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 infrequent 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 mumeric data

Step Functions - Multivariate Transformations

step_classdist(): Distances to class centroids

step_classdist_shrunken(): Compute shrunken centroid distances for classification models

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

Developer Functions

developer_functions: Developer functions for creating recipes steps

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(<step_classdist_shrunken>) 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_extract>) tidy(<step_dummy_multi_choice>) 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_indicate_na>) 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_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