Update app.R

app.R

@@ -1,58 +1,127 @@
 library(shiny)
-library(bslib)
 library(dplyr)
-library(ggplot2)
-
-df <- readr::read_csv("penguins.csv")
-# Find subset of columns that are suitable for scatter plot
-df_num <- df |> select(where(is.numeric), -Year)
-
-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)
+library(shinythemes)
+library(plotly)
+
+# Generate synthetic data
+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)
+  )
+
+  price_factor <- -1.7 * (data$pricePoint - 70)^2
+  foot_traffic_effect <- 2.5 * (data$footTraffic - 200) + 80
+  stock_effect <- 0.80 * (data$stockAvailability - 70) + 50
+
+  data$sales <- round(
+    foot_traffic_effect + data$adSpend * 0.2 + data$discountPercent * 60 +
+      data$socialMediaEngagement * 0.5 + data$competitorDistance * 150 +
+      data$websiteVisits * 0.15 + price_factor + stock_effect + runif(days, -40, 40)
+  )
+
+  data$sales <- pmax(data$sales, 0)
+  return(data)
+}
+
+store_data <- generate_complex_data()
+
+# UI
+ui <- fluidPage(
+  theme = shinytheme("cosmo"),
+  titlePanel("Fashion Sales Simulator - Plotly Edition"),
+  tabsetPanel(
+    tabPanel("Guided Analysis",
+             sidebarLayout(
+               sidebarPanel(
+                 selectInput("selected_metric", "Select a Variable:", 
+                             choices = names(store_data)[-c(1,2,10)],
+                             selected = "footTraffic"),
+                 checkboxInput("show_trend", "Show Linear Trend Line", FALSE)
+               ),
+               mainPanel(
+                 plotlyOutput("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)]),
+                 checkboxInput("show_trend_explore", "Show Linear Trend Line", FALSE),
+                 actionButton("compute_corr", "Compute Correlation")
+               ),
+               mainPanel(
+                 plotlyOutput("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 <- function(x, y) {
+    cor(store_data[[x]], store_data[[y]], use = "complete.obs")
+  }
+
+  output$guided_plot <- renderPlotly({
+    p <- plot_ly(store_data, x = ~get(input$selected_metric), y = ~sales, type = 'scatter', mode = 'markers',
+                 marker = list(color = 'blue')) %>%
+      layout(title = paste("Sales vs", input$selected_metric),
+             xaxis = list(title = input$selected_metric),
+             yaxis = list(title = "Sales"))
+
+    if (input$show_trend) {
+      model <- lm(sales ~ get(input$selected_metric), data = store_data)
+      trend_line <- data.frame(x = store_data[[input$selected_metric]], y = predict(model))
+      trend_line <- trend_line[order(trend_line$x), ]
+      p <- p %>% add_lines(x = ~trend_line$x, y = ~trend_line$y, name = "Trend Line", line = list(color = 'red'))
+    }
+    p
   })
 
-  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$by_species
-        )
-      }
-
-      p
-    },
-    res = 100
-  )
+  output$correlation <- renderText({
+    corr_value <- compute_correlation(input$selected_metric, "sales")
+    paste("Correlation with Sales:", round(corr_value, 3))
+  })
+
+  output$free_explore_plot <- renderPlotly({
+    p <- plot_ly(store_data, x = ~get(input$x_var), y = ~get(input$y_var), type = 'scatter', mode = 'markers',
+                 marker = list(color = 'green4')) %>%
+      layout(title = paste(input$x_var, "vs", input$y_var),
+             xaxis = list(title = input$x_var),
+             yaxis = list(title = input$y_var))
+
+    if (input$show_trend_explore) {
+      model <- lm(get(input$y_var) ~ get(input$x_var), data = store_data)
+      trend_line <- data.frame(x = store_data[[input$x_var]], y = predict(model))
+      trend_line <- trend_line[order(trend_line$x), ]
+      p <- p %>% add_lines(x = ~trend_line$x, y = ~trend_line$y, name = "Trend Line", line = list(color = 'red'))
+    }
+    p
+  })
+
+  observeEvent(input$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))
+    })
+  })
 }
 
 shinyApp(ui, server)