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recipes (version 0.1.8)

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

218,325

Version

0.1.8

License

GPL-2

Issues

Pull Requests

Stars

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Repository

https://github.com/tidymodels/recipes

Maintainer

Max Kuhn

Last Published

December 18th, 2019

Functions in recipes (0.1.8)

Apply a Trained Data Recipe

Make a random identification field for steps

Train a Data Recipe

Wrapper function for preparing recipes within resampling

Extract Finalized Training Set

Create a Recipe for Preprocessing Data

Internal Functions

Helper Functions for Profile Data Sets

Discretize Numeric Variables

Check to see if a recipe is trained/prepared

B-Spline Basis Functions

Holiday Feature Generator

step_bin2factor

Create a Factors from A Dummy Variable

Objects exported from other packages

step_hyperbolic

Hyperbolic Transformations

recipes_pkg_check

Update packages

Create a Formula from a Prepared Recipe

recipes: A package for computing and preprocessing design matrices.

Helpers for printing step functions

Manually Alter Roles

High Correlation Filter

Create Counts of Patterns using Regular Expressions

Isomap Embedding

Date Feature Generator

Radial Basis Function Kernel PCA Signal Extraction

Imputation via K-Nearest Neighbors

Create a lagged predictor

Mutate multiple columns

Add new variables using mutate

step_modeimpute

Impute Nominal Data Using the Most Common Value

Create Interaction Variables

Add intercept (or constant) column

Remove observations with missing values

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Methods for Select Variables in Step Functions

Box-Cox Transformation for Non-Negative Data

Collapse Some Categorical Levels

Dummy Variables Creation

step_YeoJohnson

Yeo-Johnson Transformation

ICA Signal Extraction

step_factor2string

Convert Factors to Strings

Create a Profiling Version of a Data Set

Orthogonal Polynomial Basis Functions

Linear Combination Filter

Convert values to predefined integers

Centering numeric data

Distances to Class Centroids

Logarithmic Transformation

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

step_discretize

Discretize Numeric Variables

step_string2factor

Convert Strings to Factors

Assign missing categories to "unknown"

Inverse Transformation

Convert Ordered Factors to Unordered Factors

Inverse Logit Transformation

step_downsample

Down-Sample a Data Set Based on a Factor Variable

Imputation via Bagged Trees

Sort rows using dplyr

Filter rows using dplyr

Rename variables by name

Up-Sample a Data Set Based on a Factor Variable

Center and scale numeric data

NNMF Signal Extraction

Create Dummy Variables using Regular Expressions

Nature Spline Basis Functions

step_medianimpute

Impute Numeric Data Using the Median

Scaling Numeric Data to a Specific Range

step_meanimpute

Impute Numeric Data Using the Mean

Simple Value Assignments for Novel Factor Levels

Distance between two locations

Rename multiple columns

Apply (Smoothed) Rectified Linear Transformation

Polynomial Kernel PCA Signal Extraction

Kernel PCA Signal Extraction

General Variable Filter

Filter rows by position using dplyr

Moving Window Functions

Update a recipe step

Shuffle Variables

step_rollimpute

Impute Numeric Data Using a Rolling Window Statistic

Ratio Variable Creation

Zero Variance Filter

step_spatialsign

Spatial Sign Preprocessing

Logit Transformation

step_lowerimpute

Impute Numeric Data Below the Threshold of Measurement

step_num2factor

Convert Numbers to Factors

Sample rows using dplyr

PCA Signal Extraction

Tidy the Result of a Recipe

Partial Least Squares Feature Extraction

Scaling Numeric Data

Near-Zero Variance Filter

tunable.step_bagimpute

tunable methods for recipes

Summarize a Recipe

Square Root Transformation

Select Terms in a Step Function.

check_new_values

Check for New Values

Add a New Operation to the Current Recipe

Check Variable Class

Check Range Consistency

Quantitatively check on variables

Check for Missing Values

Check if all Columns are Present

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

check that newly created variable names don't overlap