bibliometrix (version 2.0.1)

thematicEvolution: Create an Evolution Thematic Map

Description

It creates an Evolution thematic map based on co-word network analysis and clustering. The methodology is inspired by the proposal of Cobo et al. (2011).

Usage

thematicEvolution(..., weighted = FALSE, labelsize = 0.5, size = 1)

Arguments

...

is a sequence of names of thematic maps created by thematicMap function.

weighted

is a logical. If FALSE, a thematic nexus is measures by the classical inclusion index (calculated using the number of keywords). If TRUE, the inclusion index is calculated considering the occurrences of keywords.

labelsize

is numerical

size

is numerical

Value

a list containing:

nets

The thematic nexus graph for each comparison

Details

thematicEvolution starts from two or more thematic maps created by thematicMap function.

See Also

thematicMap function to create a thematic map based on co-word network analysis and clustering.

cocMatrix to compute a bibliographic bipartite network.

networkPlot to plot a bibliographic network.

Examples

# NOT RUN {
data(scientometrics)
years=c(2000)
list_df=timeslice(scientometrics, breaks = years)
M1=list_df[[1]]
M2=list_df[[2]]
NetMatrix1 <- biblioNetwork(M1, analysis = "co-occurrences", 
              network = "keywords", sep = ";")
S1 <- normalizeSimilarity(NetMatrix1, type = "association")
net1 <- networkPlot(NetMatrix1, normalize = "association",n = 50, 
     Title = "co-occurrence network",type="fruchterman",
     labelsize = 0.7, halo = FALSE, cluster = "walktrap",remove.isolates=FALSE,
     remove.multiple=FALSE, noloops=TRUE, weighted=TRUE)
res1 <- thematicMap(net1, NetMatrix1, S1, minfreq = 1)
#plot(res1$map)

NetMatrix2 <- biblioNetwork(M2, analysis = "co-occurrences", 
              network = "keywords", sep = ";")
S2 <- normalizeSimilarity(NetMatrix2, type = "association")
net2 <- networkPlot(NetMatrix2, normalize = "association",n = 50, 
     Title = "co-occurrence network",type="fruchterman",
     labelsize = 0.7, halo = FALSE, cluster = "walktrap",remove.isolates=FALSE,
     remove.multiple=FALSE, noloops=TRUE, weighted=TRUE)
res2 <- thematicMap(net2, NetMatrix2, S2, minfreq = 1)
#plot(res2$map)

nexus <- thematicEvolution(res1,res2,weighted=FALSE)

# }