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ver1.R
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# Se cargan
library(shiny)
library(shinydashboard)
library(igraph)
library(tidyverse)
library(tidyr)
library(CINNA)
library(bibliometrix)
ui <- dashboardPage(
dashboardHeader(title = "Core of Science"),
dashboardSidebar(
helpText("Note: this program performs a citation analysis ",
"based on the SAP algorithm."),
fileInput("file1", "Choose txt File",
accept = c(
"text/csv",
"text/comma-separated-values,text/plain",
".bib")
),
sidebarMenu(
menuItem("Introduction", tabName = "importance", icon = icon("th")),
menuItem("Importance" , tabName = "importance", icon = icon("th")),
menuItem("Evolution" , tabName = "evolution" , icon = icon("th")),
menuItem("Subfields" , tabName = "subfields" , icon = icon("th"))
),
selectInput("var",
label = "Choose a subfield group",
choices = list("Grupo 1",
"Grupo 2",
"Grupo 3"),
selected = "Grupo 1"),
downloadButton("downloadData", strong("Download Tree of science"))
),
dashboardBody(
tabItems(
tabItem(tabName = "importance",h2("Articles importance"),
fluidRow( box(title = "History publication",plotOutput("grafico1")),
box(title = "Most productive authors",tableOutput("tabla1"))),
fluidRow( box(title = "Most popular journals",tableOutput("tabla2")))
),
tabItem(tabName = "evolution",h2("Evolution ToS"),
fluidRow(
box(title = "Tree of Science ToS",
tableOutput("contents")
)
)
),
tabItem(tabName = "subfields",h2("Subfields"),
fluidRow(
box(title = "Subfields clasification",
tableOutput("subareas")
)
)
)
)
)
)
server <- function(input, output) {
output$contents <- renderTable({
# input$file1 will be NULL initially. After the user selects
# and uploads a file, head of that data file by default,
# or all rows if selected, will be shown.
req(input$file1)
#df <- read.(input$file1$datapath)
data_wos <- convert2df(file = input$file1$datapath,
dbsource = "wos",
format = "plaintext")
# Se Crea el ID_WOS para compararlo con las referencias
data_wos$ID_WOS <- rownames(data_wos)
data_wos$ID_WOS <- ifelse(!is.na(data_wos$VL),
paste(data_wos$ID_WOS,
data_wos$VL,
sep = ", V"),
data_wos$ID_WOS)
data_wos$ID_WOS <- ifelse(!is.na(data_wos$BP),
paste(data_wos$ID_WOS,
data_wos$BP,
sep = ", P"),
data_wos$ID_WOS)
data_wos$ID_WOS <- ifelse(!is.na(data_wos$DI),
paste(data_wos$ID_WOS,
data_wos$DI,
sep = ", DOI "),
data_wos$ID_WOS)
# Creando la lista de enlases
edgelist <-
as_tibble(data_wos) %>%
mutate(cited_references = CR) %>%
separate_rows(CR, sep = ";") %>%
filter(!grepl(pattern = "^[0-9].*",
CR)) %>%
select(ID_WOS, CR) %>%
filter(CR != "" & is.na(CR) == FALSE) %>%
mutate(ID_WOS = str_to_upper(ID_WOS),
CR = str_to_upper(CR)) %>%
unique()
# Se crea el grafo y se eliminan los bucles o ciclos y las aristas repetidas
graph <- graph.data.frame(edgelist) %>%
simplify()
# Se eliminan los vertices con indegree = 1 y con outdegree = 0
graph_1 <- delete.vertices(graph,
which(degree(graph, mode = "in") == 1 &
degree(graph, mode = "out") == 0))
# Se escoge el componente mas grande conectado
graph_2 <- giant_component_extract(graph_1, directed = TRUE)
graph_2 <- graph_2[[1]]
# Se determinan las metricas de la red
metricas.red <- tibble(
id = V(graph_2)$name,
indegree = degree(graph_2, mode = "in"),
outdegree = degree(graph_2, mode = "out"),
bet = betweenness(graph_2))
metricas.red <- metricas.red %>%
mutate(year = as.numeric(str_extract(id, "[0-9]{4}")))
# Clasificacion de las raices
Raices <- metricas.red[metricas.red$outdegree == 0, c("id","indegree")] %>%
arrange(desc(indegree))
Raices <- Raices[1:10,]
# Clasificacion de las hojas
Hojas.ext <- metricas.red[metricas.red$indegree == 0, c("id","outdegree","year")]
act.year <- as.numeric(format(Sys.Date(),'%Y'))
Hojas.ext <- Hojas.ext %>%
mutate(antiguedad = act.year - year) %>%
arrange(antiguedad)
Hojas <- filter(Hojas.ext, antiguedad <= 5)
# Se determina el numero del vertice de las Hojas
num.vertices.hojas <- c()
for (vertice in Hojas$id){
num.vertices.hojas <- c(num.vertices.hojas,which(metricas.red$id == vertice))
}
# Se determina el numero del vertice de las raices
num.vertices.raices <- c()
for (vertice in Raices$id){
num.vertices.raices <- c(num.vertices.raices,which(metricas.red$id == vertice))
}
# Calculo del SAP de las Hojas
SAP_hojas <- c()
for (vert in Hojas$id){
h <- get.all.shortest.paths(graph_2,
from = vert,
to = Raices$id,
mode = "out")
SAP_hojas <- c(SAP_hojas, length(h[[1]]))
}
Hojas <- Hojas %>%
mutate(SAP = SAP_hojas) %>%
arrange(desc(SAP))
Hojas <- Hojas[1:60,] %>%
filter(SAP > 0)
Caminos <- c()
for (vert in Hojas$id){
h <- get.all.shortest.paths(graph_2,
from = vert,
to = Raices$id,
mode = "out")
lista.nodos <- unique(unlist(h[1]))
lista.nodos <- lista.nodos[!(lista.nodos %in% num.vertices.raices)]
lista.nodos <- lista.nodos[!(lista.nodos %in% num.vertices.hojas)]
Caminos <- c(Caminos,lista.nodos)
}
# Seleccion del tronco
Tronco <- metricas.red[unique(Caminos), c("id","indegree","year")]
mas.nuevo <- max(Tronco$year, na.rm = TRUE)
Tronco <- Tronco %>%
mutate(antiguedad = mas.nuevo - year)
# Tree of science
Raices$TOS <- "Root"
Hojas$TOS <- "Leaves"
Tronco$TOS <- "Trunck"
TOS <- rbind(Raices[,c(1,3)], Tronco[,c(1,5)], Hojas[,c(1,6)])
output$downloadData <- downloadHandler(
filename = function() {
paste("arbol", ".csv", sep = "")
},
content = function(file) {
#write.csv(TOS, file, row.names = FALSE)
write.table(TOS,file,sep=";")
}
)
return(TOS[,c("id","TOS")])
})
output$grafico1 <- renderPlot({
req(input$file1)
#df <- read.(input$file1$datapath)
data_wos <- convert2df(file = input$file1$datapath,
dbsource = "wos",
format = "plaintext")
fecha.publicacion <- data_wos$PY # Año de publicacion
N <- length(fecha.publicacion) # Numero de articulos
frec.publicacion <- table(fecha.publicacion)
plot(x = frec.publicacion, # Datos
main = "Scientific anual production",
ylab = "Papers",
xlab = "Year",
col = "Red",
type = "o")
})
output$tabla1 <- renderTable({
req(input$file1)
#df <- read.(input$file1$datapath)
data_wos <- convert2df(file = input$file1$datapath,
dbsource = "wos",
format = "plaintext")
autores <- strsplit( x = data_wos$AF, split = ";", fixed = FALSE )
Author <- c()
for (w in autores) {
for (w1 in w){
Author <- c(Author,w1)
}
}
frec.autores <- as.data.frame(table(Author)) %>%
arrange(desc(Freq))
return(frec.autores[1:10,])
})
output$tabla2 <- renderTable({
req(input$file1)
#df <- read.(input$file1$datapath)
data_wos <- convert2df(file = input$file1$datapath,
dbsource = "wos",
format = "plaintext")
Journals <- as.data.frame(table(data_wos$SO)) %>%
arrange(desc(Freq)) %>%
rename(Journal = "Var1")
return(Journals[1:10,])
})
output$subareas <- renderTable({
req(input$file1)
#df <- read.(input$file1$datapath)
data_wos <- convert2df(file = input$file1$datapath,
dbsource = "wos",
format = "plaintext")
# Se Crea el ID_WOS para compararlo con las referencias
data_wos$ID_WOS <- rownames(data_wos)
data_wos$ID_WOS <- ifelse(!is.na(data_wos$VL),
paste(data_wos$ID_WOS,
data_wos$VL,
sep = ", V"),
data_wos$ID_WOS)
data_wos$ID_WOS <- ifelse(!is.na(data_wos$BP),
paste(data_wos$ID_WOS,
data_wos$BP,
sep = ", P"),
data_wos$ID_WOS)
data_wos$ID_WOS <- ifelse(!is.na(data_wos$DI),
paste(data_wos$ID_WOS,
data_wos$DI,
sep = ", DOI "),
data_wos$ID_WOS)
# Creando la lista de enlases
edgelist <-
as_tibble(data_wos) %>%
mutate(cited_references = CR) %>%
separate_rows(CR, sep = ";") %>%
filter(!grepl(pattern = "^[0-9].*",
CR)) %>%
select(ID_WOS, CR) %>%
filter(CR != "" & is.na(CR) == FALSE) %>%
mutate(ID_WOS = str_to_upper(ID_WOS),
CR = str_to_upper(CR)) %>%
unique()
# Se crea el grafo y se eliminan los bucles o ciclos y las aristas repetidas
graph <- graph.data.frame(edgelist) %>%
simplify()
# Se eliminan los vertices con indegree = 1 y con outdegree = 0
graph_1 <- delete.vertices(graph,
which(degree(graph, mode = "in") == 1 &
degree(graph, mode = "out") == 0))
# Se escoge el componente mas grande conectado
graph_2 <- giant_component_extract(graph_1, directed = TRUE)
graph_2 <- graph_2[[1]]
subareas <-
as.undirected(graph_2,
mode = "each") %>%
cluster_louvain()
graph <-
graph_2 %>%
set_vertex_attr(name = "sub_area",
value = membership(subareas))
subareas <-
as.undirected(graph_2,
mode = "each") %>%
cluster_louvain()
graph <-
graph_2 %>%
set_vertex_attr(name = "sub_area",
value = membership(subareas))
subareas_3 <-
tibble(publication = subareas$names , subarea = V(graph)$sub_area) %>%
group_by(subarea) %>%
count() %>%
arrange(desc(n))
tabla.subareas <- tibble(Authors = subareas$names , subfield = V(graph)$sub_area)
grupo1 <- tabla.subareas[tabla.subareas$subfield == subareas_3$subarea[1],]
grupo1$subfield <- "Group 1"
grupo2 <- tabla.subareas[tabla.subareas$subfield == subareas_3$subarea[2],]
grupo2$subfield <- "Group 2"
grupo3 <- tabla.subareas[tabla.subareas$subfield == subareas_3$subarea[3],]
grupo3$subfield <- "Group 3"
agrupacion <- rbind(grupo1,grupo2,grupo3)
if (input$var == "Grupo 1"){
return(grupo1)
}
if (input$var == "Grupo 2"){
return(grupo2)
}
if (input$var == "Grupo 3"){
return(grupo3)
}
})
}
shinyApp(ui, server)
tabItem(
tabName = "intento",
fluidRow(
tabBox(
title = "Subfields",
tabPanel("Group 1",
fluidRow(
box()
)
),
tabPanel("Wordcloud Group 1",
fluidRow(
box()
))
)
)
)