R的滚动日期范围内的唯一值计数 [英] Count of unique values in a rolling date range for R

问题描述

此问题已经有答案SQL ，我能够使用 sqldf 在R中实现该解决方案。但是，我一直找不到使用 data.table 来实现它的方法。

This question already has an answer for SQL, and I was able to implement that solution in R using sqldf. However, I've been unable to find a way to implement it using data.table.

问题是要计算滚动日期范围内一列的不同值，例如（并直接从链接的问题中引用）数据是否如下所示：

The problem is to count the distinct values of one column within a rolling date range, e.g. (and quoting directly from the linked question) if the data looked like this:

Date   | email 
-------+----------------
1/1/12 | [e-mail]
1/1/12 | [e-mail]
1/1/12 | [e-mail]
1/2/12 | [e-mail]
1/2/12 | [e-mail]
1/3/12 | [e-mail]
1/4/12 | [e-mail]
1/5/12 | [e-mail]
1/5/12 | [e-mail]
1/6/12 | [e-mail]
1/6/12 | [e-mail]
1/6/12 | [e-mail]

然后，如果我们使用日期时间段，则结果集看起来像这样3天

Then the result set would look something like this if we used a date period of 3 days

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

以下是使用 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('[e-mail]', '[e-mail]','[e-mail]',
           '[e-mail]', '[e-mail]','[e-mail]',
           '[e-mail]','[e-mail]','[e-mail]',
           '[e-mail]','[e-mail]','[e-mail]')
dt <- data.table(date, email)

任何对此的帮助将不胜感激。谢谢！

Any help on this would be much appreciated. Thanks!

编辑1：

这是一个玩具问题，我想将其应用于更大的数据集，因此使用笛卡尔积是有问题的。取而代之的是，我想要的东西等同于SQL中的相关子查询，例如我最初链接的问题的解决方案是：

This is a toy problem that I want to apply to a much larger data set, so use of Cartesian products is problematic. Instead, I'd like something equivalent to a correlated subquery in SQL, e.g. the solution from the question that I originally linked was:

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：
这是根据@MichaelChirico的解决方案的一些时间，应@的要求jangorecki：

Edit 2: Here is some timing based on @MichaelChirico's solution, as requested by @jangorecki:

# 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，但是使用data.table而不是sqldf导致速度提高了12.5倍

The outputs are eqivalent, but using data.table rather than sqldf resulted in a 12.5x speedup. Pretty substantial!

推荐答案

这很有效，它利用了的新非等额连接功能。 data.table 。

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，我对它的工作方式有点不满意，但想法是加入 dt 在日期自身，匹配两天前的任何日期还有今天。我不确定为什么我们必须通过设置 date = date + 2 之后进行清理。

To be honest I'm a bit miffed on how this is working exactly, but the idea is to join dt to itself on date, matching any date that is between 2 days ago and today. I'm not sure why we have to clean up by setting date = date + 2 afterwards.

以下是使用密钥的方法：

Here's an approach using keys:

setkey(dt, date)

dt[ , .(count = dt[.(seq.Date(.BY$date - 2L, .BY$date, "day")),
                   uniqueN(email), nomatch = 0L]), by = date]

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