Summary and Schedule

This is a lesson about how to use the targets R package for maintaining efficient data analysis workflows.

	Setup Instructions	Download files required for the lesson
Duration: 00h 00m	1. Introduction	Why should we care about reproducibility? How can targets help us achieve reproducibility?
Duration: 00h 12m	2. First targets Workflow	What are best practices for organizing analyses? What is a _targets.R file for? What is the content of the _targets.R file? How do you run a workflow?
Duration: 00h 24m	3. Loading Workflow Objects	Where does the workflow happen? How can we inspect the objects built by the workflow?
Duration: 00h 36m	4. The Workflow Lifecycle	What happens if we re-run a workflow? How does targets know what steps to re-run? How can we inspect the state of the workflow?
Duration: 00h 48m	5. Best Practices for targets Project Organization	What are best practices for organizing targets projects? How does the organization of a targets workflow differ from a script-based analysis?
Duration: 01h 00m	6. Managing Packages	How should I manage packages for my targets project?
Duration: 01h 12m	7. Working with External Files	How can we load external data?
Duration: 01h 24m	8. Branching	How can we specify many targets without typing everything out?
Duration: 01h 36m	9. Parallel Processing	How can we build targets in parallel?
Duration: 01h 48m	10. Reproducible Reports with Quarto	How can we create reproducible reports?
Duration: 02h 00m	11. Deploying Targets on HPC	Why would we use HPC to run Targets workflows? How can we run Targets workflows on Slurm?
Duration: 02h 12m	Finish

The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.

Running on OnDemand

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1. Go to https://ondemand.hpc.wehi.edu.au
2. Click RStudio Latest
3. Use the regular partition
4. Use 4.2.3 as the R version
5. In the “Additional Modules” box, add quarto/1.1.189 pandoc/2.3.1 cmake/3.25.1
6. Set the “Runtime hours” to 5
7. Set the CPUs to 4
8. Set the memory to 10 GB
9. In the console, paste in the following command to install the package dependencies:

R

install.packages(
  c(
    "conflicted",
    "palmerpenguins",
    "quarto",
    "tarchetypes",
    "targets",
    "tidyverse",
    "visNetwork",
    "crew",
    "crew.cluster"
  )
)

Your submission page should look like this:

Installation Problems?

Make sure Global Options → Python → “Automatically activate project-local Python environments” is unchecked!