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Easily Install and Load the 'Tidymodels' Packages
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tidymodels is a "meta-package" for modeling and statistical analysis that share 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.

  • dplyr contains a grammar for data manipulation.

  • ggplot2 implements a grammar of graphics.

  • infer is a modern approach to statistical inference.

  • 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.

  • yardstick contains tools for evaluating models (e.g. accuracy, RMSE, etc.)

There are a few modeling packages that are also installed along with tidymodels (but are not attached on startup):

  • tidypredict translates some model prediction equations to SQL for high-performance computing.

  • tidyposterior can be used to compare models using resampling and Bayesian analysis.

  • tidytext contains tidy tools for quantitative text analysis, including basic text summarization, sentiment analysis, and text modeling.

To install:


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

## ── Attaching packages ───────────────────────────────── tidymodels 0.0.1 ──
## ✔ ggplot2   3.0.0          ✔ recipes
## ✔ tibble    1.4.2          ✔ broom     0.5.0     
## ✔ purrr     0.2.5          ✔ yardstick 0.0.1     
## ✔ dplyr     0.7.6          ✔ infer     0.3.1     
## ✔ rsample   0.0.2
## ── Conflicts ──────────────────────────────────── tidymodels_conflicts() ──
## ✖ rsample::fill()    masks tidyr::fill()
## ✖ dplyr::filter()    masks stats::filter()
## ✖ dplyr::lag()       masks stats::lag()
## ✖ recipes::prepper() masks rsample::prepper()
## ✖ recipes::step()    masks stats::step()
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