Create connection between numericInput and function in a Shiny app

Question

The code below generates a map with clusters. The cluster number will depend on the k variable that is inside my function. To get this k value, I use the Weighted Sum Method (WSM) calculation. Note that for this calculation it is necessary to choose the weights of the criteria, in my case there are only two. Therefore, k can vary depending on the chosen weights. In my function I manually put ( weights <- c(0.5,0.5) ). However, I would like to put the weights from the two numericInput I created. So how to do this? Another thing, in this case, the map is only generated after the weights are selected.

This question can help: Approach without inserting all the code on the server

library(shiny)
library(rdist)
library(geosphere)
library(shinythemes)
library(leaflet)
library(shinyjs)

function.cl<-function(df,k){
  
  #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), 
                     Coverage = c (1526, 2350, 3526, 2469, 1285, 2433, 2456),
                     Production = c(526, 350, 526, 469, 285, 433, 456)), class = "data.frame", row.names = c(NA, -7L))
  
  
  #Calculation WSM
  
  weights <- c(0.5,0.5) 
  
  scaled <- df |>
    mutate(Coverage = min(Coverage) / Coverage,
           Production = Production / max(Production))
  
  scaled <- scaled |>
    rowwise() |>
    mutate(`Performance Score` = weighted.mean(c(Coverage, Production), w = weights))
  
  scaled$Rank <- (nrow(scaled) + 1) - rank(scaled$`Performance Score`)
  
  k<-subset(scaled, Rank==2)$Properties #number of clusters
  
  
  
  #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 
  df1<-df[c("Latitude","Longitude")]
  
  
  #Color and Icon for map
  ai_colors <-c("red","gray","blue","orange","green","beige")
  
  clust_colors <- ai_colors[df$cluster]
  icons <- awesomeIcons(
    icon = 'ios-close',
    iconColor = 'black',
    library = 'ion',
    markerColor =  clust_colors)
  
  # Map for all clusters:
  m1<-leaflet(df1) %>% addTiles() %>%
    addMarkers(~Longitude, ~Latitude) %>%
    addAwesomeMarkers(lat=~df$Latitude, lng = ~df$Longitude, icon=icons, label=~as.character(df$cluster)) %>% 
    addLegend( position = "topright", title="Cluster", colors = ai_colors[1:max(df$cluster)],labels = unique(df$cluster))
  
  plot1<-m1
  

  
  
  return(list(
    "Plot1" = plot1
  ))
}

ui <- bootstrapPage(
  useShinyjs(),
  navbarPage(theme = shinytheme("flatly"), collapsible = TRUE,
             "Cl", 
             tabPanel("Solution",
                      sidebarLayout(
                        sidebarPanel(
                          
                          numericInput("weight1", label = h4("Weight 1"),
                                       min = 0, max = 1, value = NA, step=0.1),
                          
                          disabled(numericInput("weight2", label = h4("Weight 2"),
                                                min = 0, max = 1, value = NA, step=0.1)),
                          
                          helpText("The sum of weights should be equal to 1")),

                        mainPanel(
                          tabsetPanel(      
                            tabPanel("Solution", (leafletOutput("Leaf1",width = "95%", height = "600")))))
                        
                      ))))

server <- function(input, output, session) {
  
  Modelcl<-reactive({
    function.cl(df,k)
  })
  
  output$Leaf1 <- renderLeaflet({
    
    Modelcl()[[1]]
  })
  
  observeEvent(input$weight1, {
    freezeReactiveValue(input, "weight2")
    updateNumericInput(session, 'weight2', value = 1 - input$weight1)
  })
  
}

shinyApp(ui = ui, server = server)

Answer 1

Why don't you redesign your function to this:

function.cl<-function(weights){
...
}

and in the reactive call on the server side you do this:

Modelcl<-reactive({
    function.cl(weights=c(input$weight1, input$weight2))
  })