R：具有2個大型數據集的模式匹配財務時間序列數據：

Question

我的問題可能很復雜， 請耐心閱讀。

我正在處理以下情況，我有兩個交易所（紐約和倫敦）的金融時間序列的兩個時間數據集

這兩個數據集如下所示：

倫敦數據集：

Date        time.second Price
2015-01-05  32417   238.2
2015-01-05  32418   238.2
2015-01-05  32421   238.2
2015-01-05  32422   238.2
2015-01-05  32423   238.2
2015-01-05  32425   238.2
2015-01-05  32427   238.2
2015-01-05  32431   238.2
2015-01-05  32435   238.47
2015-01-05  32436   238.47

紐約數據集：

NY.Date     Time    Price
2015-01-05  32416   1189.75
2015-01-05  32417   1189.665
2015-01-05  32418   1189.895
2015-01-05  32419   1190.15
2015-01-05  32420   1190.075
2015-01-05  32421   1190.01
2015-01-05  32422   1190.175
2015-01-05  32423   1190.12
2015-01-05  32424   1190.14
2015-01-05  32425   1190.205
2015-01-05  32426   1190.2
2015-01-05  32427   1190.33
2015-01-05  32428   1190.29
2015-01-05  32429   1190.28
2015-01-05  32430   1190.05
2015-01-05  32432   1190.04

可以看到，共有3列： 日期，時間（秒），價格

我想做的是使用倫敦數據集作為參考，找到紐約數據集中最近但較早的數據項。

我指的是哪個最近但更早 ？ 我的意思是，例如

倫敦數據集中的“ 2015-01-01”，“ 21610”，“ 15.6871” ，我想在紐約數據集中找到數據，並且該數據在同一日期 ， 最接近但更早或相等的時間出現 ，這將很有幫助看一下我當前的程序 ：

# I am trying to avoid using for-loop
for(i in 1:dim(london_data)[1]){ #for each row in london data set
    print(i)
    tempRow<-london_data[i,]
    dateMatch<-(which(NY_data[,1]==tempRow[1])) # select the same date
    dataNeeded<-(london_before[dateMatch,]) # subset the same date data
    # find the nearest but earlier data in NY_data set
    Found<-dataNeeded[which(dataNeeded[,2]<=tempRow[2]),] 
    # Found may be more than one row, each row is of length 3
    if(length(Found)>3)
    {    # Select the data, we only need "time" and "price", 2nd and 3rd  
         # column
         # the data is in the final row of **Found**
         selected<-Found[dim(Found)[1],2:3] 
         if(length(selected)==0) # if nothing selected, just insert 0 and 0
             temp[i,]<-c(0,0)
         else
            temp[i,]<-selected
     }
     else{ # Found may only one row, of length 3
         temp[i,]<-Found[2:3] # just insert what we want
     }
   print(paste("time is", as.numeric(selected[1]))) #Monitor the loop
 }
 res<-cbind(london_data,temp)
 colnames(res)<-c("LondonDate","LondonTime","LondonPrice","NYTime","NYPrice")

上面列出的數據集的正確輸出是**（僅部分）**：

      "LondonDate","LondonTime","LondonPrice","NYTime","NYPrice"
 [1,] "2015-01-05" "32417"      "238.2"       "32417"    "1189.665" 
 [2,] "2015-01-05" "32418"      "238.2"       "32418"    "1189.895" 
 [3,] "2015-01-05" "32421"      "238.2"       "32421"    "1190.01"  
 [4,] "2015-01-05" "32422"      "238.2"       "32422"    "1190.175" 
 [5,] "2015-01-05" "32423"      "238.2"       "32423"    "1190.12"  
 [6,] "2015-01-05" "32425"      "238.2"       "32425"    "1190.205" 
 [7,] "2015-01-05" "32427"      "238.2"       "32427"    "1190.33"  
 [8,] "2015-01-05" "32431"      "238.2"       "32430"    "1190.05"  
 [9,] "2015-01-05" "32435"      "238.47"      "32432"    "1190.04"  
 [10,] "2015-01-05" "32436"      "238.47"      "32432"    "1190.04"

我的問題是，倫敦數據集有超過5,000,000列 ，我試圖避免for循環，但我仍然需要至少一個以上程序才能成功運行，但花費了大約24小時 。

如何避免使用for循環並加速程序？

您的幫助將不勝感激。

Answer 1

解決方案是使用data.table在@Jan Gorecki注釋的基礎上構建的，這是解決方案：

library(data.table)

df1 <- data.table(Date=rep("05/01/2015", 10),   
              time.second=c(32417, 32418, 32421, 32422, 32423, 32425, 32427, 32431, 32435, 32436),  
              Price=c(238.2, 238.2, 238.2, 238.2, 238.2, 238.2, 238.2, 238.2, 238.47, 238.47))

df2 <- data.table(NY.Date=rep("05/01/2015", 16),    
              Time=c(32416, 32417, 32418, 32419, 32420, 32421, 32422, 32423, 32424, 32425, 32426, 32427, 32428, 32429, 32430, 32432),   
              Price=c(1189.75, 1189.665, 1189.895, 1190.15, 1190.075, 1190.01, 1190.175, 1190.12, 1190.14, 1190.205, 1190.2, 1190.33, 1190.29, 1190.28, 1190.05, 1190.04))


setnames(df2, c("Date", "time.second", "NYPrice"))

setkey(df1,"Date", "time.second")
setkey(df2,"Date", "time.second")

df2[, NYTime:=time.second]

df3 <- df2[df1, roll=TRUE]
df3