Function reference • recipes

Basic Functions

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

recipe(): Create a recipe for preprocessing data

formula(<recipe>): Create a Formula from a Prepared Recipe

print(<recipe>): Print a Recipe

summary(<recipe>): Summarize a recipe

prep(): Estimate a preprocessing recipe

bake(): Apply a trained preprocessing recipe

juice(): Extract transformed training set

selections selection: Methods for selecting variables in step functions

has_role() has_type() all_outcomes() all_predictors() all_date() all_date_predictors() all_datetime() all_datetime_predictors() all_double() all_double_predictors() all_factor() all_factor_predictors() all_integer() all_integer_predictors() all_logical() all_logical_predictors() all_nominal() all_nominal_predictors() all_numeric() all_numeric_predictors() all_ordered() all_ordered_predictors() all_string() all_string_predictors() all_unordered() all_unordered_predictors() current_info(): Role Selection

add_role() update_role() remove_role(): Manually Alter Roles

update_role_requirements(): Update role specific requirements

get_case_weights() averages() medians() variances() correlations() covariances() pca_wts() are_weights_used(): Helpers for steps with case weights

case_weights: Using case weights with recipes

Step Functions - Imputation

step_impute_bag() step_bagimpute() imp_vars(): Impute via bagged trees

step_impute_knn() step_knnimpute(): Impute via k-nearest neighbors

step_impute_linear(): Impute numeric variables via a linear model

step_impute_lower() step_lowerimpute(): Impute numeric data below the threshold of measurement

step_impute_mean() step_meanimpute(): Impute numeric data using the mean

step_impute_median() step_medianimpute(): Impute numeric data using the median

step_impute_mode() step_modeimpute(): Impute nominal data using the most common value

step_impute_roll() step_rollimpute(): Impute numeric data using a rolling window statistic

step_unknown(): Assign missing categories to "unknown"

Step Functions - Individual Transformations

step_BoxCox(): Box-Cox Transformation for Non-Negative Data

step_bs(): B-Spline Basis Functions

step_harmonic(): Add sin and cos terms for harmonic analysis

step_hyperbolic(): Hyperbolic Transformations

step_inverse(): Inverse Transformation

step_invlogit(): Inverse Logit Transformation

step_log(): Logarithmic Transformation

step_logit(): Logit Transformation

step_mutate(): Add new variables using dplyr

step_ns(): Natural Spline Basis Functions

step_poly(): Orthogonal Polynomial Basis Functions

step_poly_bernstein(): Generalized Bernstein Polynomial Basis

step_relu(): Apply (Smoothed) Rectified Linear Transformation

step_spline_b(): Basis Splines

step_spline_convex(): Convex Splines

step_spline_monotone(): Monotone Splines

step_spline_natural(): Natural Splines

step_spline_nonnegative(): Non-Negative Splines

step_sqrt(): Square Root Transformation

step_YeoJohnson(): Yeo-Johnson Transformation

Step Functions - Discretization

step_discretize(): Discretize Numeric Variables

discretize() predict(<discretize>): Discretize Numeric Variables

step_cut(): Cut a numeric variable into a factor

Step Functions - Dummy Variables and Encodings

step_bin2factor(): Create a Factors from A Dummy Variable

step_count(): Create Counts of Patterns using Regular Expressions

step_date(): Date Feature Generator

step_dummy(): Create traditional dummy variables

step_dummy_extract(): Extract patterns from nominal data

step_dummy_multi_choice(): Handle levels in multiple predictors together

step_factor2string(): Convert Factors to Strings

step_holiday(): Holiday Feature Generator

step_indicate_na(): Create Missing Data Column Indicators

step_integer(): Convert values to predefined integers

step_novel(): Simple Value Assignments for Novel Factor Levels

step_num2factor(): Convert Numbers to Factors

step_ordinalscore(): Convert Ordinal Factors to Numeric Scores

step_other(): Collapse Some Categorical Levels

step_percentile(): Percentile Transformation

step_regex(): Detect a regular expression

step_relevel(): Relevel factors to a desired level

step_string2factor(): Convert Strings to Factors

step_time(): Time Feature Generator

step_unknown(): Assign missing categories to "unknown"

step_unorder(): Convert Ordered Factors to Unordered Factors

Step Functions - Interactions

step_interact(): Create Interaction Variables

Step Functions - Normalization

step_center(): Centering numeric data

step_normalize(): Center and scale numeric data

step_range(): Scaling Numeric Data to a Specific Range

step_scale(): Scaling Numeric Data

Step Functions - Multivariate Transformations

step_classdist(): Distances to Class Centroids

step_depth(): Data Depths

step_geodist(): Distance between two locations

step_ica(): ICA Signal Extraction

step_isomap(): Isomap Embedding

step_kpca(): Kernel PCA Signal Extraction

step_kpca_poly(): Polynomial Kernel PCA Signal Extraction

step_kpca_rbf(): Radial Basis Function Kernel PCA Signal Extraction

step_mutate_at(): Mutate multiple columns using dplyr

step_nnmf(): Non-Negative Matrix Factorization Signal Extraction

step_nnmf_sparse(): Non-Negative Matrix Factorization Signal Extraction with lasso Penalization

step_pca(): PCA Signal Extraction

step_pls(): Partial Least Squares Feature Extraction

step_ratio() denom_vars(): Ratio Variable Creation

step_spatialsign(): Spatial Sign Preprocessing

Step Functions - Filters

step_corr(): High Correlation Filter

step_filter_missing(): Missing Value Column Filter

step_lincomb(): Linear Combination Filter

step_nzv(): Near-Zero Variance Filter

step_rm(): General Variable Filter

step_select(): Select variables using dplyr

step_zv(): Zero Variance Filter

Step Functions - Row Operations

step_arrange(): Sort rows using dplyr

step_filter(): Filter rows using dplyr

step_lag(): Create a lagged predictor

step_naomit(): Remove observations with missing values

step_impute_roll() step_rollimpute(): Impute numeric data using a rolling window statistic

step_sample(): Sample rows using dplyr

step_shuffle(): Shuffle Variables

step_slice(): Filter rows by position using dplyr

Step Functions - Others

step_intercept(): Add intercept (or constant) column

step_profile(): Create a Profiling Version of a Data Set

step_rename(): Rename variables by name using dplyr

step_rename_at(): Rename multiple columns using dplyr

step_window(): Moving Window Functions

Check Functions

check_class(): Check Variable Class

check_cols(): Check if all Columns are Present

check_missing(): Check for Missing Values

check_new_values(): Check for New Values

check_range(): Check Range Consistency

Internal Step Handling

add_step() add_check(): Add a New Operation to the Current Recipe

detect_step(): Detect if a particular step or check is used in a recipe

fully_trained(): Check to see if a recipe is trained/prepared

.get_data_types(): Get types for use in recipes

names0() dummy_names() dummy_extract_names(): Naming Tools

prepper(): Wrapper function for preparing recipes within resampling

recipes_eval_select(): Evaluate a selection with tidyselect semantics specific to recipes

recipes_extension_check(): Checks that steps have all S3 methods

update(<step>): Update a recipe step

Tidy Methods

tidy(<step_BoxCox>) tidy(<step_YeoJohnson>) tidy(<step_arrange>) tidy(<step_bin2factor>) tidy(<step_bs>) tidy(<step_center>) tidy(<check_class>) tidy(<step_classdist>) tidy(<check_cols>) tidy(<step_corr>) tidy(<step_count>) tidy(<step_cut>) tidy(<step_date>) tidy(<step_depth>) tidy(<step_discretize>) tidy(<step_dummy>) tidy(<step_dummy_multi_choice>) tidy(<step_dummy_extract>) tidy(<step_factor2string>) tidy(<step_filter>) tidy(<step_filter_missing>) tidy(<step_geodist>) tidy(<step_harmonic>) tidy(<step_holiday>) tidy(<step_hyperbolic>) tidy(<step_ica>) tidy(<step_impute_bag>) tidy(<step_impute_knn>) tidy(<step_impute_linear>) tidy(<step_impute_lower>) tidy(<step_impute_mean>) tidy(<step_impute_median>) tidy(<step_impute_mode>) tidy(<step_impute_roll>) tidy(<step_integer>) tidy(<step_interact>) tidy(<step_intercept>) tidy(<step_inverse>) tidy(<step_invlogit>) tidy(<step_isomap>) tidy(<step_kpca>) tidy(<step_kpca_poly>) tidy(<step_kpca_rbf>) tidy(<step_lag>) tidy(<step_lincomb>) tidy(<step_log>) tidy(<step_logit>) tidy(<check_missing>) tidy(<step_mutate>) tidy(<step_mutate_at>) tidy(<step_indicate_na>) tidy(<step_naomit>) tidy(<check_new_values>) tidy(<step_nnmf>) tidy(<step_nnmf_sparse>) tidy(<step_normalize>) tidy(<step_novel>) tidy(<step_ns>) tidy(<step_num2factor>) tidy(<step_nzv>) tidy(<step_ordinalscore>) tidy(<step_other>) tidy(<step_pca>) tidy(<step_percentile>) tidy(<step_pls>) tidy(<step_poly>) tidy(<step_poly_bernstein>) tidy(<step_profile>) tidy(<step_range>) tidy(<check_range>) tidy(<step_ratio>) tidy(<step_regex>) tidy(<step_relevel>) tidy(<step_relu>) tidy(<step_rename>) tidy(<step_rename_at>) tidy(<step_rm>) tidy(<step_sample>) tidy(<step_scale>) tidy(<step_select>) tidy(<step_shuffle>) tidy(<step_slice>) tidy(<step_spatialsign>) tidy(<step_spline_b>) tidy(<step_spline_convex>) tidy(<step_spline_monotone>) tidy(<step_spline_natural>) tidy(<step_spline_nonnegative>) tidy(<step_sqrt>) tidy(<step_string2factor>) tidy(<recipe>) tidy(<step>) tidy(<check>) tidy(<step_time>) tidy(<step_unknown>) tidy(<step_unorder>) tidy(<step_window>) tidy(<step_zv>): Tidy the Result of a Recipe