Communication and Social Networks (Spring 2022)/Dutch School Data Visualization challenge

From CommunityData

The goal[edit]

In 2003 and 2004, researchers repeatedly surveyed a number of Dutch school students about their friendships and their behavior. They were particularly interested in the relationship between friendships and drinking behavior. They recorded information about alcohol use, gender, age, ethnicity (whether Dutch or not), and religion.

For this project, you will think of a question that you would like to visualize with this network. For example:

  • Are people who drink more more popular?
  • Are males or females more likely to have the same drinking behavior as their friends?
  • Are people of the dominant religion more likely to be popular? More likely to be friends with each other?

There are three parts of the assignment:

1) First, I want you to think about a question and draw on a piece of paper what you want the outcome to look like. For example, if you want to visualize whether people who drink are more popular, you may decide to color nodes by in-degree and change their size or shape based on drinking behavior.

2) Second, you will do your best to recreate your idea using tidygraph and ggraph. I would like you to turn in both your drawing and your visualization.

3) Write a paragraph or two explaining what the visualization tell us about the question that you asked.

You are welcome to work with a partner if you would like. Just make it clear who you worked with and I would encourage you to be a bit more ambitious in what you try to do.


The Introduction to ggraph and tidygraph reading actually uses this dataset. You can look to that for examples to build on (Here is the R Markdown file that I used to create the web page).


Descriptions of what each measure means are at this site, maintained by the people who collected the data.

To import the data you can right-click on and save the edge and node data files above to your computer and then import them into R.

Alternatively, the following code will download the files and create a graph object. You are welcome to reuse it.


nodes = read_csv('')
edges = read_csv('')

G = graph_from_data_frame(d=edges, v=nodes) %>% as_tbl_graph()