玫瑰图和雷达图（自备）

玫瑰图

数据格式

绘图基础

绘图升级（文本调整）

玫瑰图

下载数据data/2020/2020-11-24 · mirrors_rfordatascience/tidytuesday - 码云 - 开源中国 (gitee.com)

R语言绘图—南丁格尔玫瑰图 - 知乎 (zhihu.com)

数据格式

rm(list = ls()) 
library(ggplot2)
library(dplyr)
library(stringr)
hike_data <- readRDS("hike_data.rds")
hike_data$region <- as.factor(word(hike_data$location, 1, sep = " -- "))
hike_data$length_num <- as.numeric(sapply(strsplit(hike_data$length, " "), "[[", 1))

plot_df <- hike_data %>%
  group_by(region) %>%     ##按照region列进行分组
  summarise(sum_length = sum(length_num), mean_gain = mean(as.numeric(gain)),
    n = n()) %>% ##每个分组计算总长度(sum_length)、平均增益(mean_gain)和数量(n)
  mutate(mean_gain = round(mean_gain, digits = 0))#对mean_gain列进行舍入操作，保留0位小数

plot_df
# A tibble: 11 × 4
   region                  sum_length mean_gain     n
   <fct>                        <dbl>     <dbl> <int>
 1 Central Cascades             2131.      2260   226
 2 Central Washington            453.       814    80
 3 Eastern Washington           1334.      1591   143
 4 Issaquah Alps                 383.       973    77
 5 Mount Rainier Area           1602.      1874   196
 6 North Cascades               3347.      2500   301
 7 Olympic Peninsula            1700.      1572   209
 8 Puget Sound and Islands       810.       452   191
 9 Snoqualmie Region            1915.      2206   219
10 South Cascades               1630.      1649   193
11 Southwest Washington          825.      1185   123

绘图基础

p1 <- ggplot(data = plot_df,aes(
  x = reorder(str_wrap(region, 5), sum_length),##x变量region，str_wrap()将region换行,按照sum_length排序
  y=sum_length,fill = region))+                ##fill = region 根据这个进行颜色填充
  geom_bar(width = 0.8,stat = "identity")+     #条形图
  coord_polar(theta="x",start=0)+              #坐标系 theta将角度映射到的变量（x或y）
  ylim(-500,3500)+                              ##根据最大值设置合适的圆环直径
  scale_fill_viridis(option="A",discrete=T)+
  theme_minimal()+xlab(" ")+ylab(" ")+ ##主题
  labs(title = "玫瑰图", 
       subtitle = paste( "Florence NightingaleA","Florence NightingaleB", sep = "\n"),   
       caption = "2024")+
  theme(legend.position="none")##不展示图例
p1
dev.off()

绘图升级（文本调整）

计算角度

rm(list = ls()) 
library(ggplot2)
library(dplyr)
library(stringr)
library(viridis)
hike_data <- readRDS("hike_data.rds")
hike_data$region <- as.factor(word(hike_data$location, 1, sep = " -- "))
hike_data$length_num <- as.numeric(sapply(strsplit(hike_data$length, " "), "[[", 1))

plot_df <- hike_data %>%
  group_by(region) %>%     ##按照region列进行分组
  summarise(sum_length = sum(length_num), mean_gain = mean(as.numeric(gain)),
    n = n()) %>% ##每个分组计算总长度(sum_length)、平均增益(mean_gain)和数量(n)
  mutate(mean_gain = round(mean_gain, digits = 0))#对mean_gain列进行舍入操作，保留0位小数


##需要对文本角度进行计算## 需要先进行排序计算
plot_df1 <- as.data.frame(plot_df)
##值从大到小降序排列
plot_df2 <- plot_df1[order(plot_df1$sum_length,decreasing=T),c(1:2)]
label_data<-plot_df2
library(data.table)
setDT(label_data)#构造文本
label_data[,new_label:=paste0(region,sum_length,"例")]                ##添加文本内容
label_data[,id:=1:nrow(label_data)]                                   ##添加排序号（已经降序排列）
number_of_bar <- nrow(label_data)                                     ##行数量用于计算角度
label_data[,angle:=90 - 360 * (label_data$id-0.5) /number_of_bar]     #角度计算
label_data[,":="(hjust=ifelse(angle<90,1,0),
                 angle1=ifelse(angle<90,angle+180,angle))]

head(label_data)[1:3]
              region sum_length                  new_label id     angle hjust   angle1
1:    North Cascades    3346.53    North Cascades3346.53例  1 73.636364     1 253.6364
2:  Central Cascades    2130.85  Central Cascades2130.85例  2 40.909091     1 220.9091
3: Snoqualmie Region    1915.32 Snoqualmie Region1915.32例  3  8.181818     1 188.1818


p1 <- ggplot(data = plot_df,aes(
  ##一定注意reorder(str_wrap(region, 5), sum_length,decreasing=T)顺序与计算角度顺序需要一致
  x = reorder(str_wrap(region, 5), sum_length,decreasing=T),##x变量region，str_wrap()将region换行,按照sum_length排序
  y=sum_length,fill = region))+                ##fill = region 根据这个进行颜色填充
  geom_bar(width = 0.8,stat = "identity")+     #条形图
  coord_polar(theta="x",start=0)+              #坐标系 theta将角度映射到的变量（x或y）
  ylim(-500,3500)+                              ##根据最大值设置合适的圆环直径
  scale_fill_viridis(option="A",discrete=T)+
  theme_minimal()+xlab(" ")+ylab(" ")+ ##主题
  labs(title = "玫瑰图", subtitle = paste( "Florence NightingaleA","Florence NightingaleB", sep = "\n"), caption = "2024")+
  theme(legend.position = "none",              #不展示图例
        text = element_text(color = "gray12", family = "Bell MT"),  #参数https://www.jianshu.com/p/8e33dc11ed8c
        axis.text = element_blank(),    
        axis.title = element_blank(),  
        panel.grid = element_blank())+ 
  geom_text(data=label_data, 
            aes(x=id, y= sum_length, 
                label=new_label, 
                hjust=hjust),  
            color="black", fontface="bold",  
            alpha=0.6, size=3.5, 
            angle=label_data$angle1,inherit.aes=FALSE)
p1
dev.off()