Introduction to R

SAiGENCI, University of Adelaide, Adelaide, SA 5000, AustraliaAdelaide Centre of Epigenetics, School of Biomedicine, University of Adelaide, Adelaide, SA 5000, Australia
nora.liu@adelaide.edu.au

workflowr

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Last updated: 2025-11-30

Checks: 2 0

Knit directory: Introduction_to_R/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: up-to-date

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.

Repository version: 51d13a0

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 51d13a0. 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:    .DS_Store
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  .Rprofile
    Untracked:  .gitattributes
    Untracked:  .gitignore
    Untracked:  Introduction_to_R.Rproj
    Untracked:  _workflowr.yml
    Untracked:  analysis/
    Untracked:  code/
    Untracked:  data/
    Untracked:  output/

Unstaged changes:
    Modified:   README.md

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.

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There are no past versions. Publish this analysis with wflow_publish() to start tracking its development.

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Welcome to the 2025 EMBL Australia PhD Course: Introduction to R.

In this two hour session, we will cover the essentials of working with biological data in R, including:

• Importing and manipulating data

• performing simple dimension reduction

• Visualising gene expression using plots such as boxplots and heatmaps and carrying out basic statistical tests

• Building simple machien learning classifier

This workshop is designed for participants with little or no coding or bioinformatics experience. We will work through the code together, and I encourage you to type out all of the code yourself if possible. Along the way, there will be a few short challenge exercises for you to try.