如何使用coord_polar

我在R中有下面的数据框，我想从中协调出极坐标图

下面是我的数据结构

dim(melted_TimingOfActivities)
[1] 181863      3

    l

library(ggplot2)



dput(head(melted_TimingOfActivities, 500))
structure(list(index = 1:500, variable = structure(c(1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L), .Label = c("Personal care/sleep (mins per day)", "Employment (mins per day)", 
"Study (mins per day)", "Household & family care (mins per day)", 
" Volunteer work & meetings (mins per day)", "Social life & entertainment (mins per day)", 
"Sports & outdoor activities (mins per day)", "Hobbies & games (mins per day)", 
"Mass media (mins per day)", "Travel (mins per day)", "Other spec/not specfd (mins per day)"
), class = "factor"), value = c("7:30", "11:10", "11:50", "11:40", 
"10:50", "12:10", "11:30", "11:30", "9:50", "10:20", "9:30", 
"9:10", "9:10", "9:0", "10:0", "8:20", "9:20", "9:20", "14:40", 
"9:10", "12:30", "10:20", "10:30", "9:20", "12:40", "9:0", "13:10", 
"10:30", "13:50", "10:40", "14:0", "10:40", "12:20", "11:0", 
"11:30", "12:40", "10:30", "10:0", "12:0", "13:0", "11:10", "8:30", 
"15:40", "9:30", "12:40", "11:0", "10:30", "9:20", "10:30", "10:10", 
"9:40", "10:30", "11:30", "10:0", "10:20", "10:20", "11:50", 
"11:10", "8:0", "8:0", "12:50", "11:50", "12:50", "9:40", "9:50", 
"7:50", "15:0", "13:10", "13:50", "13:40", "9:50", "11:0", "6:0", 
"8:0", "12:40", "11:20", "13:50", "14:50", "9:40", "12:50", "8:30", 
"12:30", "13:0", "13:20", "9:30", "13:50", "14:30", "18:20", 
"9:50", "10:20", "9:40", "9:0", "12:40", "14:0", "12:30", "12:40", 
"12:10", "10:0", "7:50", "9:40", "12:50", "12:0", "9:20", "11:40", 
"6:0", "5:50", "13:10", "14:0", "11:0", "9:50", "8:10", "13:0", 
"14:20", "12:30", "11:0", "9:30", "9:40", "11:40", "13:0", "13:20", 
"9:20", "11:50", "11:50", "10:50", "15:0", "10:20", "16:0", "9:10", 
"12:10", "10:10", "13:10", "11:10", "12:50", "11:40", "12:20", 
"11:50", "9:50", "11:10", "9:10", "9:20", "12:0", "10:20", "7:50", 
"9:50", "11:20", "7:40", "10:40", "14:30", "13:40", "10:0", "14:20", 
"7:20", "12:50", "9:50", "12:20", "9:50", "15:10", "12:10", "12:0", 
"14:50", "13:0", "15:10", "9:10", "11:50", "9:40", "10:10", "10:50", 
"9:0", "11:0", "10:40", "9:20", "11:0", "11:0", "12:30", "12:30", 
"13:50", "9:40", "10:40", "12:50", "7:0", "9:10", "6:40", "12:30", 
"9:30", "13:30", "12:30", "14:30", "14:30", "14:30", "13:20", 
"10:30", "15:40", "14:30", "14:10", "12:30", "7:20", "9:20", 
"11:30", "12:0", "13:0", "13:40", "15:0", "14:10", "14:40", "12:0", 
"11:50", "11:40", "12:0", "9:20", "1:0", "8:0", "9:10", "14:30", 
"12:30", "11:10", "14:20", "8:0", "8:0", "7:50", "13:50", "7:50", 
"8:10", "9:30", "9:40", "11:40", "13:20", "10:0", "8:40", "11:20", 
"10:30", "11:40", "9:0", "12:50", "10:50", "9:50", "11:0", "7:20", 
"10:40", "9:20", "8:20", "7:50", "7:10", "9:30", "13:10", "9:0", 
"13:10", "8:40", "12:0", "8:40", "8:10", "9:0", "12:30", "9:10", 
"9:20", "9:30", "9:30", "10:40", "9:0", "10:20", "10:10", "9:20", 
"9:10", "10:20", "12:0", "12:40", "11:0", "11:0", "10:30", "15:0", 
"9:50", "11:30", "12:10", "11:50", "14:20", "11:10", "9:0", "10:30", 
"12:30", "14:40", "10:10", "10:40", "8:50", "10:10", "5:50", 
"11:50", "9:30", "10:50", "12:30", "12:0", "14:40", "15:0", "15:40", 
"12:50", "8:0", "10:30", "8:20", "8:20", "8:20", "8:40", "11:30", 
"9:50", "6:40", "8:50", "11:40", "10:0", "10:40", "13:20", "7:10", 
"15:0", "11:0", "13:40", "12:0", "14:30", "13:50", "14:20", "8:40", 
"13:10", "8:40", "9:40", "7:50", "12:0", "8:40", "13:20", "9:40", 
"10:0", "9:0", "11:10", "10:0", "9:50", "15:10", "0:0", "13:0", 
"10:20", "9:40", "8:50", "11:0", "9:20", "10:40", "9:10", "15:10", 
"15:20", "8:50", "11:50", "14:30", "13:0", "12:10", "12:30", 
"10:10", "9:40", "9:20", "9:20", "10:10", "4:30", "10:20", "9:50", 
"9:20", "7:50", "9:0", "8:10", "11:40", "9:0", "10:30", "12:20", 
"11:20", "8:30", "10:20", "10:20", "12:20", "12:40", "12:40", 
"13:30", "12:40", "15:30", "11:20", "11:40", "11:40", "10:40", 
"12:20", "10:40", "13:10", "15:10", "13:50", "13:20", "14:20", 
"13:30", "6:30", "6:50", "10:30", "11:30", "9:30", "12:20", "11:20", 
"12:50", "11:20", "13:40", "11:10", "13:50", "18:20", "12:20", 
"9:50", "6:30", "14:10", "12:10", "11:10", "12:50", "9:0", "7:50", 
"10:10", "8:40", "7:0", "10:40", "12:30", "12:20", "12:0", "10:20", 
"8:50", "11:40", "9:40", "10:30", "10:40", "11:40", "10:0", "10:10", 
"11:40", "11:30", "13:10", "11:40", "13:30", "10:50", "14:20", 
"14:20", "14:30", "13:0", "12:50", "11:0", "13:50", "11:50", 
"13:10", "11:40", "8:10", "12:20", "12:20", "10:20", "9:10", 
"14:20", "11:40", "9:40", "8:30", "6:50", "11:30", "11:50", "11:20", 
"9:20", "14:30", "13:0", "8:20", "8:10", "9:0", "9:50", "9:10", 
"9:50", "10:50", "10:50", "10:20", "9:50", "12:30", "12:30", 
"10:30", "10:20", "12:50", "11:30", "9:0", "11:40", "8:20", "11:10", 
"9:0", "12:50", "10:10", "10:40", "10:10", "8:30", "10:10", "11:20", 
"12:20", "7:40", "12:10", "14:50", "13:50", "7:10", "9:40", "9:20", 
"12:50", "12:10", "13:40", "14:10", "9:20", "11:20", "12:20", 
"12:20", "9:40")), row.names = c(NA, 500L), class = "data.frame")

我在R做以下工作

 ggplot(melted_TimingOfActivities, 
        aes(x=variable, y=value), fill = variable)+ 
   geom_col(width=1)+ 
   coord_polar(theta = "y")+
   labs(x="Minutes", y="Activities", colour="Activties", fill="Activites" ) + 
   theme(legend.position="right", axis.text.x = element_text(angle = 0, hjust = 0))  

我收到了下面的情节

有没有什么简单的方法来格式化分钟来显示小时和分钟

---

这里有两个步骤可以达到目的，另外还有一个步骤可以使数据更具代表性，因为前500行数据中的样本只包括11项活动中的第一项

第0步。分配一组活动，以便其他活动有意义

第一步。将时间转换为数字并获得平均值

第二步。阴谋

library(dplyr)
library(lubridate)
set.seed(42)

# Step 0. Randomly assigning other values to "variable" so we have variety
activities <- levels(melted_TimingOfActivities$variable)
melted_TimingOfActivities2 <- melted_TimingOfActivities %>%
  mutate(variable = sample(activities, n(), replace = T)) %>%

  # Step 1. Convert text minutes:seconds into numeric, take average
  mutate(minutes = ms(value) / minutes(1)) %>%
  group_by(variable) %>%
  summarize(avg_time = mean(minutes))

# Step 2: plot
ggplot(melted_TimingOfActivities2, 
       aes(x = 1, y = avg_time, fill = variable)) +
  geom_col() +
  coord_polar(theta = "y")

库（dplyr）
图书馆（lubridate）
种子（42）
#第0步。随机分配其他值给“变量”，这样我们就有了变化
活动%
#第一步。将文本分钟：秒转换为数字，取平均值
变异（分钟=毫秒（值）/分钟（1））%>%
分组依据（变量）%>%
汇总（平均时间=平均（分钟））
#步骤2：绘图
ggplot（熔解时间工厂2，
aes（x=1，y=avg_时间，fill=variable））+
geom_col（）+
极坐标（theta=“y”）

---

这里有两个步骤可以达到目的，另外还有一个步骤可以使数据更具代表性，因为前500行数据中的样本只包括11项活动中的第一项

第0步。分配一组活动，以便其他活动有意义

第一步。将时间转换为数字并获得平均值

第二步。阴谋

library(dplyr)
library(lubridate)
set.seed(42)

# Step 0. Randomly assigning other values to "variable" so we have variety
activities <- levels(melted_TimingOfActivities$variable)
melted_TimingOfActivities2 <- melted_TimingOfActivities %>%
  mutate(variable = sample(activities, n(), replace = T)) %>%

  # Step 1. Convert text minutes:seconds into numeric, take average
  mutate(minutes = ms(value) / minutes(1)) %>%
  group_by(variable) %>%
  summarize(avg_time = mean(minutes))

# Step 2: plot
ggplot(melted_TimingOfActivities2, 
       aes(x = 1, y = avg_time, fill = variable)) +
  geom_col() +
  coord_polar(theta = "y")

库（dplyr）
图书馆（lubridate）
种子（42）
#第0步。随机分配其他值给“变量”，这样我们就有了变化
活动%
#第一步。将文本分钟：秒转换为数字，取平均值
变异（分钟=毫秒（值）/分钟（1））%>%
分组依据（变量）%>%
汇总（平均时间=平均（分钟））
#步骤2：绘图
ggplot（熔解时间工厂2，
aes（x=1，y=avg_时间，fill=variable））+
geom_col（）+
极坐标（theta=“y”）

---

您的示例数据有500行（大概是样本）的“个人护理/睡眠（每天分钟）”时间。“您是否正在寻求帮助，了解如何平均这些样本，以便为每个活动提供一个单一值，如示例饼图？@JonSpring是的，我想表示每个活动的持续时间您的示例数据有500行（大概是样本）“个人护理/睡眠（每天分钟）”的时间。“您是否在寻求帮助，了解如何平均这些样本，以便为每项活动提供一个单一值，如示例饼图？@JonSpring是的，我想表示每项活动的持续时间