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recipes (version 0.1.9)

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

0.1.9

License

GPL-2

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Repository

https://github.com/tidymodels/recipes

Maintainer

Last Published

January 7th, 2020

Functions in recipes (0.1.9)

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

check that newly created variable names don't overlap

Check Range Consistency

Apply a Trained Data Recipe

Check Variable Class

check_new_values

Check for New Values

Check for Missing Values

Check if all Columns are Present

Quantitatively check on variables

Add a New Operation to the Current Recipe

Extract Finalized Training Set

Helpers for printing step functions

Internal Functions

Create a Formula from a Prepared Recipe

recipes: A package for computing and preprocessing design matrices.

Box-Cox Transformation for Non-Negative Data

recipes_pkg_check

Update packages

Check to see if a recipe is trained/prepared

step_factor2string

Convert Factors to Strings

Dummy Variables Creation

step_YeoJohnson

Yeo-Johnson Transformation

Discretize Numeric Variables

Filter rows using dplyr

Train a Data Recipe

High Correlation Filter

Create a Recipe for Preprocessing Data

Wrapper function for preparing recipes within resampling

Methods for Select Variables in Step Functions

step_hyperbolic

Hyperbolic Transformations

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

Sort rows using dplyr

Kernel PCA Signal Extraction

Inverse Logit Transformation

Helper Functions for Profile Data Sets

step_medianimpute

Impute Numeric Data Using the Median

Polynomial Kernel PCA Signal Extraction

step_meanimpute

Impute Numeric Data Using the Mean

Objects exported from other packages

B-Spline Basis Functions

Create Counts of Patterns using Regular Expressions

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Make a random identification field for steps

Imputation via K-Nearest Neighbors

NNMF Signal Extraction

Create a lagged predictor

Isomap Embedding

Date Feature Generator

Radial Basis Function Kernel PCA Signal Extraction

Imputation via Bagged Trees

Create Interaction Variables

step_bin2factor

Create a Factors from A Dummy Variable

Linear Combination Filter

step_lowerimpute

Impute Numeric Data Below the Threshold of Measurement

Create a Profiling Version of a Data Set

tunable.step_bagimpute

tunable methods for recipes

Near-Zero Variance Filter

Convert values to predefined integers

Holiday Feature Generator

Logit Transformation

Rename multiple columns

ICA Signal Extraction

Collapse Some Categorical Levels

step_num2factor

Convert Numbers to Factors

General Variable Filter

Distances to Class Centroids

Update a recipe step

Orthogonal Polynomial Basis Functions

Centering numeric data

Manually Alter Roles

Square Root Transformation

step_discretize

Discretize Numeric Variables

Add intercept (or constant) column

Mutate multiple columns

Center and scale numeric data

PCA Signal Extraction

step_downsample

Down-Sample a Data Set Based on a Factor Variable

Create Dummy Variables using Regular Expressions

Partial Least Squares Feature Extraction

Logarithmic Transformation

Scaling Numeric Data

Distance between two locations

Add new variables using mutate

Inverse Transformation

Sample rows using dplyr

Shuffle Variables

step_modeimpute

Impute Nominal Data Using the Most Common Value

Scaling Numeric Data to a Specific Range

Summarize a Recipe

Zero Variance Filter

Ratio Variable Creation

Simple Value Assignments for Novel Factor Levels

Nature Spline Basis Functions

Rename variables by name

step_string2factor

Convert Strings to Factors

Select Terms in a Step Function.

Tidy the Result of a Recipe

Remove observations with missing values

Relevel factors to a desired level

step_rollimpute

Impute Numeric Data Using a Rolling Window Statistic

Filter rows by position using dplyr

Moving Window Functions

Up-Sample a Data Set Based on a Factor Variable

step_spatialsign

Spatial Sign Preprocessing

Apply (Smoothed) Rectified Linear Transformation

Convert Ordered Factors to Unordered Factors

Assign missing categories to "unknown"