Venn Diagram

Generates a Venn Diagram and an Upset diagram from selected categorical variables. This function converts specified variables to logical values based on a chosen "true" level. Two visual outputs are produced: a Venn diagram (via ggvenn) and an Upset plot (via UpSetR or ComplexUpset). Additionally, a summary table of "true" counts for each variable is provided.

ComplexUpset features include advanced styling, statistical annotations, custom sorting, and enhanced theming options for publication-ready figures.

Value

The function produces a Venn diagram and an Upset diagram.

Super classes

jmvcore::Analysis -> ClinicoPath::vennBase -> vennClass

Methods

Public methods

• vennClass$clone()

Inherited methods

• jmvcore::Analysis$.createImage()
• jmvcore::Analysis$.createImages()
• jmvcore::Analysis$.createPlotObject()
• jmvcore::Analysis$.load()
• jmvcore::Analysis$.render()
• jmvcore::Analysis$.save()
• jmvcore::Analysis$.savePart()
• jmvcore::Analysis$.setCheckpoint()
• jmvcore::Analysis$.setParent()
• jmvcore::Analysis$.setReadDatasetHeaderSource()
• jmvcore::Analysis$.setReadDatasetSource()
• jmvcore::Analysis$.setResourcesPathSource()
• jmvcore::Analysis$.setStatePathSource()
• jmvcore::Analysis$addAddon()
• jmvcore::Analysis$asProtoBuf()
• jmvcore::Analysis$asSource()
• jmvcore::Analysis$check()
• jmvcore::Analysis$init()
• jmvcore::Analysis$optionsChangedHandler()
• jmvcore::Analysis$postInit()
• jmvcore::Analysis$print()
• jmvcore::Analysis$readDataset()
• jmvcore::Analysis$run()
• jmvcore::Analysis$serialize()
• jmvcore::Analysis$setError()
• jmvcore::Analysis$setStatus()
• jmvcore::Analysis$translate()
• ClinicoPath::vennBase$initialize()

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Method clone()

The objects of this class are cloneable with this method.

Usage

vennClass$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
# Example 1: Basic 2-variable Venn diagram
data("mtcars")
mtcars$vs <- factor(mtcars$vs, levels = c(0, 1), labels = c("V-shaped", "Straight"))
mtcars$am <- factor(mtcars$am, levels = c(0, 1), labels = c("Automatic", "Manual"))

# Create Venn diagram showing overlap between V-shaped engines and Manual transmission
venn(data = mtcars, var1 = "vs", var1true = "V-shaped", 
     var2 = "am", var2true = "Manual")

# Example 2: 3-variable Venn diagram with penguins data
library(palmerpenguins)
data("penguins")
penguins$large_bill <- factor(ifelse(penguins$bill_length_mm > 45, "Large", "Small"))
penguins$heavy_weight <- factor(ifelse(penguins$body_mass_g > 4000, "Heavy", "Light"))
penguins$adelie_species <- factor(ifelse(penguins$species == "Adelie", "Adelie", "Other"))

venn(data = penguins, 
     var1 = "large_bill", var1true = "Large",
     var2 = "heavy_weight", var2true = "Heavy", 
     var3 = "adelie_species", var3true = "Adelie")

# Example 3: Medical/Clinical example
# Create sample clinical data
clinical_data <- data.frame(
  patient_id = 1:100,
  diabetes = sample(c("Yes", "No"), 100, replace = TRUE, prob = c(0.3, 0.7)),
  hypertension = sample(c("Yes", "No"), 100, replace = TRUE, prob = c(0.4, 0.6)),
  obesity = sample(c("Yes", "No"), 100, replace = TRUE, prob = c(0.25, 0.75))
)

# Analyze comorbidity patterns
venn(data = clinical_data,
     var1 = "diabetes", var1true = "Yes",
     var2 = "hypertension", var2true = "Yes",
     var3 = "obesity", var3true = "Yes")

# Example 4: Using ComplexUpset for advanced features
venn(data = clinical_data,
     var1 = "diabetes", var1true = "Yes",
     var2 = "hypertension", var2true = "Yes",
     var3 = "obesity", var3true = "Yes",
     upsetType = "complexUpset",
     sortBy = "freq",
     minSize = 5,
     showAnnotations = TRUE)
} # }