带有时间窗口的非常规观测数据表累积统计 [英] data.table cumulative stats of irregular observations with time window
问题描述
我有一些交易记录,例如:
I have some transactional records, like the following:
library(data.table)
customers <- 1:75
purchase_dates <- seq( as.Date('2016-01-01'),
as.Date('2018-12-31'),
by=1 )
n <- 500L
set.seed(1)
# Assume the data are already ordered and 1 row per cust_id/purch_dt
df <- data.table( cust_id = sample(customers, n, replace=TRUE),
purch_dt = sample(purchase_dates, n, replace=TRUE),
purch_amt = sample(500:50000, n, replace=TRUE)/100
)[, .(purch_amt = sum(purch_amt)),
keyby=.(cust_id, purch_dt) ]
df
# cust_id purch_dt purch_amt
# 1 2016-03-20 69.65
# 1 2016-05-17 413.60
# 1 2016-12-25 357.18
# 1 2017-03-20 256.21
# 2 2016-05-26 49.14
# 2 2018-05-31 261.87
# 2 2018-12-27 293.28
# 3 2016-12-10 204.12
# 3 2018-09-21 8.70
我想知道在365天之前的窗口内(例如,在 d-365
通过 d-1
进行日期为 d
的交易)。
I would like to know the prior transaction count and total amount, within a 365-day prior window (i.e., at d-365
through d-1
for a transaction on date d
).
我曾想过使用滚动连接,但是最多可以匹配一次以前的购买,并且可以进行多次购买。
I thought of using a rolling join, but that would match to at most one prior purchase, and there could be multiple purchases.
我能够使用带有日期过滤器的笛卡尔自连接来获得所需的结果(请参见下面的答案),但这不是一种非常节省内存的方法。
I was able to get the desired result using a Cartesian self-join with a date filter (see answer below), but that's not a very memory-efficient approach.
所需的输出:
cust_id purch_dt prior_purch_cnt prior_purch_amt purch_amt
1 2016-03-20 0 0.00 69.65
1 2016-05-17 1 69.65 413.60
1 2016-12-25 2 483.25 357.18
1 2017-03-20 3 840.43 256.21
2 2016-05-26 0 0.00 49.14
2 2018-05-31 0 0.00 261.87
2 2018-12-27 1 261.87 293.28
3 2016-12-10 0 0.00 204.12
3 2018-09-21 0 0.00 8.70
推荐答案
我想知道在365天之前的窗口内(例如,在
d-365中)的先前交易计数和总金额
到d-1
d
)。
我认为惯用的方式是:
df[, c("ppn", "ppa") :=
df[.(cust_id = cust_id, d_dn = purch_dt-365, d_up = purch_dt),
on=.(cust_id, purch_dt >= d_dn, purch_dt < d_up),
.(.N, sum(purch_amt, na.rm=TRUE))
, by=.EACHI][, .(N, V2)]
]
cust_id purch_dt purch_amt ppn ppa
1: 1 2016-03-20 69.65 0 0.00
2: 1 2016-05-17 413.60 1 69.65
3: 1 2016-12-25 357.18 2 483.25
4: 1 2017-03-20 256.21 3 840.43
5: 2 2016-05-26 49.14 0 0.00
---
494: 75 2018-01-12 381.24 2 201.04
495: 75 2018-04-01 65.83 3 582.28
496: 75 2018-06-17 170.30 4 648.11
497: 75 2018-07-22 60.49 5 818.41
498: 75 2018-10-10 66.12 4 677.86
这是非等额参加。
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