How do I make the graph and table change automatically after changing my sliderinput. I made a function, where the value of k (number of clusters) can vary from 2 to 18. I left the value of k at 8. If I change the value of k via code, shiny generates a new table and figure. However I would like to change the shiny by my slider input. Can you help me please? The executable code, as well as the shiny code are below.
library(shiny)
library(ggplot2)
library(rdist)
library(geosphere)
library(kableExtra)
library(readxl)
library(tidyverse)
#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), 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), 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), Waste = c(526, 350, 526, 469, 285, 175, 175, 350, 350, 175, 350, 175, 175, 364,
+ 175, 175, 350, 45.5, 54.6)), class = "data.frame", row.names = c(NA, -19L))
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)
data_table_1<-kable(data_table_1[order(data_table_1$cluster), c(1,4,2,3)], align = "c", row.names = FALSE) %>%
kable_styling(full_width = FALSE)
#Scatter Plot
suppressPackageStartupMessages(library(ggplot2))
df1<-as.data.frame(center_mass)
colnames(df1) <-c("Latitude", "Longitude", "cluster")
g<-ggplot(data=df, aes(x=Longitude, y=Latitude, color=factor(clusters))) + geom_point(aes(x=Longitude, y=Latitude), size = 4)
Centro_View<- g + geom_text(data=df, mapping=aes(x=eval(Longitude), y=eval(Latitude), label=Waste), size=3, hjust=-0.1)+ geom_point(data=df1, mapping=aes(Longitude, Latitude), color= "green", size=4) + geom_text(data=df1, mapping = aes(x=Longitude, y=Latitude, label = 1:k), color = "black", size = 4)
plotGD<-print(Centro_View + ggtitle("Scatter Plot") + theme(plot.title = element_text(hjust = 0.5)))
}
ui <- fluidPage(
titlePanel("Clustering "),
sidebarLayout(
sidebarPanel(
helpText(h3("Generation of clustering")),
radioButtons("filter1", h3("Waste Potential"),
choices = list("Select all properties" = 1,
"Exclude properties that produce less than L and more than S" = 2),
selected = 1),
radioButtons("filter2", h3("Coverage do cluster"),
choices = list("Use default limitations" = 1,
"Do not limite coverage" = 2
),selected = 1),
sliderInput("Slider", h3("Number of clusters"),
min = 2, max = 18, value = 8)
),
mainPanel(
plotOutput("tabela"),
plotOutput("ScatterPlot")
)))
server <- function(input, output) {
f1<-renderText({input$filter1})
f2<-renderText({input$filter2})
Modelclustering<- function.clustering(df,input$Slider,1,1))
output$tabela<-renderTable(Modelclustering[["plot_env"]][["data_table_1"]])
output$ScatterPlot<-renderPlot(Modelclustering[["plot_env"]][["plotGD"]])
}
# Run the application
shinyApp(ui = ui, server = server)
Thank you very much!
