Advanced chord diagram with R and circlize



Chord diagram is an efficient way to display flows between entities. This post shows how to build a customized version using the circlize package.

Chord section Data to Viz

Chord diagram from adjacency matrix


The circlize package developped by Zuguang Gu is the best way to build chord diagram in R. The chord diagram section of the gallery provides a step by step introduction to it.

This example explains how to build a highly customized chord diagram, adding links manually thanks to the circos.link() function.

Note that the library also offers a chordDiagram() functions that builds everything automatically, but offers less customization. (See it here.)

Important: This example has been found on stackoverflow, made by Jazzuro.

### You need several libraries
library(circlize)
library(migest)
library(dplyr)
 
### Make data
m <- data.frame(order = 1:6,
            country = c("Ausralia", "India", "China", "Japan", "Thailand", "Malaysia"),
            V3 = c(1, 150000, 90000, 180000, 15000, 10000),
            V4 = c(35000, 1, 10000, 12000, 25000, 8000),
            V5 = c(10000, 7000, 1, 40000, 5000, 4000),
            V6 = c(7000, 8000, 175000, 1, 11000, 18000),
            V7 = c(70000, 30000, 22000, 120000, 1, 40000),
            V8 = c(60000, 90000, 110000, 14000, 30000, 1),
            r = c(255,255,255,153,51,51),
            g = c(51, 153, 255, 255, 255, 255),
            b = c(51, 51, 51, 51, 51, 153),
            stringsAsFactors = FALSE)
df1 <- m[, c(1,2, 9:11)]
m <- m[,-(1:2)]/1e04
m <- as.matrix(m[,c(1:6)])
dimnames(m) <- list(orig = df1$country, dest = df1$country)
#Sort order of data.frame and matrix for plotting in circos
df1 <- arrange(df1, order)
df1$country <- factor(df1$country, levels = df1$country)
m <- m[levels(df1$country),levels(df1$country)]
 
 
### Define ranges of circos sectors and their colors (both of the sectors and the links)
df1$xmin <- 0
df1$xmax <- rowSums(m) + colSums(m)
n <- nrow(df1)
df1$rcol<-rgb(df1$r, df1$g, df1$b, max = 255)
df1$lcol<-rgb(df1$r, df1$g, df1$b, alpha=200, max = 255)
 
### Plot sectors (outer part)
par(mar=rep(0,4))
circos.clear()
 
### Basic circos graphic parameters
circos.par(cell.padding=c(0,0,0,0), track.margin=c(0,0.15), start.degree = 90, gap.degree =4)
 
### Sector details
circos.initialize(factors = df1$country, xlim = cbind(df1$xmin, df1$xmax))
 
### Plot sectors
circos.trackPlotRegion(ylim = c(0, 1), factors = df1$country, track.height=0.1,
                      #panel.fun for each sector
                      panel.fun = function(x, y) {
                      #select details of current sector
                      name = get.cell.meta.data("sector.index")
                      i = get.cell.meta.data("sector.numeric.index")
                      xlim = get.cell.meta.data("xlim")
                      ylim = get.cell.meta.data("ylim")
 
                      #text direction (dd) and adjusmtents (aa)
                      theta = circlize(mean(xlim), 1.3)[1, 1] %% 360
                      dd <- ifelse(theta < 90 || theta > 270, "clockwise", "reverse.clockwise")
                      aa = c(1, 0.5)
                      if(theta < 90 || theta > 270)  aa = c(0, 0.5)
 
                      #plot country labels
                      circos.text(x=mean(xlim), y=1.7, labels=name, facing = dd, cex=0.6,  adj = aa)
 
                      #plot main sector
                      circos.rect(xleft=xlim[1], ybottom=ylim[1], xright=xlim[2], ytop=ylim[2], 
                                  col = df1$rcol[i], border=df1$rcol[i])
 
                      #blank in part of main sector
                      circos.rect(xleft=xlim[1], ybottom=ylim[1], xright=xlim[2]-rowSums(m)[i], ytop=ylim[1]+0.3, 
                                  col = "white", border = "white")
 
                      #white line all the way around
                      circos.rect(xleft=xlim[1], ybottom=0.3, xright=xlim[2], ytop=0.32, col = "white", border = "white")
 
                      #plot axis
                      circos.axis(labels.cex=0.6, direction = "outside", major.at=seq(from=0,to=floor(df1$xmax)[i],by=5), 
                                  minor.ticks=1, labels.away.percentage = 0.15)
                    })
 
### Plot links (inner part)
### Add sum values to df1, marking the x-position of the first links
### out (sum1) and in (sum2). Updated for further links in loop below.
df1$sum1 <- colSums(m)
df1$sum2 <- numeric(n)
 
### Create a data.frame of the flow matrix sorted by flow size, to allow largest flow plotted first
df2 <- cbind(as.data.frame(m),orig=rownames(m),  stringsAsFactors=FALSE)
df2 <- reshape(df2, idvar="orig", varying=list(1:n), direction="long",
           timevar="dest", time=rownames(m),  v.names = "m")
df2 <- arrange(df2,desc(m))
 
### Keep only the largest flows to avoid clutter
df2 <- subset(df2, m > quantile(m,0.6))
 
### Plot links
for(k in 1:nrow(df2)){
    #i,j reference of flow matrix
    i<-match(df2$orig[k],df1$country)
    j<-match(df2$dest[k],df1$country)
 
#plot link
circos.link(sector.index1=df1$country[i], point1=c(df1$sum1[i], df1$sum1[i] + abs(m[i, j])),
            sector.index2=df1$country[j], point2=c(df1$sum2[j], df1$sum2[j] + abs(m[i, j])),
            col = df1$lcol[i])
 
#update sum1 and sum2 for use when plotting the next link
df1$sum1[i] = df1$sum1[i] + abs(m[i, j])
df1$sum2[j] = df1$sum2[j] + abs(m[i, j])
}

Related chart types


Chord diagram
Network
Sankey
Arc diagram
Edge bundling



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