Tidyverse-First Data Fabrication

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Documentation for package ‘fabricatr’ version 2.0.0

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

add_level	Add a level, nested in the one before it if there is one
ALL	Keep every unit at a resampling level
correlate	Generate a variable correlated with an existing variable
cross_levels	Create a full Cartesian product of declared levels
declare_level	Declare an independent level for cross-classification
draw_binary	Draw binary (0/1) outcomes
draw_binary_icc	Draw binary data with a target intra-cluster correlation
draw_binomial	Draw binomial counts
draw_categorical	Draw categorical outcomes
draw_count	Draw Poisson count data
draw_likert	Recode a latent variable into a Likert response
draw_multivariate	Draw multivariate random variables
draw_normal_ar	Draw normally distributed data with serial correlation within clusters
draw_normal_icc	Draw normally distributed data with a target intra-cluster correlation
draw_ordered	Draw ordered categorical outcomes from a latent variable
draw_quantile	Draw quantile bucket assignments
fabricate	Fabricate a data frame
fabricate_with_dots	Fabricate from a list of quosures
import_level	Import an existing data frame as a level
join_using	Name the levels to cross or link
link_levels	Sample N rows from a Cartesian product with optional correlation
modify_level	Modify columns of an existing level, or of the rows in hand
nest_level	Nest a level in the one before it (same as a later add_level())
potential_outcomes	Build potential outcomes columns
recycle	Recycle a vector to the length of the level being built
resample_data	Resample a data frame, with optional hierarchical cluster resampling
reveal_outcomes	Reveal observed outcomes from potential outcomes columns
split_quantile	Split data into quantile buckets