Hierarchical Visualizations in R and the Javascript InfoVis Toolkit

I love R.  It is really a great language and platform for statistical work and graphing.  But every technology has its limits – and other tools can be meet different needs.  So in this post, I will start with R and move on to the JavaScript InfoVis Toolkit.  And I must admit, I can’t say that I know the limits of R.  I am regularly corrected by the friendly and informed R community which makes this blog better – and helps me as well.

I have been regularly considering the best ways to visualize trees – especially large ones.  Having a background in Oracle, I often revisit the employee hierarchy that is included as part of the HR demonstration schema.


The query to construct a result set that represents the employee hierarchy relies upon Oracle’s non-standard hierarchical query syntax.  Employees are associated with other records in the employee table by a manager id.  The root node of the tree is the record with a null manager id (the top manager who has no superior within the organization).

library(RODBC)
ch = odbcConnect(“XE”,uid=”HR”,pwd=”HR”)


sql=”SELECT
      replace(m.first_name,’ ‘,’_’)||’_’||
      replace(m.last_name,’ ‘,’_’) manager_name,
      replace(e.first_name,’ ‘,’_’)||’_’||
      replace(e.last_name,’ ‘,’_’) employee_name
   FROM employees e    
   LEFT OUTER JOIN employees m 
   ON m.employee_id = e.manager_id  
   WHERE e.manager_id is not null
   START WITH e.manager_id IS NULL
   CONNECT BY PRIOR e.employee_id = e.manager_id
   ORDER siblings BY e.last_name,e.first_name”


r=sqlQuery(ch, paste(sql, collapse=’ ‘))
close(ch)


The data returned is a simple listing that pairs up each employee with his/her manager.

head(r)

      MANAGER_NAME           EMPLOYEE_NAME

1     Steven_King            Gerald_Cambrault

2     Gerald_Cambrault       Elizabeth_Bates

3     Gerald_Cambrault       Harrison_Bloom

4     Gerald_Cambrault       Tayler_Fox

5     Gerald_Cambrault       Sundita_Kumar

6     Gerald_Cambrault       Lisa_Ozer

The igraph library can then be used to plot the data.


library(igraph)
g = graph.data.frame(r, directed = T)
V(g)$label = V(g)$name
tkplot(g)


The results are a bit cluttered due to the number of nodes – but can be manually manipulated when viewed using tkplot.  The typical organizational chart structure can be obtained using the Reingold-Tilford layout.






Other possibilities include the circle layout…





…the Fruchterman-Reingold layout…
…and the Kamada-Kawai layout.