The targets R package

The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing - Published in JOSS (2021)

https://github.com/ropensci/targets

Science Score: 95.0%

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Found codemeta.json file
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Links to: joss.theoj.org, zenodo.org
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Published in Journal of Open Source Software

Keywords

data-science high-performance-computing make peer-reviewed pipeline r r-package r-targetopia reproducibility reproducible-research rstats targets workflow

Keywords from Contributors

drake makefile ropensci jags rjags rstats-package reproducible-science

Scientific Fields

Engineering Computer Science - 80% confidence

Sociology Social Sciences - 64% confidence

Artificial Intelligence and Machine Learning Computer Science - 62% confidence

Last synced: 6 months ago · JSON representation

Repository

Function-oriented Make-like declarative workflows for R

Basic Info

Host: GitHub
Owner: ropensci
License: other
Language: R
Default Branch: main
Homepage: https://docs.ropensci.org/targets/
Size: 6.95 MB

Statistics

Stars: 1,018
Watchers: 18
Forks: 76
Open Issues: 2
Releases: 57

Topics

data-science high-performance-computing make peer-reviewed pipeline r r-package r-targetopia reproducibility reproducible-research rstats targets workflow

Created over 6 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Codemeta

README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# targets 

[![ropensci](https://badges.ropensci.org/401_status.svg)](https://github.com/ropensci/software-review/issues/401)
[![JOSS](https://joss.theoj.org/papers/10.21105/joss.02959/status.svg)](https://doi.org/10.21105/joss.02959)
[![zenodo](https://zenodo.org/badge/200093430.svg)](https://zenodo.org/badge/latestdoi/200093430)
[![R Targetopia](https://img.shields.io/badge/R_Targetopia-member-blue?style=flat&labelColor=gray)](https://wlandau.github.io/targetopia/)
[![CRAN](https://www.r-pkg.org/badges/version/targets)](https://CRAN.R-project.org/package=targets)
[![status](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![check](https://github.com/ropensci/targets/actions/workflows/check.yaml/badge.svg)](https://github.com/ropensci/targets/actions?query=workflow%3Acheck)
[![codecov](https://codecov.io/gh/ropensci/targets/branch/main/graph/badge.svg?token=3T5DlLwUVl)](https://app.codecov.io/gh/ropensci/targets)

Pipeline tools coordinate the pieces of computationally demanding analysis projects. The `targets` package is a 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 | `pak::pkg_install("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).

[![](./man/figures/video.png)](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)
* [Powerful simulation pipelines with `targets`](https://www.youtube.com/watch?v=fgtIqJfzPK0). [useR! 2025 Conference](https://user2025.r-project.org/program/in-person/keynotes/will-landau) (1:00:25).
* [`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](https://ropensci.org/commcalls/jan2023-targets/) (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

* [`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")
```

Owner

Name: rOpenSci
Login: ropensci
Kind: organization
Email: info@ropensci.org
Location: Berkeley, CA

Website: https://ropensci.org/
Twitter: rOpenSci
Repositories: 307
Profile: https://github.com/ropensci

JOSS Publication

The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing

Published

January 15, 2021

DOI

10.21105/joss.02959

Volume 6, Issue 57, Page 2959

Authors

William Michael Landau

Eli Lilly and Company

Editor

Arfon Smith

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GitHub Events

Total

Create event: 48
Commit comment event: 1
Release event: 7
Issues event: 99
Watch event: 81
Delete event: 41
Issue comment event: 149
Push event: 418
Pull request event: 7
Fork event: 8

Last Year

Create event: 48
Commit comment event: 1
Release event: 7
Issues event: 99
Watch event: 81
Delete event: 41
Issue comment event: 149
Push event: 418
Pull request event: 7
Fork event: 8

Committers

Last synced: 7 months ago

All Time

Total Commits: 3,548
Total Committers: 23
Avg Commits per committer: 154.261
Development Distribution Score (DDS): 0.194

Past Year

Commits: 542
Committers: 4
Avg Commits per committer: 135.5
Development Distribution Score (DDS): 0.03

Top Committers

Name	Email	Commits
wlandau	w**u@g**m	2,860
wlandau	w**s@g**m	617
Mark Edmondson	g**b@m**e	18
Matthew T. Warkentin	m**n@m**a	11
Malcolm Barrett	m**t@g**m	11
Sam Albers	s**s@g**a	5
Alec L. Robitaille	r**c@g**m	3
Joel Nitta	j**a@g**m	3
Kirill Müller	k**l@c**m	3
Etienne Bacher	5****r	2
Kendon Bell	k****B	2
Timing Liu	3****g	2
András Svraka	s**s@g**m	1
Bill Denney	w**y@h**m	1
Hadley Wickham	h**m@g**m	1
Maëlle Salmon	m**n@y**e	1
Patrick Schratz	p**z@g**m	1
Robin	R****e	1
Russ Hyde	r**a@g**m	1
Shota Komatsu	s**u@s**m	1
Stuart Russell	s**t@d**g	1
shikokuchuo	5****o	1
Sam Kim	s**s@g**m	1

Committer Domains (Top 20 + Academic)

define.ag: 1 sansan.com: 1 humanpredictions.com: 1 cynkra.com: 1 gov.bc.ca: 1 mail.utoronto.ca: 1 markedmondson.me: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 199
Total pull requests: 47
Average time to close issues: 16 days
Average time to close pull requests: 8 days
Total issue authors: 65
Total pull request authors: 19
Average comments per issue: 3.2
Average comments per pull request: 1.45
Merged pull requests: 38
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 60
Pull requests: 5
Average time to close issues: 14 days
Average time to close pull requests: about 22 hours
Issue authors: 17
Pull request authors: 4
Average comments per issue: 1.68
Average comments per pull request: 1.8
Merged pull requests: 3
Bot issues: 0
Bot pull requests: 0

View more stats

Top Authors

Issue Authors

wlandau (97)
dipterix (10)
stemangiola (7)
noamross (3)
psychelzh (3)
Robinlovelace (3)
mkoohafkan (3)
koefoeden (3)
turbanisch (2)
mem48 (2)
mattwarkentin (2)
pat-s (2)
hadley (2)
Sage0614 (2)
asadow (2)

Pull Request Authors

wlandau (31)
krlmlr (4)
malcolmbarrett (3)
olivroy (2)
jeffery-leirness-noaa (2)
etiennebacher (2)
samkimhis (2)
erinsteiner-NOAA (2)
johnbaums (2)
joelnitta (2)
shikokuchuo (2)
Adafede (2)
maelle (2)
pat-s (2)
komatsuna4747 (1)

Top Labels

Issue Labels

type: new feature (93) type: bug (25) topic: performance (21) type: maintenance (21) status: priority (7) order: 1 (5) type: bottleneck (4) depends: different tool (4) type: edge case (4) depends: help or input (3) type: trouble (3) order: 3 (2) topic: documentation (2) depends: reprex (1) order: sooner (1) status: shelved (1) type: question (1) difficulty: advanced (1) order: 2 (1) topic: dependencies (1)

Pull Request Labels

type: new feature (1) topic: literate programming (1)

Packages

Total packages: 2
Total downloads:
- cran 14,248 last-month
Total docker downloads: 215,117

Total dependent packages: 10
(may contain duplicates)
Total dependent repositories: 78
(may contain duplicates)
Total versions: 61
Total maintainers: 1

cran.r-project.org: targets

Dynamic Function-Oriented 'Make'-Like Declarative Pipelines

Homepage: https://docs.ropensci.org/targets/
Documentation: http://cran.r-project.org/web/packages/targets/targets.pdf
License: MIT + file LICENSE
Latest release: 1.11.3
published 10 months ago

Versions: 43
Dependent Packages: 8
Dependent Repositories: 77
Downloads: 14,248 Last month
Docker Downloads: 215,117

Rankings

Stargazers count: 0.3%

Forks count: 1.1%

Dependent repos count: 2.7%

Downloads: 5.0%

Average: 6.1%

Dependent packages count: 6.6%

Docker downloads count: 21.0%

Maintainers (1)

will.landau.oss@gmail.com

Last synced: 6 months ago

conda-forge.org: r-targets

Homepage: https://docs.ropensci.org/targets/
License: MIT
Latest release: 0.14.0
published over 3 years ago

Versions: 18
Dependent Packages: 2
Dependent Repositories: 1

Rankings

Dependent packages count: 19.6%

Average: 21.9%

Dependent repos count: 24.3%

Last synced: 6 months ago

Dependencies

DESCRIPTION cran

R >= 3.5.0 depends
R6 >= 2.4.1 imports
base64url >= 1.4 imports
callr >= 3.4.3 imports
cli >= 2.0.2 imports
codetools >= 0.2.16 imports
data.table >= 1.12.8 imports
digest >= 0.6.25 imports
igraph >= 1.2.5 imports
knitr >= 1.34 imports
rlang >= 1.0.0 imports
stats * imports
tibble >= 3.0.1 imports
tidyselect >= 1.1.0 imports
tools * imports
utils * imports
vctrs >= 0.2.4 imports
withr >= 2.4.0 imports
yaml >= 2.2.1 imports
DT >= 0.14 suggests
arrow >= 3.0.0 suggests
bs4Dash >= 0.5.0 suggests
clustermq >= 0.8.95.1 suggests
curl >= 4.3 suggests
dplyr >= 1.0.0 suggests
fst >= 0.9.2 suggests
future >= 1.19.1 suggests
future.batchtools >= 0.9.0 suggests
future.callr >= 0.6.0 suggests
gargle >= 1.2.0 suggests
googleCloudStorageR >= 0.7.0 suggests
gt >= 0.2.2 suggests
keras >= 2.2.5.0 suggests
markdown >= 1.1 suggests
paws >= 0.1.11 suggests
pingr >= 2.0.1 suggests
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processx >= 3.4.3 suggests
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torch >= 0.1.0 suggests
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.github/workflows/check.yaml actions

actions/checkout v3 composite
r-lib/actions/check-r-package v2 composite
r-lib/actions/setup-pandoc v2 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/cover.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

.github/workflows/lint.yaml actions

actions/checkout v3 composite
r-lib/actions/setup-r v2 composite
r-lib/actions/setup-r-dependencies v2 composite

The targets R package

Science Score: 95.0%

Keywords

Keywords from Contributors

Scientific Fields

Repository

Basic Info

Statistics

Topics

Metadata Files

README.Rmd

Owner

JOSS Publication

The targets R package: a dynamic Make-like function-oriented pipeline toolkit for reproducibility and high-performance computing

Authors

Editor

Tags

CodeMeta (codemeta.json)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

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Past Year

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Issue Authors

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Issue Labels

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Packages

cran.r-project.org: targets

Rankings

Maintainers (1)

conda-forge.org: r-targets

Rankings

Dependencies