recipes v0.1.6

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

Name
articles/Multivariate_PLS.Rmd
articles/Subsampling-tidyposterior.png
articles/Subsampling.Rmd
Custom_Steps.Rmd
Dummies.Rmd
Ordering.Rmd
Roles.Rmd
Selecting_Variables.Rmd
Simple_Example.Rmd
Skipping.Rmd
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