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https://github.com/ropensci/targets
Function-oriented Make-like declarative workflows for R
https://github.com/ropensci/targets
data-science high-performance-computing make peer-reviewed pipeline r r-package r-targetopia reproducibility reproducible-research rstats targets workflow
Last synced: 20 days ago
JSON representation
Function-oriented Make-like declarative workflows for R
- Host: GitHub
- URL: https://github.com/ropensci/targets
- Owner: ropensci
- License: other
- Created: 2019-08-01T17:33:25.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-13T21:19:15.000Z (7 months ago)
- Last Synced: 2024-04-14T09:57:15.190Z (7 months ago)
- Topics: data-science, high-performance-computing, make, peer-reviewed, pipeline, r, r-package, r-targetopia, reproducibility, reproducible-research, rstats, targets, workflow
- Language: R
- Homepage: https://docs.ropensci.org/targets/
- Size: 5.84 MB
- Stars: 863
- Watchers: 17
- Forks: 68
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
- jimsghstars - ropensci/targets - Function-oriented Make-like declarative workflows for R (R)
README
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# targets 
[](https://github.com/ropensci/software-review/issues/401)
[](https://doi.org/10.21105/joss.02959)
[](https://zenodo.org/badge/latestdoi/200093430)
[](https://wlandau.github.io/targetopia/)
[](https://CRAN.R-project.org/package=targets)
[](https://www.repostatus.org/#active)
[](https://github.com/ropensci/targets/actions?query=workflow%3Acheck)
[](https://app.codecov.io/gh/ropensci/targets)
[](https://github.com/ropensci/targets/actions?query=workflow%3Alint)
Pipeline tools coordinate the pieces of computationally demanding analysis projects. The `targets` package is a [Make](https://www.gnu.org/software/make/)-like pipeline tool for statistics and data science in R. The package skips costly runtime for tasks that are already up to date, orchestrates the necessary computation with implicit parallel computing, and abstracts files as R objects. If all the current output matches the current upstream code and data, then the whole pipeline is up to date, and the results are more trustworthy than otherwise.
## Philosophy
A pipeline is a computational workflow that does statistics, analytics, or data science. Examples include forecasting customer behavior, simulating a clinical trial, and detecting differential expression from genomics data. A pipeline contains tasks to prepare datasets, run models, and summarize results for a business deliverable or research paper. The methods behind these tasks are user-defined R functions that live in R scripts, ideally in a folder called `"R/"` in the project. The tasks themselves are called "targets", and they run the functions and return R objects. The `targets` package orchestrates the targets and stores the output objects to make your pipeline efficient, painless, and reproducible.
## Prerequisites
1. Familiarity with the [R programming language](https://www.r-project.org/), covered in [R for Data Science](https://r4ds.had.co.nz/).
1. [Data science workflow management techniques](https://rstats.wtf/index.html).
1. [How to write functions](https://r4ds.had.co.nz/functions.html) to prepare data, analyze data, and summarize results in a data analysis project.
## Installation
If you are using `targets` [with `crew` for distributed computing](https://books.ropensci.org/targets/crew.html), it is recommended to use `crew` version `0.4.0` or higher.
```{r, eval = FALSE}
install.packages("crew")
```
There are multiple ways to install the `targets` package itself, and both the latest release and the development version are available.
Type | Source | Command
---|---|---
Release | CRAN | `install.packages("targets")`
Development | GitHub | `remotes::install_github("ropensci/targets")`
Development | rOpenSci | `install.packages("targets", repos = "https://dev.ropensci.org")`
## Get started in 4 minutes
The 4-minute video at demonstrates the example pipeline used in the [walkthrough](https://books.ropensci.org/targets/walkthrough.html) and [functions](https://books.ropensci.org/targets/functions.html) chapters of the [user manual](https://books.ropensci.org/targets/). Visit for the code and to try out the code in a browser (no download or installation required).
[](https://vimeo.com/700982360)
## Usage
To create a pipeline of your own:
1. [Write R functions](https://books.ropensci.org/targets/functions.html) for a pipeline and save them to R scripts (ideally in the `"R/"` folder of your project).
1. Call [`use_targets()`](https://docs.ropensci.org/targets/reference/use_targets.html) to write key files, including the vital `_targets.R` file which configures and defines the pipeline.
1. Follow the comments in `_targets.R` to fill in the details of your specific pipeline.
1. Check the pipeline with [`tar_visnetwork()`](https://docs.ropensci.org/targets/reference/tar_visnetwork.html), run it with [`tar_make()`](https://docs.ropensci.org/targets/reference/tar_make.html), and read output with [`tar_read()`](https://docs.ropensci.org/targets/reference/tar_read.html). [More functions](https://docs.ropensci.org/targets/reference/index.html) are available.
## Documentation
* [User manual](https://books.ropensci.org/targets/): in-depth discussion about how to use `targets`. The most important chapters are the [walkthrough](https://books.ropensci.org/targets/walkthrough.html), [help guide](https://books.ropensci.org/targets/help.html), and [debugging guide](https://books.ropensci.org/targets/debugging.html).
* [Reference website](https://docs.ropensci.org/targets/): formal documentation of all user-side functions, the statement of need, and multiple design documents of the internal architecture.
* [Developer documentation](https://books.ropensci.org/targets-design/): software design documents for developers contributing to the deep internal architecture of `targets`.
## Help
Please read the [help guide](https://books.ropensci.org/targets/help.html) to learn how best to ask for help using `targets`.
## Courses
* [Carpentries workshop](https://carpentries-incubator.github.io/targets-workshop/) by [Joel Nitta](https://github.com/joelnitta)
* [Half-day interactive tutorial with a Keras example](https://github.com/wlandau/targets-tutorial)
## Selected talks
### English
* [Get started with `targets` in 4 minutes (4:08)](https://vimeo.com/700982360)
* [`targets` in Action](https://ropensci.org/commcalls/jan2023-targets/) with [Joel Nitta](https://github.com/joelnitta) and [Eric Scott](https://github.com/Aariq). rOpenSci Community Call (1:09:56).
* [`targets` and `crew` for clinical trial simulation pipelines](https://www.youtube.com/watch?v=cyF2dzloVLo&list=PLMtxz1fUYA5C0csy8-wTfFmRYQEB30vr3). R/Pharma 2023 (1:57:22).
* [`targets` and `stantargets` for Bayesian model validation pipelines](https://youtu.be/HJI5mQJRGpY). R/Medicine 2021 (15:33)
* [Reproducible computation at scale in R with `targets`](https://youtu.be/Gqn7Xn4d5NI) New York Open Statistical Programming Meetup, December 2020 (1:54:28).
* [ds-incubator series, 2021](https://www.youtube.com/playlist?list=PLvgdJdJDL-APJqHy5CXs6m4N7hUVp5rb4) by [Mauro Lepore](https://github.com/maurolepore).
### Español
* [Introducción a targets](https://www.youtube.com/watch?v=Vj312AfdpBo). Irene Cruz, R-Ladies Barcelona, 2021-05-25 (1:25:12).
### 日本語
* [Bio"Pack"athon, 2022-03-31 (1:04:10)](https://togotv.dbcls.jp/20220331.html), [Joel Nitta](https://github.com/joelnitta)
## Example projects
* [Four-minute example](https://github.com/wlandau/targets-four-minutes)
* [Minimal example](https://github.com/wlandau/targets-minimal)
* [Machine learning with Keras](https://github.com/wlandau/targets-keras)
* [Validate a minimal Stan model](https://github.com/wlandau/targets-stan)
* [Using Target Markdown and `stantargets` to validate a Bayesian longitudinal model for clinical trial data analysis](https://github.com/wlandau/rmedicine2021-pipeline)
* [Shiny app that runs a pipeline](https://github.com/wlandau/targets-shiny)
* [Deploy a pipeline to RStudio Connect](https://github.com/sol-eng/targets-deployment-rsc)
## Apps
* [`tar_watch()`](https://docs.ropensci.org/targets/reference/tar_watch.html): a built-in Shiny app to visualize progress while a pipeline is running. Available as a Shiny module via [`tar_watch_ui()`](https://docs.ropensci.org/targets/reference/tar_watch_ui.html) and [`tar_watch_server()`](https://docs.ropensci.org/targets/reference/tar_watch_server.html).
* [`targetsketch`](https://wlandau.shinyapps.io/targetsketch): a Shiny app to help sketch pipelines ([app](https://wlandau.shinyapps.io/targetsketch), [source](https://github.com/wlandau/targetsketch)).
## Deployment
* explains how to deploy a pipeline to RStudio Connect ([example code](https://github.com/sol-eng/targets-deployment-rsc)).
* [`tar_github_actions()`](https://docs.ropensci.org/targets/reference/tar_github_actions.html) sets up a pipeline to run on GitHub Actions. The [minimal example](https://github.com/wlandau/targets-minimal) demonstrates this approach.
## Extending and customizing targets
* [R Targetopia](https://wlandau.github.io/targetopia/): a collection of [R packages](https://wlandau.github.io/targetopia/packages.html) that extend `targets`. [These packages](https://wlandau.github.io/targetopia/packages.html) simplify pipeline construction for specific fields of Statistics and data science.
* [Target factories](https://wlandau.github.io/targetopia/contributing.html#target-factories): a programming technique to write specialized interfaces for custom pipelines. Posts [here](https://ropensci.org/blog/2021/02/03/targets/) and [here](https://wlandau.github.io/targetopia/contributing.html) describe how.
## Code of conduct
Please note that this package is released with a [Contributor Code of Conduct](https://ropensci.org/code-of-conduct/).
## Citation
```{r, comment = NA_character_}
citation("targets")
```