<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/trumpworld-analysis-ownership-relations-in-his-business-network/"></div></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://r.prevos.net/trumpworld-analysis/"> The Devil is in the Data</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><img alt="Trumpworld by Buzzfeed" src="https://i1.wp.com/img.buzzfeed.com/buzzfeed-static/static/2017-01/13/13/asset/buzzfeed-prod-fastlane-01/sub-buzz-31493-1484333437-1.jpg?resize=300%2C258&ssl=1" data-recalc-dims="1">You do not need a machine learning algorithm to predict that the presidency of Donald Trump will be controversial.</p>
<p>One of the most discussed aspects of his upcoming reign is the massive potential for conflicts of interest. Trump’s complex business empire is entangled with many aspects of national and international politics.</p>
<p>Buzzfeed has mapped many of the relationships between businesses and people in what they have dubbed <a target="_blank" rel="nofollow" href="https://www.buzzfeed.com/johntemplon/help-us-map-trumpworld?utm_term=.fnvA3dNK6#.vcEvzO2p8">Trumpworld</a>. They provided direct access to the data to enable citizens data science into the wheelings and dealings of Donald J. Trump. The <a target="_blank" rel="nofollow" href="https://docs.google.com/spreadsheets/d/1Z5Vo5pbvxKJ5XpfALZXvCzW26Cl4we3OaN73K9Ae5Ss/edit">raw data set</a> consists of three subsets of connections between:</p>
<ul>
<li>Organisations</li>
<li>People</li>
<li>People and organisations</li>
</ul>
<h2>Trumpworld Analysis</h2>
<p>This post analyses the connections between organisations using the mighty<a target="_blank" rel="nofollow" href="http://igraph.org/r/"> igraph package in the R language</a>. The code snippet below converts the data to a graph that can be visualised and 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.</p>
<pre>
# Read data
trumpworld.org &lt;- read.csv(&quot;TrumpWorld/TrumpWorld DataOrg.csv&quot;)
trumpworld.org.ownership &lt;- subset(trumpworld.org, Connection==&quot;Ownership&quot;)[,1:2]

# Create graph of ownerships
library(igraph)
org.ownership &lt;- graph.edgelist(as.matrix(trumpworld.org.ownership))

# Analysis
nrow(trumpworld.org.ownership)
length(unique(c(trumpworld.org.ownership[,1], trumpworld.org.ownership[,2])))

# Plot Graph
par(mar=rep(0,4))
plot(org.ownership,
 layout=layout.fruchterman.reingold,
 vertex.label=NA,
 vertex.size=2,
 edge.arrow.size=.1
)
</pre>
<h2><a target="_blank" rel="nofollow" href="http://r.prevos.net/?attachment_id=125"><img sizes="(max-width: 300px) 100vw, 300px" alt="Trumpworld analysis - business ownership network" class="wp-image-125" src="https://i2.wp.com/r.prevos.net/wp-content/uploads/sites/11/2017/01/trumpworld-org1-300x300.png?resize=300%2C300" data-recalc-dims="1"></a>Network Analysis</h2>
<p>This network contains 309 ownership relationships between 322 firms.</p>
<p>When we plot the data we see that most relationships are between two firms. The plot is organised with the <a target="_blank" rel="nofollow" href="https://en.wikipedia.org/wiki/Force-directed_graph_drawing">Fruchterman-Reingold</a> algorithm to improve its clarity.</p>
<p>We can also see a large conglomerate in the centre. The names have been removed for clarity.</p>
<p>The Trumpland analysis continues with this conglomerate. The second code section excises this connected subnetwork so we can analyse it in more detail.</p>
<pre>
# Find most connected firm
which.max(degree(org.ownership))
# Create subnetworks
org.ownership.d &lt;- decompose(org.ownership)
# Find largest subnetwork
largest &lt;- which.max(sapply(org.ownership.d, diameter))
#Plot largest subnetwork
plot(org.ownership.d[[largest]],
 layout=layout.fruchterman.reingold,
 vertex.label.cex=.5,
 vertex.size=5,
 edge.arrow.size=.1
)
</pre>
<h2>Digging Deeper</h2>
<p>The node with the highest <a target="_blank" rel="nofollow" href="https://en.wikipedia.org/wiki/Degree_(graph_theory)">degree</a> identifies the business with the most holdings. This analysis shows that DJT Holdings LLC owns 33 other organisations, which own further organisations. We can now use the cluster function to investigate this subnetwork.</p>
<p><a target="_blank" rel="nofollow" href="https://i1.wp.com/r.prevos.net/wp-content/uploads/sites/11/2017/01/TrumplandHoldings.png"><img sizes="(max-width: 2701px) 100vw, 2701px" alt="Trumpworld holdings" class="wp-image-129" src="https://i1.wp.com/r.prevos.net/wp-content/uploads/sites/11/2017/01/TrumplandHoldings.png?resize=456%2C399" data-recalc-dims="1"></a></p>
<p>This Trumpworld analysis shows that the ownership network is clearly a <a target="_blank" rel="nofollow" href="https://en.wikipedia.org/wiki/Star_(graph_theory)">star network</a> where 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 visualise Trump’s leadership style? Is the star network an expression of his lack of trust and thus desire to oversee everything directly?</p>
<p>The post <a target="_blank" rel="nofollow" href="http://r.prevos.net/trumpworld-analysis/">Trumpworld Analysis : Ownership Relations in his Business Network</a> appeared first on <a target="_blank" rel="nofollow" href="http://r.prevos.net/">The Devil is in the Data</a>.</p>
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