r - Plot scatterplot on a map in Shiny

Question

how do I plot my scatterplot on a map? I managed to plot my scatterplot, however I wanted it to be plotted on a map. I believe that an option is to use the leaflet package, since I have the Latitude and Longitude coordinates, but I don't know how to use it. Please, if you have other options feel free. Could you help me with this problem ?? The executable code is below.

Thank you very much!

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")

  #Output table
  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)

  #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)
  plotGD<-print(Centro_View + ggtitle("Scatter Plot") + theme(plot.title = element_text(hjust = 0.5)))

  return(list(
    "Data" = data_table_1,
    "Plot" = plotGD
  ))
}

ui <- bootstrapPage(
  navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
             "Clustering", 

             tabPanel("General Solution",

                      sidebarLayout(
                        sidebarPanel(
                          radioButtons("filtro1", h3("Select properties"),
                                       choices = list("All properties" = 1, 
                                                      "Exclude properties" = 2),
                                       selected = 1),

                          tags$b(h5("(a) Choose other filters")),
                          tags$b(h5("(b) Choose clusters")),  
                          sliderInput("Slider", h5(""),
                                      min = 2, max = 8, value = 5)
                      ),

                        mainPanel(
                          tabsetPanel(      
                            tabPanel("Solution", plotOutput("ScatterPlot"))))

                      ))))


server <- function(input, output, session) {

  Modelclustering<-reactive(function.clustering(df,input$Slider,1,1))

  output$ScatterPlot <- renderPlot({
    Modelclustering()[[2]]
  })

  observeEvent(input$Slider,{
    updateSelectInput(session,'select',
                      choices=unique(df[df==input$Slider]))
  }) 


}

shinyApp(ui = ui, server = server)

Thank you very much!

Answer 1

I can think of a couple things that may help you.

library(shiny)
library(ggplot2)

useri <- shinyUI(pageWithSidebar(
headerPanel("Reactive Plot"),
sidebarPanel(
selectInput('x','X-Axis',names(iris)),
selectInput('y','Y-Axis',names(iris)),
selectInput('color','Color',c('None',names(iris[5])))),
mainPanel(uiOutput("plotui"),dataTableOutput("plot_brushed_points"))))

serveri <- shinyServer(function(input,output) {
output$plot <- renderPlot({
p <- ggplot(iris,aes_string(x=input$x, y=input$y))+geom_point()+theme_bw()
if(input$color != 'None')
  p <- p + aes_string(color=input$color)
print(p)
})
output$plotui <- renderUI(plotOutput("plot",brush = brushOpts("plot_brush")))
output$plot_brushed_points <- renderDataTable(brushedPoints(iris,input$plot_brush,input$x,input$y), options=list(searching=FALSE, paging = FALSE))
})

shinyApp(useri, serveri)

Also...

library(shiny)
library(shinydashboard)
library(shinyjs)
library(glue)

ui <- dashboardPage(
  dashboardHeader(),
  dashboardSidebar(selectInput("cols", NULL, c(2, 3, 4, 6, 12), 4)),
  dashboardBody(
    useShinyjs(),
    div(
      box(solidHeader = TRUE,
          title = "Box",
          width = 4,
          status = "info",
          sliderInput("sld", "n:", 1, 100, 50),
          plotOutput("plt")
      ), id = "box-parent")
  )) 

server <- function(input, output) {
  observe({
    cols <- req(input$cols)
    runjs(code = glue('var $el = $("#box-parent > :first");',
                      '$el.removeClass(function (index, className) {{',
                      'return (className.match(/(^|\s)col-sm-\d+/g) || []).join(" ")',
                      '}});',
                      '$el.addClass("col-sm-{cols}");'))
  })

  output$plt <- renderPlot(plot(rnorm(input$sld), rnorm(input$sld)))
}

shinyApp(ui, server)