R Notebook

Creando el ambiente

library(tidyverse)
library(forcats)
library(bibliometrix)
library(igraph)

Cargando los datos

WoS

https://images.webofknowledge.com/images/help/WOS/hs_wos_fieldtags.html

wos <- 
  convert2df("wos.txt")

Converting your wos collection into a bibliographic dataframe

Done!


Generating affiliation field tag AU_UN from C1:  Done!

Scopus

https://service.elsevier.com/app/answers/detail/a_id/14805/supporthub/scopus/~/what-ris-format-mapping-does-scopus-use-when-exporting-results-when-exchanging/

scopus <- 
  convert2df("scopus.bib", 
             dbsource = "scopus", 
             format = "bibtex")

Análisis descriptivo

scopus %>% dim()

[1] 523  38

scopus %>% names()

 [1] "AU"            "DE"            "ID"            "C1"            "CR"            "JI"            "AB"           
 [8] "PA"            "AR"            "chemicals_cas" "coden"         "RP"            "DT"            "DI"           
[15] "BE"            "FU"            "BN"            "SN"            "SO"            "LA"            "TC"           
[22] "PN"            "page_count"    "PP"            "PU"            "PM"            "DB"            "sponsors"     
[29] "TI"            "url"           "VL"            "PY"            "FX"            "AU_UN"         "AU1_UN"       
[36] "AU_UN_NR"      "SR_FULL"       "SR"           

Vamos a analizar AU

Creamos una tabla de autores

autores <- 
  scopus %>% 
  select(AU)

autores %>%  
  dim()

[1] 523   1

¿Realmente cuántos investigadores hay?

autores %>% dim()

[1] 1183    1

Quitamos el NA NA

autores <-
  autores %>% 
  filter(!(str_detect(string = AU,
                     pattern = "NA NA" )))

Cómo se vería en un diagrama de barras?

Qué tal un diagrama circular?

autores %>% 
  count(AU, sort = TRUE) %>% 
  slice(1:10) %>% 
  mutate(AU = forcats::fct_reorder(AU, n)) %>% 
  ggplot(aes(x = "", y = n, fill = AU)) + 
  geom_bar(with = 1, stat = "identity")

Ignoring unknown parameters: with

ahora si..

autores %>% 
  count(AU, sort = TRUE) %>% 
  slice(1:10) %>% 
  mutate(AU = forcats::fct_reorder(AU, n)) %>% 
  ggplot(aes(x = "", y = n, fill = AU)) + 
  geom_bar(with = 1, stat = "identity") +
  coord_polar("y", start = 0)

Ignoring unknown parameters: with

scopus %>% names()

 [1] "AU"            "DE"            "ID"            "C1"            "CR"            "JI"            "AB"           
 [8] "PA"            "AR"            "chemicals_cas" "coden"         "RP"            "DT"            "DI"           
[15] "BE"            "FU"            "BN"            "SN"            "SO"            "LA"            "TC"           
[22] "PN"            "page_count"    "PP"            "PU"            "PM"            "DB"            "sponsors"     
[29] "TI"            "url"           "VL"            "PY"            "FX"            "AU_UN"         "AU1_UN"       
[36] "AU_UN_NR"      "SR_FULL"       "SR"