这个问题已经有了answer for SQL，我能够使用sqldf在R中实现该解决方案。但是，我一直无法找到使用data.table实现它的方法。
问题是计算滚动日期范围内一列的不同值，例如（并直接引用链接的问题）如果数据如下所示：
Date | email
-------+----------------
1/1/12 | [email protected]
1/1/12 | [email protected]
1/1/12 | [email protected]
1/2/12 | [email protected]
1/2/12 | [email protected]
1/3/12 | [email protected]
1/4/12 | [email protected]
1/5/12 | [email protected]
1/5/12 | [email protected]
1/6/12 | [email protected]
1/6/12 | [email protected]
1/6/12 | [email protected]
如果我们使用3天的日期，那么结果集看起来就像这样
date | count(distinct email)
-------+------
1/1/12 | 3
1/2/12 | 3
1/3/12 | 3
1/4/12 | 3
1/5/12 | 2
1/6/12 | 2
这是使用data.table在R中创建相同数据的代码：
date <- as.Date(c('2012-01-01','2012-01-01','2012-01-01',
'2012-01-02','2012-01-02','2012-01-03',
'2012-01-04','2012-01-05','2012-01-05',
'2012-01-06','2012-01-06','2012-01-06'))
email <- c('[email protected]', '[email protected]','[email protected]',
'[email protected]', '[email protected]','[email protected]',
'[email protected]','[email protected]','[email protected]',
'[email protected]','[email protected]','[email protected]')
dt <- data.table(date, email)
任何有关这方面的帮助将非常感激。谢谢！
编辑1：
这是一个玩具问题，我想应用于更大的数据集，因此使用笛卡尔积很有问题。相反，我想要一些等同于SQL中相关子查询的东西，例如：我最初关联的问题的解决方案是：
SELECT day
,(SELECT count(DISTINCT email)
FROM tbl
WHERE day BETWEEN t.day - 2 AND t.day -- period of 3 days
) AS dist_emails
FROM tbl t
WHERE day BETWEEN '2012-01-01' AND '2012-01-06'
GROUP BY 1
ORDER BY 1;
编辑2：根据@ MichaelChirico的解决方案，根据@jangorecki的要求，这是一些时间：
# The data
> dim(temp)
[1] 2627785 4
> head(temp)
date category1 category2 itemId
1: 2013-11-08 0 2 1713
2: 2013-11-08 0 2 90485
3: 2013-11-08 0 2 74249
4: 2013-11-08 0 2 2592
5: 2013-11-08 0 2 2592
6: 2013-11-08 0 2 765
> uniqueN(temp$itemId)
[1] 13510
> uniqueN(temp$date)
[1] 127
# Timing for data.table
> system.time(dtTime <- temp[,
+ .(count = temp[.(seq.Date(.BY$date - 6L, .BY$date, "day"),
+ .BY$category1, .BY$category2 ), uniqueN(itemId), nomatch = 0L]),
+ by = c("date","category1","category2")])
user system elapsed
6.913 0.130 6.940
>
# Time for sqldf
> system.time(sqlDfTime <-
+ sqldf(c("create index ldx on temp(date, category1, category2)",
+ "SELECT date, category1, category2,
+ (SELECT count(DISTINCT itemId)
+ FROM temp
+ WHERE category1 = t.category1 AND category2 = t.category2 AND
+ date BETWEEN t.date - 6 AND t.date
+ ) AS numItems
+ FROM temp t
+ GROUP BY date, category1, category2
+ ORDER BY 1;"))
user system elapsed
87.225 0.098 87.295
输出是等效的，但使用data.table而不是sqldf导致12.5倍的加速。相当实质！
这是有效的，利用data.table的新非等值线功能。
dt[dt[ , .(date3=date, date2 = date - 2, email)],
on = .(date >= date2, date<=date3),
allow.cartesian = TRUE
][ , .(count = uniqueN(email)),
by = .(date = date + 2)]
# date V1
# 1: 2011-12-30 3
# 2: 2011-12-31 3
# 3: 2012-01-01 3
# 4: 2012-01-02 3
# 5: 2012-01-03 1
# 6: 2012-01-04 2
说实话，我对这是如何工作有点恼火，但想法是在dt上加入date，匹配2天前和今天之间的任何date。我不确定为什么我们必须通过设置date = date + 2来清理。
这是使用键的方法：
setkey(dt, date)
dt[ , .(count = dt[.(seq.Date(.BY$date - 2L, .BY$date, "day")),
uniqueN(email), nomatch = 0L]), by = date]
最近在non-equi的current development version中实现了data.table, v1.9.7连接功能，这可以通过以下方式完成：
dt[.(date3=unique(dt$date2)), .(count=uniqueN(email)), on=.(date>=date3, date2<=date3), by=.EACHI]
# date date2 count
# 1: 2011-12-30 2011-12-30 3
# 2: 2011-12-31 2011-12-31 3
# 3: 2012-01-01 2012-01-01 3
# 4: 2012-01-02 2012-01-02 3
# 5: 2012-01-03 2012-01-03 1
# 6: 2012-01-04 2012-01-04 2