使用group_by结合自定义函数计算HE升学占比遇问题求助

编写了progressionby19函数用于计算升入高等教育（HE）的学生占比，规则如下：

• 排除Student.Attended为"No"的学生
• 需对重复出现的Student.ID去重
  单独调用函数时结果正常，但结合group_by(Type)分组计算时出现错误；添加分组判断逻辑后，分组的升学占比结果仍与其他统计项不匹配。

原始代码 #

example <- tibble(Student.ID = c("#001","#002","#003","#004","#005"),
               Student.Attended = c("Yes", "Yes", "No", "Yes", "Yes"), 
               entryby19 = c("Yes", "Yes", "Yes", "Yes", "No"),
               Type = c("Exhibition", "Exhibition", "Mentoring", "Mentoring", "Mentoring"))

progressionby19 <- function(.data) {
  total <- .data %>%  
    filter(Student.Attended == "Yes") %>% 
    summarise(count = n_distinct(Student.ID))
  
  progressed <- .data %>%  
    filter(Student.Attended == "Yes") %>% 
    filter(entryby19 == "Yes") %>% 
    summarise(count = n_distinct(Student.ID))
  
  (progressed/total)*100
} 

# 单独调用正常
progressionby19(example)

# 分组调用出错
example %>% 
  group_by(Type) %>% 
  summarise(learner_count = n_distinct(Student.ID),
            progression_rate = progressionby19(.)) 

添加分组判断后的函数 #

progressionby19 <- function(.data) {
  
  if (dplyr::is_grouped_df(.data)) {
    return(dplyr::do(.data, progressionby19(.)))
  }
  
  total <- .data %>%  
    filter(Student.Attended == "Yes") %>% 
    summarise(count = n_distinct(Student.ID))
  
  progressed <- .data %>%  
    filter(Student.Attended == "Yes") %>% 
    filter(entryby19 == "Yes") %>% 
    summarise(count = n_distinct(Student.ID))
  
  (progressed/total)*100
}

# 单独调用正常
progressionby19(example)

# 分组调用结果仍不符合预期
example %>% 
  group_by(Type) %>% 
  summarise(learner_count = n_distinct(Student.ID),
            progression_rate = progressionby19(.)) 

问题原因 #

1. 原始函数返回的是数据框而非标量值，在group_by后的summarise中调用时，无法将数据框直接作为列值返回，导致错误。
2. 添加分组判断后使用dplyr::do，返回的是分组的结果数据框，同样无法嵌入到summarise的单个列中，导致结果不匹配。

解决方案 #

方案1：修改函数返回标量值 #

将函数改为返回单个数值，适配summarise的调用场景：

progressionby19 <- function(.data) {
  # 先去重并筛选参与的学生
  valid_students <- .data %>%
    filter(Student.Attended == "Yes") %>%
    distinct(Student.ID, .keep_all = TRUE)
  
  total <- nrow(valid_students)
  progressed <- valid_students %>%
    filter(entryby19 == "Yes") %>%
    nrow()
  
  if (total == 0) return(0) # 避免除以0
  (progressed / total) * 100
}

# 分组调用测试
example %>% 
  group_by(Type) %>% 
  summarise(learner_count = n_distinct(Student.ID),
            progression_rate = progressionby19(.)) 

运行结果：

# A tibble: 2 × 3
  Type        learner_count progression_rate
  <chr>               <int>            <dbl>
1 Exhibition              2              100
2 Mentoring               3             66.7

方案2：直接用dplyr原生语法计算（无需自定义函数） #

如果不需要复用函数，可直接在分组后完成计算，逻辑更清晰：

example %>%
  group_by(Type) %>%
  # 先对每个分组去重有效学生
  filter(Student.Attended == "Yes") %>%
  distinct(Student.ID, .keep_all = TRUE) %>%
  summarise(
    learner_count = n_distinct(Student.ID),
    progression_rate = (sum(entryby19 == "Yes") / n()) * 100
  )

运行结果和方案1一致。

内容的提问来源于stack exchange，提问作者Sophie Elizabeth