环状条形图(Circular barplot)是条形图的变体，图如其名，环状条形图在视觉上很吸引人，但也必须小心使用，因为环状条形图使用的是极坐标系而不是笛卡尔坐标系，每一个类别不共享相同的Y轴。环状条形图非常适合于周期性数据，本文主要介绍基于R语言实现环状条形图的绘制。本文主要参考链接:Circular barplot
R语言的环状条形图主要基于tidyverse包实现，tidyverse是一组R包的集合，这些R包共享共同的原理并旨在无缝地协同工作，具体介绍见：
tidyverse
安装命令如下：
install.packages(“tidyverse”)
本文所有代码见：R-Study-Notes
文章目录
1 基础环状条形图绘制 Basic circular barplot
1.1 最基础环状条形图的绘制 Most basic circular barplot
1.2 给环状条形图添加标签 Add labels to circular barplot
2 分组环状条形图 Circular barplot with groups
2.1 在圆中添加间隙 Add a gap in the circle
2.2 组间距设置 Space between groups
2.3 对柱状进行排序 Order bars
2.4 环状条形图自定义 Circular barchart customization
3 堆积环状条形图 Circular stacked barplot
4 参考
1 基础环状条形图绘制 Basic circular barplot
1.1 最基础环状条形图的绘制 Most basic circular barplot
环状条形图就是条形图，只不过环状条形图沿圆形而不是直线显示。
输入数据集与条形图的输入数据集相同：每个组需要一个数值（一个组=一个条形图）。（请参阅条形图部分的更多说明）。
基本上，这个方法和做一个经典的条形图是一样的。最后，我们调用coord_polar()使整个坐标系变为极坐标系，这样会使得图表呈圆形。注意，ylim()参数非常重要。如果它从0开始，这些条将从圆的中心开始。如果您提供负值，将出现一个白色的圆圈空格！此外会用到rep函数，具体介绍见R中rep函数的使用
# Libraries
# 导入包
library(tidyverse)
# Create dataset
# 创建数据
data <- data.frame(
id=seq(1,60),
individual=paste( "Mister ", seq(1,60), sep=""),
value=sample( seq(10,100), 60, replace=T)
)
head(data)
| id | individual | value |
|---|---|---|
| <int> | <fct> | <int> |
| 1 | Mister 1 | 44 |
| 2 | Mister 2 | 79 |
| 3 | Mister 3 | 81 |
| 4 | Mister 4 | 62 |
| 5 | Mister 5 | 91 |
| 6 | Mister 6 | 50 |
# Make the plot
# 画图
p <- ggplot(data, aes(x=as.factor(id), y=value)) +
# This add the bars with a blue color
# 添加蓝色条形，stat表示数据统计方式，也就是说identity提取横坐标x对应的y值
geom_bar(stat="identity", fill=alpha("blue", 0.3)) +
# The negative value controls the size of the inner circle, the positive one is useful to add size over each bar
# 设置y的范围，负值设定内圆的大小，正值设定各个条柱的最高高度
ylim(-100,120)+
# theme_minimal简约主题
theme_minimal() +
# Custom the theme: no axis title and no cartesian grid
# 自定义主题
theme(
# 移除标题坐标文字
axis.text = element_blank(),
axis.title = element_blank(),
# 移除网格
panel.grid = element_blank(),
# This remove unnecessary margin around plot
# 移除不必要空白
plot.margin = unit(rep(-2,4), "cm"))+
# This makes the coordinate polar instead of cartesian.
# 使用极坐标系
coord_polar(start = 0)
p
1.2 给环状条形图添加标签 Add labels to circular barplot
上节说明了如何制作基本的环状条形图。下一步是在每个条上添加标签，以便深入了解图形。这里我建议一种方法，在每个条的顶部添加标签，使用与条中心部分相同的角度。在下面的代码中，有一小段创建了一个带有每个标签特性的数据帧，然后我们可以在geom_text（）中调用它。
请注意，为了让标签的更好阅读，这就需要将其中一些标签翻转180度。
首先添加数据
# Libraries
library(tidyverse)
# Create dataset
# 创建数据
data <- data.frame(
id=seq(1,60),
individual=paste( "Mister ", seq(1,60), sep=""),
value=sample( seq(10,100), 60, replace=T)
)
# ----- This section prepare a dataframe for labels ---- #
# 准备数据标签
# Get the name and the y position of each label
label_data <- data
# calculate the ANGLE of the labels
# 计算标签角度
number_of_bar <- nrow(label_data)
number_of_bar
# I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
# 减去0.5是为了让标签位于条柱中心
# angle是标签角度
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar
# calculate the alignment of labels: right or left
# If I am on the left part of the plot, my labels have currently an angle < -90
# 判断标签左对齐还是右对齐，也就是标签是朝向左边还是右边
label_data$hjust<-ifelse( angle < -90, 1, 0)
# flip angle BY to make them readable
# 翻转标签
label_data$angle<-ifelse(angle < -90, angle+180, angle)
# ----- ------------------------------------------- ---- #
head(label_data)
60
| id | individual | value | hjust | angle |
|---|---|---|---|---|
| <int> | <fct> | <int> | <dbl> | <dbl> |
| 1 | Mister 1 | 86 | 0 | 87 |
| 2 | Mister 2 | 81 | 0 | 81 |
| 3 | Mister 3 | 27 | 0 | 75 |
| 4 | Mister 4 | 48 | 0 | 69 |
| 5 | Mister 5 | 57 | 0 | 63 |
| 6 | Mister 6 | 49 | 0 | 57 |
开始绘图
# Start the plot
# 开始绘图
p <- ggplot(data, aes(x=as.factor(id), y=value)) +
# This add the bars with a bskyblue color
# 添加蓝色条形，stat表示数据统计方式，也就是说identity提取横坐标x对应的y值
geom_bar(stat="identity", fill=alpha("skyblue", 0.7)) +
# The negative value controls the size of the inner circle, the positive one is useful to add size over each bar
# 设置y的范围，负值设定内圆的大小，正值设定各个条柱的最高高度
ylim(-100,120)+
# theme_minimal简约主题
theme_minimal() +
# Custom the theme: no axis title and no cartesian grid
# 自定义主题
theme(
# 移除标题坐标文字
axis.text = element_blank(),
axis.title = element_blank(),
# 移除网格
panel.grid = element_blank(),
# This remove unnecessary margin around plot
# 移除不必要空白
plot.margin = unit(rep(-2,4), "cm"))+
# This makes the coordinate polar instead of
# 设置极坐标系
coord_polar(start = 0) +
# Add the labels, using the label_data dataframe that we have created before
# 添加标签
geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE )
p
2 分组环状条形图 Circular barplot with groups
2.1 在圆中添加间隙 Add a gap in the circle
本节主要介绍在圆中添加间隙，其实大部分操作和上一节一样，只是在初始数据帧的末尾添加了几行空行就能添加间隙
添加数据
# library
library(tidyverse)
# Create dataset
# 添加数据
data <- data.frame(
individual=paste( "Mister ", seq(1,60), sep=""),
value=sample( seq(10,100), 60, replace=T)
)
# Set a number of 'empty bar'
# 设置空白柱的个数
empty_bar <- 10
# 在原始数据中添加空白数据
# Add lines to the initial dataset
to_add <- matrix(NA, empty_bar, ncol(data))
colnames(to_add) <- colnames(data)
data <- rbind(data, to_add)
data$id <- seq(1, nrow(data))
# Get the name and the y position of each label
# 和上一步一样，获得标签角度信息
label_data <- data
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)
head(label_data)
| individual | value | id | hjust | angle |
|---|---|---|---|---|
| <fct> | <int> | <int> | <dbl> | <dbl> |
| Mister 1 | 18 | 1 | 0 | 87.42857 |
| Mister 2 | 55 | 2 | 0 | 82.28571 |
| Mister 3 | 69 | 3 | 0 | 77.14286 |
| Mister 4 | 36 | 4 | 0 | 72.00000 |
| Mister 5 | 46 | 5 | 0 | 66.85714 |
| Mister 6 | 82 | 6 | 0 | 61.71429 |
绘图
# Make the plot
# 绘图
p <- ggplot(data, aes(x=as.factor(id), y=value)) + # Note that id is a factor. If x is numeric, there is some space between the first bar
geom_bar(stat="identity", fill=alpha("green", 0.3)) +
ylim(-100,120) +
theme_minimal() +
theme(
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar(start = 0) +
geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE )
p;
Warning message:
"Removed 10 rows containing missing values (position_stack)."
Warning message:
"Removed 10 rows containing missing values (geom_text)."
2.2 组间距设置 Space between groups
组间距就是在各个组之间添加若干个空白柱，本节代码会用到R语言管道，具体介绍R语言中的管道%>%
首先创建一个空白数组
# library
library(tidyverse)
# Create dataset
# 创建数据集
data <- data.frame(
individual=paste( "Mister ", seq(1,60), sep=""),
group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,
value=sample( seq(10,100), 60, replace=T)
)
# Set a number of 'empty bar' to add at the end of each group
# 在原始数据中添加空白数据
# empty_bar 表示组之间的空白距离
empty_bar <- 4
# 每一组之间4个空白
to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group), ncol(data)) )
colnames(to_add) <- colnames(data)
# 为每个空白值提供组信息，rep函数的意思就是复制值，levels(data$group)为复制的对象，each为复制的次数
to_add$group <- rep(levels(data$group), each=empty_bar)
head(to_add)
| individual | group | value |
|---|---|---|
| <lgl> | <chr> | <lgl> |
| NA | A | NA |
| NA | A | NA |
| NA | A | NA |
| NA | A | NA |
| NA | B | NA |
| NA | B | NA |
然后将空白数组与原始数据绑定
colnames(to_add) <- colnames(data)
to_add$group <- rep(levels(data$group), each=empty_bar)
data <- rbind(data, to_add)
# 管道操作类似 data<-arrange(data,data$group)
data <- data %>% arrange(group)
# 设置id
data$id <- seq(1, nrow(data))
head(data)
| individual | group | value | id |
|---|---|---|---|
| <fct> | <fct> | <int> | <int> |
| Mister 1 | A | 79 | 1 |
| Mister 2 | A | 20 | 2 |
| Mister 3 | A | 67 | 3 |
| Mister 4 | A | 47 | 4 |
| Mister 5 | A | 78 | 5 |
| Mister 6 | A | 50 | 6 |
绘图
# Get the name and the y position of each label
# 设定角度值
label_data <- data
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)
# Make the plot
# fill 按组填充颜色
p <- ggplot(data, aes(x=as.factor(id), y=value, fill=group)) +
geom_bar(stat="identity", alpha=0.5) +
ylim(-100,120) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar() +
geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE )
p
Warning message:
"Removed 16 rows containing missing values (position_stack)."
Warning message:
"Removed 16 rows containing missing values (geom_text)."
2.3 对柱状进行排序 Order bars
在这里，观察结果是按每个组内的条形高度排序的。如果您的目标是了解组内和组间的最高/最低观察值是什么，那么这将非常有用。在上一节中修改一行代码即可：
# data = data %>% arrange(group)
# 修改为
data = data %>% arrange(group, value)
# library
library(tidyverse)
# Create dataset
# 创建数据集
data <- data.frame(
individual=paste( "Mister ", seq(1,60), sep=""),
group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,
value=sample( seq(10,100), 60, replace=T)
)
# Set a number of 'empty bar' to add at the end of each group
# 在原始数据中添加空白数据
# empty_bar 表示组之间的空白距离
empty_bar <- 4
# 每一组之间4个空白
to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group), ncol(data)) )
colnames(to_add) <- colnames(data)
# 为每个空白值提供组信息，rep函数的意思就是复制值，levels(data$group)为复制的对象，each为复制的次数
to_add$group <- rep(levels(data$group), each=empty_bar)
head(to_add)
colnames(to_add) <- colnames(data)
to_add$group <- rep(levels(data$group), each=empty_bar)
data <- rbind(data, to_add)
# 管道操作类似 data<-arrange(data,data$group)
data <- data %>% arrange(group, value)
# 设置id
data$id <- seq(1, nrow(data))
head(data)
# Get the name and the y position of each label
# 设定角度值
label_data <- data
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)
# Make the plot
# fill 按组填充颜色
p <- ggplot(data, aes(x=as.factor(id), y=value, fill=group)) +
geom_bar(stat="identity", alpha=0.5) +
ylim(-100,120) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar() +
geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE )
p
| individual | group | value |
|---|---|---|
| <lgl> | <chr> | <lgl> |
| NA | A | NA |
| NA | A | NA |
| NA | A | NA |
| NA | A | NA |
| NA | B | NA |
| NA | B | NA |
| individual | group | value | id |
|---|---|---|---|
| <fct> | <fct> | <int> | <int> |
| Mister 1 | A | 20 | 1 |
| Mister 10 | A | 20 | 2 |
| Mister 7 | A | 24 | 3 |
| Mister 4 | A | 30 | 4 |
| Mister 3 | A | 49 | 5 |
| Mister 8 | A | 64 | 6 |
Warning message:
"Removed 16 rows containing missing values (position_stack)."
Warning message:
"Removed 16 rows containing missing values (geom_text)."
2.4 环状条形图自定义 Circular barchart customization
最后是，在图表中添加一些自定义项是非常明智的。这里我们添加组名（A、B、C和D），并添加一个刻度来帮助比较条形图的大小。代码有点长，但结果看来是值得的！
首先准备数据
# library
library(tidyverse)
# Create dataset
data <- data.frame(
individual=paste( "Mister ", seq(1,60), sep=""),
group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,
value=sample( seq(10,100), 60, replace=T)
)
# Set a number of 'empty bar' to add at the end of each group
empty_bar <- 3
to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group), ncol(data)) )
colnames(to_add) <- colnames(data)
to_add$group <- rep(levels(data$group), each=empty_bar)
data <- rbind(data, to_add)
data <- data %>% arrange(group)
data$id <- seq(1, nrow(data))
# Get the name and the y position of each label
label_data <- data
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)
head(label_data)
| individual | group | value | id | hjust | angle |
|---|---|---|---|---|---|
| <fct> | <fct> | <int> | <int> | <dbl> | <dbl> |
| Mister 1 | A | 38 | 1 | 0 | 87.5 |
| Mister 2 | A | 24 | 2 | 0 | 82.5 |
| Mister 3 | A | 72 | 3 | 0 | 77.5 |
| Mister 4 | A | 47 | 4 | 0 | 72.5 |
| Mister 5 | A | 96 | 5 | 0 | 67.5 |
| Mister 6 | A | 86 | 6 | 0 | 62.5 |
然后设置abcd刻度线信息
# prepare a data frame for base lines
base_data <- data %>%
group_by(group) %>%
summarize(start=min(id), end=max(id) - empty_bar) %>%
rowwise() %>%
mutate(title=mean(c(start, end)))
head(base_data)
| group | start | end | title |
|---|---|---|---|
| <fct> | <int> | <dbl> | <dbl> |
| A | 1 | 10 | 5.5 |
| B | 14 | 43 | 28.5 |
| C | 47 | 60 | 53.5 |
| D | 64 | 69 | 66.5 |
接着设置各组之间的间隔条
# prepare a data frame for grid (scales)
grid_data <- base_data
grid_data$end <- grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1
grid_data$start <- grid_data$start - 1
grid_data <- grid_data[-1,]
grid_data
| group | start | end | title |
|---|---|---|---|
| <fct> | <dbl> | <dbl> | <dbl> |
| B | 13 | 11 | 28.5 |
| C | 46 | 44 | 53.5 |
| D | 63 | 61 | 66.5 |
最后就是绘图
# Make the plot
p <- ggplot(data, aes(x=as.factor(id), y=value, fill=group)) +
# 添加条形图
geom_bar(aes(x=as.factor(id), y=value, fill=group), stat="identity", alpha=0.5) +
# 添加各组之间的线条，可以注释
geom_segment(data=grid_data, aes(x = end, y = 80, xend = start, yend = 80), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 60, xend = start, yend = 60), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 40, xend = start, yend = 40), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 20, xend = start, yend = 20), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
# Add text showing the value of each 100/75/50/25 lines，设置值坐标，可以注释
annotate("text", x = rep(max(data$id),4), y = c(20, 40, 60, 80), label = c("20", "40", "60", "80") , color="grey", size=3 , angle=0, fontface="bold", hjust=1) +
# 和前面一样
ylim(-100,120) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar() +
geom_text(data=label_data, aes(x=id, y=value+10, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=2.5, angle= label_data$angle, inherit.aes = FALSE ) +
# Add base line information
# 添加下划线
geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = "black", alpha=0.8, size=0.6 , inherit.aes = FALSE ) +
# 添加各组的名字
geom_text(data=base_data, aes(x = title, y = -18, label=group), hjust=c(1,1,0,0), colour = "black", alpha=0.8, size=4, fontface="bold", inherit.aes = FALSE)
p
Warning message:
"Removed 12 rows containing missing values (position_stack)."
Warning message:
"Removed 12 rows containing missing values (geom_text)."
3 堆积环状条形图 Circular stacked barplot
本节旨在教你如何制作分组堆积的环状条形图。我强烈建议在深入研究这个代码之前先阅读前面的代码。本节会用到gather函数来处理数据，gather函数类似excel中的透视表，将数据压平。具体使用见
R语言 tidyr包的三个重要函数：gather，spread，separate的用法和举例
该段代码和前面不同在于数据创建以及创建各组之间的间距条
首先创建数据集
# library
library(tidyverse)
library(viridis)
# Create dataset
# 创建数据集
data <- data.frame(
individual=paste( "Mister ", seq(1,60), sep=""),
group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,
value1=sample( seq(10,100), 60, replace=T),
value2=sample( seq(10,100), 60, replace=T),
value3=sample( seq(10,100), 60, replace=T)
)
head(data)
| individual | group | value1 | value2 | value3 |
|---|---|---|---|---|
| <fct> | <fct> | <int> | <int> | <int> |
| Mister 1 | A | 44 | 70 | 62 |
| Mister 2 | A | 86 | 75 | 31 |
| Mister 3 | A | 18 | 56 | 61 |
| Mister 4 | A | 20 | 64 | 99 |
| Mister 5 | A | 62 | 66 | 44 |
| Mister 6 | A | 43 | 50 | 31 |
转换数据
# Transform data in a tidy format (long format)
# key表示观察的变量就是value1,value2,value3;value代表值,-c(1,2)表示不对第一列和第二列进行转换
data <- data %>% gather(key = "observation", value="value", -c(1,2))
head(data)
dim(data)
| individual | group | observation | value |
|---|---|---|---|
| <fct> | <fct> | <chr> | <int> |
| Mister 1 | A | value1 | 44 |
| Mister 2 | A | value1 | 86 |
| Mister 3 | A | value1 | 18 |
| Mister 4 | A | value1 | 20 |
| Mister 5 | A | value1 | 62 |
| Mister 6 | A | value1 | 43 |
- 180
4
设置一系列绘图指标
# Set a number of 'empty bar' to add at the end of each group
empty_bar <- 2
nObsType <- nlevels(as.factor(data$observation))
to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group)*nObsType, ncol(data)) )
colnames(to_add) <- colnames(data)
to_add$group <- rep(levels(data$group), each=empty_bar*nObsType )
data <- rbind(data, to_add)
data <- data %>% arrange(group, individual)
data$id <- rep( seq(1, nrow(data)/nObsType) , each=nObsType)
# Get the name and the y position of each label
label_data <- data %>% group_by(id, individual) %>% summarize(tot=sum(value))
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)
# prepare a data frame for base lines
base_data <- data %>%
group_by(group) %>%
summarize(start=min(id), end=max(id) - empty_bar) %>%
rowwise() %>%
mutate(title=mean(c(start, end)))
# prepare a data frame for grid (scales)
grid_data <- base_data
grid_data$end <- grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1
grid_data$start <- grid_data$start - 1
grid_data <- grid_data[-1,]
Warning message:
"Factor `individual` contains implicit NA, consider using `forcats::fct_explicit_na`"
绘图
# Make the plot
p <- ggplot(data) +
# Add the stacked bar
geom_bar(aes(x=as.factor(id), y=value, fill=observation), stat="identity", alpha=0.5) +
scale_fill_viridis(discrete=TRUE) +
# Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.
geom_segment(data=grid_data, aes(x = end, y = 0, xend = start, yend = 0), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 50, xend = start, yend = 50), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 100, xend = start, yend = 100), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 150, xend = start, yend = 150), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 200, xend = start, yend = 200), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
# Add text showing the value of each 100/75/50/25 lines
ggplot2::annotate("text", x = rep(max(data$id),5), y = c(0, 50, 100, 150, 200), label = c("0", "50", "100", "150", "200") , color="grey", size=6 , angle=0, fontface="bold", hjust=1) +
ylim(-150,max(label_data$tot, na.rm=T)) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar() +
# Add labels on top of each bar
geom_text(data=label_data, aes(x=id, y=tot+10, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=5, angle= label_data$angle, inherit.aes = FALSE ) +
# Add base line information
geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = "black", alpha=0.8, size=0.6 , inherit.aes = FALSE ) +
geom_text(data=base_data, aes(x = title, y = -18, label=group), hjust=c(1,1,0,0), colour = "black", alpha=0.8, size=4, fontface="bold", inherit.aes = FALSE)
p
# 保存数据 Save at png
ggsave(p, file="output.png", width=10, height=10)
Warning message:
"Removed 24 rows containing missing values (position_stack)."
Warning message:
"Removed 9 rows containing missing values (geom_text)."
Warning message:
"Removed 24 rows containing missing values (position_stack)."
Warning message:
"Removed 9 rows containing missing values (geom_text)."
4 参考
Circular barplot
tidyverse
R中rep函数的使用
R语言中的管道%>%
R语言 tidyr包的三个重要函数：gather，spread，separate的用法和举例
