| Title: | Draw Venn Diagrams |
|---|---|
| Description: | A close to zero dependency package to draw and display Venn diagrams up to 7 sets, and any Boolean union of set intersections. |
| Authors: | Adrian Dusa [aut, cre, cph] |
| Maintainer: | Adrian Dusa <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.12.2 |
| Built: | 2024-12-08 05:47:20 UTC |
| Source: | https://github.com/dusadrian/venn |
A close to zero dependency package to draw and display Venn diagrams, and any boolean union of set intersections.
| Package: | venn |
| Type: | Package |
| Version: | 1.12.2 |
| Date: | 2024-10-06 |
| License: | GPL (>= 2) |
Authors:
Adrian Dusa
Department of Sociology
University of Bucharest
[email protected]
Maintainer:
Adrian Dusa
Extracts useful information such as the counts or the actual intersections between several sets.
extractInfo( x, what = c("counts", "intersections", "both"), use.names = FALSE )extractInfo( x, what = c("counts", "intersections", "both"), use.names = FALSE )
x |
A list object containing set elements, or a list of binary values |
what |
What to extract |
use.names |
Logical, use the set names to indicate intersections |
When the argument x is a list, the counts are taken from the number
of common values for each intersection, and when x is a dataframe,
(comprised of exclusively binary values 0 and 1) the counts are taken from the
number of similar rows.
A dataframe, when extracting the counts or a list if extracting intersections.
set.seed(12345) xlist <- list(A = 1:20, B = 10:30, C = sample(25:50, 15)) xdf <- as.data.frame(matrix( sample(0:1, 90, replace = TRUE), ncol = 3 )) colnames(xdf) <- LETTERS[1:3] extractInfo(xlist) # counts by default extractInfo(xlist, what = "intersections") extractInfo(xlist, what = "both") extractInfo(xdf)set.seed(12345) xlist <- list(A = 1:20, B = 10:30, C = sample(25:50, 15)) xdf <- as.data.frame(matrix( sample(0:1, 90, replace = TRUE), ncol = 3 )) colnames(xdf) <- LETTERS[1:3] extractInfo(xlist) # counts by default extractInfo(xlist, what = "intersections") extractInfo(xlist, what = "both") extractInfo(xdf)
This function takes a list of dataframes or a matrices containing x and y values, which define zones (polygons), and calculates their centroids.
getCentroid(data)getCentroid(data)
data |
A matrix or a dataframe with two columns, for x and y coordinates |
Most of the coordinates for the intersection labels in this package were calculated using the formula for a centroid of a non-self-intersecting closed polygon, approximated by 10 vertices.
A list with x and y coordinates, for each zone in the input list.
Centroid. (n.d.). In Wikipedia. Retrieved January 06, 2016, from https://en.wikipedia.org/wiki/Centroid
venn("0110") # centroid for the intersection "0110" in a 4 set diagram centroid <- getCentroid(getZones("0110"))[[1]] text(centroid[1], centroid[2], labels = "0110", cex = 0.85) # centroids for the two zones in the "E not A" zones venn(5) area <- getZones("0---1") # list of length 2 polygon(area[[1]], col="lightblue") polygon(area[[2]], col="lightblue") text(do.call("rbind", getCentroid(area)), labels = c("zone 1", "zone 2"), cex = 0.85)venn("0110") # centroid for the intersection "0110" in a 4 set diagram centroid <- getCentroid(getZones("0110"))[[1]] text(centroid[1], centroid[2], labels = "0110", cex = 0.85) # centroids for the two zones in the "E not A" zones venn(5) area <- getZones("0---1") # list of length 2 polygon(area[[1]], col="lightblue") polygon(area[[2]], col="lightblue") text(do.call("rbind", getCentroid(area)), labels = c("zone 1", "zone 2"), cex = 0.85)
This function uses a metacommand to calculate the shape of a specific zone or a list of zones.
getZones(area, snames, ellipse = FALSE)getZones(area, snames, ellipse = FALSE)
area |
A character expression written in sum of products form. |
snames |
A string containing the sets' names, separated by commas. |
ellipse |
Logical, get the zones from the shape of an ellipse, where possible |
A SOP ("sum of products") is also known as a DNF ("disjunctive normal form"), or in other
words a "union of intersections", for example A*D + B*c.
The same expression can be written in curly brackets notation:
A{1}*D{1} + B{1}*C{0}.
The expression B{1}*C{0} can also be written in a pseudo-language, as
"-10-" (assuming there are only four sets).
A "zone" is a union of set intersections. There are exactly 2^k intersections
in a Venn diagram, where k is the number of sets. To highlight an entire set,
we need a union of all possible intersections which form that set.
The argument ellipse retrieves the data from the shape of an ellipse, and it only
works with 4 and 5 sets.
A list of self-enclosed polygons, for each independent zone.
venn(3) area <- getZones("A", snames = "A, B, C") # a list of length 1 polygon(area[[1]], col="lightblue") # The very same result is obtained with: zone <- getZones("1--") # for 5 sets, the content of the 5th set but not in the first set is a # list of two zones venn(5) zones <- getZones("0---1") # this time a list of length 2 # (re)coloring the first zone (union) polygon(zones[[1]], col="lightblue") # and the second zone (union) polygon(zones[[2]], col="lightblue")venn(3) area <- getZones("A", snames = "A, B, C") # a list of length 1 polygon(area[[1]], col="lightblue") # The very same result is obtained with: zone <- getZones("1--") # for 5 sets, the content of the 5th set but not in the first set is a # list of two zones venn(5) zones <- getZones("0---1") # this time a list of length 2 # (re)coloring the first zone (union) polygon(zones[[1]], col="lightblue") # and the second zone (union) polygon(zones[[2]], col="lightblue")
This function uses a variety of input data to draw and display a Venn diagram with up to 7 sets.
venn(x, snames = "", ilabels = NULL, ellipse = FALSE, zcolor = "bw", opacity = 0.3, plotsize = 15, ilcs = 0.6, sncs = 0.85, borders = TRUE, box = TRUE, par = TRUE, ggplot = FALSE, ...)venn(x, snames = "", ilabels = NULL, ellipse = FALSE, zcolor = "bw", opacity = 0.3, plotsize = 15, ilcs = 0.6, sncs = 0.85, borders = TRUE, box = TRUE, par = TRUE, ggplot = FALSE, ...)
x |
A single number (of sets), or a metacommand formula (see details), or a list containing set values, or a dataset containing boolean values. |
snames |
An optional parameter containing the names for each set. |
ilabels |
Complex argument, see Details. |
ellipse |
Logical, force the shape to an ellipse, where possible |
zcolor |
A vector of colors for the custom zones, or predefined colors if "style" |
opacity |
Degree of opacity for the color(s) specified with
|
plotsize |
Plot size, in centimeters. |
ilcs |
Character expansion (in base plots) or size (in ggplots) for the intersection labels |
sncs |
Character expansion (in base plots) or size (in ggplots) for the set names |
borders |
Logical: draw all intersection borders |
box |
Logical: draw the outside square |
par |
Logical: use the default, custom par settings |
ggplot |
Logical: plot the Venn diagram using ggplot |
... |
Additional parameters, mainly for the outer borders of the sets |
The argument x can be either:
- a single number (of sets), between 1 and 7
- a metacommand (character) to draw custom intersection zones
- a list, containing values for the different sets: each component is a set,
and only up to 7 components are processed.
- a dataset of boolean values.
A "zone" is a union of set intersections. There are exactly 2^k
intersections in a Venn diagram, where k is the number of sets. To
highlight an entire set, we need a union of all possible intersections which
form that set.
For example, in a 3 sets diagram, the (overall) first set is composed by four
intersections:100 for what is in the first set but outside sets 2 and outside set 3101 for the intersection between sets 1 and 3, outside set 2110 for the intersection between sets 1 and 2, outside set 3111 for the intersection between all three sets.
A meta-language can be used to define these intersections, using the values of
1 for what is inside the set, 0 for what is outside
the set, and - when its either inside or outside of the set.
The command "1--" is translated as "display only the first, entire
set" is equivalent with the union of the four intersections
"100 + 101 + 110 + 111".
The parameter snames should have the same length as the number of
sets specified by the parameter x.
When the parameter x is used as a metacommand, the number of sets
is calculated as the number of characters in each intersection of the
metacommand. One such character command is "100 + 101 + 110 + 111"
or "1--", and all intersections have exactly three characters.
It is also possible to use a regular, disjunctive normal form, like
"A", which is equivalent with "Abc + AbC + ABc + ABC".
When x is an expression written in DNF, if a valid R statement
then quoting is not even necessary.
The argument snames establishes names for the different sets, or
in its absence it is taken from LETTERS. When x is a
list or a dataframe, snames is taken from their names. The length
of the snames indicates the total number of sets.
A numerical vector can be supplied with the argument ilabels, when
the argument x is a single number of sets. The vector should match
the increasing order of the binary representation for the set intersections.
This argument can also be logical, and if activated with TRUE it constructs
the intersection labels from their particular combinations of 0s and 1s.
Finally, it can also be specified as ilabels = "counts", for counting the
frequency of appearance of each intersection. When the argument x
is a list, the counts are taken from the number of common values for each
intersection, and when x is a dataframe, (comprised of exclusively
binary values 0 and 1) the counts are taken from the number of similar rows. If
a particular intersection does not have any common values (or no rows), the
count "0" is left blank and not displayed in the diagram.
The argument ellipse differentiates between two types of diagrams
for 4 and 5 sets. The idea is to allow for as much space as possible for each
intersection (also as equal as possible) and that is impossible if preserving
the shape of an ellipse. The default is to create large space for the
intersections, but users who prefer an ellipse might want to set this argument
to TRUE.
Colors to fill the desired zones (or entire sets) can be supplied via the
argument zcolor (the default is "bw" black and white,
which means no colors at all). Users can either chose the predefined color style,
using zcolor = "style", or supply a vector of custom colors for
each zone. If only one custom color is supplied, it will be recycled for all
zones.
When using zcolor = "style", any other additional arguments for
the borders are ignored.
A different set of predefined colors is used, when argument x is a
QCA type object (a truth table, either from a class tt or from a
class qca). If custom colors are provided via zcolor,
it should have a length of 3 colors: the first for the absence of the outcome
(0), the second for the presence of the outcome (1),
and the third for the contradictions (C). Remainders have no
color, by default.
The argument ilcs works only if the intersection labels
(ilabels) have information, and it sets the size of the labels via
a cex argument. In the absence of a specific value from the user,
it's default is set to 0.6 for all Venn diagrams with up to five sets, and it
automatically decreases to 0.5 for six sets and 0.45 for seven sets.
Via ..., users can specify additional parameters, mainly for the
outer borders of the sets, as specified by par(),
and since version 1.9 it is also used to pass additional aesthetics parameters
for the ggplot2 graphics. All of them are fed either to the base function
lines() which is responsible with the borders, or
to the function geom_path() from package
ggplot2.
For up to 3 sets, the shapes can be circular. For more than 3 sets, the shape cannot be circular: for 4 and 5 sets they can be ellipses, while for more than 5 sets the shapes cannot be continous (they might be monotone, but not continous). The 7 sets diagram is called "Adelaide" (Ruskey, 2005).
The most challenging diagram is the one with 6 sets, where for many years it was thought a Venn diagram didn't even exist. All diagrams are symmetric, except for the one with 6 sets, where some of the sets have different shapes. The diagram in this package is an adaptation from Mamakani, K., Myrvold W. and F. Ruskey (2011).
The argument border can be used only for custom intersections
and/or unions, it has no effect when x is a list, or a data frame,
or a truth table object.
The argument par is used to define a custom set of parameters when
producing the plot, to ensure a square shape of about 15 cm and eliminate the
outer regions. If deactivated, users can define their own size and shape of the
plot using the system function par(). By default,
the plot is always produced using a size of 1000 points for both horizontal and
vertical, unless the argument ggplot is activated, when the
argument par will have no effect.
Ruskey, F. and M. Weston. 2005. Venn diagrams. Electronic Journal of Combinatorics, Dynamic Survey DS5.
Mamakani, K., Myrvold W. and F. Ruskey. 2011. Generating all Simple Convexly-drawable Polar Symmetric 6-Venn Diagrams. International Workshop on Combinatorial Algorithms, Victoria. LNCS, 7056, 275-286.
# A simple Venn diagram with 3 sets venn(3) # with a vector of counts: 1 for "000", 2 for "001" etc. venn(3, counts = 1:8) # display the first whole set venn("1--") # same with venn("A", snames = "A, B, C") # an equivalent command, from the union of all intersections venn("100 + 110 + 101 + 111") # same with venn("A~B~C + AB~C + A~BC + ABC") # adding the labels for the intersections venn("1--", ilabels = TRUE) # using different parameters for the borders venn(4, lty = 5, col = "navyblue") # using ellipses venn(4, lty = 5, col = "navyblue", ellipse = TRUE) # a 5 sets Venn diagram venn(5) # a 5 sets Venn diagram using ellipses venn(5, ellipse = TRUE) # a 5 sets Venn diagram with intersection labels venn(5, ilabels = TRUE) # and a predefined color style venn(5, ilabels = TRUE, zcolor = "style") # a union of two sets venn("1---- + ----1") # same with venn("A + E", snames = "A, B, C, D, E") # with different colors venn("1---- , ----1", zcolor = "red, blue") # same with venn("A, E", snames = "A, B, C, D, E", zcolor = "red, blue") # same colors for the borders venn("1---- , ----1", zcolor = "red, blue", col = "red, blue") # 6 sets diagram venn(6) # 7 sets "Adelaide" venn(7) # artistic version venn(c("1000000", "0100000", "0010000", "0001000", "0000100", "0000010", "0000001", "1111111")) # without all borders venn(c("1000000", "0100000", "0010000", "0001000", "0000100", "0000010", "0000001", "1111111"), borders = FALSE) # using sum of products notation venn("A + B~C", snames = "A, B, C, D") # when x is a list set.seed(12345) x <- list(First = 1:20, Second = 10:30, Third = sample(25:50, 15)) venn(x, ilabels = "counts") # when x is a dataframe set.seed(12345) x <- as.data.frame(matrix(sample(0:1, 150, replace = TRUE), ncol = 5)) venn(x, ilabels = "counts") # producing a ggplot2 graphics venn(x, ilabels = "counts", ggplot = TRUE) # increasing the border size venn(x, ilabels = "counts", ggplot = TRUE, size = 1.5) # with dashed lines venn(x, ilabels = "counts", ggplot = TRUE, linetype = "dashed") ## Not run: # produce Venn diagrams for QCA objects library(QCA) data(CVF) obj <- truthTable(CVF, "PROTEST", incl.cut = 0.85) venn(obj) # to set opacity based on inclusion scores # (less inclusion, more transparent) venn(obj, opacity = obj$tt$incl) # custom labels for intersections pCVF <- minimize(obj, include = "?") venn(pCVF$solution[[1]], zcol = "#ffdd77, #bb2020, #1188cc") cases <- paste(c("HungariansRom", "CatholicsNIreland", "AlbaniansFYROM", "RussiansEstonia"), collapse = "\n") coords <- unlist(getCentroid(getZones(pCVF$solution[[1]][2]))) text(coords[1], coords[2], labels = cases, cex = 0.85) ## End(Not run)# A simple Venn diagram with 3 sets venn(3) # with a vector of counts: 1 for "000", 2 for "001" etc. venn(3, counts = 1:8) # display the first whole set venn("1--") # same with venn("A", snames = "A, B, C") # an equivalent command, from the union of all intersections venn("100 + 110 + 101 + 111") # same with venn("A~B~C + AB~C + A~BC + ABC") # adding the labels for the intersections venn("1--", ilabels = TRUE) # using different parameters for the borders venn(4, lty = 5, col = "navyblue") # using ellipses venn(4, lty = 5, col = "navyblue", ellipse = TRUE) # a 5 sets Venn diagram venn(5) # a 5 sets Venn diagram using ellipses venn(5, ellipse = TRUE) # a 5 sets Venn diagram with intersection labels venn(5, ilabels = TRUE) # and a predefined color style venn(5, ilabels = TRUE, zcolor = "style") # a union of two sets venn("1---- + ----1") # same with venn("A + E", snames = "A, B, C, D, E") # with different colors venn("1---- , ----1", zcolor = "red, blue") # same with venn("A, E", snames = "A, B, C, D, E", zcolor = "red, blue") # same colors for the borders venn("1---- , ----1", zcolor = "red, blue", col = "red, blue") # 6 sets diagram venn(6) # 7 sets "Adelaide" venn(7) # artistic version venn(c("1000000", "0100000", "0010000", "0001000", "0000100", "0000010", "0000001", "1111111")) # without all borders venn(c("1000000", "0100000", "0010000", "0001000", "0000100", "0000010", "0000001", "1111111"), borders = FALSE) # using sum of products notation venn("A + B~C", snames = "A, B, C, D") # when x is a list set.seed(12345) x <- list(First = 1:20, Second = 10:30, Third = sample(25:50, 15)) venn(x, ilabels = "counts") # when x is a dataframe set.seed(12345) x <- as.data.frame(matrix(sample(0:1, 150, replace = TRUE), ncol = 5)) venn(x, ilabels = "counts") # producing a ggplot2 graphics venn(x, ilabels = "counts", ggplot = TRUE) # increasing the border size venn(x, ilabels = "counts", ggplot = TRUE, size = 1.5) # with dashed lines venn(x, ilabels = "counts", ggplot = TRUE, linetype = "dashed") ## Not run: # produce Venn diagrams for QCA objects library(QCA) data(CVF) obj <- truthTable(CVF, "PROTEST", incl.cut = 0.85) venn(obj) # to set opacity based on inclusion scores # (less inclusion, more transparent) venn(obj, opacity = obj$tt$incl) # custom labels for intersections pCVF <- minimize(obj, include = "?") venn(pCVF$solution[[1]], zcol = "#ffdd77, #bb2020, #1188cc") cases <- paste(c("HungariansRom", "CatholicsNIreland", "AlbaniansFYROM", "RussiansEstonia"), collapse = "\n") coords <- unlist(getCentroid(getZones(pCVF$solution[[1]][2]))) text(coords[1], coords[2], labels = cases, cex = 0.85) ## End(Not run)