Question

我很擅长使用数据表，并想要一些帮助汇总一些数据。

Login   OpenTime            CloseTime     OpenedValueUSD    ClosedValueUSD  Year    Month   TransferredValue Identifier
859    04/02/2014 07:55 05/02/2014 15:37    10000               10000       2014    2             0                1
859    07/02/2014 03:16 07/02/2014 03:51    8960.755            8960.755    2014    2             0                2
859    11/02/2014 12:41 13/02/2014 11:56    13635.178           13606.901   2014    2             0                3
859    11/02/2014 13:34 11/02/2014 15:34    13635.178           13635.178   2014    2             13635.178        4
859    12/02/2014 13:46 14/02/2014 09:59    13660.246           13649.278   2014    2             13635.178        5
859    13/02/2014 15:33 13/02/2014 15:42    13606.901           13606.901   2014    2             13660.246        6
859    25/03/2014 14:52 26/03/2014 12:58    10000               10000       2014    3             0                7

对于每一行，我想汇总在该交易之前开立的所有交易，并在该交易开启后关闭。例如，第三行的交易在第四次交易之前开盘，但仅在第四次交易开盘后关闭。因此，我接受了该交易的OpenedValueUSD（以及任何其他适当的交易（在本例中为none））并将其放在TransferredValue列中。

这是当前代码：

tradeData[,TransferredValue:=sum(tradeData$OpenedValueUSD[OpenTime < 
           tradeData$OpenTime & CloseTime > tradeData$OpenTime & Login == 
           tradeData$Login]), by="Identifier"]

Answer 1

这是使用foverlaps()的另一种方式，它不需要按行分组。我会打电话给你的 data.table dt。

将OpenTime和CloseTime转换为POSIXct格式，如@ alex23lemm所示。
添加一个等于tmpTime的临时列OpenTime。我们将在foverlaps()中使用它。
```
dt[, tmpTime := OpenTime]
```
setkey()在Login, OpenTime, CloseTime列上。
```
setkey(dt, Login, OpenTime, CloseTime)
```
使用foverlaps()，我们现在将Login, OpenTime, tmpTime中的哪些时间间隔完全落入 Login, OpenTime, CloseTime。
```
olaps = foverlaps(dt, dt, by.x=c("Login", "OpenTime", "tmpTime"), 
                which=TRUE, nomatch=0L, type="within")
```
by.y会自动成为关键列。

删除自我重叠，即删除xid == yid。

olaps = olaps[xid != yid]
#    xid yid
# 1:   4   3
# 2:   5   3
# 3:   6   5

将xid行分配给与yid对应的值。并删除tmpTime。

dt[olaps$xid, TransferredValue := 
        dt$OpenedValueUSD[olaps$yid]][, tmpTime := NULL]

#    Login            OpenTime           CloseTime OpenedValueUSD ClosedValueUSD Year Month TransferredValue Identifier
# 1:   859 2014-02-04 07:55:00 2014-02-05 15:37:00      10000.000      10000.000 2014     2             0.00          1
# 2:   859 2014-02-07 03:16:00 2014-02-07 03:51:00       8960.755       8960.755 2014     2             0.00          2
# 3:   859 2014-02-11 12:41:00 2014-02-13 11:56:00      13635.178      13606.901 2014     2             0.00          3
# 4:   859 2014-02-11 13:34:00 2014-02-11 15:34:00      13635.178      13635.178 2014     2         13635.18          4
# 5:   859 2014-02-12 13:46:00 2014-02-14 09:59:00      13660.246      13649.278 2014     2         13635.18          5
# 6:   859 2014-02-13 15:33:00 2014-02-13 15:42:00      13606.901      13606.901 2014     2         13660.25          6
# 7:   859 2014-03-25 14:52:00 2014-03-26 12:58:00      10000.000      10000.000 2014     3             0.00          7

Answer 2

这应该产生预期的结果：

tradeData[,OpenTime:=as.POSIXct(OpenTime,format="%d/%m/%Y %H:%M")]
tradeData[,CloseTime:=as.POSIXct(CloseTime,format="%d/%m/%Y %H:%M")]

tradeData[,TransferredValue:=sum(tradeData$OpenedValueUSD[tradeData$OpenTime < OpenTime & 
                                                            tradeData$CloseTime > OpenTime]), by = 'Identifier']
tradeData


    # Login            OpenTime           CloseTime OpenedValueUSD ClosedValueUSD Year Month
# 1:   859 2014-02-04 07:55:00 2014-02-05 15:37:00      10000.000      10000.000 2014     2
# 2:   859 2014-02-07 03:16:00 2014-02-07 03:51:00       8960.755       8960.755 2014     2
# 3:   859 2014-02-11 12:41:00 2014-02-13 11:56:00      13635.178      13606.901 2014     2
# 4:   859 2014-02-11 13:34:00 2014-02-11 15:34:00      13635.178      13635.178 2014     2
# 5:   859 2014-02-12 13:46:00 2014-02-14 09:59:00      13660.246      13649.278 2014     2
# 6:   859 2014-02-13 15:33:00 2014-02-13 15:42:00      13606.901      13606.901 2014     2
# 7:   859 2014-03-25 14:52:00 2014-03-26 12:58:00      10000.000      10000.000 2014     3
# Identifier TransferredValue
# 1:          1             0.00
# 2:          2             0.00
# 3:          3             0.00
# 4:          4         13635.18
# 5:          5         13635.18
# 6:          6         13660.25
# 7:          7             0.00

数据：

tradeData <- data.table(Login = c(859, 859, 859, 859, 859, 859, 859), OpenTime = c("04/02/2014 07:55", "07/02/2014 03:16", "11/02/2014 12:41", "11/02/2014 13:34", "12/02/2014 13:46", "13/02/2014 15:33", "25/03/2014 14:52"), CloseTime = c("05/02/2014 15:37", "07/02/2014 03:51", "13/02/2014 11:56", "11/02/2014 15:34", "14/02/2014 09:59", "13/02/2014 15:42", "26/03/2014 12:58"), OpenedValueUSD = c(10000.000, 8960.755, 13635.178, 13635.178, 13660.246, 13606.901, 10000.000), ClosedValueUSD = c(10000.000, 8960.755, 13606.901, 13635.178, 13649.278, 13606.901, 10000.000), Year = c(2014, 2014, 2014, 2014, 2014, 2014, 2014), Month = c(2, 2, 2, 2, 2, 2, 3), Identifier = c(1, 2, 3, 4, 5, 6, 7))

R：使用数据表来聚合数据

2 个答案: