Build an audit from a wide matrix of scores

For auditing studies that have no INSTAR sheet, which is most of the published literature and will be for a long time. The usual shape is one row per paper, one column per framework item, scored by a reader.

Usage

audit_from_matrix(scores, id = NULL)

Arguments

scores

A data frame in wide form.

id

Optional name of the column identifying each study. If omitted, the first non-item column is used, and failing that the row number.

Value

An object of class instar_audit.

Details

Columns whose names match an item_id in instar_items are treated as items. Every other column is carried through as study metadata, so a journal or year column in the input becomes a grouping variable in summary(audit, by = ) without any further work.

Cell values are read leniently, because scoring sheets are made by people: Y, yes, TRUE, and 1 all mean reported; N, no, FALSE, and 0 mean not reported; NA, N/A, -, and empty cells mean not applicable. C (conditional) counts as reported, matching the framework's applicability codes.

Examples

scores <- data.frame(
  doi = c("10.1/a", "10.1/b"),
  journal = c("J Exp Biol", "Behav Ecol"),
  subjects_taxon = c("Y", "Y"),
  subjects_n = c("Y", "N"),
  env_field = c("NA", "Y")
)
audit <- audit_from_matrix(scores, id = "doi")
#> ℹ 15 framework items not present in `scores` and left out of the audit:
#>   subjects_source, proc_handling, proc_anaesthesia, proc_biosecurity,
#>   ethics_review, ethics_endpoints, ethics_statement, nutrition_diet,
#>   env_housing, env_acclimation, health_monitoring, health_injury, fate_end,
#>   behaviour_general, and affect_indicators.
summary(audit)
#>          item_id                            item      domain      group
#> 1 subjects_taxon Taxonomic ID, life stage, & sex    Subjects foundation
#> 2     subjects_n         Sample size & attrition    Subjects foundation
#> 3      env_field         Field site & collection Environment    welfare
#>   n_studies reported not_reported not_applicable applicable percent_reported
#> 1         2        2            0              0          2              100
#> 2         2        1            1              0          2               50
#> 3         2        1            0              1          1              100