randomizer: Randomize cases into experimental conditions

In radiant-rstats/radiant.design: Design Menu for Radiant: Business Analytics using R and Shiny

Randomize cases into experimental conditions

Description

Randomize cases into experimental conditions

Usage

randomizer(
  dataset,
  vars,
  conditions = c("A", "B"),
  blocks = NULL,
  probs = NULL,
  label = ".conditions",
  seed = 1234,
  data_filter = "",
  arr = "",
  rows = NULL,
  na.rm = FALSE,
  envir = parent.frame()
)

Arguments

dataset	Dataset to sample from
vars	The variables to sample
conditions	Conditions to assign to
blocks	A vector to use for blocking or a data.frame from which to construct a blocking vector
probs	A vector of assignment probabilities for each treatment conditions. By default each condition is assigned with equal probability
label	Name to use for the generated condition variable
seed	Random seed to use as the starting point
data_filter	Expression entered in, e.g., Data > View to filter the dataset in Radiant. The expression should be a string (e.g., "price > 10000")
arr	Expression to arrange (sort) the data on (e.g., "color, desc(price)")
rows	Rows to select from the specified dataset
na.rm	Remove rows with missing values (FALSE or TRUE)
envir	Environment to extract data from

Details

Wrapper for the complete_ra and block_ra from the randomizr package. See https://radiant-rstats.github.io/docs/design/randomizer.html for an example in Radiant

Value

A list of variables defined in randomizer as an object of class randomizer

See Also

summary.sampling to summarize results

Examples

randomizer(rndnames, "Names", conditions = c("test", "control")) %>% str()

radiant-rstats/radiant.design documentation built on May 20, 2024, 6:42 a.m.