如何在R中避免同一列分组重复统计已出现的EPISODE值？

数据背景 #

已通过cut()函数将PRODUCT_TIME分类为CATEGORY，数据结构如下：

df <- structure(list(EPISODE = c(1, 1, 1, 2, 2, 2, 2, 2, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4), PRODUCT_TIME = c(1, 2, 1, 1, 2, 3, 4, 4, 5, 6, 8, 10, 1, 2, 1, 5, 8, 2), CATEGORY = c("1", "2", "1", "1", "2", "3 to 5", "3 to 5", "3 to 5", "3 to 5", "6 to 10", "6 to 10", "6 to 10", "1", "2", "1", "3 to 5", "6 to 10", "2")), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -18L))

数据预览：

CATEGORY为有序变量，顺序为："1"、"2"、"3 to 5"、"6 to 10"。

需求 #

1. 统计每个CATEGORY中不同的EPISODE值数量；
2. 后续分组统计时，排除之前分组中已出现过的EPISODE值。

解决方案 #

利用dplyr和purrr包，按有序分类逐步筛选首次出现的EPISODE：

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

# 将CATEGORY转为有序因子，保证处理顺序正确
df$CATEGORY <- factor(df$CATEGORY, levels = c("1", "2", "3 to 5", "6 to 10"), ordered = TRUE)

# 提取每个分类对应的唯一EPISODE集合
category_episodes <- df %>%
  group_by(CATEGORY) %>%
  summarize(episodes = list(unique(EPISODE))) %>%
  arrange(CATEGORY)

# 初始化已出现的EPISODE集合
seen_episodes <- c()

# 遍历每个分类，计算新增的唯一EPISODE
result <- map_dfr(category_episodes$episodes, function(eps) {
  new_eps <- setdiff(eps, seen_episodes)
  # 更新已出现集合
  seen_episodes <<- union(seen_episodes, new_eps)
  tibble(
    count = length(new_eps),
    episodes = list(new_eps)
  )
})

# 合并结果并格式化输出
final_result <- bind_cols(category_episodes, result) %>%
  mutate(
    output = case_when(
      count == 0 ~ str_glue('分组 "{CATEGORY}" = 0个唯一EPISODE值'),
      count == 1 ~ str_glue('分组 "{CATEGORY}" = 1个唯一EPISODE值 ({episodes[[1]]})'),
      count >= 2 ~ str_glue('分组 "{CATEGORY}" = {count}个唯一EPISODE值 ({paste(episodes[[1]], collapse = ", ")})')
    )
  ) %>%
  pull(output)

# 打印结果
print(final_result)

运行结果 #

[1] "分组 \"1\" = 3个唯一EPISODE值 (1, 2, 4)"
[2] "分组 \"2\" = 0个唯一EPISODE值"          
[3] "分组 \"3 to 5\" = 1个唯一EPISODE值 (3)"  
[4] "分组 \"6 to 10\" = 0个唯一EPISODE值"     

内容的提问来源于stack exchange，提问作者Jobolo