R语言数据处理需求：分组取最小价格并实现宽表转换

初始数据与现有代码 #

用户提供的初始数据框：

df <- data.frame(
  code1 = c("ZAZ","ZAZ","ZAZ","ZAZ","ZAZ","ZAZ","JOZ","JOZ","JOZ","JOZ","JOZ","JOZ","TSV","TSV"), 
  code2 = c("NAN","NAN","NAN","NAN","NAN","NAN","NAN","NAN","NAN","NAN","NAN","NAN","TSA","TSA"),
  start = c("Date1.1","Date1.1","Date1.3","Date1.3","Date1.5","Date1.5","Date3.1","Date3.1","Date3.3","Date3.3","Date3.5","Date3.5","Date 5.1","Date 5.1"),
  end = c("Date2.1","Date2.1","Date2.3","Date2.3","Date2.5","Date2.5","Date4.1","Date4.1","Date4.3","Date4.3","Date4.5","Date4.5","Date6.1","Date6.1"),
  price = c(1,2,3,4,5,6,1,2,3,4,5,6,1,2)
)

已编写的代码片段：

df <- df %>% 
  group_by(code1, code2,start,end) %>% 
  slice_min(price) #%>% 
  #group_modify()
df <- df[order(df$price),]

需求1：分组筛选每组最小price记录 #

你的现有代码已经完成了核心逻辑，这里优化一下命名和分组状态：

df_filtered <- df %>% 
  group_by(code1, code2, start, end) %>% 
  slice_min(price) %>% 
  ungroup() # 取消分组，避免后续操作受分组状态影响

执行后，每个(code1, code2, start, end)组合只会保留price最小的行，比如ZAZ-NAN-Date1.1-Date2.1组会留下price=1的记录。

需求2：以code1、code2为键重塑数据（最多3组start/end/price） #

结合group_modify()实现分组重塑，具体代码和说明如下：

完整实现代码 #

df_reshaped <- df_filtered %>%
  group_by(code1, code2) %>%
  # 为每个分组内的记录添加序号（1到3，不足3条的后续自动填充NA）
  mutate(row_num = row_number()) %>%
  # 使用group_modify处理每个分组
  group_modify(function(.x, .y) {
    # 将当前分组的长格式数据转为宽格式
    .x %>%
      pivot_wider(
        names_from = row_num,
        values_from = c(start, end, price),
        names_glue = "{.value}{row_num}" # 生成start1、end1、price1这类列名
      )
  }) %>%
  ungroup()

group_modify()用法说明 #

• group_modify()需要传入一个自定义函数，函数包含两个固定参数：
  • .x：当前分组对应的子数据框（比如code1=ZAZ, code2=NAN的所有筛选后记录）
  • .y：当前分组的键值信息（这里是包含code1和code2的单行数据框）
• 在函数内部，我们用pivot_wider()将每个分组的长格式数据转为宽格式，最多生成3组start/end/price列，分组内记录不足3条的列会自动填充NA。

最终结果示例 #

df_reshaped的结构如下（截取部分行）：

内容的提问来源于stack exchange，提问作者JohnDoe34