A look at {Targets}

An introduction to the R package for pipeline development

Lifting data handling and analysis into pipelines is an important step towards reproducible, automated, and robust workflows. In this blog post, I outline how researchers can use the R package targets to set up such a pipeline.

We will also use my own R package flod to generate some synthetic flow cytometry data and the tidyverse suite for general data wrangling and plotting.

library(flod)

NB! `flod` is under heavy development and subject to sweeping changes

library(targets)
library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.6
✔ forcats   1.0.1     ✔ stringr   1.6.0
✔ ggplot2   4.0.1     ✔ tibble    3.3.0
✔ lubridate 1.9.4     ✔ tidyr     1.3.2
✔ purrr     1.2.0     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors