R语言数据集添加分组变量的更优实现方法咨询

你当前的实现采用eval(parse())配合循环的方式存在调试难度高、运行效率低、易触发语法错误的问题，以下是几种可实现完全相同效果的更优方案：

方案1：使用dplyr的case_when()（最推荐，简洁易读） #

这是目前同类需求下最常用的实现方式，代码逻辑清晰，无需手动处理判断优先级：

library(dplyr)

Items <- rep(c("cleats","whistle","VAR","Penalty","Bat","Pink Ball","White Ball","Super-Over","Hoops","Men's Rugby","Women's rugby"),5) 
dt <- data.frame(Items)  

dt <- dt %>% 
  mutate(Classification = case_when(
    Items %in% c('cleats','whistle','VAR','Penalty') ~ 'Football',
    Items %in% c('Bat','Pink Ball','White Ball','Super-Over') ~ 'Cricket',
    grepl('Rugby|rugby', Items) ~ 'Rugby',
    TRUE ~ 'Other Sports'
  ))

方案2：Base R 原生实现（无需加载第三方包） #

如果你不想引入依赖包，可以用嵌套ifelse实现，完全不用循环和表达式解析：

Items <- rep(c("cleats","whistle","VAR","Penalty","Bat","Pink Ball","White Ball","Super-Over","Hoops","Men's Rugby","Women's rugby"),5) 
dt <- data.frame(Items)  

dt$Classification <- ifelse(
  dt$Items %in% c('cleats','whistle','VAR','Penalty'), 'Football',
  ifelse(
    dt$Items %in% c('Bat','Pink Ball','White Ball','Super-Over'), 'Cricket',
    ifelse(
      grepl('Rugby|rugby', dt$Items), 'Rugby',
      'Other Sports'
    )
  )
)

方案3：高可维护性规则迭代实现（适合分类规则频繁调整的场景） #

如果你的分类规则经常需要增减，可以把规则单独抽离成配置，用purrr的迭代功能实现，修改规则不需要调整核心逻辑：

library(purrr)

Items <- rep(c("cleats","whistle","VAR","Penalty","Bat","Pink Ball","White Ball","Super-Over","Hoops","Men's Rugby","Women's rugby"),5) 
dt <- data.frame(Items)  

# 单独定义分类规则，新增规则仅需在此列表添加即可
rules <- list(
  list(pred = function(x) x %in% c('cleats','whistle','VAR','Penalty'), val = 'Football'),
  list(pred = function(x) x %in% c('Bat','Pink Ball','White Ball','Super-Over'), val = 'Cricket'),
  list(pred = function(x) grepl('Rugby|rugby', x), val = 'Rugby'),
  list(pred = function(x) rep(TRUE, length(x)), val = 'Other Sports')
)

# 迭代应用规则生成分类列
dt$Classification <- reduce(rules, function(res, rule) {
  ifelse(is.na(res) & rule$pred(dt$Items), rule$val, res)
}, .init = rep(NA_character_, nrow(dt)))

以上三种方案均规避了原实现中eval(parse())的安全风险和调试难题，运行效率和可维护性均优于原有循环实现。

内容的提问来源于stack exchange，提问作者Din