Venn diagram UpSetR, clearer than traditional Venn diagram
created at 07-01-2021
views: 5
I just used the Venn diagram recently, but because there are more terms, the visualization effect of VennDiagram is not good. Then another post saw the improved version of UpSetR and Uncle Y upsetplot.
UpSetR¶
1. Under normal circumstances, I like to use the most direct way to input¶
In this way, you can easily choose the intersection between any clusters you want to display.
colorPalette<-c("#e41a1c","#377eb8","#4daf4a","9ecae1","#6baed6","#4292c6")
library(UpSetR)
input <- c(
'Type 1'= 578,
'Type 2' = 284,
'Type 3' = 488,
'Type 1&Type 3' =205,
'Type 2&Type 3' =89,
'Type 1&Type 2&Type 3' =20)
data <- fromExpression(input)
upset(data, nsets = 9, sets = c('Type 1', 'Type 2' , 'Type 3'),
keep.order = TRUE,matrix.color ="#b35806", main.bar.color = colorPalette,
sets.bar.color = c("#e41a1c","#377eb8","#4daf4a"),
point.size = 4, line.size = 1.3, mainbar.y.label = "IntersectionSize",
sets.x.label = "", mb.ratio = c(0.60, 0.40), text.scale = c(2, 2, 0.5, 0.5,2, 3))
2. use table input¶
When the amount of data is large, or when a more complex display is required, you can use table input.
Let's take a look at the movies data provided by the system
movies <- read.csv(system.file("extdata","movies.csv",package = "UpSetR"), header = TRUE, sep=";")
View(movies)
Data in a similar format can be generated using the pivot_wider function in the tidyr package. There are many functions in this package that are very useful for data transformation.
upset(movies, nsets = 7, nintersects = 30, mb.ratio = c(0.5, 0.5),
order.by = c("freq", "degree"), decreasing = c(TRUE,FALSE))
Other more complex parameters can be explored on their own.
created at 07-01-2021
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