R:如何在重叠的时间段内求平均值 [英] R: how to average within overlapping time periods
问题描述
我最近发布了一个类似的问题 here，不过这有点过于简单化了.所以我们又来了:
I have recently posted a similar question here, which was a bit oversimplified, though. Thus here we go again:
假设我有一个数据帧(下面的 dput 输出)，其中包含许多不同变量的时间序列数据(本例中为 5，实际数据中更多):
Assume I have a dataframe (dput output below) with time series data of many different variables (5 in this example, many more in the real data):
date a b c d e
1 2009-10-01 00:00:00 10 20 30 40 50
2 2009-10-01 01:00:00 11 21 31 41 51
3 2009-10-01 02:00:00 12 22 32 42 52
4 2009-10-01 03:00:00 13 23 33 43 53
5 2009-10-01 04:00:00 14 24 34 44 54
6 2009-10-01 05:00:00 15 25 35 45 55
7 2009-10-01 06:00:00 16 26 36 46 56
8 2009-10-01 07:00:00 17 27 37 47 57
9 2009-10-01 08:00:00 18 28 38 48 58
10 2009-10-01 09:00:00 19 29 39 49 59
11 2009-10-01 10:00:00 20 30 40 50 60
12 2009-10-01 11:00:00 21 31 41 51 61
13 2009-10-01 12:00:00 22 32 42 52 62
14 2009-10-01 13:00:00 23 33 43 53 63
15 2009-10-01 14:00:00 24 34 44 54 64
16 2009-10-01 15:00:00 25 35 45 55 65
17 2009-10-01 16:00:00 26 36 46 56 66
18 2009-10-01 17:00:00 27 37 47 57 67
19 2009-10-01 18:00:00 28 38 48 58 68
20 2009-10-01 19:00:00 29 39 49 59 69
21 2009-10-01 20:00:00 30 40 50 60 70
22 2009-10-01 21:00:00 31 41 51 61 71
23 2009-10-01 22:00:00 32 42 52 62 72
24 2009-10-01 23:00:00 33 43 53 63 73
25 2009-10-02 00:00:00 34 44 54 64 74
和另一个数据框事件"，具有由开始和停止日期定义的不同时间段(此处为 3 个，实际数据中还有更多):
and another data frame "events" with different time periods defined by a start and stop date (3 here, many more in the real data):
id start stop
1 AGH 2009-10-01 02:00:00 2009-10-01 04:00:00
2 TRG 2009-10-01 03:00:00 2009-10-01 10:00:00
3 ZUH 2009-10-01 03:00:00 2009-10-01 20:00:00
我想得到一个不同事件中变量平均值的表格，如下所示:
I would like to get a table of the mean values of the variables within the different events like this:
id avg(y.a) avg(y.b) avg(y.c) avg(y.d) avg(y.e)
1 AGH 13.0 23.0 33.0 43.0 53.0
2 TRG 16.5 26.5 36.5 46.5 56.5
3 ZUH 21.5 31.5 41.5 51.5 61.5
我从上一篇文章中了解到，我可以使用 sqldf 包和一个相当简单的 SQL 语句来做到这一点:
I have learned from my previous post that I can do this using the sqldf package and a rather simple SQL statement:
means <- sqldf("
+ SELECT x.id, avg(y.a), avg(y.b), avg(y.c), avg(y.d), avg(y.e)
+ FROM events as x, data as y
+ WHERE y.date between x.start and x.stop
+ GROUP BY x.id
+ ")
然而，由于实际数据包含更多要平均的列，这些列在我必须处理的各种文件中的名称不同，将所有列名输入到 SQL 语句中变得有点乏味.
However, as the real data contains many more columns to average, which are named differently in the various files I have to process, typing all the column names into the SQL statements becomes a bit tedious.
因此，我更喜欢 R 中的解决方案，在那里我可以简单地按列的编号 (data[2:100]) 引用列，但困难在于，时间段不连续且重叠，而 id是字符串.
Thus I would prefer a solution in R, where I can simply refer to the columns by their number (data[2:100]) The difficulty is, though, that the time periods are non-continous and overlapping and the ids are character strings.
任何想法如何做到这一点将不胜感激！
Any ideas how to do this would be much appreciated!
dput(数据)
structure(list(date = structure(c(1254348000, 1254351600, 1254355200,
1254358800, 1254362400, 1254366000, 1254369600, 1254373200, 1254376800,
1254380400, 1254384000, 1254387600, 1254391200, 1254394800, 1254398400,
1254402000, 1254405600, 1254409200, 1254412800, 1254416400, 1254420000,
1254423600, 1254427200, 1254430800, 1254434400), class = c("POSIXct",
"POSIXt"), tzone = "Europe/Berlin"), a = 10:34, b = 20:44, c = 30:54,
d = 40:64, e = 50:74), .Names = c("date", "a", "b", "c",
"d", "e"), row.names = c(NA, -25L), class = "data.frame")
dput(事件)
structure(list(id = structure(1:3, .Label = c("AGH", "TRG", "ZUH"
), class = "factor"), start = structure(c(1254355200, 1254358800,
1254358800), class = c("POSIXct", "POSIXt"), tzone = "Europe/Berlin"),
stop = structure(c(1254362400, 1254384000, 1254420000), class = c("POSIXct",
"POSIXt"), tzone = "Europe/Berlin")), .Names = c("id", "start",
"stop"), row.names = c(NA, -3L), class = "data.frame")
推荐答案
基本问题是数据没有归一化；然而，我们可以动态生成 sql 语句:
The basic problem is due to the fact that the data is not normalized; however, short of putting it into long form we could dynamically generate the sql statement:
library(sqldf)
sql <- paste("select id, ",
toString(sprintf("avg(y.%s)", names(data)[-1])),
"from events as x, data as y
where y.date between x.start and x.stop
group by x.id")
sqldf(sql)
作为替代，我们展示了使用 reshape2 包中的 melt 将数据转换为长格式，data_long，对其进行处理以给出 means.long 并使用 dcast 将其转换回宽格式:
As an alternative, we show the use of melt in the reshape2 package to convert the data to long form, data_long, process it to give means.long and convert it back to wide form using dcast :
library(reshape2)
data_long <- melt(data, id.vars = "date")
means_long <- sqldf("
SELECT x.id, y.variable, avg(value)
FROM events as x, data_long as y
WHERE y.date between x.start and x.stop
GROUP BY x.id, y.variable
")
means <- dcast(id ~ variable, data = means_long, value.var = "avg(value)")
这篇关于R:如何在重叠的时间段内求平均值的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
