r语言 - Link selectInput with sliderInput in shiny

Question

朋友们，我希望我的selectInput链接到我的输出表中出现的集群数量。换句话说，它似乎被分为5个集群。在selectInput中，我希望它显示如下：

选择集群

1

2

3

4

5

也就是说，我的选择输出将取决于我的幻灯片输入。我该怎么做？我的可执行代码如下：

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,
"Coverage" = coverage
))
}
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),
radioButtons("filtro2", h3("Coverage"),
choices = list("Limite coverage" = 1, 
"No limite coverage" = 2
),selected = 1),
radioButtons("gasoduto", h3("Preference for the location"),
choices = list("big production" = 1, 
"small production"= 2
),selected = 1),
tags$hr(),
tags$b(h3("Satisfied?")),
radioButtons("satisfaction","", choices = list("Yes" = 1,"No " = 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),
tags$hr(),
actionButton("reset", "Clean")
),
mainPanel(
tabsetPanel(      
tabPanel("Solution", DTOutput("tabela"))))
)),
tabPanel("Route and distance",
sidebarLayout(
sidebarPanel(
selectInput("select", label = h3("Select the cluster"),"")
),
mainPanel(
tabsetPanel(
tabPanel("Distance", plotOutput(""))))
))))
server <- function(input, output) {
f1<-renderText({input$filter1})
f2<-renderText({input$filter2})

Modelclustering<-reactive(function.clustering(df,input$Slider,1,1))

output$tabela <- renderDataTable({
data_table_1 <- req(Modelclustering())[[1]]
x <- datatable(data_table_1[order(data_table_1$cluster),c(1,4,2,3)],
options = list(columnDefs = list(list(className = 'dt-center', targets = 0:3)), 
paging =TRUE,searching = FALSE,
pageLength =  10,lenghtMenu=c(5,10,15,20),scrollx=T
), rownames = FALSE)%>% formatRound(c(3:4), 2,mark = ",")%>%
formatStyle(columns = c(3:4), 'text-align' = 'center')
return(x)
})
output$ScatterPlot <- renderPlot({
Modelclustering()[[2]]
})
}
shinyApp(ui = ui, server = server)

非常感谢朋友们！

新的更新

我插入了updateSelectiInput函数(代码如下(，通过这种方式，我成功地放置了相应数量的集群。然而，我想把它以列表的形式留下，而不是像我在开头描述的那样是5：

observeEvent(input$Slider,{
updateSelectInput(session,'select',
choices=unique(df[df==input$Slider]))
}) 

Answer 1

您非常接近更新表达式。你只需要：

observeEvent(input$Slider,{
updateSelectInput(session,'select',
choices=unique(1:input$Slider))
}) 

另一种方法是使用CCD_ 1。在ui中，我们可以放置一个占位符：，而不是创建一个空的selectInput

uiOutput("select_clusters")

然后在服务器中，我们填充这个占位符：

output$select_clusters <- renderUI({
selectInput("select", label = h3("Select the cluster"), choices = 1:input$Slider)
})

---

编辑

要使observeEvent(或eventReactive(对多个输入作出反应，请将输入或反应体包装在c():中

observeEvent(c(input$SLIDER, input$FILTER),{
updateSelectInput(session,'select',
choices=unique(1:input$Slider))
}) 

但是，如果您需要这样做，我认为采用renderUI方法更有意义，并提供灵活性。这可能看起来像：

output$select_clusters <- renderUI({
req(input$slider)
req(input$filter)
df2 <- df[df$something %in% input$filter, ]
selectInput("select", 
label = h3("Select the cluster"), 
choices = df2$something)
})

一般情况下，使用update*Input功能，您只能更新现有的小部件，而不能删除它。但如果集群数量=1，则根本不需要选择输入。使用renderUI，如果条件需要，您可以使用空HTML容器(div()(来"隐藏"uiOutput/renderUI0：

what_to_do <- reactive({
req(input$Slider)
if (input$Slider == 1) {
x <- div() 
} else {
x <- selectInput("select", 
label = h3("Select the cluster"), 
choices = 1:input$Slider)
}
return(x)
})
output$select_clusters <- renderUI({
what_to_do()
})