Question

我在R中有50列到250万行的数据帧，表示时间序列。时间列属于POSIXct类。为了进行分析，我反复需要在特定时间找到给定类的系统状态。

我目前的方法如下（简化和可重复）：

set.seed(1)
N <- 10000
.time <- sort(sample(1:(100*N),N))
class(.time) <- c("POSIXct", "POSIXt")
df <- data.frame(
  time=.time,
  distance1=sort(sample(1:(100*N),N)),
  distance2=sort(sample(1:(100*N),N)),
  letter=sample(letters,N,replace=TRUE)
)

# state search function
time.state <- function(df,searchtime,searchclass){
  # find all rows in between the searchtime and a while (here 10k seconds)
  # before that
  rows <- which(findInterval(df$time,c(searchtime-10000,searchtime))==1)
  # find the latest state of the given class within the search interval
  return(rev(rows)[match(T,rev(df[rows,"letter"]==searchclass))])
}  

# evaluate the function to retrieve the latest known state of the system
# at time 500,000.
df[time.state(df,500000,"a"),]

然而，拨打which的费用非常高。或者，我可以先按类过滤，然后找时间，但这不会更改评估时间。据Rprof说，which和==花费了大部分时间。

是否有更有效的解决方案？时间点排序微弱增加。

Answer 1

由于which，==和[都与数据框的大小成线性关系，因此解决方案是为批量操作生成子集数据框，如下所示：

# function that applies time.state to a series of time/class cominations
time.states <- function(df,times,classes,day.length=24){
  result <- vector("list",length(times))
  day.end <- 0
  for(i in 1:length(times)){
    if(times[i] > day.end){
      # create subset interval from 1h before to 24h after
      day.begin <- times[i]-60*60
      day.end <- times[i]+day.length*60*60
      df.subset <- df[findInterval(df$time,c(day.begin,day.end))==1,]
    }
    # save the resulting row from data frame
    result[[i]] <- df.subset[time.state(df.subset,times[i],classes[i]),]
  }
  return(do.call("rbind",result))
}

dT=diff(range(df$times))和dT/day.length大，这会使评估时间缩短dT/(day.length+1)。

优化时间序列数据框中的搜索

1 个答案: