Skills DirectoryData & Analytics
Data analysis, BI, visualization, datasets, statistics, and ML workflows
Browse data & analytics skills
Showing 10,969–10,992 of 13,031 skills
R microbenchmark package for precise timing. Use for sub-millisecond accurate timing of R expressions.
R lobstr package for memory inspection. Use for understanding R object memory usage and structure.
R bench package for benchmarking. Use for high precision timing of R expressions.
R packages for profiling and benchmarking. Use for measuring code performance and memory usage.
R usethis package for workflow automation. Use for package setup, Git, GitHub, and project configuration.
R testthat package for unit testing. Use for writing and running unit tests for R packages.
R styler package for code formatting. Use for automatically formatting R code to follow style guidelines.
R roxygen2 package for documentation. Use for in-source documentation with special comments.
R renv package for dependency management. Use for creating isolated, reproducible R project environments.
R rcmdcheck package for R CMD check. Use for running R CMD check with better output.
R pryr package for R internals. Use for exploring R's internal workings and memory usage.
R pkgdown package for package websites. Use for generating documentation websites from R packages.
R lintr package for static code analysis. Use for checking R code style and finding potential issues.
R devtools package for package development. Use for loading, documenting, testing, and building R packages.
R covr package for test coverage. Use for measuring and reporting test coverage of R packages.
R box package for modular code. Use for creating reusable modules with explicit imports/exports.
R package development with devtools, usethis, roxygen2. Use for creating and maintaining R packages.
R OOP with R6, S3, S4. Use for object-oriented programming patterns.
R R6 package for OOP. Use for encapsulated object-oriented programming with reference semantics.
R documentation with pkgdown, roxygen2, rmarkdown. Use for package websites and documentation.
R development packages. Use for package development, testing, documentation, code style, and IDE setup.
R validate package for data validation. Use for defining and checking data validation rules.
R pointblank package for data quality. Use for data validation and quality reporting.
R assertr package for assertion pipelines. Use for assertive programming with data frames.