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Small Sample Size Testing Dataset

Source: R/data_toolssummary_docs.R
toolssummary_small_sample.Rd

Minimal dataset with very small sample size to test edge cases with limited data, small group sizes, and basic functionality with minimal observations. Designed to validate graceful handling of small samples and ensure summarytools functions work appropriately with limited data scenarios.

Usage

toolssummary_small_sample

Format

A data frame with 20 observations and 7 variables:

id

Integer. Simple identifier (1-20)

group

Factor. Three-level grouping variable ("A", "B", "C")

value_numeric

Numeric. Primary numeric variable with 1 missing value

value_integer

Integer. Integer variable (1-100 range)

category_binary

Factor. Binary categorical variable ("Yes", "No")

category_small

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

score

Numeric. Score variable (0-10 range) with 1 decimal place

Source

Simulated data generated using create_toolssummary_test_data.R

Details

This minimal dataset tests enhanced summary functionality with very small sample sizes, which can reveal edge cases in statistical calculations, grouping operations, and summary presentation algorithms. It validates summarytools behavior with limited data.

Key Features:

  • Only 20 observations total for minimal data testing

  • Simple variable structure with basic data types

  • Single missing value for missing data handling

  • Small group sizes when stratified (6-7 observations per group)

  • Basic data types only (numeric, integer, factor)

Common Use Cases:

  • Testing minimum sample size handling

  • Validating small group statistics

  • Edge case detection in grouping algorithms

  • Minimum viable summary generation

summarytools Integration Testing:

  • dfSummary: Minimal data overview and visualization

  • freq: Small sample frequency analysis

  • descr: Descriptive statistics with limited observations

  • ctable: Cross-tabulation with small cell counts

Recommended Usage Scenarios:

  • Small sample behavior validation

  • Edge case testing for grouping operations

  • Minimum data requirements assessment

  • Quality control for small datasets

See also

toolssummary, toolssummary_datasets

Examples

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

# Basic small sample analysis
result <- toolssummary(
  data = toolssummary_small_sample,
  vars = c("value_numeric", "category_binary", "score"),
  useSummarytools = TRUE,
  showDfSummary = TRUE
)

# Small group analysis
result_grouped <- toolssummary(
  data = toolssummary_small_sample,
  vars = c("value_numeric", "score"),
  groupVar = "group",
  useSummarytools = TRUE,
  showCrosstabs = TRUE
)
} # }