It includes a core set of packages that are loaded on startup:
broom takes the messy output of built-in functions in R, such as
t.test, and turns them into tidy data frames.
dials has tools to create and manage values of tuning parameters.
parsnip is a tidy, unified interface to creating models.
recipesis a general data preprocessor with a modern interface. It can create model matrices that incorporate feature engineering, imputation, and other help tools.
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.)
You can install the released version of tidymodels from CRAN with:
Install the development version from GitHub with:
When loading the package, the versions and conflicts are listed:
library(tidymodels) #> ── Attaching packages ────────────────────── tidymodels 0.1.0 ── #> ✓ broom 0.5.6 ✓ recipes 0.1.13 #> ✓ dials 0.0.7 ✓ rsample 0.0.7 #> ✓ dplyr 1.0.0 ✓ tibble 3.0.1 #> ✓ ggplot2 3.3.2 ✓ tune 0.1.0 #> ✓ infer 0.5.2 ✓ workflows 0.1.1 #> ✓ parsnip 0.1.1 ✓ yardstick 0.0.6 #> ✓ purrr 0.3.4 #> ── Conflicts ───────────────────────── tidymodels_conflicts() ── #> x purrr::discard() masks scales::discard() #> x dplyr::filter() masks stats::filter() #> x dplyr::lag() masks stats::lag() #> x recipes::step() masks stats::step()
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.