recipes v0.1.4


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Preprocessing Tools to Create Design Matrices

An extensible framework to create and preprocess design matrices. Recipes consist of one or more data manipulation and analysis "steps". Statistical parameters for the steps can be estimated from an initial data set and then applied to other data sets. The resulting design matrices can then be used as inputs into statistical or machine learning models.

Functions in recipes

Name Description
biomass Biomass Data
formula.recipe Create a Formula from a Prepared Recipe
juice Extract Finalized Training Set
selections Methods for Select Variables in Step Functions
yj_trans Internal Functions
recipe Create a Recipe for Preprocessing Data
has_role Role Selection
step step sets the class of the step and check is for checks.
step_dummy Dummy Variables Creation
step_factor2string Convert Factors to Strings
discretize Discretize Numeric Variables
fixed Helper Functions for Profile Data Sets
step_inverse Inverse Transformation
step_invlogit Inverse Logit Transformation
fully_trained Check to see if a recipe is trained/prepared
detect_step Detect if a particular step or check is used in a recipe
step_kpca Kernel PCA Signal Extraction
credit_data Credit Data
recipes recipes: A package for computing and preprocessing design matrices.
step_lag Create a lagged predictor
okc OkCupid Data
print.recipe Print a Recipe
names0 Naming Tools
prep Train a Data Recipe
reexports Objects exported from other packages
recipes_pkg_check Update packages
roles Manually Alter Roles
step_bin2factor Create a Factors from A Dummy Variable
prepper Wrapper function for preparing recipes within resampling
step_bs B-Spline Basis Functions
step_center Centering Numeric Data
step_arrange Sort rows using dplyr
step_bagimpute Imputation via Bagged Trees
rand_id Make a random identification field for steps
step_classdist Distances to Class Centroids
step_filter Filter rows using dplyr
step_geodist Distance between two locations
step_date Date Feature Generator
step_BoxCox Box-Cox Transformation for Non-Negative Data
step_YeoJohnson Yeo-Johnson Transformation
step_depth Data Depths
step_pca PCA Signal Extraction
step_count Create Counts of Patterns using Regular Expressions
step_corr High Correlation Filter
step_discretize Discretize Numeric Variables
step_pls Partial Least Squares Feature Extraction
step_ica ICA Signal Extraction
step_modeimpute Impute Nominal Data Using the Most Common Value
step_integer Convert values to predefined integers
step_interact Create Interaction Variables
step_intercept Add intercept (or constant) column
step_mutate Add new variables using mutate
step_lincomb Linear Combination Filter
step_range Scaling Numeric Data to a Specific Range
step_downsample Down-Sample a Data Set Based on a Factor Variable
step_log Logarithmic Transformation
step_num2factor Convert Numbers to Factors
step_nzv Near-Zero Variance Filter
step_ratio Ratio Variable Creation
step_holiday Holiday Feature Generator
step_hyperbolic Hyperbolic Transformations
step_upsample Up-Sample a Data Set Based on a Factor Variable
step_isomap Isomap Embedding
step_medianimpute Impute Numeric Data Using the Median
step_meanimpute Impute Numeric Data Using the Mean
step_window Moving Window Functions
terms_select Select Terms in a Step Function.
tidy.recipe Tidy the Result of a Recipe
step_rm General Variable Filter
step_naomit Remove observations with missing values
step_nnmf NNMF Signal Extraction
step_knnimpute Imputation via K-Nearest Neighbors
step_rollimpute Impute Numeric Data Using a Rolling Window Statistic
step_logit Logit Transformation
step_sample Sample rows using dplyr
step_sqrt Square Root Transformation
step_ordinalscore Convert Ordinal Factors to Numeric Scores
step_spatialsign Spatial Sign Preprocessing
step_scale Scaling Numeric Data
step_zv Zero Variance Filter
summary.recipe Summarize a Recipe
step_lowerimpute Impute Numeric Data Below the Threshold of Measurement
step_other Collapse Some Categorical Levels
step_poly Orthogonal Polynomial Basis Functions
step_profile Create a Profiling Version of a Data Set
step_string2factor Convert Strings to Factors
step_unorder Convert Ordered Factors to Unordered Factors
step_novel Simple Value Assignments for Novel Factor Levels
step_ns Nature Spline Basis Functions
step_regex Create Dummy Variables using Regular Expressions
step_relu Apply (Smoothed) Rectified Linear Transformation
step_shuffle Shuffle Variables
step_slice Filter rows by position using dplyr
check_cols Check if all Columns are Present
check_missing Check for Missing Values
covers Raw Cover Type Data
check_type Quantitatively check on variables
Smithsonian Smithsonian Museums
check_range Check Range Consistency
check_name check that newly created variable names don't overlap
add_step Add a New Operation to the Current Recipe
bake Apply a Trained Data Recipe
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