在R语言中按分组变量将指定列导出为独立CSV文件

需求说明 #

将单个DataFrame按subject_nr分组，每个分组导出为独立CSV文件（文件名如101.csv、102.csv），且仅保留item、start_time、end_time三列数据。

示例数据 #

df <- data.frame("subject_nr" = c("101", "101", "101", "102", "102", "103", "103", "103"),
                 "item" = c("a", "b", "c", "a", "b", "a", "b", "c"),
                 "start_time" = c(50, 52, 55, 53, 54.5, 12, 15, 17),
                 "end_time" = c(51, 54, 60, 53.5, 55.5, 14, 16.5, 19),
                 "extra_variable"= c("s", "t", "u", "v", "w", "x", "y", "z"))

预期输出示例（101.csv） #

"item","start_time","end_time"
"a",50,51
"b",52,54
"c",55,60

实现方法 #

方法1：基础R实现

无需额外安装包，用split()拆分数据框，再通过lapply()遍历导出：

# 按subject_nr拆分数据框
split_df <- split(df, df$subject_nr)

# 遍历每个子集，导出指定列到CSV
lapply(names(split_df), function(subj) {
  # 提取当前分组的指定列
  subset_data <- split_df[[subj]][, c("item", "start_time", "end_time")]
  # 导出CSV，文件名用分组名，不包含行名
  write.csv(subset_data, file = paste0(subj, ".csv"), row.names = FALSE)
})

方法2：tidyverse工具链实现

使用dplyr处理分组，purrr执行批量导出：

# 首次使用需安装包
# install.packages("tidyverse")
library(tidyverse)

# 筛选指定列 -> 按subject_nr分组 -> 批量导出
df %>%
  select(item, start_time, end_time, subject_nr) %>%
  group_by(subject_nr) %>%
  group_walk(function(data, key) {
    write.csv(data[, -4], file = paste0(key$subject_nr, ".csv"), row.names = FALSE)
  })

更简洁的写法：

df %>%
  select(item, start_time, end_time, subject_nr) %>%
  group_split(subject_nr) %>%
  walk(function(subset) {
    subj <- unique(subset$subject_nr)
    write.csv(subset[, -4], paste0(subj, ".csv"), row.names = FALSE)
  })

内容的提问来源于stack exchange，提问作者user9974638