Adjust the way to calculate the distance between two points in shiny code

[JojoSouza]

The shiny code below generates the route between two points, using the googleway function. Note that in addition, the distance between the two points is calculated, but this is the Euclidean distance and not the real distance using the googleway functions. The correct way to calculate the distance is by doing the following:

test<-google_directions(origin = c(-24.872139, -50.038787), destination = c(-24.9062992895515, -50.0125745903862), mode = "driving", alternatives = TRUE)

Distance<-sum(as.numeric(direction_steps(test)$distance$value)) 

However, I would like this form of calculation in the shiny code below, in order to calculate the route properly. Therefore, can you help me adjust the code in shiny?

Code in Shiny

library(shiny)
library(rdist)
library(dplyr)
library(geosphere)
library(shinythemes)
library(leaflet)
library(googleway)

set_key( "AIzaSyBD6kgTlgcTa6iwLwoWtKrKQI6QNodEkmo")

function.cl<-function(df,k,Filter1,Filter2){
  
  #database df
  df<-structure(list(Properties = c(1,2,3,4,5,6,7), 
                     Latitude = c(-23.8, -23.8, -23.9, -23.9, -23.9,-23.4,-23.5), 
                     Longitude = c(-49.6, -49.3, -49.4, -49.8, -49.6,-49.4,-49.2), 
                     Waste = c(526, 350, 526, 469, 285, 433, 456)), class = "data.frame", row.names = c(NA, -7L))
  
  #clusters
  coordinates<-df[c("Latitude","Longitude")]
  d<-as.dist(distm(coordinates[,2:1]))
  fit.average<-hclust(d,method="average") 
  clusters<-cutree(fit.average, k) 
  nclusters<-matrix(table(clusters))  
  df$cluster <- clusters 
  
  #database df1
  df1<-matrix(nrow=k,ncol=2)
  for(i in 1:k){
    df1[i,]<-c(weighted.mean(subset(df,cluster==i)$Latitude),
               weighted.mean(subset(df,cluster==i)$Longitude))}
  df1<-cbind(df1,matrix(c(1:k),ncol=1)) %>%
    data.frame()
  colnames(df1)<-c("Latitude","Longitude","cluster")
  
  #specific cluster and specific propertie
  print(Filter1)
  print(Filter2)
  df_spec_clust <- df1[df1$cluster == Filter1,]
  df_spec_prop<-df[df$Properties==Filter2,]
  
  #Table to join df and df1
  data_table <- df[order(df$cluster, as.numeric(df$Properties)),]
  data_table_1 <- aggregate(. ~ cluster, df[,c("cluster","Properties")], toString)
  

  # Map for route
  if(nrow(df_spec_clust>0) & nrow(df_spec_prop>0)) {

    df2<-google_directions(origin = df_spec_clust[,1:2], destination = df_spec_prop[,2:3], 
                           mode = "driving")
    
    
    df_routes <- data.frame(polyline = direction_polyline(df2))
    
    
    m3<-google_map() %>%
      add_polylines(data = df_routes, polyline = "polyline")
    
    plot1<-m3 
  } else {
    plot1 <- NULL
  }
  
  df2<-google_directions(origin = df_spec_clust[,1:2], destination = df_spec_prop[,2:3], 
                         mode = "driving")
  
  
  DISTANCE<- merge(df,df1,by = c("cluster"), suffixes = c("_df","_df1"))
  
  (DISTANCE$distance <- purrr::pmap_dbl(.l = list(DISTANCE$Longitude_df,
                                                    DISTANCE$Latitude_df,
                                                    DISTANCE$Longitude_df1,
                                                    DISTANCE$Latitude_df1),
                                          .f = ~distm(c(..1,..2),c(..3,..4))))
  
  

  return(list(
    "Plot1" = plot1,
    "DIST" = DISTANCE,
    "Data" = data_table_1,
    "Data1" = data_table
  ))
}

ui <- bootstrapPage(
  navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
             "Cl", 
             tabPanel("Solution",
                      sidebarLayout(
                        sidebarPanel(
                          tags$b(h3("Choose the cluster number?")),
                          sliderInput("Slider", h5(""),
                                      min = 2, max = 5, value = 3),
                          selectInput("Filter1", label = h4("Select just one cluster to show"),""),
                          selectInput("Filter2",label=h4("Select the cluster property designated above"),""),
                          h4("The distance is:"),
                          textOutput("dist"),
                        ),
                        mainPanel(
                          tabsetPanel(      
                            tabPanel("Gmaps", (google_mapOutput("Gmaps",width = "95%", height = "600")))
                        
                      ))))))

server <- function(input, output, session) {
  
  Modelcl<-reactive({
    function.cl(df,input$Slider,input$Filter1,input$Filter2)
  })
  

  output$Gmaps <- renderGoogle_map({
    Modelcl()[[1]]
  })
  
  observeEvent(input$Slider, {
    abc <- req(Modelcl()$Data)
    updateSelectInput(session,'Filter1',
                      choices=sort(unique(abc$cluster)))
  }) 
  
  observeEvent(c(input$Slider,input$Filter1),{
    abc <- req(Modelcl()$Data1) %>% filter(cluster == as.numeric(input$Filter1))
    updateSelectInput(session,'Filter2',
                      choices=sort(unique(abc$Properties)))})
  
  output$dist <- renderText({
    DIST <- data.frame(Modelcl()[[2]])
    DIST$distance[DIST$cluster == input$Filter1 & DIST$Properties == input$Filter2]
  })
  
  
}

shinyApp(ui = ui, server = server)

[nirgrahamuk]

This app is overly convoluted for what you are asking surely ? as what you are asking could be asked for some fixed data (i.e. no need to make things dynamic or provide views on them through a larger app.
Probably if you constructed a minimal example in a plain script / non shiny, you might even arrive at a satisfactory solution for yourself, or failing that, greatly increase your chances of support here by providing significantly less code for us to look at.