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Reads a standalone Betwixt review HTML file and reconstructs the original candidate state, the persisted reviewed state, review metadata, and review provenance.

Usage

read_review(path)

Arguments

path

A single character string giving the path to a standalone Betwixt review HTML file.

Value

A named list with four elements:

metadata

Review-level metadata including title, description, project_id, sequence, and status.

provenance

Review provenance including the original and saved filenames, creation and lifecycle timestamps, reviewer name and email, and the optional review-level comment.

candidate

A data frame reconstructing the candidate state presented for review. It contains row number, evidence fields, descriptive fields, and candidate semantic assertion values.

reviewed

A data frame reconstructing the persisted reviewed state. It contains the current descriptive and semantic values, assertion qualifications, row finalisation state, row outcome, and optional row-level comments.

Empty optional evidence fields and comments are returned as NA_character_. row_number is returned as integer and reviewed$finalised as logical.

Details

read_review() does not finalise semantic knowledge or construct a subsequent stabilised semantic state. It reconstructs the review artefact as saved by Betwixt.

The returned candidate and reviewed data frames preserve the same evidence, descriptive, and semantic fields. The reviewed data additionally contains assertion qualifications, row finalisation state, row outcome, and optional row-level comments.

Review-wide information such as reviewer identity, lifecycle timestamps, and the review-level comment is returned separately in provenance.

Betwixt review files preserve both original candidate values and the current reviewed values in machine-readable HTML attributes and controls. read_review() uses these persisted values to reconstruct the two states independently.

A finalised Betwixt review is a completed review artefact. Reading such a file does not promote reviewed assertions into a subsequent stabilised semantic state or apply them to an external knowledge system.

Examples

if (FALSE) { # \dontrun{
review <- read_review("muis-garments-review_1-finalised.html")

review$metadata
review$provenance
review$candidate
review$reviewed
} # }