如何总结数据点之间的重叠 [英] How to summarise overlaps between data points

问题描述

我有一组通过RFID读取器的动物数据，看起来像这样-

I have a data set of animals passing an RFID reader, it looks like this -

ID    date_time                 
A     2019-11-02 08:07:47    
B     2019-11-02 08:07:48 
A     2019-11-02 08:07:49
A     2019-11-02 08:07:50
A     2019-11-02 08:09:12
A     2019-11-02 08:09:13
B     2019-11-02 08:09:17

我最近问了这个问题，(将多个行合并为一个时间间隔)，现在我的数据如下所示- (将数据组织成十秒的间隔)

I asked this question recently, (combine multiple rows into one time interval), and now my data looks like this - (with the data organised into ten second intervals)

ID     start_date_time.      end_date_time
A      2019-11-02 08:07:47   2019-11-02 08:07:50
B      2019-11-02 08:07:48   2019-11-02 08:07:48
A      2019-11-02 08:09:12   2019-11-02 08:09:13
B      2019-11-02 08:09:17   2019-11-02 08:09:47

我还添加了一个列，该列总结了时间间隔

I have also added a column which summarises the intervals

dat$Interval = interval(dat$start_date_time,dat$end_date_time)

我现在需要找到并总结这些间隔在何处相交并将其产生为一个计数，以显示动物互动(或同时出现在RFID阅读器上)的次数- (并且无需重复反向互动，即A-B，B-A)

I now need to find and summarise where these intervals intersect and produce this as a count, to show the number of times animals interact (or are present at the RFID reader at the same time) something like this - (and without repeating reverse interactions, i.e. A-B, B-A)

ID     ID2     Interactions(n)
A      A       0
A      B       1    
A      C       3

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推荐答案

这个问题不太容易回答.也许这可能是一个很好的起点:

Not really easy to answer, this question. Maybe this might be a good starting point:

library(tidyverse)
library(lubridate)

cbind(
  dat[rep(1:nrow(dat[-1, ]), nrow(dat[-1, ]):1), c(1, 4)],          
  setNames(
    dat[unlist(sapply(2:nrow(dat), seq, to = nrow(dat))), c(1, 4)],
    c('ID2', 'Interval2')
    )
  ) %>%
  mutate(
    interacts = intersect(Interval, Interval2),
    Interval  = NULL,
    Interval2 = NULL
  ) %>%
  filter(!is.na(as.numeric(interacts))) %>%
  count(ID, ID2)

# # A tibble: 1 x 3
#   ID    ID2       n
#   <chr> <chr> <int>
# 1 A     B         1

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