R data.table具有时间窗口的不规则观测的累积统计数据
我有一些事务记录，如下所示：R data.table具有时间窗口的不规则观测的累积统计数据,r,data.table,cumulative-sum,R,Data.table,Cumulative Sum,我有一些事务记录，如下所示： 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 ordere
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
以下是带有日期范围筛选器的笛卡尔自联接：
df_prior <- df[df, on=.(cust_id), allow.cartesian=TRUE
][i.purch_dt < purch_dt &
i.purch_dt >= purch_dt - 365
][, .(prior_purch_cnt = .N,
prior_purch_amt = sum(i.purch_amt)),
keyby=.(cust_id, purch_dt)]
df2 <- df_prior[df, on=.(cust_id, purch_dt)]
df2[is.na(prior_purch_cnt), `:=`(prior_purch_cnt=0,
prior_purch_amt=0
)]
df2
# 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
df_prior=purch_dt-365
][，（优先购买权=.N，
优先购买金额=总额（即购买金额），
keyby=（客户id，购买日期）]
df2
我想知道之前365天窗口内的先前交易计数和总金额（即日期为d
的交易在d-365
至d-1
）
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
df[，c（“ppn”，“ppa”）：=
df[（客户id=客户id，客户dn=采购dt-365，客户up=采购dt），
on=（客户id，采购dt>=d\U dn，采购dt
这是一个“非相等联接”。谢谢——不知何故，我忘记了这个特性（根据文档，v1.9.8+），在子集中创建新变量作为联接时，我的头脑有点崩溃。我不知道我们可以这么做。@C8H10N4O2:）希望在包的加入小插曲编写完成后，更多的人会知道。
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