app.R

#
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
#    http://shiny.rstudio.com/
#
#install.packages('leaflet')
#install.packages('leaflet.extras')
Sys.setlocale("LC_CTYPE", locale="Arabic")
library(shiny)
library(leaflet)
library(tidyverse)
library(leaflet.extras)
#needed for color palette
library(RColorBrewer)
# df_geocode <- read.csv('geocoded.csv')
# df_geocode$w <- as.character(df_geocode$w)
# df <- readxl::read_xlsx('E:/Misc/01. Tech/01. Programing/03. Projects/04. Election Analysis/Scraper/spiders/output/sheet.xlsx',
#                        sheet = 'Sheet1')
# df %>%  group_by(w,wilaya_id,Surname_neutral) %>%  summarise(n())  %>%  arrange(desc(`n()`)) %>% rename(n = `n()`) -> df_grouped
# 
# merge(x = df_grouped, y= df_geocode) -> df
df_slice <- df
ui<-navbarPage("Interactive App", id="nav",
               
               
               tabPanel("Tribal Distribution",
                        div(class="outer",
                            tags$head(
                              # Include custom CSS inspired by :https://shiny.rstudio.com/gallery/superzip-example.html
                              includeCSS("www/styles.css")
                            ),
                            #Main window of leaflet map
                            leafletOutput("plotmap", width="100%", height="100%"),
                            #Left-side filter pane
                            absolutePanel(id = "controls", class = "panel panel-default", fixed = TRUE,
                                          draggable = FALSE, top = 60, left =20 , right = "auto", bottom = "auto",
                                          width = 330, height = "auto",

                                          h2("Tribal Distribution"),
                                          checkboxInput(inputId = 'inCluster',label = "Enable proximity clustering",value=TRUE),
                                          selectInput(inputId = 'inTribe',label = 'Tribe:',choices = c('All',as.character(sort(unique(df$Surname_neutral),decreasing = F))), selected = 'All'),
                                          conditionalPanel("input.inMake != 'All'",
                                                           selectInput(inputId = 'inModel',label = 'Car Model:',choices = "")),
                                          sliderInput(inputId = 'inYear',label="Manufacturing Year",min=1900,max=2017,value=c(1900,2017)),
                                          sliderInput(inputId = 'inPrice',label="Price Range in K$",min=0,max=1000,value=c(0,1000))
                            )#,
                        #     #Right-side plots pane
                        #     absolutePanel(id = "Plots",h2(HTML('<p style="text-align: center;">Cars Statistics</p>')), class = "panel panel-default", fixed = TRUE,
                        #                   draggable = FALSE, top = 60, left = "auto", right = 20, bottom = "auto",
                        #                   width = 400, height = "auto",
                        #                   plotOutput(outputId = 'plotPrice',height =300,
                        #                              click = 'plotPrice_click',
                        #                              brush=brushOpts(id='plotPrice_brush')),
                        #                   plotOutput(outputId = 'histMonth',height = 200)
                        #                   ,radioButtons("in_agg_sel","Select aggregation method", c("Car Counts" = "CNT", "Price Average" = "AVG") , inline=T)
                        #                   
                        #     ),
                        #     
                        #     tags$div(id="cite"," This data has been collected in March 2017")
                          )
               ),
               
               tabPanel("Explorer",
                        fluidRow(
                          column(12,
                                 dataTableOutput('mytable')
                          )
                        )
               )
)


















# Define server procedures for visulaisation
server<-function(input, output,session) {
  showModal(modalDialog(
    title = "Introduction",
    includeHTML('www/Intro.htm'),
    width = 370, height = "auto",
    easyClose = TRUE
  ))
  #draw leaflet map based on lat-lon bounds in the data
  output$plotmap <- renderLeaflet({
    leaflet(data = df) %>% addProviderTiles(providers$CartoDB.DarkMatter) %>%
      # add option to select map layout
      addLayersControl(
        baseGroups = c("OSM (default)", "Toner", "Toner Lite"),
        options = layersControlOptions(collapsed = TRUE)) %>%
      fitBounds(~min(Lon), ~min(Lat), ~max(Lon), ~max(Lat))
  })  
  # #function Filter the data based on user selection
  df_slice<-reactive({
    bounds <- input$plotmap_bounds
    latRng <- range(bounds$north, bounds$south)
    LonRng <- range(bounds$east, bounds$west)
    print(c(latRng,LonRng))
    if (input$inTribe=='All' ){
      slice<-subset(df,Lat >= latRng[1] & Lat <= latRng[2] &
                      Lon >= LonRng[1] & Lon <= LonRng[2] )
    }else if (input$inMake!='All' ){
      slice<-subset(df,latRng[1] & Lat <= latRng[2] &
                      Lon >= LonRng[1] & Lon <= LonRng[2]) #&
                      # Surname_neutral==input$inTribe)
    }

  })
  # 
  # #function Filter the data based on brush at the scatter plot
  # df_slice_b<-reactive({
  #   if (is.null(input$plotPrice_brush)){
  #     return(df_slice())
  #   }else{
  #     return(brushedPoints(df_slice(),input$plotPrice_brush) )
  #   }
  # })
  # 
  ###################################################################################
  #Function to interactivly draw the marker for listings
  DrawMarkers<-reactive({

    # if (input$inCluster==TRUE)
    # {
    #   #Draw markers clustered together
    #   leafletProxy('plotmap',data=df_slice_b()) %>%
    #     clearShapes() %>%
    #     clearMarkers()%>%
    #     clearMarkerClusters()%>%
    #     addCircleMarkers(
    #       ~Lon, ~Lat,
    #       stroke=F,
    #       fillOpacity=.8,
    #       popup = ~paste('<strong>Manufacturer:</strong>', Make,
    #                      '</br><strong>Model:</strong>', Model,
    #                      '</br><strong>Year:</strong>', Year,
    #                      '</br><strong>Price:</strong>', Price,'AUD',
    #                      '</br><strong>URL:</strong>', paste('<a href="',url,'" target="_blank">Go to Gumtree Listing</a>'),
    #                      '</br><strong>Odometer:</strong>', Km,
    #                      '</br><strong>Transmision:</strong>', Trans,
    #                      '</br><strong>Registraion:</strong>', Reg,
    #                      '</br><strong>Fuel:</strong>', Fuel,
    #                      '</br><strong>AC:</strong>', AC),
    #       popupOptions  = labelOptions(noHide = F, direction = 'top', textOnly = T,textsize=9)
    #       ,color=~pal(PriceCluster)
    #       ,clusterOptions = markerClusterOptions()
    #     )
    # }
    #Draw individual marker per listing
    # if (input$inCluster==FALSE)
    # {
      leafletProxy('plotmap',data=df_slice()) %>%
        clearShapes() %>%
        clearMarkers()%>%
        #addWebGLHeatmap(lng=~Lon, lat=~Lat, size = 60000)
        clearMarkerClusters()%>%
        addCircleMarkers(
          ~Lat,
          ~Lon,
          stroke=F,
          fillOpacity=.8,
          radius=4,
          popup = ~paste(w),
          clusterOptions = markerClusterOptions()
        )
        # }
  })
  # 
  # 
  # ###################################################################################
  # 
  # 
  # 
  # #Populate model drop down list based on Maker selection
  # observeEvent(input$inMake,{
  #   x<-input$inMake
  #   Models <-unique(Makers_Models[Makers_Models$Make==as.character(x),2])
  #   updateSelectInput(session, inputId = 'inModel',label = 'Car Model:',choices = c('All',as.character(Models)),selected = 'All')
  #   DrawMarkers()
  #   
  #   
  # })
  # 
  # observeEvent(input$plotPrice_click,{
  #   DrawMarkers()
  # })
  # observeEvent(input$plotPrice_brush,{ DrawMarkers() })
  # observeEvent(input$inYear,{ DrawMarkers() })
  # observeEvent(input$inMake,{ DrawMarkers() })
  # observeEvent(input$inModel,{ DrawMarkers() })
  # observeEvent(input$inPrice,{ DrawMarkers() })
  # observeEvent(input$inCluster,{ DrawMarkers() })
  # observeEvent(input$plotPrice_click,{DrawMarkers()})
  # 
  # #Draw histogram
  # output$histMonth<-renderPlot({
  #   if (input$in_agg_sel=='CNT'){
  #     ggplot(data=df_slice_b(), aes(x=DateListed))+geom_bar(aes(y=..count..),fill='light blue')+
  #       labs(
  #         x = "Month",
  #         y = "Cars Count",
  #         title='Cars Listed Per Month')+
  #       theme(plot.title = element_text(hjust = 0.5, face='bold'))
  #   }else if(input$in_agg_sel=='AVG'){
  #     ggplot(df_slice_b(), aes(x=DateListed, y=Price)) + stat_summary(fun.y="mean", geom="bar",fill='light blue')+
  #       labs(
  #         x = "Month",
  #         y = "average listings price (AUD)",
  #         title='Average listings Price ')+
  #       theme(plot.title = element_text(hjust = 0.5, face='bold'))
  #   }
  # })
  # #Draw Pric .Vs. Year scatter Plot
  # output$plotPrice<-renderPlot({
  #   ggplot(df_slice(), aes(x=Year, y=Price,colour = PriceCluster)) +
  #     geom_point() +
  #     scale_colour_manual(name = "PriceCluster",values = PriceColors)+
  #     scale_y_continuous(labels = comma)+
  #     labs(
  #       x = "Manuf. Year",
  #       y = "Price(AUD)",
  #       colour = "Price Cluster",
  #       title='Price.VS.Age Distribution')+
  #     theme(legend.position = "bottom")+
  #     theme(plot.title = element_text(hjust = 0.5,face='bold'))
  #   
  # })
  # # Render Table 
  # output$mytable <- renderDataTable(df_slice_b())
  
  observe({
    DrawMarkers()
  
    })
  
}

shinyApp(ui, server)