library(igraph)##
## Attaching package: 'igraph'## The following objects are masked from 'package:stats':
##
## decompose, spectrum## The following object is masked from 'package:base':
##
## uniona <- read.csv("/Users/Akwelle/Desktop/scopus-coauthor-deliverables.csv")
g <- graph.data.frame(a[,c("Authors", "Title", "Affiliations","Year", "Source.title")])V(g)$type <- V(g)$name %in% a[,c("Authors")]
table(V(g)$type)[2]## TRUE
## 506i<-table(V(g)$type)[2]mat <- t(get.incidence(g))
mat <- mat%*%t(mat)
dim(mat)## [1] 506 506g<-graph.adjacency(mat, mode = "undirected", weighted = TRUE, diag=F)cent<-data.frame(bet=betweenness(g, normalized=T, directed = FALSE)/max(betweenness(g, normalized=T, directed = FALSE)),eig=evcent(g)$vector, degree=degree(g, mode="total"))
cent$name<-rownames(cent) #Ids in this case
head(cent);tail(cent)## bet eig degree
## Yi J.S., Garcia Morales E.E., Reed N.S., Willink A., Nieman C.L. NaN 1 0
## Li Y., Fu X., Guo X., Liang H., Cao D., Shi J. NaN 1 0
## Poe J. NaN 1 0
## Kang H., Lyu S.-R., Yun S. NaN 1 0
## Brown J.R., Hilbig H. NaN 1 0
## O'Hearn M., Lauren B.N., Wong J.B., Kim D.D., Mozaffarian D. NaN 1 0
## name
## Yi J.S., Garcia Morales E.E., Reed N.S., Willink A., Nieman C.L. Yi J.S., Garcia Morales E.E., Reed N.S., Willink A., Nieman C.L.
## Li Y., Fu X., Guo X., Liang H., Cao D., Shi J. Li Y., Fu X., Guo X., Liang H., Cao D., Shi J.
## Poe J. Poe J.
## Kang H., Lyu S.-R., Yun S. Kang H., Lyu S.-R., Yun S.
## Brown J.R., Hilbig H. Brown J.R., Hilbig H.
## O'Hearn M., Lauren B.N., Wong J.B., Kim D.D., Mozaffarian D. O'Hearn M., Lauren B.N., Wong J.B., Kim D.D., Mozaffarian D.## bet eig
## Preston B.L., Warren R.C., Stewart P. NaN 1
## Li B.D.L., Brown W.A., Ampil F.L., Burton G.V., Yu H., McDonald J.C. NaN 1
## Sawyer T.F. NaN 1
## Schaumberg D.A., Christen W.G., Glynn R.J., Buring J.E. NaN 1
## Edwards A.B., Cherry R.L., Peterson J. NaN 1
## Edwards J.C., Maldonado Jr. F.G., Engelgau G.R. NaN 1
## degree
## Preston B.L., Warren R.C., Stewart P. 0
## Li B.D.L., Brown W.A., Ampil F.L., Burton G.V., Yu H., McDonald J.C. 0
## Sawyer T.F. 0
## Schaumberg D.A., Christen W.G., Glynn R.J., Buring J.E. 0
## Edwards A.B., Cherry R.L., Peterson J. 0
## Edwards J.C., Maldonado Jr. F.G., Engelgau G.R. 0
## name
## Preston B.L., Warren R.C., Stewart P. Preston B.L., Warren R.C., Stewart P.
## Li B.D.L., Brown W.A., Ampil F.L., Burton G.V., Yu H., McDonald J.C. Li B.D.L., Brown W.A., Ampil F.L., Burton G.V., Yu H., McDonald J.C.
## Sawyer T.F. Sawyer T.F.
## Schaumberg D.A., Christen W.G., Glynn R.J., Buring J.E. Schaumberg D.A., Christen W.G., Glynn R.J., Buring J.E.
## Edwards A.B., Cherry R.L., Peterson J. Edwards A.B., Cherry R.L., Peterson J.
## Edwards J.C., Maldonado Jr. F.G., Engelgau G.R. Edwards J.C., Maldonado Jr. F.G., Engelgau G.R.cent$bet<-cent$bet/max(cent$bet)
#cent$eig<-cent$eig/max(cent$eig)
summary(cent)## bet eig degree name
## Min. : NA Min. :1 Min. :0 Length:506
## 1st Qu.: NA 1st Qu.:1 1st Qu.:0 Class :character
## Median : NA Median :1 Median :0 Mode :character
## Mean :NaN Mean :1 Mean :0
## 3rd Qu.: NA 3rd Qu.:1 3rd Qu.:0
## Max. : NA Max. :1 Max. :0
## NA's :506actors<- a[!duplicated(a$Authors), c("Authors", "Title", "ISSN", "Publisher", "Year")]V(g)$label<-V(g)$name
V(g)$name<-1:length(V(g))links<-as.data.frame(cbind(get.edgelist(g), E(g)$Year, E(g)$Affiliations))links$V1<-as.numeric(as.character(links$V1))
links$V2<-as.numeric(as.character(links$V2))
links$V3<-round(as.numeric(as.character(links$V3)),3)colnames(links)<-c("source","target", "value", "affiliations")
links[,1:2]<-(links[,1:2]-1)V(g)$ISSN <- actors$ISSN[match(V(g)$label, actors$Authors)]
V(g)$Publisher <- actors$Publisher[match(V(g)$label, actors$Authors)]
V(g)$Year <- actors$Year[match(V(g)$label, actors$Authors)]summary(V(g)$Publisher)## Length Class Mode
## 506 character charactersummary(actors$Year)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2000 2011 2016 2015 2020 2022summary(V(g)$Year[1:i])## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2000 2011 2016 2015 2020 2022nodes <- data.frame(name= c(paste("ID: ", V(g)$label,
", ISSN: ", V(g)$ISSN,
", First Gen. College: ", V(g)$firstgen,
", Publisher: ", V(g)$Publisher, sep="")),
Year = abs(V(g)$Year),
size=abs(V(g)$Year))
nodes$name<-as.character(nodes$name)nodes$group<-NA
nodes$group[1:i]<-cut(nodes$Year[1:i], c(2000,2005,2010,2015,max(nodes$Year[1:i])), right=TRUE)
table(is.na(nodes$group))##
## FALSE TRUE
## 499 7table(nodes$group)##
## 1 2 3 4
## 36 79 125 259head(nodes[is.na(nodes$group),],20)## name
## 500 ID: Sternfeld B., Cauley J., Harlow S., Liu G., Lee M., ISSN: 00029262, First Gen. College: , Publisher:
## 501 ID: Preston B.L., Warren R.C., Stewart P., ISSN: 07493797, First Gen. College: , Publisher:
## 502 ID: Li B.D.L., Brown W.A., Ampil F.L., Burton G.V., Yu H., McDonald J.C., ISSN: 00034932, First Gen. College: , Publisher:
## 503 ID: Sawyer T.F., ISSN: 10934510, First Gen. College: , Publisher:
## 504 ID: Schaumberg D.A., Christen W.G., Glynn R.J., Buring J.E., ISSN: 00257079, First Gen. College: , Publisher: Lippincott Williams and Wilkins
## 505 ID: Edwards A.B., Cherry R.L., Peterson J., ISSN: 15333175, First Gen. College: , Publisher:
## 506 ID: Edwards J.C., Maldonado Jr. F.G., Engelgau G.R., ISSN: 10402446, First Gen. College: , Publisher: Association of American Medical Colleges
## Year size group
## 500 2000 2000 NA
## 501 2000 2000 NA
## 502 2000 2000 NA
## 503 2000 2000 NA
## 504 2000 2000 NA
## 505 2000 2000 NA
## 506 2000 2000 NAnodes$group<-ifelse(is.na(nodes$group), "Year", ifelse(nodes$group==1, "2000-2005", ifelse(nodes$group==2, "2005-2010", ifelse(nodes$group==3, "2010=2015", "after 2015"))))
counts<-data.frame(table(nodes$group))
counts$labels <- paste(counts$Var1, ", N= ", counts$Freq, sep="")
nodes$groups <- counts$labels[match(nodes$group, counts$Var1)]
head(nodes)## name
## 1 ID: Yi J.S., Garcia Morales E.E., Reed N.S., Willink A., Nieman C.L., ISSN: 08982643, First Gen. College: , Publisher: SAGE Publications Inc.
## 2 ID: Li Y., Fu X., Guo X., Liang H., Cao D., Shi J., ISSN: 14712431, First Gen. College: , Publisher: NLM (Medline)
## 3 ID: Poe J., ISSN: 21623104, First Gen. College: , Publisher: STAR Scholars Network
## 4 ID: Kang H., Lyu S.-R., Yun S., ISSN: 21623104, First Gen. College: , Publisher: University Printing Services
## 5 ID: Brown J.R., Hilbig H., ISSN: 00071234, First Gen. College: , Publisher: Cambridge University Press
## 6 ID: O'Hearn M., Lauren B.N., Wong J.B., Kim D.D., Mozaffarian D., ISSN: 07351097, First Gen. College: , Publisher: Elsevier Inc.
## Year size group groups
## 1 2022 2022 after 2015 after 2015, N= 259
## 2 2022 2022 after 2015 after 2015, N= 259
## 3 2022 2022 after 2015 after 2015, N= 259
## 4 2022 2022 after 2015 after 2015, N= 259
## 5 2022 2022 after 2015 after 2015, N= 259
## 6 2022 2022 after 2015 after 2015, N= 259library(networkD3)
library(magrittr)
library(htmlwidgets)##
## Attaching package: 'htmlwidgets'## The following object is masked from 'package:networkD3':
##
## JSlibrary(htmltools)
netviz<-forceNetwork(Links = links, Nodes = nodes,
Source = 'source', Target = 'target',
NodeID = 'name',
Group = "groups", # color nodes by group calculated earlier
charge = -30, # node repulsion
linkDistance = JS("function(d) { return d.linkDistance; }"),#JS("function(d){return d.value}"),
linkWidth = JS("function(d) { return Math.sqrt(d.value)*2; }"),
opacity = 0.8,
Value = "value",
Nodesize = 'size',
radiusCalculation = JS("Math.sqrt(d.nodesize*30)+4"),
zoom = T,
fontSize=14,
bounded= F,
legend= TRUE,
linkColour = ifelse(links$value == 1, "#CCFFFF", ifelse(links$value == 2, "#e3eaa7", "#abb2b9 ")),
colourScale = JS("d3.scaleOrdinal(d3.schemeCategory10)"))## Warning: It looks like Source/Target is not zero-indexed. This is required in
## JavaScript and so your plot may not render.HTMLaddons <-
"function(el, x) {
d3.select('body').style('background-color', ' #212f3d ')
d3.selectAll('.legend text').style('fill', 'white')
d3.selectAll('.link').append('svg:title')
.text(function(d) { return 'No. of courses common: ' + d.value + ', Campus: ' + d.campus ; })
var options = x.options;
var svg = d3.select(el).select('svg')
var node = svg.selectAll('.node');
var link = svg.selectAll('link');
var mouseout = d3.selectAll('.node').on('mouseout');
function nodeSize(d) {
if (options.nodesize) {
return eval(options.radiusCalculation);
} else {
return 6;
}
}
d3.selectAll('.node').on('click', onclick)
function onclick(d) {
if (d3.select(this).on('mouseout') == mouseout) {
d3.select(this).on('mouseout', mouseout_clicked);
} else {
d3.select(this).on('mouseout', mouseout);
}
}
function mouseout_clicked(d) {
node.style('opacity', +options.opacity);
link.style('opacity', +options.opacity);
d3.select(this).select('circle').transition()
.duration(750)
.attr('r', function(d){return nodeSize(d);});
d3.select(this).select('text').transition()
.duration(1250)
.attr('x', 0)
.style('font', options.fontSize + 'px ');
}
}
"
netviz$x$links$linkDistance <- (1/links$value)*50
netviz$x$links$campus <- links$value
onRender(netviz, HTMLaddons)We should care about this because it allows us to see how the different actors in the network interact with one another based on a variety of characteristics, which can help us better understand the implications of the network.