Claude Skills by LeoLin990405

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

R network visualization with ggraph, visNetwork. Use for graph layouts and interactive networks.

R ggraph package for network visualization. Use for grammar of graphics style network plots.

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

R sentiment analysis with tidytext, syuzhet, sentimentr. Use for sentiment scoring and emotion detection.

R sentimentr package for sentence-level sentiment. Use for polarity-based sentiment analysis with valence shifters.

R syuzhet package for sentiment analysis. Use for extracting sentiment and emotion from text.

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

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

R quanteda package for text analysis. Use for corpus management, tokenization, and document-feature matrices.

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

R text2vec package for text vectorization. Use for word embeddings, GloVe, and efficient text processing.

R tidytext package for text mining. Use for tokenization, sentiment analysis, TF-IDF, and tidy text analysis.

R tm package for text mining. Use for classic text mining infrastructure and preprocessing.

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

R LDAvis package for topic model visualization. Use for interactive visualization of LDA topic models.

R topic modeling with topicmodels, LDAvis, stm. Use for LDA, CTM, and topic visualization.

R stm package for structural topic models. Use for topic modeling with document-level covariates.

R topicmodels package for topic modeling. Use for LDA and CTM topic models on document-term matrices.

R parallel computing and high performance packages. Use for multi-core processing, distributed computing, Spark integration, and C++ acceleration with Rcpp.

R RcppParallel package for parallel C++. Use for multi-threaded C++ code with Intel TBB.

R high-performance with Rcpp, RcppParallel, RcppArmadillo. Use for C++ integration and optimization.

R distributed computing with sparklyr, future.batchtools. Use for cluster and cloud computing.

R sparklyr package for Apache Spark. Use for distributed data processing with dplyr interface.

R local parallel computing with parallel, future, furrr. Use for multi-core processing.

R doParallel package for parallel foreach backend. Use for registering parallel backends for foreach loops.

R foreach package for parallel loops. Use for parallel iteration with various backends.

R furrr package for parallel purrr. Use for future-powered map functions.

R future package for parallel computing. Use for unified async and parallel evaluation.

R parallel package for parallel computing. Use for multicore and cluster-based parallel processing.

R learning resources. Use for finding books, courses, tutorials, cheat sheets, podcasts, and community resources.

R learning books and references. Use for finding R programming books and documentation.

R community resources and help. Use for finding R community forums and support.

R learning courses and tutorials. Use for finding R programming courses.

R spatial analysis packages. Use for geographic data, maps, GIS operations, and spatial statistics.

R sp package for spatial data. Use for spatial data classes and methods (legacy, see sf for modern approach).

R spatstat package for spatial point patterns. Use for analyzing and modeling spatial point pattern data.

R mapping with tmap, leaflet, mapview. Use for static and interactive maps.

R cartography package for thematic mapping. Use for creating publication-quality thematic maps.

R ggmap package for maps with ggplot2. Use for Google Maps and Stamen tiles with ggplot2.

R mapsf package for thematic mapping. Use for creating thematic maps with sf objects.

R mapview package for quick interactive maps. Use for rapid spatial data exploration.

R tmap package for thematic maps. Use for publication-quality static and interactive maps.

R raster spatial data with terra, raster, stars. Use for gridded data, satellite imagery, and raster analysis.

R raster package for raster data. Use for reading, writing, and analyzing raster/gridded data.

R stars package for spatiotemporal arrays. Use for raster data cubes and time series of rasters.

R terra package for spatial data. Use for raster and vector data analysis, successor to raster package.

R vector spatial data with sf, sp, terra. Use for points, lines, polygons, and geometric operations.