时间戳

首页 > 解决方案 > 计算进入和退出之间的持续时间 - 代码优化

r - 计算进入和退出之间的持续时间 - 代码优化

问题描述

我有一些数据：

我正在尝试根据入口、出口、建筑物 ID 和唯一标识符（车号）计算进入建筑物的时间。

现在，我按唯一 ID 对数据框进行排序，然后按日期排序，然后应用此算法：

    For each row {
        if row.type = 'entry' and nextRow.type = 'exit' and row.uid = nextRow.uid {
        Calculate time difference and add this data to another df.
        }
    }

尽管我只有 6000 行，但它需要一些时间来运行......我对 R 不是很熟悉，我认为有一些方法可以加速这个......

代码如下：

# Sort rows: 
BldActivity <- BldActivity[order(BldActivity$UniqueId, BldActivity$DateOfEvent),]


df = data.frame(NULL)
DurationOfStay <- data.frame(NULL) 

for(i in 1:nrow(BldActivity)) {
    row <- BldActivity[i,]
    # do stuff with row
    if(row$Type == 'entry') {
        rowNext <- BldActivity[i+1,] 
        if(!is.na(rowNext$Type)) {
            if(rowNext$Type == 'exit' && row$UniqueId == rowNext$UniqueId)
            {
                        newRow <- data.frame( Entry_DateOfEvent = row$DateOfEvent,
                                        Exit_DateOfEvent  = rowNext$DateOfEvent,

                                        BuildingID = row$BuildingID,
                                        BuildingName = row$`Building Name`,
                                        UniqueId = row$UniqueId,
                                        DurationOfStay = difftime(rowNext$DateOfEvent, row$DateOfEvent, units="mins")
                                        )

                    DurationOfStay <- rbind(DurationOfStay,newRow)
            }
        }
    }
}

您能否指出可能的改进？

这是一个输入示例：

DateOfEvent Type    UniqueId    BuildingID  Building Name
2019/03/22 09:15:43 entry   04352e5b6051c311048a5803f8716700    1e98f5c0e699    Building 2
2019/03/22 09:51:45 exit    04352e5b6051c311048a5803f8716700    1e98f5c0e699    Building 2
2019/03/22 10:31:28 entry   066b9a3995acd495318ad70e0d876f00    062e933d6b9f    Building 1
2019/03/22 11:15:02 exit    066b9a3995acd495318ad70e0d876f00    062e933d6b9f    Building 1
2019/03/22 11:11:42 entry   0e027aba359aaecbe8fe3eaf5a1bbb00    062e933d6b9f    Building 1
2019/03/22 14:44:27 exit    0e027aba359aaecbe8fe3eaf5a1bbb00    062e933d6b9f    Building 1
2019/03/22 09:55:03 entry   1747dbaef11176b9ab90f2cfbf056210    1e98f5c0e699    Building 2
2019/03/22 18:13:08 exit    1747dbaef11176b9ab90f2cfbf056210    1e98f5c0e699    Building 2
2019/03/21 14:23:53 entry   3e0d2c4b1b159a24f4dc5fa084b59f00    1e98f5c0e699    Building 2
2019/03/21 15:36:31 exit    3e0d2c4b1b159a24f4dc5fa084b59f00    1e98f5c0e699    Building 2

输出只是输入/输出的列值和计算的持续时间。

谢谢

菲利普

标签： r

解决方案

library(data.table)
setDT(BldActivity)
DurationOfStay <- dcast(BldActivity, UniqueId + BuildingID + `Building Name` ~ Type, value.var = "DateOfEvent")
DurationOfStay[, DurationOfStay := difftime(exit, entry, units="mins"), ]

推荐阅读