Shiny apps runs locally and preciously deployed without issue, now it won't load the data but I haven't changed the data or filepaths

...Hello,
I am very new to shiny and am building an app for the upcoming election.
I had previously deployed versions of the attached app with no issues and the .shp and .csv data loaded just fine. Yesterday, I added in some css styling and now the app will load but it won't read any of the data once deployed. The app works fine on my local rstudio console and when opened in a browser from the console, but the map and plotly/ggplot charts wont display when deployed. I haven't changed any of the data or file paths to the .shp and .csv files I only changed the formatting/ styling of the app. I also had a colleague try in case the issue was my local environment but he is encountering the same issues.

Session information:
R version 3.5.2 (2018-12-20)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 17134)

Here is a link to my personal shinyapps account where the old versions of the app is running with no issues using the same data files and file paths https://rchappell.shinyapps.io/Updated_Dashboard/
I just need to upload the app on my company's paid shinyapps.io account and it doesn't work.

I am just confused as it used to deploy with no issues but now the data/ map/charts won't read/display. I am sure I have just done something silly but for the life of me I can't see what it is and neither can my colleague. I can supply the data if required it just wont let me upload it here.
I have one .csv file and one .shp file used for the app.

Things I have tried:

changing the instances
time-out period
upload time out etc from within the shinyapps.io settings
clearing my local r studio environment
updating r and re-installing all the required packages

Thanks in advance any and all help will be very much appreciated.
Thank you

#Load req packages
library(rgdal);library(maptools);library(ggplot2);library(plyr);
library(dplyr);library(rgeos);library(sp);
library(raster);library(RColorBrewer);library(ggpubr);library(ggthemes);
library(scales);library(OpenStreetMap);library(rJava);library(ggmap);
library(RJSONIO);library(osmar);library(leaflet);library(ggmap);
library(maps);library(htmlwidgets);library(htmltools);library(tidyverse); 
library(reshape2);library(ggalt); library(ggplot2); library(rsconnect); library(data.table);
library(leaflet.minicharts); library(shiny); library(sf); library(shinythemes);
library(plotly); library(shinydashboard); library(htmlTable)



#Load csv containing all the data we've aggreagated at the CED level
df<-read.csv("data/CED_aggregated_data_DB.csv")
df$X <- NULL #remove cols you dont need
is.num <- sapply(df, is.numeric)#remove the decimal points in the whole df
df[is.num] <- lapply(df[is.num], round, 0)

#create the percentage values to display in the pop-ups 
#df$Participation_rate_pop_up<-df$CED_participation_rate_2018/100
#df$Unemployment_rate_pop_up<-df$Unemployment_rate/100

#create sep data frames with the diff cols you will use for distributions
df.age <- reshape2::melt(df[,c(1, 5:40)])
#we are taking the suffix 'females", "males" etc and creatung a separate column in each df
#we are using this separate column to create the breakdowns in the distributions repeat for each one that you want a breakdown for
setDT(df.age)[, paste0("variable",2:3):= tstrsplit(variable, "_age_")]
df.income <- reshape2::melt(df[,c(1, 47:72)])
setDT(df.income)[, paste0("variable",2:3):= tstrsplit(variable, "_wkly_")]
df.edu<-reshape2::melt(df[,c(1, 102:119)])
setDT(df.edu)[, paste0("variable",2:3):= tstrsplit(variable, "_edu_")]
df.ind<-reshape2::melt(df[,c(1, 73:94)])
setDT(df.ind)[, paste0("variable",2:3):= tstrsplit(variable, "_emp_")]
df.dss<-reshape2::melt(df[,c(1, 128:137)])
df.tenure<-reshape2::melt(df[,c(1, 138:140)])
df.dwelling<-reshape2::melt(df[,c(1,141:143)])
df.self_emp<-reshape2::melt(df[,c(1,99:101)])
df.mig<-reshape2::melt(df[,c(1, 120:127)])
df.proj.pop<-reshape2::melt(df[,c(1, 41:46)])
df.elect.parties<-reshape2::melt(df[,c(1, 147:151)])
#create a df with the unemp and participation rates to add to the elected parties
df.workforce<-(df[,c(1, 152:153)])
df.table<-merge(df.workforce,df.elect.parties, by= "Elect_div")
#reshape the data tabel to suit the table format we want displayed below the map
#long format is more conducive to adding in more information down the track
df.melted<-melt(df.table, idvars="Elect_div", measure.vars=c("Unemployment_rate_2018", "CED_participation_rate_2018","Elected_Party_2016", "Elected_Party_2013",
                                                          "Elected_Party_2010", "Elected_Party_2007", "Elected_Party_2004"))
#we dont need the variable colum because we are using row names in the table
#be sure to update the row names in the table to reflect the order or the variables (see melt function above)
df.melted$variable <- NULL
#we want the value column first from a display point of view for the table
df.melted<-df.melted[,c(ncol(df.melted),1:(ncol(df.melted)-1))]

#load(file="AppData/merged_shape_data.rda")

qld<-readOGR(dsn=path.expand("./data/shape_files"), layer="E_AUGEC_region")
qld<-qld[qld$Elect_div %in% c("Blair","Bonner","Bowman","Brisbane", #filter to only have qld elects
                              "Capricornia","Dawson","Dickson","Fadden",
                              "Fairfax","Fisher","Flynn","Forde",
                              "Griffith","Groom","Herbert","Hinkler",
                              "Kennedy","Leichhardt","Lilley",
                              "Longman","Maranoa","McPherson",
                              "Moncrieff","Moreton","Oxley",
                              "Petrie","Rankin","Ryan",
                              "Wide Bay","Wright"),]
qld_centre <- data.frame(gCentroid(qld)) #find centres of polygons for labelling
centroids <- as.data.frame(coordinates(qld))
names(centroids) <- c("long", "lat")#capture the lat longs for the centres of each elctoral division
centroids$Elect_div <- factor(qld$Elect_div)#add the elect_divs to the centroids data


#merge the csv to the shape file based on elect_div
qld.stats <- merge(qld, df, by = "Elect_div")
#create a twitter share button so users can easily share on twitter
#the link also includes suggested text for the tweet
url <- " https://twitter.com/intent/tweet?text=Dig%20deeper%20into%20your%20federal%20electorate's%20data%20using%20this%20app%20developed%20by%20RIDL&hashtags=AUSVOTES&hashtags=RIDL&url=https://regionalinnovationdatalab.shinyapps.io/Dashboard/"


#~~~~~~~~~~~~~~~~~~~APPLICATION/ Dashboard~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

ui<- fluidPage(tags$head(HTML("<title>RIDL: election app</title> <link rel='icon' type='image/gif/png' href='grif_uni_cent.png'>")),
                
  includeCSS("www/style/bootstrap.css"),
#navbarPage(title=(div(img(src="au_grif.gif",style="margin-top: -14px; padding-right:10px;padding-bottom:10px", height = 60, "Developed by the Regional Innovation Data Lab"),#tags$a(href='https://www.griffith.edu.au/griffith-business-school/policy-innovation-hub/regional-innovation-data-lab'),
              
            titlePanel(title = (div(img(src="grif_uni_cent.png",style="margin-top: -14px; padding-right:10px;padding-bottom:10px", height = 100,"2019 Federal Electorate Profiles"),
            br(),          
             
            
              column(6,tags$a(href=url, "Share on Twitter", class="twitter-share-button"),
                      includeScript("http://platform.twitter.com/widgets.js"),
                      leafletOutput("map",  height = "1000px"),
                      htmlOutput("table"),
                   
              actionButton(inputId='Top10', label="Top 10 seats to watch", style="color: #FFF; background-color: #FF0000; border-color: #000033; padding-bottom:10px; font-size:10px",
                           onclick ="window.open('https://apsc.govcms.gov.au/machinery-government-mog-changes-guide', '_blank')"),
              actionButton(inputId='MP', label="How MPs vote on issues that matter", style="color: #FFF; background-color: #000033; border-color: #000033; padding-bottom:10px; font-size:10px",
                           onclick ="window.open('https://theyvoteforyou.org.au/', '_blank')"),
               
              actionButton(inputId = 'pdf', label= "FAQ doc", style="color: #FFF; background-color: #0099CC; border-color: #000033; padding-bottom:10px; font-size:10px",
                           onclick = "window.open('ridl_app_support_doc.pdf'),'_blank')")), 
              
               
               
              column(6,actionButton(inputId='Find', label="Find your electorate", style="color: #FFF; background-color: #FF0000; border-color: #000033; padding-bottom:10px; font-size:15px",
                                    onclick ="https://electorate.aec.gov.au/', '_blank')"),
                     br(),
                     
                                    selectInput("division", "",
                                    label="Select an electorate, graphs below will be updated.",
                                    choices = qld.stats$Elect_div), selectInput("stats", "",label="Select a statistic to display spatially on the map.",
                                                                                choices= list("Sitting Party"="PartyNm", 
                                                                                              "Work-force participation rate"='CED_participation_rate_2018',
                                                                                              "Unemployment rate"='Unemployment_rate_2018')),
               
                             column(6,plotlyOutput("ageBar", height="300px"),plotlyOutput("projpopBar", height = "300px"),
                                      plotlyOutput("eduBar", height = "300px"), plotlyOutput("incomeBar", height = "300px"),
                                      plotlyOutput("indBar",height="300px"),plotlyOutput("selfempBar", height = "300px"),                
                                      plotlyOutput("migBar",height="300px"),plotlyOutput("dssBar",height="300px"),               
                                      plotlyOutput("tenureBar", height = "300px"),plotlyOutput("dwellingBar",height="300px")))               
              
  ))))
br()
#~~~~~~AESTHETIC STUFF~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
#colour scheme for bar charts
mycol=c("#000033", "#0099CC", "#339900","#FFCC33","#FF0000")

#colour scheme for bar charts with male female breakdowns
#blue=female red=male
MF=c("#000033","#FF0000")
#remove scientific notation from y axis labels
point <- format_format(big.mark = " ", decimal.mark = ",", scientific = FALSE)

#~~~~SERVER CODE~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
server <- function(input, output, session) {
 #add buttons
  output$Find <- renderUI({
    actionButton("Find")  
  })
  output$Top10 <- renderUI({
    actionButton("Top10")  
  })
  output$MP <- renderUI({
    actionButton("MP")  
  })
  output$pdf <- renderUI({
    actionButton("pdf")  
  })
  
  #creating a reative table that displays the data from df.melted based on the selected electoral division
   selectedData<-eventReactive(df.melted$Elect_div==input$division,  {
    
    # Create the table (using table from htmlTables package)
    HTML(
      htmlTable(subset(df.melted,df.melted$Elect_div==input$division), 
                  align="r",
                  header=c("",""),
                  rnames= paste(c("2018 unemployment rate %","2018 participation rate %", "Party elected 2016",
                            "Party elected 2013", "Party elected 2010", "Party elected 2007", "Party elected 2004")), 
             
                  caption="Historic elected party data from the Australian Electoral Commission (AEC)",
                  tfoot="&dagger;Participation & Unemployment rate calculated using data from Australian Government Dept of Jobs & Small Business (2018)")           
             
     )
    
  })
  output$table <- renderUI({selectedData()})
  
#Create the plots for all the different bar charts we want displayed using the dfs we created above (age, income etc.)
#using ggplot and plotly to create the bar charts/ distributions
  
  #population per electorate by age and sex
  output$ageBar <- renderPlotly({
      ageplot<-ggplot(
      subset(df.age,df.age$Elect_div==input$division),
      aes(variable2, value)) + geom_bar(stat = "identity", position ="stack", aes(fill = variable3))+
      scale_x_discrete(labels=c("0-4","5-9","10-14","15-19","20-24","25-29","30-34", "35-39","40-44", 
                                "45-49","50-54","55-59","60-64","65-69", 
                                "70-74","75-79", "80-84",">85")) +
      theme_classic() + scale_fill_manual(values = MF, guide=FALSE) +
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+
      labs(title = "Age of population", subtitle = "Data from ABS", x = "", y = "", fill= "Sex")
    
  ggplotly(ageplot)%>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })
  
 #projected population per CED 
  output$projpopBar <- renderPlotly({
    projpopplot<-ggplot(subset(df.proj.pop, df.proj.pop$Elect_div == input$division),
                    aes(variable, value)) + geom_bar(stat = "identity", aes(fill = value))+
      scale_x_discrete(labels = c("2016", "2021", "2026", "2031", "2036", "2041")) +
      scale_y_continuous(labels = point) + #remove scientific notation
      theme_classic() + scale_fill_gradientn(colours = mycol, guide=FALSE) + 
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+ 
      labs(title = "Projected population", subtitle = "Data from Qld Gov", x = "", y = "")
   ggplotly(projpopplot)%>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })
  
  #highest level of education per CED by Sex
   output$eduBar <- renderPlotly({
    edplot<-ggplot(subset(df.edu, df.edu$Elect_div == input$division),
                   aes(x=fct_inorder(variable2),y= value)) + geom_bar(stat = "identity", position ="stack", aes(fill = variable3))+
      scale_x_discrete(labels = c("Year 8","Year 9","Year 10","Year 11",  "Year 12", "Grad Dip", "Advanced Dip", "Bachelor's", "Post-Graduate")) +
      theme_classic() + scale_fill_manual(values = MF, guide=FALSE) +
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+
      labs(title = "Highest level of schooling completed", subtitle = "Data from ABS", x = "", y = "", fill="Sex")
    ggplotly(edplot)%>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })
  
  #weekly income per CED by Sex 
  output$incomeBar <- renderPlotly({
    incplot<-ggplot(
      subset(df.income,df.income$Elect_div==input$division),
      aes(x=fct_inorder(variable2),y= value)) + geom_bar(stat = "identity", position ="stack", aes(fill = variable3))+
      scale_x_discrete(labels=c("$1-149","$150-299","$300-399","$400-499","$500-649", "$650-799", "$800-999", "$1000-1249", "$1250-1499", "$1500-1749",
                                "$1750-1999", "$2000-2999", ">$3000"))+
      theme_classic() + scale_fill_manual(values = MF, guide=FALSE)+
      theme(axis.text.x = element_text(angle=45, hjust = 1,size = 8, face= 'bold'))+
      labs(title = "Weekly personal income",  subtitle = "Data from ABS",x = "", y = "", fill="Sex")
   ggplotly(incplot)%>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })
  
  #employment by industry per CED by sex
  output$indBar <- renderPlotly({
   indplot<- ggplot(subset(df.ind, df.ind$Elect_div == input$division),
                    aes(x=fct_inorder(variable2),y= value)) + geom_bar(stat = "identity", position ="stack", aes(fill = variable3))+
      scale_x_discrete(labels = c("Arts & Rec", "Hlth & Soc Services", "Real estate", 
                                  "Finance", "Logistics","Sci & Tech", "Media & Telecom",
                                  "Public Serv & Safety", "Mining", "Retail", "Agri,Foresty & Fishing")) +
      theme_classic() + scale_fill_manual(values = MF, guide=FALSE) +
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+
      labs(title = "Employment by industry/sector", subtitle = "Data from ABS", x = "", y = "", fill="Sex")
 
   ggplotly(indplot)%>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })

  #number of ppl who are self employed per CED
  output$selfempBar <- renderPlotly({
    selfplot<-ggplot(subset(df.self_emp, df.self_emp$Elect_div == input$division),
           aes(variable, value)) + geom_bar(stat = "identity", aes(fill = value))+
      scale_x_discrete(labels = c("Total", "Male", "Female", "Indigenous")) +
      theme_classic() + scale_fill_gradientn(colours = mycol, guide=FALSE) +
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+ 
      labs(title = "Self-employment",  subtitle = "Data from ABS", x = "", y = "")
    ggplotly(selfplot)%>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })
  
  #number of people who had a change of address (interstate and overseas)
  output$migBar <- renderPlotly({
    migplot<-ggplot(subset(df.mig, df.mig$Elect_div == input$division),
                     aes(variable, value)) + geom_bar(stat = "identity", aes(fill = value))+
      scale_x_discrete(labels = c("ACT", "NSW", "NT", "SA", "Tas", "Vic", "WA", "O/S")) +
      theme_classic() + scale_fill_gradientn(colours = mycol, guide=FALSE) +
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+ 
      labs(title = "Change of address",  subtitle = "Data from ABS", x = "", y = "")
    ggplotly(migplot)%>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })
  
  #DSS payments per CED
  output$dssBar <- renderPlotly({
    dssplot<-ggplot(subset(df.dss, df.dss$Elect_div == input$division),
           aes(variable, value)) + geom_bar(stat = "identity", aes(fill = value))+
      scale_x_discrete(labels = c("Age Pension", "Carer", "Disability", "Low Income", "Newstart", 
                                  "Pensioner", "Cwlth Rent Assist", "Cwlth Seniors Hlth", "Family Tax Ben A", "Family Tax Ben B")) +
      theme_classic() + scale_fill_gradientn(colours = mycol, guide=FALSE) +
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+ 
      labs(title = "Dept of Social Services (DSS) payments 2018", subtitle = "Data from DSS", x = "", y = "")
      
  ggplotly(dssplot)%>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })
  
  #tenure type per CED
  output$tenureBar <- renderPlotly({
    tenureplot<-ggplot(subset(df.tenure, df.tenure$Elect_div == input$division),
           aes(variable, value)) + geom_bar(stat = "identity", aes(fill = value))+
      scale_x_discrete(labels = c("Owned w mortgage", "Owned outright","Rented")) +
      theme_classic() + scale_fill_gradientn(colours = mycol, guide=FALSE) +
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+ 
      labs(title = "Tenure type", subtitle = "Data from ABS", x = "", y = "")
  ggplotly(tenureplot) %>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE))})
  
  #dwelling structure per CED
  output$dwellingBar <- renderPlotly({
    dwelplot<-ggplot(subset(df.dwelling, df.dwelling$Elect_div == input$division),
           aes(variable, value)) + geom_bar(stat = "identity", aes(fill = value))+
      scale_x_discrete(labels = c("Flat/Appartment","Semi-detached", "Separate home")) +
      theme_classic() + scale_fill_gradientn(colours = mycol, guide=FALSE) +
      theme(axis.text.x = element_text(angle=45, hjust = 1, size = 8, face= 'bold'))+ 
      labs(title = "Dwelling structure type", subtitle = "Data from ABS", x = "", y = "")
   ggplotly(dwelplot) %>% config(displayModeBar = F) %>% layout(xaxis=list(fixedrange=TRUE)) %>% layout(yaxis=list(fixedrange=TRUE)) })
  
  
  #~~~create reactive colour palettes we want the political party colours to be displayed as mapped to each
  #party when Sitting Party (PartyAb) is selected and a different one for all the other selected stats
  factpal <-reactive({
    colorFactor(c("red","#33165F","blue"), domain = qld.stats[[input$stats == "PartyAb"]])
  })
  
  colorpal <- reactive({
    colorFactor(colorRamp(c("red", "blue")), domain = qld.stats[[input$stats]], reverse = FALSE)
 
  })
    
   
  
  #create the base map that will be displayed regardless of selected input
  output$map<-renderLeaflet({
    
    leaflet(qld.stats) %>%
      addProviderTiles(providers$OpenStreetMap.BlackAndWhite) %>% #(providers$OpenStreetMap.BlackAndWhite)%>% 
      # Centre the map in the middle of our co-ordinates
      fitBounds(min(137.99),max(-29.18),min(153.55),max(-9.12))
    
  }) 
  
  #when sitting party or Party Ab is selected from input$stats set colour palette to factpal which is set to the party colours
  #use othe colour palette for all other selected input$stats
  observe({
    if (input$stats == "PartyAb") {
      pal <- factpal()

    } else {
      pal <- colorpal()
    }
    
    #creating the lables for the pop-ups on each clickable map-shape (polygon)
   
    labels <- sprintf(
      "<strong>%s</strong><br/>
      Sitting member: %s <br/><br/>
      Sitting party: %s <br/><br/>
      Margin for seat (percentage): %s<br/><br/>
      Population: %s<br/>", 
      qld.stats$Elect_div, qld.stats$sitting_member,qld.stats$PartyNm, qld.stats$margins,
      qld.stats$CED_pop_total) %>% lapply(htmltools::HTML)
      #scales::percent(qld.stats$Unemployment_rate_pop_up), scales::percent(qld.stats$Participation_rate_pop_up)
    
    
    #creating a proxy map that displays the various stats from the stats drp down 
    leafletProxy("map", data = qld.stats) %>%
      clearShapes() %>%
      addPolygons(
        layerId = qld.stats$Elect_div,
        fillColor = ~pal(qld.stats[[input$stats]]),
        fillOpacity = 0.4,
        weight = 0.5,
        opacity = 1,
        color = "#444444",
        dashArray = "3",
        label = labels,
        highlight = highlightOptions(
          weight = 3,
          color = "#FFFFFF",
          dashArray = "",
          fillOpacity = 0.9,
          bringToFront = TRUE),
        labelOptions = labelOptions(
          style = list("font-weight" = "normal", padding = "3px 5px"),
          textsize = "13px",
          direction = "auto")
      )
    
    #we are adding a legend to display the raw data that aligns with the spatially depicted stat from the stats drop-down
    #this information is also displayed in the pop-ups for each clickable electorate
    varname<-switch(input$stats,
                    "PartyNm"="Sitting Party",                                                                                                                                                                                             "Electorate Population"="CED_pop_total",
      'CED_participation_rate_2018'="Work-force participation rate %",
      'Unemployment_rate_2018'="Unemployment rate %")
    
    
     leafletProxy("map", data = qld.stats) %>% clearControls() %>%
      addLegend(pal = pal, opacity = 0.7,  title = varname,
                values = ~qld.stats[[input$stats]],labels = c(min(input$stats), max(input$stats)),
                position = "topleft")
  })
  
  
  #we want to create a reactivity so users can either select the division 
  #from the drop down menu or by clicking on the map
  
  observe({
    event <- input$map_shape_click
    if (is.null(event))
      return()
    updateSelectInput(session, "division", selected = event$id)
  })
  
  #we want to create reactivity so that the map to zooms in on and focus on the selected electorate
  observe({
    selectedPolygon <- subset(qld.stats, qld.stats$Elect_div == input$division)
    leafletProxy("map", data = qld.stats) %>%
      removeShape("highlightedPolygon") %>%
      fitBounds(selectedPolygon@bbox[1,1],
                selectedPolygon@bbox[2,1],
                selectedPolygon@bbox[1,2],
                selectedPolygon@bbox[2,2]) %>%
      addPolylines(weight = 4, color = "white",
                   data = selectedPolygon, layerId = "highlightedPolygon")
  })
  
  
}




shinyApp(ui, server)

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