Coverage breakdowns as a flat tibble

Accessor returning the per-dimension coverage breakdowns of an sm_coverage_audit() result as a single flat (long) tibble, optionally filtered to one dimension.

Usage

sm_coverage_breakdowns(x, dimension = NULL, tidy = TRUE)

Arguments

x

An sm_coverage object.

dimension

Optional dimension name (e.g. "year") to filter to.

tidy

Logical (default TRUE). The stable long contract: returns columns dimension, level, n_reference, n_matched, recall, n_corpus, precision, f1. With tidy = FALSE, the legacy recall-only shape (dimension, level, n_reference, n_matched, recall) is returned for backward compatibility.

Value

A tibble (see tidy). Type-stable: a 0-row tibble with the documented columns when there are no breakdowns. Slices present on only one side (corpus vs reference) carry NA in the other side's counts/metric.

Details

This accessor honours the package's accessor return-type stability contract (see scimapR-stability): with tidy = TRUE the column set is guaranteed and will only change via a lifecycle deprecation.

See also

sm_coverage_audit(), scimapR-stability

Other coverage: sm_coverage_audit(), sm_journal_in_index(), sm_match_types(), sm_reconcile()

Examples

corpus <- sm_example_corpus(n_works = 30, seed = 1)
ref <- corpus$works[1:25, c("work_id", "doi", "title", "year")]
cov <- sm_coverage_audit(corpus, ref, by = "year")
sm_coverage_breakdowns(cov, dimension = "year")
#> # A tibble: 9 × 8
#>   dimension level n_reference n_matched recall n_corpus precision    f1
#>   <chr>     <chr>       <int>     <int>  <dbl>    <int>     <dbl> <dbl>
#> 1 year      2015            1         1      1        1     1     1    
#> 2 year      2016            2         2      1        2     1     1    
#> 3 year      2017            2         2      1        3     0.667 0.8  
#> 4 year      2018            2         2      1        2     1     1    
#> 5 year      2020            6         6      1        8     0.75  0.857
#> 6 year      2021            3         3      1        4     0.75  0.857
#> 7 year      2022            3         3      1        4     0.75  0.857
#> 8 year      2023            3         3      1        3     1     1    
#> 9 year      2024            3         3      1        3     1     1