thematicMap function

Create a thematic map

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

thematicMap(
  M,
  field = "ID",
  n = 250,
  minfreq = 5,
  ngrams = 1,
  stemming = FALSE,
  size = 0.5,
  n.labels = 1,
  community.repulsion = 0.1,
  repel = TRUE,
  remove.terms = NULL,
  synonyms = NULL,
  cluster = "walktrap",
  subgraphs = FALSE
)

Arguments

• M: is a bibliographic dataframe.
• field: is the textual attribute used to build up the thematic map. It can be field = c("ID","DE", "TI", "AB"). biblioNetwork or cocMatrix.
• n: is an integer. It indicates the number of terms to include in the analysis.
• minfreq: is a integer. It indicates the minimum frequency (per thousand) of a cluster. It is a number in the range (0,1000).
• 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).
• size: is numerical. It indicates del size of the cluster circles and is a number in the range (0.01,1).
• n.labels: is integer. It indicates how many labels associate to each cluster. Default is n.labels = 1.
• community.repulsion: is a real. It indicates the repulsion force among network communities. It is a real number between 0 and 1. Default is community.repulsion = 0.1.
• 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").
• subgraphs: is a logical. If TRUE cluster subgraphs are returned.

Returns

a list containing:

map		The thematic map as ggplot2 object
clusters		Centrality and Density values for each cluster.
words		A list of words following in each cluster
nclust		The number of clusters
net		A list containing the network output (as provided from the networkPlot function)

Details

thematicMap starts from a co-occurrence keyword network to plot in a two-dimensional map the typological themes of a domain.

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(scientometrics, package = "bibliometrixData")
res <- thematicMap(scientometrics, field = "ID", n = 250, minfreq = 5, size = 0.5, repel = TRUE)
plot(res$map)
## End(Not run)

See Also

biblioNetwork function to compute a bibliographic network.

cocMatrix to compute a bibliographic bipartite network.

networkPlot to plot a bibliographic network.