Comtradr data clean

Description

This function takes (import) trade data downloaded from comtrade - potentially using the comtradr package, cleans it and transforms it into a network. Adding a number of country level attributes to nodes in the network, including: regional partition, GDP, GDP per capita, GDP growth and FDI. However, it is important to note the limits of using comtradr to construct a network. Firstly when downloading the data with comtradr, you must specify reporters and partners – yet you cannot put “all” for both – only for either reporters or partners. Then for the other you are limited to a character vector of country names, length five or fewer. Therefore, this will not give you a full network. However, this function can be applied to trade data downloaded from UN Comtrade (download csv and read into R as a dataframe), or any other trade data. You just make sure it has the following column names: reporter_iso, partner_iso, trade_value_usd and year. Some dataformats may have different names. Also - it is important to note that this function is for import data.

Usage

Comtradrclean(DF, YEAR, threshold, cutoff)

Arguments

Value

International Trade Network - igraph object

Electrical Automotive Goods 2016 Network

Description

Electrical Automotive Goods 2016 Network. Electrical automotive goods category as defined by Amighini & Gogoni (2014)

Usage

ELEnet16

References

Amighini, A. and Gorgoni, S. (2014) The International Reorganisation of Auto Production, The World Economy, 37(7), pp. 923–952.

List of Electrical Automotive Goods Networks (2006-2016)

Description

List of Electrical Automotive Goods Networks for 2006 - 2016. Electrical automotive goods category as defined by Amighini & Gogoni (2014)

Usage

ELEnetList

References

Amighini, A. and Gorgoni, S. (2014) The International Reorganisation of Auto Production, The World Economy, 37(7), pp. 923–952. (list of igraph objects)

Single Clean ITN Plot

Description

This function plots a single/clean ITN

Usage

ITN_make_plot(gs, LABEL, REGION)

Arguments

Value

Panel of ITN plots

Examples

##Load graph
data("ELEnet16")

##Otherwise download data from WITS and create an
##International Trade Network using WITSclean()

##Plot the network - No Label, colour by region
ITN_plot_example<-ITN_make_plot(ELEnet16,FALSE,TRUE)

ITN plot on world map

Description

This function plots the international trade network on a world map

Usage

ITN_map_plot(gs)

Arguments

Value

Plot of the ITN on world map

Examples

require(maps)
##Load the ITN
data(ELEnet16)

## Plot ITN on map - node size based on outdegree
ITN_map_plot(ELEnet16)

Adjust ITN

Description

This function adjusts ITN matrices so they are the same size

Usage

ITNadjust(MATlist, j)

Arguments

Value

Matrix

Examples

##Create a list of random matrices (of different sizes)
##Labels - letters of alphabet (can represent actor names)
mat1<- matrix(round(runif(10*10)), 10, 10)
rownames(mat1)<-LETTERS[1:10]
colnames(mat1)<-LETTERS[1:10]

mat2<- matrix(round(runif(10*10)), 10, 10)
rownames(mat2)<-LETTERS[10:19]
colnames(mat2)<-LETTERS[10:19]

mat3<- matrix(round(runif(12*12)), 12, 12)
rownames(mat3)<-LETTERS[15:26]
colnames(mat3)<-LETTERS[15:26]

##Create matrix list
MATlist<-list(mat1,mat2,mat3)

##Adjust matrix 1 so that it has additional rows/actors not
##in the original matrix

mat1adjust<-ITNadjust(MATlist,1)

ITN Blockmodel Plot

Description

This function calculates block membership for the ITN and then plots the network, with node colour according to block membership.

Usage

ITNblock_plot(gs, LABEL)

Arguments

Value

Network Plot - nodes coloured based on block membership

Examples

require(igraph)
require(sna)
require(intergraph)

##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(75,0.05,directed = TRUE)

##Add edge weights
E(ITN)$weight<-runif(ecount(ITN), 0, 1)

##Blockmodel plot
block_plot<-ITNblock_plot(ITN,FALSE)

ITN Blockmodel & Structural Equivalence

Description

This function calculates block membership for ITN and structural equivalence between countries

Usage

ITNblock_se(gs)

Arguments

Value

List object containing block membership and structural equivalence matrix results

Examples

require(igraph)
require(sna)
require(intergraph)

##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(50,0.05,directed = TRUE)

##Add edge weights
E(ITN)$weight<-runif(ecount(ITN), 0, 1)

##Blockmodel & structural equivalence analysis
blockse<-ITNblock_se(ITN)

ITN Centrality

Description

This function calculates a number of centrality metrics for the weighted International Trade Network (ITN)

Usage

ITNcentrality(gs)

Arguments

Value

Table of centrality results (dataframe)

Examples

require(igraph)
##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(75,0.05,directed = TRUE)

##Add edge weights
E(ITN)$weight<-runif(ecount(ITN), 0, 1)

##Add vertex names
V(ITN)$name<-1:vcount(ITN)

##Calculate the centrality measures
ITNCENT<-ITNcentrality(ITN)

ITN Centrality for binary networks

Description

This function calculates a number of centrality metrics for the binary International Trade Network (ITN)

Usage

ITNcentrality_binary(gs)

Arguments

Value

Table of centrality results (dataframe)

Examples

require(igraph)
##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(75,0.05,directed = TRUE)


##Add vertex names
V(ITN)$name<-1:vcount(ITN)

##Calculate the centrality measures
ITNCENT<-ITNcentrality_binary(ITN)

ITN Cluster

Description

This function calculates cluster membership for ITN

Usage

ITNcluster(gs)

Arguments

Value

Cluster object containing various cluster membership results

Examples

##Load ITN
data(ELEnet16)

##Cluster Analysis
CLU<-ITNcluster(ELEnet16)

ITN Correlation Plot

Description

This function plots the correlation between degree and strength scores

Usage

ITNcorr(gs)

Arguments

Value

Correlation plot

Examples

require(igraph)

##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(75,0.05,directed = TRUE)

##Add edge weights
E(ITN)$weight<-runif(ecount(ITN), 0, 1)

##Plot correlation matrix between degree and strength scores.
corr_plot<-ITNcorr(ITN)

ITN Degree Distribution

Description

This function plots the ITN (probability) degree distribtuion

Usage

ITNdegdist(gs)

Arguments

Value

Panel of ITN degree distribution plots

Examples

require(igraph)

##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(75,0.05,directed = TRUE)

##Plot degree distribution
deg_dist_plot<-ITNdegdist(ITN)

Dynamic ITN

Description

This function produces a dynamic network object for ITNs. It cleans and adjusts the individual networks, so they are the same size. This dynamic network object can then be used to create animations, mapping changes over time and to calculate temporal network statistics

Usage

ITNdynamic(NETlist)

Arguments

Value

It returns the Dynamic Network Object

Examples

require(igraph)

##Create a set of random International Trade Networks (igraph objects)
##and add vertex names
ITN1<-erdos.renyi.game(75,0.05,directed = TRUE)
V(ITN1)$name<-1:vcount(ITN1)
ITN2<-erdos.renyi.game(100,0.01,directed = TRUE)
V(ITN2)$name<-1:vcount(ITN2)
ITN3<-erdos.renyi.game(55,0.1,directed = TRUE)
V(ITN3)$name<-1:vcount(ITN3)

##Create network list
NETlist<-list(ITN1,ITN2,ITN3)

##Create Dynamic Network Object

ITNdyn<-ITNdynamic(NETlist)

ITN Histogram Degree Distribution

Description

This function plots the histogram degree distribution for the ITN

Usage

ITNhistdegdist(gs)

Arguments

Value

Panel of ITN histogram degree distribution plots

Examples

require(igraph)

##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(75,0.05,directed = TRUE)

##Add edge weights
E(ITN)$weight<-runif(ecount(ITN), 0, 1)

##Plot degree distribution histogram
hist_deg_dist<-ITNhistdegdist(ITN)

ITN - Exports vs Imports Plot

Description

The following function produces a plot showing imports (in degree) vs exports (out degree). This allows us to identify whether in the ITN, countries that export high levels also import high levels. The plot can be produced for either weighted or binary import and export ties.

Usage

ITNimvex(gs, weighted)

Arguments

Value

Imports Vs Exports Plot

Examples

require(igraph)

##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(75,0.05,directed = TRUE)

##Add edge weights
E(ITN)$weight<-runif(ecount(ITN), 0, 1)

##Plot binary import vs exports
imvex_plot<-ITNimvex(ITN,FALSE)

ITN Plots

Description

This function creates a panel of four plots of the ITN for a quick inspection. These include plots: (i) highlighting clusters using the fast greedy algorithm.(ii)node colours for communities detected using the spinglass algorithm. (iii)nodes coloured by regional partition and (iv)with nodes coloured by regional partition and node size based on outdegree centrality.

Usage

ITNplotset(gs)

Arguments

Value

Panel of ITN plots

Examples

##Load the network
data(ELEnet16)

##Plot set of network visualisations
ITNplotset(ELEnet16)

ITN Properties

Description

This function calculates network level properties for the ITN. These include: -Size (number of nodes) -Density -Reciprocity -Diameter -Average path length -Average node strength -Average Degree -Betweenness Centralisation -Closeness Centralisation -Eigenvector Centralisation -Out Degree Centralisation -In Degree Centralisation -All Degree Centralisation -Clustering coefficent (transitivity) -Clustering Weighted -Region Homophily -Degree Assortativity

Usage

ITNproperties(gs, weighted)

Arguments

Value

Table of centrality results (dataframe)

Examples

##Load the network
data(ELEnet16)

##Calculate the network properties
ITNPROP<-ITNproperties(ELEnet16,TRUE)

ITN Properties Base

Description

This function calculates network level properties for the ITN. These include: -Size (number of nodes) -Density -Reciprocity -Diameter -Average path length -Average node strength -Average Degree -Betweenness Centralisation -Closeness Centralisation -Eigenvector Centralisation -Out Degree Centralisation -In Degree Centralisation -All Degree Centralisation -Clustering coefficent (transitivity) -Clustering Weighted -Degree Assortativity

Usage

ITNproperties_base(gs, weighted)

Arguments

Value

Table of centrality results (dataframe)

Examples

##Load the network
data(ELEnet16)

##Calculate the network properties
ITNPROP<-ITNproperties_base(ELEnet16,TRUE)

WITS data clean

Description

This function takes (import) trade data downloaded from WITS, cleans it and transforms it into a network. Adding a number of country level attributes to nodes in the network, including: regional partition, GDP, GDP per capita, GDP growth and FDI.

Usage

WITSclean(CSVfile, YEAR, threshold, cutoff)

Arguments

Value

International Trade Network - igraph object

abs_diff_mat

Description

This takes a dataframe of node attributes and convert one into a absolute difference matrix

Usage

abs_diff_mat(DF, attrname)

Arguments

Value

Absolute difference matrix

cap_lat_lon

Description

Dataframe of capital city latitude and longitude coordinates

Usage

cap_lat_lon

Core-Periphery for Weighted Networks

Description

This function implements rich club core-periphery algorithm (Ma & Mondragón, 2015) to identify members of the core and periphery in weighted networks

Usage

core_periphery_weighted(gs, type)

Arguments

Value

List - 1.)igraph object with core-periphery results added as a node attribute. 2.) Dataframe of core-periphery results.

References

Ma A, Mondragón RJ (2015) Rich-Cores in Networks. PLoS ONE 10(3): e0119678. https://doi.org/10.1371/journal.pone.0119678

Examples

require(igraph)
##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(50,0.05,directed = TRUE)

##Add edge weights
E(ITN)$weight<-runif(ecount(ITN), 0, 1)

##Add vertex names
V(ITN)$name<-1:vcount(ITN)

##Implement core-periphery algorithm
ITNcp<-core_periphery_weighted(ITN,"directed")

Group level E-I Index

Description

This function calculates the E-I Index (External-internal) at the group/attribute level

Usage

ei_group(gs, attrname)

Arguments

Value

Group level results dataframe

Examples

require(igraph)
##Create random network (igraph object)
gs<-erdos.renyi.game(75,0.05,directed = TRUE)

##Add vertex names
V(gs)$name<-1:vcount(gs)

## Add an attribute
V(gs)$letters<- rep(LETTERS[1:5],15)

##Calculate the Group E-I Results
EI_GROUP_DATAFRAME<-ei_group(gs,"letters")

Individual/Node level E-I Index

Description

This function calculates the E-I Index (External-internal) at the individual/node level

Usage

ei_ind(gs, attrname)

Arguments

Value

Group level results dataframe

Examples

require(igraph)
##Create random network (igraph object)
gs<-erdos.renyi.game(30,0.05,directed = TRUE)

##Add vertex names
V(gs)$name<-1:vcount(gs)

## Add an attribute
V(gs)$letters<- rep(LETTERS[1:5],6)

##Calculate the Individual E-I Results
EI_IND_DATAFRAME<-ei_ind(gs,"letters")

Network level E-I Index

Description

This function calculates the E-I Index (External-internal) at the network level

Usage

ei_network(gs, attrname)

Arguments

Value

Group level results dataframe

Examples

require(igraph)
##Create random network (igraph object)
gs<-erdos.renyi.game(75,0.05,directed = TRUE)

##Add vertex names
V(gs)$name<-1:vcount(gs)

## Add an attribute
V(gs)$letters<- rep(LETTERS[1:5],15)

##Calculate the Group E-I Results
EI_NETWORK<-ei_network(gs,"letters")

get.backbone

Description

This function extracts the backbone of a network

Usage

get.backbone(G, alpha, directed = TRUE)

Arguments

Value

Backbone of the network

References

Serrano, M. Á., Boguñá, M. and Vespignani, A. (2009) Extracting the multiscale backbone of complex weighted networks, Proceedings of the National Academy of Sciences, 106(16), pp. 6483–6488.

Examples

require(igraph)

##Create a random (directed) network
gs<-erdos.renyi.game(50,0.2,directed = TRUE)

##Add edge weights to the network
E(gs)$weight<-runif(ecount(gs), 0, 1)

##Extract backbone at 0.05 significance level
backbone<-get.backbone(gs,0.1)

isEmpty

Description

This function check whether data is numeric(0) and give returns an NA if this is true and the value of the data otherwise.

Usage

isEmpty(x)

Arguments

Value

NA or the data

make_trade_network

Description

This function takes (import) trade data and cleans it and transforms it into a network. This function can be applied to trade data downloaded from UN Comtrade (download csv and read into R as a dataframe), or any other trade data. You just make sure it has the following column names: reporter_iso, partner_iso and edge_weight. Some dataformats may have different names. Also - it is important to note that this function is for import data.

Usage

make_trade_network(DF, threshold, cutoff)

Arguments

Value

International Trade Network - igraph object

Mixing Matrix

Description

This function calculates the mixing matrix for an igraph object

Usage

mixing_matrix_igraph(gs, attrname)

Arguments

Value

Mixing matrix

Examples

require(igraph)
##Create random International Trade Network (igraph object)
gs<-erdos.renyi.game(50,0.05,directed = TRUE)

##Add vertex attributes
V(gs)$LETTER<-rep(LETTERS[1:5],10)

##Add vertex names
V(gs)$name<-1:vcount(gs)

##Calculate mixing matrix
mixing_matrix<-mixing_matrix_igraph(gs,"LETTER")

Plot Degree Distribution

Description

This function plots degree distribution for any graph

Usage

plot_degree_distribution(graph, a)

Arguments

Value

Panel of ITN degree distribution plots

Examples

require(igraph)
##Create random International Trade Network (igraph object)
ITN<-erdos.renyi.game(75,0.05,directed = TRUE)

##Plot out degree distribution
plot_degree_distribution(ITN,"in")

receiver_mat

Description

This takes a dataframe of node attributes and convert one into a matrix of receiver attributes

Usage

receiver_mat(DF, attrname)

Arguments

Value

Receiver matrix

region_circle_plot

Description

This function creates a chord diagram/circle plot for levels of trade between regional partitions

Usage

region_circle_plot(gs)

Arguments

Value

Circle Plot

Examples

##Load graph
data("ELEnet16")

##Create region circle plot
region_circle_plot(ELEnet16)

reorder_df

Description

Reorders the rows of one dataframe according to another vector (id vector)

Usage

reorder_df(df, col_sort, reorder_data)

Arguments

Value

Reordered dataframe

Examples

df <- data.frame(a = letters[1:3],b = LETTERS[4:6],c = 7:9)

reorder_data<-c("c","a","b")

df_new<-reorder_df(df,"a",reorder_data)

df_new

round_df

Description

This function rounds the numeric variables in a dataframe containing numeric and non-numeric data

Usage

round_df(x, digits)

Arguments

Value

Dataframe with rounded numbers

Examples

##Create dataframe
ID = c("a","b","c","d","e")
Value1 = c(3.445662,6.44566,8.75551,1.114522,1.5551)
Value2 = c(8.2,1.7,6.4,19.45459,10.34524)
df<-data.frame(ID,Value1,Value2)

##Round to 2 digits
rounddf<-round_df(df,2)

sender_mat

Description

This takes a dataframe of node attributes and convert one into a matrix of sender attributes

Usage

sender_mat(DF, attrname)

Arguments

Value

Sender matrix