闪亮的地图上的散点图
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我如何在地图上绘制散点图？我设法绘制了散点图，但是我希望将其绘制在地图上。我相信一种选择是使用传单包，因为我具有经度和纬度坐标，但是我不知道如何使用它。请，如果您有其他选择，请随时。您能帮我解决这个问题吗？可执行代码如下。
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！
答案
我认为可以帮助您的几件事。
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)
也...
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)
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