问题描述
我喜欢 R，但有些问题实在是太难了.
I love R but some problems are just plain hard.
挑战是在基于时间的窗口大于或等于 6 小时的不规则时间序列中找到滚动和小于 30 的第一个实例.我有这个系列的样本
The challenge is to find the first instance of a rolling sum that is less than 30 in an irregular time series having a time-based window greater than or equal to 6 hours. I have a sample of the series
Row Person DateTime Value
1 A 2014-01-01 08:15:00 5
2 A 2014-01-01 09:15:00 5
3 A 2014-01-01 10:00:00 5
4 A 2014-01-01 11:15:00 5
5 A 2014-01-01 14:15:00 5
6 B 2014-01-01 08:15:00 25
7 B 2014-01-01 10:15:00 25
8 B 2014-01-01 19:15:00 2
9 C 2014-01-01 08:00:00 20
10 C 2014-01-01 09:00:00 5
11 C 2014-01-01 13:45:00 1
12 D 2014-01-01 07:00:00 1
13 D 2014-01-01 08:15:00 13
14 D 2014-01-01 14:15:00 15
For Person A, Rows 1 & 5 create a minimum 6 hour interval with a running sum of 25 (which is less than 30).
For Person B, Rows 7 & 8 create a 9 hour interval with a running sum of 27 (again less than 30).
For Person C, using Rows 9 & 10, there is no minimum 6 hour interval (it is only 5.75 hours) although the running sum is 26 and is less than 30.
For Person D, using Rows 12 & 14, the interval is 7.25 hours but the running sum is 30 and is not less than 30.
给定 n 个观察值，必须比较 n*(n-1)/2 个区间.例如，当 n=2 时，只需要计算 1 个区间.对于 n=3，有 3 个区间.以此类推.
Given n observations, there are n*(n-1)/2 intervals that must be compared. For example, with n=2 there is just 1 interval to evaluate. For n=3 there are 3 intervals. And so on.
我假设这是子集和问题的一种变体(http://en.wikipedia.org/wiki/Subset_sum_problem)
I assume that this is an variation of the subset sum problem (http://en.wikipedia.org/wiki/Subset_sum_problem)
虽然可以对数据进行排序，但我怀疑这需要一个蛮力解决方案来测试每个间隔.
While the data can be sorted I suspect this requires a brute force solution testing each interval.
任何帮助将不胜感激.
这是 DateTime 列格式为 POSIXct 的数据:
here's the data with DateTime column formatted as POSIXct:
df <- structure(list(Person = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L,
2L, 3L, 3L, 3L, 4L, 4L, 4L), .Label = c("A", "B", "C", "D"), class = "factor"),
DateTime = structure(c(1388560500, 1388564100, 1388566800,
1388571300, 1388582100, 1388560500, 1388567700, 1388600100,
1388559600, 1388563200, 1388580300, 1388556000, 1388560500,
1388582100), class = c("POSIXct", "POSIXt"), tzone = ""),
Value = c(5L, 5L, 5L, 5L, 5L, 25L, 25L, 2L, 20L, 5L, 1L,
1L, 13L, 15L)), .Names = c("Person", "DateTime", "Value"), row.names = c("1",
"2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13",
"14"), class = "data.frame")
推荐答案
我发现这也是 R 中的一个难题.所以我给它做了一个包！
I have found this to be a difficult problem in R as well. So I made a package for it!
library("devtools")
install_github("boRingTrees","mgahan")
require(boRingTrees)
当然，您必须正确计算出单位的上限.
Of course, you will have to figure out your units correctly for the upper bound.
如果您有兴趣，这里还有一些文档.https://github.com/mgahan/boRingTrees
Here is some more documentation if you are interested.https://github.com/mgahan/boRingTrees
对于@beginneR 提供的数据df，您可以使用以下代码获得6 小时滚动总和.
For the data df that @beginneR provided, you could use the following code to get a 6 hour rolling sum.
require(data.table)
setDT(df)
df[ , roll := rollingByCalcs(df,dates="DateTime",target="Value",
by="Person",stat=sum,lower=0,upper=6*60*60)]
Person DateTime Value roll
1: A 2014-01-01 01:15:00 5 5
2: A 2014-01-01 02:15:00 5 10
3: A 2014-01-01 03:00:00 5 15
4: A 2014-01-01 04:15:00 5 20
5: A 2014-01-01 07:15:00 5 25
6: B 2014-01-01 01:15:00 25 25
7: B 2014-01-01 03:15:00 25 50
8: B 2014-01-01 12:15:00 2 2
9: C 2014-01-01 01:00:00 20 20
10: C 2014-01-01 02:00:00 5 25
11: C 2014-01-01 06:45:00 1 26
12: D 2014-01-01 00:00:00 1 1
13: D 2014-01-01 01:15:00 13 14
14: D 2014-01-01 07:15:00 15 28
原来的帖子对我来说很不清楚，所以这可能不是他想要的.如果提供了具有所需输出的列，我想我可以提供更多帮助.
The original post is pretty unclear to me, so this might not be exactly what he wanted. If a column with the desired output was presented, I imagine I could be of more help.
这篇关于R:计算不规则时间序列的滚动和，这些时间序列由 id 变量分组，具有基于时间的窗口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！