bibliometrix: thematicEvolution – R documentation

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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.

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.

Value

a list containing:

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

Examples

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

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

bibliometrix

Comprehensive Science Mapping Analysis

v3.1.1

GPL-3

Authors

Massimo Aria [cre, aut], Corrado Cuccurullo [aut]

Initial release