CODEWITHSUNDEEP
rlistdataframenestednested-lists
Jerwin Wieser
Jerwin Wieser

Reputation: 125

R dataframe to nested list

I want to convert a dataframe in this format (tbl) to the following nested list (tbllst):

library(tidyr)

tbl <- tribble(
  ~Col1, ~Col2, ~Col3,
  "Var1", "Var1_1", "Var1_1_1", 
  "Var1", "Var1_1", "Var1_1_2", 
  "Var1", "Var1_2", "Var1_2_1", 
  "Var1", "Var1_2", "Var1_2_2", 
)

tbllst <- list(
  Col1 = list(
    "Var1" = list(
      Col2 = list(
        "Var1_1" = list(
          Col3 = c(
            "Var1_1_1", 
            "Var1_1_2"
          )
        ),
        "Var1_2" = list(
          Col3 = c(
            "Var1_2_1", 
            "Var1_2_2"
          )
        )
      )
    )
  )
)

Is there an automated way of achieving this?

Upvotes: 3

Views: 326

Answers (2)

ThomasIsCoding
ThomasIsCoding

Reputation: 102730

Here is another option using data.table + rrapply

library(data.table)
library(rrapply)

dt <- setDT(tbl)[, Map(function(...) list2DF(.(...)), names(.SD), .SD)]
rrapply(dt[, lapply(.SD, list), c(head(names(dt), -1))], how = "unmelt")

which gives

$Col1
$Col1$Var1
$Col1$Var1$Col2
$Col1$Var1$Col2$Var1_1
$Col1$Var1$Col2$Var1_1$Col3
[1] "Var1_1_1" "Var1_1_2"


$Col1$Var1$Col2$Var1_2
$Col1$Var1$Col2$Var1_2$Col3
[1] "Var1_2_1" "Var1_2_1"

Upvotes: 2

Joris C.
Joris C.

Reputation: 6244

The function rrapply() in the rrapply-package has an option how = "unmelt" that converts a melted data.frame to a nested list, where each row in the data.frame becomes a node path in the nested list.

To apply this function, we first need to transform the tbldata.frame to the input format that is required by rrapply():

library(purrr)
library(dplyr)
library(rrapply)

## put data.frame in format for rrapply-function
tbl1 <- imap_dfc(tbl, ~bind_cols(.y, .x)) %>%
  group_by(across(num_range(prefix = "...", range = 1:5))) %>%
  summarize(`...6` = list(c(`...6`)))

tbl1
#> # A tibble: 2 x 6
#> # Groups:   ...1, ...2, ...3, ...4 [2]
#>   ...1  ...2  ...3  ...4   ...5  ...6     
#>   <chr> <chr> <chr> <chr>  <chr> <list>   
#> 1 Col1  Var1  Col2  Var1_1 Col3  <chr [2]>
#> 2 Col1  Var1  Col2  Var1_2 Col3  <chr [2]>

## unmelt to nested list
ls_tbl <- rrapply(tbl1, how = "unmelt")

str(ls_tbl)
#> List of 1
#>  $ Col1:List of 1
#>   ..$ Var1:List of 1
#>   .. ..$ Col2:List of 2
#>   .. .. ..$ Var1_1:List of 1
#>   .. .. .. ..$ Col3: chr [1:2] "Var1_1_1" "Var1_1_2"
#>   .. .. ..$ Var1_2:List of 1
#>   .. .. .. ..$ Col3: chr [1:2] "Var1_2_1" "Var1_2_2"

Note that the purpose of the group_by() and summarize() operations is only to get multiple var1_%_% under a single Col3 node. The following is considerably easier (but does not produce exactly the same result):

ls_tbl <- rrapply(imap_dfc(tbl, ~bind_cols(.y, .x)), how = "unmelt")

str(ls_tbl)
#> List of 1
#>  $ Col1:List of 1
#>   ..$ Var1:List of 1
#>   .. ..$ Col2:List of 2
#>   .. .. ..$ Var1_1:List of 2
#>   .. .. .. ..$ Col3: chr "Var1_1_1"
#>   .. .. .. ..$ Col3: chr "Var1_1_2"
#>   .. .. ..$ Var1_2:List of 2
#>   .. .. .. ..$ Col3: chr "Var1_2_1"
#>   .. .. .. ..$ Col3: chr "Var1_2_2"

Upvotes: 3

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