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Time Series Summary Test Dataset

Source: R/data_tinytable_docs.R
tinytable_timeseries_summary.Rd

Longitudinal dataset with repeated measures per subject across multiple timepoints. Designed to test time-based table formatting, longitudinal data presentation, and outcome tracking over time with realistic dropout patterns.

Usage

tinytable_timeseries_summary

Format

A data frame with 150 observations and 10 variables:

subject_id

Character. Subject identifier (TS_01 to TS_30)

timepoint

Factor. Assessment timepoint ("T1" to "T5")

months_from_baseline

Integer. Time since baseline (0, 3, 6, 12, 24 months)

assessment_date

Date. Date of assessment

primary_outcome

Numeric. Primary outcome score (0-100) with time-dependent missing

secondary_outcome_1

Numeric. Secondary outcome correlated with primary

secondary_outcome_2

Numeric. Independent secondary outcome

response_status

Factor. Treatment response ("Responder", "Non-responder")

compliance_pct

Numeric. Treatment compliance percentage (75-100%)

dose_adjustment

Factor. Dose modification ("None", "Increase", "Decrease", "Hold")

Source

Simulated data generated using create_tinytable_test_data.R

Details

This dataset represents a longitudinal clinical study with 30 subjects followed over 5 timepoints (24 months). It includes realistic patterns of improvement, dropout, and missing data that increase over time.

Key Features:

  • 30 subjects with 5 timepoints each (150 total observations)

  • Time-dependent outcome improvement pattern

  • Realistic dropout patterns (increasing missing data over time)

  • Compliance and dose adjustment tracking

  • Response status categorization

  • Multiple correlated outcomes

Recommended TinyTable Usage:

  • Table Type: "Grouped Summary" by timepoint

  • Grouping Variable: timepoint or response_status

  • Variables: primary_outcome, secondary_outcome_1, compliance_pct

  • Themes: "Clinical" for longitudinal study reporting

See also

tinytable, tinytable_datasets

Examples

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

# Outcomes by timepoint
result <- tinytable(
  data = tinytable_timeseries_summary,
  vars = c("primary_outcome", "secondary_outcome_1", "compliance_pct"),
  group_var = "timepoint",
  table_type = "grouped",
  show_missing = TRUE
)

# Response analysis
result_response <- tinytable(
  data = tinytable_timeseries_summary,
  vars = c("primary_outcome", "secondary_outcome_2"),
  group_var = "response_status",
  table_type = "descriptive"
)
} # }