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# Demonstrate numeric filtering behavior
numeric_demo <- teal::init(
  data = demo_data,
  modules = teal::modules(
    tm_g_scatterplot(
      label = "Age vs BMI",
      dataname = "ADSL",
      x = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(

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# Demonstrate categorical filtering behavior
categorical_demo <- teal::init(
  data = demo_data,
  modules = teal::modules(
    tm_variable_browser(
      label = "Variable Browser",
      dataname = "ADSL"
    ),
    tm_g_distribution(
      label = "Treatment Distribution",

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library(teal)
library(teal.modules.general)
library(teal.data)
library(pharmaverseadam)

# Load data to demonstrate filter panel structure
data("adsl", package = "pharmaverseadam")
data("adae", package = "pharmaverseadam")

# Create teal_data object

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# Advanced select_spec options
select_spec(
  choices = variable_choices("ADSL", c("AGE", "BMIBL")),
  selected = "AGE",
  multiple = TRUE,          # Allow multiple variable selection
  fixed = TRUE,            # Prevent user from changing selection
  always_selected = "AGE"  # Keep AGE always selected
)

# Advanced filter_spec options

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# Example 3: Both select_spec and filter_spec - Full control
app_select_and_filter <- teal::init(
  data = demo_data,
  modules = teal::modules(
    tm_g_scatterplot(
      label = "Example 3: Select + Filter Specs",
      dataname = "ADSL",
      x = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(

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# Example 2: Only filter_spec - Row filtering only
app_filter_only <- teal::init(
  data = demo_data,
  modules = teal::modules(
    tm_g_scatterplot(
      label = "Example 2: Filter Spec Only",
      dataname = "ADSL",
      x = data_extract_spec(
        dataname = "ADSL",
        select = select_spec(

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library(teal)
library(teal.modules.general)
library(teal.data)
library(pharmaverseadam)

# Load data
data("adsl", package = "pharmaverseadam")

# Create teal_data object
demo_data <- teal_data()

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Dataset → data_extract_spec → Module Processing → User Interface → Analysis Output
   ↓            ↓                    ↓               ↓              ↓
Raw Data → Column Selection → Row Filtering → UI Controls → Final Analysis
         → Variable Choices → Data Subset → User Options → Processed Results

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# Pattern 1: Domain-based nesting
modules(
  label = "Safety Domain",
  modules(label = "Overview", ...),
  modules(label = "Detailed Analysis", ...),
  modules(label = "Regulatory Tables", ...)
)

# Pattern 2: Analysis-type nesting
modules(

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library(teal)
library(teal.modules.general)
library(teal.data)
library(pharmaverseadam)

# Step 1: Load multiple ADaM datasets
data("adsl", package = "pharmaverseadam")
data("adae", package = "pharmaverseadam")
data("adlb", package = "pharmaverseadam")