将虚拟变量转换为标签向量：DataFrame种族列格式转换需求

原始数据 #

df_before <- structure(list(ID = 1:4, White = c(1L, 0L, 0L, 0L), Black = c(0L, 1L, 0L, 0L), Asian = c(0L, 0L, 1L, 1L)), class = "data.frame", row.names = c(NA, -4L))

目标数据 #

df_after <- structure(list(ID = 1:4, Race = c("White", "Black", "Asian", "Asian")), class = "data.frame", row.names = c(NA, -4L))

解决方案 #

方法1：使用tidyverse（tidyr + dplyr）

通过宽表转长表筛选出有效分类：

library(tidyverse)

df_after <- df_before %>%
  pivot_longer(cols = -ID, names_to = "Race", values_to = "value") %>%
  filter(value == 1) %>%
  select(-value)

方法2：使用base R

通过列索引匹配分类名称：

race_cols <- c("White", "Black", "Asian")
df_after <- data.frame(
  ID = df_before$ID,
  Race = race_cols[max.col(df_before[, race_cols])]
)

方法3：使用dplyr逐行判断

通过条件分支直接映射分类：

library(dplyr)

df_after <- df_before %>%
  rowwise() %>%
  mutate(Race = case_when(
    White == 1 ~ "White",
    Black == 1 ~ "Black",
    Asian == 1 ~ "Asian"
  )) %>%
  ungroup() %>%
  select(ID, Race)

内容的提问来源于stack exchange，提问作者Jamie