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使用大型資料集的For回圈的極端處理時間

2022-01-19 07:15:25 後端開發

我準備了一個例程，將一個單元格（“DBH”）的值復制到另一行中的另一個單元格（“DG”），其中被復制的行緊跟在被復制的行之后（及時，而不是必須在表中）。

PlotID 'R8' 代表 8 個測量值，'R7' 代表第 7 個，依此類推。

該回圈旨在檢查該行是否具有先前的測量值，如果有，則記錄該測量值的唯一行識別符號。然后，它使用前面的行識別符號來搜索要通過第二個嵌套回圈復制的前面的記錄。

問題是我使用的完整資料集包含 650,000 條記錄以北。我知道這種結構會有些低效，但是當我運行它時，運行時每小時只移動大約 10,000 條記錄。有什么提高計算效率的建議嗎？

  # sort TreeInit Table to facilitate desired iteration
  FVS_TreeInit$StandPlotTree_ID <- paste(FVS_TreeInit$StandPlot_ID,"T",FVS_TreeInit$Tree_ID,sep="")
  
  # loop through FVS_TreeInit and add previous diameter increment identifier to current row
  for (i in seq_len(nrow(FVS_TreeInit))){
    if (FVS_TreeInit[i,"Plot_ID"] == "R8"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R7T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R7"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R6T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R6"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R5T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R5"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R4T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R4"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R3T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R3"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R2T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R2"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R1T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R1"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R0T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }
    } else{
      FVS_TreeInit[i,"GD"] <- NA
    }
    if (i%%1000 == 0){
      print(paste(i," rows iterated...",sep=""))
    }
  }
}

一些示例資料（.csv）：

Stand_ID,Plot_ID,StandPlot_ID,Tree_ID,DBH,DG,Species,Age,StandPlotTree_ID,StandPlotTree_ID_Past
D18P186001,R0,D18P186001R0,1,4.76378225,,ABBA,,D18P186001R0T1,
D18P186001,R0,D18P186001R0,2,1.259843219,,BEPA,,D18P186001R0T2,
D18P186001,R0,D18P186001R0,3,0.275590695,,BEPA,,D18P186001R0T3,
D18P186001,R0,D18P186001R0,4,0.629921609,,BEPA,,D18P186001R0T4,
D18P186001,R0,D18P186001R0,5,0.1968505,,BEPA,,D18P186001R0T5,
D18P186001,R0,D18P186001R0,6,3.70078925,,ABBA,26,D18P186001R0T6,
D18P186001,R0,D18P186001R0,7,2.834647125,,ABBA,,D18P186001R0T7,
D18P186001,R0,D18P186001R0,8,1.259843219,,,,D18P186001R0T8,
D18P186001,R0,D18P186001R0,9,2.283465875,,,,D18P186001R0T9,
D18P186001,R0,D18P186001R0,10,1.181103,,,,D18P186001R0T10,
D18P186001,R0,D18P186001R0,11,1.535433938,,BEPA,,D18P186001R0T11,
D18P186001,R0,D18P186001R0,12,4.212600625,,ABBA,,D18P186001R0T12,
D18P186001,R0,D18P186001R0,13,3.30708825,,ABBA,,D18P186001R0T13,
D18P186001,R0,D18P186001R0,14,1.259843219,,ABBA,,D18P186001R0T14,
D18P186001,R0,D18P186001R0,15,4.212600625,,ABBA,28,D18P186001R0T15,
D18P186001,R0,D18P186001R0,16,3.149608,,ABBA,28,D18P186001R0T16,
D18P186001,R0,D18P186001R0,17,1.181103,,ABBA,,D18P186001R0T17,
D18P186001,R0,D18P186001R0,18,0.944882438,,BEPA,,D18P186001R0T18,
D18P186001,R0,D18P186001R0,19,0.393701,,BEPA,,D18P186001R0T19,
D18P186001,R0,D18P186001R0,20,0.314960805,,BEPA,,D18P186001R0T20,
D18P186001,R0,D18P186001R0,21,2.362206,,ABBA,22,D18P186001R0T21,
D18P186001,R0,D18P186001R0,22,0.314960805,,BEPA,,D18P186001R0T22,
D18P186001,R0,D18P186001R0,23,1.062992719,,ABBA,,D18P186001R0T23,
D18P186001,R0,D18P186001R0,24,3.93701,,ABBA,25,D18P186001R0T24,
D18P186001,R0,D18P186001R0,25,4.76378225,,ABBA,,D18P186001R0T25,
D18P186001,R0,D18P186001R0,26,0.748031891,,BEPA,,D18P186001R0T26,
D18P186001,R0,D18P186001R0,27,0.944882438,,BEPA,,D18P186001R0T27,
D18P186001,R0,D18P186001R0,28,3.267718375,,ABBA,,D18P186001R0T28,
D18P186001,R0,D18P186001R0,29,0.314960805,,BEPA,,D18P186001R0T29,
D18P186001,R0,D18P186001R0,30,0.472441219,,BEPA,,D18P186001R0T30,
D18P186001,R0,D18P186001R0,31,4.606301625,,ABBA,,D18P186001R0T31,
D18P186001,R0,D18P186001R0,32,4.055120375,,ABBA,,D18P186001R0T32,
D18P186001,R0,D18P186001R0,33,0.433071109,,,,D18P186001R0T33,
D18P186001,R0,D18P186001R0,34,1.023622562,,,,D18P186001R0T34,
D18P186001,R0,D18P186001R0,35,0.5905515,,,,D18P186001R0T35,
D18P186001,R0,D18P186001R0,36,0.748031891,,,,D18P186001R0T36,
D18P186001,R0,D18P186001R0,37,3.425198625,,ABBA,30,D18P186001R0T37,
D18P186001,R0,D18P186001R0,38,4.29134075,,ABBA,,D18P186001R0T38,
D18P186001,R0,D18P186001R0,39,3.93701,,ABBA,,D18P186001R0T39,
D18P186001,R0,D18P186001R0,40,4.251970875,,ABBA,,D18P186001R0T40,
D18P186001,R0,D18P186001R0,41,4.09449025,,ABBA,,D18P186001R0T41,
D18P186001,R0,D18P186001R0,42,6.181105625,,ABBA,24,D18P186001R0T42,
D18P186001,R0,D18P186001R0,43,3.858269875,,ABBA,,D18P186001R0T43,
D18P186001,R0,D18P186001R0,44,3.7401595,,ABBA,,D18P186001R0T44,
D18P186001,R0,D18P186001R0,45,0.5905515,,ABBA,,D18P186001R0T45,
D18P186001,R0,D18P186001R0,46,1.259843219,,ABBA,,D18P186001R0T46,
D18P186001,R0,D18P186001R0,47,3.89763975,,ABBA,,D18P186001R0T47,
D18P186001,R0,D18P186001R0,48,0.708661781,,ABBA,,D18P186001R0T48,
D18P186001,R0,D18P186001R0,49,0.314960805,,ABBA,,D18P186001R0T49,
D18P186001,R0,D18P186001R0,50,4.1338605,,ABBA,,D18P186001R0T50,
D18P186001,R0,D18P186001R0,51,1.889764875,,BEPA,,D18P186001R0T51,
D18P186001,R0,D18P186001R0,52,0.314960805,,,,D18P186001R0T52,
D18P186001,R0,D18P186001R0,53,0.629921609,,BEPA,,D18P186001R0T53,
D18P186001,R0,D18P186001R0,54,0.354330891,,BEPA,,D18P186001R0T54,
D18P186001,R0,D18P186001R0,55,1.456693719,,BEPA,,D18P186001R0T55,
D18P186001,R0,D18P186001R0,56,2.047245125,,,,D18P186001R0T56,
D18P186001,R0,D18P186001R0,57,2.480316375,,,,D18P186001R0T57,
D18P186001,R0,D18P186001R0,58,1.968505,,,,D18P186001R0T58,
D18P186001,R0,D18P186001R0,59,3.93701,,ABBA,,D18P186001R0T59,
D18P186001,R0,D18P186001R0,60,1.102362781,,ABBA,,D18P186001R0T60,
D18P186001,R0,D18P186001R0,61,0.472441219,,BEPA,,D18P186001R0T61,
D18P186001,R0,D18P186001R0,62,0.905512281,,BEPA,,D18P186001R0T62,
D18P186001,R0,D18P186001R0,63,0.944882438,,BEPA,,D18P186001R0T63,
D18P186001,R0,D18P186001R0,64,1.574804,,ABBA,,D18P186001R0T64,
D18P186001,R0,D18P186001R0,65,0.472441219,,BEPA,,D18P186001R0T65,
D18P186001,R0,D18P186001R0,66,5.15748325,,ABBA,,D18P186001R0T66,
D18P186001,R0,D18P186001R0,67,5.35433375,,ABBA,29,D18P186001R0T67,
D18P186001,R0,D18P186001R0,68,0.511811281,,ABBA,,D18P186001R0T68,
D18P186001,R0,D18P186001R0,69,4.68504175,,ABBA,,D18P186001R0T69,
D18P186001,R0,D18P186001R0,70,6.8897675,,ABBA,23,D18P186001R0T70,
D18P186001,R0,D18P186001R0,71,0.275590695,,BEPA,,D18P186001R0T71,
D18P186001,R0,D18P186001R0,72,4.606301625,,ABBA,20,D18P186001R0T72,
D18P186001,R0,D18P186001R0,73,0.511811281,,BEPA,,D18P186001R0T73,
D18P186001,R0,D18P186001R0,74,0.275590695,,BEPA,,D18P186001R0T74,
D18P186001,R0,D18P186001R0,75,0.393701,,BEPA,,D18P186001R0T75,
D18P186001,R0,D18P186001R0,76,0.472441219,,BEPA,,D18P186001R0T76,
D18P186001,R0,D18P186001R0,77,0.669291719,,ABBA,,D18P186001R0T77,
D18P186001,R0,D18P186001R0,78,0.354330891,,BEPA,,D18P186001R0T78,
D18P186001,R0,D18P186001R0,79,4.409451125,,ABBA,,D18P186001R0T79,
D18P186001,R0,D18P186001R0,80,7.12598825,,ABBA,,D18P186001R0T80,
D18P186001,R0,D18P186001R0,81,4.724412,,ABBA,,D18P186001R0T81,
D18P186001,R1,D18P186001R1,1,5.86614475,,ABBA,,D18P186001R1T1,
D18P186001,R1,D18P186001R1,2,1.3779535,,BEPA,,D18P186001R1T2,
D18P186001,R1,D18P186001R1,3,,,BEPA,,D18P186001R1T3,
D18P186001,R1,D18P186001R1,4,,,BEPA,,D18P186001R1T4,
D18P186001,R1,D18P186001R1,5,0.236220609,,BEPA,,D18P186001R1T5,
D18P186001,R1,D18P186001R1,6,4.1338605,,ABBA,31,D18P186001R1T6,
D18P186001,R1,D18P186001R1,7,3.425198625,,ABBA,,D18P186001R1T7,
D18P186001,R1,D18P186001R1,9,2.480316375,,,,D18P186001R1T9,
D18P186001,R1,D18P186001R1,10,1.259843219,,,,D18P186001R1T10,
D18P186001,R1,D18P186001R1,11,1.574804,,BEPA,,D18P186001R1T11,
D18P186001,R1,D18P186001R1,12,4.88189225,,ABBA,,D18P186001R1T12,
D18P186001,R1,D18P186001R1,13,3.89763975,,ABBA,,D18P186001R1T13,
D18P186001,R1,D18P186001R1,14,1.3779535,,ABBA,,D18P186001R1T14,
D18P186001,R1,D18P186001R1,15,5.196853125,,ABBA,33,D18P186001R1T15,
D18P186001,R1,D18P186001R1,16,4.055120375,,ABBA,33,D18P186001R1T16,
D18P186001,R1,D18P186001R1,17,1.220473062,,ABBA,,D18P186001R1T17,
D18P186001,R1,D18P186001R1,18,0.9842525,,BEPA,,D18P186001R1T18,
D18P186001,R1,D18P186001R1,19,0.393701,,BEPA,,D18P186001R1T19,
D18P186001,R1,D18P186001R1,20,0.472441219,,BEPA,,D18P186001R1T20,
D18P186001,R1,D18P186001R1,21,2.716536938,,ABBA,27,D18P186001R1T21,
D18P186001,R1,D18P186001R1,22,,,BEPA,,D18P186001R1T22,
D18P186001,R1,D18P186001R1,23,1.181103,,ABBA,,D18P186001R1T23,
D18P186001,R1,D18P186001R1,24,5.000002625,,ABBA,30,D18P186001R1T24,
D18P186001,R1,D18P186001R1,25,4.88189225,,ABBA,,D18P186001R1T25,
D18P186001,R1,D18P186001R1,26,,,BEPA,,D18P186001R1T26,
D18P186001,R1,D18P186001R1,27,1.023622562,,BEPA,,D18P186001R1T27,
D18P186001,R1,D18P186001R1,28,4.330711,,ABBA,,D18P186001R1T28,
D18P186001,R1,D18P186001R1,29,0.354330891,,BEPA,,D18P186001R1T29,
D18P186001,R1,D18P186001R1,30,0.511811281,,BEPA,,D18P186001R1T30,
D18P186001,R1,D18P186001R1,31,5.74803475,,ABBA,,D18P186001R1T31,
D18P186001,R1,D18P186001R1,32,5.27559325,,ABBA,,D18P186001R1T32,
D18P186001,R1,D18P186001R1,33,,,,,D18P186001R1T33,
D18P186001,R1,D18P186001R1,34,0.944882438,,,,D18P186001R1T34,
D18P186001,R1,D18P186001R1,35,0.669291719,,,,D18P186001R1T35,
D18P186001,R1,D18P186001R1,36,1.062992719,,,,D18P186001R1T36,
D18P186001,R1,D18P186001R1,37,4.5275615,,ABBA,35,D18P186001R1T37,
D18P186001,R1,D18P186001R1,38,5.629924375,,ABBA,,D18P186001R1T38,
D18P186001,R1,D18P186001R1,39,4.88189225,,ABBA,,D18P186001R1T39,
D18P186001,R1,D18P186001R1,40,5.47244375,,ABBA,,D18P186001R1T40,
D18P186001,R1,D18P186001R1,41,4.842522375,,ABBA,,D18P186001R1T41,
D18P186001,R1,D18P186001R1,42,7.204728,,ABBA,29,D18P186001R1T42,
D18P186001,R1,D18P186001R1,43,4.842522375,,ABBA,,D18P186001R1T43,
D18P186001,R1,D18P186001R1,44,4.606301625,,ABBA,,D18P186001R1T44,
D18P186001,R1,D18P186001R1,45,0.748031891,,ABBA,,D18P186001R1T45,
D18P186001,R1,D18P186001R1,46,1.3779535,,ABBA,,D18P186001R1T46,
D18P186001,R1,D18P186001R1,47,4.37008125,,ABBA,,D18P186001R1T47,
D18P186001,R1,D18P186001R1,48,0.9842525,,ABBA,,D18P186001R1T48,
D18P186001,R1,D18P186001R1,49,0.354330891,,ABBA,,D18P186001R1T49,
D18P186001,R1,D18P186001R1,50,5.07874275,,ABBA,,D18P186001R1T50,
D18P186001,R1,D18P186001R1,51,2.401576062,,BEPA,,D18P186001R1T51,
D18P186001,R1,D18P186001R1,52,,,,,D18P186001R1T52,
D18P186001,R1,D18P186001R1,53,,,BEPA,,D18P186001R1T53,
D18P186001,R1,D18P186001R1,54,,,BEPA,,D18P186001R1T54,
D18P186001,R1,D18P186001R1,55,1.614174062,,BEPA,,D18P186001R1T55,
D18P186001,R1,D18P186001R1,56,2.401576062,,,,D18P186001R1T56,
D18P186001,R1,D18P186001R1,57,2.992127562,,,,D18P186001R1T57,
D18P186001,R1,D18P186001R1,58,2.204725562,,,,D18P186001R1T58,
D18P186001,R1,D18P186001R1,59,4.96063275,,ABBA,,D18P186001R1T59,
D18P186001,R1,D18P186001R1,60,1.456693719,,ABBA,,D18P186001R1T60,
D18P186001,R1,D18P186001R1,61,,,BEPA,,D18P186001R1T61,
D18P186001,R1,D18P186001R1,62,0.905512281,,BEPA,,D18P186001R1T62,
D18P186001,R1,D18P186001R1,63,1.023622562,,BEPA,,D18P186001R1T63,
D18P186001,R1,D18P186001R1,64,1.732284438,,ABBA,,D18P186001R1T64,
D18P186001,R1,D18P186001R1,65,0.629921609,,BEPA,,D18P186001R1T65,
D18P186001,R1,D18P186001R1,66,6.65354675,,ABBA,,D18P186001R1T66,
D18P186001,R1,D18P186001R1,67,7.204728,,ABBA,34,D18P186001R1T67,
D18P186001,R1,D18P186001R1,68,1.259843219,,ABBA,,D18P186001R1T68,
D18P186001,R1,D18P186001R1,69,5.66929425,,ABBA,,D18P186001R1T69,
D18P186001,R1,D18P186001R1,70,7.6771695,,ABBA,28,D18P186001R1T70,
D18P186001,R1,D18P186001R1,71,0.5905515,,BEPA,,D18P186001R1T71,
D18P186001,R1,D18P186001R1,72,5.629924375,,ABBA,25,D18P186001R1T72,
D18P186001,R1,D18P186001R1,73,0.275590695,,BEPA,,D18P186001R1T73,
D18P186001,R1,D18P186001R1,74,0.314960805,,BEPA,,D18P186001R1T74,
D18P186001,R1,D18P186001R1,75,0.354330891,,BEPA,,D18P186001R1T75,
D18P186001,R1,D18P186001R1,76,0.472441219,,BEPA,,D18P186001R1T76,
D18P186001,R1,D18P186001R1,77,0.944882438,,ABBA,,D18P186001R1T77,
D18P186001,R1,D18P186001R1,78,0.393701,,BEPA,,D18P186001R1T78,
D18P186001,R1,D18P186001R1,79,5.433073875,,ABBA,,D18P186001R1T79,
D18P186001,R1,D18P186001R1,80,8.03150025,,ABBA,,D18P186001R1T80,
D18P186001,R1,D18P186001R1,81,,,ABBA,,D18P186001R1T81,
D18P186001,R1,D18P186001R1,82,0.157480402,,BEPA,,D18P186001R1T82,
D18P186001,R1,D18P186001R1,83,0.118110305,,BEPA,,D18P186001R1T83,
D18P186001,R1,D18P186001R1,84,0.118110305,,BEPA,,D18P186001R1T84,
D18P186001,R1,D18P186001R1,85,0.118110305,,BEPA,,D18P186001R1T85,
D18P186001,R1,D18P186001R1,86,0.118110305,,BEPA,,D18P186001R1T86,
D18P186001,R1,D18P186001R1,87,0.157480402,,BEPA,,D18P186001R1T87,
D18P186001,R1,D18P186001R1,88,0.275590695,,BEPA,,D18P186001R1T88,
D18P186001,R1,D18P186001R1,89,1.102362781,,BEPA,,D18P186001R1T89,
D18P186001,R1,D18P186001R1,90,0.314960805,,BEPA,,D18P186001R1T90,
D18P186001,R1,D18P186001R1,91,0.1968505,,BEPA,,D18P186001R1T91,
D18P186001,R1,D18P186001R1,92,0.629921609,,ABBA,,D18P186001R1T92,
D18P186001,R1,D18P186001R1,93,0.1968505,,ABBA,,D18P186001R1T93,
D18P186001,R1,D18P186001R1,94,0.393701,,ABBA,,D18P186001R1T94,
D18P186001,R1,D18P186001R1,95,0.314960805,,BEPA,,D18P186001R1T95,
D18P186001,R1,D18P186001R1,96,0.393701,,BEPA,,D18P186001R1T96,
D18P186001,R1,D18P186001R1,97,0.275590695,,BEPA,,D18P186001R1T97,
D18P186001,R1,D18P186001R1,98,0.393701,,BEPA,,D18P186001R1T98,
D18P186001,R1,D18P186001R1,99,0.354330891,,BEPA,,D18P186001R1T99,
D18P186001,R1,D18P186001R1,100,0.354330891,,BEPA,,D18P186001R1T100,
D18P186001,R2,D18P186001R2,1,6.3779565,,ABBA,,D18P186001R2T1,
D18P186001,R2,D18P186001R2,2,1.417323562,,BEPA,,D18P186001R2T2,
D18P186001,R2,D18P186001R2,3,,,BEPA,,D18P186001R2T3,
D18P186001,R2,D18P186001R2,4,,,BEPA,,D18P186001R2T4,
D18P186001,R2,D18P186001R2,5,0.314960805,,BEPA,,D18P186001R2T5,
D18P186001,R2,D18P186001R2,6,4.251970875,,ABBA,33,D18P186001R2T6,
D18P186001,R2,D18P186001R2,7,3.58267925,,ABBA,,D18P186001R2T7,
D18P186001,R2,D18P186001R2,8,,,,,D18P186001R2T8,
D18P186001,R2,D18P186001R2,9,2.5590565,,,,D18P186001R2T9,
D18P186001,R2,D18P186001R2,10,1.220473062,,,,D18P186001R2T10,
D18P186001,R2,D18P186001R2,11,1.614174062,,BEPA,,D18P186001R2T11,
D18P186001,R2,D18P186001R2,12,5.07874275,,ABBA,,D18P186001R2T12,
D18P186001,R2,D18P186001R2,13,4.1338605,,ABBA,,D18P186001R2T13,
D18P186001,R2,D18P186001R2,14,1.3779535,,ABBA,,D18P186001R2T14,
D18P186001,R2,D18P186001R2,15,5.433073875,,ABBA,35,D18P186001R2T15,
D18P186001,R2,D18P186001R2,16,4.37008125,,ABBA,35,D18P186001R2T16,
D18P186001,R2,D18P186001R2,17,1.220473062,,ABBA,,D18P186001R2T17,
D18P186001,R2,D18P186001R2,18,1.181103,,BEPA,,D18P186001R2T18,
D18P186001,R2,D18P186001R2,19,0.393701,,BEPA,,D18P186001R2T19,
D18P186001,R2,D18P186001R2,20,0.551181391,,BEPA,,D18P186001R2T20,
D18P186001,R2,D18P186001R2,21,2.834647125,,ABBA,29,D18P186001R2T21,
D18P186001,R2,D18P186001R2,22,,,BEPA,,D18P186001R2T22,
D18P186001,R2,D18P186001R2,23,1.259843219,,ABBA,,D18P186001R2T23,
D18P186001,R2,D18P186001R2,24,5.3149635,,ABBA,32,D18P186001R2T24,
D18P186001,R2,D18P186001R2,25,5.039372875,,ABBA,,D18P186001R2T25,
D18P186001,R2,D18P186001R2,26,,,BEPA,,D18P186001R2T26,
D18P186001,R2,D18P186001R2,27,0.9842525,,BEPA,,D18P186001R2T27,
D18P186001,R2,D18P186001R2,28,4.48819125,,ABBA,,D18P186001R2T28,
D18P186001,R2,D18P186001R2,29,0.393701,,BEPA,,D18P186001R2T29,
D18P186001,R2,D18P186001R2,30,0.551181391,,BEPA,,D18P186001R2T30,
D18P186001,R2,D18P186001R2,31,6.06299525,,ABBA,,D18P186001R2T31,
D18P186001,R2,D18P186001R2,32,5.55118425,,ABBA,,D18P186001R2T32,
D18P186001,R2,D18P186001R2,33,,,,,D18P186001R2T33,
D18P186001,R2,D18P186001R2,34,1.023622562,,,,D18P186001R2T34,
D18P186001,R2,D18P186001R2,35,0.905512281,,,,D18P186001R2T35,
D18P186001,R2,D18P186001R2,36,1.141732938,,,,D18P186001R2T36,
D18P186001,R2,D18P186001R2,37,4.76378225,,ABBA,37,D18P186001R2T37,
D18P186001,R2,D18P186001R2,38,5.94488525,,ABBA,,D18P186001R2T38,
D18P186001,R2,D18P186001R2,39,5.118113,,ABBA,,D18P186001R2T39,
D18P186001,R2,D18P186001R2,40,5.826774875,,ABBA,,D18P186001R2T40,
D18P186001,R2,D18P186001R2,41,5.039372875,,ABBA,,D18P186001R2T41,
D18P186001,R2,D18P186001R2,42,7.5590595,,ABBA,31,D18P186001R2T42,
D18P186001,R2,D18P186001R2,43,5.07874275,,ABBA,,D18P186001R2T43,
D18P186001,R2,D18P186001R2,44,4.842522375,,ABBA,,D18P186001R2T44,
D18P186001,R2,D18P186001R2,45,0.748031891,,ABBA,,D18P186001R2T45,
D18P186001,R2,D18P186001R2,46,1.417323562,,ABBA,,D18P186001R2T46,
D18P186001,R2,D18P186001R2,47,4.5275615,,ABBA,,D18P186001R2T47,
D18P186001,R2,D18P186001R2,48,1.102362781,,ABBA,,D18P186001R2T48,
D18P186001,R2,D18P186001R2,49,0.5905515,,ABBA,,D18P186001R2T49,
D18P186001,R2,D18P186001R2,50,5.27559325,,ABBA,,D18P186001R2T50,
D18P186001,R2,D18P186001R2,51,2.5590565,,BEPA,,D18P186001R2T51,
D18P186001,R2,D18P186001R2,52,,,,,D18P186001R2T52,
D18P186001,R2,D18P186001R2,53,,,BEPA,,D18P186001R2T53,
D18P186001,R2,D18P186001R2,54,,,BEPA,,D18P186001R2T54,
D18P186001,R2,D18P186001R2,55,1.653544125,,BEPA,,D18P186001R2T55,
D18P186001,R2,D18P186001R2,56,2.401576062,,,,D18P186001R2T56,
D18P186001,R2,D18P186001R2,57,3.149608,,,,D18P186001R2T57,
D18P186001,R2,D18P186001R2,58,2.440946125,,,,D18P186001R2T58,
D18P186001,R2,D18P186001R2,59,5.15748325,,ABBA,,D18P186001R2T59,
D18P186001,R2,D18P186001R2,60,1.653544125,,ABBA,,D18P186001R2T60,
D18P186001,R2,D18P186001R2,61,,,BEPA,,D18P186001R2T61,
D18P186001,R2,D18P186001R2,62,0.944882438,,BEPA,,D18P186001R2T62,
D18P186001,R2,D18P186001R2,63,1.102362781,,BEPA,,D18P186001R2T63,
D18P186001,R2,D18P186001R2,64,1.811024562,,ABBA,,D18P186001R2T64,
D18P186001,R2,D18P186001R2,65,0.708661781,,BEPA,,D18P186001R2T65,
D18P186001,R2,D18P186001R2,66,7.12598825,,ABBA,,D18P186001R2T66,
D18P186001,R2,D18P186001R2,67,7.51968925,,ABBA,36,D18P186001R2T67,
D18P186001,R2,D18P186001R2,68,1.3779535,,ABBA,,D18P186001R2T68,
D18P186001,R2,D18P186001R2,69,6.14173575,,ABBA,,D18P186001R2T69,
D18P186001,R2,D18P186001R2,70,8.89764275,,ABBA,30,D18P186001R2T70,
D18P186001,R2,D18P186001R2,71,0.787402,,BEPA,,D18P186001R2T71,
D18P186001,R2,D18P186001R2,72,5.826774875,,ABBA,27,D18P186001R2T72,
D18P186001,R2,D18P186001R2,73,0.354330891,,BEPA,,D18P186001R2T73,
D18P186001,R2,D18P186001R2,74,0.511811281,,BEPA,,D18P186001R2T74,
D18P186001,R2,D18P186001R2,75,0.433071109,,BEPA,,D18P186001R2T75,
D18P186001,R2,D18P186001R2,76,0.551181391,,BEPA,,D18P186001R2T76,
D18P186001,R2,D18P186001R2,77,1.062992719,,ABBA,,D18P186001R2T77,
D18P186001,R2,D18P186001R2,78,0.511811281,,BEPA,,D18P186001R2T78,
D18P186001,R2,D18P186001R2,79,5.826774875,,ABBA,,D18P186001R2T79,
D18P186001,R2,D18P186001R2,80,8.50394175,,ABBA,,D18P186001R2T80,
D18P186001,R2,D18P186001R2,81,,,ABBA,,D18P186001R2T81,
D18P186001,R2,D18P186001R2,82,0.157480402,,BEPA,,D18P186001R2T82,
D18P186001,R2,D18P186001R2,83,0.118110305,,BEPA,,D18P186001R2T83,
D18P186001,R2,D18P186001R2,84,,,BEPA,,D18P186001R2T84,
D18P186001,R2,D18P186001R2,85,,,BEPA,,D18P186001R2T85,
D18P186001,R2,D18P186001R2,86,0.118110305,,BEPA,,D18P186001R2T86,
D18P186001,R2,D18P186001R2,87,,,BEPA,,D18P186001R2T87,
D18P186001,R2,D18P186001R2,88,0.314960805,,BEPA,,D18P186001R2T88,
D18P186001,R2,D18P186001R2,89,,,BEPA,,D18P186001R2T89,
D18P186001,R2,D18P186001R2,90,0.433071109,,BEPA,,D18P186001R2T90,
D18P186001,R2,D18P186001R2,91,0.078740201,,BEPA,,D18P186001R2T91,
D18P186001,R2,D18P186001R2,92,0.708661781,,ABBA,,D18P186001R2T92,
D18P186001,R2,D18P186001R2,93,0.393701,,ABBA,,D18P186001R2T93,
D18P186001,R2,D18P186001R2,94,0.472441219,,ABBA,,D18P186001R2T94,
D18P186001,R2,D18P186001R2,95,,,BEPA,,D18P186001R2T95,
D18P186001,R2,D18P186001R2,96,0.393701,,BEPA,,D18P186001R2T96,
D18P186001,R2,D18P186001R2,97,0.393701,,BEPA,,D18P186001R2T97,
D18P186001,R2,D18P186001R2,98,0.393701,,BEPA,,D18P186001R2T98,
D18P186001,R2,D18P186001R2,99,0.472441219,,BEPA,,D18P186001R2T99,
D18P186001,R2,D18P186001R2,100,0.433071109,,BEPA,,D18P186001R2T100,
D18P186001,R3,D18P186001R3,1,6.92913775,,ABBA,,D18P186001R3T1,
D18P186001,R3,D18P186001R3,2,1.456693719,,BEPA,,D18P186001R3T2,
D18P186001,R3,D18P186001R3,3,,,BEPA,,D18P186001R3T3,
D18P186001,R3,D18P186001R3,4,,,BEPA,,D18P186001R3T4,
D18P186001,R3,D18P186001R3,5,,,BEPA,,D18P186001R3T5,
D18P186001,R3,D18P186001R3,6,4.48819125,,ABBA,37,D18P186001R3T6,
D18P186001,R3,D18P186001R3,7,3.858269875,,ABBA,,D18P186001R3T7,
D18P186001,R3,D18P186001R3,8,,,,,D18P186001R3T8,
D18P186001,R3,D18P186001R3,9,2.716536938,,,,D18P186001R3T9,
D18P186001,R3,D18P186001R3,10,,,,,D18P186001R3T10,
D18P186001,R3,D18P186001R3,11,1.535433938,,BEPA,,D18P186001R3T11,
D18P186001,R3,D18P186001R3,12,5.590554125,,ABBA,,D18P186001R3T12,
D18P186001,R3,D18P186001R3,13,4.37008125,,ABBA,,D18P186001R3T13,
D18P186001,R3,D18P186001R3,14,1.338583438,,ABBA,,D18P186001R3T14,
D18P186001,R3,D18P186001R3,15,5.984255125,,ABBA,39,D18P186001R3T15,
D18P186001,R3,D18P186001R3,16,4.88189225,,ABBA,39,D18P186001R3T16,
D18P186001,R3,D18P186001R3,17,1.338583438,,ABBA,,D18P186001R3T17,
D18P186001,R3,D18P186001R3,18,1.141732938,,BEPA,,D18P186001R3T18,
D18P186001,R3,D18P186001R3,19,,,BEPA,,D18P186001R3T19,
D18P186001,R3,D18P186001R3,20,0.669291719,,BEPA,,D18P186001R3T20,
D18P186001,R3,D18P186001R3,21,3.070867875,,ABBA,33,D18P186001R3T21,
D18P186001,R3,D18P186001R3,22,,,BEPA,,D18P186001R3T22,
D18P186001,R3,D18P186001R3,23,1.259843219,,ABBA,,D18P186001R3T23,
D18P186001,R3,D18P186001R3,24,5.905515,,ABBA,36,D18P186001R3T24,
D18P186001,R3,D18P186001R3,25,5.433073875,,ABBA,,D18P186001R3T25,
D18P186001,R3,D18P186001R3,26,,,BEPA,,D18P186001R3T26,
D18P186001,R3,D18P186001R3,27,1.023622562,,BEPA,,D18P186001R3T27,
D18P186001,R3,D18P186001R3,28,4.96063275,,ABBA,,D18P186001R3T28,
D18P186001,R3,D18P186001R3,29,,,BEPA,,D18P186001R3T29,
D18P186001,R3,D18P186001R3,30,0.511811281,,BEPA,,D18P186001R3T30,
D18P186001,R3,D18P186001R3,31,6.6141765,,ABBA,,D18P186001R3T31,
D18P186001,R3,D18P186001R3,32,6.06299525,,ABBA,,D18P186001R3T32,
D18P186001,R3,D18P186001R3,33,,,,,D18P186001R3T33,
D18P186001,R3,D18P186001R3,34,0.9842525,,,,D18P186001R3T34,
D18P186001,R3,D18P186001R3,35,,,,,D18P186001R3T35,
D18P186001,R3,D18P186001R3,36,1.220473062,,,,D18P186001R3T36,
D18P186001,R3,D18P186001R3,37,5.236223375,,ABBA,41,D18P186001R3T37,
D18P186001,R3,D18P186001R3,38,6.4960665,,ABBA,,D18P186001R3T38,

uj5u.com熱心網友回復：

真正的答案

基本上，您撰寫的代碼就像您用其他語言撰寫了很多但從未使用過的代碼一樣R。由于所有嵌套回圈，很難準確理解發生了什么，而且我還沒有運行您的代碼（請參閱此處了解如何可重現地包含資料）。

我認為盡管可以洗掉所有回圈，例如將第一個條件替換為：

FVS_TreeInit$GD[
    FVS_TreeInit$Plot_ID == "R8" & 
    FVS_TreeInit$StandPlotTree_ID == paste(FVS_TreeInit$Stand_ID,"R7T",FVS_TreeInit$Tree_ID,sep="")
    ]  <- FVS_TreeInit$DBH

如果您使用(here)會明顯更快，因為它的實作速度如此之快。data.table

便宜又簡單的答案

有一種既便宜又簡單的方法可以在不重構所有代碼的情況下加速你所擁有的東西，但它仍然會很慢。

問題是您經常使用df[i, "columnName"]. 這是一種非常緩慢的方法。看看這個例子。

創建兩個隨機的大（10e6行）字串資料框：

# Random data function borrowed from: https://www.r-bloggers.com/2017/03/fast-data-lookups-in-r-dplyr-vs-data-table/
random_string_column <- function(n) {
  stringi::stri_rand_strings(n = n, length = 8)
}
random_data_frame <- function(n) tibble(
  col1 = random_string_column(n),
  col2 = random_string_column(n)
)
df <- random_data_frame(10^6)
df2 <- random_data_frame(10^6)
head(df)

輸出（df2 類似）：

# A tibble: 6 x 2
  col1     col2
  <chr>    <chr>   
1 NykzxkNO qGusGkMa
2 mgivTVky xlCtwEba
3 zar6FbC4 dOYeQPfg
4 89fKLJFv RPsP2CZc
5 V2FH3Smt ObToBJFC
6 gXa0j5XZ QYKK6hD2

現在讓我們按照自己的方式進行 1000 次隨機查找：

rand_indeces  <- sample(c(1:nrow(df), 1000))
system.time(
    for(i in rand_indeces) {
        if(df[i, "col2"]=="aaaaa") { #it never will
            break
        }
    }
)

輸出：

   user  system elapsed 
 138.58    0.23  140.72

兩分鐘多。現在讓我們看看另一個資料框（新的df，所以我們可以看到它不是因為快取），df[i, "col2"]=="aaaaa"用df2$col2[i] == "aaaaa".

system.time(
    for(i in rand_indeces) {
        if(df2$col2[i] == "aaaaa") { #it never will
            break
        }
    }
)

輸出：

   user  system elapsed 
   4.70    0.00    4.75

這應該可以為您節省不少時間。但是，從根本上說，在 R 中以這種方式進行查找很慢（請參閱此處和此處）。

如果您必須進行單獨查找，您可以考慮創建一個哈希表，或者通過第一個鏈接中的方法或通過這里environments的一些較新的方法。我覺得這個包看起來很有趣——還沒用過。collections

但是，到目前為止，最好的辦法是對您的代碼進行矢量化，如本回復頂部所述。

uj5u.com熱心網友回復：

（劇透：跳到雙重合并部分以獲得比嵌套回圈快 150 倍的結果。我希望這種速度改進會隨著更大的資料而變得更好。）

中級改進

首先，您的內部回圈都可以從for回圈中減少到更快，也許會改變

      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R0T",FVS_TreeInit[i,"Tree_ID"],sep="")
      for (f in seq_len(nrow(FVS_TreeInit))){
        if (FVS_TreeInit[f,"StandPlotTree_ID"] == find){
          FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
          break
        }
      }

到

      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R0T",FVS_TreeInit[i,"Tree_ID"],sep="")
      f <- FVS_TreeInit[,"StandPlotTree_ID"] == find
      FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]

值得注意的是，我們已經從迭代for回圈變為矢量化比較，這在 R 中往往要快得多。但是，應該注意我們對幾個 做同樣的事情"R*"，所以我們可以大大減少你的外回圈；其中大部分是代碼高爾夫（減少字符數來做同樣的事情），但一般不要重復自己“：如果你在多個地方有相同（相同）的代碼，你會增加改變一個的機會而不是所有其他人。

在該主題中，到目前為止，我可以使用以下方法獲得相同的結果：

  # loop through FVS_TreeInit and add previous diameter increment identifier to current row
  for (i in seq_len(nrow(FVS_TreeInit))){
    if (FVS_TreeInit[i,"Plot_ID"] == "R8"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R7T",FVS_TreeInit[i,"Tree_ID"],sep="")
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R7"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R6T",FVS_TreeInit[i,"Tree_ID"],sep="")
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R6"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R5T",FVS_TreeInit[i,"Tree_ID"],sep="")
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R5"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R4T",FVS_TreeInit[i,"Tree_ID"],sep="")
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R4"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R3T",FVS_TreeInit[i,"Tree_ID"],sep="")
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R3"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R2T",FVS_TreeInit[i,"Tree_ID"],sep="")
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R2"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R1T",FVS_TreeInit[i,"Tree_ID"],sep="")
    } else if (FVS_TreeInit[i,"Plot_ID"] == "R1"){
      find <- paste(FVS_TreeInit[i,"Stand_ID"],"R0T",FVS_TreeInit[i,"Tree_ID"],sep="")
    } else {
      find <- NA
    }
    FVS_TreeInit[i,"GD"] <- NA
    if (!is.na(find)) {
      f <- FVS_TreeInit[,"StandPlotTree_ID"] == find
      if (any(f)) FVS_TreeInit[i,"GD"] <- FVS_TreeInit[f,"DBH"]
    }
    if (i%%1000 == 0){
      print(paste(i," rows iterated...",sep=""))
    }
  }

好的，這更短，并縮小到真正重要的內容。值得注意的是（對我而言），我們基于某些特定值有效地將資料“連接”在一起。我認為我們可以通過更簡單的步驟（合并）來完成所有這些作業。

雙重合并

這實際上只是基于三個 ID 將資料連接回自身：Stand_ID、Tree_ID和Plot_ID，其中后者是時間組件（因此需要針對自連接進行調整）。

源于您的R*值反映時間的想法，我認為我們可以安全地假設R(n)應該分配給R(n 1)，所以讓我們以編程方式創建這個中間框架：

prevPlot <- data.frame(prev_Plot_ID = unique(FVS_TreeInit$Plot_ID))
prevPlot$Plot_ID <- paste0("R", as.integer(gsub("R", "", prevPlot$prev_Plot_ID))-1)
prevPlot <- subset(prevPlot, prev_Plot_ID != "R0")
prevPlot
#   prev_Plot_ID Plot_ID
# 2           R1      R0
# 3           R2      R1
# 4           R3      R2

We'll merge/join this onto the original FVS_Tree_Init in order to identify the "previous Plot_ID" and then merge back on itself to bring in the new value.

prevValues <- subset(merge(FVS_TreeInit, prevPlot, by = "Plot_ID", all.x = TRUE),
                     select = c(Stand_ID, prev_Plot_ID, Tree_ID, DBH))
names(prevValues)[4] <- "GD"
head(prevValues)
#     Stand_ID prev_Plot_ID Tree_ID        GD
# 1 D18P186001           R1      39 3.9370100
# 2 D18P186001           R1      40 4.2519709
# 3 D18P186001           R1       1 4.7637823
# 4 D18P186001           R1       2 1.2598432
# 5 D18P186001           R1       3 0.2755907
# 6 D18P186001           R1       4 0.6299216
new_FVS_TreeInit <- merge(
  FVS_TreeInit, prevValues,
  by.x = c("Stand_ID", "Plot_ID", "Tree_ID"),
  by.y = c("Stand_ID", "prev_Plot_ID", "Tree_ID"),
  all.x = TRUE)
identical(expected, new_FVS_TreeInit[names(expected)])
# [1] TRUE

where expected is the output from your nested for loop, and new_FVS_TreeInit[names(expected)] is just a step to ensure all columns are in the same order (for comparison with identical, not required for data processing).

僅供參考，這突出了您使用連接字串作為單數鍵的矛盾（一種相當 Excel 的連接方法......它確實有效），前提是在一個或多個鍵上合并/連接資料。一旦你摒棄了單鍵 Excel 方法并認識到一個或多個方法也同樣有效——并且更易于閱讀和維護——我認為將思維方式轉變為合并可以改變編程。請參閱如何加入（合并）資料幀（內、外、左、右）和INNER JOIN、LEFT JOIN、RIGHT JOIN 和 FULL JOIN 有什么區別？有關連接的“演算”的更多討論。

基準

使用此示例資料，它的執行速度提高了大約 150倍。

bench::mark(
  forloop = {
   # your code
  },
  doublemerge = {
    # my code
  }
)
# Warning: Some expressions had a GC in every iteration; so filtering is disabled.
# # A tibble: 2 x 13
#   expression       min   median `itr/sec` mem_alloc `gc/sec` n_itr  n_gc total_time result          memory      time      gc       
#   <bch:expr>  <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl> <int> <dbl>   <bch:tm> <list>          <list>      <list>    <list>   
# 1 forloop      272.4ms 284.86ms      3.51     656KB     7.02     2     4      570ms <df [318 x 11]> <Rprofmem ~ <bench_t~ <tibble ~
# 2 doublemerge    1.4ms   1.55ms    528.       346KB     1.99   265     1      502ms <df [318 x 11]> <Rprofmem ~ <bench_t~ <tibble ~

uj5u.com熱心網友回復：

單程data.table解決方案：

這可以在按Stand_ID, Tree_ID, then排序后一次性完成Plot_ID（在我的機器上大約 6 毫秒，包括讀取資料）。

首先，我將示例資料字串復制到 object data。

library(data.table)

dt <- fread(input = data) # read into a data.table

setorder(dt[, `:=`(Plot_ID = as.integer(substr(Plot_ID, 2, 2)), DG = as.numeric(DG))], # convert Plot_ID to an integer and DG to numeric (from logical)
         # order by Stand_ID, Tree_ID, Plot_ID
         Stand_ID, Tree_ID, Plot_ID)[
          # create a temporary column indicating the rows that will pull DBH from the previous row
          , pull := c(FALSE, diff(Plot_ID) == 1L) & Stand_ID == shift(Stand_ID) & Tree_ID == shift(Tree_ID)][
          # pull DBH from the previous row where appropriate
          pull == TRUE, DG := shift(dt$DBH)[dt$pull]][,
          # clean up
          `:=`(pull = NULL, Plot_ID = paste0("R", Plot_ID))]

uj5u.com熱心網友回復：

我相信這可以用更少的代碼以更簡單的方式重新構建。看起來您正在對資料進行連接，其中查找鍵是當前 Plot_ID 的修改版本，其中“R#”應鏈接到“R#-1T”。

在 R 中執行此操作的一種更慣用的方法是為所有行創建一次適當的鍵，然后基于該查找鍵進行連接。這將比您的回圈方法快幾個數量級，因為它只需要編譯一次查找代碼而不是 650,000 次，并且連接針對速度進行了優化。請參閱此頁面以深入解釋為什么矢量化在 R 中如此有幫助：https ://www.noamross.net/archives/2014-04-16-vectorization-in-r-why/

我在下面使用 dplyr 執行此操作，但該方法在 base r 中的作業方式非常相似。

library(dplyr)
FVS_TreeInit %>%

  # create key for all rows
  mutate(prior_key = paste0(Stand_ID,  "R", 
    parse_number(Plot_ID)-1, "T",  # will be nonsense R-1T when starts from R0, that's ok,
    Tree_ID)) %>%                  # it just won't replace GD there

  # Use that key to join to a version of the data with just StandPlotTree_ID and
  # DBH, which I rename as GD. Match `prior_key` with `StandPlotTree_ID` there.
  left_join(FVS_TreeInit %>% transmute(StandPlotTree_ID, GD = DBH),
            by = c("prior_key" = "StandPlotTree_ID"))

基準

您的原始代碼在提供的示例資料上運行需要 3.7 秒。我建議的更換耗時 0.027 秒，大約是 140 倍的更換速度。我預計隨著資料的增加，時差會變得更大。

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