recipes

Add a New Operation to the Current Recipe

Apply a Trained Data Recipe

Check Variable Class

Check if all Columns are Present

Check for Missing Values

check that newly created variable names don't overlap

Check for New Values

Check Range Consistency

Quantitatively check on variables

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

Discretize Numeric Variables

Helper Functions for Profile Data Sets

Helpers for printing step functions

Create a Formula from a Prepared Recipe

Check to see if a recipe is trained/prepared

Get the keep_original_cols value of a recipe step

Role Selection

Extract Finalized Training Set

Naming Tools

Train a Data Recipe

Wrapper function for preparing recipes within resampling

Print a Recipe

Make a random identification field for steps

Create a Recipe for Preprocessing Data

recipes: A package for computing and preprocessing design matrices.

Internal Functions

Update packages

Objects exported from other packages

S3 methods for tracking which additional packages are needed for steps.

Manually Alter Roles

Methods for Selecting Variables in Step Functions

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

Box-Cox Transformation for Non-Negative Data

Yeo-Johnson Transformation

Sort rows using dplyr

Create a Factors from A Dummy Variable

B-Spline Basis Functions

Centering numeric data

Distances to Class Centroids

High Correlation Filter

Create Counts of Patterns using Regular Expressions

Cut a numeric variable into a factor

Date Feature Generator

Data Depths

Discretize Numeric Variables

Down-Sample a Data Set Based on a Factor Variable

Dummy Variables Creation

Convert Factors to Strings

Filter rows using dplyr

Distance between two locations

Holiday Feature Generator

Hyperbolic Transformations

ICA Signal Extraction

Imputation via Bagged Trees

Imputation via K-Nearest Neighbors

Imputation of numeric variables via a linear model.

Impute Numeric Data Below the Threshold of Measurement

Impute Numeric Data Using the Mean

Impute Numeric Data Using the Median

Impute Nominal Data Using the Most Common Value

Impute Numeric Data Using a Rolling Window Statistic

Create Missing Data Column Indicators

Convert values to predefined integers

Create Interaction Variables

Add intercept (or constant) column

Inverse Transformation

Inverse Logit Transformation

Isomap Embedding

Kernel PCA Signal Extraction

Polynomial Kernel PCA Signal Extraction

Radial Basis Function Kernel PCA Signal Extraction

Create a lagged predictor

Linear Combination Filter

Logarithmic Transformation

Logit Transformation

Add new variables using dplyr

Mutate multiple columns using dplyr

Remove observations with missing values

NNMF Signal Extraction

Center and scale numeric data

Simple Value Assignments for Novel Factor Levels

Natural Spline Basis Functions

Convert Numbers to Factors

Near-Zero Variance Filter

Convert Ordinal Factors to Numeric Scores

Collapse Some Categorical Levels

PCA Signal Extraction

Partial Least Squares Feature Extraction

Orthogonal Polynomial Basis Functions

Create a Profiling Version of a Data Set

Scaling Numeric Data to a Specific Range

Ratio Variable Creation

Create Dummy Variables using Regular Expressions

Relevel factors to a desired level

Apply (Smoothed) Rectified Linear Transformation

Rename variables by name using dplyr

Rename multiple columns using dplyr

General Variable Filter

Sample rows using dplyr

Scaling Numeric Data

Select variables using dplyr

Shuffle Variables

Filter rows by position using dplyr

Spatial Sign Preprocessing

Square Root Transformation

Convert Strings to Factors

Assign missing categories to "unknown"

Convert Ordered Factors to Unordered Factors

Up-Sample a Data Set Based on a Factor Variable

Moving Window Functions

Zero Variance Filter

Summarize a Recipe

Select Terms in a Step Function.

Tidy the Result of a Recipe

Find recommended methods for generating parameter values

Update a recipe step