tidymodels is a “meta-package” for modeling and statistical analysis that shares the underlying design philosophy, grammar, and data structures of the tidyverse.
It includes a core set of packages that are loaded on startup:
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broomtakes the messy output of built-in functions in R, such aslm,nls, ort.test, and turns them into tidy data frames. -
dialshas tools to create and manage values of tuning parameters. -
dplyrcontains a grammar for data manipulation. -
ggplot2implements a grammar of graphics. -
inferis a modern approach to statistical inference. -
parsnipis a tidy, unified interface to creating models. -
purrris a functional programming toolkit. -
recipesis a general data preprocessor with a modern interface. It can create model matrices that incorporate feature engineering, imputation, and other help tools. -
rsamplehas infrastructure for resampling data so that models can be assessed and empirically validated. -
tibblehas a modern re-imagining of the data frame. -
tunecontains the functions to optimize model hyper-parameters. -
workflowshas methods to combine pre-processing steps and models into a single object. -
yardstickcontains tools for evaluating models (e.g. accuracy, RMSE, etc.).
A list of all tidymodels functions across different CRAN packages can be found at https://www.tidymodels.org/find/.
You can install the released version of tidymodels from CRAN with:
install.packages("tidymodels")Install the development version from GitHub with:
# install.packages("pak")
pak::pak("tidymodels/tidymodels")When loading the package, the versions and conflicts are listed:
library(tidymodels)
#> ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
#> ✔ broom 1.0.5 ✔ recipes 1.0.10
#> ✔ dials 1.2.1 ✔ rsample 1.2.0
#> ✔ dplyr 1.1.4 ✔ tibble 3.2.1
#> ✔ ggplot2 3.5.0 ✔ tidyr 1.3.1
#> ✔ infer 1.0.6 ✔ tune 1.2.0
#> ✔ modeldata 1.3.0 ✔ workflows 1.1.4
#> ✔ parsnip 1.2.1 ✔ workflowsets 1.1.0
#> ✔ purrr 1.0.2 ✔ yardstick 1.3.1
#> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ──
#> ✖ purrr::discard() masks scales::discard()
#> ✖ dplyr::filter() masks stats::filter()
#> ✖ dplyr::lag() masks stats::lag()
#> ✖ recipes::step() masks stats::step()
#> • Learn how to get started at https://www.tidymodels.org/start/This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
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For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
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Most issues will likely belong on the GitHub repo of an individual package. If you think you have encountered a bug with the tidymodels metapackage itself, please submit an issue.
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Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
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Check out further details on contributing guidelines for tidymodels packages and how to get help.
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