useR!2017 tutorial: Data Carpentry -- Open and Reproducible Science with R
Three hour mini-lesson based on Reproducible Science Curriculum material. This presentation is a modification of a 2-hour lesson put together by Hilmar Lapp presented at the CBB Retreat in Wrightsville Beach on Sep 26, 2015.
Further resources
These are also in the last slide of the presentation. If you have others, we welcome pull requests.
- Entire suppl. doc generated from Rmarkdown: Finnegan et al. 2015. “Paleontological Baselines for Evaluating Extinction Risk in the Modern Oceans.” Science 348 (6234): 567–70.
- Data Analysis for the Life Sciences - a book completely written in R markdown
- FitzJohn et al. 2014. “How Much of the World Is Woody?” The Journal of Ecology. doi:10.1111/1365-2745.12260. Start to end replicable analysis on Github.
- Boettiger et al. “RNeXML: A Package for Reading and Writing Richly Annotated Phylogenetic, Character, and Trait Data in R.” Methods in Ecology and Evolution, September. Code archive and DOI assignment at Zenodo
Related reading:
- Ram, Karthik. 2013. “Git Can Facilitate Greater Reproducibility and Increased Transparency in Science.” Source Code for Biology and Medicine 8 (1): 7.
- Sandve et al. 2013. “Ten Simple Rules for Reproducible Computational Research.” PLoS Computational Biology 9 (10). Public Library of Science: e1003285.
- Boettiger, Carl. 2015. “An Introduction to Docker for Reproducible Research.” ACM SIGOPS Operating Systems Review 49 (1). ACM: 71–79.