R/WITSclean_function.R
In MatthewSmith430/ITNr: Analysis of the International Trade Network
Defines functions
WITSclean
Documented in
WITSclean
#' @title 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.
#' @param CSVfile WITS csv file
#' @param YEAR Year
#' @param threshold Apply a threshold - TRUE, Extract the backbone - FALSE
#' @param cutoff Threshold - cutoff level, Backbone - significance level
#' @export
#' @return International Trade Network - igraph object
WITSclean<-function(CSVfile,YEAR,threshold,cutoff){
DATAV1<-utils::read.csv(CSVfile)
DATA<-dplyr::filter(DATAV1,DATAV1$Year==YEAR)
Sender<-as.vector(DATA[,"PartnerISO3"])
Sender<- gsub('SER', 'SRB', Sender)
Sender<- gsub('TMP', 'TLS', Sender)
Sender<- gsub('ZAR', 'COD', Sender)
Sender<- gsub('ROM', 'ROU', Sender)
Sender<- gsub('SUD', 'SDN', Sender)
Sender<- gsub('MNT', 'MNE', Sender)
Receiver<-as.vector(DATA[,"ReporterISO3"])
Receiver<- gsub('SER', 'SRB', Receiver)
Receiver<- gsub('TMP', 'TLS', Receiver)
Receiver<- gsub('ZAR', 'COD', Receiver)
Receiver<- gsub('ROM', 'ROU', Receiver)
Receiver<- gsub('SUD', 'SDN', Receiver)
Receiver<- gsub('MNT', 'MNE', Receiver)
VAL<-DATA[,"TradeValue.in.1000.USD"]
FULLel<-data.frame(Sender=Sender,
Receiver=Receiver,
VAL=VAL,stringsAsFactors = FALSE)
WDIDataSeries<-WDI::WDI_data
WDICountryInfo<-WDIDataSeries$country
WD<-as.data.frame(WDICountryInfo,stringsAsFactors = FALSE)
COUNTRYlist<-WDICountryInfo[,"iso3c"]
REGIONlist<-WDICountryInfo[,"region"]
INCOMElist<-WDICountryInfo[,"income"]
CountryRegion<-cbind(COUNTRYlist,REGIONlist)
CountryIncome<-cbind(COUNTRYlist,INCOMElist)
##List all of the aggregate entities.
AggReg<-c("All","EUN","UNS","OAS","FRE","BAT",
"SPE","VAT","UMI","ATA","PCN","AIA","COK",
"SHN","MSR","NIU",
"BES","BLM","BUN","BVT","CCK","CXR","FLK",#Small regions
"HMD","IOT","NFK","SGS","TKL",#small regions
"ESH","SPM","ATF"
)
AggRegMat<-matrix("Aggregates",length(AggReg),2)
AggRegMat[,1]<-AggReg
CountryRegion<-rbind(CountryRegion,AggRegMat)
CountryIncome<-rbind(CountryIncome,AggRegMat)
CR<-as.data.frame(CountryRegion,stringsAsFactors = FALSE)
ALL_AGG<-dplyr::filter(CR,REGIONlist=="Aggregates")
ALL_AGG<-ALL_AGG$COUNTRYlist
ALL_AGG<-as.vector(ALL_AGG)
WDIgdp1<-WDI::WDI(country="all",indicator = "NY.GDP.PCAP.KD", start = YEAR, end=YEAR )
WDIgdp1<-as.data.frame(WDIgdp1,stringsAsFactors=FALSE)
#WDIgdp1<-merge(WD,WDIgdp1,by="iso2c")
WDIgdp1$iso3<-WD$iso3c[match(WDIgdp1$iso2c,WD$iso2c)]
WDIgdp2<-cbind(as.vector(WDIgdp1$iso3),
as.vector(WDIgdp1$NY.GDP.PCAP.KD))
colnames(WDIgdp2)<-c("iso3","GDP")
WDIgdp2<-as.data.frame(WDIgdp2,stringsAsFactors=FALSE)
WDIGDPgrowth1<-WDI::WDI(country="all",indicator = "NY.GDP.MKTP.KD.ZG", start = YEAR, end=YEAR )
WDIGDPgrowth1<-as.data.frame(WDIGDPgrowth1,stringsAsFactors=FALSE)
WDIGDPgrowth1$iso3<-WD$iso3c[match(WDIGDPgrowth1$iso2c,WD$iso2c)]
WDIGDPgrowth2<-cbind(as.vector(WDIGDPgrowth1$iso3),
as.vector(WDIGDPgrowth1$NY.GDP.MKTP.KD.ZG))
colnames(WDIGDPgrowth2)<-c("iso3","GDPgrowth")
WDIGDPgrowth2<-as.data.frame(WDIGDPgrowth2,stringsAsFactors=FALSE)
WDIGDPPC1<-WDI::WDI(country="all",indicator = "NY.GDP.PCAP.PP.KD", start = YEAR, end=YEAR )
WDIGDPPC1<-as.data.frame(WDIGDPPC1,stringsAsFactors=FALSE)
WDIGDPPC1$iso3<-WD$iso3c[match(WDIGDPPC1$iso2c,WD$iso2c)]
WDIGDPPC2<-cbind(as.vector(WDIGDPPC1$iso3),as.vector(WDIGDPPC1$NY.GDP.PCAP.PP.KD))
colnames(WDIGDPPC2)<-c("iso3","GDPPC")
WDIGDPPC2<-as.data.frame(WDIGDPPC2,stringsAsFactors=FALSE)
WDIFDI1<-WDI::WDI(country="all",indicator = "BN.KLT.DINV.CD", start = YEAR, end=YEAR )
WDIFDI1<-as.data.frame(WDIFDI1,stringsAsFactors=FALSE)
WDIFDI1$iso3<-WD$iso3c[match(WDIFDI1$iso2c,WD$iso2c)]
WDIFDI2<-cbind(as.vector(WDIFDI1$iso3),as.vector(WDIFDI1$BN.KLT.DINV.CD))
colnames(WDIFDI2)<-c("iso3","FDI")
WDIFDI2<-as.data.frame(WDIFDI2,stringsAsFactors=FALSE)
##Country Region is all countries and their regions
# This gives a full list of countries that are aggregates
#AggregatesList<-subset(CountryRegion, REGIONlist %in% "Aggregates")
##Total Exports per country
TotalCountryExports<-subset(FULLel,Receiver %in% "All")
#Grand Total Exports
AllAllTotal<-as.matrix(subset(TotalCountryExports,Sender %in% "All"))
GrandTotal<-as.numeric(AllAllTotal[,3])
Share<-list()
for (i in 1:length(VAL)){
Share[[i]]<-(VAL[i]/GrandTotal)*100
}
Share <-plyr::ldply(Share, data.frame)
#Share<-dplyr::as_data_frame(Share)
colnames(Share)<-"Share"
FULLel<-cbind(FULLel,Share)
G1<-igraph::graph_from_data_frame(FULLel,direct=TRUE)
igraph::E(G1)$weight<-FULLel[,4]
igraph::V(G1)$id<-igraph::V(G1)$name
CountryNames<-igraph::V(G1)$name
## Adds an NA to any countries not cover in WDI data
NotCovered<-subset(CountryNames,!(CountryNames %in% CountryRegion[,1]))
NotCoveredWDI<-subset(CountryNames,!(CountryNames %in% WDIgdp2$iso3))
mm<-matrix("NA",length(NotCovered),2)
mm[,1]<-NotCovered
CountryRegion2<-rbind(CountryRegion,mm)
CountryIncome2<-rbind(CountryIncome,mm)
mm2<-matrix("NA",length(NotCoveredWDI),2)
mm2[,1]<-NotCoveredWDI
colnames(mm2)<-colnames(WDIgdp2)
WDIgdp3<-rbind(WDIgdp2,mm2)
#GDPPC
colnames(mm2)<-colnames(WDIGDPPC2)
WDIGDPPC3<-rbind(WDIGDPPC2,mm2)
#GDPgrowth
colnames(mm2)<-colnames(WDIGDPgrowth2)
WDIGDPgrowth3<-rbind(WDIGDPgrowth2,mm2)
#FDI
colnames(mm2)<-colnames(WDIFDI2)
WDIFDI3<-rbind(WDIFDI2,mm2)
RegionListAttr<-list()
IncomeListAttr<-list()
GDPListattr<-list()
GDPPCListattr<-list()
GDPgrowthListAttr<-list()
FDIListAttr<-list()
for (i in 1:length(CountryNames)){
RegionListAttr[[i]]<-subset(CountryRegion2,COUNTRYlist %in% CountryNames[i])
IncomeListAttr[[i]]<-subset(CountryIncome2,COUNTRYlist %in% CountryNames[i])
GDPListattr[[i]]<-subset(WDIgdp3,WDIgdp3$iso3 %in% CountryNames[i])
GDPPCListattr[[i]]<-subset(WDIGDPPC3,WDIGDPgrowth3$iso3 %in% CountryNames[i])
GDPgrowthListAttr[[i]]<-subset(WDIGDPgrowth3,WDIGDPgrowth3$iso3 %in% CountryNames[i])
FDIListAttr[[i]]<-subset(WDIFDI3,WDIFDI3$iso3 %in% CountryNames[i])
}
dfREG<-plyr::ldply(RegionListAttr, data.frame)
#dfREG<-dplyr::as_data_frame(dfREG)
dfINC<-plyr::ldply(IncomeListAttr,data.frame)
#dfINC<-dplyr::as_data_frame(dfINC)
dfGDP<-plyr::ldply(GDPListattr,data.frame)
#dfGDP<-dplyr::as_data_frame(dfGDP)
dfGDPPC<-plyr::ldply(GDPPCListattr, data.frame)
#dfGDPPC<-dplyr::as_data_frame(dfGDPPC)
dfGDPgrowth<-plyr::ldply(GDPgrowthListAttr, data.frame)
#dfGDPgrowth<-dplyr::as_data_frame(dfGDPgrowth)
dfFDI<-plyr::ldply(FDIListAttr, data.frame)
#dfFDI<-dplyr::as_data_frame(dfFDI)
target<-CountryNames
dfREG<-dfREG[match(target,dfREG$COUNTRYlist),]
RR<-as.vector(dfREG[,2])
RR1<-c(RR)
RR1<-unlist(RR1)
RR2<-as.factor(RR1)
H<-as.character(dfREG$REGIONlist)
dfINC<-dfINC[match(target,dfINC$COUNTRYlist),]
KK<-as.vector(dfINC[,2])
KK1<-c(KK)
KK1<-unlist(KK1)
KK2<-as.factor(KK1)
U<-as.character(dfINC$INCOMElist)
A<-levels(RR2)
B<-1:length(A)
KEY<-cbind(A,B)
Ainc<-levels(KK2)
Binc<-1:length(Ainc)
dfGDP<-dfGDP[match(target,dfGDP$iso3),]
dfGDPPC<-dfGDPPC[match(target,dfGDPPC$iso3),]
dfGDPgrowth<-dfGDPgrowth[match(target,dfGDPgrowth$iso3),]
dfFDI<-dfFDI[match(target,dfFDI$iso3),]
GGDP<-as.vector(dfGDP[,2])
GGDP<-suppressWarnings(as.numeric(GGDP))
GGDPPC<-as.vector(dfGDPPC[,2])
GGDPPC<-suppressWarnings(as.numeric(GGDPPC))
GGDPgrowth<-as.vector(dfGDPgrowth[,2])
GFDI<-as.vector(dfFDI[,2])
IH<-fastmatch::fmatch(KK,Ainc)
CH<-fastmatch::fmatch(RR, A)
igraph::V(G1)$regionNAME<-H
igraph::V(G1)$region<-CH
igraph::V(G1)$income<-IH
igraph::V(G1)$GDP<-GGDP
igraph::V(G1)$GDPPC<-GGDPPC
igraph::V(G1)$logGDP<-log(GGDP)
igraph::V(G1)$logGDPPC<-log(GGDPPC)
igraph::V(G1)$GDPgrowth<-GGDPgrowth
igraph::V(G1)$FDI<-GFDI
#Delete Aggregated Vertices etc
#KEY2<-as.data.frame(KEY,stringsAsFactors=FALSE)
#NAcheck<-"NA" %in% KEY2$A
#Aggrow<-as.data.frame(KEY2[KEY2$A=="Aggregates", ])
#AggNumber<-as.numeric(as.vector(Aggrow$B))
DEL_LIST<-subset(igraph::V(G1)$name,
igraph::V(G1)$name %in% ALL_AGG)
G1<-igraph::delete_vertices(G1,c(DEL_LIST,"All"))
G3<-igraph::delete.vertices(G1, which(is.na(igraph::V(G1)$region)))
#if (NAcheck==TRUE){
# NArow<-as.data.frame(KEY2[KEY2$A=="NA", ])
# NANumber<-as.numeric(as.vector(NArow$B))
# G2<-igraph::delete.vertices(G1, which(igraph::V(G1)$region==NANumber))
# G3<-igraph::delete.vertices(G2, which(igraph::V(G2)$region==AggNumber))
# G3<-igraph::delete.vertices(G2, which(is.na(igraph::V(G2)$region)))
#} else G3<-igraph::delete.vertices(G1, which(is.na(igraph::V(G1)$region)))
#G3<-igraph::delete.vertices(G2, which(igraph::V(G2)$regionNAME=="Aggregates"))
BACK_BONE_PREP<-igraph::get.data.frame(G3,what="vertices")
#Apply the threshold/backbone
if(threshold==TRUE){
G4<-igraph::delete.edges(G3,which(igraph::E(G3)$weight<cutoff))
} else {
G4<-get.backbone(G3,cutoff,TRUE)
BB_ID<-igraph::get.data.frame(G4,what="vertices")
BB_ATTR<-merge(BB_ID,BACK_BONE_PREP,by.all="name",all.x=TRUE,
all.y=FALSE)
BB_ATTR<-BB_ATTR[order(match(BB_ATTR[, "name"],igraph::V(G4)$name)),]
igraph::V(G4)$region<-BB_ATTR$region
igraph::V(G4)$regionNAME<-BB_ATTR$regionNAME
igraph::V(G4)$income<-BB_ATTR$income
igraph::V(G4)$GDP<-BB_ATTR$GDP
igraph::V(G4)$GDPPC<-BB_ATTR$GDPPC
igraph::V(G4)$logGDP<-BB_ATTR$logGDP
igraph::V(G4)$logGDPPC<-BB_ATTR$logGDPPC
igraph::V(G4)$GDPgrowth<-BB_ATTR$GDPgrowth
igraph::V(G4)$FDI<-BB_ATTR$FDI
igraph::V(G4)$id<-BB_ATTR$id
}
G5<-igraph::delete.vertices(G4, which(igraph::degree(G4)==0))
EW<-igraph::E(G5)$weight
EW2<-as.numeric(EW)
G6<-igraph::delete_edge_attr(G5,"weight")
igraph::E(G6)$weight<-EW2
return(G6)
}
MatthewSmith430/ITNr documentation built on April 15, 2024, 8:05 a.m.
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Defines functions WITSclean
Documented in WITSclean
#' @title 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.
#' @param CSVfile WITS csv file
#' @param YEAR Year
#' @param threshold Apply a threshold - TRUE, Extract the backbone - FALSE
#' @param cutoff Threshold - cutoff level, Backbone - significance level
#' @export
#' @return International Trade Network - igraph object
WITSclean<-function(CSVfile,YEAR,threshold,cutoff){
DATAV1<-utils::read.csv(CSVfile)
DATA<-dplyr::filter(DATAV1,DATAV1$Year==YEAR)
Sender<-as.vector(DATA[,"PartnerISO3"])
Sender<- gsub('SER', 'SRB', Sender)
Sender<- gsub('TMP', 'TLS', Sender)
Sender<- gsub('ZAR', 'COD', Sender)
Sender<- gsub('ROM', 'ROU', Sender)
Sender<- gsub('SUD', 'SDN', Sender)
Sender<- gsub('MNT', 'MNE', Sender)
Receiver<-as.vector(DATA[,"ReporterISO3"])
Receiver<- gsub('SER', 'SRB', Receiver)
Receiver<- gsub('TMP', 'TLS', Receiver)
Receiver<- gsub('ZAR', 'COD', Receiver)
Receiver<- gsub('ROM', 'ROU', Receiver)
Receiver<- gsub('SUD', 'SDN', Receiver)
Receiver<- gsub('MNT', 'MNE', Receiver)
VAL<-DATA[,"TradeValue.in.1000.USD"]
FULLel<-data.frame(Sender=Sender,
Receiver=Receiver,
VAL=VAL,stringsAsFactors = FALSE)
WDIDataSeries<-WDI::WDI_data
WDICountryInfo<-WDIDataSeries$country
WD<-as.data.frame(WDICountryInfo,stringsAsFactors = FALSE)
COUNTRYlist<-WDICountryInfo[,"iso3c"]
REGIONlist<-WDICountryInfo[,"region"]
INCOMElist<-WDICountryInfo[,"income"]
CountryRegion<-cbind(COUNTRYlist,REGIONlist)
CountryIncome<-cbind(COUNTRYlist,INCOMElist)
##List all of the aggregate entities.
AggReg<-c("All","EUN","UNS","OAS","FRE","BAT",
"SPE","VAT","UMI","ATA","PCN","AIA","COK",
"SHN","MSR","NIU",
"BES","BLM","BUN","BVT","CCK","CXR","FLK",#Small regions
"HMD","IOT","NFK","SGS","TKL",#small regions
"ESH","SPM","ATF"
)
AggRegMat<-matrix("Aggregates",length(AggReg),2)
AggRegMat[,1]<-AggReg
CountryRegion<-rbind(CountryRegion,AggRegMat)
CountryIncome<-rbind(CountryIncome,AggRegMat)
CR<-as.data.frame(CountryRegion,stringsAsFactors = FALSE)
ALL_AGG<-dplyr::filter(CR,REGIONlist=="Aggregates")
ALL_AGG<-ALL_AGG$COUNTRYlist
ALL_AGG<-as.vector(ALL_AGG)
WDIgdp1<-WDI::WDI(country="all",indicator = "NY.GDP.PCAP.KD", start = YEAR, end=YEAR )
WDIgdp1<-as.data.frame(WDIgdp1,stringsAsFactors=FALSE)
#WDIgdp1<-merge(WD,WDIgdp1,by="iso2c")
WDIgdp1$iso3<-WD$iso3c[match(WDIgdp1$iso2c,WD$iso2c)]
WDIgdp2<-cbind(as.vector(WDIgdp1$iso3),
as.vector(WDIgdp1$NY.GDP.PCAP.KD))
colnames(WDIgdp2)<-c("iso3","GDP")
WDIgdp2<-as.data.frame(WDIgdp2,stringsAsFactors=FALSE)
WDIGDPgrowth1<-WDI::WDI(country="all",indicator = "NY.GDP.MKTP.KD.ZG", start = YEAR, end=YEAR )
WDIGDPgrowth1<-as.data.frame(WDIGDPgrowth1,stringsAsFactors=FALSE)
WDIGDPgrowth1$iso3<-WD$iso3c[match(WDIGDPgrowth1$iso2c,WD$iso2c)]
WDIGDPgrowth2<-cbind(as.vector(WDIGDPgrowth1$iso3),
as.vector(WDIGDPgrowth1$NY.GDP.MKTP.KD.ZG))
colnames(WDIGDPgrowth2)<-c("iso3","GDPgrowth")
WDIGDPgrowth2<-as.data.frame(WDIGDPgrowth2,stringsAsFactors=FALSE)
WDIGDPPC1<-WDI::WDI(country="all",indicator = "NY.GDP.PCAP.PP.KD", start = YEAR, end=YEAR )
WDIGDPPC1<-as.data.frame(WDIGDPPC1,stringsAsFactors=FALSE)
WDIGDPPC1$iso3<-WD$iso3c[match(WDIGDPPC1$iso2c,WD$iso2c)]
WDIGDPPC2<-cbind(as.vector(WDIGDPPC1$iso3),as.vector(WDIGDPPC1$NY.GDP.PCAP.PP.KD))
colnames(WDIGDPPC2)<-c("iso3","GDPPC")
WDIGDPPC2<-as.data.frame(WDIGDPPC2,stringsAsFactors=FALSE)
WDIFDI1<-WDI::WDI(country="all",indicator = "BN.KLT.DINV.CD", start = YEAR, end=YEAR )
WDIFDI1<-as.data.frame(WDIFDI1,stringsAsFactors=FALSE)
WDIFDI1$iso3<-WD$iso3c[match(WDIFDI1$iso2c,WD$iso2c)]
WDIFDI2<-cbind(as.vector(WDIFDI1$iso3),as.vector(WDIFDI1$BN.KLT.DINV.CD))
colnames(WDIFDI2)<-c("iso3","FDI")
WDIFDI2<-as.data.frame(WDIFDI2,stringsAsFactors=FALSE)
##Country Region is all countries and their regions
# This gives a full list of countries that are aggregates
#AggregatesList<-subset(CountryRegion, REGIONlist %in% "Aggregates")
##Total Exports per country
TotalCountryExports<-subset(FULLel,Receiver %in% "All")
#Grand Total Exports
AllAllTotal<-as.matrix(subset(TotalCountryExports,Sender %in% "All"))
GrandTotal<-as.numeric(AllAllTotal[,3])
Share<-list()
for (i in 1:length(VAL)){
Share[[i]]<-(VAL[i]/GrandTotal)*100
}
Share <-plyr::ldply(Share, data.frame)
#Share<-dplyr::as_data_frame(Share)
colnames(Share)<-"Share"
FULLel<-cbind(FULLel,Share)
G1<-igraph::graph_from_data_frame(FULLel,direct=TRUE)
igraph::E(G1)$weight<-FULLel[,4]
igraph::V(G1)$id<-igraph::V(G1)$name
CountryNames<-igraph::V(G1)$name
## Adds an NA to any countries not cover in WDI data
NotCovered<-subset(CountryNames,!(CountryNames %in% CountryRegion[,1]))
NotCoveredWDI<-subset(CountryNames,!(CountryNames %in% WDIgdp2$iso3))
mm<-matrix("NA",length(NotCovered),2)
mm[,1]<-NotCovered
CountryRegion2<-rbind(CountryRegion,mm)
CountryIncome2<-rbind(CountryIncome,mm)
mm2<-matrix("NA",length(NotCoveredWDI),2)
mm2[,1]<-NotCoveredWDI
colnames(mm2)<-colnames(WDIgdp2)
WDIgdp3<-rbind(WDIgdp2,mm2)
#GDPPC
colnames(mm2)<-colnames(WDIGDPPC2)
WDIGDPPC3<-rbind(WDIGDPPC2,mm2)
#GDPgrowth
colnames(mm2)<-colnames(WDIGDPgrowth2)
WDIGDPgrowth3<-rbind(WDIGDPgrowth2,mm2)
#FDI
colnames(mm2)<-colnames(WDIFDI2)
WDIFDI3<-rbind(WDIFDI2,mm2)
RegionListAttr<-list()
IncomeListAttr<-list()
GDPListattr<-list()
GDPPCListattr<-list()
GDPgrowthListAttr<-list()
FDIListAttr<-list()
for (i in 1:length(CountryNames)){
RegionListAttr[[i]]<-subset(CountryRegion2,COUNTRYlist %in% CountryNames[i])
IncomeListAttr[[i]]<-subset(CountryIncome2,COUNTRYlist %in% CountryNames[i])
GDPListattr[[i]]<-subset(WDIgdp3,WDIgdp3$iso3 %in% CountryNames[i])
GDPPCListattr[[i]]<-subset(WDIGDPPC3,WDIGDPgrowth3$iso3 %in% CountryNames[i])
GDPgrowthListAttr[[i]]<-subset(WDIGDPgrowth3,WDIGDPgrowth3$iso3 %in% CountryNames[i])
FDIListAttr[[i]]<-subset(WDIFDI3,WDIFDI3$iso3 %in% CountryNames[i])
}
dfREG<-plyr::ldply(RegionListAttr, data.frame)
#dfREG<-dplyr::as_data_frame(dfREG)
dfINC<-plyr::ldply(IncomeListAttr,data.frame)
#dfINC<-dplyr::as_data_frame(dfINC)
dfGDP<-plyr::ldply(GDPListattr,data.frame)
#dfGDP<-dplyr::as_data_frame(dfGDP)
dfGDPPC<-plyr::ldply(GDPPCListattr, data.frame)
#dfGDPPC<-dplyr::as_data_frame(dfGDPPC)
dfGDPgrowth<-plyr::ldply(GDPgrowthListAttr, data.frame)
#dfGDPgrowth<-dplyr::as_data_frame(dfGDPgrowth)
dfFDI<-plyr::ldply(FDIListAttr, data.frame)
#dfFDI<-dplyr::as_data_frame(dfFDI)
target<-CountryNames
dfREG<-dfREG[match(target,dfREG$COUNTRYlist),]
RR<-as.vector(dfREG[,2])
RR1<-c(RR)
RR1<-unlist(RR1)
RR2<-as.factor(RR1)
H<-as.character(dfREG$REGIONlist)
dfINC<-dfINC[match(target,dfINC$COUNTRYlist),]
KK<-as.vector(dfINC[,2])
KK1<-c(KK)
KK1<-unlist(KK1)
KK2<-as.factor(KK1)
U<-as.character(dfINC$INCOMElist)
A<-levels(RR2)
B<-1:length(A)
KEY<-cbind(A,B)
Ainc<-levels(KK2)
Binc<-1:length(Ainc)
dfGDP<-dfGDP[match(target,dfGDP$iso3),]
dfGDPPC<-dfGDPPC[match(target,dfGDPPC$iso3),]
dfGDPgrowth<-dfGDPgrowth[match(target,dfGDPgrowth$iso3),]
dfFDI<-dfFDI[match(target,dfFDI$iso3),]
GGDP<-as.vector(dfGDP[,2])
GGDP<-suppressWarnings(as.numeric(GGDP))
GGDPPC<-as.vector(dfGDPPC[,2])
GGDPPC<-suppressWarnings(as.numeric(GGDPPC))
GGDPgrowth<-as.vector(dfGDPgrowth[,2])
GFDI<-as.vector(dfFDI[,2])
IH<-fastmatch::fmatch(KK,Ainc)
CH<-fastmatch::fmatch(RR, A)
igraph::V(G1)$regionNAME<-H
igraph::V(G1)$region<-CH
igraph::V(G1)$income<-IH
igraph::V(G1)$GDP<-GGDP
igraph::V(G1)$GDPPC<-GGDPPC
igraph::V(G1)$logGDP<-log(GGDP)
igraph::V(G1)$logGDPPC<-log(GGDPPC)
igraph::V(G1)$GDPgrowth<-GGDPgrowth
igraph::V(G1)$FDI<-GFDI
#Delete Aggregated Vertices etc
#KEY2<-as.data.frame(KEY,stringsAsFactors=FALSE)
#NAcheck<-"NA" %in% KEY2$A
#Aggrow<-as.data.frame(KEY2[KEY2$A=="Aggregates", ])
#AggNumber<-as.numeric(as.vector(Aggrow$B))
DEL_LIST<-subset(igraph::V(G1)$name,
igraph::V(G1)$name %in% ALL_AGG)
G1<-igraph::delete_vertices(G1,c(DEL_LIST,"All"))
G3<-igraph::delete.vertices(G1, which(is.na(igraph::V(G1)$region)))
#if (NAcheck==TRUE){
# NArow<-as.data.frame(KEY2[KEY2$A=="NA", ])
# NANumber<-as.numeric(as.vector(NArow$B))
# G2<-igraph::delete.vertices(G1, which(igraph::V(G1)$region==NANumber))
# G3<-igraph::delete.vertices(G2, which(igraph::V(G2)$region==AggNumber))
# G3<-igraph::delete.vertices(G2, which(is.na(igraph::V(G2)$region)))
#} else G3<-igraph::delete.vertices(G1, which(is.na(igraph::V(G1)$region)))
#G3<-igraph::delete.vertices(G2, which(igraph::V(G2)$regionNAME=="Aggregates"))
BACK_BONE_PREP<-igraph::get.data.frame(G3,what="vertices")
#Apply the threshold/backbone
if(threshold==TRUE){
G4<-igraph::delete.edges(G3,which(igraph::E(G3)$weight<cutoff))
} else {
G4<-get.backbone(G3,cutoff,TRUE)
BB_ID<-igraph::get.data.frame(G4,what="vertices")
BB_ATTR<-merge(BB_ID,BACK_BONE_PREP,by.all="name",all.x=TRUE,
all.y=FALSE)
BB_ATTR<-BB_ATTR[order(match(BB_ATTR[, "name"],igraph::V(G4)$name)),]
igraph::V(G4)$region<-BB_ATTR$region
igraph::V(G4)$regionNAME<-BB_ATTR$regionNAME
igraph::V(G4)$income<-BB_ATTR$income
igraph::V(G4)$GDP<-BB_ATTR$GDP
igraph::V(G4)$GDPPC<-BB_ATTR$GDPPC
igraph::V(G4)$logGDP<-BB_ATTR$logGDP
igraph::V(G4)$logGDPPC<-BB_ATTR$logGDPPC
igraph::V(G4)$GDPgrowth<-BB_ATTR$GDPgrowth
igraph::V(G4)$FDI<-BB_ATTR$FDI
igraph::V(G4)$id<-BB_ATTR$id
}
G5<-igraph::delete.vertices(G4, which(igraph::degree(G4)==0))
EW<-igraph::E(G5)$weight
EW2<-as.numeric(EW)
G6<-igraph::delete_edge_attr(G5,"weight")
igraph::E(G6)$weight<-EW2
return(G6)
}
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