按最近时间“之后”组合数据集

Question

我想根据最近时间之后合并两个数据集，但我不知道如何用data.table 指定。

x 数据集是here，'y' 数据集如下：

#y data.table 
structure(list(date.time = structure(c(1551107760, 1551171420, 
1551181500, 1551181560, 1551182400, 1551188100, 1551194700, 1551262320, 
1551262380, 1551264420, 1551267960, 1551272400, 1551338580, 1551343140, 
1551343380, 1551349800, 1551351600, 1551369000, 1551424680, 1551425520, 
1551429000, 1551446880), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
    id = c(12L, 3L, 3L, 10L, 10L, 11L, 4L, 10L, 3L, 11L, 4L, 
    12L, 2L, 10L, 12L, 12L, 12L, 12L, 2L, 12L, 2L, 10L)), row.names = c(NA, 
-22L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x000001dc21061ef0>, sorted = "date.time")

如果我指定roll=nearest：

require(data.table)
setDT(x)
setDT(y)

x[, reference.x := date.time] #ref to check join 
setkey(y, date.time)
setkey(x, date.time)

#join dataframe 
results <- x[y, roll="nearest"]

您将看到该函数正常工作，但是我希望加入基于x 的最近时间，该时间发生在y 之后。在结果中您会看到reference.x 的日期和时间早于y 的日期和时间。

> results
            date.time    class         reference.x id
 1: 2019-02-25 15:16:00 8.2 14.8 2019-02-25 15:16:00 12 #on or after
 2: 2019-02-26 08:57:00 8.2 14.8 2019-02-26 08:57:00  3 #on or after 
 3: 2019-02-26 11:45:00 8.2 14.8 2019-02-26 11:45:00  3 #on or after
 4: 2019-02-26 11:46:00 8.2 14.8 2019-02-26 11:45:00 10 #before (incorrect) 
 5: 2019-02-26 12:00:00 8.2 14.8 2019-02-26 11:59:00 10
 6: 2019-02-26 13:35:00 8.2 14.8 2019-02-26 13:35:00 11
 7: 2019-02-26 15:25:00 8.2 14.8 2019-02-26 15:25:00  4
 8: 2019-02-27 10:12:00 8.2 14.8 2019-02-27 07:10:00 10
 9: 2019-02-27 10:13:00 8.2 14.8 2019-02-27 07:10:00  3
10: 2019-02-27 10:47:00 8.2 14.8 2019-02-27 07:10:00 11
11: 2019-02-27 11:46:00 8.2 14.8 2019-02-27 07:10:00  4
12: 2019-02-27 13:00:00 8.2 14.8 2019-02-27 07:10:00 12
13: 2019-02-28 07:23:00 8.2 14.8 2019-02-28 07:26:00  2
14: 2019-02-28 08:39:00 8.2 14.8 2019-02-28 08:39:00 10
15: 2019-02-28 08:43:00 8.2 14.8 2019-02-28 08:42:00 12
16: 2019-02-28 10:30:00 8.2 14.8 2019-02-28 10:30:00 12
17: 2019-02-28 11:00:00 8.2 14.8 2019-02-28 10:59:00 12
18: 2019-02-28 15:50:00 8.2 14.8 2019-02-28 15:49:00 12
19: 2019-03-01 07:18:00 8.2 14.8 2019-03-01 07:18:00  2
20: 2019-03-01 07:32:00 8.2 14.8 2019-03-01 07:32:00 12
21: 2019-03-01 08:30:00 8.2 14.8 2019-03-01 08:30:00  2
22: 2019-03-01 13:28:00 8.2 14.8 2019-03-01 13:27:00 10
>

structure(list(date.time = structure(c(1551107760, 1551171420, 
1551181500, 1551181560, 1551182400, 1551188100, 1551194700, 1551262320, 
1551262380, 1551264420, 1551267960, 1551272400, 1551338580, 1551343140, 
1551343380, 1551349800, 1551351600, 1551369000, 1551424680, 1551425520, 
1551429000, 1551446880), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
    class = c("8.2 14.8", "8.2 14.8", "8.2 14.8", "8.2 14.8", 
    "8.2 14.8", "8.2 14.8", "8.2 14.8", "8.2 14.8", "8.2 14.8", 
    "8.2 14.8", "8.2 14.8", "8.2 14.8", "8.2 14.8", "8.2 14.8", 
    "8.2 14.8", "8.2 14.8", "8.2 14.8", "8.2 14.8", "8.2 14.8", 
    "8.2 14.8", "8.2 14.8", "8.2 14.8"), reference.x = structure(c(1551107760, 
    1551171420, 1551181500, 1551181500, 1551182340, 1551188100, 
    1551194700, 1551251400, 1551251400, 1551251400, 1551251400, 
    1551251400, 1551338760, 1551343140, 1551343320, 1551349800, 
    1551351540, 1551368940, 1551424680, 1551425520, 1551429000, 
    1551446820), tzone = "UTC", class = c("POSIXct", "POSIXt"
    )), id = c(12L, 3L, 3L, 10L, 10L, 11L, 4L, 10L, 3L, 11L, 
    4L, 12L, 2L, 10L, 12L, 12L, 12L, 12L, 2L, 12L, 2L, 10L)), sorted = "date.time", class = c("data.table", 
"data.frame"), row.names = c(NA, -22L), .internal.selfref = <pointer: 0x000001dc21061ef0>)

任何帮助将不胜感激。

Answer 1

您正在寻找的选项是roll = -Inf

来自文档 (?data.table)：

-Inf 改为向后滚动；即，下一个观察结果向后进行（NOCB）。

> results <- x[y, roll=-Inf]
> head(results)

             date.time    class         reference.x id
1: 2019-02-25 15:16:00 8.2 14.8 2019-02-25 15:16:00 12
2: 2019-02-26 08:57:00 8.2 14.8 2019-02-26 08:57:00  3
3: 2019-02-26 11:45:00 8.2 14.8 2019-02-26 11:45:00  3
4: 2019-02-26 11:46:00 8.2 14.8 2019-02-26 11:47:00 10
5: 2019-02-26 12:00:00 8.2 14.8 2019-02-26 12:01:00 10
6: 2019-02-26 13:35:00 8.2 14.8 2019-02-26 13:35:00 11