R Graphics · Venn Diagram

Explaining Set Relationships with Venn Diagrams in R

Construct two- and three-set diagrams, label intersections, show counts, use transparency, and build diagrams from real sets.

6 examplesReproducible R code

Before you begin

Choose the variation that serves the analysis

Venn diagrams communicate overlap among a small number of sets. They are effective for conceptual relationships and simple counts, but complex set systems are better handled by an UpSet plot or table.

Examples: 06
Levels: Beginner + Intermediate

Example 01Base RBeginner

Draw a Two-Set Venn Diagram

Two overlapping circles divide observations into A only, B only, and their intersection.

Transparent fills keep the overlap visible. Place set labels outside the circles and intersection information centrally.

RRun this code

plot.new(); plot.window(xlim = c(0,10), ylim = c(0,7), asp = 1)
symbols(c(4,6), c(3.5,3.5), circles = c(2.4,2.4), inches = FALSE, add = TRUE,
        bg = c(adjustcolor("#2563eb",.4), adjustcolor("#f59e0b",.4)))
text(c(2.5,7.5,5), c(6,6,3.5), c("Set A","Set B","A ∩ B"), font = 2)

Example 02Base RBeginner

Display Region Counts

Counts quantify the exclusive and shared portions of the sets.

Place each count inside its corresponding region and state the universe or sample from which sets were formed.

RRun this code

plot.new(); plot.window(xlim=c(0,10), ylim=c(0,7), asp=1)
symbols(c(4,6), c(3.5,3.5), circles=c(2.4,2.4), inches=FALSE, add=TRUE,
        bg=c(adjustcolor("#2563eb",.4),adjustcolor("#f59e0b",.4)))
text(c(3,5,7), 3.5, c(18,12,21), cex=1.4, font=2)

Example 03Base RBeginner

Extend the Diagram to Three Sets

Three circles create regions for exclusive, pairwise, and three-way intersections.

Keep labels and counts sparse. With more than three or four sets, the number of intersections becomes difficult to read.

RRun this code

plot.new(); plot.window(xlim=c(0,10), ylim=c(0,8), asp=1)
cols <- adjustcolor(c("#2563eb","#f59e0b","#10b981"),.35)
symbols(c(4,6,5), c(4.5,4.5,2.8), circles=rep(2.4,3), inches=FALSE, add=TRUE, bg=cols)
text(c(2.6,7.4,5), c(6.8,6.8,.3), c("A","B","C"), font=2)

Example 04Base RIntermediate

Build Sets from Data

Real sets can be created from logical conditions or category membership.

Use intersect(), setdiff(), and union() to calculate the values printed in each diagram region.

RRun this code

A <- rownames(mtcars)[mtcars$mpg > 20]
B <- rownames(mtcars)[mtcars$hp > 110]
c(A_only = length(setdiff(A,B)),
  both = length(intersect(A,B)),
  B_only = length(setdiff(B,A)))

Example 05Base RIntermediate

Approximate Relative Set Sizes

Circle sizes can roughly reflect the relative sizes of sets.

Area-proportional diagrams are difficult to construct exactly; treat manually scaled circles as an approximation and report counts.

RRun this code

nA <- 35; nB <- 60
plot.new(); plot.window(xlim=c(0,12), ylim=c(0,8), asp=1)
symbols(c(4,7), c(4,4), circles=sqrt(c(nA,nB)/pi), inches=FALSE, add=TRUE,
        bg=adjustcolor(c("#2563eb","#f59e0b"),.35))
text(c(3,8), 4, c(nA,nB), font=2)

Example 06Base RIntermediate

Know When to Use an Alternative

An UpSet-style count plot is clearer when many intersections must be compared.

Use bars or an intersection matrix for four or more sets, where overlapping circles become visually ambiguous.

RRun this code

intersection_counts <- c("A only"=22,"B only"=18,"C only"=15,
                         "A & B"=9,"A & C"=7,"B & C"=6,"A & B & C"=4)
barplot(sort(intersection_counts), horiz=TRUE, las=1,
        col="#2563eb", border=NA, xlab="Elements")