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server.R
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server.R
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shiny::shinyServer(function(input,output,session) {
values <- reactiveValues()
#--------------# Excel data #---------------#
filePath <- reactiveVal()
observe({
inFile <- input$file1
if (is.null(inFile)) {
return(NULL)
} else {
filePath(inFile$datapath)
selectionWorksheet <- sort(unique(readxl::excel_sheets(inFile$datapath)))
updateSelectInput(session, "worksheet", choices = selectionWorksheet)
}
})
#observe({
#inFile <- input$file1
#if (is.null(inFile)) {
# return (NULL)
# }
# else {#Set up the selection for the worksheet names within the selected file
# filePath <<- inFile$datapath
# selectionWorksheet <- sort(unique(readxl::excel_sheets(inFile$datapath)))
# updateSelectInput(session, "worksheet", choices = selectionWorksheet)
# }
#})
observe({
if(is.null(input$file1)) {
shinyjs::disable("getData")
}
else {shinyjs::enable("getData")
}
})
observeEvent(input$getData, {
datxl <- readxl::read_excel(filePath(), sheet=input$worksheet)
values$datxl <- as.data.frame(
unclass(datxl), stringsAsFactors = TRUE)
datxl <- values$datxl %>% dplyr::group_by(News)
NamesDiarios <- as.data.frame(datxl$Newspapers)
values$outxl <- table(NamesDiarios)
})
##---------------scraping--------
observeEvent(input$runsearch, {
req(input$search)
if(input$language == "spanish"){
url1 <- URLencode(paste0("https://www.google.com/search?q=",
gsub(" ","+",input$search),
"&hl=es-419&tbm=nws&start="))
news_array <- c()
diarios_array <- c()
for(i in 0:input$numberpag){
page <- i * 10
new_url <- paste(url1,toString(page),sep="")
reddit_wbpg <- read_html(new_url)
diario<-reddit_wbpg %>%
html_nodes('.BNeawe.UPmit') %>%
html_text()
(Noticias<-reddit_wbpg %>%
html_nodes('.zBAuLc.l97dzf') %>%
html_text())
news_array <- append(news_array, Noticias)
diarios_array <- append(diarios_array, diario)
}
#creacion de tabla de Datos
Diario<-data.frame(Diario=diarios_array)
Diario$Diario<- stringr::str_trim(Diario$Diario,side = "both")
Diario$Diario<- stringr::str_to_lower(Diario$Diario,locale = "es")
Diario$Diario<-chartr('áéíóúñ','aeioun',Diario$Diario)
Noticia<-data.frame(Noticia=news_array)
Noticia$Noticia<- stringr::str_to_lower(Noticia$Noticia,locale = "es")
Noticia$Noticia<-chartr('áéíóúñ','aeioun',Noticia$Noticia)
Data<-cbind(ID=row.names(Diario),Diario, Noticia)
values$Data <- Data %>% dplyr::group_by(Noticia) %>% dplyr::filter (! duplicated(Noticia))
NamesDiarios<- as.data.frame(Data$Diario)
values$out <- table(NamesDiarios)
#View(values$Data)
}
else {url1 <- URLencode(paste0("https://www.google.com/search?q=",
gsub(" ","+",input$search),
"&hl=en-US&gl=US&ceid=US%3Aen&tbm=nws&start="))
news_array <- c()
diarios_array <- c()
for(i in 0:input$numberpag){
page <- i * 10
new_url <- paste(url1,toString(page),sep="")
reddit_wbpg <- read_html(new_url)
(diario<-reddit_wbpg %>%
html_nodes('.BNeawe.UPmit') %>%
html_text())
(Noticias<-reddit_wbpg %>%
html_nodes('.zBAuLc.l97dzf') %>%
html_text())
news_array <- append(news_array, Noticias)
diarios_array <- append(diarios_array, diario)
}
#creacion de tabla de Datos
Diario<-data.frame(Diario=diarios_array)
Diario$Diario<-stringr::str_trim(Diario$Diario,side = "both")
Diario$Diario<- stringr::str_to_lower(Diario$Diario,locale = "es")
Diario$Diario<-chartr('áéíóúñ','aeioun',Diario$Diario)
Noticia<-data.frame(Noticia=news_array)
Noticia$Noticia<-stringr::str_to_lower(Noticia$Noticia,locale = "es")
Noticia$Noticia<-chartr('áéíóúñ','aeioun',Noticia$Noticia)
Data<-cbind(ID=row.names(Diario),Diario, Noticia)
values$Data <- Data %>% dplyr::group_by(Noticia) %>% dplyr::filter (! duplicated(Noticia))
NamesDiarios<- as.data.frame(Data$Diario)
values$out <- table(NamesDiarios)
# View(values$Data)
}
beepr::beep(2)
})
#tabla resumen
output$sum <- DT::renderDT({
if(input$load =="null"){
return (NULL)
}
else if(input$load =="import"){
req(values$outxl)
tabla <- data.frame(values$outxl)%>%
dplyr::arrange(desc(Freq))}
else if (input$load =="load"){
req(values$out)
tabla <- data.frame(values$out)%>%
dplyr::arrange(desc(Freq))}
DT::datatable(data = tabla,
extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
scrollX = TRUE,
fixedColumns = TRUE,
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
lengthMenu = list(c(5, 25, 100, -1),
c('5', '25', '100','All'))))
})
#tabla de noticas por diario
output$text <- DT::renderDT({
if(input$load =="null"){
return (NULL)
}
else if(input$load =="import"){
req(values$datxl)
tabla <- data.frame(values$datxl)}
else if (input$load =="load"){
req(values$Data)
tabla <- data.frame(values$Data)}
DT::datatable(data = tabla, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
scrollX = TRUE,
fixedColumns = TRUE,
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
lengthMenu = list(c(5, 25, 100, -1),
c('5', '25', '100','All'))))
})
### Permitir que este disonible el nombre del periodico
observe({
req(values$Data)
dat <- values$Data
lista <- unique(dat$Diario)
updateSelectizeInput(
session = session,
inputId = "selectnews",
label = "Select/deselect all options",
choices = as.character(lista),
server = TRUE,
selected = NULL
)
})
# Realizar proceso de creacion de la matrix dtm
observeEvent(input$dtm.update, {
req(input$selectnews)
if(is.null(input$selectnews)) {return(shinyalert::shinyalert("Oops!",
"Something went wrong.
please select at least
one newspaper",
type = "error"))}
.data <- dplyr::filter(values$Data, Diario %in% c(input$selectnews))
#View(.data)
diario <- .data
values$diario <- data.frame(diario =.data$Diario)
#View(values$diario)
stp <- unlist(strsplit(input$stopwords,","))
stp <- trimws(stp)
cpus <- parallel::detectCores()
ngram <- as.integer(input$ngrams)
Stemm <- trimws(input$Stemm)
odtm <- textmineR::CreateDtm(
doc_vec = .data$Noticia,
doc_names = .data$ID,
ngram_window = c(1,ngram),
lower = FALSE,
remove_punctuation = FALSE,
remove_numbers = FALSE,
cpus = cpus)
if(input$checkStemming){
dtm <- textmineR::CreateDtm(
doc_vec = .data$Noticia,
doc_names = .data$ID,
ngram_window = c(1,ngram),
stopword_vec = c(stopwords::stopwords(input$Language),letters,stp),
lower = TRUE,
remove_punctuation = TRUE,
remove_numbers = input$removenumber,
stem_lemma_function = function(x) SnowballC::wordStem(x, Stemm),
cpus = cpus)}
else{dtm <- textmineR::CreateDtm(
doc_vec = .data$Noticia,
doc_names = .data$ID,
lower = TRUE,
stopword_vec = c(stopwords::stopwords(input$Language),letters,stp),
ngram_window = c(1,ngram),
remove_punctuation = TRUE,
remove_numbers = input$removenumber,
cpus = cpus)
}
values$dtm <- quanteda::as.dfm(dtm)
CONVERT <- quanteda::convert(
values$dtm,
to = "topicmodels")
values$dtmt <- removeSparseTerms(CONVERT,
sparse= input$sparce)
values$dtmF <- chinese.misc::m3m(values$dtmt,to="dgCMatrix")
Original <- dim(odtm)
Without_Sparsity <- dim(values$dtm)
Final <- dim (values$dtmt)
values$dimen <- rbind(Original,
Final)
colnames (values$dimen) <- c ("document", "term")
values$tf_mat <- textmineR::TermDocFreq(dtm = values$dtmF)
values$freq <- colSums(as.matrix(values$dtmF)) #
#values$freq <- colSums2(values$dtmF)
values$wf <- data.frame(word=names(values$freq), freq=values$freq)
beepr::beep(2)
})
###_ DTM xl_ inicio#####
observeEvent(input$dtmexcel.update, {
req(values$datxl)
.data <- data.frame(values$datxl)
stp <- unlist(strsplit(input$stopwordsxl,","))
stp <- trimws(stp)
cpus <- parallel::detectCores()
ngram <- as.integer(input$ngramsxl)
Stemm <- trimws(input$Stemmxl)
odtm <- textmineR::CreateDtm(
doc_vec = .data$News,
doc_names = .data$ID,
ngram_window = c(1,ngram),
lower = FALSE,
remove_punctuation = FALSE,
remove_numbers = FALSE,
cpus = cpus)
if(input$checkStemming){
dtm <- textmineR::CreateDtm(
doc_vec = .data$News,
doc_names = .data$ID,
ngram_window = c(1,ngram),
stopword_vec = c(stopwords::stopwords(input$Languagexl),letters,stp),
lower = TRUE,
remove_punctuation = TRUE,
remove_numbers = input$removenumberxl,
stem_lemma_function = function(x) SnowballC::wordStem(x, Stemm),
cpus = cpus)}
else{dtm <- textmineR::CreateDtm(
doc_vec = .data$News,
doc_names = .data$ID,
lower = TRUE,
stopword_vec = c(stopwords::stopwords(input$Languagexl),letters,stp),
ngram_window = c(1,ngram),
remove_punctuation = TRUE,
remove_numbers = input$removenumberxl,
cpus = cpus)
}
values$dtmxl <- quanteda::as.dfm(dtm)
CONVERT <- quanteda::convert(
values$dtmxl,
to = "topicmodels")
values$dtmtxl <- removeSparseTerms(CONVERT,
sparse= input$sparcexl)
values$dtmFxl <- chinese.misc::m3m(values$dtmtxl, to="dgCMatrix")
#values$dtmFxl <- as(values$dtmtxl,"CsparseMatrix")
Originalxl <- dim(odtm)
Without_Sparsity <- dim(values$dtm)
Finalxl <- dim (values$dtmtxl)
values$dimenxl <- rbind(Originalxl,
Finalxl)
# colnames (values$dimen) <- c ("document", "term")
values$tf_mat <- textmineR::TermDocFreq(dtm = values$dtmFxl)
values$freq <- colSums(as.matrix(values$dtmFxl)) #
#values$freq <- colSums2(values$dtmFxl)
values$wf <- data.frame(word=names(values$freq), freq=values$freq)
beepr::beep(2)
})
output$Table_dimxl <- DT::renderDT({
dat <- as.matrix(values$dimenxl)
colnames (dat) <- c ("document", "term")
DT::datatable(data = dat,
options = list(pageLength = 5,
searching = FALSE,
rownames = TRUE))
})
#tabla dimenciones de dtm
output$Table_dim <- DT::renderDT({
DT::datatable(data = as.matrix(values$dimen),
options = list(pageLength = 5,
searching = FALSE,
rownames = TRUE))
})
output$data_b <- DT::renderDT({
DT::datatable(data = values$tf_mat, extensions = 'Buttons',
options = list(
dom = 'Bfrtip',
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
lengthMenu = list(c(5, 25, 100, -1),
c('5', '25', '100','All'))
)
)
})
# grafico de barras
output$plot_gg <- highcharter::renderHighchart({
export
values$wf %>% dplyr::top_n(input$b, freq) %>%
highcharter::hchart("column",
highcharter::hcaes(x = word, y = freq),
color = input$colorbar,
borderColor = "black") %>%
highcharter::hc_add_theme(highcharter::hc_theme_ggplot2()) %>%
highcharter::hc_xAxis(title = list(text = "Term")) %>%
highcharter::hc_yAxis(title = list(text = "Frequency")) %>%
highcharter::hc_exporting(
enabled = TRUE,
formAttributes = list(target = "_blank"),
buttons = list(contextButton = list(
text = "Export",
theme = list(fill = "transparent"),
menuItems = export)
)
)
})
# nube de palabras
output$plot_gf<- highcharter::renderHighchart({
export
values$wf %>% dplyr::top_n(input$c, freq) %>%
highcharter::hchart( "wordcloud",
highcharter::hcaes(name = word, weight = freq)) %>%
highcharter::hc_exporting(
enabled = TRUE,
formAttributes = list(target = "_blank"),
buttons = list(contextButton = list(
text = "Export",
theme = list(fill = "transparent"),
menuItems = export)))
})
# co-ocurrencia
output$plotcoc <- renderPlot({
if(input$load == "import"){
dtm <- values$dtmFxl }
else if(input$load == "load"){
dtm <- values$dtmF }
dtm <- dtmremovetfidf(dtm, top = input$d)
term_correlations <- dtmcorr(dtm)
values$plotco <- textplot::textplot_correlation_glasso(term_correlations,
exclude_zero = TRUE)
values$plotco
})
output$downplotco <- downloadHandler(
filename = function() {
paste("coocurrenceplot",input$downplotcooc, sep=".")
},
# is a function with argument file. content writes the plot to the device
content = function(file) {
if(input$downplotcooc == "png")
png(file) # open the png device
else
pdf(file) # open the pdf device
print(values$plotco) # draw the plot
dev.off() # turn the device off
})
#############################number topic###############
observe({
if (isTRUE(input$num1>=input$num2||input$num3>input$num2)) {
shinyjs::disable("Run.model1")
}
else if(isTRUE(input$num5>=input$num4)) {
shinyjs::disable("Run.model1")
}
else if(isTRUE(input$num1 < 2 )) {
shinyjs::disable("Run.model1")
}
else if(is.na(as.numeric(input$num1))) {
shinyjs::disable("Run.model1")
}
else if(is.na(as.numeric(input$num2))) {
shinyjs::disable("Run.model1")
}
else if(is.na(as.numeric(input$num3))) {
shinyjs::disable("Run.model1")
}
else if(is.na(as.numeric(input$num4))) {
shinyjs::disable("Run.model1")
}
else if(is.na(as.numeric(input$num5))) {
shinyjs::disable("Run.model1")
}
else if(is.na(as.numeric(input$num6))) {
shinyjs::disable("Run.model1")
}
#else if(is.null(values$dtmF)) {
# shinyjs::disable("Run.model1")
# }
else {shinyjs::enable("Run.model1")
}
})
output$OthKcoh <- renderText({
stpCohe <- unlist(strsplit(input$OtherKCoherence,","))
stpCohe <- as.numeric(trimws(stpCohe))
if (anyNA(stpCohe)) {
"Invalid input"
}
})
observeEvent(input$Run.model1,{
ptm <- proc.time()
stpCohe <- unlist(strsplit(input$OtherKCoherence,","))
stpCohe <- as.numeric(trimws(stpCohe))
seqk <- c(seq(from=input$num1,to=input$num2,by=input$num3),stpCohe)# Candidate number of topics k
iterations <- input$num4 # Parameters control Gibbs sampling
burnin <- input$num5 # Parameters control Gibbs sampling
alpha <- input$num6 # Parameters control
cores <- parallel::detectCores()
#dtm <- values$dtmF
if(input$load == "import"){
dtm <- values$dtmFxl }
else if(input$load == "load"){
dtm <- values$dtmF }
values$coherence_list <- textmineR::TmParallelApply(X = seqk ,
FUN = function(k){
m <- textmineR::FitLdaModel(dtm= dtm ,
k = k,
iterations =iterations ,
burnin = burnin,
alpha = alpha,
beta = colSums(dtm) / sum(dtm) * 100,
optimize_alpha = TRUE,
calc_likelihood = TRUE,
calc_coherence = TRUE,
calc_r2 = FALSE,
cpus = cores)
m$k <- k
m
},export= ls(), # c("nih_sample_dtm"), # export only needed for Windows machines
cpus = cores)
values$coherence_mat <- data.frame(k = sapply(values$coherence_list,
function(x) nrow(x$phi)),
coherence = sapply(values$coherence_list,
function(x) mean(x$coherence)),
stringsAsFactors = FALSE)
beepr::beep(2)
values$end_time <- proc.time() - ptm
})
output$timeCoherence <- renderPrint({
req(values$end_time)
print( values$end_time)
})
output$plot_gi <- highcharter::renderHighchart({
req(values$coherence_list)
values$coherence_mat
export
values$coherence_mat %>%
highcharter::hchart("line", highcharter::hcaes(x = k, y = coherence)) %>%
highcharter::hc_add_theme(highcharter::hc_theme_ggplot2())%>%
highcharter::hc_credits(
enabled = TRUE, text = "Source: LDAbiplots",
style = list(fontSize = "12px")
)%>%
highcharter::hc_exporting(
enabled = TRUE,
formAttributes = list(target = "_blank"),
buttons = list(contextButton = list(
text = "Export",
theme = list(fill = "transparent"),
menuItems = export)))
})
########LDA#####################
observe({
if (isTRUE(input$num27 >= input$num26|| input$num25 < 2)) {
shinyjs::disable("Run.model5")
}
#else if(is.null(values$dtmF)) {
# shinyjs::disable("Run.model5")
#}
else if(is.na(as.numeric(input$num25))) {
shinyjs::disable("Run.model5")
}
else if(is.na(as.numeric(input$num26))) {
shinyjs::disable("Run.model5")
}
else if(is.na(as.numeric(input$num27))) {
shinyjs::disable("Run.model5")
}
else if(is.na(as.numeric(input$num28))) {
shinyjs::disable("Run.model5")
}
else {
shinyjs::enable("Run.model5")
}
})
observeEvent(input$Run.model5,{
set.seed(12345)
shinybusy::show_modal_spinner(
spin = "atom",
color = "#428ded",
text = "Please wait...Work in progress. You'll have to be
patient, because the result may take a while"
)
k <- input$num25
iter <- input$num26
burnin <- input$num27
alpha <- input$num28
cpus <- parallel::detectCores()
if(input$load == "import"){
dtm <- values$dtmFxl
}
else if(input$load == "load"){
dtm <- values$dtmF
}
values$model <- textmineR::FitLdaModel(dtm = dtm, # parameter
k = k ,# Number of topics k
iterations = iter, # parameter
burnin = burnin, #parameter
alpha = alpha,# parameter
beta = colSums(dtm)/sum(dtm)*100,
optimize_alpha = TRUE, # parameter
calc_likelihood = TRUE,
calc_coherence = TRUE,
calc_r2 = FALSE,
cpus = cpus)
beepr::beep(2)
remove_modal_spinner()
})
output$summLDA <- DT::renderDT({
req(values$model)
if(input$load == "import"){
dtm <- values$dtmFxl
}
else if(input$load == "load"){
dtm <- values$dtmF
}
model <- values$model
top_terms <- textmineR::GetTopTerms(phi = model$phi, M = input$Topterm)
top_term <- apply(top_terms, 2, function(x){
paste(x, collapse = ", ")
})
prevalence <- colSums(model$theta) / sum(model$theta) * 100
# textmineR has a naive topic labeling tool based on probable bigrams
values$labels <- textmineR::LabelTopics(assignments = model$theta > input$assignments,
dtm = dtm,
M = input$Labels)
summar <- data.frame(topic = rownames(model$phi),
label = values$labels,
coherence = round(model$coherence, 3),
prevalence = round(prevalence,3),
top_terms = top_term)
DT::datatable(data = summar, extensions = 'Buttons',
options = list(dom = 'Bfrtip',
scrollX = TRUE,
fixedColumns = TRUE,
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
lengthMenu = list(c(5, 25, 100, -1),
c('5', '25', '100','All'))))%>%
DT::formatRound( columns= c("coherence","prevalence"),
digits=5)
})
output$theta <- DT::renderDT({
req(values$model$theta)
tidy_theta <- data.frame(ID = rownames(values$model$theta),
round(values$model$theta,5),
stringsAsFactors = FALSE) %>%
tidyr::gather(topic, gamma, -ID)
if(input$load == "import"){
Newspaper <- values$datxl$Newspapers
values$tidy_theta <- data.frame(Newspaper = Newspaper,tidy_theta)
values$tidy_theta
}
else if(input$load == "load"){
Newspaper <- values$diario
values$tidy_theta <- data.frame(Newspaper = Newspaper,tidy_theta)
values$tidy_theta
}
DT::datatable(data = values$tidy_theta ,
extensions = 'Buttons',
filter = 'top',
colnames=c("Newspaper","ID news","Topic", "theta"),
options = list(dom = 'Bfrtip',
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
lengthMenu = list(c(5,100,1000,10000,-1),
c('5', '100', '1000','10000','All')
)
)
)%>% DT::formatRound( columns= c("gamma"),digits=5)
})
output$phi <- DT::renderDT({
req(values$model$phi)
tidy_beta <- data.frame(topic = as.integer(stringr::str_replace_all(rownames(values$model$phi), "t_", "")
),
round(values$model$phi,5),
stringsAsFactors = FALSE)%>%
tidyr::gather(term, beta, -topic)
DT::datatable(data = tidy_beta ,
extensions = 'Buttons',
filter = 'top',
colnames=c("topic","term", "phi"),
options = list(dom = 'Bfrtip',
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
lengthMenu = list(c(5,100,1000,10000, -1),
c('5', '100', '1000','10000','All')
)
)
)
})
output$plot_worcloud <- highcharter::renderHighchart({
req(values$model)
tidy_beta <- data.frame(
topic = as.integer(stringr::str_replace_all(rownames(values$model$phi),
"t_", "")),
round(values$model$phi,5),stringsAsFactors = FALSE)%>%
tidyr::gather(term, beta, -topic)
export
tidy_beta %>% dplyr::filter(topic==input$num29)%>%
dplyr::top_n(input$cloud, beta) %>%
highcharter::hchart(
"wordcloud",
highcharter::hcaes(name = term,
weight = beta)) %>%
highcharter::hc_credits(
enabled = TRUE, text = "Source: LDAbiplots",
style = list(fontSize = "12px")
)%>%
highcharter::hc_exporting(
enabled = TRUE,
formAttributes = list(target = "_blank"),
buttons = list(
contextButton = list(
text = "Export",
theme = list(fill = "transparent"),
menuItems = export)))
})
output$plot_heatmap <- highcharter::renderHighchart({
req(values$tidy_theta)
colr <- list( list(0.005, '#9ecae1'),
list(0.01, '#4292c6'),
list(0.05,'#2171b5'),
list(0.10,'#08519c'),
list(0.30,'#fc8d59'),
list(0.45,'#ef6548'),
list(0.75,'#d7301f'),
list(0.95,'#b30000'),
list(1, '#7f0000'))
if(input$load == "import"){
heatm <- values$tidy_theta %>%
dplyr::group_by(topic,Newspaper) %>%
dplyr::summarise(proportion= mean(gamma))
export
heatm %>%
highcharter::hchart("heatmap", highcharter::hcaes(x = Newspaper,
y = as.integer(stringr::str_replace_all(topic,"t_", " ")) ,
value =proportion)) %>%
highcharter::hc_colorAxis(stops= colr,
min=min(heatm$proportion),
max= max(heatm$proportion)) %>%
highcharter::hc_yAxis(title = list(text = "Topic"))%>%
highcharter::hc_credits(
enabled = TRUE, text = "Source: LDABiplots",
style = list(fontSize = "12px")
)%>%
highcharter::hc_exporting(
enabled = TRUE,
formAttributes = list(target = "_blank"),
buttons = list(contextButton = list(
text = "Export",
theme = list(fill = "transparent"),
menuItems = export)))
}
else if(input$load == "load"){
heatm <- values$tidy_theta %>%
dplyr::group_by(topic,diario) %>%
dplyr::summarise(proportion= mean(gamma))
export
heatm %>%
highcharter::hchart("heatmap", highcharter::hcaes(x = diario,
y = as.integer(stringr::str_replace_all(topic,"t_", " ")) ,
value =proportion)) %>%
highcharter::hc_colorAxis(stops= colr,
min=min(heatm$proportion),
max= max(heatm$proportion)) %>%
highcharter::hc_yAxis(title = list(text = "Topic"))%>%
highcharter::hc_credits(
enabled = TRUE, text = "Source: LDABiplots",
style = list(fontSize = "12px")
)%>%
highcharter::hc_exporting(
enabled = TRUE,
formAttributes = list(target = "_blank"),
buttons = list(contextButton = list(
text = "Export",
theme = list(fill = "transparent"),
menuItems = export)))
}
})
#output$num_cluster <- renderPlot({
# req(values$model$phi)
# phi <- values$model$phi
#p1 <- fviz_nbclust(phi, hcut, nstart = 2, method = "wss")+
# labs(subtitle = "Elbow method")
#p2 <- fviz_nbclust(phi, hcut, method = "silhouette")+
# labs(subtitle = "Silhouette method")
# p3 <- fviz_nbclust(phi, hcut, nstart = 2, method = "gap_stat", nboot = 50, verbose = FALSE)+
# labs(subtitle = "Gap statistic method")
# figure <- ggpubr::ggarrange(p1,p2, ncol = 2, nrow = 2)
# figure
#})
output$plot_cluster <- renderPlot({
req(values$model$phi)
phi <- values$model$phi
label <- values$labels
top_terms <- textmineR::GetTopTerms(phi = phi, M = input$numM)
top_term <- apply(top_terms, 2, function(x){
paste(x, collapse = ", ")
})
cluster_dist <- textmineR::CalcHellingerDist(phi)
hc <- hclust(as.dist(cluster_dist),input$methodaglo)
hc$labels <- paste(rownames(phi),top_term )
values$p <-factoextra::fviz_dend(hc,
k = input$numberclu,
#k_colors = c("#2E9FDF", "#00AFBB", "#E7B800", "#FC4E07"),
color_labels_by_k = TRUE, # color labels by groups
rect = TRUE ,
type = input$type)
values$p
})
output$downcluster <- downloadHandler(
filename = function() {
paste("cluster-",Sys.Date(),input$butdowncluster, sep=".")
},
# is a function with argument file. content writes the plot to the device
content = function(file) {
if(input$butdowncluster == "png")
png(file) # open the png device
else
pdf(file) # open the pdf device
print(values$p) # draw the plot
dev.off() # turn the device off
}
)
output$cophenetic <- renderText({
req(values$model$phi)
phi <- values$model$phi
cluster_dist <- textmineR::CalcHellingerDist(phi)
hc <- hclust(as.dist(cluster_dist),input$methodaglo)
cor(x = as.dist(cluster_dist), cophenetic(hc))
})
biptr <- reactive({
biptr = input$biptransf
biptr
})
observeEvent(input$runbiplot,{
req(values$tidy_theta)
if(input$load == "load"){
bip <- values$tidy_theta %>%
dplyr::group_by(topic,diario) %>%
dplyr::summarise(proportion= mean(gamma))%>%
tidyr::spread(diario, proportion)
bip <- data.frame(bip)
names<-bip$topic}
else if(input$load == "import"){
bip <- values$tidy_theta %>%
group_by(topic,Newspaper) %>%
dplyr::summarise(proportion= mean(gamma))%>%
tidyr::spread(Newspaper, proportion)
bip <- data.frame(bip)
names<-bip$topic}
bip <- dplyr::select(bip,-"topic")
row.names(bip)<-names
if (input$selectypebiplot == 'HJ_Biplot') {
values$Biplot <- HJBiplot(bip,Transform.Data = biptr())
}
else if(input$selectypebiplot == 'JK_Biplot') {
values$Biplot <- JKBiplot(bip,Transform.Data = biptr())
}
else if(input$selectypebiplot == 'GH_Biplot') {
values$Biplot <- GHBiplot(bip,Transform.Data = biptr())
}
})
output$eigen <- DT::renderDT({
req(values$Biplot)
dat <- values$Biplot
dat <- data.frame(dat$eigenvalues)
DT::datatable(data = dat,
colnames=c("value"),
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
#lengthMenu = list(c(5,10,20000,-1),
c('5', '10', 'All')
)
)%>%
DT::formatRound( columns= c("dat.eigenvalues"),
digits=5)
})
output$variance <- DT::renderDT({
req(values$Biplot)
dat <- values$Biplot
dat <- data.frame(dat$explvar)
DT::datatable(data = dat,
colnames=c("Percent %"),
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
#lengthMenu = list(c(5,10,20000,-1),
c('5', '10', 'All')
)
)%>%
DT::formatRound( columns= c("dat.explvar"),
digits= 2)
})
output$loading <- DT::renderDT({
req(values$Biplot)
dat <- values$Biplot
dat <- data.frame(dat$loadings)
DT::datatable(data = dat%>% mutate_if(is.numeric, round, digits=5),
#colnames=c("Percent %"),
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
scrollX = TRUE,
fixedColumns = TRUE,
buttons = c('pageLength',
'copy',
'csv',
'excel',
'pdf',
'print'),
pagelength = 5,
#lengthMenu = list(c(5,10,20000,-1),
c('5', '10', 'All')
)
)
})
output$indcoor <- DT::renderDT({
req(values$Biplot)
dat <- values$Biplot
dat <- data.frame(dat$coord_ind)
DT::datatable(data = dat%>% mutate_if(is.numeric, round, digits=5),
#colnames=c("Percent %"),
extensions = 'Buttons',
options = list(dom = 'Bfrtip',
scrollX = TRUE,