按每日的特定时间切割 POSIXct

Question

我有兴趣计算时间序列数据集中特定时间段的平均值。

给定这样的时间序列：

dtm=as.POSIXct("2007-03-27 05:00", tz="GMT")+3600*(1:240)
Count<-c(1:240)
DF<-data.frame(dtm,Count)

在过去，我已经能够用

计算每日平均值

DF$Day<-cut(DF$dtm,breaks="day")
Day_Avg<-aggregate(DF$Count~Day,DF,mean)

但现在我正试图将一天分成特定的时间段，但我不确定如何设置我的“休息时间”。

相对于 0:00:24:00 的每日平均值，例如，我如何才能获得中午到中午的平均值？

或者更奇怪的是，我如何设置中午到中午的平均值，不包括晚上 7 点到早上 6 点的夜间时间（或者相反，只包括早上 6 点到晚上 7 点的白天时间）。

Answer 1

让我快速重复你的代码。

dtm <- as.POSIXct("2007-03-27 05:00", tz="GMT")+3600*(1:240)
Count <- c(1:240)
DF<-data.frame(dtm,Count)

DF$Day<-cut(DF$dtm,breaks="day")
Day_Avg<-aggregate(DF$Count~Day,DF,mean)

如果您在函数调用中每次偏移 12 小时，您仍然可以使用 cut 并在 "day" 上休息。我会保存中午到中午开始的那一天，所以我会减去 12 小时。

# Get twelve hours in seconds
timeOffset <- 60*60*12
# Subtract the offset to get the start day of the noon to noon
DF$Noon_Start_Day <- cut((DF$dtm - timeOffset), breaks="day")
# Get the mean
NtN_Avg <- aggregate(DF$Count ~ Noon_Start_Day, DF, mean)

排除某些时间的一种方法是将日期转换为POSIXlt。然后您可以访问hour 等等。

# Indicate which times are good (use whatever boolean test is needed here)
goodTimes <- !(as.POSIXlt(DF$dtm)$hour >= 19) & !(as.POSIXlt(DF$dtm)$hour <= 6)
new_NtN_Avg <- aggregate(Count ~ Noon_Start_Day, data=subset(DF, goodTimes), mean)

我在 stackoverflow 上的这个问题上找到了一些帮助：r-calculate-means-for-subset-of-a-group

Answer 2

中午到中午的问题可以很容易地用数值解决。关键是（GMT）一天的开始有一个time_t 值，它总是能被86400 整除。这是由POSIX 指定的。例如，参见：http://en.wikipedia.org/wiki/Unix_time

cuts <- unique(as.numeric(DF$dtm) %/% (86400/2)) * (86400/2)  # half-days
cuts <- c(cuts, cuts[length(cuts)]+(86400/2))                 # One more at the end
cuts <- as.POSIXct(cuts, tz="GMT", origin="1970-01-01")       # Familiar format
DF$halfday <-  cut(DF$dtm, cuts)                              # This is the cut you want.

Halfday_Avg <- aggregate(Count~halfday, data=DF, FUN=mean)

Halfday_Avg
##                halfday Count
## 1  2007-03-27 00:00:00   3.5
## 2  2007-03-27 12:00:00  12.5
## 3  2007-03-28 00:00:00  24.5
## 4  2007-03-28 12:00:00  36.5
## 5  2007-03-29 00:00:00  48.5
## 6  2007-03-29 12:00:00  60.5
## 7  2007-03-30 00:00:00  72.5
## 8  2007-03-30 12:00:00  84.5
## 9  2007-03-31 00:00:00  96.5
## 10 2007-03-31 12:00:00 108.5
## 11 2007-04-01 00:00:00 120.5
## 12 2007-04-01 12:00:00 132.5
## 13 2007-04-02 00:00:00 144.5
## 14 2007-04-02 12:00:00 156.5
## 15 2007-04-03 00:00:00 168.5
## 16 2007-04-03 12:00:00 180.5
## 17 2007-04-04 00:00:00 192.5
## 18 2007-04-04 12:00:00 204.5
## 19 2007-04-05 00:00:00 216.5
## 20 2007-04-05 12:00:00 228.5
## 21 2007-04-06 00:00:00 237.5

现在扩展它以解决其余问题。这里给出的是早上 6 点到晚上 7 点的时间范围。

intraday <- as.numeric(DF$dtm) %% 86400

# Subset DF by the chosen range
New_Avg <- aggregate(Count~halfday, data=DF[intraday >= 6*3600 & intraday <= 19*3600,], FUN=mean)

New_Avg
##                halfday Count
## 1  2007-03-27 00:00:00   3.5
## 2  2007-03-27 12:00:00  10.5
## 3  2007-03-28 00:00:00  27.5
## 4  2007-03-28 12:00:00  34.5
## 5  2007-03-29 00:00:00  51.5
## 6  2007-03-29 12:00:00  58.5
## 7  2007-03-30 00:00:00  75.5
## 8  2007-03-30 12:00:00  82.5
## 9  2007-03-31 00:00:00  99.5
## 10 2007-03-31 12:00:00 106.5
## 11 2007-04-01 00:00:00 123.5
## 12 2007-04-01 12:00:00 130.5
## 13 2007-04-02 00:00:00 147.5
## 14 2007-04-02 12:00:00 154.5
## 15 2007-04-03 00:00:00 171.5
## 16 2007-04-03 12:00:00 178.5
## 17 2007-04-04 00:00:00 195.5
## 18 2007-04-04 12:00:00 202.5
## 19 2007-04-05 00:00:00 219.5
## 20 2007-04-05 12:00:00 226.5

Answer 3

xts 是时间序列分析的完美包

library(xts)

originalTZ <- Sys.getenv("TZ")

Sys.setenv(TZ = "GMT")

data.xts <- as.xts(1:240, as.POSIXct("2007-03-27 05:00", tz = "GMT") + 3600 * (1:240))

head(data.xts)
##                     [,1]
## 2007-03-27 06:00:00    1
## 2007-03-27 07:00:00    2
## 2007-03-27 08:00:00    3
## 2007-03-27 09:00:00    4
## 2007-03-27 10:00:00    5
## 2007-03-27 11:00:00    6


# You can filter data using ISO-style subsetting
data.xts.filterd <- data.xts["T06:00/T19:00"]

# You can use builtin functions to apply any function FUN on daily data.
apply.daily(data.xts.filtered, mean)
##                      [,1]
## 2007-03-27 18:00:00   7.5
## 2007-03-28 18:00:00  31.5
## 2007-03-29 18:00:00  55.5
## 2007-03-30 18:00:00  79.5
## 2007-03-31 18:00:00 103.5
## 2007-04-01 18:00:00 127.5
## 2007-04-02 18:00:00 151.5
## 2007-04-03 18:00:00 175.5
## 2007-04-04 18:00:00 199.5
## 2007-04-05 18:00:00 223.5


# OR

# now let's say you want to find noon to noon average.

period.apply(data.xts, c(0, which(.indexhour(data.xts) == 11)), FUN = mean)
##                      [,1]
## 2007-03-27 11:00:00   3.5
## 2007-03-28 11:00:00  18.5
## 2007-03-29 11:00:00  42.5
## 2007-03-30 11:00:00  66.5
## 2007-03-31 11:00:00  90.5
## 2007-04-01 11:00:00 114.5
## 2007-04-02 11:00:00 138.5
## 2007-04-03 11:00:00 162.5
## 2007-04-04 11:00:00 186.5
## 2007-04-05 11:00:00 210.5


# now if you want to exclude time from 7 PM to 6 AM
data.xts.filtered <- data.xts[!data.xts %in% data.xts["T20:00/T05:00"]]

head(data.xts.filtered, 20)
##                     [,1]
## 2007-03-27 06:00:00    1
## 2007-03-27 07:00:00    2
## 2007-03-27 08:00:00    3
## 2007-03-27 09:00:00    4
## 2007-03-27 10:00:00    5
## 2007-03-27 11:00:00    6
## 2007-03-27 12:00:00    7
## 2007-03-27 13:00:00    8
## 2007-03-27 14:00:00    9
## 2007-03-27 15:00:00   10
## 2007-03-27 16:00:00   11
## 2007-03-27 17:00:00   12
## 2007-03-27 18:00:00   13
## 2007-03-27 19:00:00   14
## 2007-03-28 06:00:00   25
## 2007-03-28 07:00:00   26
## 2007-03-28 08:00:00   27
## 2007-03-28 09:00:00   28
## 2007-03-28 10:00:00   29
## 2007-03-28 11:00:00   30


period.apply(data.xts.filtered, c(0, which(.indexhour(data.xts.filtered) == 11)), FUN = mean)
##                          [,1]
## 2007-03-27 11:00:00   3.50000
## 2007-03-28 11:00:00  17.78571
## 2007-03-29 11:00:00  41.78571
## 2007-03-30 11:00:00  65.78571
## 2007-03-31 11:00:00  89.78571
## 2007-04-01 11:00:00 113.78571
## 2007-04-02 11:00:00 137.78571
## 2007-04-03 11:00:00 161.78571
## 2007-04-04 11:00:00 185.78571
## 2007-04-05 11:00:00 209.78571





Sys.setenv(TZ = originalTZ)