Download button not working in shiny

Friends, could you help me make the Download button work in my code. I created the button for Downloand, which is basically to download the table and figure in PDF. The executable code is below.

library(shiny)
library(ggplot2)
library(rdist)
library(geosphere)
library(kableExtra)
library(readxl)
library(tidyverse)
library(DT)

#database
df<-structure(list(Properties = c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35), Latitude = c(-23.8, -23.8, -23.9, -23.9, -23.9,  -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, -23.9, 
                                                                                                                                                 + -23.9, -23.9, -23.9, -23.9, -23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9,-23.9), Longitude = c(-49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.6, -49.7, 
                                                                                                                                                                                                                                                                                                     + -49.7, -49.7, -49.7, -49.7, -49.6, -49.6, -49.6, -49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6,-49.6), Waste = c(526, 350, 526, 469, 285, 175, 175, 350, 350, 175, 350, 175, 175, 364, 
                                                                                                                                                                                                                                                                                                                                                                                                                                                                          + 175, 175, 350, 45.5, 54.6,350,350,350,350,350,350,350,350,350,350,350,350,350,350,350,350)), class = "data.frame", row.names = c(NA, -35L))

function.clustering<-function(df,k,Filter1,Filter2){
  
  if (Filter1==2){
    Q1<-matrix(quantile(df$Waste, probs = 0.25)) 
    Q3<-matrix(quantile(df$Waste, probs = 0.75))
    L<-Q1-1.5*(Q3-Q1)
    S<-Q3+1.5*(Q3-Q1)
    df_1<-subset(df,Waste>L[1]) 
    df<-subset(df_1,Waste<S[1])
  }
  
  #cluster
  coordinates<-df[c("Latitude","Longitude")]
  d<-as.dist(distm(coordinates[,2:1]))
  fit.average<-hclust(d,method="average") 
  
  
  #Number of clusters
  clusters<-cutree(fit.average, k) 
  nclusters<-matrix(table(clusters))  
  df$cluster <- clusters 
  
  #Localization
  center_mass<-matrix(nrow=k,ncol=2)
  for(i in 1:k){
    center_mass[i,]<-c(weighted.mean(subset(df,cluster==i)$Latitude,subset(df,cluster==i)$Waste),
                       weighted.mean(subset(df,cluster==i)$Longitude,subset(df,cluster==i)$Waste))}
  coordinates$cluster<-clusters 
  center_mass<-cbind(center_mass,matrix(c(1:k),ncol=1)) 
  
  #Coverage
  coverage<-matrix(nrow=k,ncol=1)
  for(i in 1:k){
    aux_dist<-distm(rbind(subset(coordinates,cluster==i),center_mass[i,])[,2:1])
    coverage[i,]<-max(aux_dist[nclusters[i,1]+1,])}
  coverage<-cbind(coverage,matrix(c(1:k),ncol=1))
  colnames(coverage)<-c("Coverage_meters","cluster")
  
  #Sum of Waste from clusters
  sum_waste<-matrix(nrow=k,ncol=1)
  for(i in 1:k){
    sum_waste[i,]<-sum(subset(df,cluster==i)["Waste"])
  }
  sum_waste<-cbind(sum_waste,matrix(c(1:k),ncol=1))
  colnames(sum_waste)<-c("Potential_Waste_m3","cluster")
  
  #Table1
  data_table <- Reduce(merge, list(df, coverage, sum_waste))
  data_table <- data_table[order(data_table$cluster, as.numeric(data_table$Properties)),]
  data_table_1 <- aggregate(. ~ cluster + Coverage_meters + Potential_Waste_m3, data_table[,c(1,7,6,2)], toString)
  
  #Plot1
  #Scatter Plot
  suppressPackageStartupMessages(library(ggplot2))
  df1<-as.data.frame(center_mass)
  colnames(df1) <-c("Latitude", "Longitude", "cluster")
  g<-ggplot(data=df,  aes(x=Longitude, y=Latitude,  color=factor(clusters))) + geom_point(aes(x=Longitude, y=Latitude), size = 4)
  Centro_View<- g +  geom_text(data=df, mapping=aes(x=eval(Longitude), y=eval(Latitude), label=Waste), size=3, hjust=-0.1)+ geom_point(data=df1, mapping=aes(Longitude, Latitude), color= "green", size=4) + geom_text(data=df1, mapping = aes(x=Longitude, y=Latitude, label = 1:k), color = "black", size = 4)
  plot1<-print(Centro_View + ggtitle("Scatter Plot") + theme(plot.title = element_text(hjust = 0.5)))
  
  return(list(
    "Data" = data_table_1,
    "Plot" = plot1
    
  ))
  
}


ui <- fluidPage(
  
  titlePanel("Clustering "),
  
  
  sidebarLayout(
    sidebarPanel(
      helpText(h3("Generation of clustering")),
      
      radioButtons("filter1", h3("Waste Potential"),
                   choices = list("Select all properties" = 1, 
                                  "Exclude properties that produce less than L and more than S" = 2),
                   selected = 1),
      
      tags$hr(),
      
      radioButtons("filter2", h3("Are you satisfied with the solution"),
                   choices = list("Yes" = 1, 
                                  "No" = 2),
                   selected = 1),
      
      sliderInput("Slider", h3("Number of clusters"),
                  min = 2, max = 34, value = 8),
      
      tags$hr(),
      
      downloadButton("downloadData", "Download")),
    
    
    mainPanel(
      tabsetPanel( 
        tabPanel("Table",DTOutput("tabela")),
        tabPanel("Figure",plotOutput("ScatterPlot"))
        
      ))))

server <- function(input, output) {
  
  values <- reactiveValues(df = NULL)
  
  Modelclustering<-reactive(function.clustering(df,input$Slider,input$filter1,input$filter2))
  
  output$tabela <- renderDataTable({
    data_table_1 <- req(Modelclustering())[[1]]
    x <- datatable(data_table_1[order(data_table_1$cluster), c(1, 4, 2, 3)],
                   options = list(
                     paging =TRUE,
                     pageLength =  5,lengthChange=FALSE)
    ) %>% formatRound(c(3:4), 2)
    return(x)
  })
  
  output$ScatterPlot <- renderPlot({
    Modelclustering()[[2]]
  })

output$downloadData <- downloadHandler(
  filename = function() {
    paste(input$dataset, ".csv", sep = "")
  },
  content = function(file) {
    write.csv(datasetInput(), file, row.names = FALSE)
  })
}

# Run the application 
shinyApp(ui = ui, server = server)

Thank you very much!

Hi, I couldn't find downloadHandler your code,

To downloadButton get work, it needs to be declared to which object will return to download.

You might want to see this tutorial for download in shiny

if you want to export a pdf you will probably have to use rmarkdown/knitr

but the 'structure' would be the same as in the tutorial example you were linked to.
provide a downloadHandler with a filename function and a content function.
what you provide as content is up to you.
why dont you set up the download button to first produce a csv.
then study and make yourself a simple example of outputting a pdf with rmarkdown (without shiny being a consideration).
then combine your knowledge and make your download button output your pdf.

This topic was automatically closed 54 days after the last reply. New replies are no longer allowed.

Thanks for the reply friend. I inserted the "output$downloadData" on the server. But I couldn't make it work, could you help me ?? This example that you commented generates csv but not PDF. Would it be similar to do ??
Thank you again!