Reset button in shiny

Question

Friends, could you help me make the Clear button work in my code. I created the button but I couldn't get it to work.. When pressing the Clear button, I would like to clear the generated table, as well as the figure. In addition, if the filters were used, I would like them to return to the standard it was.

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")

  #Table1
  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)

  #Plot1
  #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)
  plot1<-print(Centro_View + ggtitle("Scatter Plot") + theme(plot.title = element_text(hjust = 0.5)))

  return(list(
    "Data" = data_table_1,
    "Plot" = plot1

  ))

}


ui <- fluidPage(

  titlePanel("Clustering "),


  sidebarLayout(
    sidebarPanel(
      helpText(h3("Generation of clustering")),

      radioButtons("filter1", h3("Waste Potential"),
                   choices = list("Select all properties" = 1, 
                                  "Exclude properties that produce less than L and more than S" = 2),
                   selected = 1),

      tags$hr(),

      radioButtons("filter2", h3("Are you satisfied with the solution"),
                   choices = list("Yes" = 1, 
                                  "No" = 2),
                   selected = 1),

      sliderInput("Slider", h3("Number of clusters"),
                  min = 2, max = 34, value = 8),

    tags$hr(),
    actionButton("reset", "Clean all"),

    downloadButton("downloadData", "Download")),


    mainPanel(
      tabsetPanel( 
        tabPanel("Table",DTOutput("tabela")),
        tabPanel("Figure",plotOutput("ScatterPlot"))

    ))))

server <- function(input, output) {

  values <- reactiveValues(df = NULL)

  Modelclustering<-reactive(function.clustering(df,input$Slider,input$filter1,input$filter2))

  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(
                     paging =TRUE,
                     pageLength =  5,lengthChange=FALSE)
                   ) %>% formatRound(c(3:4), 2)
    return(x)
  })

   output$ScatterPlot <- renderPlot({
    Modelclustering()[[2]]
  })
}

observeEvent(input$reset,{
  values$df <- NULL
})


# Run the application 
shinyApp(ui = ui, server = server)

Thank you very much friends!

Answer 1

@Jovani -

To reset your radio buttons, use updateRadioButtons in your server .

You will need session for this, make sure this is an argument in your server function (added below).

Also, make sure your observeEvent is inside your server function (I assume it was since you said it was working).

server <- function(input, output, session) {

  values <- reactiveValues(df = NULL)

  Modelclustering<-reactive(function.clustering(df,input$Slider,input$filter1,input$filter2))

  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(
                     paging =TRUE,
                     pageLength =  5,lengthChange=FALSE)
    ) %>% formatRound(c(3:4), 2)
    return(x)
  })

  output$ScatterPlot <- renderPlot({
    Modelclustering()[[2]]
  })

  observeEvent(input$reset,{
    values$df <- NULL
    updateRadioButtons(session, "filter1", selected = 1)
    updateRadioButtons(session, "filter2", selected = 1)
  })
}