Chapter 6 Resources
This section provides a collection of useful resources related to reproducible research, open science, Git and GitHub, R Markdown, data management, transparency, and reproducible analytical workflows.
The resources below include:
- books,
- online courses,
- blogs,
- tutorials,
- policy frameworks,
- technical guides,
- and practical examples.
These materials can help deepen understanding of reproducible methodologies and support the development of reproducible workflows in research, industry, and public-sector analytics.
6.1 Blogs, websites, books, and courses
6.1.1 Reproducible research and open science
Matthew Shotwell’s slides (2011):
“Approaches and Barriers to Reproducible Practices in Biostatistics”NIH Training Module:
Reproducibility Through Rigor and TransparencyGandrud, Christopher.
Reproducible Research with R and RStudio, CRC Press, 2013.Xie, Yihui.
Dynamic Documents with R and knitr, CRC Press, 2013.ROpenSci blog post:
“Reproducible research is still a challenge”
by R. FitzJohn, M. Pennell, A. Zanne, and W. Cornwell.Karl Broman’s course:
“Tools for Reproducible Research”
University of Wisconsin–Madison.Johns Hopkins University course on Coursera:
“Reproducible Research”
by Roger Peng, Jeff Leek, and Brian Caffo.Stodden, Victoria, Friedrich Leisch, and Roger D. Peng (Editors).
Implementing Reproducible Research, CRC Press, 2014.ROpenSci reproducibility guide:
Reproducible Research and Open Science
6.1.2 Git and version control
StackOverflow discussion:
“Why should I use version control?”Learn GitHub interactively:
Learn Git on GitHubKarl Broman’s reproducibility tools course:
Tools for Reproducible ResearchBC Government policy framework:
BC Government Framework for GitHub
Version control systems such as Git are fundamental tools for collaborative, transparent, and reproducible analytical workflows.
6.1.3 R, R Markdown, and data science workflows
Reproducible Research with R and RStudio:
http://christophergandrud.github.io/RepResR-RStudio/Making slides with the Xaringan package in R Markdown:
https://arm.rbind.io/slides/xaringan.htmlData wrangling with R:
https://cengel.github.io/R-data-wrangling/Data cleaning with R and tidyverse:
https://towardsdatascience.com/data-cleaning-with-r-and-the-tidyverse-detecting-missing-values-ea23c519bc62Gallery of missing data visualization tools:
https://cran.r-project.org/web/packages/naniar/vignettes/naniar-visualisation.htmlHow R handles missing values:
https://stats.idre.ucla.edu/r/faq/how-does-r-handle-missing-values/
These resources provide practical examples for building reproducible analytical workflows using R and related tools.
6.1.4 Reproducibility and scientific challenges
What does research reproducibility mean?
https://stm.sciencemag.org/content/8/341/341ps12Challenge to scientists: Does your ten-year-old code still run?
https://www.nature.com/articles/d41586-020-02462-7
These resources discuss broader scientific and computational reproducibility challenges, including software sustainability, documentation, and long-term maintainability.
6.1.5 Data privacy and security
- Data privacy versus security:
https://dataprivacymanager.net/security-vs-privacy/
Reproducibility must often be balanced with privacy, confidentiality, and security requirements, particularly in public-sector and health-related analytics.
6.2 Additional suggested resources
Additional useful topics for further exploration include:
- FAIR data principles,
- open science frameworks,
- reproducible machine learning,
- computational notebooks,
- Quarto publishing,
- Docker and containerization,
- workflow automation,
- cloud-based collaboration tools,
- and research data management practices.
As reproducible workflows continue to evolve, staying familiar with emerging tools and practices becomes increasingly valuable.
