如何合并含特定列不同值的R数据框并处理性别列？

首先修正输入数据的小问题：原代码中id向量长度（4）与其他列（5）不匹配，会触发错误，先修正为长度一致的版本：

# 修正后的输入数据
dfnames1 <- data.frame(id = c(1,2,3,4,5), name1 = c("Helen", "Von", "Erik", "Brook", "Adel"), gender = c("F", "Neutral", "M", "Neutral", "F"))
dfnames2 <- data.frame(id = c(1,2,3,4,5), name2 = c("Helen", "Von", "Erik", "Brook", "Adel"), gender2 = c("Neutral", "M", "M", "Uni", "M"))

解决方案 #

步骤1：合并数据框 #

通过id主键将两个数据框合并，这里提供基础R和tidyverse两种实现方式：

基础R合并

merged_df <- merge(dfnames1, dfnames2, by = "id")

tidyverse合并（需先加载dplyr）

library(dplyr)
merged_df <- inner_join(dfnames1, dfnames2, by = "id")

步骤2：按规则处理性别列 #

根据需求的规则，我们先将gender2中的"Uni"映射为"M"（匹配预期输出），再编写逻辑处理性别：

基础R实现

# 替换"Uni"为"M"
merged_df$gender2[merged_df$gender2 == "Uni"] <- "M"

# 定义性别处理函数
process_gender <- function(g1, g2) {
  non_neutral <- c(g1, g2)[c(g1, g2) != "Neutral"]
  if (length(non_neutral) == 0) {
    return("Neutral")
  } else if (length(unique(non_neutral)) == 1) {
    return(unique(non_neutral))
  } else if (all(c("F", "M") %in% non_neutral)) {
    return("FM")
  }
}

# 应用函数生成最终性别列
merged_df$gender <- mapply(process_gender, merged_df$gender, merged_df$gender2)

# 整理成目标格式
dfnames <- merged_df[, c("id", "name1", "gender")]
names(dfnames)[2] <- "name"

tidyverse实现

dfnames <- merged_df %>%
  mutate(
    # 替换"Uni"为"M"
    gender2 = ifelse(gender2 == "Uni", "M", gender2),
    # 按规则生成最终性别
    gender = case_when(
      gender == "Neutral" & gender2 == "Neutral" ~ "Neutral",
      gender == "Neutral" ~ gender2,
      gender2 == "Neutral" ~ gender,
      (gender %in% c("F", "M") & gender2 %in% c("F", "M") & gender != gender2) ~ "FM",
      TRUE ~ gender
    )
  ) %>%
  select(id, name = name1, gender)

最终结果 #

运行上述代码后，得到符合预期的数据框：

> dfnames
  id    name gender
1  1  Helen      F
2  2    Von      M
3  3   Erik      M
4  4  Brook      M
5  5   Adel     FM

内容的提问来源于stack exchange，提问作者Erik Brole