检查Posixct时间是否在间隔内

时间：2016-03-04 08:08:51

标签： r count intervals lubridate

问题： 我想要合并两个数据帧，具体取决于一个数据帧的日期/时间是否在另一个数据帧的间隔内。

流量：日期和时间（Posixct），频率

mydata：间隔，频率和

我现在想计算来自流量的Posixct时间是否在mydata的间隔内，如果这是真的，我想计算mydata中“Frequencies of sum”列中的频率。

我遇到的两个问题： 1.交通数据帧的行数明显多于mydata。我不知道如何告诉R循环遍历流量中的每个观察以检查mydata中的一行。

  1. 在mydata的频率间隔中可以有多个观察拟合。我希望R将不同流量观测的所有频率相加，以获得频率的总分。间隔也是重叠的。

    2. 以下是数据：

    DateTime <- c("2014-11-01 04:00:00", "2014-11-01 04:03:00",  "2014-11-01 04:06:00", "2014-11-01 04:08:00", "2014-11-01 04:10:00", "2014-11-01 04:12:00", "2015-08-01 04:13:00", "2015-08-01 04:45:00", "2015-08-01 14:15:00", "2015-08-01 14:13:00")
DateTime <- as.POSIXct(DateTime)
Frequency <- c(1,2,3,5,12,1,2,2,1,1)                 
traffic <- data.frame(DateTime, Frequency)

library(lubridate)
DateTime1 <- c("2014-11-01 04:00:00", "2015-08-01 04:03:00",  "2015-08-01 14:00:00")
DateTime2 <- c("2014-11-01 04:15:00", "2015-08-01 04:13:00",  "2015-08-01 14:15:00")
DateTime1 <- as.POSIXct(DateTime1)
DateTime2 <- as.POSIXct(DateTime2)
mydata <- data.frame(DateTime1, DateTime2)
mydata$Interval <- as.interval(DateTime1, DateTime2)
mydata$SumFrequency <- NA

    预期结果应该是这样的：

    mydata$SumFrequency <- c(24, 2, 2)
head(mydata)

    我从包lubridate尝试了int_overlaps。 任何有关如何解决这个问题的提示都非常感谢！

5 个答案:

答案 0 :(得分：3)

使用data.table方法between按时过滤流量数据集：

setDT(traffic)
setDT(mydata)

mydata[,SumFrequency := as.numeric(SumFrequency)] # coerce logical to numeric for next step.
mydata[,SumFrequency := sum( traffic[ DateTime %between% c(DateTime1, DateTime2), Frequency] ), by=1:nrow(mydata)]

给出：

             DateTime1           DateTime2                                           Interval SumFrequency
1: 2014-11-01 04:00:00 2014-11-01 04:15:00   2014-11-01 04:00:00 CET--2014-11-01 04:15:00 CET           24
2: 2015-08-01 04:03:00 2015-08-01 04:13:00 2015-08-01 04:03:00 CEST--2015-08-01 04:13:00 CEST            2
3: 2015-08-01 14:00:00 2015-08-01 14:15:00 2015-08-01 14:00:00 CEST--2015-08-01 14:15:00 CEST            2

如果mydata中有很多行，最好创建一个索引列并在by子句中使用它：

mydata[, idx := .I]
mydata[, SumFrequency := sum( traffic[DateTime %between% c(DateTime1, DateTime2),Frequency] ),by=idx]

这就是：

             DateTime1           DateTime2                                           Interval SumFrequency idx
1: 2014-11-01 04:00:00 2014-11-01 04:15:00   2014-11-01 04:00:00 CET--2014-11-01 04:15:00 CET           24   1
2: 2015-08-01 04:03:00 2015-08-01 04:13:00 2015-08-01 04:03:00 CEST--2015-08-01 04:13:00 CEST            2   2
3: 2015-08-01 14:00:00 2015-08-01 14:15:00 2015-08-01 14:00:00 CEST--2015-08-01 14:15:00 CEST            2   3

答案 1 :(得分：3)

来自 data.table 包的foverlaps的简短解决方案：

mydata <- data.table(DateTime1, DateTime2, key = c("DateTime1", "DateTime2"))
traffic <- data.table(start = DateTime, end = DateTime, Frequency, key = c("start","end"))

foverlaps(traffic, mydata, type="within", nomatch=0L)[, .(sumFreq = sum(Frequency)), 
                                                      by = .(DateTime1, DateTime2)]

给出：

             DateTime1           DateTime2 sumFreq
1: 2014-11-01 04:00:00 2014-11-01 04:15:00      24
2: 2015-08-01 04:03:00 2015-08-01 04:13:00       2
3: 2015-08-01 14:00:00 2015-08-01 14:15:00       2

答案 2 :(得分：2)

我看到两个解决方案：

data.frame和plyr

您可以使用%within%中的lubridate函数以及for循环或使用plyr循环函数（如dlply 

DateTime <- c("2014-11-01 04:00:00", "2014-11-01 04:03:00",  "2014-11-01 04:06:00", "2014-11-01 04:08:00", "2014-11-01 04:10:00", "2014-11-01 04:12:00", "2015-08-01 04:13:00", "2015-08-01 04:45:00", "2015-08-01 14:15:00", "2015-08-01 14:13:00")
DateTime <- as.POSIXct(DateTime)
Frequency <- c(1,2,3,5,12,1,2,2,1,1)                 
traffic <- data.frame(DateTime, Frequency)

library(lubridate)
DateTime1 <- c("2014-11-01 04:00:00", "2015-08-01 04:03:00",  "2015-08-01 14:00:00")
DateTime2 <- c("2014-11-01 04:15:00", "2015-08-01 04:13:00",  "2015-08-01 14:15:00")
DateTime1 <- as.POSIXct(DateTime1)
DateTime2 <- as.POSIXct(DateTime2)
mydata <- data.frame(DateTime1, DateTime2)
mydata$Interval <- as.interval(DateTime1, DateTime2)

library(plyr)
# Create a group-by variable
mydata$NumInt <- 1:nrow(mydata)
mydata$SumFrequency <- dlply(mydata, .(NumInt),
                             function(row){
                               sum(
                                 traffic[traffic$DateTime %within% row$Interval, "Frequency"]
                               )
                             })

mydata
#>             DateTime1           DateTime2
#> 1 2014-11-01 04:00:00 2014-11-01 04:15:00
#> 2 2015-08-01 04:03:00 2015-08-01 04:13:00
#> 3 2015-08-01 14:00:00 2015-08-01 14:15:00
#>                                             Interval NumInt SumFrequency
#> 1   2014-11-01 04:00:00 CET--2014-11-01 04:15:00 CET      1           24
#> 2 2015-08-01 04:03:00 CEST--2015-08-01 04:13:00 CEST      2            2
#> 3 2015-08-01 14:00:00 CEST--2015-08-01 14:15:00 CEST      3            2

使用data.table和函数foverlaps

data.table已经实现了一个重叠连接的功能，您可以在一个小技巧中使用它 此功能为foverlaps（我使用data.table 1.9.6下方） （请参阅How to perform join over date ranges using data.table? and this presentation）

请注意，您无需使用lubridate

创建间隔 
DateTime <- c("2014-11-01 04:00:00", "2014-11-01 04:03:00",  "2014-11-01 04:06:00", "2014-11-01 04:08:00", "2014-11-01 04:10:00", "2014-11-01 04:12:00", "2015-08-01 04:13:00", "2015-08-01 04:45:00", "2015-08-01 14:15:00", "2015-08-01 14:13:00")
DateTime <- as.POSIXct(DateTime)
Frequency <- c(1,2,3,5,12,1,2,2,1,1)                 
traffic <- data.table(DateTime, Frequency)

library(lubridate)
DateTime1 <- c("2014-11-01 04:00:00", "2015-08-01 04:03:00",  "2015-08-01 14:00:00")
DateTime2 <- c("2014-11-01 04:15:00", "2015-08-01 04:13:00",  "2015-08-01 14:15:00")
mydata <- data.table(DateTime1 = as.POSIXct(DateTime1), DateTime2 = as.POSIXct(DateTime2))

# Use function `foverlaps`  for overlapping joins

# Here's the trick : create a dummy variable to artificially have an interval
traffic[, dummy:=DateTime]
setkey(mydata, DateTime1, DateTime2)

# do the join
mydata2 <- foverlaps(traffic, mydata, by.x=c("DateTime", "dummy"), type ="within", nomatch=0L)[, dummy := NULL][]
mydata2
#>              DateTime1           DateTime2            DateTime Frequency
#> 1: 2014-11-01 04:00:00 2014-11-01 04:15:00 2014-11-01 04:00:00         1
#> 2: 2014-11-01 04:00:00 2014-11-01 04:15:00 2014-11-01 04:03:00         2
#> 3: 2014-11-01 04:00:00 2014-11-01 04:15:00 2014-11-01 04:06:00         3
#> 4: 2014-11-01 04:00:00 2014-11-01 04:15:00 2014-11-01 04:08:00         5
#> 5: 2014-11-01 04:00:00 2014-11-01 04:15:00 2014-11-01 04:10:00        12
#> 6: 2014-11-01 04:00:00 2014-11-01 04:15:00 2014-11-01 04:12:00         1
#> 7: 2015-08-01 04:03:00 2015-08-01 04:13:00 2015-08-01 04:13:00         2
#> 8: 2015-08-01 14:00:00 2015-08-01 14:15:00 2015-08-01 14:15:00         1
#> 9: 2015-08-01 14:00:00 2015-08-01 14:15:00 2015-08-01 14:13:00         1

# summarise with a sum by grouping by each line of mydata
setkeyv(mydata2, key(mydata))
mydata2[mydata, .(SumFrequency = sum(Frequency)), by = .EACHI]
#>              DateTime1           DateTime2 SumFrequency
#> 1: 2014-11-01 04:00:00 2014-11-01 04:15:00           24
#> 2: 2015-08-01 04:03:00 2015-08-01 04:13:00            2
#> 3: 2015-08-01 14:00:00 2015-08-01 14:15:00            2

答案 3 :(得分：1)

就第2点而言，您可以使用聚合作为实例

aggData <- aggregate(traffic$Frequency~format(traffic$DateTime, "%Y%m%d h:m"), data=traffic, sum)

以分钟为间隔对所有频率求和。

对于第1点而言，并非合并工作？ 

merge(x = myData, y = aggData, by = "DateTime", all.x = TRUE)

外部合并解释为here

答案 4 :(得分：1)

使用for.loop我们可以这样做：

for(i in 1:nrow(mydata)) {
  mydata$SumFrequency[i] <- sum(traffic$Frequency[traffic$DateTime %within% mydata$Interval[i]])
}

> mydata
#            DateTime1           DateTime2                                           Interval SumFrequency
#1 2014-11-01 04:00:00 2014-11-01 04:15:00   2014-11-01 04:00:00 CET--2014-11-01 04:15:00 CET           24
#2 2015-08-01 04:03:00 2015-08-01 04:13:00 2015-08-01 04:03:00 CEST--2015-08-01 04:13:00 CEST            2
#3 2015-08-01 14:00:00 2015-08-01 14:15:00 2015-08-01 14:00:00 CEST--2015-08-01 14:15:00 CEST            2
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