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
- Go to https://ondemand.hpc.wehi.edu.au
- Click RStudio Latest
- Use the
regular
partition - Use
4.2.3
as the R version - In the “Additional Modules” box, add
quarto/1.1.189 pandoc/2.3.1 cmake/3.25.1
- Set the “Runtime hours” to 5
- Set the CPUs to 4
- Set the memory to 10 GB
- 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: