Overview
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:
broom
takes the messy output of built-in functions in R, such aslm
,nls
, ort.test
, and turns them into tidy data frames.dials
has tools to create and manage values of tuning parameters.dplyr
contains a grammar for data manipulation.ggplot2
implements a grammar of graphics.infer
is a modern approach to statistical inference.parsnip
is a tidy, unified interface to creating models.purrr
is a functional programming toolkit.recipes
is a general data preprocessor with a modern interface. It can create model matrices that incorporate feature engineering, imputation, and other help tools.rsample
has infrastructure for resampling data so that models can be assessed and empirically validated.tibble
has a modern re-imagining of the data frame.tune
contains the functions to optimize model hyper-parameters.workflows
has methods to combine pre-processing steps and models into a single object.yardstick
contains 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/
Contributing
This project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
For questions and discussions about tidymodels packages, modeling, and machine learning, please post on RStudio Community.
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.
Either way, learn how to create and share a reprex (a minimal, reproducible example), to clearly communicate about your code.
Check out further details on contributing guidelines for tidymodels packages and how to get help.