R中按小时计算的时间戳数据中的平均班级计数答案

【问题标题】：Average class counts in time-stamped data by the hour in RR中按小时计算的时间戳数据中的平均班级计数
【发布时间】：2019-04-05 13:39:57
【问题描述】：

我正在学习 R，因此我寻求帮助以提供一个示例来说明如何编写以下转换代码。

这是我正在处理的数据示例：

> head(clas1)
   GMT_DATE GMT_TIME    clas1
1: 6/1/2016 00:04:00 Foraging
2: 6/1/2016 00:08:00 Foraging
3: 6/1/2016 00:12:00 Foraging
4: 6/1/2016 00:16:00 Foraging
5: 6/1/2016 00:20:00 Foraging
6: 6/1/2016 00:24:00 Foraging

此数据每四分钟有一个时间戳 (GMT_TIME)，持续数天 (GMT_DATE)。每个时间戳在Foraging、Feeding 或Standing 三个不同的类中都有一个关联的clas1 事件。

我想要的输出是一个数据框，它将平均一天中每个小时的每个班级的小时计数（从0 到23）。这是一个没有值的示例，包括sd：

Time_day avg_Standing sd_Standing avg_Feeding _sd_Feeding avg_Foraging sd_Foraging
0
1
2
3
4
5

让这变得困难的事实是我有一个字符串而不是值。我希望有人至少能让我走上正轨。任何帮助表示赞赏！

PS：请在下方找到dput() 示例：

> dput(clas1[c(1:600),])
structure(list(GMT_DATE = c("6/1/2016", "6/1/2016", "6/1/2016", 
"6/1/2016", "6/1/2016", "6/1/2016", "6/1/2016", "6/1/2016", "6/1/2016", 
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"04:08:00", "04:12:00", "04:16:00", "04:20:00", "04:24:00", "04:28:00", 
"04:32:00", "04:36:00", "04:40:00", "04:44:00", "04:48:00", "04:52:00", 
"04:56:00", "05:00:00", "05:04:00", "05:08:00", "05:12:00", "05:16:00", 
"05:20:00", "05:24:00", "05:28:00", "05:32:00", "05:36:00", "05:40:00", 
"05:44:00", "05:48:00", "05:52:00", "05:56:00", "06:00:00", "06:04:00", 
"06:08:00", "06:12:00", "06:16:00", "06:20:00", "06:24:00", "06:28:00", 
"06:32:00", "06:36:00", "06:40:00", "06:44:00", "06:48:00", "06:52:00", 
"06:56:00", "07:00:00", "07:04:00", "07:08:00", "07:12:00", "07:16:00", 
"07:20:00", "07:24:00", "07:28:00", "07:32:00", "07:36:00", "07:40:00", 
"07:44:00", "07:48:00", "07:52:00", "07:56:00", "08:00:00", "08:04:00", 
"08:08:00", "08:12:00", "08:16:00", "08:20:00", "08:24:00", "08:28:00", 
"08:32:00", "08:36:00", "08:40:00", "08:44:00", "08:48:00", "08:52:00", 
"08:56:00", "09:00:00", "09:04:00", "09:08:00", "09:12:00", "09:16:00", 
"09:20:00", "09:24:00", "09:28:00", "09:32:00", "09:36:00", "09:40:00", 
"09:44:00", "09:48:00", "09:52:00", "09:56:00", "10:00:00", "10:04:00", 
"10:08:00", "10:12:00", "10:16:00", "10:20:00", "10:24:00", "10:28:00", 
"10:32:00", "10:36:00", "10:40:00", "10:44:00", "10:48:00", "10:52:00", 
"10:56:00", "11:00:00", "11:04:00", "11:08:00", "11:12:00", "11:16:00", 
"11:20:00", "11:24:00", "11:28:00", "11:32:00", "11:36:00", "11:40:00", 
"11:44:00", "11:48:00", "11:52:00", "11:56:00", "12:00:00", "12:04:00", 
"12:08:00", "12:12:00", "12:16:00", "12:20:00", "12:24:00", "12:28:00", 
"12:32:00", "12:36:00", "12:40:00", "12:44:00", "12:48:00", "12:52:00", 
"12:56:00", "13:00:00", "13:04:00", "13:08:00", "13:12:00", "13:16:00", 
"13:20:00", "13:24:00", "13:28:00", "13:32:00", "13:36:00", "13:40:00", 
"13:44:00", "13:48:00", "13:52:00", "13:56:00", "14:00:00", "14:04:00", 
"14:08:00", "14:12:00", "14:16:00", "14:20:00", "14:24:00", "14:28:00", 
"14:32:00", "14:36:00", "14:40:00", "14:44:00", "14:48:00", "14:52:00", 
"14:56:00", "15:00:00", "15:04:00", "15:08:00", "15:12:00", "15:16:00", 
"15:20:00", "15:24:00", "15:28:00", "15:32:00", "15:36:00", "15:40:00", 
"15:44:00", "15:48:00", "15:52:00", "15:56:00", "16:00:00"), 
    clas1 = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 
    1L, 1L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 3L, 
    1L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 
    1L, 2L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 2L, 1L, 1L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 1L, 3L, 1L, 
    1L, 3L, 1L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 2L, 
    2L, 1L, 3L, 1L, 3L, 2L, 2L, 1L, 2L, 2L, 1L, 3L, 3L, 3L, 3L, 
    1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 2L, 
    3L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 2L, 
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 
    2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 3L, 
    3L, 3L, 3L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 3L, 3L, 1L, 3L, 3L, 
    3L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 3L, 2L, 
    2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 
    1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 1L, 3L, 1L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 2L, 3L, 
    1L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 1L, 3L, 3L, 3L, 
    1L, 2L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 1L, 2L, 2L, 3L, 3L, 1L, 3L, 3L, 1L, 3L, 1L, 3L, 3L, 3L, 
    3L, 3L, 2L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 
    2L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 
    1L, 3L, 3L, 3L, 3L, 2L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 1L, 1L, 
    2L, 3L, 3L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 
    1L, 1L, 3L, 3L, 3L, 3L, 1L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 1L, 
    1L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L), .Label = c("Feeding", "Foraging", "Standing"
    ), class = "factor")), row.names = c(NA, -600L), class = c("data.table", 
"data.frame"), .internal.selfref = <pointer: 0x00000000026c1ef0>)

【问题讨论】：

我认为你应该首先尝试绘制它。这太宽泛了，可以形成一个完整的数据清理（如果有人为你做的话）会破坏学习的目的。
@NelsonGon 感谢您的评论。我试图重新表述我的问题以使其更精确，所以它正好到了我正在挣扎的地步。 ploting 本身没有问题。
我认为您需要aggregate 或ave 或summarise。查看有关按组汇总数据的问题。

标签： r

【解决方案1】：

使用ggplot 和tidyverse，您可以先聚合数据，然后绘制它：

library(ggplot2)
library(tidyverse)

(agg_df <- df %>% 
  as_tibble() %>%
  mutate(tms  = as.POSIXct(paste0(GMT_DATE, "-", GMT_TIME), format = "%m/%d/%Y-%H:%M:%S"),
         hour = hour(tms),
         day  = day(tms)) %>% ## create aux columns with day and hour
  group_by(day, hour) %>%
  summarise(tab = list(as.data.frame(table(clas1)))) %>%
  unnest() %>%
  group_by(hour, clas1) %>%
  summarise(stats = list(mean_cl_normal(Freq))) %>%
  unnest())

# # A tibble: 72 x 5
# # Groups:   hour [24]
#     hour clas1        y   ymin  ymax
#    <int> <fct>    <dbl>  <dbl> <dbl>
#  1     0 Feeding    0     0     0   
#  2     0 Foraging  14.5   8.15 20.9 
#  3     0 Standing   0     0     0   
#  4     1 Feeding    6.5 -25.3  38.3 
#  5     1 Foraging   2   -10.7  14.7 
#  6     1 Standing   6.5 -12.6  25.6 
#  7     2 Feeding    4.5  -1.85 10.9 
#  8     2 Foraging   9     9     9   
#  9     2 Standing   1.5  -4.85  7.85
# 10     3 Feeding   11   -14.4  36.4 
# # ... with 62 more rows

agg_df 现在包含所有必要的列：

y：每小时平均计数 clas1（几天内的平均值）
ymin, ymax：假设正常规律的平均计数的上下置信区间

这个数据框现在可以用来绘图了：

ggplot(agg_df, aes(x = hour, y = y, fill = clas1, ymin = ymin, ymax = ymax)) +
    geom_col(position = position_dodge2()) +
    geom_errorbar(position = position_dodge2())

注意

使用正常法则来获得计数的置信范围并不是最好的主意，但如果您有大量的平均天数（至少 50 天左右），则可以证明这一点。基于泊松的方法会更准确。

【讨论】：

谢谢！那效果很好。是的，我的平均天数非常多（大约 2 个月）。但是，我看到所有三个班级的每小时计数总和是一天中每个小时的 29.5161291。 clas1 有 4 分钟的时间戳，这意味着每小时的计数总和应该是 60 分钟 /4 分钟 = 15。我错了吗？