The network has 2669 nodes (3 modes: Person (640), Organization (2015), Federal Agency (14)) and 3380 arcs.
Converted into Pajek format by Vladimir Batagelj, May 29, 2022. The properties Connection and Source(s) are not considered.
Data set from the Github / BuzzFeedNews repository TrumpWorld Data.
Copied from the repository: This repository contains the data behind TrumpWorld, as described in this BuzzFeed News article. In addition to the tabular files already represented in the TrumpWorld spreadsheet, this repository also includes a GraphML file of the data, for ease of importing into network analysis and visualization software.
If you have suggestions for expanding or improving the dataset, please email trump@buzzfeed.com. If you’d like to send your tip securely and anonymously, see these instructions. (Please do not submit GitHub pull requests.)
Update, April 24, 2017: We’ve simplified TrumpWorld’s tabular data. Previously, the TrumpWorld data was distributed across three CSV files: person-person-connections.csv, person-org-connections.csv, and org-org-connections.csv. Now all of that data has been consolidated into a single CSV: trumpworld.csv. No information has been removed from TrumpWorld; it has just been streamlined. In addition, the GraphML representation of TrumpWorld now include each node’s type (i.e., “person” / “organization”).
> wdir <- "C:/Users/vlado/DL/data/TrumpWorld"
> setwd(wdir)
> T <- read.csv("trumpworld.csv",sep=",",head=TRUE)
> dim(T)
[1] 3380 6
> str(T)
'data.frame': 3380 obs. of 6 variables:
$ Entity.A.Type: chr "Organization" "Organization" "Organization" "Organization" ...
$ Entity.A : chr "4 SHADOW TREE LANE MEMBER CORP." "40 WALL DEVELOPMENT ASSOCIATES LLC" ...
$ Entity.B.Type: chr "Organization" "Organization" "Organization" "Organization" ...
$ Entity.B : chr "4 SHADOW TREE LANE LLC" "40 WALL STREET LLC" "40 WALL STREET COMMERCIAL LLC" "40 WALL STREET LLC" ...
$ Connection : chr "Ownership" "Ownership" "Ownership" "Ownership" ...
$ Source.s. : chr "https://www.documentcloud.org/documents/2838696-Trump-2016-Financial-Disclosure.html" ...
Check different modes and count the arcs among them.
> ty <- factor(c(T$Entity.A.Type,T$Entity.B.Type))
> levels(ty)
[1] "Federal Agency" "Organization" "Person"
> tyL <- levels(ty)
> nd <- factor(c(T$Entity.A,T$Entity.B))
> ndL <- levels(nd)
> length(ndL)
[1] 2669
> tyA <- factor(T$Entity.A.Type,levels=tyL)
> tyB <- factor(T$Entity.B.Type,levels=tyL)
> table(tyA,tyB)
tyB
tyA Federal Agency Organization Person
Federal Agency 0 0 0
Organization 0 782 153
Person 18 2046 381
Determine the mode sets and check that they are mutually disjoint.
> F <- T$Entity.B[T$Entity.B.Type=="Federal Agency"] > F <- sort(union(F,NULL)) > F [1] "CENTRAL INTELLIGENCE AGENCY" [2] "DEPARTMENT OF AGRICULTURE" [3] "DEPARTMENT OF DEFENSE" [4] "DEPARTMENT OF EDUCATION" [5] "DEPARTMENT OF ENERGY" [6] "DEPARTMENT OF HEALTH AND HUMAN SERVICES" [7] "DEPARTMENT OF HOMELAND SECURITY" [8] "DEPARTMENT OF HOUSING AND URBAN DEVELOPMENT" [9] "DEPARTMENT OF JUSTICE" [10] "DEPARTMENT OF THE INTERIOR" [11] "DEPARTMENT OF THE TREASURY" [12] "DEPARTMENT OF TRANSPORTATION" [13] "ENVIRONMENTAL PROTECTION AGENCY" [14] "SMALL BUSINESS ADMINISTRATION" > P <- sort(union(T$Entity.A[T$Entity.A.Type=="Person"],T$Entity.B[T$Entity.B.Type=="Person"])) > length(P) [1] 640 > head(P) [1] "AARON SCHOCK" "ABDUL RAHMAN AL-ASSIR" "ABDUL WAHID AL ULAMA" [4] "ABE WALLACH" "ABIGAIL KLEM" "ADO MACHIDA" > O <- sort(union(T$Entity.A[T$Entity.A.Type=="Organization"],T$Entity.B[T$Entity.B.Type=="Organization"])) > length(O) [1] 2015 > head(O) [1] "1116 SOUTH MAIN STREET, LLC" [2] "1186 BROADWAY, LLC" [3] "1290 AVENUE OF THE AMERICAS, A TENANCY-IN-COMMON" [4] "1291 AVENUE OF THE AMERICAS, A TENANCY-IN-COMMON" [5] "1292 AVENUE OF THE AMERICAS, A TENANCY-IN-COMMON" [6] "1293 AVENUE OF THE AMERICAS, A TENANCY-IN-COMMON" > L <- c(P,O,F) > C <- c(rep(1,length(P)),rep(2,length(O)),rep(3,length(F))) > length(C) [1] 2669 > length(L) [1] 2669 > intersect(F,P) character(0) > intersect(F,O) character(0) > intersect(P,O) character(0)
Factorize the nodes and export Pajek files.
> source("https://raw.githubusercontent.com/bavla/Rnet/master/R/Pajek.R")
> vector2clu(C,Clu="Trump.clu")
> u <- factor(T$Entity.A,levels=L)
> v <- factor(T$Entity.B,levels=L)
> uvLab2net(L,u,v,rep(1,length(u)),Net="Trump.net",dir=TRUE)
The files Trump.net and Trump.clu were combined using Pajek into a Pajek project file Trump.paj.
> n <- nrow(T)
> PO <- rep(NA,n)
> k <- 0
> for(i in 1:n)
+ if((T$Entity.A.Type[i]=="Person")&&(T$Entity.B.Type[i]=="Organization")){
+ k <- k+1; PO[k] <- T$Connection[i]}
> k
[1] 2046
> head(PO)
[1] "Former director"
[2] "Former director"
[3] "Former executive vice president of acquisitions and finance"
[4] "Chairman and former deputy general counsel"
[5] "Former partner"
[6] "General counsel"
>
> OO <- rep(NA,n)
> k <- 0
> for(i in 1:n)
+ if((T$Entity.A.Type[i]=="Organization")&&(T$Entity.B.Type[i]=="Organization")){
+ k <- k+1; OO[k] <- T$Connection[i]}
> k
[1] 782
> head(OO)
[1] "Ownership" "Ownership" "Ownership" "Ownership" "Ownership" "Ownership"