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Mixed Data Types with Complex Categorical Variables

Source: R/data_toolssummary_docs.R
toolssummary_mixed_datatypes.Rd

Comprehensive dataset containing continuous, ordinal, categorical, and date variables with complex missing patterns and diverse scales. Designed to test enhanced table formatting capabilities with mixed variable types, ordered factors, and sophisticated categorical analysis using summarytools comprehensive functionality.

Usage

toolssummary_mixed_datatypes

Format

A data frame with 250 observations and 15 variables:

record_id

Integer. Record identifier (1-250)

data_source

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

region

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

score_continuous

Numeric. Continuous scale score (0-100) with 1 decimal place

score_ordinal

Integer. Ordinal rating scale (1-5) with ~8% missing

measurement_value

Numeric. Measurement in arbitrary units (50-300) with ~5% missing

severity

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

priority_level

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

binary_flag

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

quality_status

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

category_multi

Factor. Multi-level category ("Type_A" to "Type_H")

assessment_date

Date. Assessment date (2024-01-01 to 2024-12-31)

completion_date

Date. Completion date (2024-02-01 to 2025-01-31) with ~12% missing

count_variable

Integer. Count data (0-20)

percentage_score

Numeric. Percentage score (0-100) with 1 decimal place

Source

Simulated data generated using create_toolssummary_test_data.R

Details

This dataset is specifically designed to test summarytools ability to handle diverse data types in a single comprehensive analysis. It includes variables with different scales, measurement levels, and complex categorical structures that challenge standard summary approaches.

Key Features:

  • Multiple continuous scales with different ranges and precisions

  • Properly ordered factors with meaningful level hierarchies

  • Binary and multi-category nominal factors

  • Date variables for temporal analysis

  • Complex missing data patterns across variable types

  • Count data and percentage scores

summarytools Integration Testing:

  • dfSummary: Comprehensive overview handling all data types

  • freq: Advanced frequency analysis for categorical and ordinal variables

  • descr: Extended descriptive statistics for continuous variables

  • ctable: Cross-tabulations across different categorical structures

Recommended Usage Scenarios:

  • Mixed data type comprehensive summaries

  • Ordered factor analysis and presentation

  • Multi-source data integration assessment

  • Quality control and data validation

See also

toolssummary, toolssummary_datasets

Examples

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

# Comprehensive mixed data analysis
result <- toolssummary(
  data = toolssummary_mixed_datatypes,
  vars = c("score_continuous", "severity", "priority_level", "binary_flag"),
  useSummarytools = TRUE,
  showDfSummary = TRUE,
  showDescr = TRUE,
  showFreq = TRUE
)

# Analysis by data source
result_source <- toolssummary(
  data = toolssummary_mixed_datatypes,
  vars = c("score_continuous", "measurement_value", "severity"),
  groupVar = "data_source",
  useSummarytools = TRUE,
  showCrosstabs = TRUE
)
} # }