analyze-r-package

# analyze-r-package Analyze an R/Bioconductor package to understand its structure, purpose, and key characteristics. ## Usage Invoke this skill when you want to understand an R package's architecture: - "Analyze this R package" - "Tell me about this package structure" ## Prerequisites - Working directory is an R package root (contains DESCRIPTION file) - Package has standard R structure (R/, NAMESPACE, etc.) ## Process 1. **Read Package Metadata**: Analyze `DESCRIPTION` for name, purpose, version, dependencies, and classify its type (Data, Analysis, Infrastructure, Utility) based on `biocViews`. 2. **Identify Exports**: Parse `NAMESPACE` for exported functions and (if present) S4 classes/methods. Categorize exports broadly (Data access, Processing, Visualization, Utility). 3. **Examine Directory Structure**: Note the presence of `data/`, `inst/extdata/`, `vignettes/`, `tests/testthat/`, and `src/`. 4. **Detect Data Access Patterns**: Search `R/` source files for remote data access (ExperimentHub, AnnotationHub, DuckDB, AWS S3, HuggingFace, etc.) vs local data access. 5. **Identify Classes**: List R class definitions (S3, S4, R6, S7) and their properties/slots/fields. 6. **Read README**: Extract high-level purpose and key features. 7. **Analyze Testing**: Check `tests/testthat/` structure, file count, and remote vs local test data. 8. **List Vignettes**: Check `vignettes/` for `.Rmd` files, extracting titles and purposes. ## Output Format Produce a structured markdown summary: ```markdown ## Package Analysis: [Package Name] ### Classification - **Type**: [Data/Analysis/Infrastructure/Utility] - **Purpose**: [1-2 sentence summary] - **Version**: [version number] ### Key Exports ([count] total) - **Data Access Functions**: [list] - **Data Processing Functions**: [list] - **Utility Functions**: [list] ### Data Access Pattern - **Type**: [None / Local Only / Remote / Hybrid] - **Technologies**: [e.g., ExperimentHub, DuckDB] ### Classes - [ClassName] - [Type: S3/S4/R6/S7] - [description] ### Documentation & Testing - **Vignettes**: [List with titles] - **Testing**: [Framework, count, data types] ### Special Characteristics [List notable patterns that should be documented] ### Dependencies of Note [List key Bioconductor or specialized packages] ``` ## Examples ### Example: Data Package Analysis **User**: "Analyze this R package" **Agent**: (Analyzes `parkinsonsMetagenomicData` repository) ```markdown ## Package Analysis: parkinsonsMetagenomicData ### Classification - **Type**: Data Package - **Purpose**: Provides uniformly processed gut microbiome data via remote parquet files accessed through DuckDB. - **Version**: 0.99.0 ### Key Exports (18 total) **Data Access Functions** (5): - `returnSamples()` - Main high-level data retrieval function - `loadParquetData()` - Load filtered data from DuckDB connection **Discovery Functions** (5): - `parquet_colinfo()` - Inspect column structure - `biobakery_files()` - List available data types ### Data Access Pattern - **Type**: Hybrid (Remote primary, Local for testing) - **Technologies**: DuckDB for remote parquet access, TreeSummarizedExperiment output ### Documentation & Testing **Vignettes** (4): 1. codebook.Rmd - Data Codebook 2. full-workflow.Rmd - Comprehensive tutorial **Testing**: - Framework: testthat (3 files) - Test data: inst/extdata/ (parquet, TSV, RDS) ### Special Characteristics - Uses DuckDB for efficient remote parquet file querying without full download ``` ## Integration This analysis output is consumed by `create-package-instructions` and `update-package-instructions`. --- **See also**: [create-package-instructions](../create-package-instructions/SKILL.md)