targets-dsfest

This repo contains slides and code for the talk 'Hit your reproducibility {targets}' at the UK Government Data Science Festival on 29 September 2020.

You can view the slides in your browser (press F for fullscreen, P for presenter notes and O for a slide overview).

What is {targets}?

The {targets} package by Will Landau is an R-focused solution for managing your analytical workflows.

What does that mean? {targets} ‘remembers’ the relationships between objects in your workflow. When you update your analysis, {targets} makes sure that only the impacted objects are re-run. This means you don’t have to recreate everything from scratch each time.

Demo code

This repo also includes files used as part of a {targets} demo used in the slides:

• targets.R contains the pipeline
• run-pipeline-beavers.R has code to execute and inspect the pipeline
• _targets/ is the cache of objects and metadata (like {targets} 'memory')
• report-beavers.Rmd is a dummy R Markdown report to be rendered in the pipeline
• report-beavers-mistake.html is a rendered version of the R Markdown file, but it has an error!
• report-beavers.html is the final rendered version of the R Markdown after the mistake is corrected

The slides describe an example using these materials, but in short:

1. tar_script() creates a _targets.R file, which is where you declare you write functions and options and create your targets with tar_targets(), declaring the pipeline with tar_pipeline()
2. tar_manifest() lets you check the configuration of your targets
3. tar_visnetwork visualises your pipeline as a graph network
4. tar_make() executes your pipeline, which caches outputs and metadata in a _targets/ directory that can be read from with tar_read() and tar_load()
5. tar_outdated() prints any targets that need to be updated following any changes to other targets, after which you can reinspect your pipeline and re-make it