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recipes (version 0.1.14)

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

215,376

Version

0.1.14

License

GPL-2

Issues

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Repository

https://github.com/tidymodels/recipes

Homepage

https://recipes.tidymodels.org

Maintainer

Max Kuhn

Last Published

October 17th, 2020

Functions in recipes (0.1.14)

check_new_values

Check for New Values

Check Range Consistency

Helpers for printing step functions

Add a New Operation to the Current Recipe

Apply a Trained Data Recipe

Helper Functions for Profile Data Sets

Discretize Numeric Variables

recipes: A package for computing and preprocessing design matrices.

Create a Recipe for Preprocessing Data

Create a Formula from a Prepared Recipe

Check for Missing Values

Extract Finalized Training Set

check that newly created variable names don't overlap

recipes_pkg_check

Update packages

step_YeoJohnson

Yeo-Johnson Transformation

Distances to Class Centroids

Filter rows using dplyr

step_lowerimpute

Impute Numeric Data Below the Threshold of Measurement

Distance between two locations

High Correlation Filter

Check if all Columns are Present

Wrapper function for preparing recipes within resampling

Check Variable Class

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

step_meanimpute

Impute Numeric Data Using the Mean

Internal Functions

Box-Cox Transformation for Non-Negative Data

Manually Alter Roles

Train a Data Recipe

Methods for Select Variables in Step Functions

Remove observations with missing values

Add intercept (or constant) column

step_discretize

Discretize Numeric Variables

Polynomial Kernel PCA Signal Extraction

Sort rows using dplyr

Radial Basis Function Kernel PCA Signal Extraction

step_downsample

Down-Sample a Data Set Based on a Factor Variable

Inverse Transformation

Center and scale numeric data

Simple Value Assignments for Novel Factor Levels

NNMF Signal Extraction

step_factor2string

Convert Factors to Strings

Holiday Feature Generator

Dummy Variables Creation

Apply (Smoothed) Rectified Linear Transformation

PCA Signal Extraction

Update a recipe step

Relevel factors to a desired level

Collapse Some Categorical Levels

Sample rows using dplyr

Imputation via Bagged Trees

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

Check to see if a recipe is trained/prepared

Quantitatively check on variables

Create a lagged predictor

Isomap Embedding

step_hyperbolic

Hyperbolic Transformations

Inverse Logit Transformation

step_bin2factor

Create a Factors from A Dummy Variable

Logarithmic Transformation

Logit Transformation

Add new variables using mutate

Scaling Numeric Data

Partial Least Squares Feature Extraction

General Variable Filter

Up-Sample a Data Set Based on a Factor Variable

Convert Ordered Factors to Unordered Factors

Orthogonal Polynomial Basis Functions

step_rollimpute

Impute Numeric Data Using a Rolling Window Statistic

Linear Combination Filter

Mutate multiple columns

Natural Spline Basis Functions

step_spatialsign

Spatial Sign Preprocessing

step_num2factor

Convert Numbers to Factors

Objects exported from other packages

required_pkgs.step_depth

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

Select Terms in a Step Function.

Summarize a Recipe

Create Dummy Variables using Regular Expressions

Ratio Variable Creation

B-Spline Basis Functions

Make a random identification field for steps

Square Root Transformation

Date Feature Generator

Centering numeric data

step_impute_linear

Imputation of numeric variables via a linear model.

step_medianimpute

Impute Numeric Data Using the Median

ICA Signal Extraction

Cut a numeric variable into a factor

Create Counts of Patterns using Regular Expressions

Convert values to predefined integers

Imputation via K-Nearest Neighbors

Create Interaction Variables

Kernel PCA Signal Extraction

Create a Profiling Version of a Data Set

Near-Zero Variance Filter

step_modeimpute

Impute Nominal Data Using the Most Common Value

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Rename variables by name

Scaling Numeric Data to a Specific Range

Filter rows by position using dplyr

Rename multiple columns

Shuffle Variables

step_string2factor

Convert Strings to Factors

Assign missing categories to "unknown"

Zero Variance Filter

Tidy the Result of a Recipe

tunable.step_bagimpute

tunable methods for recipes

Moving Window Functions