bibliometrix (version 3.1.4)

thematicEvolution: Perform a Thematic Evolution Analysis

Description

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).

Usage

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

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 two 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.

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.

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

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, package = "bibliometrixData")
years=c(2000)

nexus <- thematicEvolution(scientometrics,field="ID", years=years, n=100,minFreq=2)

# }