在R语言中如何基于地区列表匹配州并汇总死亡数据？

现有数据 #

a) 地区-州代码映射列表 #

regions <- list(
    west = c("WA", "OR", "CA", "NV", "AZ", "ID", "MT", "WY",
                     "CO", "NM", "UT"),
    south = c("TX", "OK", "AR", "LA", "MS", "AL", "TN", "KY",
                        "GA", "FL", "SC", "NC", "VA", "WV"),
    midwest = c("KS", "NE", "SD", "ND", "MN", "MO", "IA", "IL",
                            "IN", "MI", "WI", "OH"),
    northeast = c("ME", "NH", "NY", "MA", "RI", "VT", "PA", 
                                "NJ", "CT", "DE", "MD", "DC")
)

b) 州死亡数数据集（示例） #

# A tibble:
  state      Deaths 
  <chr>          <int>    
1 AL             29549      
2 AK               741      
3 AR             50127   
4 NJ            15142   
5 CA            175213   
6 IA            1647   
...

最优实现方法 #

推荐使用tidyverse工具链（dplyr + purrr）完成，步骤清晰且处理效率高：

完整代码 #

library(tidyverse)

# 将regions列表转换为长格式映射表
region_mapping <- imap_dfr(regions, ~ tibble(region = .y, state = .x))

# 假设原死亡数数据集名为death_data，执行匹配与汇总
death_summary <- death_data %>%
  left_join(region_mapping, by = "state") %>%
  group_by(region) %>%
  summarize(total_deaths = sum(Deaths, na.rm = TRUE))

关键步骤说明 #

• imap_dfr()：遍历regions列表时同时保留地区名（列表元素名）和对应州代码，直接合并为标准tibble，比基础R的stack()更易控制列名
• left_join()：确保原数据中所有州都被保留，未匹配到地区的州（比如示例中的AK）会标记为NA，可根据需求后续处理
• sum(..., na.rm = TRUE)：避免因存在未匹配的NA值导致汇总结果出错

基础R替代方案 #

如果不想加载tidyverse包，也可以用基础R实现：

# 转换regions为数据框
region_mapping <- stack(regions)
colnames(region_mapping) <- c("state", "region")

# 合并数据并按地区汇总
merged_data <- merge(death_data, region_mapping, by = "state", all.x = TRUE)
death_summary <- aggregate(Deaths ~ region, data = merged_data, sum, na.rm = TRUE)

内容的提问来源于stack exchange，提问作者Nicolás Sacco