Analyze Relationships and Linkages Across Datasets in an R Database

Evaluates common key variables (e.g., subject identifiers, visit times) across multiple datasets in an R database list. Computes cross-table overlap, record coverage, key cardinality (1:1, 1:many, many:many), and identifies orphan records.

Usage

database_relationships(db, id_cols = NULL)

# S3 method for class 'cbe_database_relationships'
as_tbl_graph(x, ...)

find_shared_keys(db)

check_key_integrity(
  db,
  id_col,
  master_dataset = NULL,
  compare = FALSE,
  tolerance = 1e-07,
  ...
)

Arguments

db

A named list of data frames (an R database).

id_cols

Optional character vector of key/ID column names to evaluate. If NULL, shared keys are automatically detected across datasets.

x

A cbe_database_relationships object.

...

Additional arguments passed to compare_df.

id_col

Character string specifying the identifier column to check.

master_dataset

Character string naming the primary cohort table. If provided, checks which IDs in other tables are absent from the master cohort table.

compare

Logical; if TRUE and master_dataset is supplied, runs compare_df on overlapping variables between the master dataset and child datasets, attaching the comparison results.

tolerance

Numeric comparison tolerance passed to compare_df.

Value

A tibble summarizing dataset pairs, shared key columns, record counts, overlap counts, coverage percentages, and cardinality.

Examples

db <- list(
  patients = data.frame(id = 1:5, age = c(20, 30, 40, 50, 60)),
  vitals = data.frame(id = c(1, 1, 2, 2, 3, 6), bp = c(120, 122, 130, 132, 110, 115))
)
database_relationships(db)
#> # A tibble: 1 × 11
#>   from_table to_table key_column from_rows to_rows from_unique_keys
#>   <chr>      <chr>    <chr>          <int>   <int>            <int>
#> 1 patients   vitals   id                 5       6                5
#> # ℹ 5 more variables: to_unique_keys <int>, shared_keys <int>,
#> #   from_coverage_pct <dbl>, to_coverage_pct <dbl>, cardinality <chr>