does not take into account grouping by user_id. When I tried to modify the formula by adding this grouping as by = c("user_id", "order_date") or by = list(user_id, order_date), it did not work. It seems it is something very basic, any hints on how to get around this detail?
Also, keep in mind that I'm after a solution that works, even if it is not based on the above code or data.table at all!
Thanks!
回答1:
Here's one way:
library(data.table)
orderDT = with(df, data.table(id = user_id, completed = !cancelled_order, d = order_date))
vec = list(minus = 14L, plus = 14L)
orderDT[, c("dplus", "dminus") := .(
orderDT[!(completed)][orderDT[, .(id, d_plus = d + vec$plus, d_tom = d + 1L)], on=.(id, d <= d_plus, d >= d_tom), .N, by=.EACHI]$N
,
orderDT[!(completed)][orderDT[, .(id, d_minus = d - vec$minus, d_yest = d - 1L)], on=.(id, d >= d_minus, d <= d_yest), .N, by=.EACHI]$N
)]
id completed d dplus dminus
1: 1 TRUE 2015-01-28 2 0
2: 1 FALSE 2015-01-31 1 0
3: 1 FALSE 2015-02-08 0 1
4: 1 TRUE 2015-02-23 0 0
5: 1 TRUE 2015-03-23 0 0
6: 2 TRUE 2015-01-25 2 0
7: 2 FALSE 2015-01-28 1 0
8: 2 FALSE 2015-02-06 0 1
9: 2 TRUE 2015-02-21 0 0
10: 2 TRUE 2015-03-26 0 0
(I found OP's column names cumbersome and so shortened them.)
How it works
Each of the columns can be run on its own, like
orderDT[!(completed)][orderDT[, .(id, d_plus = d + vec$plus, d_tom = d + 1L)], on=.(id, d <= d_plus, d >= d_tom), .N, by=.EACHI]$N
And this can be broken down into steps by simplifying:
orderDT[!(completed)][
orderDT[, .(id, d_plus = d + vec$plus, d_tom = d + 1L)],
on=.(id, d <= d_plus, d >= d_tom),
.N,
by=.EACHI]$N
# original version
orderDT[!(completed)][
orderDT[, .(id, d_plus = d + vec$plus, d_tom = d + 1L)],
on=.(id, d <= d_plus, d >= d_tom),
.N,
by=.EACHI]
# don't extract the N column of counts
orderDT[!(completed)][
orderDT[, .(id, d_plus = d + vec$plus, d_tom = d + 1L)],
on=.(id, d <= d_plus, d >= d_tom)]
# don't create the N column of counts
orderDT[!(completed)]
# don't do the join
orderDT[, .(id, d_plus = d + vec$plus, d_tom = d + 1L)]
# see the second table used in the join
This uses a "non-equi" join, taking inequalities to define the date ranges. For more details, see the documentation page found by typing ?data.table.
回答2:
I might have made this solution a bit complex:
library(dplyr)
library(tidyr)
vec <- c(7,14)
reslist <- lapply(vec, function(x){
df %>% merge(df %>% rename(cancelled_order2 = cancelled_order, order_date2 = order_date)) %>%
filter(abs(order_date-order_date2)<=x) %>%
group_by(user_id, order_date) %>% arrange(order_date2) %>% mutate(cumcancel = cumsum(cancelled_order2)) %>%
mutate(before = cumcancel - cancelled_order2,
after = max(cumcancel) - cumcancel) %>%
filter(order_date == order_date2) %>%
select(user_id, cancelled_order, order_date, before, after) %>%
mutate(within = x)})
do.call(rbind, reslist) %>% gather(key, value, -user_id, -cancelled_order, -order_date, -within) %>%
mutate(col = paste0(key,"_",within)) %>% select(-within, - key) %>% spread(col, value) %>% arrange(user_id, order_date)
PS: I did spot a mistake in your output example (user_id 1, order_date 2015-02-23 ,minus14 should be 0, since there are 15 days between 02/08 and 02/23)
回答3:
I recommend to use runner package. There is a function runner which executes any R function within sliding window.
To obtain sum from current 7-days window and 14-days window excluding current element one can use sum(x[length(x)]) for each window.
library(runner)
df %>%
group_by(user_id) %>%
mutate(
minus_7 = runner(cancelled_order, k = 7, idx = order_date,
f = function(x) sum(x[length(x)])),
minus_14 = runner(cancelled_order, k = 14, idx = order_date,
f = function(x) sum(x[length(x)])))
# A tibble: 10 x 5
# Groups: user_id [2]
user_id cancelled_order order_date minus_7 minus_14
<int> <dbl> <date> <dbl> <dbl>
1 1 0 2015-01-28 0 0
2 1 1 2015-01-31 1 1
3 1 1 2015-02-08 1 1
4 1 0 2015-02-23 0 0
5 1 0 2015-03-23 0 0
6 2 0 2015-01-25 0 0
7 2 1 2015-01-28 1 1
8 2 1 2015-02-06 1 1
9 2 0 2015-02-21 0 0
10 2 0 2015-03-26 0 0
For future elements it's bit tricky, because it's still 7-days window but lagged by -6 days (i:(i+6) = 7 days). Also in this case, first element of each window is excluded with sum(x[-1]).
df %>%
group_by(user_id) %>%
mutate(
plus_7 = runner(cancelled_order, k = 7, lag = -6, idx = order_date,
f = function(x) sum(x[-1])),
plus_14 = runner(cancelled_order, k = 14, lag = -13, idx = order_date,
f = function(x) sum(x[-1]))
)
# A tibble: 10 x 5
# Groups: user_id [2]
user_id cancelled_order order_date plus_7 plus_14
<int> <dbl> <date> <dbl> <dbl>
1 1 0 2015-01-28 1 2
2 1 1 2015-01-31 0 1
3 1 1 2015-02-08 0 0
4 1 0 2015-02-23 0 0
5 1 0 2015-03-23 0 0
6 2 0 2015-01-25 1 2
7 2 1 2015-01-28 0 1
8 2 1 2015-02-06 0 0
9 2 0 2015-02-21 0 0
10 2 0 2015-03-26 0 0
More information in package and function documentation.
来源：https://stackoverflow.com/questions/41615967/calculate-the-number-of-occurrences-of-a-specific-event-in-the-past-and-future-w