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https://github.com/wlandau/targets-minimal

A minimal example data analysis project with the targets R package
https://github.com/wlandau/targets-minimal

data-science high-performance-computing pipeline r reproducibility reproducible-research rstats statistics targets workflow

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A minimal example data analysis project with the targets R package

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>"

)

```



# `targets` package minimal example



[![Launch RStudio Cloud](https://img.shields.io/badge/RStudio-Cloud-blue)](https://rstudio.cloud/project/1430691)



This repository is an example data analysis workflow with [`targets`](https://docs.ropensci.org/targets). The pipeline reads the data from a file, preprocesses it, visualizes it, and fits a regression model. 



## How to access



You can try out this example project as long as you have a browser and an internet connection. [Click here](https://rstudio.cloud/project/1430691) to navigate your browser to an RStudio Cloud instance. Alternatively, you can clone or download this code repository and install the R packages [listed here](https://github.com/wlandau/targets-minimal/blob/03835c2aa4679dcf3f28c623a06d7505b18bee17/DESCRIPTION#L25-L30).



## How to run



1. Open the R console and call `renv::restore()` to install the required R packages.

2. call the [`tar_make()`](https://wlandau.github.io/targets/reference/tar_make.html) function to run the pipeline.

3. Then, call `tar_read(hist)` to retrieve the histogram. 

4. Experiment with [other functions](https://wlandau.github.io/targets/reference/index.html) such as [`tar_visnetwork()`](https://wlandau.github.io/targets/reference/tar_visnetwork.html) to learn how they work.



## File structure



The most important files are:



```{r, eval = FALSE}

├── _targets.R

├── R/

├──── functions.R

├── data/

├──── raw_data.csv

└── index.Rmd

```



File | Purpose

---|---

[`_targets.R`](https://github.com/wlandau/targets-minimal/blob/main/_targets.R) | The special R script that declares the [`targets`](https://docs.ropensci.org/targets) pipeline. See `tar_script()` for details.

[`R/functions.R`](https://github.com/wlandau/targets-minimal/blob/main/R/functions.R) | An R script with user-defined functions. Unlike [`_targets.R`](https://github.com/wlandau/targets-minimal/blob/main/_targets.R), there is nothing special about the name or location of this script. In fact, for larger projects, it is good practice to partition functions into multiple files.

[`data/raw_data.csv`](https://github.com/wlandau/targets-minimal/blob/main/data/raw_data.csv) | The raw `airquality` dataset.

[`index.Rmd`](https://github.com/wlandau/targets-minimal/blob/main/index.Rmd): an R Markdown report that reruns in the pipeline whenever the histogram of ozone changes ([details](https://books.ropensci.org/targets/files.html#literate-programming)).



## Continuous deployment



Minimal pipelines with low resource requirements are appropriate for continuous deployment. For example, when this particular GitHub repository is updated, its `targets` pipeline runs in a [GitHub Actions workflow](https://github.com/wlandau/targets-minimal/actions). The workflow pushes the results to the [`targets-runs`](https://github.com/wlandau/targets-minimal/tree/targets-runs) branch, and [GitHub Pages](https://pages.github.com/) hosts the latest version of the rendered R Markdown report at . Subsequent runs restore the output files from the previous run so that up-to-date targets do not rebuild. Follow these steps to set up continuous deployment for your own minimal pipeline:



1. Ensure your project stays within the storage and compute limitations of GitHub (i.e. your pipeline is minimal). For storage, you may choose the [AWS-backed storage formats](https://books.ropensci.org/targets/cloud.html#storage) (e.g. `tar_target(..., format = "aws_qs")`) for large outputs to reduce the burden on GitHub storage.

2. Ensure GitHub Actions are enabled in the Settings tab of your GitHub repository's website.

3. Set up your project with [`renv`](https://rstudio.github.io/renv/) ([details here](https://rstudio.github.io/renv/articles/ci.html)).

    * Call `targets::tar_renv(extras = character(0))` to write a `_packages.R` file to expose hidden dependencies.

    * Call `renv::init()` to initialize the `renv` lockfile `renv.lock` or `renv::snapshot()` to update it.

    * Commit `renv.lock` to your Git repository.

4. Write the [`.github/workflows/targets.yaml`](https://github.com/wlandau/targets-minimal/blob/main/.github/workflows/targets.yaml) workflow file using `targets::tar_github_actions()` and commit this file to Git.

5. Push to GitHub. A GitHub Actions workflow should run the pipeline and upload the results to the `targets-runs` branch of your repository. Subsequent runs should add new commits but not necessarily rerun targets.