lukesonnet/randomizr

Easy to use tools for common forms of random assignment

randomizr is designed to make conducting field, lab, survey, or online experiments easier by automating the random assignment process.

Installation from CRAN is easy:

install.packages("randomizr")

If you'd like to install the most current development release, use the following code:

install.packages("devtools")
devtools::install_github("DeclareDesign/randomizr")

There are five main random assignment functions in randomizr: simple_ra(), complete_ra(), block_ra(), cluster_ra(), and block_and_cluster_ra(), which correspond to common experimental designs.

complete_ra() is the workhorse function that will be most appropriate for a large number of experimental situations: it assigns m of N units to treatment:

library(randomizr)
Z <- complete_ra(N = 100, m = 50)
table(Z)
#> Z
#>  0  1 
#> 50 50

A more complicated design that, for example, assigns different numbers of clusters to three different treatments can be accomodated like this:

# This makes a cluster variable: one unit in cluster "a", two in "b"...
clust_var <- rep(letters, times = 1:26)

Z <- cluster_ra(clust_var=clust_var, m_each = c(7, 7, 12),
                condition_names=c("control", "placebo", "treatment"))
table(Z, clust_var)
#>            clust_var
#> Z            a  b  c  d  e  f  g  h  i  j  k  l  m  n  o  p  q  r  s  t  u
#>   control    1  2  0  0  0  0  0  0  0  0 11 12 13  0  0  0  0  0 19  0  0
#>   placebo    0  0  3  0  5  0  0  8  0  0  0  0  0 14  0  0  0 18  0 20  0
#>   treatment  0  0  0  4  0  6  7  0  9 10  0  0  0  0 15 16 17  0  0  0 21
#>            clust_var
#> Z            v  w  x  y  z
#>   control    0  0  0 25  0
#>   placebo    0  0 24  0  0
#>   treatment 22 23  0  0 26

Happy randomizing!