A chord diagram in R

A chord diagram is a graphical representation of the data in a matrix’s interrelationships. The data is arranged in a radial pattern around a circle, with the relationships between the data points commonly depicted as arcs linking the dots (Wikipedia). Each entity is represented by a fragment on the outer part of the circular layout. Then, arcs are drawn between each entities. The size of the arc is proportional to the importance of the flow.

Using the htmlwidgets interface architecture, the chorddiag package in R allows us to build interactive chord diagrams using the JavaScript visualization library D3 from within R.

We’ll look at how to make interactive chord diagrams in this blog post. You can view how many regions from each environmental challenges are by hovering over the arcs for each region/section below.

require(tidyverse)
require(viridis)
require(chorddiag)  #devtools::install_github("mattflor/chorddiag")

We create a dataframe of four regions in Tanzania with with a count of people responded to the causes of environmental challenges

regions = data.frame(Mwanza = c(50, 25, 5, 12),
                      Dar = c(10, 55, 5, 20),
                      Tanga = c(45,12,29, 20),
                      Arusha = c(24,67,27,15))

regions

  Mwanza Dar Tanga Arusha
1     50  10    45     24
2     25  55    12     67
3      5   5    29     27
4     12  20    20     15

We then convert the region data frame to matrix and assign the corresponding environmental challenges as row names

regions = as.matrix(regions)
row.names(regions) = c("Climate", "Waste", "Pollution", "Deforestation")

We can then plot the chord diagram

regions %>% chorddiag(type = "bipartite", 
                      showTicks = F, 
                      groupnameFontsize = 14, 
                      groupnamePadding = 10, 
                      margin = 90, showGroupnames = T)

Sometimes we might not be interested with default colors and wish to define our own colors. Since we have four groups, let’s define the color and use it to make similar plot but with a different flavour from the color

groupColors <- c("#000000", "#FFDD89", "#957244", "#F26223")

regions %>% chorddiag(type = "bipartite", 
                      showTicks = F, 
                      groupColors = groupColors,
                      groupnameFontsize = 14, 
                      groupnamePadding = 10, 
                      margin = 90, 
                      showGroupnames = T)

Chord diagrams can also be used to show transition matrices. Let’s pretend that each row in the dummy data below represents the number of customers each company has this year. Let’s imagine, though, that some of your clients decide to switch firms next year (shown in the columns). You may hover your mouse over the visualization to check which companies are losing the most clients and which are keeping the most.

df = data_frame(`Company A` = c(800, 200, 100, 50, 140, 200, 140),
                `Company B` = c(100, 2000, 300, 400, 50, 0, 290),
                `Company C` = c(200, 500, 4000, 80, 120, 320, 600),
                `Company D` = c(500, 200, 300, 5000, 250, 140, 450),
                `Company E` = c(600, 300, 150, 600, 6000, 30, 0),
                `Company F` = c(500, 400, 100, 300, 250, 4500, 140),
                `Company G` = c(300, 50, 0, 150, 600, 250, 7000))
df

# A tibble: 7 x 7
  `Company A` `Company B` `Company C` `Company D` `Company E` Company ~1 Compa~2
        <dbl>       <dbl>       <dbl>       <dbl>       <dbl>      <dbl>   <dbl>
1         800         100         200         500         600        500     300
2         200        2000         500         200         300        400      50
3         100         300        4000         300         150        100       0
4          50         400          80        5000         600        300     150
5         140          50         120         250        6000        250     600
6         200           0         320         140          30       4500     250
7         140         290         600         450           0        140    7000
# ... with abbreviated variable names 1: `Company F`, 2: `Company G`

df = as.matrix(df)
row.names(df) = c(colnames(df))

chorddiag(data = df, 
          type = "directional", 
          showTicks = F, 
          groupnameFontsize = 14, 
          groupnamePadding = 10, 
          margin = 90)