basic R shiny app with data visualizationdataset

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+#
+# This is a Shiny web application. You can run the application by clicking
+# the 'Run App' button above.
+#
+# Find out more about building applications with Shiny here:
+#
+#    http://shiny.rstudio.com/
+#
+library(shinythemes)
+library(shiny)
+library(DT)
+library(data.table)
+library(ggplot2)
+library(shinycssloaders)
+
+
+# Defining Non Changing Variables
+data <- fread("2020.10.01.csv")
+data_na_removed <- na.omit(data)
+
+# Encoding the Label Column
+# 1 - Benign 2 - Malicious 3 - Outlier
+data_encoded <- data_na_removed
+data_encoded$label <- factor(data_na_removed$label,
+                               levels = c("benign", "malicious", "outlier"),
+                               labels = c(1, 2, 3))
+data_encoded$label = as.numeric(data_encoded$label)
+# Define Default Values
+pch = 16
+features <- c("Average Input","Incoming Bytes","Outgoing Bytes",
+              "Destination IP", "Destination Port", "Entropy", 
+              "Inbound Packets", "Outbound Packets", "Protocol",
+              "Source IP", "Source Port", "Start Time (s)", 
+              "End Time (s)", "Total Entropy", "Type", "Duration")
+feature_variables <- c("avg_ipt", "bytes_in", "bytes_out", "dest_ip", 
+                       "dest_port", "entropy", "num_pkts_out", "num_pkts_in",
+                       "proto", "src_ip", "src_port", "time_end", "time_start",
+                       "total_entropy", "label", "duration")
+
+# Define Elementary Functions
+get_color <- function(a = 1) {
+    return(alpha("#e95420", a))
+}
+
+# Define UI for application
+ui <- fluidPage(
+    theme = shinytheme("united"),
+    # Application title
+    titlePanel("A Comprehensive Approach To Analysis and Detection of Emerging 
+               Threats due to Network Intrusion"),
+
+    navbarPage(
+        "Network Intrusion Detection Demo",
+        tabPanel(
+            icon("home"),
+            p("Through this application, it is intended to develop a demo of a",
+            strong("Network Intrusion Detection System"), 
+            "using different Machine Learning Techniques using the 
+            LUFlow Network Intrusion Detection Data Set. This page is intended
+            to display the information about the dataset."
+            ,style="text-align:justify;color:black;
+            background-color:lavender;padding:15px;border-radius:10px"),
+            br(),
+            p("The data used in this application are publicly available on the",
+            em("LUFlow Network Intrusion Detection Data Set"), "Kaggle page. 
+            The Data Set contains telemetry cap- tured using Cisco’s Joy tool. 
+            This tool records multiple measurements asso- ciated with flows. 
+            Features are engineered from these measurements, which are also 
+            outlined below",style="text-align:justify;color:black;
+            background-color:papayawhip;padding:15px;border-radius:10px"),
+            hr(),
+            tags$style(".fa-database {color:#e95420}"),
+            h3(p(icon("database",lib = "font-awesome"),
+                 em("Dataset Exploration "),
+                 style="color:black;text-align:center")),
+            fluidRow(column(DT::dataTableOutput("renderData"),
+                            width = 12)),
+            hr(),
+            p(em("Developed by"), br("Kumar Priyansh, Ritu Dimri,
+                                     Sandeep Perumalla, Hemanth Katikala"), 
+              style="text-align:center; font-family: times")
+        ),
+        tabPanel(
+            "Data Visualization",
+            p("This part allows you to visualize features via different types of
+              plots. You can select whatever features you want to plot and hit
+              the \"Plot Graph\" button. Please keep in mind that all plots",
+              strong("might not be useful"), 
+              "and you need to select which plots you want to visualize. If you
+              want to save an image of the currently visualized plot, please
+              right click on the plot and click on the relevant",
+              strong("save image"),
+              "option."
+              ,style="text-align:justify;color:black;
+            background-color:lavender;padding:15px;border-radius:10px"),
+            sidebarLayout(
+                sidebarPanel(
+                    selectInput(
+                        "plotType",
+                        p("Type of Plot:"),
+                        choices = c(Histogram = "hist",
+                                    "Scatter Plot" = "scatter",
+                                    "Mosaic Plot" = "mosaic")
+                    ),
+                    # Only show this panel if the plot type is a histogram
+                    conditionalPanel(
+                        condition = "input.plotType == 'hist'",
+                        selectInput(
+                            "plotVariable",
+                            p("Feature to Visualize:"),
+                            choices = features
+                        ),
+                        selectInput(
+                            "plotVariant",
+                            p("Plot Variant:"),
+                            choices = c("Normal", "Log 10 Scale")
+                        )   
+                    ),
+                    
+                    # Only show this panel if the plot type is a scatter plot
+                    conditionalPanel(
+                        condition = "input.plotType == 'scatter'",
+                        selectInput(
+                            "plotVariable1",
+                            p("First Feature to Visualize:"),
+                            choices = features
+                        ),
+                        uiOutput("secondSelection")
+                    ),
+                    
+                    # Single Mosiac Plot for now
+                    conditionalPanel(
+                        condition = "input.plotType == 'mosaic'",
+                        selectInput(
+                            "mosaicVariable",
+                            p("Select Features to Visualize:"),
+                            choices = c("Labels vs Protocols" = "labproto")
+                        )
+                    ),
+                    actionButton("plot", "Plot Graph",
+                                 width = "100%", icon = icon("chart-line"),
+                                 style="color: #fff; background-color: #e95420;
+                                 outline: none")
+                ),
+                mainPanel(
+                    withSpinner(
+                        plotOutput("selectedFeatureVariableForVisualization"),
+                        type = 6, color = "#e95420"
+                    )
+                )
+            )
+        ),
+        tabPanel(
+            "Compare Models"
+        )
+    )
+)
+
+# Define server logic
+server <- function(input, output) {
+    output$renderData <- DT::renderDataTable(
+        DT::datatable({
+            data_na_removed
+        },
+        options = list(
+            initComplete = JS(
+                "function(settings, json) {",
+                "$(this.api().table().header()).css({'background-color': 
+                'moccasin', 'color': '1c1b1b'});",
+                "}"),
+            columnDefs=list(list(className='dt-center',targets="_all"))),
+        style = 'bootstrap',
+        class = 'cell-border stripe',
+        rownames = FALSE,
+        colnames = features)
+    )
+    
+    output$secondSelection <- renderUI({
+        selectedFeature <- input$plotVariable1
+        selectInput(
+            "plotVariable2",
+            p("Second Feature to Visualize:"),
+            choices = features[!features %in% selectedFeature]
+        )
+    })
+
+    output$selectedFeatureVariableForVisualization <- renderPlot({
+        input$plot
+        isolate({
+            plotType <- input$plotType
+            if (plotType == 'hist') {
+                selectedFeature <- input$plotVariable
+                plotVariant <- input$plotVariant
+                positionInFeatureArray <- which(features == selectedFeature)
+                selectedFeatureVariable <- feature_variables[positionInFeatureArray]
+                if (plotVariant == "Normal") {
+                    hist(data_encoded[[selectedFeatureVariable]], 
+                         main = paste("Histogram Plot of", selectedFeature, sep = " ", collapse = NULL),
+                         ylab = "Frequency", xlab = selectedFeature,
+                         col = get_color(), pch = pch)
+                } else {
+                    nonZeroSelectedFeature = data_encoded[data_encoded[[selectedFeatureVariable]] > 0]
+                    hist(log(nonZeroSelectedFeature[[selectedFeatureVariable]]), 
+                         main = paste("Log 10 Base Histogram Plot of", selectedFeature, sep = " ", collapse = NULL),
+                         ylab = "Frequency", xlab = selectedFeature,
+                         col = get_color(), pch = pch)
+                } 
+            } else if (plotType == 'scatter') {
+                firstFeature <- feature_variables[which(features == 
+                                                            input$plotVariable1)]
+                secondFeature <- feature_variables[which(features == 
+                                                             input$plotVariable2)]
+                try(plot(data_encoded[[firstFeature]], data_encoded[[secondFeature]],
+                         main = paste("Scatter Plot of", input$plotVariable1,
+                                      "vs", input$plotVariable2, sep = " ", collapse = NULL),
+                         ylab = input$plotVariable2, xlab = input$plotVariable1,
+                         col = get_color(0.02),
+                         pch = 16,), silent = TRUE)
+            } else {
+                selectedFeatures <- input$mosaicVariable
+                if (selectedFeatures == 'labproto') {
+                    proto_label_mosaic <- table(data_encoded$proto, data_encoded$label)
+                    mosaicplot(~ factor(proto)+factor(label, labels=c("benign","malicious","outlier")),
+                               data = data_encoded,xlab = "Protocol", ylab = "Category", 
+                               main= "Mosaic plot of Protocol vs Category",shade = TRUE)
+                }
+            }
+        })
+    })
+}
+
+# Run the application 
+shinyApp(ui = ui, server = server)