Trumpworld Analysis : Ownership Relations in his Business Network

Trumpworld by BuzzfeedYou do not need a machine learning algorithm to predict that the presidency of Donald Trump will be controversial.

One of the most discussed aspects of his reign is the massive potential for conflicts of interest. Trump’s complex business empire is entangled with national and international politics.

Buzzfeed has mapped many of the relationships between businesses and people in what they have dubbed Trumpworld. They provided the data to enable citizens data science into the wheelings and dealings of Donald J. Trump. The raw data set consists of three subsets of connections between:

  • Organisations
  • People
  • People and organisations

Trumpworld Analysis

This post analyses the connections between organisations using the mighty igraph package in the R language. The code snippet below converts the data to a graph that can be analysed using social network analysis techniques. I have downloaded the table of the raw data file as CSV files. This data is subsetted to contain only ownership relationships.

# Read data <- read.csv("TrumpWorld DataOrg.csv") <- subset(, Connection==&quot;Ownership&quot;)[,1:2]

# Create graph of ownerships
org.ownership <- graph.edgelist(as.matrix(

# Analysis

# Plot Graph

Trumpworld analysis - business ownership networkNetwork Analysis

This network contains 309 ownership relationships between 322 firms.

When we plot the data, we see that most relationships are between two firms. The plot is organised with the Fruchterman-Reingold algorithm to improve its clarity.

We can also see a large cluster in the centre. The names have been removed for clarity.

The Trumpland analysis continues with this conglomerate. The second code section excises this connected subnetwork so we can analyse it in more detail.

# Find most connected firm
# Create subnetworks
org.ownership.d <- decompose(org.ownership)
# Find largest subnetwork
largest <- which.max(sapply(org.ownership.d, diameter))
#Plot largest subnetwork

Digging Deeper

The node with the highest degree identifies the business with the most holdings. This analysis shows that DJT Holdings LLC owns 33 other organisations. These organisations own other organisations. We can now use the cluster function to investigate this subnetwork.

Trumpworld holdings

This Trumpworld analysis shows that the ownership network is clearly a star network. DJT Holdings LLC centrally controls all organisations. Perhaps this graph visualises the management style of the soon to be president Trump. Trump centrally controls his empire, which is typical for a family business.

Does this chart visualise Trump’s leadership style? Is the star network an expression of his lack of trust and thus desire to oversee everything directly?

View this code and associated files on GitHub.

Finding antipodes using the globe and ggmap packages

The antipodes of each point on the Earth's surface

The antipode of each point on the Earth’s surface—the points where the blue and yellow overlap, are the land antipodes.

When I was a kid, I was was fascinated by the conundrum of what happens when you drill a hole straight through the centre of the earth. I always believed that I would turn up in Australia. But is this really the case?

The antipodes of any place on earth is the place that is diametrically opposite to it. A pair of antipodes are connected by a straight line running through the centre of the Earth. These points are as far away from each other as is possible on this planet. Two people are antipodes when they live on opposite sides of the globe. Their feet (πούς/pous in Ancient Greek) are directly opposite each other.

How can we use coding in R to solve this conundrum?

Using R to find antipodes

We can roughly recreate the antipodean map on Wikipedia with the globe package. This package, written by Adrian Baddeley, plots 2D and 3D views of the earth. The package contains a data file with major coastlines that can be used to create a flipped map of the world.

The package contains a data file with major coastlines that can be used to create a flipped map of the world. To turn a spatial location into its antipode you subtract 180 degrees from the longitude and reverse the sign of the latitude, shown below.

# Create antipodean map
flip <- earth
flip$coords[,1] <- flip$coords[,1]-180
flip$coords[,2] <- -flip$coords[,2]
par(mar=rep(0,4)) # Remove plotting margins
globeearth(eye=c(90,0), col="blue")
globepoints(loc=flip$coords, eye=c(90,0), col="red", pch=".")

We can also use the ggmap package to visualise antipodes. This package, developed by David Kahle antipodean R-guru Hadley Wickham, has a neat geocoding function to obtain a spatial location. The antipode function takes the description of a location and a zoom level to plot a dot on the antipode location. The gridExtra package is used to created a faceted map, which is not otherwise possible in ggmap.


antipode <- function(location, zm=6) {
    # Map location
    lonlat <- geocode(location)
    loc1 <- get_map(lonlat, zoom=zm)
    map1 <- ggmap(loc1) + geom_point(data=lonlat, aes(lon, lat, col="red", size=10)) + 
    # Define antipode
    lonlat$lon <- lonlat$lon-180
    if (lonlat$lon < -180) lonlat$lon <- 360 + lonlat$lon
    lonlat$lat <- -lonlat$lat
    loc2 <- get_map(lonlat, zoom=zm)
    map2 <- ggmap(loc2) + geom_point(data=lonlat, aes(lon, lat, col="red", size=10)) + 
    grid.arrange(map1, map2, nrow=1)

antipode("Rector Nelissenstraat 47 Hoensbroek", 4)

This code solves the problem I was thinking about as a child. Running the code shows that the antipodes location of the home I grew up in is not in Australia, but quite a way south of New Zealand. Another childhood fantasy shattered …

You can also view this code on GitHub.

Antipodes using ggmap and gridExtra.