Learn R Programming
⚠️There's a newer version (1.1.0) of this package.Take me there.

recipes (version 0.1.1)

Preprocessing Tools to Create Design Matrices

Description

An extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.

Copy Link

Version

Install

install.packages('recipes')

Monthly Downloads

142,662

Version

0.1.1

License

GPL-2

Issues

94

Pull Requests

4

Stars

573

Forks

111

Maintainer

Max Kuhn

Last Published

November 20th, 2017

Functions in recipes (0.1.1)

has_role

Role Selection
covers

Raw Cover Type Data
bake

Apply a Trained Data Recipe
credit_data

Credit Data
biomass

Biomass Data
discretize

Discretize Numeric Variables
add_step

Add a New Step to Current Recipe
prep

Train a Data Recipe
formula.recipe

Create a Formula from a Prepared Recipe
print.recipe

Print a Recipe
juice

Extract Finalized Training Set
names0

Naming Tools
add_role

Manually Add Roles
okc

OkCupid Data
step_classdist

Distances to Class Centroids
step_count

Create Counts of Patterns using Regular Expressions
selections

Methods for Select Variables in Step Functions
step

A General Step Wrapper
step_bs

B-Spline Basis Functions
recipe

Create a Recipe for Preprocessing Data
step_bagimpute

Imputation via Bagged Trees
yj_trans

Internal Functions
step_bin2factor

Create a Factors from A Dummy Variable
recipes

recipes: A package for computing and preprocessing design matrices.
reexports

Objects exported from other packages
step_BoxCox

Box-Cox Transformation for Non-Negative Data
step_YeoJohnson

Yeo-Johnson Transformation
step_corr

High Correlation Filter
step_invlogit

Inverse Logit Transformation
step_isomap

Isomap Embedding
step_hyperbolic

Hyperbolic Transformations
step_interact

Create Interaction Variables
step_intercept

Add intercept (or constant) column
step_ica

ICA Signal Extraction
step_lincomb

Linear Combination Filter
step_log

Logarithmic Transformation
step_date

Date Feature Generator
step_regex

Create Dummy Variables using Regular Expressions
step_relu

Apply (Smoothed) Rectified Linear Transformation
step_factor2string

Convert Factors to Strings
step_center

Centering Numeric Data
step_holiday

Holiday Feature Generator
step_depth

Data Depths
step_dummy

Dummy Variables Creation
step_knnimpute

Imputation via K-Nearest Neighbors
step_meanimpute

Impute Numeric Data Using the Mean
step_kpca

Kernel PCA Signal Extraction
step_modeimpute

Impute Nominal Data Using the Most Common Value
step_logit

Logit Transformation
step_ordinalscore

Convert Ordinal Factors to Numeric Scores
step_other

Collapse Some Categorical Levels
step_lowerimpute

Impute Numeric Data Below the Threshold of Measurement
step_ns

Nature Spline Basis Functions
step_pca

PCA Signal Extraction
step_nzv

Near-Zero Variance Filter
step_range

Scaling Numeric Data to a Specific Range
terms_select

Select Terms in a Step Function.
step_ratio

Ratio Variable Creation
tidy.recipe

Tidy the Result of a Recipe
step_zv

Zero Variance Filter
step_poly

Orthogonal Polynomial Basis Functions
summary.recipe

Summarize a Recipe
step_rm

General Variable Filter
step_shuffle

Shuffle Variables
step_scale

Scaling Numeric Data
step_spatialsign

Spatial Sign Preprocessing
step_unorder

Convert Ordered Factors to Unordered Factors
step_window

Moving Window Functions
step_sqrt

Square Root Transformation
step_string2factor

Convert Strings to Factors