Last updated: 2019-12-05

Checks: 2 0

Knit directory: reproducible_bioinformatics/

This reproducible R Markdown analysis was created with workflowr (version 1.5.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

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.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

Ignored files:
    Ignored:    .Rhistory

Unstaged changes:
    Deleted:    assets/new_rmarkdown.png

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
Rmd f19271d Dave Tang 2019-12-05 wflow_publish(files = c(“analysis/docker.Rmd”, “analysis/index.Rmd”))
html 7b114c5 First Last 2019-12-04 Build site.
Rmd a4180a4 First Last 2019-12-04 wflow_publish(files = c(“analysis/conda.Rmd”, “analysis/index.Rmd”))
html 60a5900 First Last 2019-12-04 Build site.
html d83e8cb davetang 2019-12-03 Build site.
Rmd 88b592b davetang 2019-12-03 New workflowr project
Rmd 4e0dfff davetang 2019-12-03 Start workflowr project.

My aim for the workshop is to introduce computational tools and demonstrate how they can be used to help promote reproducibility when performing bioinformatic analyses. Many of these tools, especially workflowr help adhere to these Ten Simple Rules for Reproducible Computational Research:

Links to workshop material.