Imagining your data structure

Most simply, fabricatr will create a single-level data structure given a number of units.

library(fabricatr)
fabricate(N = 100, temp_fahrenheit = rnorm(N, mean = 80, sd = 20))

## Warning: `is_lang()` is deprecated as of rlang 0.2.0.
## Please use `is_call()` instead.
## This warning is displayed once per session.

## Warning: `lang_name()` is deprecated as of rlang 0.2.0.
## Please use `call_name()` instead.
## This warning is displayed once per session.

Social science data is often hierarchical. For example, schools have classrooms that have students. fabricatr shines here with the add_level command. By default, new levels are nested within the levels above them.

library(fabricatr)
fabricate(
  # five schools
  school  = add_level(N = 5,
  n_classrooms = sample(10:15, N, replace = TRUE)),
  # 10 to 15 classrooms per school
  classroom  = add_level(N = n_classrooms),
  # 15 students per classroom
  student = add_level(N = 15)
  )

## Warning: `lang_modify()` is deprecated as of rlang 0.2.0.
## Please use `call_modify()` instead.
## This warning is displayed once per session.

The real world often produces messy, overlapping hierarchies. For example, student data may be collected from middle school and also high school, in which case students are nested in two different schools, but those schools are not nested within each other. Here’s how to make such “cross-classified” data. The rho parameter governs how correlated primary_rank and secondary_rank should be.

dat <- 
fabricate(
  primary_schools = add_level(N = 5, primary_rank = 1:N),
  secondary_schools = add_level(N = 6, secondary_rank = 1:N, nest = FALSE),
  students = link_levels(N = 15, by = join(primary_rank, secondary_rank, rho = 0.9))
)

## `link_levels()` calls are faster if the `mvnfast` package is installed.

ggplot(dat, aes(primary_rank, secondary_rank)) + geom_point(position = position_jitter(width = 0.1, height = 0.1), alpha = 0.5) + theme_bw()

Similarly, you can create longitudinal data via cross_levels:

fabricate(
 students = add_level(N = 2),
 years = add_level(N = 20, year = 1981:2000, nest = FALSE),
 student_year = cross_levels(by = join(students, years))
)

Imagining your variables

R has lots of great tools for simulating variables. In some cases, though, common kinds of outcome variables are surprisingly tough to simulate. fabricatr collects a small number of functions to create variable types commonly used by social scientists, with simple syntax. We describe two examples here, but see our variable creation vignette for the rest.

dat <- 
  fabricate(
    N = 1000,
    clusters = sample(LETTERS, N, replace = TRUE),
    Y1 = draw_normal_icc(clusters = clusters, ICC = .2),
    Y2 = draw_binary_icc(clusters = clusters, ICC = .2)
  )
ICC::ICCbare(clusters, Y1, dat)

## [1] 0.09726701

ICC::ICCbare(clusters, Y2, dat)

## [1] 0.176036

dat <- 
fabricate(
  N = 100,
  test_ability = rnorm(N),
  test_score = draw_ordered(test_ability, breaks = c(-.5, 0, .5))
)
ggplot(dat, aes(test_ability, test_score)) + geom_point() + theme_bw()

Where to go next

This post is a high-level teaser for fabricatr’s functionality, but for a deeper introduction, check out the fabricatr getting started vignette. You can also download and print this cheatsheet.

You can install fabricatr from CRAN:

install.packages("fabricatr")
library(fabricatr)

Graeme Blair is an Assistant Professor of Political Science at UCLA.

Jasper Cooper is a Postdoctoral Research Associate at the Kahneman-Treisman Center for Behavioral Science and Public Policy at Princeton University.

Alexander Coppock is an Assistant Professor of Political Science at Yale University.

Macartan Humphreys is a Professor of Political Science at Columbia University and a Director of the research group “Institutions and Political Inequality” at the WZB Berlin Social Science Center.