R组变量

Question

我有一个数据帧，如下

tmpdf <- data.frame(licensePlate = c("Y80901", "Y80901", "Y80901", "AMG-999", "AMG-999", "W3188", "W3188"), 
starttime= c("2015-09-18 09:55", "2015-09-18 23:00", "2015-09-20 15:00", "2015-09-17 15:42", "2015-09-21 09:22", "2015-09-17 09:00", "2015-09-21 14:00"), 
endtime = c("2015-09-18 17:55", "2015-09-20 11:00", "2015-09-21 12:00", "2015-09-18 13:00", "2015-09-21 14:22", "2015-09-21 12:00", "2015-09-21 16:00")) 
    tmpdf 
     licensePlate  starttime   endtime 
    1  Y80901 2015-09-18 09:55 2015-09-18 17:55 
    2  Y80901 2015-09-18 23:00 2015-09-20 11:00 
    3  Y80901 2015-09-20 15:00 2015-09-21 12:00 
    4  AMG-999 2015-09-17 15:42 2015-09-18 13:00 
    5  AMG-999 2015-09-21 09:22 2015-09-21 14:22 
    6  W3188 2015-09-17 09:00 2015-09-21 12:00 
    7  W3188 2015-09-21 14:00 2015-09-21 16:00 

我要计数的最后n天（例如，最后5天从9/17至9/21）在每各licensePlate的一天使用的时间，我的预期结果如下：

Period   LicensePlate  Used Time 

1 2015-09-17   Y80901    0 
2 2015-09-17   AMG-999    8.3  
3 2015-09-17   W3188    15 
4 2015-09-18   Y80901    9 
5 2015-09-18   AMG-999    13 
6 2015-09-18   W3188    24 
7 2015-09-19   Y80901    24 
8 2015-09-19   AMG-999    0 
9 2015-09-19   W3188    24 
10 2015-09-20  Y80901    20 
11 2015-09-20  AMG-999    0 
12 2015-09-20  W3188    24 
13 2015-09-21  Y80901    12 
14 2015-09-21  AMG-999    5 
15 2015-09-21  W3188    14 

我觉得dplyr/data.table和lubridate可以用来获得我的结果， 我可能需要测量以天为单位的时间段，但我不知道如何在起点/范围内减少 开始/结束的时间间隔每行不同。

Answer 1

这里的东西，让你开始。这是差不多您想要的输出，因为它不会显示每个期间缺少licensePlate。

的第一步将是由licensePlate和Period您的日期数据转换为有效的POSIXct类，然后扩展到每分钟的水平（可能在这个解决方案中最昂贵的部分）和聚合，同时总结结果（我在这里没有使用as.Date，因为它处理的值不好，其值为00和1之间的值为POSIX）。

library(data.table) 
setDT(tmpdf)[, `:=`(starttime = as.POSIXct(starttime), endtime = as.POSIXct(endtime))] 
res <- tmpdf[, .(licensePlate, Period = seq(starttime, endtime, by = "1 min")), by = 1:nrow(tmpdf)] 
res[, .(Used_Time = round(.N/60L, 1L)), keyby = .(Period = substr(Period, 1L, 10L), licensePlate)] 
#   Period licensePlate Used_Time 
# 1: 2015-09-17  AMG-999  8.3 
# 2: 2015-09-17  W3188  15.0 
# 3: 2015-09-18  AMG-999  13.0 
# 4: 2015-09-18  W3188  24.0 
# 5: 2015-09-18  Y80901  9.0 
# 6: 2015-09-19  W3188  24.0 
# 7: 2015-09-19  Y80901  24.0 
# 8: 2015-09-20  W3188  24.0 
# 9: 2015-09-20  Y80901  20.0 
# 10: 2015-09-21  AMG-999  5.0 
# 11: 2015-09-21  W3188  14.0 
# 12: 2015-09-21  Y80901  12.0 

Answer 2

你是正确的，plyr可以用来解决这个问题。一个可能的解决方案：

tmpdf$starttime <- as.POSIXct(tmpdf$starttime) #convert date/time columns to date/time values in R 
tmpdf$endtime <- as.POSIXct(tmpdf$endtime) #convert date/time columns to date/time values in R 
newdf <- ddply(tmpdf,.(as.Date(starttime),licensePlate),function(df){ 
df$diffdays <- as.double(difftime(df$endtime,df$starttime,units='days')) 
df 
}) 
#If you want to only have the Period, LicensePlate, and Used Time columns remaining: 
newdf <- subset(newdf,select=c(1,2,5)) 
colnames(newdf) <- c('Period','LicensePlate','UsedTime') 

希望它有帮助！

Answer 3

尝试这个 - 它帮助

`tmpdf <- data.frame(licensePlate = c("Y80901", "Y80901", "Y80901", "AMG-999", "AMG-999", "W3188", "W3188"), 
        starttime= c("2015-09-18 09:55", "2015-09-18 23:00", "2015-09-20 15:00", "2015-09-17 15:42", "2015-09-21 09:22", "2015-09-17 09:00", "2015-09-21 14:00"), 
        endtime = c("2015-09-18 17:55", "2015-09-20 11:00", "2015-09-21 12:00", "2015-09-18 13:00", "2015-09-21 14:22", "2015-09-21 12:00", "2015-09-21 16:00")) 

tmpdf 
str(tmpdf) 
library(lubridate) 
tmpdf$starttime=ymd_hm(paste(tmpdf$starttime)) 
tmpdf$endtime=ymd_hm(paste(tmpdf$endtime)) 
tmpdf$Period=day(tmpdf$starttime) 
tmpdf$diff=difftime(tmpdf$endtime,tmpdf$starttime) 
tmpdf` 

Answer 4

深吸一口气。这里是我的解决方案

初始化数据

tmpdf <- data.frame(licensePlate = c("Y80901", "Y80901", "Y80901", "AMG-999", "AMG-999", "W3188", "W3188"), 
       starttime= c("2015-09-18 09:55", "2015-09-18 23:00", "2015-09-20 15:00", "2015-09-17 15:42", "2015-09-21 09:22", "2015-09-17 09:00", "2015-09-21 14:00"), 
       endtime = c("2015-09-18 17:55", "2015-09-20 11:00", "2015-09-21 12:00", "2015-09-18 13:00", "2015-09-21 14:22", "2015-09-21 12:00", "2015-09-21 16:00")) 

'converting to POSIXct for better date/time handling' 
    tmpdf$starttime <- as.POSIXct(tmpdf$starttime, tz = "GMT") 
    tmpdf$endtime <- as.POSIXct(tmpdf$endtime, tz = "GMT") 

数据准备

要执行所需的操作您的完整使用数据必须被转换到日常使用的数据。所以我写了下面的函数来将数据准备成需要的格式。

#splits single usage data into two 
splitToTwo <- function(list){ 
newList <- NULL 

for (i in 1:nrow(list)){ 

tmp <- list[i,] 

# set the end time of the first split as 23:59:59 
list[i,]$endtime <- as.Date(list[i,]$starttime) + hours(23) + minutes(59) + seconds(59) 

# set the start time of the second split as 00:00:01 
tmp$starttime <- list[i,]$endtime + seconds(2) 

# add the new df to the list 
tmp <- rbind(tmp,list[i,]) 
newList <- rbind(newList,tmp) 
} 
return(newList) 
} 


#recursive function. Split the usage data into two till there are completely normalised to daily usage data 
setDailyUsage <- function(tmpdf){ 

    # create a exclusive subset where the usage spawns more than a day 
    list <- tmpdf[as.Date(tmpdf$endtime) - as.Date(tmpdf$starttime) > 0, ] 

    # replace tmpdf with usage that started and ended the same day 
    tmpdf <- tmpdf[ as.Date(tmpdf$endtime) - as.Date(tmpdf$starttime) == 0,] 

    # call to our split function to split the dataset with usage spawning more than one day 
    split <- splitToTwo(list) 

    # add the now split data to our exclusive 
    tmpdf <- rbind(tmpdf,split) 

    if (nrow(tmpdf[as.Date(tmpdf$endtime) - as.Date(tmpdf$starttime) > 0, ])>0){ 
     tmpdf <- setDailyUsage(tmpdf) 
    } 

return(tmpdf) 

} 

准备好的数据

我们准备的数据

preparedData <- setDailyUsage(tmpdf) 
    licensePlate   starttime    endtime 
1   Y80901 2015-09-18 09:55:00 2015-09-18 17:55:00 
5  AMG-999 2015-09-21 09:22:00 2015-09-21 14:22:00 
7   W3188 2015-09-21 14:00:00 2015-09-21 16:00:00 
21  Y80901 2015-09-18 23:00:00 2015-09-18 23:59:59 
3   Y80901 2015-09-21 00:00:01 2015-09-21 12:00:00 
31  Y80901 2015-09-20 15:00:00 2015-09-20 23:59:59 
4  AMG-999 2015-09-18 00:00:01 2015-09-18 13:00:00 
41  AMG-999 2015-09-17 15:42:00 2015-09-17 23:59:59 
61   W3188 2015-09-17 09:00:00 2015-09-17 23:59:59 
2   Y80901 2015-09-20 00:00:01 2015-09-20 11:00:00 
211  Y80901 2015-09-19 00:00:01 2015-09-19 23:59:59 
611  W3188 2015-09-18 00:00:01 2015-09-18 23:59:59 
612  W3188 2015-09-19 00:00:01 2015-09-19 23:59:59 
6   W3188 2015-09-21 00:00:01 2015-09-21 12:00:00 
613  W3188 2015-09-20 00:00:01 2015-09-20 23:59:59 

数据操作

现在我们创建一个新的DF表示按规定格式的数据。这将在UsedTime列中初始为空值。

preparedData$duration <- preparedData$endtime - preparedData$starttime 
noOfUniquePlates <- length(unique(preparedData$licensePlate)) 
Period <- rep(seq(from=(min(as.Date(preparedData$starttime))),to=(max(as.Date(preparedData$starttime))), by="day"),noOfUniquePlates) 
noOfUniqueDays <- length(unique(Period)) 
LicensePlate <- rep(unique(preparedData$licensePlate),each=noOfUniqueDays) 
UsedTime <- 0 

newDF <- data.frame(Period,LicensePlate,UsedTime) 

现在在newDF的每一行简单mapply功能，寻找在preparedData DF正确的使用数据。

findUsage <- function(p,l){ 
    sum(preparedData[as.Date(preparedData$starttime) == p & as.Date(preparedData$endtime) == p & preparedData$licensePlate == l , ]$duration) 
} 
newDF$UsedTime <- mapply(findUsage, newDF$Period, newDF$LicensePlate) 
newDF$UsedTime <- newDF$UsedTime/60 

    > newDF[with(newDF,order(Period)),] 
     Period LicensePlate UsedTime 
1 2015-09-17  Y80901 0.000000 
6 2015-09-17  AMG-999 8.299722 
11 2015-09-17  W3188 14.999722 
2 2015-09-18  Y80901 8.999722 
7 2015-09-18  AMG-999 12.999722 
12 2015-09-18  W3188 23.999444 
3 2015-09-19  Y80901 23.999444 
8 2015-09-19  AMG-999 0.000000 
13 2015-09-19  W3188 23.999444 
4 2015-09-20  Y80901 19.999444 
9 2015-09-20  AMG-999 0.000000 
14 2015-09-20  W3188 23.999444 
5 2015-09-21  Y80901 11.999722 
10 2015-09-21  AMG-999 5.000000 
15 2015-09-21  W3188 13.999722 

我不得不限制解释以保持答案的简短。如果您在评论中需要澄清，请告诉我。