**Sustainable Research » Renglish**, and kindly contributed to R-bloggers)

I recently thought about ways to visualize medications and their co-occurences in a group of children. As long as you want to visualize up to 4 different medications you can simply use Venn diagrams. There is a very nice R-package to generate these kind of graphics for you (for a description see: Chen and Boutros, 2011). But this is of little help here.

The problem I faced involved 29 different medications and 50 children. So my data was stored in a table with 29 columns – one for each medication – and 50 rows – one for each child, so that the cells indicate whether or not the child took the medication.

M <- matrix(sample(0:1, 1450, replace=TRUE, prob=c(0.9,0.1)), nc=29)

### The Solution – Social Network Analysis

There are a several R-packages to analyze and visualize social network data – I will focus on “igraph” in this post. The problem I had was that I was not – and probably I am still not – familiar with the concepts and nomenclature of this field. The key to using the data described above in terms of network analysis was understanding that such data is called an **affiliation matrix**, where individuals are affiliated with certain events. As “igraph” likes **adjacency matrices**, where every column and row represents a different node – in our case a medication. The diagonal gives the number of times a medication was given (more information can be found on Daizaburo Shizuka site).

We transform an affilition matrix into an adjacency matrix in R simply by:

adj=M%*%t(M)

Now we can make a first bare-minimum plot:

require(igraph)

g=graph.adjacency(adj,mode=”undirected”, weighted=TRUE,diag=FALSE)

summary(g)

plot(g, main=”The bare minimum”)

### Adding information and spicing it up a notch

In all likelihood You want to add at least three kinds of information:

- Labels for the nodes
- Size of the nodes to represent the total number of events, aka medications
- Size of the links to represent the overlap between medications

name<-sample(c(LETTERS, letters, 1:99), 29, replace=TRUE)

number<-diag(adj)*5+5

width<-(E(g)$weight/2)+1

plot(g, main=”A little more information”, vertex.size=number,vertex.label=name,edge.width=width)

The “igraph” package lets you adopt quite a few parameters so you should consult with the manual. I only changed some of the colors, layout, fonts, etc.

plot(g, main=”Spice it up a notch”, vertex.size=number, vertex.label=name, edge.width=width, layout=layout.lgl, vertex.color=”red”, edge.color=”darkgrey”, vertex.label.family =”sans”, vertex.label.color=”black”)

Here is just the code:

^{?}View Code RSPLUS

require(igraph) setwd("~/Desktop/") # Generate example data M <- matrix(sample(0:1, 1450, replace=TRUE, prob=c(0.9,0.1)), nc=29) # Transform matrices adj=M%*%t(M) # Make a simple plot g<-graph.adjacency(adj,mode="undirected", weighted=TRUE,diag=FALSE) summary(g) plot(g, main="The bare minimum") # Add more information name<-sample(c(LETTERS, letters, 1:99), 29, replace=TRUE) number<-diag(adj)*5+5 width<-(E(g)$weight/2)+1 plot(g, main="A little more information", vertex.size=number,vertex.label=name,edge.width=width) # Adjust some plotting parameters plot(g, main="Spice it up a notch", vertex.size=number, vertex.label=name, edge.width=width, layout=layout.lgl, vertex.color="red", edge.color="darkgrey", vertex.label.family ="sans", vertex.label.color="black") |

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