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:
broomtakes the messy output of built-in functions in R, such as
t.test, and turns them into tidy data frames.
dplyrcontains a grammar for data manipulation.
ggplot2implements a grammar of graphics.
inferis a modern approach to statistical inference.
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.
yardstickcontains 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):
tidypredicttranslates some model prediction equations to SQL for high-performance computing.
tidyposteriorcan be used to compare models using resampling and Bayesian analysis.
tidytextcontains tidy tools for quantitative text analysis, including basic text summarization, sentiment analysis, and text modeling.
When loading the package, the versions and conflicts are listed:
## ── Attaching packages ───────────────────────────────── tidymodels 0.0.1 ──
## ✔ ggplot2 3.0.0 ✔ recipes 0.1.3.9000 ## ✔ 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()