pipeflow

A beginner-friendly framework for building and managing data analysis pipelines in R.

https://github.com/rpahl/pipeflow

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A beginner-friendly framework for building and managing data analysis pipelines in R.

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
require(pipeflow)
require(badger)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#",
  prompt = F,
  fig.path = "images/README-",
  tidy = FALSE,
  cache = FALSE,
  out.width = "100%"
)

old <- options(width = 100L)
```



[![CRAN release](https://www.r-pkg.org/badges/version/pipeflow)](https://cran.r-project.org/package=pipeflow)
[![Dependencies](https://tinyverse.netlify.app/badge/pipeflow)](https://CRAN.R-project.org/package=pipeflow)
[![Code coverage](https://codecov.io/gh/rpahl/pipeflow/branch/main/graph/badge.svg)](https://app.codecov.io/gh/rpahl/pipeflow)
[![R-CMD-check status](https://github.com/rpahl/pipeflow/workflows/R-CMD-check/badge.svg)](https://github.com/rpahl/pipeflow/actions)
[![Test coverage](https://github.com/rpahl/pipeflow/workflows/test-coverage/badge.svg)](https://github.com/rpahl/pipeflow/actions)
[![Linter status](https://github.com/rpahl/pipeflow/workflows/lint/badge.svg)](https://github.com/rpahl/pipeflow/actions)

[![Downloads per month](https://cranlogs.r-pkg.org/badges/last-month/pipeflow)](https://cran.r-project.org/package=pipeflow)
[![Downloads total](https://cranlogs.r-pkg.org/badges/grand-total/pipeflow)](https://cran.r-project.org/package=pipeflow)
[![Last commit](https://img.shields.io/github/last-commit/rpahl/pipeflow.svg)](https://github.com/rpahl/pipeflow/commits/main)
[![Lifecycle status](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)



# pipeflow 

A lightweight yet powerful framework for building robust data
analysis pipelines. With `pipeflow`, you initialize a pipeline with your
dataset and construct workflows step by step simply by adding R functions.
You can modify, remove, or insert steps and parameters at any stage,
while `pipeflow` ensures the pipeline's integrity.

Thanks to its intuitive interface, using `pipeflow` quickly pays off in
the beginning while in the long run will help you to keep a clear and
structured overview of your project.



### Why use `pipeflow`

* Easy to learn yet suited for growingly complex workflows
* Automatically manages function and parameter dependencies
* Promotes structured and modular code
* Facilitates reusability and collaboration
* Simplifies error handling and debugging

### Installation

```{r, eval = FALSE}
# Install release version from CRAN
install.packages("pipeflow")

# Install development version from GitHub
devtools::install_github("rpahl/pipeflow")
```


### Usage

```{r}
library(pipeflow)
```

### Getting Started

It is recommended to read the vignettes in the order they are listed below:

* [Get started with pipeflow](https://rpahl.github.io/pipeflow/articles/v01-get-started.html)
* [Modifying existing pipelines](https://rpahl.github.io/pipeflow/articles/v02-modify-pipeline.html)
* [Combining pipelines](https://rpahl.github.io/pipeflow/articles/v03-combine-pipelines.html)
* [Collecting output](https://rpahl.github.io/pipeflow/articles/v04-collect-output.html)


### Advanced topics

* [How to use pipeflow with split data sets](https://rpahl.github.io/pipeflow/articles/v05-split-and-combine.html)
* [How pipelines can modify themselves at runtime](https://rpahl.github.io/pipeflow/articles/v06-self-modify-pipeline.html)


```{r, include = FALSE}
options(old)
```

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