bibliometrix (version 3.1.4)

thematicMap: Create a thematic map

Description

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).

Usage

thematicMap(
  M,
  field = "ID",
  n = 250,
  minfreq = 5,
  ngrams = 1,
  stemming = FALSE,
  size = 0.5,
  n.labels = 1,
  repel = TRUE
)

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.

repel

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

Value

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

Details

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.

See Also

biblioNetwork function to compute a bibliographic network.

cocMatrix to compute a bibliographic bipartite network.

networkPlot to plot a bibliographic network.

Examples

# NOT RUN {
data(scientometrics, package = "bibliometrixData")
res <- thematicMap(scientometrics, field = "ID", n = 250, minfreq = 5, size = 0.5, repel = TRUE)
plot(res$map)

# }