thematicMap: Create a thematic map
In xiangtuochen/bibliometrixfullname: Comprehensive Science Mapping Analysis

Description Usage Arguments Details Value See Also Examples

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,
  repel = TRUE
)

`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`.
`repel`	is logical. If it is TRUE ggplot uses geom_label_repel instead of geom_label.

thematicMap starts from a co-occurrence keyword network to plot in a two-dimesional 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.

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)

biblioNetwork function to compute a bibliographic network.

cocMatrix to compute a bibliographic bipartite network.

networkPlot to plot a bibliographic network.

1
2
3

data(scientometrics, package = "bibliometrixData")
res <- thematicMap(scientometrics, field = "ID", n = 250, minfreq = 5, size = 0.5, repel = TRUE)
plot(res$map)