delfin

Instalando los paquetes

install.packages("tosr")

## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)

install.packages("bibliometrix")

## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)

install.packages("promises")

## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)

install.packages("tidyverse")

## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)

install.packages("igraph")

## Installing package into '/home/rstudio-user/R/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)

Cargando los paquetes

library("bibliometrix")

## To cite bibliometrix in publications, please use:
## 
## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, 
##                                  Journal of Informetrics, 11(4), pp 959-975, Elsevier.
##                         
## 
## https://www.bibliometrix.org
## 
##                         
## For information and bug reports:
##                         - Send an email to info@bibliometrix.org   
##                         - Write a post on https://github.com/massimoaria/bibliometrix/issues
##                         
## Help us to keep Bibliometrix free to download and use by contributing with a small donation to support our research team (https://bibliometrix.org/donate.html)
## 
##                         
## To start with the shiny web-interface, please digit:
## biblioshiny()

library("tidyverse")

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✓ ggplot2 3.3.5     ✓ purrr   0.3.4
## ✓ tibble  3.1.2     ✓ dplyr   1.0.7
## ✓ tidyr   1.1.3     ✓ stringr 1.4.0
## ✓ readr   2.0.0     ✓ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library("tosr")
library("igraph")

## 
## Attaching package: 'igraph'

## The following objects are masked from 'package:dplyr':
## 
##     as_data_frame, groups, union

## The following objects are masked from 'package:purrr':
## 
##     compose, simplify

## The following object is masked from 'package:tidyr':
## 
##     crossing

## The following object is masked from 'package:tibble':
## 
##     as_data_frame

## The following objects are masked from 'package:stats':
## 
##     decompose, spectrum

## The following object is masked from 'package:base':
## 
##     union

Creando el ToS

tos <- tosR("WoS_293.txt")

## [1] "1"
## 
## Converting your wos collection into a bibliographic dataframe
## 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

## Computing TOS SAP

## Computing TOS subfields

tos %>% rmarkdown::paged_table()

Cargando los datos

wos <- convert2df("WoS_293.txt")

## 
## Converting your wos collection into a bibliographic dataframe
## 
## Done!
## 
## 
## Generating affiliation field tag AU_UN from C1:  Done!

wos %>% rmarkdown::paged_table()

Creando la red

net <- 
  biblioNetwork(wos,
                analysis = "coupling",
                network = "references", 
                sep = ";") %>% 
  graph_from_adjacency_matrix(mode = "directed") %>% 
  simplify()

Análisis general

metricas <- 
  tibble(id = V(net)$name,
         grado_entrada = degree(net, mode = "in"),
         grado_salida = degree(net, mode = "out"),
         internediacion = betweenness(net))

metricas %>% rmarkdown::paged_table()

Exportando grafo

write_graph(net, "net.graphml", "graphml")