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README.Rmd
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---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# targets <img src='man/figures/logo.png' align="right" height="139"/>
[](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)
The `targets` package is a [Make](https://www.gnu.org/software/make/)-like pipeline tool for statistics and data science in R. With `targets`, you can maintain a reproducible workflow without repeating yourself. `targets` 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. An up-to-date `targets` pipeline is tangible evidence that the output aligns with the code and data, which substantiates trust in the results.
## 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
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 <https://vimeo.com/700982360> 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 <https://github.com/wlandau/targets-four-minutes> for the code and <https://rstudio.cloud/project/3946303> 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`.
* [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`.
## Courses
* [Official half-day interactive tutorial](https://github.com/wlandau/targets-tutorial).
## Recorded talks
### English
* [Get started with `targets` in 4 minutes (4:08)](https://vimeo.com/700982360)
* [R/Medicine 2021 (15.33)](https://youtu.be/HJI5mQJRGpY)
* [R/Pharma 2020 (9:24)](https://www.youtube.com/watch?v=GRqKJBaC5g4&list=PLMtxz1fUYA5C0YflXsR8EEAQXfjntlV1H&index=6)
* [LA R Users Meetup, October 2020 (1:14:40)](https://www.youtube.com/watch?v=Qq25BUxpJu4)
* [New York Open Statistical Programming Meetup, December 2020 (1:54:28)](https://youtu.be/Gqn7Xn4d5NI)
* [ds-incubator series, 2021](https://www.youtube.com/playlist?list=PLvgdJdJDL-APJqHy5CXs6m4N7hUVp5rb4)
* [Lille R User Group, June 2021 (45:54)](https://youtu.be/FODSavXGjYg)
### Español
* [R-Ladies Barcelona, 2021-05-25 (1:25:12)](https://www.youtube.com/watch?v=Vj312AfdpBo).
### 日本語
- [Bio"Pack"athon, 2022-03-31
(1:04:10)](https://togotv.dbcls.jp/20220331.html)
## 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
* <https://solutions.rstudio.com/r/workflows/> 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.
## Help
* Post to the [GitHub discussion forum](https://github.com/ropensci/targets/discussions) to ask questions. To get the best help about a specific issue, create a reproducible example with [`targets::tar_reprex()`](https://docs.ropensci.org/targets/reference/tar_reprex.html) or [`reprex::reprex()`](https://reprex.tidyverse.org/reference/reprex.html).
* The [RStudio Community](https://community.rstudio.com/) forum is full of friendly enthusiasts of R and the tidyverse. Use the [`targets` tag](https://community.rstudio.com/tag/targets).
* [Stack Overflow](https://stackoverflow.com/) broadcasts to the entire open source community. Use the [`targets-r-package` tag](https://stackoverflow.com/questions/tagged/targets-r-package).
## 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")
```