Update app.R

app.R

@@ -1,58 +1,163 @@
 library(shiny)
-library(bslib)
 library(dplyr)
-library(ggplot2)
+library(shinythemes)
+library(highcharter)
 
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
+# Generate synthetic data with U-shaped & inverted U-shaped effects
+generate_complex_data <- function() {
+  set.seed(123)
+  days <- 30
+  data <- data.frame(
+    day = 1:days,
+    date = seq.Date(from = as.Date("2024-01-01"), by = "day", length.out = days),
+    footTraffic = sample(200:600, days, replace = TRUE),
+    adSpend = sample(400:1200, days, replace = TRUE),
+    discountPercent = sample(0:40, days, replace = TRUE),
+    socialMediaEngagement = sample(200:1200, days, replace = TRUE),
+    competitorDistance = sample(1:15, days, replace = TRUE),
+    websiteVisits = sample(400:1000, days, replace = TRUE),
+    pricePoint = sample(40:100, days, replace = TRUE),
+    stockAvailability = sample(40:100, days, replace = TRUE)
+  )
+  
+  # U-shaped effect on pricePoint
+  price_factor <- -0.7 * (data$pricePoint - 70)^2
+  
+  # Inverted U-shaped effect on foot traffic
+  foot_traffic_effect <- 2.5 * (data$footTraffic - 400) + 80
+  
+  # Realistic effect of stock availability (moderate stock works better)
+  stock_effect <- 0.60 * (data$stockAvailability - 70) + 50
+  
+  # Final sales equation
+  data$sales <- round(
+    foot_traffic_effect +      # Inverted U-shaped
+      data$adSpend * 0.2 +     
+      data$discountPercent * 10 + 
+      data$socialMediaEngagement * 0.1 + 
+      (data$competitorDistance) * 45 + 
+      data$websiteVisits * 0.15 + 
+      price_factor +           # U-shaped
+      stock_effect +           # Realistic stock effect
+      runif(days, -100, 100)   # Random noise
+  )
+  
+  data$sales <- pmax(data$sales, 0) # Ensure non-negative sales
+  return(data)
+}
 
-ui <- page_sidebar(
-  theme = bs_theme(bootswatch = "minty"),
-  title = "Penguins explorer",
-  sidebar = sidebar(
-    varSelectInput("xvar", "X variable", df_num, selected = "Bill Length (mm)"),
-    varSelectInput("yvar", "Y variable", df_num, selected = "Bill Depth (mm)"),
-    checkboxGroupInput("species", "Filter by species",
-      choices = unique(df$Species), selected = unique(df$Species)
+store_data <- generate_complex_data()
+
+# UI
+ui <- fluidPage(
+  theme = shinytheme("cosmo"),
+  
+  titlePanel("Fashion Sales Simulator - Highcharter Edition"),
+  
+  tabsetPanel(
+    tabPanel("Guided Analysis",
+             sidebarLayout(
+               sidebarPanel(
+                 selectInput("selected_metric", "Select a Variable:", 
+                             choices = names(store_data)[-c(1,2,10)],
+                             selected = "footTraffic")
+               ),
+               mainPanel(
+                 highchartOutput("guided_plot"),
+                 verbatimTextOutput("correlation")
+               )
+             )
     ),
-    hr(), # Add a horizontal rule
-    checkboxInput("by_species", "Show species", TRUE),
-    checkboxInput("show_margins", "Show marginal plots", TRUE),
-    checkboxInput("smooth", "Add smoother"),
-  ),
-  plotOutput("scatter")
+    
+    tabPanel("Free Exploration",
+             sidebarLayout(
+               sidebarPanel(
+                 selectInput("x_var", "Select X Variable:", choices = names(store_data)[-c(1,2,10)]),
+                 selectInput("y_var", "Select Y Variable:", choices = names(store_data)[-c(1,2,10)]),
+                 actionButton("compute_corr", "Compute Correlation")
+               ),
+               mainPanel(
+                 highchartOutput("free_explore_plot"),
+                 verbatimTextOutput("free_corr_output")
+               )
+             )
+    )
+  )
 )
 
+# Server
 server <- function(input, output, session) {
-  subsetted <- reactive({
-    req(input$species)
-    df |> filter(Species %in% input$species)
+  
+  # Compute correlation dynamically
+  compute_correlation <- function(x, y) {
+    cor(store_data[[x]], store_data[[y]], use = "complete.obs")
+  }
+  
+  # Guided Analysis Highchart Plot
+  output$guided_plot <- renderHighchart({
+    data_points <- lapply(1:nrow(store_data), function(i) {
+      list(store_data[[input$selected_metric]][i], store_data$sales[i])
+    })
+    
+    highchart() %>%
+      hc_chart(type = "scatter") %>%
+      hc_title(text = paste("Sales vs", input$selected_metric)) %>%
+      hc_xAxis(title = list(text = input$selected_metric)) %>%
+      hc_yAxis(title = list(text = "Sales")) %>%
+      hc_add_series(
+        data = data_points,
+        type = "scatter",
+        name = "Data Points",
+        marker = list(radius = 4),
+        color = "blue"
+      ) %>%
+      hc_tooltip(pointFormat = paste0(
+        "<b>", input$selected_metric, ":</b> {point.x}<br>",
+        "<b>Sales:</b> {point.y}"
+      )) %>%
+      hc_exporting(enabled = TRUE)
+  })
+  
+  output$correlation <- renderText({
+    corr_value <- compute_correlation(input$selected_metric, "sales")
+    paste("Correlation with Sales:", round(corr_value, 3))
+  })
+  
+  # Free Exploration Scatter Plot with Highcharter
+  output$free_explore_plot <- renderHighchart({
+    req(input$x_var, input$y_var)
+    
+    data_points <- lapply(1:nrow(store_data), function(i) {
+      list(store_data[[input$x_var]][i], store_data[[input$y_var]][i])
+    })
+    
+    highchart() %>%
+      hc_chart(type = "scatter") %>%
+      hc_title(text = paste(input$x_var, "vs", input$y_var)) %>%
+      hc_xAxis(title = list(text = input$x_var)) %>%
+      hc_yAxis(title = list(text = input$y_var)) %>%
+      hc_add_series(
+        data = data_points,
+        type = "scatter",
+        name = "Data Points",
+        marker = list(radius = 4),
+        color = "red"
+      ) %>%
+      hc_tooltip(pointFormat = paste0(
+        "<b>", input$x_var, ":</b> {point.x}<br>",
+        "<b>", input$y_var, ":</b> {point.y}"
+      )) %>%
+      hc_exporting(enabled = TRUE)
+  })
+  
+  # Compute correlation in Free Exploration
+  observeEvent(input$compute_corr, {
+    output$free_corr_output <- renderText({
+      corr_value <- compute_correlation(input$x_var, input$y_var)
+      paste("Correlation between", input$x_var, "and", input$y_var, ":", round(corr_value, 3))
+    })
   })
-
-  output$scatter <- renderPlot(
-    {
-      p <- ggplot(subsetted(), aes(!!input$xvar, !!input$yvar)) +
-        theme_light() +
-        list(
-          theme(legend.position = "bottom"),
-          if (input$by_species) aes(color = Species),
-          geom_point(),
-          if (input$smooth) geom_smooth()
-        )
-
-      if (input$show_margins) {
-        margin_type <- if (input$by_species) "density" else "histogram"
-        p <- p |> ggExtra::ggMarginal(
-          type = margin_type, margins = "both",
-          size = 8, groupColour = input$by_species, groupFill = input$by_species
-        )
-      }
-
-      p
-    },
-    res = 100
-  )
 }
 
+# Run App
 shinyApp(ui, server)