# ropensci/targets

Function-oriented Make-like declarative workflows for R

Repository: https://github.com/ropensci/targets
Canonical: https://ross.abutalabs.com/products/targets
Homepage: https://docs.ropensci.org/targets/
Language: R
License: NOASSERTION
License Family: other
Topics: reproducibility, high-performance-computing, r, data-science, pipeline, rstats, r-package, workflow, targets, reproducible-research, make, r-targetopia, peer-reviewed
Last push: 2026-05-13T17:53:48+00:00

## Health v2 (maintenance only)
Score: 77/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 82, release rhythm 57, longevity 100
- inputs: {"age_days": 2589, "days_push": 112, "days_rel": 205, "gap_med": 33.0, "n_releases_24m": 11}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1094, forks 84 (observed 2026-08-28T04:03:33.923951+00:00)

## What it is
targets is a Make-like, function-oriented pipeline tool for statistics and data science workflows in R. It skips computation for up-to-date targets, orchestrates implicit parallel computing, and stores outputs to make analyses efficient and reproducible.

## Use cases
- run reproducible data science pipelines in R
- skip recomputation of unchanged analysis steps
- orchestrate parallel statistical analyses
- manage dependencies between data processing and modeling tasks
- build reproducible research workflows for papers
- run computationally demanding simulation studies
- cache intermediate results of long-running R analyses

## When to choose
- your R analysis project has interdependent steps and slow recomputation
- you want Make-like incremental builds for statistics or data science
- you need reproducible, evidence-backed pipelines for research or business deliverables
- you want implicit parallel/distributed computing via crew or clustermq

## When to avoid
- your project is not in R
- you need a general-purpose CI/CD or container orchestration tool
- your workflow is a simple linear script with no costly steps

## Facets
- artifact type: library
- maturity: active
- function: workflow-automation, scheduling, developer-tools
- domain: data-science, developer-tools
- platform: cross-platform, cli
- tags: make-like, pipeline, reproducible-research, rstats, parallel-computing, r-package, automation, reproducibility, r

## Member repositories
- ropensci/targets (main) score 77

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:33.923951+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:47:38.271568+00:00, confidence not recorded.
  - readme: https://github.com/ropensci/targets (fetched 2026-08-28T04:03:33.923951+00:00, sha 1daa4610fc18)
  - homepage: https://docs.ropensci.org/targets/ (fetched 2026-08-29T12:50:36.915774+00:00, sha 299d69ac35db)
  - site_page: https://docs.ropensci.org/targets/news/index.html (fetched 2026-08-29T12:50:36.918533+00:00, sha 21c4f0fe3d95)
  - site_page: https://docs.ropensci.org/targets/authors.html (fetched 2026-08-29T12:50:36.924211+00:00, sha db9646754f28)
  - site_page: https://ropensci.org/about (fetched 2026-08-29T12:50:36.926260+00:00, sha 21e3e45abb74)
  - site_page: https://docs.ropensci.org/ (fetched 2026-08-29T12:50:36.928333+00:00, sha db4231ca5a42)
  - site_page: https://books.ropensci.org/targets-design (fetched 2026-08-29T12:50:36.922345+00:00, sha 544d4f84edd4)
- Data as of 2026-08-30T08:39:29.467469+00:00.