如何根据一天/月/年的时间间隔添加列的值？

Question

我能够使用以下方法每天（大约上午 12:00 - 上午 12:00）添加降水数据：

 pptnew<-data        
 pptnew<-aggregate(.~timestampDMY,data=pptnew, FUN = sum)
 #timestampDMY is a factor with Date Month and Year Values (made a factor      
 from POSIXlt using format argument to get rid of minutes/seconds) because 
 aggregate probably doesnot work on POSIXlt class.

但是，现在我想根据时间间隔添加一列的值（即以毫米/30 分钟为单位的降水量） - 比如说一天的上午 9:00 到第二天的上午 9:00。

以下是一个虚拟数据集（ 下载）：

  Date_Time      Rainfall_mm
17-05-2013 19:00:00 0.8
17-05-2013 19:30:00 0.5
17/05/2013 20:00:00 0.3
17/05/2013 20:30:00 0
17/05/2013 21:00:00 0
17/05/2013 21:30:00 0
17/05/2013 22:00:00 0
17/05/2013 22:30:00 0
17/05/2013 23:00:00 0
17/05/2013 23:30:00 0
17/05/2013 00:00:00 1.3
18/05/2013 00:30:00 1.5
18/05/2013 01:00:00 1.7
18/05/2013 01:30:00 1.9
18/05/2013 02:00:00 2.1
18/05/2013 02:30:00 2.3
18/05/2013 03:00:00 2.5
18/05/2013 03:30:00 2.7
18/05/2013 04:00:00 2.9
18/05/2013 04:30:00 3.1
18/05/2013 05:00:00 3.3
18/05/2013 05:30:00 0
18/05/2013 06:00:00 0
18/05/2013 06:30:00 0
18/05/2013 07:00:00 0
18/05/2013 07:30:00 0
18/05/2013 08:00:00 0
18/05/2013 08:30:00 0
18/05/2013 09:00:00 0
18/05/2013 09:30:00 0
18/05/2013 10:00:00 0.6
18/05/2013 10:30:00 0.8
18/05/2013 11:00:00 0.5
18/05/2013 11:30:00 0
18/05/2013 12:00:00 0
18/05/2013 12:30:00 0
18/05/2013 13:00:00 0
18/05/2013 13:30:00 0
18/05/2013 14:00:00 0
18/05/2013 14:30:00 0
18/05/2013 15:00:00 0
18/05/2013 15:30:00 0
18/05/2013 16:00:00 0
18/05/2013 16:30:00 0
18/05/2013 17:00:00 0
18/05/2013 17:30:00 0
18/05/2013 18:00:00 0
18/05/2013 18:30:00 0
18/05/2013 19:00:00 0
18/05/2013 19:30:00 0
18/05/2013 20:00:00 0.3
18/05/2013 20:30:00 0.3
18/05/2013 21:00:00 0.7
18/05/2013 21:30:00 0.9
18/05/2013 22:00:00 1
18/05/2013 22:30:00 1
18/05/2013 23:00:00 
18/05/2013 23:30:00 
18/05/2013 00:00:00 
19/05/2013 00:30:00 
19/05/2013 01:00:00 
19/05/2013 01:30:00 
19/05/2013 02:00:00 
19/05/2013 02:30:00 
19/05/2013 03:00:00 
19/05/2013 03:30:00 
19/05/2013 04:00:00 0
19/05/2013 04:30:00 0
19/05/2013 05:00:00 0
19/05/2013 05:30:00 0.2
19/05/2013 06:00:00 0.2
19/05/2013 06:30:00 0.4
19/05/2013 07:00:00 0.7
19/05/2013 07:30:00 0.1
19/05/2013 08:00:00 0
19/05/2013 08:30:00 0
19/05/2013 09:00:00 0
19/05/2013 09:30:00 0
19/05/2013 10:00:00 0
19/05/2013 10:30:00 0
19/05/2013 11:00:00 0
19/05/2013 11:30:00 0
19/05/2013 12:00:00 0
19/05/2013 12:30:00 0
19/05/2013 13:00:00 0
19/05/2013 13:30:00 0
19/05/2013 14:00:00 0
19/05/2013 14:30:00 0
19/05/2013 15:00:00 0
19/05/2013 15:30:00 0.8
19/05/2013 16:00:00 0.5
19/05/2013 16:30:00 0.3
19/05/2013 17:00:00 0
19/05/2013 17:30:00 0
19/05/2013 18:00:00 0
19/05/2013 18:30:00 0
19/05/2013 19:00:00 0
19/05/2013 19:30:00 0
19/05/2013 20:00:00 0
19/05/2013 20:30:00 1.3
19/05/2013 21:00:00 1.5
19/05/2013 21:30:00 1.7
19/05/2013 22:00:00 1.9
19/05/2013 22:30:00 2.1
19/05/2013 23:00:00 2.3
19/05/2013 23:30:00 2.5
19/05/2013 00:00:00 2.7
20/05/2013 00:30:00 2.9
20/05/2013 01:00:00 3.1
20/05/2013 01:30:00 3.3
20/05/2013 02:00:00 0
20/05/2013 02:30:00 0
20/05/2013 03:00:00 0
20/05/2013 03:30:00 0
20/05/2013 04:00:00 0
20/05/2013 04:30:00 0
20/05/2013 05:00:00 0
20/05/2013 05:30:00 0
20/05/2013 06:00:00 0
20/05/2013 06:30:00 0.6
20/05/2013 07:00:00 0.8
20/05/2013 07:30:00 0.5
20/05/2013 08:00:00 0
20/05/2013 08:30:00 0
20/05/2013 09:00:00 0
20/05/2013 09:30:00 0
20/05/2013 10:00:00 0
20/05/2013 10:30:00 0
20/05/2013 11:00:00 0
20/05/2013 11:30:00 0
20/05/2013 12:00:00 0
20/05/2013 12:30:00 0
20/05/2013 13:00:00 0
20/05/2013 13:30:00 0
20/05/2013 14:00:00 0
20/05/2013 14:30:00 0
20/05/2013 15:00:00 0
20/05/2013 15:30:00 0
20/05/2013 16:00:00 0
20/05/2013 16:30:00 0.3
20/05/2013 17:00:00 0.3
20/05/2013 17:30:00 0.7
20/05/2013 18:00:00 0.9
20/05/2013 18:30:00 1
20/05/2013 19:00:00 1
20/05/2013 19:30:00 
20/05/2013 20:00:00 
20/05/2013 20:30:00 
20/05/2013 21:00:00 
20/05/2013 21:30:00 
20/05/2013 22:00:00 
20/05/2013 22:30:00 
20/05/2013 23:00:00 
20/05/2013 23:30:00 
20/05/2013 00:00:00 
21/05/2013 00:30:00 0
21/05/2013 01:00:00 0
21/05/2013 01:30:00 0
21/05/2013 02:00:00 0.2
21/05/2013 02:30:00 0.2
21/05/2013 03:00:00 0.4
21/05/2013 03:30:00 0.7
21/05/2013 04:00:00 0.1
21/05/2013 04:30:00 0
21/05/2013 05:00:00 0
21/05/2013 05:30:00 0
21/05/2013 06:00:00 0
21/05/2013 06:30:00 0
21/05/2013 07:00:00 0
21/05/2013 07:30:00 0
21/05/2013 08:00:00 0
21/05/2013 08:30:00 0
21/05/2013 09:00:00 0
21/05/2013 09:30:00 0
21/05/2013 10:00:00 0
21/05/2013 10:30:00 0
21/05/2013 11:00:00 0
21/05/2013 11:30:00 0
21/05/2013 12:00:00 0.8
21/05/2013 12:30:00 0.5
21/05/2013 13:00:00 0.3
21/05/2013 13:30:00 0
21/05/2013 14:00:00 0
21/05/2013 14:30:00 0
21/05/2013 15:00:00 0
21/05/2013 15:30:00 0
21/05/2013 16:00:00 0
21/05/2013 16:30:00 0
21/05/2013 17:00:00 1.3
21/05/2013 17:30:00 1.5
21/05/2013 18:00:00 1.7
21/05/2013 18:30:00 1.9
21/05/2013 19:00:00 2.1
21/05/2013 19:30:00 2.3
21/05/2013 20:00:00 2.5
21/05/2013 20:30:00 2.7
21/05/2013 21:00:00 2.9
21/05/2013 21:30:00 3.1
21/05/2013 22:00:00 3.3
21/05/2013 22:30:00 0
21/05/2013 23:00:00 0
21/05/2013 23:30:00 0
21/05/2013 00:00:00 0
22/05/2013 00:30:00 0
22/05/2013 01:00:00 0
22/05/2013 01:30:00 0
22/05/2013 02:00:00 0

先感谢您。 （请注意，一天中可能缺少值）。

问候 Sate

Answer 1

您是否知道数据集中有多个缺失的数据点？

library(dplyr)
library(tidyr)
df <- separate(df, Date_Time, c("dmyVar", "hmsVar"), sep = " ")

# see unique values available
unique(df$hmsVar)

# aggregate by timestamp, as you requested
df %>%
  group_by(hmsVar) %>%
  summarise(sumByHour = sum(Rainfall_meas_mm, na.rm = TRUE))
Source: local data frame [48 x 2]

     hmsVar sumByHour
      (chr)     (dbl)
1  00:00:00       4.0
2  00:30:00       4.4
3  01:00:00       4.8
4  01:30:00       5.2
5  02:00:00       2.3
6  02:30:00       2.5
7  03:00:00       2.9
8  03:30:00       3.4
9  04:00:00       3.0
10 04:30:00       3.1
..      ...       ...