调整 selecInput 使其同时适用于两种不同的方法

我使用两种多标准方法来查找用于生成地图的簇号。方法有VIKOR and TOPSIS。对于每种方法，我需要知道标准的目标是什么，即是否最大化（max) 或最小化 (min）。我有两个标准，所以我创建了两个selectInput供用户选择是否愿意max or min。为了VIKOR方法，我能够通过以下方法进行调整selecInput，无需手动将标准目标放入代码中（正如您所看到的，我将# in maxmin <- c('min' ,'max')。到目前为止还好。

问题在于TOPSIS，因为与标准的目标相关，而不是max and min it is + or -，所以我无法建立此链接selecInput，正如我所做的那样VIKOR。你可以看到TOPSIS, I did i <- c("-", "+").

那么我该如何调整这个，也就是说，我可以与selectInput同时使用两种方法？

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

function.cl<-function(df,k,maxmin){
  
  #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))
  
  #Vikor
  df1 <- df[c(4:5)]
  df1<-data.matrix(df1)
  weights <- c(0.3,0.7) 
  #maxmin <- c('min','max')
  v <- 0.5
  scaled1<-VIKOR(df1,weights,maxmin,v)
  
  #Topsis
  i <- c("-", "+") 
  scaled2<-topsis(df1, weights, i)
  scaled2$rank <- rank(-scaled2$score,ties.method= "first")
  colnames(scaled2)<-c("Alternatives","score","Ranking2")
  
  #Merge both methdos
  table1 <- scaled1 %>% 
    left_join(scaled2 %>% 
                mutate(Alternatives = as.numeric(Alternatives))) %>% 
    select(Alternatives, contains("Ranking"))  
  
  #mode
  ModeFunc <- function(Vec) {
    tmp <- sort(table(Vec),decreasing = TRUE)
    Nms <- names(tmp)
    if(max(tmp) > 1) {
      as.numeric(Nms[1])
    } else NA}
  
  
  table2 <- table1 |> rowwise() |> 
    mutate(Mode = ModeFunc(c_across(Ranking:Ranking2))) %>% 
    data.frame()
  
  k<-subset(table2, Mode==3)$Alternatives #cluster number
  
  #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(
                          
                          selectInput("maxmin1", label = h5("Maximize or Minimize?"),
                                      choices = list("", "max", "min"), selected = "min"),
                          
                          selectInput("maxmin2", label = h5("Maximize or Minimize?"),
                                      choices = list("", "max", "min"), selected = "max")),
                        
                        
                        mainPanel(
                          tabsetPanel(      
                            tabPanel("Solution", (leafletOutput("Leaf1",width = "95%", height = "600")))))
                        
                      ))))

server <- function(input, output, session) {
  
  Modelcl<-reactive({
    function.cl(df,k,maxmin=c(input$maxmin1, input$maxmin2))
  })
  
  output$Leaf1 <- renderLeaflet({
    req(maxmin=c(input$maxmin1, input$maxmin2))
    Modelcl()[[1]]
  })
  
}

shinyApp(ui = ui, server = server)

如果您从 selectInputs 传入的值是maxmin，你可以这样做将其转换为+ and -:

i=list("max"="+", min="-")
scaled2<-topsis(df1, weights, as.vector(unlist(i[maxmin])))