r-analytics-skill

R language data analysis and visualization skill. Use when user asks to (1) run R scripts or code, (2) install/update R packages, (3) perform data analysis with R, (4) create visualizations with ggplot2/plotly, (5) statistical analysis, (6) data manipulation with tidyverse/dplyr/data.table. Triggers on keywords like "R语言", "R脚本", "ggplot", "tidyverse", "数据分析", "可视化".

R DALEX package for model explanations. Use for explaining complex machine learning models.

R iml package for interpretable ML. Use for model-agnostic interpretability methods.

R lime package for local explanations. Use for explaining individual predictions with local interpretable models.

R packages for ML interpretability. Use for explaining and interpreting machine learning models.

R vip package for variable importance. Use for computing and visualizing variable importance scores.

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

R hunspell package for spell checking. Use for spell checking and morphological analysis.

R text mining with tidytext, tm, quanteda. Use for tokenization, TF-IDF, document-term matrices.

R stringdist package for string distance. Use for approximate string matching and distance metrics.

R tokenizers package for text tokenization. Use for fast, consistent tokenization of text.

R natural language processing packages. Use for text mining, sentiment analysis, topic modeling, tokenization, and text vectorization.

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

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

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

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

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

R data manipulation, formats, and database packages. Use for data wrangling with dplyr/data.table, reading files (CSV, Excel, JSON, Arrow), and database connections (SQL, MongoDB, Redis).

R cluster package for clustering algorithms. Use for PAM, CLARA, AGNES, DIANA, and other clustering methods.

R dbscan package for density-based clustering. Use for DBSCAN, OPTICS, and HDBSCAN clustering.

R factoextra package for cluster visualization. Use for visualizing clustering results and PCA.

R mclust package for model-based clustering. Use for Gaussian mixture models and model-based clustering.

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

R irlba package for fast SVD/PCA. Use for truncated SVD and PCA on large matrices.

R Rtsne package for t-SNE. Use for t-distributed stochastic neighbor embedding visualization.

R packages for dimensionality reduction. Use for PCA, t-SNE, UMAP, and other dimension reduction methods.

R umap package for UMAP. Use for Uniform Manifold Approximation and Projection visualization.

R ndtv package for network animation. Use for visualizing and animating dynamic networks.

R networkDynamic package for temporal networks. Use for creating and manipulating dynamic/temporal networks.

R dynamic/temporal networks with ndtv, networkDynamic, tsna. Use for time-varying networks and network evolution.

R tsna package for temporal network analysis. Use for analyzing temporal paths and metrics in dynamic networks.

R network analysis packages. Use for graph analysis, social network analysis, network visualization, and community detection.