在R中添加Gender分类变量并绘制分组条形图

1. 生成Gender分类变量 #

你的数据集是宽格式（Male和Female为单独列），需要转成长格式来生成Gender分类变量，使用tidyverse包的pivot_longer函数即可完成：

library(tidyverse)

# 假设原数据集名为school_data
school_data_long <- school_data %>%
  pivot_longer(
    cols = c(Male, Female),  # 指定要转换的列
    names_to = "Gender",     # 新分类变量的名称
    values_to = "Count"      # 对应数值列的名称
  )

转换后的数据结构示例：

A tibble: 12 × 6 #

No Region District Name of School Gender Count

1 1 South East Gaborone Bokamoso CJSS Male 545
2 1 South East Gaborone Bokamoso CJSS Female 516
3 2 South East Gaborone Marang CJSS Male 550
4 2 South East Gaborone Marang CJSS Female 567

… with 8 more rows #

2. 绘制分组条形图 #

根据需求，这里提供两种绘图方式：

方式一：按学校分性别展示 #

每个地区下的学校分别展示男女生人数：

ggplot(school_data_long, aes(x = Region, y = Count, fill = Gender)) +
  geom_col(position = "dodge") +  # 设置分组条形布局
  geom_text(
    aes(label = Count), 
    position = position_dodge(width = 0.9), 
    vjust = -0.5  # 数值标签位于条形上方
  ) +
  labs(
    title = "分性别学生人数（按地区）",
    x = "地区",
    y = "学生人数",
    fill = "性别"
  ) +
  theme_minimal()

方式二：按地区汇总后展示 #

先汇总每个地区的男女生总人数，再绘图：

# 先汇总数据
school_data_summary <- school_data_long %>%
  group_by(Region, Gender) %>%
  summarise(Total_Count = sum(Count), .groups = "drop")

# 绘制汇总后的分组条形图
ggplot(school_data_summary, aes(x = Region, y = Total_Count, fill = Gender)) +
  geom_col(position = "dodge") +
  geom_text(
    aes(label = Total_Count), 
    position = position_dodge(width = 0.9), 
    vjust = -0.5
  ) +
  labs(
    title = "地区分性别总学生人数",
    x = "地区",
    y = "总人数",
    fill = "性别"
  ) +
  theme_minimal()

内容的提问来源于stack exchange，提问作者Katlego Motsatsing