我有一个> 100万个单独的DateTime检测间隔表（即在此时间内连续检测到一个个体），还有一个表DateTime间隔表，表示门何时关闭。单个检测被分类为“ IN”或“ OUT”
使用％within％，我已经能够确定检测结果是否在任何间隔内。
但是，我想做些不同的事情。对于每个门关闭间隔，我想知道在外部检测到多少个人，在内部检测到多少个人。我相信，最简单的方法是将每个检测事件分配给一个门控周期，但是我无法弄清楚如果没有非常混乱的嵌套ifelse语句，该函数将如何编写。
预期输出（不是真实数据）：
Tag site species StartDateTime_UTC EndDateTime_UTC interval Location
<fct> <fct> <chr> <dttm> <dttm> <dbl> <chr>
1 5004.24 IC1 Striped Bass 2014-09-29 22:40:40 2014-09-29 22:46:35 1 IN
2 5004.24 IC1 Striped Bass 2014-09-29 22:49:15 2014-09-29 22:50:05 1 IN
3 5004.24 RGD1 Striped Bass 2014-10-01 23:01:12 2014-10-01 23:11:23 2 IN
4 5004.24 RGD1 Striped Bass 2014-10-01 23:16:18 2014-10-02 00:13:17 2 IN
5 5004.24 RGD1 Striped Bass 2014-10-02 00:15:47 2014-10-02 00:30:08 2 IN
6 5004.24 RGD1 Striped Bass 2014-10-02 00:33:12 2014-10-02 01:10:21 2 IN
7 5004.24 RGD1 Striped Bass 2014-10-02 01:13:01 2014-10-02 01:20:12 2 IN
8 5004.24 RGD1 Striped Bass 2014-10-02 04:14:15 2014-10-02 04:21:11 2 IN
9 5004.24 RGD1 Striped Bass 2014-10-02 04:23:31 2014-10-02 04:26:06 NA IN
10 5004.24 RGD1 Striped Bass 2014-10-02 04:28:21 2014-10-02 04:32:16 NA IN
11 5004.24 RGD1 Striped Bass 2014-10-02 22:00:06 2014-10-02 22:44:08 NA IN
12 5004.24 RGD1 Striped Bass 2014-10-02 22:46:58 2014-10-02 23:08:21 5 IN
13 5004.24 RGD1 Striped Bass 2014-10-02 23:10:36 2014-10-03 00:26:00 5 IN
14 5004.24 RGD1 Striped Bass 2014-10-03 00:28:55 2014-10-03 00:51:35 5 IN
15 5004.24 RGD1 Striped Bass 2014-10-03 00:55:06 2014-10-03 01:08:01 5 IN
16 5004.24 RGD1 Striped Bass 2014-10-03 01:10:36 2014-10-03 01:17:21 6 IN
17 5004.24 RGD1 Striped Bass 2014-10-03 01:20:41 2014-10-03 01:21:01 6 IN
18 5004.24 RGD1 Striped Bass 2014-10-03 01:30:41 2014-10-03 01:31:07 6 IN
19 5004.24 RGD1 Striped Bass 2014-10-03 01:35:02 2014-10-03 01:38:12 7 IN
20 5004.24 RGD1 Striped Bass 2014-10-03 01:42:02 2014-10-03 01:58:18 7 IN
当门关闭从而“ NA”有效时，可能不会发生某些检测
还有一些更好的选择吗？
dput：
检测：
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"Striped Bass", "Striped Bass", "Striped Bass", "Striped Bass",
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"Striped Bass", "Striped Bass", "Striped Bass", "Striped Bass",
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"IN", "IN", "IN", "IN")), row.names = c(NA, -100L), class = c("tbl_df",
"tbl", "data.frame"))
门关闭间隔
new("Interval", .Data = c(-81060, -117060, -59400, -16200, -76680,
-51000, -81120), start = structure(c(1412238660, 1412362800,
1412434800, 1412454600, 1412542980, 1412602200, 1412690400), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), tzone = "UTC")
因此，示例数据存在一些问题，难以回答您的问题。
也就是说，我们简化并纠正这些问题：
library(tidyverse) # needed because your data is in tibble format
library(lubridate) # needed for time interval class used in your example
# I called your example data "Detections" and "Intervals", not importing them here
set.seed(914)
row.sample <- c(1,5,2, sample(1:100, 10)) # to include the samples not in the given intervals mixed with good values
use.cols <- c(1,4,6,7,9)
Detections.use <- Detections[row.sample, use.cols]
Detections.use
# A tibble: 13 x 5
Tag site species StartDateTime_UTC EndDateTime_UTC
<fct> <fct> <chr> <dttm> <dttm>
1 5004.24 IC1 Striped Bass 2014-09-29 22:40:40 2014-09-29 22:46:35
2 5004.24 RGD1 Striped Bass 2014-10-02 00:15:47 2014-10-02 00:30:08
3 5004.24 IC1 Striped Bass 2014-09-29 22:49:15 2014-09-29 22:50:05
4 5004.24 RGD1 Striped Bass 2014-10-04 18:38:40 2014-10-04 18:52:30
5 5004.24 RGD1 Striped Bass 2014-10-05 07:15:43 2014-10-05 08:05:47
6 5004.24 RGD1 Striped Bass 2014-10-03 02:21:36 2014-10-03 02:24:01
7 5004.24 RGD1 Striped Bass 2014-10-03 12:13:46 2014-10-03 12:18:02
8 5004.24 RGD1 Striped Bass 2014-10-03 09:30:15 2014-10-03 10:14:19
9 5004.24 RGD1 Striped Bass 2014-10-04 14:09:39 2014-10-04 14:09:45
10 5004.24 RGD1 Striped Bass 2014-10-03 02:17:05 2014-10-03 02:18:05
11 5004.24 RGD1 Striped Bass 2014-10-05 03:08:26 2014-10-05 03:12:31
12 5004.24 RGD1 Striped Bass 2014-10-03 01:42:02 2014-10-03 01:58:18
13 5004.24 RGD1 Striped Bass 2014-10-02 00:33:12 2014-10-02 01:10:21
# Convert your intervals to a table, then correct them to make them positive time intevals
Interval.table <- tibble(Intervals, "ID" = seq(from = 1, to = length(Intervals))) %>%
mutate(end_time = Intervals$start,
start_time = end_time+Intervals$.Data,
new_Interval = start_time %--% end_time) %>%
select(ID, new_Interval)
Interval.table
# A tibble: 7 x 2
ID new_Interval
<int> <Interval>
1 1 2014-10-01 10:00:00 UTC--2014-10-02 08:31:00 UTC
2 2 2014-10-02 10:29:00 UTC--2014-10-03 19:00:00 UTC
3 3 2014-10-03 22:30:00 UTC--2014-10-04 15:00:00 UTC
4 4 2014-10-04 16:00:00 UTC--2014-10-04 20:30:00 UTC
5 5 2014-10-04 23:45:00 UTC--2014-10-05 21:03:00 UTC
6 6 2014-10-05 23:20:00 UTC--2014-10-06 13:30:00 UTC
7 7 2014-10-06 15:28:00 UTC--2014-10-07 14:00:00 UTC
现在解决您的问题lubridate程序包包含一个函数%within%，该函数测试时间是否在时间间隔内，但是奇怪的是，对于tidyverse函数，它似乎不能很好地向量化。我无法弄清楚如何使它在典型的dplyr / tidyr语法中工作，但是使用for循环，我们可以制作一个向量，其中包含每次适合的间隔ID和不适合的NA不在任何给定的插入项之内。
y <- c()
for (i in 1:length(Detections.use$StartDateTime_UTC)){
z<- which(Detections.use$StartDateTime_UTC[i] %within% Interval.table$new_Interval)
y[i] <- ifelse(isTRUE(z>0), z, NA)
}
Detections.use$Interval <- y
Detections.use
# A tibble: 13 x 6
Tag site species StartDateTime_UTC EndDateTime_UTC Interval
<fct> <fct> <chr> <dttm> <dttm> <int>
1 5004.24 IC1 Striped Bass 2014-09-29 22:40:40 2014-09-29 22:46:35 NA
2 5004.24 RGD1 Striped Bass 2014-10-02 00:15:47 2014-10-02 00:30:08 1
3 5004.24 IC1 Striped Bass 2014-09-29 22:49:15 2014-09-29 22:50:05 NA
4 5004.24 RGD1 Striped Bass 2014-10-04 18:38:40 2014-10-04 18:52:30 4
5 5004.24 RGD1 Striped Bass 2014-10-05 07:15:43 2014-10-05 08:05:47 5
6 5004.24 RGD1 Striped Bass 2014-10-03 02:21:36 2014-10-03 02:24:01 2
7 5004.24 RGD1 Striped Bass 2014-10-03 12:13:46 2014-10-03 12:18:02 2
8 5004.24 RGD1 Striped Bass 2014-10-03 09:30:15 2014-10-03 10:14:19 2
9 5004.24 RGD1 Striped Bass 2014-10-04 14:09:39 2014-10-04 14:09:45 3
10 5004.24 RGD1 Striped Bass 2014-10-03 02:17:05 2014-10-03 02:18:05 2
11 5004.24 RGD1 Striped Bass 2014-10-05 03:08:26 2014-10-05 03:12:31 5
12 5004.24 RGD1 Striped Bass 2014-10-03 01:42:02 2014-10-03 01:58:18 2
13 5004.24 RGD1 Striped Bass 2014-10-02 00:33:12 2014-10-02 01:10:21 1
编辑以添加sapply解决方案--- >>
这也可以用sapply稍微平滑一点，并且不会留下工件：
which.join <- function(x, y) { z <- which(x %within% y) z <- ifelse(isTRUE(z > 0), z, NA) z } Detections.use$Interval <- sapply(Detections.use$StartDateTime_UTC, function(x) which.join(x,Interval.table$new_Interval)另一种方法可能是使用here所示的Fuzzyjoin程序包>