每组可视化每天的摘要统计信息

Question

假设以下数据框,

head(df, 9)
         Day               variable     value
1 2015-10-18   Number_Flows.minimum  401.0000
2 2015-10-18   Number_Flows.maximum 2068.0000
3 2015-10-18   Number_Flows.average 1578.9474
4 2015-10-18 Number_srcaddr.minimum   95.0000
5 2015-10-18 Number_srcaddr.maximum  292.0000
6 2015-10-18 Number_srcaddr.average  222.6316
7 2015-10-18 Number_dstaddr.minimum   65.0000
8 2015-10-18 Number_dstaddr.maximum  411.0000
9 2015-10-18 Number_dstaddr.average  202.5789

我想要做的就是情节minimum,maximum,average每个Number_Flows,Number_srcaddr等我宁愿有条显示的价值,但我打开其他的方法为好,只要我得到(例如,对于贴在下面重复的例子)共22张图表(每天11张).

我尝试了各种各样的东西但没有运气

library(dplyr)
library(ggplot2)


ggplot(df %>% mutate(group = paste(Day, gsub('\\..*', '', variable), sep = '-')), aes(x = Day, y = value))+geom_bar(stat = 'identity')+facet_wrap(~group)
ggplot(df %>% mutate(group = paste(Day, gsub('\\..*', '', variable), sep = '-')), aes(x = Day, y = value))+geom_bar(stat = 'identity')+facet_wrap(~group)
ggplot(df %>% mutate(group = paste(Day, gsub('\\..*', '', variable), sep = '-')), aes(x = Day, y = value))+geom_line()+facet_wrap(~group)

数据

dput(df)
structure(list(Day = structure(c(1445115600, 1445115600, 1445115600, 
1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 
1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 
1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 
1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 
1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 1445115600, 
1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 
1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 
1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 
1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 
1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 1445202000, 
1445202000, 1445202000, 1445202000), class = c("POSIXct", "POSIXt"
), tzone = ""), variable = c("Number_Flows.minimum", "Number_Flows.maximum", 
"Number_Flows.average", "Number_srcaddr.minimum", "Number_srcaddr.maximum", 
"Number_srcaddr.average", "Number_dstaddr.minimum", "Number_dstaddr.maximum", 
"Number_dstaddr.average", "Sum_packets.minimum", "Sum_packets.maximum", 
"Sum_packets.average", "Sum_duration_nannosecs.minimum", "Sum_duration_nannosecs.maximum", 
"Sum_duration_nannosecs.average", "Average_Duration.minimum", 
"Average_Duration.maximum", "Average_Duration.average", "Average_Bytes.minimum", 
"Average_Bytes.maximum", "Average_Bytes.average", "Bytes_per_packet.minimum", 
"Bytes_per_packet.maximum", "Bytes_per_packet.average", "Sum_of_Bytes.minimum", 
"Sum_of_Bytes.maximum", "Sum_of_Bytes.average", "Actual_Batch_Duration_secs.minimum", 
"Actual_Batch_Duration_secs.maximum", "Actual_Batch_Duration_secs.average", 
"packets_per_second.minimum", "packets_per_second.maximum", "packets_per_second.average", 
"Number_Flows.minimum", "Number_Flows.maximum", "Number_Flows.average", 
"Number_srcaddr.minimum", "Number_srcaddr.maximum", "Number_srcaddr.average", 
"Number_dstaddr.minimum", "Number_dstaddr.maximum", "Number_dstaddr.average", 
"Sum_packets.minimum", "Sum_packets.maximum", "Sum_packets.average", 
"Sum_duration_nannosecs.minimum", "Sum_duration_nannosecs.maximum", 
"Sum_duration_nannosecs.average", "Average_Duration.minimum", 
"Average_Duration.maximum", "Average_Duration.average", "Average_Bytes.minimum", 
"Average_Bytes.maximum", "Average_Bytes.average", "Bytes_per_packet.minimum", 
"Bytes_per_packet.maximum", "Bytes_per_packet.average", "Sum_of_Bytes.minimum", 
"Sum_of_Bytes.maximum", "Sum_of_Bytes.average", "Actual_Batch_Duration_secs.minimum", 
"Actual_Batch_Duration_secs.maximum", "Actual_Batch_Duration_secs.average", 
"packets_per_second.minimum", "packets_per_second.maximum", "packets_per_second.average"
), value = c(401, 2068, 1578.94736842105, 95, 292, 222.631578947368, 
65, 411, 202.578947368421, 4181, 130567, 33860.2631578947, 2647278, 
10876533, 5438303.63157895, 1543.937984, 20335.58603, 4202.062837, 
692.4193548, 77207.90476, 14689.4305788105, 231.6654261, 943.7592654, 
465.315475931579, 1244970, 123223816, 19865244, 9, 30, 27.1578947368421, 
179, 4352, 1265.94736842105, 609, 2352, 1578.94736842105, 89, 
299, 219.105263157895, 92, 402, 193.578947368421, 1124, 60473, 
19022.6842105263, 944317, 20088618, 5254959.84210526, 1550.602627, 
9749.356239, 3236.99523905263, 258.9441708, 17451.96293, 5789.86937011053, 
140.2998221, 717.4807734, 424.926870810526, 157697, 33505216, 
9510806.21052632, 5, 30, 24.9473684210526, 114, 2179, 772.947368421053
)), .Names = c("Day", "variable", "value"), row.names = c(NA, 
66L), class = "data.frame")

Answer 1

我喜欢使用线条来表示趋势,而丝带则表示值的范围.

类似于@docendo我会separate先,但我会spread在之后:

library(tidyverse)

df %>%
  separate(variable, c("type", "var"), sep = "\\.") %>% 
  spread(var, value) %>% 
  ggplot(aes(Day)) +
  geom_line(aes(y = average), size = 1) +
  geom_ribbon(aes(ymin = minimum, ymax = maximum), alpha = 0.2) +
  facet_wrap(~type, scales = 'free_y') +
  theme(axis.text.x=element_text(angle = 90, vjust = 0.5))

如果你有更多的日子,这看起来会更好.

Answer 2

我将在绘制之前将"变量"列分开:

library(dplyr)
library(ggplot2)
library(tidyr)

df %>% 
  separate(variable, c("type", "var"), sep = "\\.") %>% 
  ggplot(aes(x = Day, y = value, color = var)) +
  geom_point() +
  facet_wrap(~type) + 
  theme(axis.text.x=element_text(angle = -90, hjust = 0))

你可以通过使用免费的y-scales,bar而不是point等来轻松地提供更多信息.