thematicEvolution function

Perform a Thematic Evolution Analysis

It performs a Thematic Evolution Analysis based on co-word network analysis and clustering. The methodology is inspired by the proposal of Cobo et al. (2011).

thematicEvolution(
  M,
  field = "ID",
  years,
  n = 250,
  minFreq = 2,
  size = 0.5,
  ngrams = 1,
  stemming = FALSE,
  n.labels = 1,
  repel = TRUE,
  remove.terms = NULL,
  synonyms = NULL,
  cluster = "walktrap"
)

Arguments

• M: is a bibliographic data frame obtained by the converting function convert2df.
• field: is a character object. It indicates the content field to use. Field can be one of c=("ID","DE","TI","AB"). Default value is field="ID".
• years: is a numeric vector of one or more unique cut points.
• n: is numerical. It indicates the number of words to use in the network analysis
• minFreq: is numerical. It indicates the min frequency of words included in to a cluster.
• size: is numerical. It indicates del size of the cluster circles and is a number in the range (0.01,1).
• ngrams: is an integer between 1 and 4. It indicates the type of n-gram to extract from texts. An n-gram is a contiguous sequence of n terms. The function can extract n-grams composed by 1, 2, 3 or 4 terms. Default value is ngrams=1.
• stemming: is logical. If it is TRUE the word (from titles or abstracts) will be stemmed (using the Porter's algorithm).
• n.labels: is integer. It indicates how many labels associate to each cluster. Default is n.labels = 1.
• repel: is logical. If it is TRUE ggplot uses geom_label_repel instead of geom_label.
• remove.terms: is a character vector. It contains a list of additional terms to delete from the documents before term extraction. The default is remove.terms = NULL.
• synonyms: is a character vector. Each element contains a list of synonyms, separated by ";", that will be merged into a single term (the first word contained in the vector element). The default is synonyms = NULL.
• cluster: is a character. It indicates the type of cluster to perform among ("optimal", "louvain","leiden", "infomap","edge_betweenness","walktrap", "spinglass", "leading_eigen", "fast_greedy").

Returns

a list containing:

nets		The thematic nexus graph for each comparison
incMatrix		Some useful statistics about the thematic nexus

Details

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

Reference:

Cobo, M. J., Lopez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.

Examples

## Not run:

data(managemeent, package = "bibliometrixData")
years=c(2004,2015)

nexus <- thematicEvolution(management,field="ID",years=years,n=100,minFreq=2)
## End(Not run)

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.