Create a Sampling Design

sampling_design() is the entry point for creating survey sampling specifications. It creates an empty design object that can be built up using pipe-able verbs like stratify_by(), cluster_by(), draw(), and stage().

sampling_design(title = NULL)

Arguments

title

Optional character string providing a title for the design. Useful for documentation and printing purposes.

Value

A sampling_design object that can be piped to other design functions.

Details

The sampling design paradigm separates the specification of a sampling plan from its execution. This allows designs to be:

• Reused across different data frames

• Partially executed (e.g., stage by stage)

• Inspected and validated before execution

• Documented and shared

The design specification is frame-independent: it describes how to sample, not what to sample from.

Design Flow

A typical design workflow follows this pattern:

sampling_design() |>
  stratify_by(...) |>
  cluster_by(...) |>
  draw(...) |>
  execute(frame)

See also

stratify_by() for defining strata, cluster_by() for defining clusters, draw() for specifying selection parameters, stage() for multi-stage designs, execute() for running designs

Examples

if (FALSE) { # \dontrun{
# Simple random sample
sampling_design() |>
  draw(n = 100) |>
  execute(my_frame, seed = 42)

# Stratified sample with proportional allocation
sampling_design(title = "Regional Survey 2024") |>
  stratify_by(region, alloc = "proportional") |>
  draw(n = 1000) |>
  execute(population_frame, seed = 42)

# Multi-stage cluster sample
sampling_design() |>
  stage(label = "Schools") |>
    cluster_by(school_id) |>
    draw(n = 50, method = "pps_brewer", mos = enrollment) |>
  stage(label = "Students") |>
    draw(n = 20) |>
  execute(school_frame, seed = 42)
} # }