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shiny/app-numeric.R

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+############################################
+# Data Professor                           #
+# http://youtube.com/dataprofessor         #
+# http://github.com/dataprofessor          #
+# http://facebook.com/dataprofessor        #
+# https://www.instagram.com/data.professor #
+############################################
+
+# Import libraries
+library(shiny)
+library(data.table)
+library(randomForest)
+
+# Read in the RF model
+model <- readRDS("model.rds")
+
+
+####################################
+# User interface                   #
+####################################
+
+ui <- pageWithSidebar(
+
+  # Page header
+  headerPanel('Iris Predictor'),
+
+  # Input values
+  sidebarPanel(
+    HTML("<h3>Input parameters</h4>"),
+    numericInput("Sepal.Length", label = "Sepal Length", value = 5.0),
+    numericInput("Sepal.Width", label = "Sepal Width", value = 3.6),
+    numericInput("Petal.Length", label = "Petal Length", value = 1.4),
+    numericInput("Petal.Width", label = "Petal Width", value = 0.2),
+
+    actionButton("submitbutton", "Submit", class = "btn btn-primary")
+  ),
+
+  mainPanel(
+    tags$label(h3('Status/Output')), # Status/Output Text Box
+    verbatimTextOutput('contents'),
+    tableOutput('tabledata') # Prediction results table
+
+  )
+)
+
+####################################
+# Server                           #
+####################################
+
+server<- function(input, output, session) {
+
+  # Input Data
+  datasetInput <- reactive({  
+
+    df <- data.frame(
+      Name = c("Sepal Length",
+               "Sepal Width",
+               "Petal Length",
+               "Petal Width"),
+      Value = as.character(c(input$Sepal.Length,
+                             input$Sepal.Width,
+                             input$Petal.Length,
+                             input$Petal.Width)),
+      stringsAsFactors = FALSE)
+
+    Species <- 0
+    df <- rbind(df, Species)
+    input <- transpose(df)
+    write.table(input,"input.csv", sep=",", quote = FALSE, row.names = FALSE, col.names = FALSE)
+
+    test <- read.csv(paste("input", ".csv", sep=""), header = TRUE)
+
+    Output <- data.frame(Prediction=predict(model,test), round(predict(model,test,type="prob"), 3))
+    print(Output)
+
+  })
+
+  # Status/Output Text Box
+  output$contents <- renderPrint({
+    if (input$submitbutton>0) { 
+      isolate("Calculation complete.") 
+    } else {
+      return("Server is ready for calculation.")
+    }
+  })
+
+  # Prediction results table
+  output$tabledata <- renderTable({
+    if (input$submitbutton>0) { 
+      isolate(datasetInput()) 
+    } 
+  })
+
+}
+
+####################################
+# Create the shiny app             #
+####################################
+shinyApp(ui = ui, server = server)