.get_data_types Get types for use in recipes add_step Add a New Operation to the Current Recipe bake Apply a trained preprocessing recipe case-weight-helpers Helpers for steps with case weights case_weights Using case weights with recipes 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 detect_step Detect if a particular step or check is used in a recipe developer_functions Developer functions for creating recipes steps discretize Discretize Numeric Variables formula.recipe Create a formula from a prepared recipe fully_trained Check to see if a recipe is trained/prepared has_role Role Selection juice Extract transformed training set names0 Naming Tools prep Estimate a preprocessing recipe prepper Wrapper function for preparing recipes within resampling print.recipe Print a Recipe recipe Create a recipe for preprocessing data recipes_eval_select Evaluate a selection with tidyselect semantics specific to recipes recipes_extension_check Checks that steps have all S3 methods roles Manually alter roles selections Methods for selecting variables in step functions step_BoxCox Box-Cox transformation for non-negative data step_YeoJohnson Yeo-Johnson transformation step_arrange Sort rows using dplyr step_bin2factor Create a factors from A dummy variable step_bs B-spline basis functions step_center Centering numeric data step_classdist Distances to class centroids step_classdist_shrunken Compute shrunken centroid distances for classification models step_corr High correlation filter step_count Create counts of patterns using regular expressions step_cut Cut a numeric variable into a factor step_date Date feature generator step_depth Data depths step_discretize Discretize Numeric Variables 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_filter Filter rows using dplyr step_filter_missing Missing value column filter step_geodist Distance between two locations step_harmonic Add sin and cos terms for harmonic analysis step_holiday Holiday feature generator step_hyperbolic Hyperbolic transformations step_ica ICA signal extraction step_impute_bag Impute via bagged trees step_impute_knn Impute via k-nearest neighbors step_impute_linear Impute numeric variables via a linear model step_impute_lower Impute numeric data below the threshold of measurement step_impute_mean Impute numeric data using the mean step_impute_median Impute numeric data using the median step_impute_mode Impute nominal data using the most common value step_impute_roll Impute numeric data using a rolling window statistic step_indicate_na Create missing data column indicators 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_kpca Kernel PCA signal extraction step_kpca_poly Polynomial kernel PCA signal extraction step_kpca_rbf Radial basis function kernel PCA signal extraction step_lag Create a lagged predictor step_lincomb Linear combination filter step_log Logarithmic transformation step_logit Logit transformation step_mutate Add new variables using dplyr step_mutate_at Mutate multiple columns using dplyr step_naomit Remove observations with missing values step_nnmf Non-negative matrix factorization signal extraction step_nnmf_sparse Non-negative matrix factorization signal extraction with lasso penalization step_normalize Center and scale numeric data step_novel Simple value assignments for novel factor levels step_ns Natural 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 infrequent categorical levels step_pca PCA signal extraction step_percentile Percentile transformation step_pls Partial least squares feature extraction step_poly Orthogonal polynomial basis functions step_poly_bernstein Generalized bernstein polynomial basis 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 Detect a regular expression step_relevel Relevel factors to a desired level step_relu Apply (smoothed) rectified linear transformation step_rename Rename variables by name using dplyr step_rename_at Rename multiple columns using dplyr step_rm General variable filter step_sample Sample rows using dplyr step_scale Scaling mumeric data step_select Select variables using dplyr step_shuffle Shuffle variables step_slice Filter rows by position using dplyr step_spatialsign Spatial sign preprocessing 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_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_window Moving window functions step_zv Zero variance filter summary.recipe Summarize a recipe tidy.step_BoxCox Tidy the result of a recipe update.step Update a recipe step update_role_requirements Update role specific requirements