Preprocessing Tools to Create Design Matrices


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Documentation for package ‘recipes’ version 0.1.3

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

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 Add 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
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.

-- S --

selections Methods for Select Variables in Step Functions
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_holiday Holiday Feature Generator
step_hyperbolic Hyperbolic Transformations
step_ica ICA Signal Extraction
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_modeimpute Impute Nominal Data Using the Most Common Value
step_naomit Remove observations with missing values
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_scale Scaling Numeric Data
step_shuffle Shuffle Variables
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_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_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_holiday Holiday Feature Generator
tidy.step_hyperbolic Hyperbolic Transformations
tidy.step_ica ICA Signal Extraction
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_modeimpute Impute Nominal Data Using the Most Common Value
tidy.step_naomit Remove observations with missing values
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_scale Scaling Numeric Data
tidy.step_shuffle Shuffle Variables
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