Last updated: 2019-04-09
Checks: 1 1
Knit directory: rrresearch/
This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
The R Markdown file has unstaged changes. To know which version of the R Markdown file created these results, you’ll want to first commit it to the Git repo. If you’re still working on the analysis, you can ignore this warning. When you’re finished, you can run wflow_publish to commit the R Markdown file and build the HTML.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.
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Ignored files:
Ignored: .DS_Store
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Ignored: analysis/.DS_Store
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Untracked files:
Untracked: analysis/05_literate-prog.Rmd
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Unstaged changes:
Modified: analysis/index.Rmd
Staged changes:
Modified: analysis/_site.yml
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 412fe0e | Anna Krystalli | 2019-04-09 | commit index |
| html | 558735a | Anna Krystalli | 2019-04-09 | commit draft docs |
| Rmd | 2462ad6 | Anna Krystalli | 2019-02-16 | Start workflowr project. |
Welcome to the R for Reproducible Research Resource site.
In order to ensure robustness of outputs and maximise the benefits of ACCE research to future researchers and society more generally, it is important to share the underlying code and data. But for sharing to have any impact, such materials need to be created FAIR (findable, accessible, interoperable, reusable), i.e. they must be adequately described, archived, and made discoverable to an appropriate standard.
Additionally, if analyses are to be deemed robust, they must be at the very least reproducible, but ideally well documented and reviewable.
R and Rstudio tools and conventions offer a powerful framework for making modern, open, reproducible and collaborative computational workflows more accessible to researchers.
This course focuses on data and project management through R and Rstudio, will introduce students to best practice and equip them with modern tools and techniques for managing data and computational workflows to their full potential. The course is designed to be relevant to students with a wide range of backgrounds, working with anything from relatively small sets of data collected from field or experimental observations, to those taking a more computational approach and bigger datasets.
Understand the basics of good research data management and be able to produce clean datasets with appropriate metadata.
Manage computational projects for reproducibility, reuse and collaboration.
Use version control to track the evolution of research projects.
Use R tools and conventions to document code and analyses and produce reproducible reports.
Be able to publish, share materials and collaborate through the web.
Understand why this all matters!
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