在 Shiny 的地图上绘制散点图 [英] Plot scatterplot on a map in Shiny

问题描述

如何在地图上绘制散点图?我设法绘制了我的散点图，但是我希望将它绘制在地图上.我相信一个选项是使用传单包，因为我有纬度和经度坐标，但我不知道如何使用它.请，如果您有其他选择，请随意.你能帮我解决这个问题吗?可执行代码如下.

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.

非常感谢！

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)

非常感谢！

推荐答案

我能想到一些对你有帮助的事情.

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)

还有……

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)

这篇关于在 Shiny 的地图上绘制散点图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！