R：使用數據表來聚合數據

Question

我是新手使用數據表，想要一些幫助聚合一些數據。

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

對於每一行，我想匯總在該交易之前開立的所有交易，並在該交易開啟后關閉。 例如，第三行的交易在第四次交易之前開盤，但僅在第四次交易開盤后關閉。 因此，我接着使用OpenedValueUSD進行交易（以及任何其他適當的交易（在本例中為無））並將其放在TransferredValue列中。

這是當前代碼：

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

Answer 1

這是使用foverlaps()的另一種方式，它不需要按行分組。 我會打電話給你的data.table dt 。

1. 將OpenTime和CloseTime轉換為POSIXct格式，如@ alex23lemm所示。

2. 添加一個臨時列tmpTime ，它等於OpenTime 。 我們將在foverlaps()使用它。

    dt[, tmpTime := OpenTime] 

3. Login, OpenTime, CloseTime上的setkey() 。

    setkey(dt, Login, OpenTime, CloseTime) 

4. 使用foverlaps() ，我們現在將在Login, OpenTime, tmpTime哪些時間間隔完全落在 Login, OpenTime, CloseTime 。

    olaps = foverlaps(dt, dt, by.x=c("Login", "OpenTime", "tmpTime"), which=TRUE, nomatch=0L, type="within") 

   by.y自動被視為關鍵列。

5. 刪除自重疊，即刪除xid == yid那些。

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

6. 將對應於yid的值分配給xid行。 並刪除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

這應該產生預期的結果：

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

數據：

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))