Last updated: 2019-04-10

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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.

R Markdown file: up-to-date

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Repository version: a3cfb2c

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    Ignored:    analysis/.DS_Store
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Unstaged changes:
    Modified:   analysis/_site.yml

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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 a3cfb2c annakrystalli 2019-04-10 update navmenu
html 3bf423c Anna Krystalli 2019-04-10 add setup
html f001244 Anna Krystalli 2019-04-09 correct right navbar icon
html 0e2d0ed Anna Krystalli 2019-04-09 add icons to navbar
html 3339a89 Anna Krystalli 2019-04-09 update navbar in docs
Rmd 9f84775 Anna Krystalli 2019-04-09 Refine index
html cd7663f Anna Krystalli 2019-04-09 update site yml
Rmd f848f40 Anna Krystalli 2019-04-09 Update index content
html 74ffa63 Anna Krystalli 2019-04-09 commit site
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.

Course description

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.


By the end of the workshop, participants will be able to:

Schedule

Day 1

Introduction

Research Data Management

  • Data Hygiene
  • Good filenaming
  • Metadata

Research Code Management

Literate Programming

  • Intoduction to long form documentation in Rmarkdown

Git & GitHub through Rstudio

  • Version control
  • Sharing code

Day 2

Research Code Management

Collaborative Git & GitHub

  • Collaborating on code

Packaging code

  • Writing & documenting functions
  • Capturing metadata incl. dependencies
  • Checking & Testing functions

Putting it all together: a Research Compendium

  • Creating a research compendium

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.