Question

我有从不同的采样制度收集的数据，从每三秒钟到每小时一次。

我希望以最接近10分钟的常规间隔（也是在其他时间间隔获得此数据的子样本，但我认为一旦我有正确的代码就可以很容易地更改间隔。）

我在这里看过类似的帖子（例如：How to subsample a data frame based on a datetime column in R），但没有答案可以允许缓冲区或最接近＆＃39;想法。

问题是：

不会定期对数据进行采样。所以我不能要求R每隔十分钟给我一次所有数据，但是我需要每隔十分钟就有一次
我还想缓冲这样，以便它不会给我不到9分钟的时间间隔。
目前，我的date_time列正在被视为一个因素＆＃39;：

类（样品$ DATE_TIME） [1]＆＃34;因素＆＃34;

示例数据：

device_no date_time纬度经度温度 1 23/04/2012 15:57:22 -33.2415715 19.4810864 27.7 1 23/04/2012 15:58:08 -33.2415396 19.4810666 27.7 1 23/04/2012 15:58:58 -33.2415963 19.48109 27.2 1 23/04/2012 15:59:46 -33.2415137 19.4810624 27.2 1 23/04/2012 16:00:33 -33.2415019 19.4810885 27 1 23/04/2012 16:01:21 -33.241561 19.4810867 26.3 1 23/04/2012 16:02:10 -33.2415579 19.4810926 26.4 1 23/04/2012 16:02:57 -33.2414687 19.4810465 25.6 1 23/04/2012 16:03:45 -33.2415096 19.4810736 24.6 1 23/04/2012 16:05:20 -33.2415707 19.4810614 24.8 1 23/04/2012 16:06:22 -33.2415188 19.4810708 24.6 1 23/04/2012 16:07:12 -33.2415754 19.4810538 24.6 1 23/04/2012 16:08:00 -33.2415054 19.4810874 24.2 1 23/04/2012 16:08:48 -33.2414794 19.4810908 24.3 1 23/04/2012 16:09:36 -33.2415538 19.4810802 24 1 23/04/2012 16:10:25 -33.2413946 19.4811353 23.4 1 23/04/2012 16:11:14 -33.2414529 19.4811084 23.7 1 23/04/2012 16:12:01 -33.2413949 19.4810978 23.5 1 24/04/2012 05:26:39 -33.2415605 19.4810589 23.1 1 24/04/2012 05:56:51 -33.2414826 19.4811049 23 1 24/04/2012 06:01:22 -33.2415975 19.4810535 23 1 24/04/2012 06:02:09 -33.2384224 19.482825 21.7 1 24/04/2012 06:02:58 -33.2380158 19.4833283 20.6 1 24/04/2012 06:03:45 -33.241538 19.4810951 19.9 1 24/04/2012 06:04:34 -33.2416337 19.4810291 19.3 1 24/04/2012 06:05:22 -33.2410841 19.4819002 19.9 1 24/04/2012 06:06:11 -33.2401989 19.4817907 19.9 1 24/04/2012 06:06:57 -33.241593 19.4810426 19.4 1 24/04/2012 06:07:46 -33.241575 19.4810011 18.6 1 24/04/2012 06:08:34 -33.2415497 19.4810493 18.8 1 24/04/2012 06:09:22 -33.2415104 19.4810216 18.4 1 24/04/2012 06:10:11 -33.2416627 19.4810065 18 1 24/04/2012 06:10:59 -33.2414893 19.4811548 18.2 1 24/04/2012 06:11:44 -33.2420604 19.4810295 18.7 1 24/04/2012 06:12:33 -33.2408584 19.4803685 35.5 1 24/04/2012 06:13:20 -33.2407331 19.4805824 38.3 1 24/04/2012 06:25:58 -33.2411718 19.4810405 39.9 1 24/04/2012 06:26:49 -33.2415396 19.4810794 41.4 1 24/04/2012 06:27:56 -33.2415588 19.481089 40 1 24/04/2012 06:28:54 -33.2415257 19.4810381 41 1 24/04/2012 06:29:42 -33.239857 19.4807259 37.6 1 24/04/2012 06:30:29 -33.2409401 19.480927 36.4

样本结果：

device_no date_time纬度经度温度 1 23/04/2012 15:57:22 -33.2415715 19.4810864 27.7 1 23/04/2012 16:07:12 -33.2415754 19.4810538 24.6 1 24/04/2012 05:26:39 -33.2415605 19.4810589 23.1 1 24/04/2012 05:56:51 -33.2414826 19.4811049 23 1 24/04/2012 06:06:57 -33.241593 19.4810426 19.4 1 24/04/2012 06:25:58 -33.2411718 19.4810405 39.9`

抱歉，我的屏幕截图不会发布，这些数据不会显示为表格...

Answer 1

# Create some data

# start at Jan 1, 2014
start=as.POSIXct("1-Jan-2014 00:00",format="%d-%b-%Y %H:%M")

# end at Jan 31, 2014
end=as.POSIXct("31-Jan-2014 23:00",format="%d-%b-%Y %H:%M")

# sequence the time from start to end and create a continuous time 
# set with observations every 5 minutes with a sd of 1 minute
time=seq.POSIXt(start,end,by=rnorm(1,1*60*5,60))

# create a loop

# you want the buffer for time > 9 minutes
time.diff=9
new.time=time[1]
# seq 10 minute intervals (this is the data we are going to try and match)
time.10min=seq.POSIXt(start,end,by=1*60*10)

# skip the first observation
for(i in 2:length(time.10min)){
  # Look for where the minimum difference occurs in your original dataset
  obs=time[which(min(abs(time.10min[i]-time))==abs(time.10min[i]-time))]

  # see if it meets you buffer requirements. If it doesn't meet your buffer requirements
  # it will take the next observation in your dataset
  if(obs-new.time[i-1]<time.diff){
    obs=time[which(min(abs(time.10min[i]-time))==abs(time.10min[i]-time))+1]
    new.time[length(new.time)+1]=obs
  } else {
    new.time[length(new.time)+1]=obs
  }
}

head(new.time)


# Good luck and hopefully this helps.

使用缓冲区将不规则数据子采样到最近的时间间隔/子集

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