将15分钟的时间序列数据汇总到每日

Question

这是我的文本文件中的数据:(我已经显示了10,000行中的10行)索引是rownames,temp是时间序列,m是以mm为单位的值.

     "Index" "temp" "m"
   1 "2012-02-07 18:15:13" "4297"
   2 "2012-02-07 18:30:04" "4296"
   3 "2012-02-07 18:45:10" "4297"
   4 "2012-02-07 19:00:01" "4297"
   5 "2012-02-07 19:15:07" "4298"
   6 "2012-02-07 19:30:13" "4299"
   7 "2012-02-07 19:45:04" "4299"
   8 "2012-02-07 20:00:10" "4299"
   9 "2012-02-07 20:15:01" "4300"
   10 "2012-02-07 20:30:07" "4301"

我使用这个导入r:

    x2=read.table("data.txt", header=TRUE)

我尝试使用以下代码将时间序列聚合到每日数据:

   c=aggregate(ts(x2[, 2], freq = 96), 1, mean)

我已将频率设置为96,因为15分钟的数据24小时将覆盖96个值.

它告诉我这个:

    Time Series:
   Start = 1 
   End = 5 
   Frequency = 1 
   [1] 5366.698 5325.115 5311.969 5288.542 5331.115

但我想要与原始数据相同的格式,即我也想要值旁边的时间序列.我需要帮助才能实现这一目标.

Answer 1

使用apply.daily从xts封装的数据转换为之后xts的对象:

这样的事情应该有效:

x2 = read.table(header=TRUE, text='     "Index" "temp" "m"
1 "2012-02-07 18:15:13" "4297"
2 "2012-02-07 18:30:04" "4296"
3 "2012-02-07 18:45:10" "4297"
4 "2012-02-07 19:00:01" "4297"
5 "2012-02-07 19:15:07" "4298"
6 "2012-02-07 19:30:13" "4299"
7 "2012-02-07 19:45:04" "4299"
8 "2012-02-07 20:00:10" "4299"
9 "2012-02-07 20:15:01" "4300"
10 "2012-02-07 20:30:07" "4301"')

x2$temp = as.POSIXct(strptime(x2$temp, "%Y-%m-%d %H:%M:%S"))
require(xts)
x2 = xts(x = x2$m, order.by = x2$temp)
apply.daily(x2, mean)
##                       [,1]
## 2012-02-07 20:30:07 4298.3

更新:您的问题以可重复的格式(与假数据)

我们并不总是需要实际数据集来帮助排除故障....

set.seed(1) # So you can get the same numbers as I do
x = data.frame(datetime = seq(ISOdatetime(1970, 1, 1, 0, 0, 0), 
                              length = 384, by = 900), 
               m = sample(2000:4000, 384, replace = TRUE))
head(x)
#              datetime    m
# 1 1970-01-01 00:00:00 2531
# 2 1970-01-01 00:15:00 2744
# 3 1970-01-01 00:30:00 3146
# 4 1970-01-01 00:45:00 3817
# 5 1970-01-01 01:00:00 2403
# 6 1970-01-01 01:15:00 3797
require(xts)
x2 = xts(x$m, x$datetime)
head(x2)
#                     [,1]
# 1970-01-01 00:00:00 2531
# 1970-01-01 00:15:00 2744
# 1970-01-01 00:30:00 3146
# 1970-01-01 00:45:00 3817
# 1970-01-01 01:00:00 2403
# 1970-01-01 01:15:00 3797
apply.daily(x2, mean)
#                         [,1]
# 1970-01-01 23:45:00 3031.302
# 1970-01-02 23:45:00 3043.250
# 1970-01-03 23:45:00 2896.771
# 1970-01-04 23:45:00 2996.479

更新2:解决方案替代方案

(使用我在上面的更新中提供的假数据.)

data.frame(time = x[seq(96, nrow(x), by=96), 1],
           mean = aggregate(ts(x[, 2], freq = 96), 1, mean))
#               time     mean
# 1 1970-01-01 23:45 3031.302
# 2 1970-01-02 23:45 3043.250
# 3 1970-01-03 23:45 2896.771
# 4 1970-01-04 23:45 2996.479