Introduction

Last updated on 2024-09-24 | Edit this page

Estimated time: 12 minutes

Overview

Questions

Why should we care about reproducibility?
How can targets help us achieve reproducibility?

Objectives

Explain why reproducibility is important for science
Describe the features of targets that enhance reproducibility

Instructor Note

Episode summary: Introduce the idea of reproducibility and why / who would want to use targets

What is reproducibility?

Reproducibility is the ability for others (including your future self) to reproduce your analysis.

We can only have confidence in the results of scientific analyses if they can be reproduced.

However, reproducibility is not a binary concept (not reproducible vs. reproducible); rather, there is a scale from less reproducible to more reproducible.

targets goes a long ways towards making your analyses more reproducible.

Other practices you can use to further enhance reproducibility include controlling your computing environment with tools like Docker, conda, or renv, but we don’t have time to cover those in this workshop.

What is `targets`?

targets is a workflow management package for the R programming language developed and maintained by Will Landau.

The major features of targets include:

Automation of workflow
Caching of workflow steps
Batch creation of workflow steps
Parallelization at the level of the workflow

This allows you to do the following:

return to a project after working on something else and immediately pick up where you left off without confusion or trying to remember what you were doing
change the workflow, then only re-run the parts that that are affected by the change
massively scale up the workflow without changing individual functions

… and of course, it will help others reproduce your analysis.

Who should use `targets`?

targets is by no means the only workflow management software. There is a large number of similar tools, each with varying features and use-cases. For example, snakemake is a popular workflow tool for python, and make is a tool that has been around for a very long time for automating bash scripts. targets is designed to work specifically with R, so it makes the most sense to use it if you primarily use R, or intend to. If you mostly code with other tools, you may want to consider an alternative.

The goal of this workshop is to learn how to use targets to reproducible data analysis in R.

Where to get more information

targets is a sophisticated package and there is a lot more to learn that we can cover in this workshop.

Here are some recommended resources for continuing on your targets journey:

The targets R package user manual by the author of targets, Will Landau, should be considered required reading for anyone seriously interested in targets.
The targets discussion board is a great place for asking questions and getting help. Before you ask a question though, be sure to read the policy on asking for help.
The targets package webpage includes documentation of all targets functions.
The tarchetypes package webpage includes documentation of all tarchetypes functions. You will almost certainly use tarchetypes along with targets, so it’s good to consult both.
Reproducible computation at scale in R with targets is a tutorial by Will Landau analyzing customer churn with Keras.
Recorded talks and example projects listed on the targets README.

About the example dataset

For this workshop, we will analyze an example dataset of measurements taken on adult foraging Adélie, Chinstrap, and Gentoo penguins observed on islands in the Palmer Archipelago, Antarctica.

The data are available from the palmerpenguins R package. You can get more information about the data by running ?palmerpenguins.

The three species of penguins in the palmerpenguins dataset. Artwork by @allison_horst.

The goal of the analysis is to determine the relationship between bill length and depth by using linear models.

We will gradually build up the analysis through this lesson, but you can see the final version at https://github.com/joelnitta/penguins-targets.

Key Points

We can only have confidence in the results of scientific analyses if they can be reproduced by others (including your future self)
targets helps achieve reproducibility by automating workflow
targets is designed for use with the R programming language
The example dataset for this workshop includes measurements taken on penguins in Antarctica