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recipes (version 0.1.7)

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

173,696

Version

0.1.7

License

GPL-2

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Repository

https://github.com/tidymodels/recipes

Maintainer

Last Published

September 15th, 2019

Functions in recipes (0.1.7)

Create a Formula from a Prepared Recipe

Internal Functions

Extract Finalized Training Set

Create a Recipe for Preprocessing Data

Train a Data Recipe

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

Wrapper function for preparing recipes within resampling

Discretize Numeric Variables

B-Spline Basis Functions

step_bin2factor

Create a Factors from A Dummy Variable

Check to see if a recipe is trained/prepared

step_YeoJohnson

Yeo-Johnson Transformation

Box-Cox Transformation for Non-Negative Data

Raw Cover Type Data

recipes: A package for computing and preprocessing design matrices.

Dummy Variables Creation

step_discretize

Discretize Numeric Variables

Inverse Transformation

Inverse Logit Transformation

step_downsample

Down-Sample a Data Set Based on a Factor Variable

Quantitatively check on variables

recipes_pkg_check

Update packages

step_factor2string

Convert Factors to Strings

Methods for Select Variables in Step Functions

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

Radial Basis Function Kernel PCA Signal Extraction

Create a lagged predictor

Holiday Feature Generator

step_hyperbolic

Hyperbolic Transformations

Create Counts of Patterns using Regular Expressions

Logit Transformation

High Correlation Filter

Create Interaction Variables

Add intercept (or constant) column

Polynomial Kernel PCA Signal Extraction

Kernel PCA Signal Extraction

Helper Functions for Profile Data Sets

Helpers for printing step functions

Scaling Numeric Data to a Specific Range

NNMF Signal Extraction

step_modeimpute

Impute Nominal Data Using the Most Common Value

Center and scale numeric data

step_lowerimpute

Impute Numeric Data Below the Threshold of Measurement

Make a random identification field for steps

Sort rows using dplyr

step_num2factor

Convert Numbers to Factors

Near-Zero Variance Filter

Add new variables using mutate

Orthogonal Polynomial Basis Functions

Filter rows using dplyr

Create a Profiling Version of a Data Set

Imputation via Bagged Trees

Centering numeric data

Distances to Class Centroids

Rename variables by name

Shuffle Variables

Rename multiple columns

Convert values to predefined integers

Nature Spline Basis Functions

Simple Value Assignments for Novel Factor Levels

ICA Signal Extraction

Isomap Embedding

Distance between two locations

General Variable Filter

Ratio Variable Creation

step_rollimpute

Impute Numeric Data Using a Rolling Window Statistic

Moving Window Functions

Filter rows by position using dplyr

Zero Variance Filter

step_spatialsign

Spatial Sign Preprocessing

step_ordinalscore

Convert Ordinal Factors to Numeric Scores

Imputation via K-Nearest Neighbors

Objects exported from other packages

Collapse Some Categorical Levels

Square Root Transformation

Assign missing categories to "unknown"

Update a recipe step

Apply (Smoothed) Rectified Linear Transformation

Create Dummy Variables using Regular Expressions

step_string2factor

Convert Strings to Factors

Scaling Numeric Data

step_medianimpute

Impute Numeric Data Using the Median

step_meanimpute

Impute Numeric Data Using the Mean

Convert Ordered Factors to Unordered Factors

Sample rows using dplyr

Manually Alter Roles

Tidy the Result of a Recipe

tunable.step_bagimpute

tunable methods for recipes

Up-Sample a Data Set Based on a Factor Variable

Linear Combination Filter

Mutate multiple columns

Date Feature Generator

Logarithmic Transformation

Remove observations with missing values

PCA Signal Extraction

Partial Least Squares Feature Extraction

Summarize a Recipe

Select Terms in a Step Function.

Check Range Consistency

check that newly created variable names don't overlap

Check if all Columns are Present

Check for Missing Values

Apply a Trained Data Recipe

Smithsonian Museums

check_new_values

Check for Missing Values

Add a New Operation to the Current Recipe