kassambara / tidymodels Goto Github PK

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:

require(devtools)
devtools::install_github("tidymodels/tidymodels")

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

library(tidymodels)

## ── Attaching packages ───────────────────────────────── tidymodels 0.0.1 ──

## ✔ ggplot2   3.0.0     ✔ recipes   0.1.3
## ✔ tibble    1.4.2     ✔ broom     0.5.0
## ✔ purrr     0.2.5     ✔ yardstick 0.0.1
## ✔ dplyr     0.7.6     ✔ infer     0.3.0
## ✔ rsample   0.0.2

## ── Conflicts ──────────────────────────────────── tidymodels_conflicts() ──
## ✖ rsample::fill() masks tidyr::fill()
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ✖ recipes::step() masks stats::step()