Venn Calculator

How to use Venn Calculator?

ggVennDiagram has a series of set operation functions, and this can be used as the Venn calculator.

 library(ggVennDiagram)
 set.seed(20231225)
y = list(
 A = sample(letters, 8) |> sort(),
 B = sample(letters, 8) |> sort(),
 C = sample(letters, 8) |> sort(),
 D = sample(letters, 8) |> sort())
 
y
 #> $A
 #> [1] "a" "e" "g" "o" "p" "s" "t" "v"
 #> 
 #> $B
 #> [1] "a" "d" "f" "i" "k" "s" "y" "z"
 #> 
 #> $C
 #> [1] "b" "g" "k" "o" "r" "s" "u" "w"
 #> 
 #> $D
 #> [1] "b" "c" "e" "h" "k" "q" "s" "y"

First of all, we need to construct a Venn class object with this list. If you print this object, it will give meta information of the object.

venn_y = Venn(y)
 
venn_y
 #> An object of class 'Venn':
 #> Slots: sets, names;
 #> No. Sets: 4 SetNames: A, B, C, D.

• Find the overlapping members of two or more sets.

   overlap(venn_y, 1:2) # members in both the first two sets
   #> [1] "a" "s"
   overlap(venn_y) # members in all the sets
   #> [1] "s"

• Find the different members between sets and set unions

   discern(venn_y, 1) # members in set 1, but not in the resting sets
   #> [1] "p" "t" "v"
   discern(venn_y, c("A","B"), 3) # members in set A & B, but not in the 3rd set
   #> [1] "a" "e" "p" "t" "v" "d" "f" "i" "y" "z"

• Find the specific members in one or more sets

   discern_overlap(venn_y, 1) # specific items in set 1
   #> [1] "p" "t" "v"
   discern_overlap(venn_y, 1:2) # specific items in set 1 and set 2
   #> [1] "a"

• Find the union of two or more sets

   unite(venn_y, 1:2) # union of set 1 and 2
   #> [1] "a" "e" "g" "o" "p" "s" "t" "v" "d" "f" "i" "k" "y" "z"
   unite(venn_y, "all") # union of all four sets
   #> [1] "a" "e" "g" "o" "p" "s" "t" "v" "d" "f" "i" "k" "y" "z" "b" "r" "u" "w" "c"
   #> [20] "h" "q"
   unite(venn_y, c("A", "B", "C"))
   #> [1] "a" "e" "g" "o" "p" "s" "t" "v" "d" "f" "i" "k" "y" "z" "b" "r" "u" "w"

Combined results were provided as VennPlotData object.

pd = process_data(venn_y)
pd
 #> Class VennPlotData - '401f'
 #> Type: ellipse; No. sets: 4; No. regions: 15.
 #> To view this shape, use `plot_shape_edge(get_shape_by_id('401f'))`.
 #> To view its components, use `venn_setedge()`, `venn_setlabel()`, etc.

• venn_set(): get set data from the object.

   venn_set(pd)
   #> # A tibble: 4 ×ばつ 4
   #> id name item count
   #> <chr> <chr> <named list> <int>
   #> 1 1 A <chr [8]> 8
   #> 2 2 B <chr [8]> 8
   #> 3 3 C <chr [8]> 8
   #> 4 4 D <chr [8]> 8

• venn_region(): get subsets data from the object.

   venn_region(pd)
   #> # A tibble: 15 ×ばつ 4
   #> id name item count
   #> <chr> <chr> <list> <int>
   #> 1 1 A <chr [3]> 3
   #> 2 2 B <chr [4]> 4
   #> 3 3 C <chr [3]> 3
   #> 4 4 D <chr [3]> 3
   #> 5 1/2 A/B <chr [1]> 1
   #> 6 1/3 A/C <chr [2]> 2
   #> 7 1/4 A/D <chr [1]> 1
   #> 8 2/3 B/C <chr [0]> 0
   #> 9 2/4 B/D <chr [1]> 1
   #> 10 3/4 C/D <chr [1]> 1
   #> 11 1/2/3 A/B/C <chr [0]> 0
   #> 12 1/2/4 A/B/D <chr [0]> 0
   #> 13 1/3/4 A/C/D <chr [0]> 0
   #> 14 2/3/4 B/C/D <chr [1]> 1
   #> 15 1/2/3/4 A/B/C/D <chr [1]> 1

Please note in order to keep the result concise, the containing items are nested. You may use the following methods to further process it.

• Method 1

   venn_region(pd) |> tidyr::unnest(item)
   #> # A tibble: 21 ×ばつ 4
   #> id name item count
   #> <chr> <chr> <chr> <int>
   #> 1 1 A p 3
   #> 2 1 A t 3
   #> 3 1 A v 3
   #> 4 2 B d 4
   #> 5 2 B f 4
   #> 6 2 B i 4
   #> 7 2 B z 4
   #> 8 3 C r 3
   #> 9 3 C u 3
   #> 10 3 C w 3
   #> # i 11 more rows

• Method 2

   venn_region(pd) |> dplyr::rowwise() |> dplyr::mutate(item = paste0(item, collapse = ", "))
   #> # A tibble: 15 ×ばつ 4
   #> # Rowwise: 
   #> id name item count
   #> <chr> <chr> <chr> <int>
   #> 1 1 A "p, t, v" 3
   #> 2 2 B "d, f, i, z" 4
   #> 3 3 C "r, u, w" 3
   #> 4 4 D "c, h, q" 3
   #> 5 1/2 A/B "a" 1
   #> 6 1/3 A/C "g, o" 2
   #> 7 1/4 A/D "e" 1
   #> 8 2/3 B/C "" 0
   #> 9 2/4 B/D "y" 1
   #> 10 3/4 C/D "b" 1
   #> 11 1/2/3 A/B/C "" 0
   #> 12 1/2/4 A/B/D "" 0
   #> 13 1/3/4 A/C/D "" 0
   #> 14 2/3/4 B/C/D "k" 1
   #> 15 1/2/3/4 A/B/C/D "s" 1

Reference

Some of these above-mentioned functions are originally developed by Turgut Yigit Akyol in RVenn.

• Akyol T (2019). RVenn: Set Operations for Many Sets. R package version 1.1.0, https://CRAN.R-project.org/package=RVenn.