如何拆分R数据框中含换行符的单元格生成新列？

原始数据复现 #

首先加载示例数据框：

df <- structure(list(
  `Frequency\nPercent` = c("car", "window", "ball", "ups"),
  AI = c("2\n0.00", "3\n0.00", "1\n0.00", "2\n0.00"),
  BLK = c("0\n0.00", "218\n0.29", "48\n0.06", "0\n0.00"),
  HIANIC = c("1\n0.00", "8\n0.01", "4\n0.01", "0\n0.00"),
  NATRICAN = c("9\n0.01", "7\n0.01", "8\n0.01", "0\n0.00"),
  UNK = c("15\n0.02", "83\n0.11", "36\n0.05", "0\n0.00"),
  yy = c("111\n0.15", "897\n1.20", "756\n1.02", "1\n0.00")
), class = "data.frame", row.names = c(NA, -4L))

方法一：使用tidyverse工具链批量处理 #

借助dplyr和tidyr的批量处理能力，高效拆分所有目标列：

library(tidyverse)

# 拆分并命名新列，同时转换数据类型
df_processed <- df %>%
  mutate(
    across(
      -`Frequency\nPercent`, 
      ~ str_split_fixed(., "\n", 2) %>% 
        as_tibble(.name_repair = ~ paste0(cur_column(), c("_freq", "_percent"))) %>%
        # 将频率转为整数，百分比转为数值
        mutate(
          across(ends_with("_freq"), as.integer),
          across(ends_with("_percent"), as.numeric)
        )
    )
  ) %>%
  unnest(cols = -`Frequency\nPercent`)

方法二：Base R 原生实现 #

无需额外安装包，用Base R函数完成拆分：

# 筛选需要拆分的列（排除第一列）
split_cols <- setdiff(names(df), "Frequency\nPercent")

# 遍历拆分每个列
split_list <- lapply(split_cols, function(col) {
  split_data <- str_split_fixed(df[[col]], "\n", 2)
  colnames(split_data) <- paste0(col, c("_freq", "_percent"))
  # 转换数据类型
  split_data[, 1] <- as.integer(split_data[, 1])
  split_data[, 2] <- as.numeric(split_data[, 2])
  as.data.frame(split_data)
})

# 合并结果
df_processed_base <- cbind(df[, "Frequency\nPercent", drop = FALSE], do.call(cbind, split_list))

两种方法最终都会生成包含原始分类列（Frequency\nPercent）和所有拆分后的频率、百分比列的数据框，例如AI列会拆分为AI_freq（整数类型）和AI_percent（数值类型）。

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