Claude Skills by LeoLin990405

R stringr package for string manipulation. Use for pattern matching, extraction, replacement, and string operations.

R tidyr package for data tidying. Use for pivot_longer, pivot_wider, separate, unite, and handling missing values.

R packages for data validation. Use for validating data quality and constraints.

R assertr package for assertion pipelines. Use for assertive programming with data frames.

R pointblank package for data quality. Use for data validation and quality reporting.

R validate package for data validation. Use for defining and checking data validation rules.

R development packages. Use for package development, testing, documentation, code style, and IDE setup.

R documentation with pkgdown, roxygen2, rmarkdown. Use for package websites and documentation.

R R6 package for OOP. Use for encapsulated object-oriented programming with reference semantics.

R OOP with R6, S3, S4. Use for object-oriented programming patterns.

R package development with devtools, usethis, roxygen2. Use for creating and maintaining R packages.

R box package for modular code. Use for creating reusable modules with explicit imports/exports.

R covr package for test coverage. Use for measuring and reporting test coverage of R packages.

R devtools package for package development. Use for loading, documenting, testing, and building R packages.

R lintr package for static code analysis. Use for checking R code style and finding potential issues.

R pkgdown package for package websites. Use for generating documentation websites from R packages.

R pryr package for R internals. Use for exploring R's internal workings and memory usage.

R rcmdcheck package for R CMD check. Use for running R CMD check with better output.

R renv package for dependency management. Use for creating isolated, reproducible R project environments.

R roxygen2 package for documentation. Use for in-source documentation with special comments.

R styler package for code formatting. Use for automatically formatting R code to follow style guidelines.

R testthat package for unit testing. Use for writing and running unit tests for R packages.

R usethis package for workflow automation. Use for package setup, Git, GitHub, and project configuration.

R packages for profiling and benchmarking. Use for measuring code performance and memory usage.

R bench package for benchmarking. Use for high precision timing of R expressions.

R lobstr package for memory inspection. Use for understanding R object memory usage and structure.

R microbenchmark package for precise timing. Use for sub-millisecond accurate timing of R expressions.

R profvis package for interactive profiling. Use for visualizing R code profiling data.

R testing with testthat, covr, mockery. Use for unit tests, coverage, and mocking.

R mockery package for mocking in tests. Use for stubbing functions and mocking dependencies.

R Rcpp package for C++ integration. Use for seamless R and C++ integration.

R interfaces to other languages. Use for calling Python, Java, JavaScript, and other languages from R.

R V8 package for JavaScript integration. Use for running JavaScript code and using JS libraries from R.

R cpp11 package for C++ integration. Use for modern C++11 integration with R.

R reticulate package for Python integration. Use for calling Python from R, importing Python modules, and data exchange.

R learning and education packages. Use for interactive R tutorials and educational content.

R learnr package for interactive tutorials. Use for creating Shiny-based interactive R tutorials.

R swirl package for interactive learning. Use for learning R programming through interactive console tutorials.

R logging packages for application logging. Use for structured logging, debugging, and monitoring R applications.

R futile.logger package for log4j-style logging. Use for hierarchical logging with layouts and appenders.

R log4r package for simple logging. Use for fast, simple log4j-style logging.

R logging package for Python-style logging. Use for logging with handlers and formatters.

R machine learning packages. Use for classification, regression, clustering, deep learning, gradient boosting (xgboost, lightgbm), random forests, neural networks, and time series forecasting.

R AnomalyDetection package from Twitter. Use for detecting anomalies in time series data.

R anomaly detection. Use for outlier detection, breakpoint detection with AnomalyDetection, anomalize, and changepoint.

R anomalize package for tidy anomaly detection. Use for detecting anomalies in time series with tidyverse workflow.

R gradient boosting packages. Use for xgboost, lightgbm, gbm, and catboost.

R lightgbm package for gradient boosting. Use for fast, distributed, high-performance gradient boosting.

R xgboost package for gradient boosting. Use for high-performance classification, regression, and ranking.

R packages for clustering analysis. Use for k-means, hierarchical clustering, and other clustering methods.