Here is a post which discusses Venn diagram from list of clusters and co-occurring factors.

For easy solution use package venneuler:

Copyrequire(venneuler)
v <- venneuler(c(A=450, B=1800, "A&B"=230))
plot(v)

For more advanced and customized solutions check package VennDiagram.

Copylibrary(VennDiagram) 
venn.diagram(list(B = 1:1800, A = 1571:2020), fill = c("lightblue", "green"), 
             alpha = c(0.5, 0.5), lwd =0, "venn_diagram.tiff")

Something quite interesting that I learned from the VizBi conference (workshop on visualizing biological data) last month concerns depicting relationships linearly vs by area. Humans can proportionally relate objects of different lengths. That is if A = 10 and B = 5, A looks exactly twice as long, and if A = 10 and B = 9.8, B looks shorter. If A and B are depicted by area, things get difficult to discern because perceived differences in area relate to one another not by the power of 1.0 but by 0.6-0.7. Thus, one loses the degree of difference between objects when using area. · Although Venn diagrams are popular and convenient, this information is something to keep in mind when trying to depict the relationships. · Added 27 Apr 2011: Try Hohli at charts.hohli.com. With Hohli, you can create a Venn diagram and customize its look and feel. One can specify a title, choose data points, and alter the background. Hohli also allows you to create other charts, including scatter plots and other line charts.

Some combinations of set intersections are impossible to represent exactly with circles so some heuristics and approximations have to be used. Unfortunately, what the venneuler package uses leads to problems with large overlaps. The VennDiagram package seems to be doing a better job in this case.