It includes a core set of packages that are loaded on startup:
broomtakes the messy output of built-in functions in R, such as
t.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 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 220.127.116.1100 ── #> ✔ broom 1.0.4 ✔ recipes 1.0.6 #> ✔ dials 1.2.0 ✔ rsample 1.1.1 #> ✔ dplyr 1.1.2 ✔ tibble 3.2.1 #> ✔ ggplot2 3.4.2 ✔ tidyr 1.3.0 #> ✔ infer 1.0.4 ✔ tune 1.1.1 #> ✔ modeldata 1.1.0 ✔ workflows 1.1.3 #> ✔ parsnip 1.1.0 ✔ workflowsets 1.0.1 #> ✔ purrr 1.0.1 ✔ yardstick 1.2.0 #> ── Conflicts ───────────────────────────────────────── tidymodels_conflicts() ── #> ✖ purrr::discard() masks scales::discard() #> ✖ dplyr::filter() masks stats::filter() #> ✖ dplyr::lag() masks stats::lag() #> ✖ recipes::step() masks stats::step() #> • Search for functions across packages at https://www.tidymodels.org/find/
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.