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recipes (version 0.1.3)

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.

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Install

install.packages('recipes')

Monthly Downloads

126,886

Version

0.1.3

License

GPL-2

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Repository

https://github.com/topepo/recipes

Maintainer

Last Published

June 16th, 2018

Functions in recipes (0.1.3)

Extract Finalized Training Set

recipes: A package for computing and preprocessing design matrices.

Methods for Select Variables in Step Functions

step_discretize

Discretize Numeric Variables

Add intercept (or constant) column

Check to see if a recipe is trained/prepared

Create a Recipe for Preprocessing Data

step_bin2factor

Create a Factors from A Dummy Variable

Inverse Transformation

B-Spline Basis Functions

Create Interaction Variables

Inverse Logit Transformation

step_meanimpute

Impute Numeric Data Using the Mean

Internal Functions

step_modeimpute

Impute Nominal Data Using the Most Common Value

PCA Signal Extraction

Centering Numeric Data

Objects exported from other packages

Collapse Some Categorical Levels

Distances to Class Centroids

step sets the class of the step and check is for checks.

Remove observations with missing values

Ratio Variable Creation

Box-Cox Transformation for Non-Negative Data

Apply (Smoothed) Rectified Linear Transformation

Simple Value Assignments for Novel Factor Levels

Create a Profiling Version of a Data Set

Scaling Numeric Data to a Specific Range

Create Dummy Variables using Regular Expressions

step_YeoJohnson

Yeo-Johnson Transformation

Convert Ordered Factors to Unordered Factors

Create Counts of Patterns using Regular Expressions

Holiday Feature Generator

Date Feature Generator

General Variable Filter

High Correlation Filter

step_hyperbolic

Hyperbolic Transformations

step_factor2string

Convert Factors to Strings

Kernel PCA Signal Extraction

Create a lagged predictor

Moving Window Functions

Zero Variance Filter

Partial Least Squares Feature Extraction

step_downsample

Down-Sample a Data Set Based on a Factor Variable

Logit Transformation

ICA Signal Extraction

Orthogonal Polynomial Basis Functions

step_rollimpute

Impute Numeric Data Using a Rolling Window Statistic

Dummy Variables Creation

Scaling Numeric Data

Up-Sample a Data Set Based on a Factor Variable

Isomap Embedding

Imputation via K-Nearest Neighbors

step_lowerimpute

Impute Numeric Data Below the Threshold of Measurement

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Near-Zero Variance Filter

Linear Combination Filter

Shuffle Variables

Logarithmic Transformation

step_spatialsign

Spatial Sign Preprocessing

Tidy the Result of a Recipe

Nature Spline Basis Functions

Square Root Transformation

step_string2factor

Convert Strings to Factors

Summarize a Recipe

Select Terms in a Step Function.

step_num2factor

Convert Numbers to Factors

Check for Missing Values

Check if all Columns are Present

Detect if a particular step or check is used in a recipe

Add a New Operation to the Current Recipe

Raw Cover Type Data

Apply a Trained Data Recipe

Check Range Consistency

Train a Data Recipe

Helper Functions for Profile Data Sets

Manually Add Roles

Discretize Numeric Variables

Create a Formula from a Prepared Recipe

Imputation via Bagged Trees