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Small Sample Size Test Dataset

Source: R/data_tinytable_docs.R
tinytable_small_sample.Rd

Minimal dataset with very small sample size to test edge cases with minimal data, small group sizes, and basic table formatting functionality.

Usage

tinytable_small_sample

Format

A data frame with 15 observations and 6 variables:

id

Integer. Simple identifier (1-15)

group

Factor. Binary grouping variable ("A", "B")

value_1

Numeric. Primary numeric variable with 1 missing value

value_2

Numeric. Secondary numeric variable (0-100 range)

category

Factor. Three-level factor ("X", "Y", "Z")

flag

Factor. Binary flag ("Yes", "No")

Source

Simulated data generated using create_tinytable_test_data.R

Details

This minimal dataset tests table formatting with very small sample sizes, which can reveal edge cases in statistical calculations, grouping operations, and table layout algorithms.

Key Features:

  • Only 15 observations total

  • Simple variable structure

  • Single missing value for testing

  • Small group sizes when stratified

  • Basic data types only

Common Use Cases:

  • Testing minimum sample size handling

  • Validating small group statistics

  • Edge case detection in grouping algorithms

  • Minimum viable table formatting

Recommended TinyTable Usage:

  • Table Type: "Summary" or "Raw Data Display"

  • Variables: value_1, value_2, category

  • Grouping: group (results in very small subgroups)

  • Themes: Any theme for basic formatting testing

See also

tinytable, tinytable_datasets

Examples

if (FALSE) { # \dontrun{
# Load the dataset
data(tinytable_small_sample)

# Basic small sample table
result <- tinytable(
  data = tinytable_small_sample,
  vars = c("value_1", "value_2", "category"),
  table_type = "summary"
)

# Small group analysis
result_grouped <- tinytable(
  data = tinytable_small_sample,
  vars = c("value_1", "value_2"),
  group_var = "group",
  table_type = "descriptive"
)
} # }