Last updated: 2020-10-21

Checks: 2 0

Knit directory: rr_tools/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). 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 results in this page were generated with repository version 34cf544. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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:    .Rproj.user/

Untracked files:
    Untracked:  README.html
    Untracked:  analysis/exercise.rmd
    Untracked:  figure/

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 repository in which changes were made to the R Markdown (analysis/index.Rmd) and HTML (docs/index.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 34cf544 jean997 2020-10-21 wflow_publish(all = TRUE)
html 34cf544 jean997 2020-10-21 wflow_publish(all = TRUE)
html b0d3bd7 jean997 2020-10-21 Build site.
Rmd de9e202 jean997 2020-10-21 added more words
html 0bb9529 jean997 2020-10-21 Build site.
Rmd 7e6b37c jean997 2020-10-21 wflow_publish(“analysis/index.Rmd”)
html 2ce37af jean997 2020-10-21 Build site.
Rmd 06a1c66 jean997 2020-10-21 wflow_publish(c(“analysis/index.Rmd”, “analysis/file_org.rmd”,
Rmd f3d4d87 jean997 2020-10-21 add content to version_control plus some small edits
Rmd 0809fee jean997 2020-10-21 initial structure
Rmd fc35dfd jean997 2020-10-21 Start workflowr project.

Introduction

The aim of this website is to give an overview of several tools for reproducible statistical and data science research. Many of these tools will also help with workflow, organization, productivity, and your general sense of peace and well being as you go about your work.

Here are some caveats before we get going:

This website was built using workflowr which is covered in the section on “Workbooks and Reports” below!

What is Reproducibility?

Effort, Pain, and Learning Curves

Each step that you take towards reproducibility will require some modification of your work habits. It is natural to work initially in ways that turn out later to be messy and hard to recreate. This is why it is so common for people to lose files or send the wrong version of something. Learning new tools, installing software, and creating new habits all take up time and can be awkward and uncomfortable. The payoff is in fewer headaches down the road and confidence in the results you report or publish on.

Developing your work habits and your programming skills is an ongoing project. I organized the content below into categories that are about the order that I have adopted different habits but fee free to hop around.

Content

  1. File Names and Directory Structure
  2. Version Control (Git)
    • Git
  3. Code and Programming Practices
  4. Workbooks and Reports
    • R Markdown and Sweave with knitr
    • workflowr for project organization
    • Software environments
  5. Pipelines
    • Snakemake
    • DSC for simulations

Resources

Advanced R Book by Hadley Wickham

R Packages Book by Hadley Wickham

Efficient R by Collin Gillespe and Robin Lovelace

Happy Git with R by Jenny Bryan

R Markdown Cookbook by Yihui Xie, Christophe Dervieux, and Emily Riederer

Conda

workflowr developed by John Blischak

Snakemake

DSC