使用标签作为新列的值将列表转换为数据框（使用 R）答案

【问题标题】：Convert list into dataframe using tags as the values of a new column (with R)使用标签作为新列的值将列表转换为数据框（使用 R）
【发布时间】：2021-01-05 23:47:47
【问题描述】：

我有一个这样的列表：

require(tidyverse)
months <- c("january", "february", "march", "october", "december") 
weekdays <- c("Sunday", "Monday", "Tuesday")
seasons <- c("Summer", "Winter", "Fall", "autumn")
timeWords_list <- tibble::lst(months,  weekdays, seasons)

我想将此列表转换为两列-data.frame：我需要列表的所有值都在第一列并且标签是第二列的值

结果应该是这样的：

df <- data.frame(first_column  = c("january", "february", "march", "october", "december", "Sunday", "Monday", "Tuesday", "Summer", "Winter", "Fall", "autumn"), 
                 second_column = c("months", "months", "months", "months", "months", "weekdays", "weekdays" ,"weekdays", "seasons", "seasons", "seasons", "seasons"))

第二列可以是字符串或因子，但应与列表的标签相对应。请注意，列表可能有不同的大小，其元素可能有不同的长度。

【问题讨论】：

标签： r data-transform

【解决方案1】：

一个简单的选择是stack 来自base R

df1 <- stack(timeWords_list)
names(df1) <- c("first_column", "second_column")

【讨论】：

【解决方案2】：

我认为@akrun 的stack 方法是迄今为止最有效的方法。这是另一个使用 unlist + rep 的基本 R 选项

data.frame(
  first_col = unlist(timeWords_list, use.names = FALSE),
  second_col = rep(names(timeWords_list), lengths(timeWords_list))
)

给了

   first_col second_col
1    january     months
2   february     months
3      march     months
4    october     months
5   december     months
6     Sunday   weekdays
7     Monday   weekdays
8    Tuesday   weekdays
9     Summer    seasons
10    Winter    seasons
11      Fall    seasons
12    autumn    seasons

【讨论】：

【解决方案3】：

使用tibble::enframe 和tidyr::unnest

tibble::enframe(timeWords_list,name = 'first_column',value = 'second_column') %>%
  tidyr::unnest(second_column)

# first_column second_column
#   <chr>        <chr>        
# 1 months       january      
# 2 months       february     
# 3 months       march        
# 4 months       october      
# 5 months       december     
# 6 weekdays     Sunday       
# 7 weekdays     Monday       
# 8 weekdays     Tuesday      
# 9 seasons      Summer       
#10 seasons      Winter       
#11 seasons      Fall         
#12 seasons      autumn

【讨论】：