Create a data.frame from a structured list

Converts a structured, column-oriented list into a flat data.frame, using the first element of the list to define the expected structure (length and names).

Usage

list_to_df_expand(x, category_col = "category", dropped_attr = "dropped")

Arguments

x

A named list. Each element is expected to have the same length and names as the first element.

category_col

Name of the column containing category labels. If NULL, no category column is added and category labels are used as row names instead.

dropped_attr

Name of the attribute used to store excluded elements (stored unchanged). If NULL, no such attribute is added.

Value

A data.frame with one row per category and one or more columns per retained list element.

Details

Only elements matching the structure of the first element are included in the result. Other elements are excluded. Optionally, excluded elements can be stored unchanged as an attribute.

Nested lists or multi-element vectors are expanded into multiple columns so that the returned data frame never contains list columns.

This function is intended for column-oriented or table-like list structures, such as those commonly found in JSON metadata or dimension specifications.

Note

This function is written and documented with help from ChatGPT.

Examples

x <- list(
  A = c(a = 1, b = 2, c = 3),
  B = list(
    a = c(x = 10, y = 20),
    b = c(x = 11, y = 21),
    c = c(x = 12, y = 22)
  ),
  bad = c(a = 1, b = 2)  # wrong length -> excluded
)

df <- list_to_df_expand(x)
df
#>   category A B.x B.y
#> 1        a 1  10  20
#> 2        b 2  11  21
#> 3        c 3  12  22

attr(df, "dropped")
#> $bad
#> a b 
#> 1 2 
#> 

# Use row names instead of a category column:
df2 <- list_to_df_expand(x, category_col = NULL)
df2
#>   A B.x B.y
#> a 1  10  20
#> b 2  11  21
#> c 3  12  22

# Disable storing excluded elements:
df3 <- list_to_df_expand(x, dropped_attr = NULL)
df3
#>   category A B.x B.y
#> 1        a 1  10  20
#> 2        b 2  11  21
#> 3        c 3  12  22