R 使用大内存技术和并行计算查找最频繁的项？

在不使用回归的情况下，我如何找到延迟最频繁的月份？以下

csv

是100MB文件的示例。我知道我应该使用

bigmemory

技术，但不确定如何实现这一点。在这里，月份存储为整数而不是因子

Year,Month,DayofMonth,DayOfWeek,DepTime,CRSDepTime,ArrTime,CRSArrTime,UniqueCarrier,FlightNum,TailNum,ActualElapsedTime,CRSElapsedTime,AirTime,ArrDelay,DepDelay,Origin,Dest,Distance,TaxiIn,TaxiOut,Cancelled,CancellationCode,Diverted,CarrierDelay,WeatherDelay,NASDelay,SecurityDelay,LateAircraftDelay
2006,1,11,3,743,745,1024,1018,US,343,N657AW,281,273,223,6,-2,ATL,PHX,1587,45,13,0,,0,0,0,0,0,0
2006,1,11,3,1053,1053,1313,1318,US,613,N834AW,260,265,214,-5,0,ATL,PHX,1587,27,19,0,,0,0,0,0,0,0
2006,1,11,3,1915,1915,2110,2133,US,617,N605AW,235,258,220,-23,0,ATL,PHX,1587,4,11,0,,0,0,0,0,0,0
2006,1,11,3,1753,1755,1925,1933,US,300,N312AW,152,158,126,-8,-2,AUS,PHX,872,16,10,0,,0,0,0,0,0,0
2006,1,11,3,824,832,1015,1015,US,765,N309AW,171,163,132,0,-8,AUS,PHX,872,27,12,0,,0,0,0,0,0,0
2006,1,11,3,627,630,834,832,US,295,N733UW,127,122,108,2,-3,BDL,CLT,644,6,13,0,,0,0,0,0,0,0
2006,1,11,3,825,820,1041,1021,US,349,N177UW,136,121,111,20,5,BDL,CLT,644,4,21,0,,0,0,0,20,0,0
2006,1,11,3,942,945,1155,1148,US,356,N404US,133,123,121,7,-3,BDL,CLT,644,4,8,0,,0,0,0,0,0,0
2006,1,11,3,1239,1245,1438,1445,US,775,N722UW,119,120,103,-7,-6,BDL,CLT,644,4,12,0,,0,0,0,0,0,0
2006,1,11,3,1642,1645,1841,1845,US,1002,N104UW,119,120,105,-4,-3,BDL,CLT,644,4,10,0,,0,0,0,0,0,0
2006,1,11,3,1836,1835,NA,2035,US,1103,N425US,NA,120,NA,NA,1,BDL,CLT,644,0,17,0,,1,0,0,0,0,0
2006,1,11,3,NA,1725,NA,1845,US,69,0,NA,80,NA,NA,NA,BDL,DCA,313,0,0,1,A,0,0,0,0,0,0

---

假设您的data.frame被称为

dd

。如果您想查看所有年份中每个月的天气延迟总数，您可以这样做

delay <- aggregate(WeatherDelay~Month, dd, sum)
delay[order(-delay$WeatherDelay),]

---

delay这更接近你想要的吗？我不太了解R，所以无法对行进行求和，但这至少可以将它们聚合起来。我也在学习

delays <- read.csv("tmp.csv", stringsAsFactors = FALSE)

delay <- aggregate(cbind(ArrDelay, DepDelay, WeatherDelay, NASDelay, SecurityDelay, LateAircraftDelay) ~ Month, delays, sum)
delay

注意：我对您的文档做了一些更改，以便在月份栏中提供一些多样性：

Year,Month,DayofMonth,DayOfWeek,DepTime,CRSDepTime,ArrTime,CRSArrTime,UniqueCarrier,FlightNum,TailNum,ActualElapsedTime,CRSElapsedTime,AirTime,ArrDelay,DepDelay,Origin,Dest,Distance,TaxiIn,TaxiOut,Cancelled,CancellationCode,Diverted,CarrierDelay,WeatherDelay,NASDelay,SecurityDelay,LateAircraftDelay
2006,1,11,3,743,745,1024,1018,US,343,N657AW,281,273,223,6,-2,ATL,PHX,1587,45,13,0,,0,0,0,0,0,0
2006,1,11,3,1053,1053,1313,1318,US,613,N834AW,260,265,214,-5,0,ATL,PHX,1587,27,19,0,,0,0,0,0,0,0
2006,2,11,3,1915,1915,2110,2133,US,617,N605AW,235,258,220,-23,0,ATL,PHX,1587,4,11,0,,0,0,0,0,0,0
2006,2,11,3,1753,1755,1925,1933,US,300,N312AW,152,158,126,-8,-2,AUS,PHX,872,16,10,0,,0,0,0,0,0,0
2006,1,11,3,824,832,1015,1015,US,765,N309AW,171,163,132,0,-8,AUS,PHX,872,27,12,0,,0,0,0,0,0,0
2006,1,11,3,627,630,834,832,US,295,N733UW,127,122,108,2,-3,BDL,CLT,644,6,13,0,,0,0,0,0,0,0
2006,3,11,3,825,820,1041,1021,US,349,N177UW,136,121,111,20,5,BDL,CLT,644,4,21,0,,0,0,0,20,0,0
2006,1,11,3,942,945,1155,1148,US,356,N404US,133,123,121,7,-3,BDL,CLT,644,4,8,0,,0,0,0,0,0,0
2006,3,11,3,1239,1245,1438,1445,US,775,N722UW,119,120,103,-7,-6,BDL,CLT,644,4,12,0,,0,0,0,0,0,0
2006,3,11,3,1642,1645,1841,1845,US,1002,N104UW,119,120,105,-4,-3,BDL,CLT,644,4,10,0,,0,0,0,0,0,0
2006,3,11,3,1836,1835,NA,2035,US,1103,N425US,NA,120,NA,NA,1,BDL,CLT,644,0,17,0,,1,0,0,0,0,0
2006,1,11,3,NA,1725,NA,1845,US,69,0,NA,80,NA,NA,NA,BDL,DCA,313,0,0,1,A,0,0,0,0,0,0

我没有投反对票，但我差一点。我认为你可以做得更好。哪些列表示延迟？为什么有些
df$ArrDelay
是负数，例如？？“我应该使用大内存技术”。你更想让人们为你做些什么？“在200x年，哪些天/月的延迟最频繁？”（提示：避免回归）再次，请阅读并采取相应的行动。显然我的问题太广泛了。我不想要答案。我想要一个提示。我认为我应该使用大内存技术，而不是传统的聚合方法。我只是没有机会在适当的时间看到反对票并结束问题。现在我甚至不能删除它！如果你看我的问题更仔细地说，然后公平地判断你是如何做到的。哦，我已经了解了航空公司的数据。为许多沮丧的时刻做好准备。如果这是你正在学习的课程的家庭作业，你应该试着从课程的指导老师或你的同学那里获得提示/起点。回到那一点n您有特定的编程问题…我们在
R
中是否有
并行聚合
？比如在一些编程语言中如何有
并行聚合
？
Year,Month,DayofMonth,DayOfWeek,DepTime,CRSDepTime,ArrTime,CRSArrTime,UniqueCarrier,FlightNum,TailNum,ActualElapsedTime,CRSElapsedTime,AirTime,ArrDelay,DepDelay,Origin,Dest,Distance,TaxiIn,TaxiOut,Cancelled,CancellationCode,Diverted,CarrierDelay,WeatherDelay,NASDelay,SecurityDelay,LateAircraftDelay
2006,1,11,3,743,745,1024,1018,US,343,N657AW,281,273,223,6,-2,ATL,PHX,1587,45,13,0,,0,0,0,0,0,0
2006,1,11,3,1053,1053,1313,1318,US,613,N834AW,260,265,214,-5,0,ATL,PHX,1587,27,19,0,,0,0,0,0,0,0
2006,2,11,3,1915,1915,2110,2133,US,617,N605AW,235,258,220,-23,0,ATL,PHX,1587,4,11,0,,0,0,0,0,0,0
2006,2,11,3,1753,1755,1925,1933,US,300,N312AW,152,158,126,-8,-2,AUS,PHX,872,16,10,0,,0,0,0,0,0,0
2006,1,11,3,824,832,1015,1015,US,765,N309AW,171,163,132,0,-8,AUS,PHX,872,27,12,0,,0,0,0,0,0,0
2006,1,11,3,627,630,834,832,US,295,N733UW,127,122,108,2,-3,BDL,CLT,644,6,13,0,,0,0,0,0,0,0
2006,3,11,3,825,820,1041,1021,US,349,N177UW,136,121,111,20,5,BDL,CLT,644,4,21,0,,0,0,0,20,0,0
2006,1,11,3,942,945,1155,1148,US,356,N404US,133,123,121,7,-3,BDL,CLT,644,4,8,0,,0,0,0,0,0,0
2006,3,11,3,1239,1245,1438,1445,US,775,N722UW,119,120,103,-7,-6,BDL,CLT,644,4,12,0,,0,0,0,0,0,0
2006,3,11,3,1642,1645,1841,1845,US,1002,N104UW,119,120,105,-4,-3,BDL,CLT,644,4,10,0,,0,0,0,0,0,0
2006,3,11,3,1836,1835,NA,2035,US,1103,N425US,NA,120,NA,NA,1,BDL,CLT,644,0,17,0,,1,0,0,0,0,0
2006,1,11,3,NA,1725,NA,1845,US,69,0,NA,80,NA,NA,NA,BDL,DCA,313,0,0,1,A,0,0,0,0,0,0