如何用tidyverse将列转为多布尔列？求简化重复代码方案

我有多组带时间后缀的category列，希望用mutate()和across()将其转换为每个类别对应的布尔列。目前通过重复3次相同代码实现了功能，但如果类别数量增多，这种方式会非常繁琐。尝试用map()简化代码但未成功，推测问题出在across()的.names参数无法与case_when()中使用的字符串相关联，尝试的简化代码无法运行，求助更简洁的实现方法。

原始实现代码 #

data <- data.frame(category_t1 = c("A","B","C","C","A","B"),
                   category_t2 = c("A","C","B","B","B",NA),
                   category_t3 = c("C","C",NA,"B",NA,"A"))

data %>% mutate(across(starts_with("category"), 
                       ~case_when(.x == "A" ~ TRUE, !is.na(.x) ~ FALSE),
                       .names = "{str_replace(.col, 'category', 'A')}"),
                across(starts_with("category"), 
                       ~case_when(.x == "B" ~ TRUE, !is.na(.x) ~ FALSE),
                       .names = "{str_replace(.col, 'category', 'B')}"),
                across(starts_with("category"), 
                       ~case_when(.x == "C" ~ TRUE, !is.na(.x) ~ FALSE),
                       .names = "{str_replace(.col, 'category', 'C')}"))

运行后输出 #

category_t1 category_t2 category_t3  A_t1  A_t2  A_t3  B_t1  B_t2  B_t3  C_t1  C_t2
1         A           A           C  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE
2         B           C           C FALSE FALSE FALSE  TRUE FALSE FALSE FALSE  TRUE
3         C           B        <NA> FALSE FALSE    NA FALSE  TRUE    NA  TRUE FALSE
4         C           B           B FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE FALSE
5         A           B        <NA>  TRUE FALSE    NA FALSE  TRUE    NA FALSE FALSE
6         B        <NA>           A FALSE    NA  TRUE  TRUE    NA FALSE FALSE    NA

尝试的简化代码（无法运行） #

data %>% mutate(across(starts_with("category"),
                       ~case_when(.x == mod ~ TRUE, !is.na(.x) ~ FALSE),
                       mod = levels(as.factor(data$category_t1)),
                       .names = "{str_replace(.col, 'category', mod)}"))

方法1：用purrr::map批量生成across调用 #

这是最贴近原始思路的简化方式，先提取所有唯一类别，再通过map遍历每个类别自动生成对应的across操作，避免重复代码：

library(dplyr)
library(purrr)
library(stringr)

data <- data.frame(category_t1 = c("A","B","C","C","A","B"),
                   category_t2 = c("A","C","B","B","B",NA),
                   category_t3 = c("C","C",NA,"B",NA,"A"))

# 提取所有非NA的唯一类别
unique_cats <- unique(unlist(data[, starts_with("category")])) %>% na.omit()

# 遍历每个类别，生成对应的across逻辑
cat_across <- map(unique_cats, function(cat) {
  across(starts_with("category"),
         ~case_when(.x == cat ~ TRUE, !is.na(.x) ~ FALSE),
         .names = str_replace(.col, "category", cat))
})

# 展开所有across操作并执行mutate
data %>% mutate(!!!cat_across)

方法2：长表转宽表（直观易读） #

通过长格式转换统一处理类别匹配，再转回宽格式，逻辑更清晰：

library(dplyr)
library(tidyr)

data %>%
  # 将category列转成长表，保留时间后缀信息
  pivot_longer(starts_with("category"), 
               names_to = "time", 
               values_to = "category") %>%
  # 为每个类别生成布尔标记
  mutate(
    A = category == "A" & !is.na(category),
    B = category == "B" & !is.na(category),
    C = category == "C" & !is.na(category)
  ) %>%
  # 转回宽格式，还原原始列结构
  pivot_wider(
    names_from = time,
    values_from = c(category, A, B, C),
    names_sep = "_"
  ) %>%
  # 调整列顺序与原始输出一致
  select(starts_with("category"), starts_with("A"), starts_with("B"), starts_with("C"))

内容的提问来源于stack exchange，提问作者jrdavalos