R中每小时按时间戳数据计算的平均班级数量
我正在学习R，所以我寻求帮助以举一个示例，说明如何编写以下转换。
以下是我正在使用的数据的示例：
> 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
此数据每隔四分钟（GMT_TIME）有几天（GMT_DATE）的时间戳。每个时间戳在三个不同的类clas1，Foraging或Feeding中都有一个关联的Standing事件。
我想要的输出是一个数据帧，该数据帧将平均每天（从0到23的每个班级的小时计数。这是一个没有值的示例，其中包括sd：
Time_day avg_Standing sd_Standing avg_Feeding _sd_Feeding avg_Foraging sd_Foraging
0
1
2
3
4
5
困难的事实是我有一个字符串而不是值。我希望有人至少可以使我走上正确的道路。任何帮助表示赞赏！
PS：请在下面找到一个dput()示例：
> dput(clas1[c(1:600),])
structure(list(GMT_DATE = c("6/1/2016", "6/1/2016", "6/1/2016",
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"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>)
1 个答案:
答案 0 :(得分：0)
使用ggplot和tidyverse，您可以先汇总数据，然后绘制数据：
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clas1的平均计数（按天平均） -
ymin, ymax：假设正常法则，平均计数的上下置信区间
此数据框现在可用于绘制：
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
使用正常定律来获得计数的置信度并不是最好的主意，但是如果您有大量的平均天数（至少50天左右），则可以证明是合理的。基于泊松的方法将更为准确。
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