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recipes (version 0.1.16)

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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Version

Install

install.packages('recipes')

Monthly Downloads

197,839

Version

0.1.16

License

MIT + file LICENSE

Issues

Pull Requests

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Repository

https://github.com/tidymodels/recipes

Homepage

https://recipes.tidymodels.org

Maintainer

Max Kuhn

Last Published

April 16th, 2021

Functions in recipes (0.1.16)

Check Range Consistency

check_new_values

Check for New Values

Check for Missing Values

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

Check if all Columns are Present

check that newly created variable names don't overlap

Quantitatively check on variables

Apply a Trained Data Recipe

Add a New Operation to the Current Recipe

Check Variable Class

Discretize Numeric Variables

recipes_pkg_check

Update packages

Wrapper function for preparing recipes within resampling

Extract Finalized Training Set

Objects exported from other packages

get_keep_original_cols

Get the keep_original_cols value of a recipe step

Check to see if a recipe is trained/prepared

B-Spline Basis Functions

required_pkgs.step_depth

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

Manually Alter Roles

Create a Recipe for Preprocessing Data

Helper Functions for Profile Data Sets

Make a random identification field for steps

Methods for Selecting Variables in Step Functions

Distances to Class Centroids

Train a Data Recipe

High Correlation Filter

step_discretize

Discretize Numeric Variables

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

Create a Formula from a Prepared Recipe

step_bin2factor

Create a Factors from A Dummy Variable

Sort rows using dplyr

Filter rows using dplyr

Date Feature Generator

Centering numeric data

Helpers for printing step functions

step_impute_median

Impute Numeric Data Using the Median

Dummy Variables Creation

step_impute_mode

Impute Nominal Data Using the Most Common Value

ICA Signal Extraction

step_factor2string

Convert Factors to Strings

step_impute_bag

Imputation via Bagged Trees

recipes: A package for computing and preprocessing design matrices.

Internal Functions

step_YeoJohnson

Yeo-Johnson Transformation

step_impute_roll

Impute Numeric Data Using a Rolling Window Statistic

Box-Cox Transformation for Non-Negative Data

step_indicate_na

Create Missing Data Column Indicators

step_impute_knn

Imputation via K-Nearest Neighbors

Distance between two locations

step_impute_mean

Impute Numeric Data Using the Mean

step_impute_lower

Impute Numeric Data Below the Threshold of Measurement

Kernel PCA Signal Extraction

step_impute_linear

Imputation of numeric variables via a linear model.

Add new variables using dplyr

Logit Transformation

Inverse Transformation

Add intercept (or constant) column

step_downsample

Down-Sample a Data Set Based on a Factor Variable

NNMF Signal Extraction

Remove observations with missing values

Mutate multiple columns using dplyr

Near-Zero Variance Filter

step_num2factor

Convert Numbers to Factors

Center and scale numeric data

Isomap Embedding

Holiday Feature Generator

step_hyperbolic

Hyperbolic Transformations

Create Counts of Patterns using Regular Expressions

Inverse Logit Transformation

Simple Value Assignments for Novel Factor Levels

Cut a numeric variable into a factor

Natural Spline Basis Functions

Radial Basis Function Kernel PCA Signal Extraction

Create a lagged predictor

PCA Signal Extraction

Polynomial Kernel PCA Signal Extraction

Collapse Some Categorical Levels

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Rename multiple columns using dplyr

Convert values to predefined integers

Apply (Smoothed) Rectified Linear Transformation

Create Interaction Variables

Linear Combination Filter

Scaling Numeric Data to a Specific Range

Ratio Variable Creation

Square Root Transformation

Select variables using dplyr

Rename variables by name using dplyr

Logarithmic Transformation

General Variable Filter

Up-Sample a Data Set Based on a Factor Variable

Zero Variance Filter

Partial Least Squares Feature Extraction

step_string2factor

Convert Strings to Factors

Select Terms in a Step Function.

Summarize a Recipe

Moving Window Functions

Orthogonal Polynomial Basis Functions

Shuffle Variables

Sample rows using dplyr

Scaling Numeric Data

step_spatialsign

Spatial Sign Preprocessing

Create a Profiling Version of a Data Set

Filter rows by position using dplyr

Tidy the Result of a Recipe

Create Dummy Variables using Regular Expressions

Relevel factors to a desired level

Assign missing categories to "unknown"

Convert Ordered Factors to Unordered Factors

Find recommended methods for generating parameter values

Update a recipe step