基于R ggplot绘制多人口统计组的Likert量表堆叠条形图

问题说明 #

需要为Likert量表调查绘制堆叠条形图，对比单问题（Q2）在多个人口统计分组中的回答分布，包括整体受访者、不同性别、不同宗教重要性群体等。现有代码仅能生成单条整体堆叠条形，需整合多分组数据实现多条形对比。

解决方案步骤 #

核心思路是将不同分组的数据整理为统一的长格式，让ggplot可以一次性渲染所有分组的条形。

1. 加载依赖包

library(tidyverse)

2. 导入示例数据

survey <- structure(list(Gender = c("Man", "Woman", "Woman", "Decline to answer", "Man", 
                                   "Man", "Woman", "Man", "Woman", "Man"), 
                         Religion_Importance = c("Very important", 
                                                 "Not too important", "Not too important", "Not important at all", 
                                                 "Somewhat important", "Very important", "Not too important", 
                                                 "Not important at all", "Somewhat important", "Very important"
                         ), 
                         Q2 = structure(c(1L, 2L, 2L, 3L, 1L, 3L, 1L, 2L, 4L, 1L), 
                                        levels = c("Always or almost always", 
                                                   "Generally", "Not generally", 
                                                   "Never or almost never", "NA"), 
                                        class = "factor")), 
                    row.names = c(NA, -10L), 
                    class = c("tbl_df", "tbl", "data.frame"))

3. 整理多分组数据

将整体、性别、宗教重要性三个维度的分组数据合并为统一结构：

# 1. 整体受访者数据
overall_data <- survey %>%
  filter(!is.na(Q2)) %>%
  mutate(group_type = "整体受访者",
         group = "整体") %>%
  select(group_type, group, Q2)

# 2. 性别分组数据
gender_data <- survey %>%
  filter(!is.na(Q2)) %>%
  mutate(group_type = "性别",
         group = Gender) %>%
  select(group_type, group, Q2)

# 3. 宗教重要性分组数据
religion_data <- survey %>%
  filter(!is.na(Q2)) %>%
  mutate(group_type = "宗教重要性",
         group = Religion_Importance) %>%
  select(group_type, group, Q2)

# 合并所有数据
combined_data <- bind_rows(overall_data, gender_data, religion_data)

# 统一Q2的因子顺序（修正原代码中的Q20笔误）
combined_data <- combined_data %>%
  mutate(Q2 = factor(Q2, levels = c("Always or almost always", 
                                    "Generally", 
                                    "Not generally", 
                                    "Never or almost never")))

4. 绘制堆叠条形图

ggplot(combined_data, aes(x = group, fill = Q2)) +
  geom_bar(position = "fill", width = 0.7) +  # position="fill"实现百分比堆叠
  coord_flip() +  # 翻转坐标轴，方便阅读长标签
  facet_wrap(~group_type, scales = "free_y", ncol = 1) +  # 按分组类型分面，自由调整y轴
  labs(y = "占比(%)", x = "", fill = "Q2回答") +
  scale_y_continuous(labels = scales::percent) +  # 将y轴转为百分比显示
  theme_bw() +
  theme(
    strip.background = element_rect(fill = "#f0f0f0"),
    strip.text = element_text(face = "bold")
  )

关键调整说明 #

• 如果不需要展示某类分组（如Decline to answer），可在对应数据块添加filter(Gender != "Decline to answer")
• 可通过scale_fill_brewer()或scale_fill_manual()自定义填充颜色
• 调整facet_wrap的ncol参数可以改变分面的列数，比如ncol=2会分成两列显示

内容的提问来源于stack exchange，提问作者haley