# Preprocessing Tools to Create Design Matrices

## Documentation for package ‘recipes’ version 0.1.4

- DESCRIPTION file.
- User guides, package vignettes and other documentation.

## Help Pages

A B C D F H I J N O P R S T U

recipes-package | recipes: A package for computing and preprocessing design matrices.

## -- A --

add_check | Add a New Operation to the Current Recipe
add_role | Manually Alter Roles
add_step | Add a New Operation to the Current Recipe
all_nominal | Role Selection
all_numeric | Role Selection
all_outcomes | Role Selection
all_predictors | Role Selection

## -- B --

bake | Apply a Trained Data Recipe
bake.recipe | Apply a Trained Data Recipe
biomass | Biomass Data

## -- C --

check_cols | Check if all Columns are Present
check_missing | Check for Missing Values
check_range | Check Range Consistency
covers | Raw Cover Type Data
credit_data | Credit Data
current_info | Role Selection

## -- D --

denom_vars | Ratio Variable Creation
detect_step | Detect if a particular step or check is used in a recipe
discretize | Discretize Numeric Variables
discretize.default | Discretize Numeric Variables
discretize.numeric | Discretize Numeric Variables
dummy_names | Naming Tools

## -- F --

formula.recipe | Create a Formula from a Prepared Recipe
fully_trained | Check to see if a recipe is trained/prepared

## -- H --

has_role | Role Selection
has_type | Role Selection

## -- I --

imp_vars | Imputation via Bagged Trees

## -- J --

juice | Extract Finalized Training Set

## -- N --

names0 | Naming Tools

## -- O --

okc | OkCupid Data

## -- P --

predict.discretize | Discretize Numeric Variables
prep | Train a Data Recipe
prep.recipe | Train a Data Recipe
prepper | Wrapper function for preparing recipes within resampling
print.recipe | Print a Recipe

## -- R --

recipe | Create a Recipe for Preprocessing Data
recipe.data.frame | Create a Recipe for Preprocessing Data
recipe.default | Create a Recipe for Preprocessing Data
recipe.formula | Create a Recipe for Preprocessing Data
recipe.matrix | Create a Recipe for Preprocessing Data
recipes | recipes: A package for computing and preprocessing design matrices.
roles | Manually Alter Roles

## -- S --

selections | Methods for Select Variables in Step Functions
Smithsonian | Smithsonian Museums
step_arrange | Sort rows using dplyr
step_bagimpute | Imputation via Bagged Trees
step_bin2factor | Create a Factors from A Dummy Variable
step_BoxCox | Box-Cox Transformation for Non-Negative Data
step_bs | B-Spline Basis Functions
step_center | Centering Numeric Data
step_classdist | Distances to Class Centroids
step_corr | High Correlation Filter
step_count | Create Counts of Patterns using Regular Expressions
step_date | Date Feature Generator
step_depth | Data Depths
step_discretize | Discretize Numeric Variables
step_downsample | Down-Sample a Data Set Based on a Factor Variable
step_dummy | Dummy Variables Creation
step_factor2string | Convert Factors to Strings
step_filter | Filter rows using dplyr
step_geodist | Distance between two locations
step_holiday | Holiday Feature Generator
step_hyperbolic | Hyperbolic Transformations
step_ica | ICA Signal Extraction
step_integer | Convert values to predefined integers
step_interact | Create Interaction Variables
step_intercept | Add intercept (or constant) column
step_inverse | Inverse Transformation
step_invlogit | Inverse Logit Transformation
step_isomap | Isomap Embedding
step_knnimpute | Imputation via K-Nearest Neighbors
step_kpca | Kernel PCA Signal Extraction
step_lag | Create a lagged predictor
step_lincomb | Linear Combination Filter
step_log | Logarithmic Transformation
step_logit | Logit Transformation
step_lowerimpute | Impute Numeric Data Below the Threshold of Measurement
step_meanimpute | Impute Numeric Data Using the Mean
step_medianimpute | Impute Numeric Data Using the Median
step_modeimpute | Impute Nominal Data Using the Most Common Value
step_mutate | Add new variables using 'mutate'
step_naomit | Remove observations with missing values
step_nnmf | NNMF Signal Extraction
step_novel | Simple Value Assignments for Novel Factor Levels
step_ns | Nature Spline Basis Functions
step_num2factor | Convert Numbers to Factors
step_nzv | Near-Zero Variance Filter
step_ordinalscore | Convert Ordinal Factors to Numeric Scores
step_other | Collapse Some Categorical Levels
step_pca | PCA Signal Extraction
step_pls | Partial Least Squares Feature Extraction
step_poly | Orthogonal Polynomial Basis Functions
step_profile | Create a Profiling Version of a Data Set
step_range | Scaling Numeric Data to a Specific Range
step_ratio | Ratio Variable Creation
step_regex | Create Dummy Variables using Regular Expressions
step_relu | Apply (Smoothed) Rectified Linear Transformation
step_rm | General Variable Filter
step_rollimpute | Impute Numeric Data Using a Rolling Window Statistic
step_sample | Sample rows using dplyr
step_scale | Scaling Numeric Data
step_shuffle | Shuffle Variables
step_slice | Filter rows by position using dplyr
step_spatialsign | Spatial Sign Preprocessing
step_sqrt | Square Root Transformation
step_string2factor | Convert Strings to Factors
step_unorder | Convert Ordered Factors to Unordered Factors
step_upsample | Up-Sample a Data Set Based on a Factor Variable
step_window | Moving Window Functions
step_YeoJohnson | Yeo-Johnson Transformation
step_zv | Zero Variance Filter
summary.recipe | Summarize a Recipe

## -- T --

terms_select | Select Terms in a Step Function.
tidy.check | Tidy the Result of a Recipe
tidy.check_cols | Check if all Columns are Present
tidy.check_missing | Check for Missing Values
tidy.check_range | Check Range Consistency
tidy.recipe | Tidy the Result of a Recipe
tidy.step | Tidy the Result of a Recipe
tidy.step_arrange | Sort rows using dplyr
tidy.step_bagimpute | Imputation via Bagged Trees
tidy.step_bin2factor | Create a Factors from A Dummy Variable
tidy.step_BoxCox | Box-Cox Transformation for Non-Negative Data
tidy.step_bs | B-Spline Basis Functions
tidy.step_center | Centering Numeric Data
tidy.step_classdist | Distances to Class Centroids
tidy.step_corr | High Correlation Filter
tidy.step_count | Create Counts of Patterns using Regular Expressions
tidy.step_date | Date Feature Generator
tidy.step_depth | Data Depths
tidy.step_discretize | Discretize Numeric Variables
tidy.step_downsample | Down-Sample a Data Set Based on a Factor Variable
tidy.step_dummy | Dummy Variables Creation
tidy.step_factor2string | Convert Factors to Strings
tidy.step_filter | Filter rows using dplyr
tidy.step_geodist | Distance between two locations
tidy.step_holiday | Holiday Feature Generator
tidy.step_hyperbolic | Hyperbolic Transformations
tidy.step_ica | ICA Signal Extraction
tidy.step_integer | Convert values to predefined integers
tidy.step_interact | Create Interaction Variables
tidy.step_inverse | Inverse Transformation
tidy.step_invlogit | Inverse Logit Transformation
tidy.step_isomap | Isomap Embedding
tidy.step_knnimpute | Imputation via K-Nearest Neighbors
tidy.step_kpca | Kernel PCA Signal Extraction
tidy.step_lincomb | Linear Combination Filter
tidy.step_log | Logarithmic Transformation
tidy.step_logit | Logit Transformation
tidy.step_lowerimpute | Impute Numeric Data Below the Threshold of Measurement
tidy.step_meanimpute | Impute Numeric Data Using the Mean
tidy.step_medianimpute | Impute Numeric Data Using the Median
tidy.step_modeimpute | Impute Nominal Data Using the Most Common Value
tidy.step_mutate | Add new variables using 'mutate'
tidy.step_naomit | Remove observations with missing values
tidy.step_nnmf | NNMF Signal Extraction
tidy.step_novel | Simple Value Assignments for Novel Factor Levels
tidy.step_ns | Nature Spline Basis Functions
tidy.step_num2factor | Convert Numbers to Factors
tidy.step_nzv | Near-Zero Variance Filter
tidy.step_ordinalscore | Convert Ordinal Factors to Numeric Scores
tidy.step_other | Collapse Some Categorical Levels
tidy.step_pca | PCA Signal Extraction
tidy.step_pls | Partial Least Squares Feature Extraction
tidy.step_poly | Orthogonal Polynomial Basis Functions
tidy.step_profile | Create a Profiling Version of a Data Set
tidy.step_range | Scaling Numeric Data to a Specific Range
tidy.step_ratio | Ratio Variable Creation
tidy.step_regex | Create Dummy Variables using Regular Expressions
tidy.step_relu | Apply (Smoothed) Rectified Linear Transformation
tidy.step_rm | General Variable Filter
tidy.step_rollimpute | Impute Numeric Data Using a Rolling Window Statistic
tidy.step_sample | Sample rows using dplyr
tidy.step_scale | Scaling Numeric Data
tidy.step_shuffle | Shuffle Variables
tidy.step_slice | Filter rows by position using dplyr
tidy.step_spatialsign | Spatial Sign Preprocessing
tidy.step_sqrt | Square Root Transformation
tidy.step_string2factor | Convert Strings to Factors
tidy.step_unorder | Convert Ordered Factors to Unordered Factors
tidy.step_upsample | Up-Sample a Data Set Based on a Factor Variable
tidy.step_window | Moving Window Functions
tidy.step_YeoJohnson | Yeo-Johnson Transformation
tidy.step_zv | Zero Variance Filter

## -- U --

update_role | Manually Alter Roles