This function processes nested data frames representing tables from hierarchical clusters, and transforms them into a flat, easy-to-read format. Each column corresponds to a dplyr::distinct variable, simplifying downstream analysis and use.
Arguments
- list_independent_tables
A list of nested tibbles, typically the output of
grp_tab_in_cluster(), where each tibble represents independent tables grouped by clusters.
Value
A list of unnested tibbles, where each tibble contains the following columns:
table_name: The name of the table.field: The field name associated with the table.indicator: Indicators related to the table.spanning_*: Columns derived from the spanning metadata (expanded into multiple columns).hrc_spanning_*: Columns derived from hierarchical spanning metadata (expanded into multiple columns).
Examples
if (FALSE) { # \dontrun{
# Example data
data(metadata_pizza_lettuce)
# Convert wide metadata to long format
metadata_pizza_lettuce_long <- wide_to_long(metadata_pizza_lettuce)
# Identify hierarchical relationships
list_hrc_identified <- identify_hrc(metadata_pizza_lettuce_long)
# Split tables into clusters
list_split <- split_in_clusters(list_hrc_identified)
# Detect inclusion relationships
list_desc_links <- create_edges(list_split)
# Group tables based on inclusion relationships
list_translation_tables <- grp_tab_names(list_desc_links)
# Regroup tables within each cluster
list_independent_tables <- grp_tab_in_cluster(list_split, list_translation_tables)
# Flatten the nested data for downstream use
list_tab_to_treat <- tab_to_treat(list_independent_tables)
# View structure of the results
str(list_tab_to_treat)
} # }