References
- Allaire, J., Cheng, J., Xie, Y., McPherson, J., Chang, W., Allen, J., Wickham, H., Atkins, A., and Hyndman, R. (2016), rmarkdown: Dynamic Documents for R, R package version 0.9.6. Available at https://CRAN.R-project.org/package=rmarkdown
- Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., and Horton, N. J. (2014), “R markdown: Integrating a Reproducible Analysis Tool Into Introductory Statistics,” Technology Innovations in Statistics Education, 8.
- Boettiger, C. (2015), “An Introduction to Docker for Reproducible Research,” ACM SIGOPS Operating Systems Review, 49, 71–79.
- Bostock, M. (2013), “Why Use Make,” available at https://bost.ocks.org/mike/make/. Accessed: 2016-09-15.
- Broman, K. (2016), “Minimal Make: A Minimal Tutorial on Make,” available at http://kbroman.org/minimal_make/.
- Bryan, J. (2018), “Excuse Me, Do You Have a Moment to Talk About Version Control?” The American Statistician, this issue.
- Edwards, S. H. (2003), “Using Test-Driven Development in the Classroom: Providing Students With Automatic, Concrete Feedback on Performance,” in Proceedings of the International Conference on Education and Information Systems: Technologies and Applications EISTA (vol. 3), Citeseer.
- Finzer, W. (2013), “The Data Science Education Dilemma,” Technology Innovations in Statistics Education, 7.
- Horton, N., Baumer, B., and Wickham, H. (2014), “Teaching Precursors to Data Science in Introductory and Second Courses in Statistics,” in Conference Proceedings of ICOTS-9, Flagstaff, AZ.
- Kaplan, D. (2007), “Computing and Introductory Statistics,” Technology Innovations in Statistics Education, 1.
- Knuth, D. E. (1984), “Literate Programming,” The Computer Journal, 27, 97–111.
- Loeliger, J., and McCullough, M. (2012), Version Control With Git: Powerful Tools and Techniques for Collaborative Software Development, Sebastapol, CA: O’Reilly Media, Inc.
- Nolan, D., and Lang, D. T. (2010), “Computing in the Statistics Curricula,” The American Statistician, 64, 97–107.
- R Core Team (2016), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/
- Ram, K. (2013), “Git Can Facilitate Greater Reproducibility and Increased Transparency in Science,” Source Code for Biology and Medicine, 8, 7.
- RStudio Team (2016), RStudio: Integrated Development Environment for R, Boston, MA: RStudio, Inc., available at http://www.rstudio.com/
- Rundel, C. (2017), “GitHub Classroom Tools,” available at https://github.com/rundel/ghclass.
- Stallman, R. M., McGrath, R., and Smith, P. D. (2016), GNU Make: A Program for Directing Recompilation. Available at https://www.gnu.org/software/make/.
- Waller, L. A. (2017), “Documenting and Evaluating Data Science Contributions in Academic Promotion in Departments of Statistics and Biostatistics,” bioRxiv. Available at http://www.biorxiv.org/content/early/2017/01/25/103093.
- Wilcox, C. (2015), “The Role of Automation in Undergraduate Computer Science Education,” in Proceedings of the 46th ACM Technical Symposium on Computer Science Education, ACM, pp. 90–95.
- Workgroup, A. S. A. U. G. (2014), “The 2014 American Statistical Association Curriculum Guidelines for Undergraduate Programs in Statistical Science,” available at http://www.amstat.org/education/curriculumguidelines.cfm.
- Xie, Y. (2015), Dynamic Documents with R and knitr (2nd ed.), Boca Raton, FL: Chapman and Hall.
- ——— (2016), knitr: A General-Purpose Package for Dynamic Report Generation in R, R package version 1.13.1.