Data
D 51 completed
Other
containerized / r · small
62
Files
6,643
LOC
0
Frameworks
6
Languages
Pipeline State
completedRun ID
#940615Phase
doneProgress
0%Started
2026-04-15 08:03:59Finished
2026-04-15 08:03:59LLM tokens
0Pipeline Metadata
Stage
CatalogedDecision
proceedNovelty
30.31Framework unique
—Isolation
—Last stage change
2026-05-10 03:34:57Deduplication group #51636
Member of a group with 37 similar repo(s) — canonical #1208548 view group →
Repobility's GitHub App fixes findings like these · https://github.com/apps/repobility-bot
AI Prompt
I want to build a containerized data analysis and visualization pipeline using R. The project needs to handle data updates, potentially using cron jobs or scripts like `update_datasets.r`. It should be deployable via Docker, and the structure suggests it might involve reading configuration from `config.json` and processing data stored in various directories like `data/` and `data_wonder/`. Please set up the necessary shell scripts for deployment and dependency management, ensuring the R environment is correctly set up within the container.
r docker data-analysis scripting containerization data-pipeline shell json
Generated by gemma4:latest
Catalog Information
I want to build a containerized data analysis and visualization pipeline using R. The project needs to handle data updates, potentially using cron jobs or scripts like update_datasets.r. It should be deployable via Docker, and the structure suggests it might involve reading configuration from config.json and processing data stored in various directories like data/ and data_wonder/. Please set up the necessary shell scripts for deployment and dependency management, ensuring the R environm
Tags
r docker data-analysis scripting containerization data-pipeline shell json
Quality Score
D
51.3/100
Structure
47
Code Quality
63
Documentation
39
Testing
0
Practices
74
Security
90
Dependencies
50
Strengths
- Consistent naming conventions (snake_case)
- Good security practices — no major issues detected
- Containerized deployment (Docker)
- Properly licensed project
Weaknesses
- No tests found — high risk of regressions
- No CI/CD configuration — manual testing and deployment
- Potential hardcoded secrets in 1 files
- 857 duplicate lines detected — consider DRY refactoring
- 1 'god files' with >500 LOC need decomposition
Recommendations
- Add a test suite — start with critical path integration tests
- Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
- Add a linter configuration to enforce code style consistency
- Move hardcoded secrets to environment variables or a secrets manager
Languages
Frameworks
None detected
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