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Multi-Modal Data Summary Test Dataset

Source: R/data_tinytable_docs.R
tinytable_multimodal_summary.Rd

Mixed data types dataset with continuous, ordinal, categorical, and date variables. Designed to test comprehensive table formatting with diverse variable types, complex missing patterns, and mixed-scale measurements.

Usage

tinytable_multimodal_summary

Format

A data frame with 200 observations and 13 variables:

record_id

Integer. Record identifier (1-200)

data_source

Factor. Data origin ("EHR", "Registry", "Clinical_Trial", "Survey")

region

Factor. Geographic region ("North", "South", "East", "West", "Central")

score_1

Numeric. Scale score 0-100 with 1 decimal place

score_2

Numeric. Rating scale 1-5 with 2 decimal places and ~8% missing

measurement_a

Numeric. Measurement in arbitrary units (50-300) with ~4% missing

severity

Ordered Factor. Severity level ("Mild" < "Moderate" < "Severe")

priority

Ordered Factor. Priority level ("Low" < "Medium" < "High" < "Critical")

flag_positive

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

quality_check

Factor. Quality assessment ("Pass", "Fail")

category_type

Factor. Category classification ("Type_A" to "Type_H")

start_date

Date. Study start date

end_date

Date. Study end date

Source

Simulated data generated using create_tinytable_test_data.R

Details

This dataset is specifically designed to test tinytable's ability to handle mixed data types in a single table. It includes variables with different scales, ordinal factors, dates, and complex missing patterns.

Key Features:

  • Multiple continuous scales (0-100, 1-5, 50-300)

  • Ordinal factors with proper level ordering

  • Binary and multi-category factors

  • Date variables for temporal data

  • Complex missing data patterns

  • Multiple data sources for subgroup analysis

Recommended TinyTable Usage:

  • Table Type: "Data Summary" or "Custom Format"

  • Grouping Variable: data_source or region

  • Variables: score_1, score_2, severity, priority

  • Themes: "Modern" for contemporary aesthetics

See also

tinytable, tinytable_datasets

Examples

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

# Mixed data types summary
result <- tinytable(
  data = tinytable_multimodal_summary,
  vars = c("score_1", "score_2", "severity", "priority"),
  table_type = "summary",
  show_missing = TRUE
)

# Data source comparison
result_source <- tinytable(
  data = tinytable_multimodal_summary,
  vars = c("score_1", "measurement_a"),
  group_var = "data_source",
  table_type = "descriptive"
)
} # }