如果我们想要获得单个数据集，请使用map循环list，然后使用bind_rows

library(purrr)
library(dplyr)
map_dfr(riddle, bind_rows)

如果要移除0行或0列的数据集，请使用递归函数检查alldim属性的值是否大于0

library(rrapply)
riddle2 <- rrapply(riddle, condition = function(x) all(dim(x)>0), 
     classes = "data.frame", how= "prune")

• 比较结构
  一个二个一个一个
  对于不同类型的更新版本，可以转换为单一类型，绑定后再使用type.convert

rrapply(riddle, condition = function(x) all(dim(x)>0),
  f =  function(x) x %>% 
  mutate(across(everything(), as.character)),
     classes = "data.frame", how= "flatten") %>%
  bind_rows %>%
  type.convert(as.is = TRUE)

• 输出

# A tibble: 35 × 3
       a b     c    
   <int> <chr> <chr>
 1     0 g     0    
 2     0 f     0    
 3     0 e     0    
 4     0 d     0    
 5     0 c     0    
 6     0 b     0    
 7     0 a     0    
 8     0 8     g    
 9     0 8     f    
10     0 8     e    
# … with 25 more rows

如果有重复的列名，我们可以使用make.unique使其唯一，因为data.frame需要唯一的列名。

rrapply(riddle, condition = function(x) all(dim(x)>0),  
    f =  function(x) 
      {
     # change to unique column names
     names(x) <- make.unique(names(x))
   x %>%  
        # convert all columns to character if there
        # are mismatch in column types in any list elements
        mutate(across(everything(), as.character))
     },      classes = "data.frame", how= "flatten") %>% 
     # bind the flattened list of data.frame/tibbles to single dataset
    bind_rows %>%
     # do the column type conversion 
     type.convert(as.is = TRUE)