tidylearn: A Unified Tidy Interface to R's Machine Learning Ecosystem

Provides a unified tidyverse-compatible interface to R's machine learning ecosystem - from data ingestion to model publishing. The tl_read() family reads data from files ('CSV', 'Excel', 'Parquet', 'JSON'), databases ('SQLite', 'PostgreSQL', 'MySQL', 'BigQuery'), and cloud sources ('S3', 'GitHub', 'Kaggle'). The tl_model() function wraps established implementations from 'glmnet', 'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster', 'dbscan', and others with consistent function signatures and tidy tibble output. Results flow into unified 'ggplot2'-based visualization and optional formatted 'gt' tables via the tl_table() family. The underlying algorithms are unchanged; 'tidylearn' simply makes them easier to use together. Access raw model objects via the $fit slot for package-specific functionality. Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>, LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>, elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>, and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.

Version:	0.3.0
Depends:	R (≥ 3.6.0)
Imports:	dplyr (≥ 1.0.0), ggplot2 (≥ 3.3.0), tibble (≥ 3.0.0), tidyr (≥ 1.0.0), purrr (≥ 0.3.0), rlang (≥ 0.4.0), magrittr, stats, e1071, gbm, glmnet, nnet, randomForest, rpart, rsample, ROCR, yardstick, cluster (≥ 2.1.0), dbscan (≥ 1.1.0), MASS, smacof (≥ 2.1.0)
Suggests:	arules, arulesViz, bigrquery, car, caret, DBI, DT, GGally, ggforce, gridExtra, gt, jsonlite, keras, knitr, lmtest, mclust, moments, nanoparquet, NeuralNetTools, onnx, parsnip, paws.storage, readr, readxl, recipes, reticulate, RMariaDB, rmarkdown, RPostgres, rpart.plot, RSQLite, scales, shiny, shinydashboard, tensorflow, testthat (≥ 3.0.0), workflows, xgboost
Published:	2026-04-09
DOI:	10.32614/CRAN.package.tidylearn
Author:	Cesaire Tobias [aut, cre]
Maintainer:	Cesaire Tobias <cesaire at sheetsolved.com>
BugReports:	https://github.com/ces0491/tidylearn/issues
License:	MIT + file LICENSE
URL:	https://github.com/ces0491/tidylearn
NeedsCompilation:	no
Citation:	tidylearn citation info
Materials:	README, NEWS
CRAN checks:	tidylearn results

Documentation:

Reference manual:	tidylearn.html , tidylearn.pdf
Vignettes:	Automated Machine Learning with tidylearn (source, R code) Data Ingestion with tidylearn (source, R code) Getting Started with tidylearn (source, R code) Integration Workflows: Combining Supervised and Unsupervised Learning (source, R code) Reporting with tidylearn (source, R code) Supervised Learning with tidylearn (source, R code) Unsupervised Learning with tidylearn (source, R code)

Downloads:

Package source:	tidylearn_0.3.0.tar.gz
Windows binaries:	r-devel: tidylearn_0.3.0.zip, r-release: tidylearn_0.2.0.zip, r-oldrel: tidylearn_0.2.0.zip
macOS binaries:	r-release (arm64): tidylearn_0.2.0.tgz, r-oldrel (arm64): tidylearn_0.2.0.tgz, r-release (x86_64): tidylearn_0.3.0.tgz, r-oldrel (x86_64): tidylearn_0.3.0.tgz
Old sources:	tidylearn archive

Linking:

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