REPOGEO REPORT · LITE

ropensci/drake

Default branch main · commit 48324887 · scanned 5/19/2026, 11:51:58 AM

GitHub: 1,342 stars · 130 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

70 /100

Needs work

Category recall

1 / 2

Avg rank #2.0 when recommended

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface ropensci/drake, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Correct README title and add a strong opening statement

Why:

CURRENT

# drak

<center>

</center>
<table class="table">...

COPY-PASTE FIX

# drake: An R-focused pipeline toolkit for reproducible and high-performance computing workflows

`drake` is a powerful R package designed to manage complex data analysis pipelines, ensuring reproducibility and accelerating computation through intelligent caching and dependency tracking. It's particularly well-suited for high-performance computing environments where efficient resource utilization and robust workflow management are critical.

<center>

</center>
<table class="table">...

mediumabout#2

Refine the 'About' description to emphasize workflow orchestration

Why:

CURRENT

An R-focused pipeline toolkit for reproducibility and high-performance computing

COPY-PASTE FIX

An R-focused **workflow orchestration** toolkit for **reproducible and high-performance computing pipelines**, intelligently caching results and tracking dependencies.

lowcomparison#3

Add a 'Comparison to Alternatives' section in the README

Why:

COPY-PASTE FIX

## Comparison to Alternatives

`drake` stands out from tools like `make` by offering fine-grained, R-native dependency tracking and caching of R objects, not just files. While `targets` is a modern successor, `drake` provides a robust and mature framework for complex R workflows. Unlike general version control systems (Git/GitHub) or environment managers (renv), `drake` focuses specifically on orchestrating computational steps within your R projects.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

1 / 2

50% of queries surface ropensci/drake

Avg rank

#2.0

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ropensci/targets

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

ropensci/targets · recommended 1×
rstudio/renv · recommended 1×
Git · recommended 1×
GitHub · recommended 1×
GitLab · recommended 1×

CATEGORY QUERY
What are the best tools for creating robust and reproducible research pipelines in R?
you: #2
AI recommended (in order):
1. targets (ropensci/targets)
2. drake (ropensci/drake) ← you
3. renv (rstudio/renv)
4. Git
5. GitHub
6. GitLab
7. Bitbucket
8. R Markdown (rstudio/rmarkdown)
9. Docker
10. Make
Show full AI answer
CATEGORY QUERY
How can I accelerate complex data science workflows in R using high-performance computing?
you: not recommended
AI recommended (in order):
1. data.table
2. Rcpp
3. foreach
4. doParallel
5. future
6. SparkR
7. sparklyr
8. Microsoft R Open
9. Intel Math Kernel Library
10. gpuR
11. torch
12. tensorflow
13. Slurm
14. PBS
15. HTCondor
AI recommended 15 alternatives but never named ropensci/drake. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of ropensci/drake?
pass
AI named ropensci/drake explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts ropensci/drake in production, what risks or prerequisites should they evaluate first?
pass
AI named ropensci/drake explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo ropensci/drake solve, and who is the primary audience?
pass
AI named ropensci/drake explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of ropensci/drake. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/ropensci/drake.svg)](https://repogeo.com/en/r/ropensci/drake)

HTML

<a href="https://repogeo.com/en/r/ropensci/drake"><img src="https://repogeo.com/badge/ropensci/drake.svg" alt="RepoGEO" /></a>

Pro

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ropensci/drake — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite