Nota de los autores: Las instrucciones que encontrará a continuación no contienen acentos ortográficos dado que al ejecutar las instrucciones en el programa estás no serían reconocidas. Le deseamos una buena práctica.

# ANEXO No 1 
#2. SCRIPT PARA REALIZAR ANALISIS BIBLIOMETRICO CON CODIGO DE R

#Se limpia la memoria y se carga la libreria bibliometrix

	rm(list = ls())
	library(bibliometrix)
# Situación 1. Se cuenta con un solo arcchivo de Wos y de Scopus


	M1 <- convert2df("scopus.bib", dbsource="scopus",format="bibtex")
	M2 <- convert2df("WoS.txt", dbsource="wos",format="plaintext")

	M <- mergeDbSources(M1, M2, remove.duplicated=TRUE)


Situación 2. Se cuenta con más de un archivo en WoS y Scopus

	M1 <- convert2df("scopus1.bib", dbsource="scopus",format="bibtex")
	M2 <- convert2df("scopus2.bib", dbsource="scopus",format="bibtex")

#Se leen los archivos de WoS"

	M3 <- convert2df("WoS1.txt", dbsource="wos",format="plaintext")
	M4 <- convert2df("WoS2.txt", dbsource="wos",format="plaintext")
	M5 <- convert2df("WoS3.txt", dbsource="wos",format="plaintext")

# Se combinan los archivos y se eliminan los duplicados	

	M <- mergeDbSources(M1, M2, M3,M4,M5,remove.duplicated=TRUE)
	
#3. Se procede a dar inicio al analisis Bibliometrico con codigo R"

#3.1 Para dar Inicio al analisis se obtiene un resumen de las estadisticas, con las
siguientes alternativas:

	results <- biblioAnalysis(M, sep = ";")

	options(width=100)
	S <- summary(object = results, k = 10, pause = FALSE)

#3.1.2 Se efectuan los graficos de la informacion suministrada en los incisos anteriores

	graph<-plot(x = results, k = 10, pause = FALSE)
	graph$MostProdAuthors
	graph$MostProdCountries
	graph$AnnualScientProd
	graph$AverArtCitperYear
	graph$AverTotCitperYear

#3.2 Se obtienen los articulos y autores mas citados 

#3.2.1 Se determinan los articulos mas citados	

	CR <- citations(M, field = "article", sep = ";")
	cbind(CR$Cited[1:10])
	

#3.2.2 Se obtienen los autores mas citados 
 
	CR <- citations(M, field = "author", sep = ";")
	cbind(CR$Cited[1:10])


#3.2.3 Suministra la informacion de los autores y articulos locales  mas citados

	CR <- localCitations(M, sep = ";")
	CR$Papers[1:10,]
	CR$Authors[1:10,]
	
#3.2.4 Numero de documentos publicados anualmente por fuentes

	library(reshape2)
	library(ggplot2)

	SG<-sourceGrowth(M, top = 5, cdf = TRUE)

	df<-melt(SG, id='Year')
	ggplot(df,aes(Year, value, group=variable, color=variable))+geom_line()+theme(legend.position = "bottom")+ guides(color = guide_legend(ncol = 1,
	byrow = F,title=""))

	row.names(SG)<-NULL
	View(SG)

#3.3 Indice G,H,M

	authors<- gsub(","," ",names(results$Authors)[1:15])
	indices <- Hindex(M, field = "author", elements=authors, sep = ";", years = 50)
	indices$H

	Fuente <- gsub(","," ",names(results$Source)[1:10])
	indices <- Hindex(M, field = "source", elements= Fuente, sep = ";", years = 50)
	indices$H

#Para conocer el indice de algun autor en particular, en este caso se hace para WANG Y", ZHOU Y

	indices <- Hindex(M, field = "author", elements=c("WANG Y","ZHOU Y"), sep = ";", years = 10)
	indices$H

#3.4 Autores mas productivos a traves del tiempo

	topAU <- authorProdOverTime(M, k = 10, graph = TRUE)
	View(topAU$dfAU) #Cuadro autores
	View(topAU$dfPapersAU) #Cuadro artículos

#3.5 Ranking del factor de dominancia de los autores

	DF <- dominance(results, k = 10)
	DF

#3.6 Coeficiente de Lotka

	L <- lotka(results)
	L
	L$Beta
	L$C
	L$R2
	L$p.value
	L$fitted
	L$AuthorProd

#3.7 Se hace la comparacin de la distribucion de valores observados con la distribucion teorica

	Observed=L$AuthorProd[,3]
	Theoretical=10^(log10(L$C)-2*log10(L$AuthorProd[,1]))

	plot(L$AuthorProd[,1], Theoretical, type="l", col="red", ylim=c(0,1), xlab="Articles", ylab= "Freq. of Authors", main="Scientific Productivity")
	lines(L$AuthorProd[,1], Observed, col="blue")
	legend(x="topright", c("Theoretical (B=2)","Observed"), col=c("red","blue"), lty=c(1,1,1), cex=0.6, bty="n")

#3.8 Creación y trazado del mapa de la estructura conceptual de un campo científicoc

	cs <- conceptualStructure(M,field = "ID",method = "CA",minDegree = 5,clust = 5,  "auto",
	k.max = 5,stemming = FALSE,labelsize = 10,documents = 2,graph = TRUE)


3.8.1 Teniendo claro lo anterior, las instrucciones a ejecutar son las siguientes:

	CS<- conceptualStructure(M,field="ID", method="CA", minDegree=5,clust=5, stemming = FALSE, labelsize= 10, documents=10)
	CS$graph_terms
	CS$graph_documents_Contrib
	CS$graph_documents_TC

	CS <- conceptualStructure(M,field="ID", method="MCA", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)
	CS$graph_terms
	CS$graph_documents_Contrib
	CS$graph_documents_TC

	CS <- conceptualStructure(M,field="ID", method="MDS", minDegree=4, clust=5, stemming=FALSE, labelsize=10, documents=10)
	CS$graph_terms
	CS$graph_documents_Contrib
	CS$graph_documents_TC

#3.9. Ley de bradford

	X<-bradford(M)
	table<-X$table
	row.names(table)<-NULL
	View(head(table,10))

#3.10. Temas de tendencia

	res <- fieldByYear(M, field = "DE", min.freq = 50, n.items = 3, graph = TRUE)

#3.11 Aparicion por años de las palabras claves mas importantes

	topKW=KeywordGrowth(M, Tag = "DE", sep = ";", top=10, cdf=TRUE)
	topKW

#3.12 Raices historicas del tema

	res <- rpys(M, sep=";", graph = TRUE)
	res
 	
#3.13. Mapa de agrupacion de palabras claves 

	res <- thematicMap(M, field = "ID", n = 250, minfreq = 5, size = 0.5, repel = TRUE, stemming = 	FALSE, n.labels = 3)
	plot(res$map)
	View(res$clusters)

#3.14 Diagrma de Sankey
# Relacion entre palabras claves, autores y referencias citadas con el diagrama de Sankey

threeFieldsPlot(M, fields=c("DE","AU","CR"),n=c(20,20,20))#Abre el grafico en el navegador
threeFieldsPlot(M, fields=c("AU","DE","CR"),n=c(20,20,20))#Abre el grafico en el navegador
threeFieldsPlot(M, fields=c("AU","CR","DE"),n=c(20,20,20))#Abre el grafico en el navegador
threeFieldsPlot(M, fields=c("AU","SO","DE"),n=c(20,20,20))#Abre el grafico en el navegador

#3.15. Analisis de acoplamiento

	CM <-couplingMap(M, analysis = "documents", field = "CR", n = 100, impact.measure = "global", minfreq = 5, stemming = FALSE, size = 0.5, n.labels = 2, repel = TRUE)
	CM$map
	View(CM$clusters)

	CM <-couplingMap(M, analysis = "authors", field = "CR", n = 500, impact.measure = "global", minfreq = 5, stemming = FALSE, size = 0.5, n.labels = 1, repel = TRUE)
	CM$map
	View(CM$clusters)

	CM <-couplingMap(M, analysis = "sources", field = "CR", n = 500, impact.measure = "global", minfreq = 5, stemming = FALSE, size = 0.5, n.labels = 1, repel = TRUE)
	CM$map
	View(CM$clusters)

#4. REDES BIBLIOGRAFICA
#4.1 Se crea el historial de citacion

	options(width=130)
	histResults <- histNetwork(M, min.citations = 1, sep = ";")
	net <- histPlot(histResults, n=15, size = 10, labelsize=5)

#4.2. Redes de co-citacion
#4.2.1 Cocotación de autores

	A<-metaTagExtraction(M,"CR_AU",sep=";")
	NetMatrix <- biblioNetwork(A, analysis = "co-citation", network = "authors", sep = ";")
	net <- networkPlot(NetMatrix, n = 45, Title = "Co-Citación de Autores", type = "fruchterman", size.cex=TRUE, size=20,labelsize=0.8,label.n=10, edgesize=5, edges.min=5, remove.isolates=TRUE)

#4.2.2 Co-Citacion de Referencias

	NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
	net <- networkPlot(NetMatrix, n = 25, Title = "Co-Citación de Referencias", type = "fruchterman", size.cex=TRUE, size=20,labelsize=0.8,label.n=20, edgesize=5, edges.min=5, remove.isolates=TRUE,halo=FALSE)

#4.2.3 Co-Citacion de Fuentes

	S<-metaTagExtraction(M,"CR_SO",sep=";")
	NetMatrix <- biblioNetwork(S, analysis = "co-citation", network = "sources", sep = ";")
	net <- networkPlot(NetMatrix, n = 45, Title = "Co-Citacion de Fuentes", type = "fruchterman", size.cex=TRUE, size=20,labelsize=0.8, label.n=10, edgesize=5,  edges.min=5, remove.isolates=TRUE)

#4.3. Redes de colaboracion

#4.3.1. Colaboracion Entre Paises

	C<-metaTagExtraction(M,"AU_CO",sep=";")
	NetMatrix <- biblioNetwork(C, analysis = "collaboration", network = "countries", sep = ";")
	net <- networkPlot(NetMatrix, n = 20, Title = "Colaboración entre países", type = "circle", size=TRUE, remove.multiple=FALSE,labelsize=0.7,cluster="none")

#4.3.2. Colaboracion Entre Autores

	NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
	net <- networkPlot(NetMatrix, n = 30, Title = "Colaboración entre autores", type = "auto", size=TRUE, remove.multiple=FALSE,
	labelsize=0.7,cluster="none")
	
#4.3.3. Colaboracion Entre Universidades

	NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "universities", sep = ";")
	net <- networkPlot(NetMatrix, n = 20, Title = "Colaboracion entre universidades", type = "circle",
	size=TRUE, remove.multiple=FALSE, labelsize=0.7, cluster="none")


#4.4. Redes de acoplamiento

#4.4.1. Acoplamiento de Autores

	NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "authors", sep = ";")
	net <- networkPlot(NetMatrix, normalize = "salton", n = 30, Title = "Acoplamiento de autores",
	type = "fruchterman", size=5, size.cex=TRUE, labelsize=0.8, edges.min=5, remove.isolates=TRUE)

#4.4.2. Acoplamiento de Referencias

	NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "references", sep = ";")
	net <- networkPlot(NetMatrix, normalize = "salton", n = 30, Title = "Acoplamiento de referencias",
	type = "fruchterman", size=5,size.cex=TRUE, labelsize=0.8, edges.min=5, remove.isolates=TRUE)

#4.4.3. Acoplamiento de Fuentes

	NetMatrix <- biblioNetwork(M, analysis = "coupling", network = "sources", sep = ";")
	net <- networkPlot(NetMatrix,  normalize = "salton", n = 20, Title = "Acoplamiento de fuentes",
	type = "fruchterman", size=5, size.cex=TRUE, labelsize=0.8, edges.min=5, remove.isolates=TRUE)

#4.4.4. Acoplamiento de Paises 

	P<-metaTagExtraction(M,"AU_CO", sep=";")
	NetMatrix <- biblioNetwork(P, analysis = "coupling", network = "countries", sep = ";")
	net <- networkPlot(NetMatrix,  normalize = "salton", n = 30, Title = "Acoplamiento de paises", 
	type = "fruchterman", size=5, size.cex=TRUE, labelsize=0.8, edges.min=5, remove.isolates=TRUE)


#4.5. Redes de co-ocurrencias

#4.5.1. Co-Ocurrencias Palabras Claves 

	NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
	net <- networkPlot(NetMatrix, normalize = "association", n = 30, Title = "Co-Ocurrencias palabras claves",
	type = "fruchterman", size=5,size.cex=TRUE, labelsize=0.8, edges.min=5, remove.isolates=TRUE)

#4.5.2. Co-Ocurrencias Palabras Claves del Autor

	NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "author_keywords", sep = ";")
	net <- networkPlot(NetMatrix, normalize = "association", n = 30, Title = "Co-Ocurrencias palabras claves del autor",
	type = "fruchterman", size=5,size.cex =TRUE,labelsize=0.8, edges.min=5, remove.isolates=TRUE)

#4.5.3. Co-Ocurrencias Autores

	NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "authors", sep = ";")
	net <- networkPlot(NetMatrix, normalize = "association", n = 30, Title = "Co-Ocurrencias autores",
	type = "fruchterman", size=5,size.cex=TRUE,labelsize=0.8, edges.min=5, remove.isolates=TRUE)

#4.6 Resumen de estadisticas de una red bibliografica 

	netstat <- networkStat(NetMatrix, stat = "all", type = " betweenness ")
	summary(netstat,k=10)

#4.7 ARBOL DE LA CIENCIA (Esta instruccion tarda un poco en generar resultados por lo que le solicitamos tener un poco de paciencia)

	library(tosr)
	ToS <- tosR("scopus.bib","WoS.txt")
	View(ToS)


#4.7.1 Exportar datos del arbol de la ciencia a excel

	#4.7 ARBOL DE LA CIENCIA (Esta instruccion tarda un poco en generar resultados por lo que le solicitamos tener un poco de paciencia)

	library(openxlsx)
	write.xlsx(ToS, "Arbol de la ciencia.xlsx",col.names = TRUE, row.names = F)

#4.7.1 Exportar datos del arbol de la ciencia a Excel

	library(openxlsx)
	write.xlsx(ToS, "Arbol de la ciencia.xlsx",col.names = TRUE, row.names = F)