Skills DirectoryData & Analytics
Data analysis, BI, visualization, datasets, statistics, and ML workflows
Browse data & analytics skills
Showing 10,897–10,920 of 13,063 skills
R parallel package for parallel computing. Use for multicore and cluster-based parallel processing.
R future package for parallel computing. Use for unified async and parallel evaluation.
R furrr package for parallel purrr. Use for future-powered map functions.
R foreach package for parallel loops. Use for parallel iteration with various backends.
R doParallel package for parallel foreach backend. Use for registering parallel backends for foreach loops.
R local parallel computing with parallel, future, furrr. Use for multi-core processing.
R sparklyr package for Apache Spark. Use for distributed data processing with dplyr interface.
R distributed computing with sparklyr, future.batchtools. Use for cluster and cloud computing.
R high-performance with Rcpp, RcppParallel, RcppArmadillo. Use for C++ integration and optimization.
R RcppParallel package for parallel C++. Use for multi-threaded C++ code with Intel TBB.
R parallel computing and high performance packages. Use for multi-core processing, distributed computing, Spark integration, and C++ acceleration with Rcpp.
R topicmodels package for topic modeling. Use for LDA and CTM topic models on document-term matrices.
R stm package for structural topic models. Use for topic modeling with document-level covariates.
R topic modeling with topicmodels, LDAvis, stm. Use for LDA, CTM, and topic visualization.
R LDAvis package for topic model visualization. Use for interactive visualization of LDA topic models.
R tokenizers package for text tokenization. Use for fast, consistent tokenization of text.
R tm package for text mining. Use for classic text mining infrastructure and preprocessing.
R tidytext package for text mining. Use for tokenization, sentiment analysis, TF-IDF, and tidy text analysis.
R stringdist package for string distance. Use for approximate string matching and distance metrics.
R quanteda package for text analysis. Use for corpus management, tokenization, and document-feature matrices.
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 syuzhet package for sentiment analysis. Use for extracting sentiment and emotion from text.
R sentimentr package for sentence-level sentiment. Use for polarity-based sentiment analysis with valence shifters.