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recipes (version 0.1.5)

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

0.1.5

License

GPL-2

Issues

Pull Requests

Stars

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Repository

https://github.com/tidymodels/recipes

Maintainer

Max Kuhn

Last Published

March 21st, 2019

Functions in recipes (0.1.5)

Apply a Trained Data Recipe

Raw Cover Type Data

Objects exported from other packages

Manually Alter Roles

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

Train a Data Recipe

Discretize Numeric Variables

Helper Functions for Profile Data Sets

Wrapper function for preparing recipes within resampling

Create a Recipe for Preprocessing Data

Sort rows using dplyr

Imputation via Bagged Trees

Filter rows using dplyr

Distance between two locations

Internal Functions

Date Feature Generator

ICA Signal Extraction

Box-Cox Transformation for Non-Negative Data

step_YeoJohnson

Yeo-Johnson Transformation

Dummy Variables Creation

Convert values to predefined integers

Add intercept (or constant) column

Create Interaction Variables

step_factor2string

Convert Factors to Strings

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Collapse Some Categorical Levels

Linear Combination Filter

Holiday Feature Generator

Logarithmic Transformation

Create Dummy Variables using Regular Expressions

step_num2factor

Convert Numbers to Factors

Apply (Smoothed) Rectified Linear Transformation

step_hyperbolic

Hyperbolic Transformations

Shuffle Variables

Near-Zero Variance Filter

Kernel PCA Signal Extraction

Ratio Variable Creation

Scaling Numeric Data to a Specific Range

Filter rows by position using dplyr

Tidy the Result of a Recipe

Select Terms in a Step Function.

Check if all Columns are Present

Check for Missing Values

recipes: A package for computing and preprocessing design matrices.

recipes_pkg_check

Update packages

Centering Numeric Data

Distances to Class Centroids

Create a lagged predictor

Square Root Transformation

step_spatialsign

Spatial Sign Preprocessing

Isomap Embedding

PCA Signal Extraction

Partial Least Squares Feature Extraction

Smithsonian Museums

Sample rows using dplyr

Add a New Operation to the Current Recipe

Imputation via K-Nearest Neighbors

step_lowerimpute

Impute Numeric Data Below the Threshold of Measurement

Logit Transformation

Simple Value Assignments for Novel Factor Levels

Extract Finalized Training Set

Scaling Numeric Data

Up-Sample a Data Set Based on a Factor Variable

Moving Window Functions

check that newly created variable names don't overlap

Make a random identification field for steps

Check Range Consistency

Create a Formula from a Prepared Recipe

Check to see if a recipe is trained/prepared

step_bin2factor

Create a Factors from A Dummy Variable

Nature Spline Basis Functions

General Variable Filter

Methods for Select Variables in Step Functions

step_rollimpute

Impute Numeric Data Using a Rolling Window Statistic

step_string2factor

Convert Strings to Factors

Convert Ordered Factors to Unordered Factors

step_discretize

Discretize Numeric Variables

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

B-Spline Basis Functions

step_downsample

Down-Sample a Data Set Based on a Factor Variable

High Correlation Filter

Create Counts of Patterns using Regular Expressions

step_meanimpute

Impute Numeric Data Using the Mean

step_medianimpute

Impute Numeric Data Using the Median

Remove observations with missing values

Inverse Transformation

Inverse Logit Transformation

step_modeimpute

Impute Nominal Data Using the Most Common Value

Add new variables using mutate

Zero Variance Filter

NNMF Signal Extraction

Summarize a Recipe

Orthogonal Polynomial Basis Functions

Create a Profiling Version of a Data Set

Update a recipe step

Quantitatively check on variables