tidymodels

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 as lm, nls, or t.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:

library("devtools")
install_github("tidymodels/tidymodels")

When loading the package, the versions and conflicts are listed:

library(tidymodels)
#> Registered S3 method overwritten by 'tune':
#>   method                   from   
#>   required_pkgs.model_spec parsnip
#> ── Attaching packages ────────────────────────────────────── tidymodels 0.1.4 ──
#> ✓ broom        0.7.9          ✓ recipes      0.1.17    
#> ✓ dials        0.0.10         ✓ rsample      0.1.0     
#> ✓ dplyr        1.0.7          ✓ tibble       3.1.5     
#> ✓ ggplot2      3.3.5          ✓ tidyr        1.1.4     
#> ✓ infer        1.0.0          ✓ tune         0.1.6.9000
#> ✓ modeldata    0.1.1          ✓ workflows    0.2.3     
#> ✓ parsnip      0.1.7          ✓ workflowsets 0.1.0     
#> ✓ purrr        0.3.4          ✓ yardstick    0.0.8
#> ── 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()
#> • Use suppressPackageStartupMessages() to eliminate package startup messages

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.