Build fast interactive data analysis pipelines that scale.
{pipeflow} simply lets you add R functions one by one, wiring them into a pipeline that stays consistent as you go. Modify, remove, or insert steps at any stage, and manage all parameters in one place.
Thanks to its intuitive interface, using {pipeflow} quickly pays off in the beginning while in the long run helps keeping a clear and structured overview of your project.
- Lightweight and intuitive API
- All parameters managed in one place
- Pipeline verified at definition time
- Filter pipeline steps via views
- Branch and merge pipeline steps
- Fast dependency resolution (C++-powered DAG)
- Subset pipelines with data.table-style
[filters - Embed reusable workflows in steps (nested pipelines)
# Install release version from CRAN
install.packages("pipeflow")
# Install development version from GitHub
devtools::install_github("rpahl/pipeflow")library(pipeflow)
p <- pip_new("demo") |>
pip_add("numbers", \(n = 5) seq_len(n)) |>
pip_add("squared", \(x = ~numbers) x^2) |>
pip_add("total", \(x = ~squared) sum(x))
p
# <pipeflow> demo (3 steps)
# -------------------------
# step params depends state
# 1: numbers n new
# 2: squared x numbers new
# 3: total x squared new
# -------------------------
# <ready> last run: never
pip_run(p)
# info [2026-09-27 14:59:46.748 UTC]: Starting run of pipeflow 'demo'
# info [2026-09-27 14:59:46.748 UTC]: Step 1/3 numbers
# info [2026-09-27 14:59:46.750 UTC]: Step 2/3 squared
# info [2026-09-27 14:59:46.752 UTC]: Step 3/3 total
# info [2026-09-27 14:59:46.753 UTC]: Finished run of pipeflow 'demo'
pip_collect(p)
# $numbers
# [1] 1 2 3 4 5
#
# $squared
# [1] 1 4 9 16 25
#
# $total
# [1] 55It is recommended to read the vignettes in the order they are listed below: