Question

我正在分析在规定时间段内的几种动物行为。

我观看动物及其行为的视频。我记录每个行为的显示时间。他们将在录制期间多次显示每个行为（对应于不同的事件）。有时在录制过程中可以同时显示2或3个行为，但它们通常不会在同一时间完全开始/结束（因此它们部分重叠）。

我最终得到了每个行为的一系列事件，对于每个事件，我都有它们的起始点，持续时间和终点（见下文中的示例）。

我需要从这个数据中提取行为1与行为2重叠的总量/行为1与行为3重叠/行为2与行为3重叠。这样我就可以找到行为之间的相关性同时显示，哪些不显示，......

我只是编程的初学者（主要是R），我发现很难开始。你能告诉我怎么办吗？非常感谢！

针对3种行为的一系列事件的示例：

Event tracked           Onset  Duration   End
Behaviour 1 _event 1    7.40    548.88  556.28
Behaviour 1 _event 2    36.20   0.47    36.67
Behaviour 1 _event 3    48.45   0.25    48.70
Behaviour 1 _event 4    68.92   1.53    70.45
Behaviour 1 _event 5    75.48   0.22    75.70
Behaviour 1 _event 6    89.75   0.66    90.41
Behaviour 1 _event 7    94.62   0.16    94.78
Behaviour 1 _event 8    101.78  0.22    102.00
Behaviour 1 _event 9    108.86  0.59    109.45
Behaviour 1 _event 10   146.35  0.66    147.00
Behaviour 1 _event 11   150.20  0.75    150.95
Behaviour 1 _event 12   152.98  0.66    153.64
Behaviour 1 _event 13   157.84  0.56    158.41
Behaviour 2_event 1     7.52    0.38    7.90
Behaviour 2_event 2     18.73   0.16    18.88
Behaviour 2_event 3     19.95   2.25    22.20
Behaviour 2_event 4     26.41   0.25    26.66
Behaviour 2_event 5     35.91   0.16    36.07
Behaviour 2_event 6     37.29   0.34    37.63
Behaviour 2_event 7     38.13   0.72    38.85
Behaviour 2_event 8     40.19   0.31    40.51
Behaviour 2_event 9     44.26   0.16    44.41
Behaviour 2_event 10    45.32   0.16    45.48
Behaviour 2_event 11    54.84   1.44    56.27
Behaviour 2_event 12    56.65   1.19    57.84
Behaviour 2_event 13    61.59   1.03    62.62
Behaviour 2_event 14    81.13   3.83    84.96
Behaviour 2_event 15    86.65   0.31    86.96
Behaviour 2_event 16    90.15   0.19    90.34
Behaviour 2_event 17    96.97   0.53    97.50
Behaviour 2_event 18    107.12  0.22    107.34
Behaviour 2_event 19    118.53  0.41    118.94
Behaviour 2_event 20    127.76  0.25    128.01
Behaviour 2_event 21    129.45  0.69    130.13
Behaviour 2_event 22    130.60  2.31    132.91
Behaviour 2_event 23    141.01  0.41    141.41
Behaviour 2_event 24    152.85  0.37    153.23
Behaviour 2_event 25    156.54  0.13    156.66
Behaviour 3_event 1     7.71    1.94    9.65
Behaviour 3_event 2     11.12   1.53    12.65
Behaviour 3_event 3     19.01   0.19    19.20
Behaviour 3_event 4     20.01   3.97    23.98
Behaviour 3_event 5     24.95   4.22    29.16
Behaviour 3_event 6     29.70   2.19    31.88
Behaviour 3_event 7     33.23   2.50    35.73
Behaviour 3_event 8     36.82   0.44    37.26
Behaviour 3_event 9     38.20   1.16    39.35
Behaviour 3_event 10    39.91   2.13    42.04
Behaviour 3_event 11    42.49   3.62    46.11
Behaviour 3_event 12    47.09   0.53    47.62
Behaviour 3_event 13    48.15   0.34    48.49
Behaviour 3_event 14    49.40   2.13    51.52
Behaviour 3_event 15    57.57   2.25    59.82
Behaviour 3_event 16    60.89   0.88    61.76
Behaviour 3_event 17    66.85   6.78    73.63
Behaviour 3_event 18    75.65   3.03    78.68

Answer 1

为了进行您想要进行的研究，最简单的方法是将数据转换为状态变量的时间序列（即是否显示行为1,2,3等）。所以您想要将您拥有的数据集转换为类似

的数据集

time   animal    behav_1  behav_2  behav_3
0      1         FALSE    TRUE     FALSE
0      2         TRUE     FALSE    FALSE
1      1         FALSE    TRUE     FALSE
1      2         TRUE     FALSE    TRUE
...    ...       ...      ...      ...

每行告诉特定动物是否在给定时间显示三种行为中的每一种。（我假设你有多个动物，你想保持他们的行为数据分开。）

然后您可以轻松地估计您感兴趣的许多数量。例如，您可以计算动物行为的概率1，因为它正在做行为2

计算专栏data$behav_1_and_2 <- data$behav_1 & data$behav_2
将合并behav_1_and_2的总和除以behav_2的总和：sum(data$behav_1_and_2) / sum(data$behav_2)

好的，但是你如何改造数据呢？首先，确定要检查的时间点数。也许你应该增加约0.1。

num_animals <- 10 // How many animals you have
time_seq <- seq(from = 0, to = 600, by = 0.1) // to should be end of video
data <- expand.grid(time = time_seq, animal = num_animals)

它可以获得所需数据框的前两列。然后，您需要计算三个行为列。定义一个函数，该函数接受行为列的time，animal和名称，如果动物当时正在执行该行为，则返回TRUE，如果FALSE，则返回has_behavior <- function(time, animal, behavior) { ... }不

// First create empty columns
data$behav_1 <- logical(nrow(data))
data$behav_2 <- logical(nrow(data))
data$behav_3 <- logical(nrow(data))

// Now loop through rows
for (i in 1:nrow(data)) {
    data$behav_1[i] <- has_behavior(data$time[i], data$animal[i], 1)
    data$behav_2[i] <- has_behavior(data$time[i], data$animal[i], 2)
    data$behav_3[i] <- has_behavior(data$time[i], data$animal[i], 3)
}

（我将让你弄明白如何制作这个功能。）手头有了这个功能，你可以用循环创建最后三列：

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使用此格式的数据，您应该能够更轻松地研究问题。您可以轻松地计算这些汇总数量，如前所述。该数据框也被设置为对进行时间序列建模有用。它也是tidy，因此可以轻松地使用dplyr等数据包进行数据汇总，并ggplot2进行可视化。（您可以在免费在线书籍R for Data Science by Hadley Wickham中了解有关最后两种工具的更多信息。）

分析2个人的几种行为

1 个答案: