基于规则条件标记R语言数据集ID的实现方案问询

样本数据集 #

首先定义示例数据集：

data <- data.frame(id = c(1,1,1,1,1,1, 2,2,2, 3,3,3),
                   cat1 = c("A","A","A","B","B","B", "A","A","A", "A","A","B"),
                   levels = c("L1","L3","L4","L2","L1","L3", "L1","L2","L2", "L1","L2","L1"))

样本数据预览：

id cat1 levels
1   1    A     L1
2   1    A     L3
3   1    A     L4
4   1    B     L2
5   1    B     L1
6   1    B     L3
7   2    A     L1
8   2    A     L2
9   2    A     L2
10  3    A     L1
11  3    A     L2
12  3    B     L1

标记规则 #

需按以下规则为每个id统一标记label：

• 规则a：若该id的cat1="A"对应的levels包含L3或L4，且存在cat1="B"，标记为Rule_satisfied
• 规则b：若该id的cat1="A"对应的levels仅包含L1或L2，且不存在cat1="B"，标记为Rule_NotSatisfied
• 规则c：若该id的cat1="A"对应的levels仅包含L1或L2，但存在cat1="B"，标记为Rule_violation

实现代码 #

使用dplyr包进行分组逻辑判断，步骤清晰且高效：

# 首次使用需先安装dplyr：install.packages("dplyr")
library(dplyr)

data.1 <- data %>%
  # 按id分组，确保同一id的标记统一
  group_by(id) %>%
  mutate(
    # 计算两个核心判断条件
    has_high_level = any(cat1 == "A" & levels %in% c("L3", "L4")),
    has_B = any(cat1 == "B"),
    # 根据规则匹配生成label
    label = case_when(
      has_high_level & has_B ~ "Rule_satisfied",
      !has_high_level & !has_B ~ "Rule_NotSatisfied",
      !has_high_level & has_B ~ "Rule_violation",
      TRUE ~ "Unknown" # 兜底逻辑，实际不会触发
    )
  ) %>%
  # 取消分组，恢复原始数据结构
  ungroup() %>%
  # 移除中间辅助计算列（可选操作）
  select(-has_high_level, -has_B)

输出结果 #

运行上述代码后，得到目标数据集：

id cat1 levels           label
1   1    A     L1  Rule_satisfied
2   1    A     L3  Rule_satisfied
3   1    A     L4  Rule_satisfied
4   1    B     L2  Rule_satisfied
5   1    B     L1  Rule_satisfied
6   1    B     L3  Rule_satisfied
7   2    A     L1 Rule_NotSatisfied
8   2    A     L2 Rule_NotSatisfied
9   2    A     L2 Rule_NotSatisfied
10  3    A     L1   Rule_violation
11  3    A     L2   Rule_violation
12  3    B     L1   Rule_violation

内容的提问来源于stack exchange，提问作者amisos55