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reproducr: Behavioural Reproducibility Auditing for R Projects

Source: R/reproducr-package.R
reproducr-package.Rd

You finish an analysis. The code runs. The numbers look right. But are they stable?

reproducr makes behavioural reproducibility risks visible and trackable. It scans your scripts for known silent breaking changes, flags stochastic calls missing set.seed(), certifies analytical outputs as baselines, and detects numerical drift across runs.

Workflow

Tier 1 – Scan & score

report <- audit_script("analysis.R")
risks  <- risk_score(report)
print(risks)

Tier 2 – Baseline & drift

model <- lm(mpg ~ wt, data = mtcars)
certify(list(coefs = coef(model)), tag = "submission-v1")

# Later, after any environment change:
check_drift(list(coefs = coef(model)), against = "submission-v1")

Tier 3 – Report & export

repro_report(report, risks, format = "html", style = "pharma")
repro_badge(report, risks, output = "README")

Key functions

FunctionPurpose
audit_script()Parse a script and extract all pkg::fn calls
risk_score()Check calls against the breaking-changes database
certify()Hash and store analytical outputs as a baseline
check_drift()Compare current outputs against a stored baseline
list_certs()List all certifications in a .reproducr file
repro_report()Render a human-readable audit report
repro_badge()Generate a reproducibility status badge
check_db_staleness()Check database entries against current CRAN versions

The breaking-changes database

The internal database covers known silent breaking changes in: dplyr, tidyr, ggplot2, readr, purrr, stringr, broom, data.table, lme4, lubridate, and base R. Community contributions are welcome – see vignette("contributing-to-the-database").

The database is kept current via a weekly GitHub Actions workflow that calls check_db_staleness() and opens an issue automatically when any entry's to_version ceiling falls below the current CRAN release.

See also

Useful links:

Author

Maintainer: Ndoh Penn ndohpenn9@gmail.com (ORCID)