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tutorizeR

tutorizeR helps teachers convert .Rmd and .qmd material into interactive learnr or quarto-live resources with linting, reusable MCQ banks, and export/report tooling.

Part of the research ecosystem

This repository is part of Aurélien Nicosia's open research and teaching ecosystem in computational statistics, scientific R software, reproducible data science and statistical education.

Installation

# install.packages("remotes")
remotes::install_github("AurelienNicosiaULaval/tutorizeR")

To use r-universe:

install.packages(
  "tutorizeR",
  repos = c(
    "https://aureliennicosiaulaval.r-universe.dev",
    "https://cloud.r-project.org"
  )
)

End-to-end workflow

library(tutorizeR)

# 1) Load reusable question bank
qb <- load_question_bank("inst/question-bank")

# 2) Lint source before conversion
lint <- lint_source("lesson.qmd", question_bank = qb, strict = FALSE)
print(lint)

# 3) Convert with mixed MCQ strategy (inline + bank)
rep <- tutorize(
  input = "lesson.qmd",
  format = "learnr",
  assessment = "both",
  question_bank = qb,
  mcq_source = "mixed",
  lint_strict = TRUE,
  overwrite = TRUE
)

print(rep)

# 4) Export conversion report JSON for CI tracing
write_tutorize_report(rep, "lesson-report.json", format = "json")

# 5) Export LMS manifest
manifest <- export_lms_manifest("lesson.qmd", profile = "canvas")
print(manifest)

Reproducibility checklist (reviewer/journal-ready)

# 1) Install dependencies
Rscript -e 'remotes::install_github("AurelienNicosiaULaval/tutorizeR")'

# 2) Lint and tests
Rscript -e "lintr::lint_package()"
Rscript -e "devtools::test()"

# 3) Build and CRAN-style check from a tarball
R CMD build .
R CMD check --as-cran --no-manual tutorizeR_0.4.3.tar.gz

# 4) Manual smoke path (requires learnr in the environment)
Rscript -e "library(tutorizeR); tutorize('tests/testthat/fixtures/rmd/basic_code.Rmd', format = 'learnr', overwrite = TRUE, output_dir = tempdir(), verbose = FALSE)"

Expected on this repository:

JOSS submission note

For JOSS, you submit the manuscript source (paper/paper.md) and bibliography (paper/paper.bib). You do not need to attach a PDF in the repository for submission. If you want a local PDF preview, render it with:

cd paper
Rscript -e "rmarkdown::render('paper.md', output_format = 'pdf_document', output_file = 'paper.pdf')"

For reviewers/authors, full submission steps are in:

JOSS 2026 scope checks (important):

Main API

Teacher tags

Inside R chunks:

MCQ block schemas

Explicit question block:

```{tutorizeR-mcq}
question: "2 + 2 = ?"
answers:
  - text: "4"
    correct: true
  - text: "5"
    correct: false


Question-bank reference block:

```text
```{tutorizeR-mcq-ref}
ids: [mean-basic, sum-basic]
strategy: ordered
shuffle_answers: false


## Addins

- Convert active file
- Convert folder
- Preview conversion (Source / Output / Diff / Lint / Logs)

## CLI mode

```bash
Rscript inst/scripts/tutorizeR-cli.R --input=lesson.qmd --format=learnr --assessment=both --overwrite=true
Rscript inst/scripts/tutorizeR-cli.R --dir=course_material --recursive=true --format=learnr

Known limitations

Documentation