Venn diagrams

This function fits Venn diagrams using an interface that is almost identical to euler(). Strictly speaking, Venn diagrams are Euler diagrams where every intersection is visible, regardless of whether or not it is zero. In almost every incarnation of Venn diagrams, however, the areas in the diagram are also non-proportional to the input; this is also the case here.

Usage

venn(combinations, ...)

# Default S3 method
venn(
  combinations,
  input = c("disjoint", "union"),
  names = letters[length(combinations)],
  ...
)

# S3 method for class 'table'
venn(combinations, ...)

# S3 method for class 'data.frame'
venn(
  combinations,
  weights = NULL,
  by = NULL,
  sep = "_",
  factor_names = TRUE,
  ...
)

# S3 method for class 'matrix'
venn(combinations, ...)

# S3 method for class 'list'
venn(combinations, ...)

Arguments

combinations: set relationships as a named numeric vector, matrix, or data.frame (see methods (by class))
...: arguments passed down to other methods
input: type of input: disjoint identities ('disjoint') or unions ('union').
names: a character vector for the names of each set of the same length as 'combinations'. Must not be NULL if combinations is a one-length numeric.
weights: a numeric vector of weights of the same length as the number of rows in combinations.
by: a factor or character matrix to be used in base::by() to split the data.frame or matrix of set combinations
sep: a character to use to separate the dummy-coded factors if there are factor or character vectors in 'combinations'.
factor_names: whether to include factor names when constructing dummy codes

Value

Returns an object of class 'eulerr_venn', 'venn', 'euler' with items

shapes: a data frame of the precomputed ellipse parameters (one row per set, columns type, h, k, a, b, phi). venn() always uses ellipses.
ellipses: the legacy 5-column data frame (h, k, a, b, phi) — kept for back-compat alongside the canonical shapes slot.
original.values: set relationships in the input
fitted.values: set relationships in the solution

Methods (by class)

venn(default): a named numeric vector, with combinations separated by an ampersand, for instance A&B = 10. Missing combinations are treated as being 0.
venn(table): A table with max(dim(x)) < 3.
venn(data.frame): a data.frame of logicals, binary integers, or factors.
venn(matrix): a matrix that can be converted to a data.frame of logicals (as in the description above) via base::as.data.frame.matrix().
venn(list): a list of vectors, each vector giving the contents of that set (with no duplicates). Vectors in the list do not need to be named.

Examples

# The trivial version
f1 <- venn(5, names = letters[1:5])
plot(f1)


# Using data (a numeric vector)
f2 <- venn(c(A = 1, "B&C" = 3, "A&D" = 0.3))

# The table method
venn(pain, factor_names = FALSE)
#> 3 set Venn diagram 
#> 
#>               type     h     k    a    b  phi width height side
#> widespread ellipse -0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> regional   ellipse  0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> male       ellipse  0.00  0.36 1.05 1.05 3.76    NA     NA   NA

# Using grouping via the 'by' argument through the data.frame method
venn(fruits, by = list(sex, age))
#> female.adult 
#> 3 set Venn diagram 
#> 
#>           type     h     k    a    b  phi width height side
#> banana ellipse -0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> apple  ellipse  0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> orange ellipse  0.00  0.36 1.05 1.05 3.76    NA     NA   NA
#> ------------------------------------------------------------ 
#> male.child 
#> 3 set Venn diagram 
#> 
#>           type     h     k    a    b  phi width height side
#> banana ellipse -0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> apple  ellipse  0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> orange ellipse  0.00  0.36 1.05 1.05 3.76    NA     NA   NA
#> ------------------------------------------------------------ 
#> male.adult 
#> 3 set Venn diagram 
#> 
#>           type     h     k    a    b  phi width height side
#> banana ellipse -0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> apple  ellipse  0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> orange ellipse  0.00  0.36 1.05 1.05 3.76    NA     NA   NA
#> ------------------------------------------------------------ 
#> female.child 
#> 3 set Venn diagram 
#> 
#>           type     h     k    a    b  phi width height side
#> banana ellipse -0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> apple  ellipse  0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> orange ellipse  0.00  0.36 1.05 1.05 3.76    NA     NA   NA


# Using the matrix method
venn(organisms)
#> 5 set Venn diagram 
#> 
#>           type      h      k a   b   phi width height side
#> animal ellipse  0.176  0.096 1 0.6 0.000    NA     NA   NA
#> mammal ellipse -0.037  0.197 1 0.6 1.257    NA     NA   NA
#> plant  ellipse -0.198  0.026 1 0.6 2.513    NA     NA   NA
#> sea    ellipse -0.086 -0.181 1 0.6 3.770    NA     NA   NA
#> spiny  ellipse  0.145 -0.137 1 0.6 5.027    NA     NA   NA

# Using weights
venn(organisms, weights = c(10, 20, 5, 4, 8, 9, 2))
#> 5 set Venn diagram 
#> 
#>           type      h      k a   b   phi width height side
#> animal ellipse  0.176  0.096 1 0.6 0.000    NA     NA   NA
#> mammal ellipse -0.037  0.197 1 0.6 1.257    NA     NA   NA
#> plant  ellipse -0.198  0.026 1 0.6 2.513    NA     NA   NA
#> sea    ellipse -0.086 -0.181 1 0.6 3.770    NA     NA   NA
#> spiny  ellipse  0.145 -0.137 1 0.6 5.027    NA     NA   NA

# A venn diagram from a list of sample spaces (the list method)
venn(plants[c("erigenia", "solanum", "cynodon")])
#> 3 set Venn diagram 
#> 
#>             type     h     k    a    b  phi width height side
#> erigenia ellipse -0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> solanum  ellipse  0.42 -0.36 1.05 1.05 3.76    NA     NA   NA
#> cynodon  ellipse  0.00  0.36 1.05 1.05 3.76    NA     NA   NA

Usage

Arguments

Value

Methods (by class)

See also

Examples