Creating Co-occurrence Data

There are several ways to create co-occurrence data for chord diagrams.

From DataFrames

The most common approach is to use a DataFrame where each row represents an observation:

df = DataFrame(
    Group1 = ["A", "A", "B", "B", "C"],
    Group2 = ["X", "Y", "X", "Y", "X"],
    Group3 = ["1", "1", "2", "2", "1"]
)

cooc = cooccurrence_matrix(df, [:Group1, :Group2, :Group3])

The function automatically:

• Extracts unique labels from each column
• Groups labels by their source column
• Counts co-occurrences between labels from different groups
• Creates a symmetric co-occurrence matrix

From Raw Matrices

For more control, create a CoOccurrenceMatrix directly:

matrix = [10 5 2; 5 8 3; 2 3 6]
labels = ["A", "B", "C"]
groups = [
    GroupInfo{String}(:Group1, ["A", "B"], 1:2),
    GroupInfo{String}(:Group2, ["C"], 3:3)
]

cooc = CoOccurrenceMatrix(matrix, labels, groups)

Handling Missing Values

Missing values are automatically skipped:

df = DataFrame(
    A = ["a1", missing, "a2"],
    B = ["b1", "b1", missing]
)
cooc = cooccurrence_matrix(df, [:A, :B])  # Handles missing gracefully

Normalization

Convert counts to frequencies:

cooc = cooccurrence_matrix(df, [:V, :D, :J]; normalize=true)
# or
cooc_norm = normalize(cooc)