r - 计算R中最近x天内ID的出现

Question

这是我关于stackoverflow的第一篇文章，所以如果我的文章不够详细，请原谅我。
我有一个包含两列(日期和组ID)的数据表。在当前日期，我想计算最近x天内发生的组出现次数。在下面的示例中，我们可以说过去30天。

date = c("2014-04-01", "2014-04-12", "2014-04-07", "2014-05-03", "2014-04-14", "2014-05-04", "2014-03-31", "2014-04-18", "2014-04-23", "2014-04-01")
group = c("G","G","F","G","E","E","H","H","H","A")
dt = data.table(cbind(group,date))

   group       date
1:     G 2014-04-01
2:     G 2014-04-12
3:     F 2014-04-07
4:     G 2014-05-03
5:     E 2014-04-14              
6:     E 2014-05-04
7:     H 2014-03-31
8:     H 2014-04-18
9:     H 2014-04-23
10:    A 2014-04-01

因此，我想要的新列将如下所示:

   group       date   count
1:     G 2014-04-01       0
2:     G 2014-04-12       1
3:     F 2014-04-07       0
4:     G 2014-05-03       1 (not including first G since it is outside 30 days)      
5:     E 2014-04-14       0       
6:     E 2014-05-04       1
7:     H 2014-03-31       0
8:     H 2014-04-18       1
9:     H 2014-04-23       2
10:    A 2014-04-01       0 

我能够使用dplyr对当前日期的组出现次数进行非窗口计数，但是我正在努力寻找一种进行30天计数的方法。对于非窗口计数，我执行了以下操作:

dt = data.table(dt %>%
 group_by(group) %>%
 mutate(count = row_number() - 1))

    group       date count
 1:     G 2014-04-01     0
 2:     G 2014-04-12     1
 3:     F 2014-04-07     0
 4:     G 2014-05-03     2
 5:     E 2014-04-14     0
 6:     E 2014-05-04     1
 7:     H 2014-03-31     0
 8:     H 2014-04-18     1
 9:     H 2014-04-23     2
10:     A 2014-04-01     0

这是数据集的一个小样本，整个数据集包含几百万行，因此我需要高效的东西。任何提示或建议，将不胜感激。先感谢您!

Answer 1

data.table选项

dt[, date := as.Date(date)][, count := cumsum(date <= first(date) + 30) - 1, group]

给

> dt
    group       date count
 1:     G 2014-04-01     0
 2:     G 2014-04-12     1
 3:     F 2014-04-07     0
 4:     G 2014-05-03     1
 5:     E 2014-04-14     0
 6:     E 2014-05-04     1
 7:     H 2014-03-31     0
 8:     H 2014-04-18     1
 9:     H 2014-04-23     2
10:     A 2014-04-01     0

遵循类似想法的dplyr选项

dt %>%
  mutate(date = as.Date(date)) %>%
  group_by(group) %>%
  mutate(count = cumsum(date <= first(date) + 30) - 1) %>%
  ungroup()