Update app.R

app.R

@@ -44,6 +44,18 @@ plot_factor <- function(pi_star_list, pi_star_se_list, factor_name, zStar = 1.96
 ui <- fluidPage(
   titlePanel("Exploring strategize with the candidate choice conjoint data"),
   
+  tags$p(
+    style = "text-align: left; margin-top: -10px;",
+    tags$a(
+      href = "https://strategizelab.org/",
+      target = "_blank",
+      title = "strategizelab.org",
+      style = "color: #337ab7; text-decoration: none;",
+      "strategizelab.org ",
+      icon("external-link", style = "font-size: 12px;")
+    )
+  ),
+  
   sidebarLayout(
     sidebarPanel(
       h4("Analysis Options"),
@@ -56,10 +68,12 @@ ui <- fluidPage(
                     choices = c("All", "Democrat", "Independent", "Republican"),
                     selected = "All")
       ),
-      # Add a single numeric input for lambda
       numericInput("lambda_input", "Lambda (regularization):", 
                    value = 0.01, min = 1e-6, max = 10, step = 0.01),
       actionButton("compute", "Compute Results", class = "btn-primary"),
+      br(),
+      selectInput("previousResults", "View Previous Results:",
+                  choices = NULL),
       hr(),
       h4("Visualization"),
       selectInput("factor", "Select Factor to Display:",
@@ -69,7 +83,8 @@ ui <- fluidPage(
       p("1. Select a case type and, for Average case, a respondent group."),
       p("2. Specify the single lambda to be used by strategize."),
       p("3. Click 'Compute Results' to generate optimal strategies."),
-      p("4. Choose a factor to view its distribution.")
+      p("4. Choose a factor to view its distribution."),
+      p("5. Use 'View Previous Results' to toggle among past computations.")
     ),
     
     mainPanel(
@@ -85,6 +100,11 @@ ui <- fluidPage(
                  p("**Average Case**: Optimizes candidate characteristics for a selected respondent group."),
                  p("**Adversarial Case**: Finds equilibrium strategies for Democrats and Republicans, identified by 'Pro-life' stance.")
         )
+      ),
+      br(),
+      wellPanel(
+        h4("Currently Selected Computation:"),
+        verbatimTextOutput("selection_summary")
       )
     )
   )
@@ -96,7 +116,10 @@ server <- function(input, output, session) {
   load("Processed_OnoData.RData")
   Primary2016 <- read.csv("PrimaryCandidates2016 - Sheet1.csv")
   
-  # Update factor choices dynamically
+  # Prepare a storage structure for caching multiple results
+  cachedResults <- reactiveValues(data = list())
+  
+  # Dynamic update of factor choices
   observe({
     if (input$case_type == "Average") {
       factors <- colnames(FACTOR_MAT_FULL)[!colnames(FACTOR_MAT_FULL) %in% c("Office")]
@@ -106,13 +129,12 @@ server <- function(input, output, session) {
     updateSelectInput(session, "factor", choices = factors, selected = factors[1])
   })
   
-  # Reactive computation triggered by button
-  result <- eventReactive(input$compute, {
+  # Observe "Compute Results" button to generate a new result and cache it
+  observeEvent(input$compute, {
     withProgress(message = "Computing optimal strategies...", value = 0, {
-      # Increment progress
       incProgress(0.2, detail = "Preparing data...")
       
-      # Common hyperparameters (mirroring QRun_Apps.R)
+      # Common hyperparameters
       params <- list(
         nSGD = 1000L,
         batch_size = 50L,
@@ -128,17 +150,27 @@ server <- function(input, output, session) {
       # Grab the single user-chosen lambda
       my_lambda <- input$lambda_input
       
+      # We'll define a label to track the result uniquely
+      # Include the case type, group (if Average), and lambda in the label
+      if (input$case_type == "Average") {
+        label <- paste("Case=Average, Group=", input$respondent_group, ", Lambda=", my_lambda, sep="")
+      } else {
+        label <- paste("Case=Adversarial, Lambda=", my_lambda, sep="")
+      }
+      
       if (input$case_type == "Average") {
         # Subset data for Average case
         if (input$respondent_group == "All") {
-          indices <- which( my_data$Office == "President" )
+          indices <- which(my_data$Office == "President")
         } else {
-          indices <- which(my_data_FULL$R_Partisanship == input$respondent_group & 
-                             my_data$Office == "President")
+          indices <- which(
+            my_data_FULL$R_Partisanship == input$respondent_group & 
+              my_data$Office == "President"
+          )
         }
         
         FACTOR_MAT <- FACTOR_MAT_FULL[indices, 
-                      !colnames(FACTOR_MAT_FULL) %in% c("Office","Party.affiliation","Party.competition")]
+                                      !colnames(FACTOR_MAT_FULL) %in% c("Office","Party.affiliation","Party.competition")]
         Yobs <- Yobs_FULL[indices]
         X <- X_FULL[indices, ]
         log_pr_w <- log_pr_w_FULL[indices]
@@ -147,7 +179,7 @@ server <- function(input, output, session) {
         
         incProgress(0.4, detail = "Running strategize...")
         
-        # Compute with strategize using a single lambda
+        # Compute with strategize
         Qoptimized <- strategize(
           Y = Yobs,
           W = FACTOR_MAT,
@@ -159,7 +191,7 @@ server <- function(input, output, session) {
           diff = TRUE, 
           adversarial = FALSE,
           use_regularization = TRUE, 
-          K = 1L, # Base analysis
+          K = 1L,
           nSGD = params$nSGD,
           penalty_type = params$penalty_type,
           folds = params$nFolds,
@@ -169,8 +201,10 @@ server <- function(input, output, session) {
           conda_env = params$conda_env,
           conda_env_required = params$conda_env_required
         )
-      } else { # Adversarial case
-        # Use full data, drop specific factors
+        
+      } else {
+        # Adversarial case
+        
         DROP_FACTORS <- c("Office", "Party.affiliation", "Party.competition")
         FACTOR_MAT <- FACTOR_MAT_FULL[, !colnames(FACTOR_MAT_FULL) %in% DROP_FACTORS]
         Yobs <- Yobs_FULL
@@ -178,8 +212,8 @@ server <- function(input, output, session) {
         log_pr_w <- log_pr_w_FULL
         assignmentProbList <- assignmentProbList_FULL[!names(assignmentProbList_FULL) %in% DROP_FACTORS]
         
-        # Prepare slate_list (simplified from QRun_Apps.R)
         incProgress(0.3, detail = "Preparing slate data...")
+        
         FactorOptions <- apply(FACTOR_MAT, 2, table)
         prior_alpha <- 10
         Primary_D <- Primary2016[Primary2016$Party == "Democratic", colnames(FACTOR_MAT)]
@@ -205,33 +239,31 @@ server <- function(input, output, session) {
         
         slate_list <- list("Democratic" = Primary_D_slate, "Republican" = Primary_R_slate)
         
-        # subset data 
-        indices <- which( my_data$R_Partisanship %in% c("Republican","Democrat") & 
-                           my_data$Office == "President" )
+        indices <- which(
+          my_data$R_Partisanship %in% c("Republican","Democrat") & 
+            my_data$Office == "President"
+        )
         FACTOR_MAT <- FACTOR_MAT_FULL[indices, 
-                                      !colnames(FACTOR_MAT_FULL) %in% c("Office",
-                                                                        "Party.competition",
-                                                                        "Party.affiliation")]
+                                      !colnames(FACTOR_MAT_FULL) %in% c("Office","Party.competition","Party.affiliation")]
         Yobs <- Yobs_FULL[indices]
         my_data_red <- my_data_FULL[indices,]
         pair_id <- pair_id_FULL[indices]
-        cluster_var <- cluster_var_FULL[ indices ]
-        my_data_red$Party.affiliation_clean <- ifelse(my_data_red$Party.affiliation == "Republican Party",
-                                                      yes = "Republican", no = ifelse(my_data_red$Party.affiliation == "Democratic Party",
-                                                                                      yes = "Democrat",no = "Independent"))
+        cluster_var <- cluster_var_FULL[indices]
+        my_data_red$Party.affiliation_clean <- ifelse(
+          my_data_red$Party.affiliation == "Republican Party",
+          yes = "Republican",
+          no = ifelse(my_data_red$Party.affiliation == "Democratic Party","Democrat","Independent")
+        )
         
-        # subset cols 
         assignmentProbList <- assignmentProbList_FULL[colnames(FACTOR_MAT)]
         slate_list$Democratic <- slate_list$Democratic[names(assignmentProbList)]
         slate_list$Republican <- slate_list$Republican[names(assignmentProbList)]
         
         incProgress(0.4, detail = "Running strategize...")
         
-        # Compute with strategize using a single lambda
         Qoptimized <- strategize(
           Y = Yobs,
           W = FACTOR_MAT,
-          #X = X,
           X = NULL,
           p_list = assignmentProbList,
           slate_list = slate_list,
@@ -249,7 +281,6 @@ server <- function(input, output, session) {
           use_regularization = TRUE,
           force_gaussian = FALSE,
           adversarial = TRUE,
-          #nFolds_glm = 3L, 
           K = 1L,
           nMonte_adversarial = 20L,
           nSGD = params$nSGD,
@@ -263,37 +294,61 @@ server <- function(input, output, session) {
         )
         
         # Identify Democrat vs Republican based on "Pro-life" stance
-        prolife_probs <- c(Qoptimized$pi_star_point$Democrat$Position.on.abortion["Pro-life"],
-                           Qoptimized$pi_star_point$Republican$Position.on.abortion["Pro-life"])
+        prolife_probs <- c(
+          Qoptimized$pi_star_point$Democrat$Position.on.abortion["Pro-life"],
+          Qoptimized$pi_star_point$Republican$Position.on.abortion["Pro-life"]
+        )
         which_repub <- which.max(prolife_probs)
         if (which_repub == 1) {
-          # Swap
-          Qoptimized$pi_star_point <- list(k1 = Qoptimized$pi_star_point$k2, k2 = Qoptimized$pi_star_point$k1)
-          Qoptimized$pi_star_se <- list(k1 = Qoptimized$pi_star_se$k2, k2 = Qoptimized$pi_star_se$k1)
+          # Swap if the first is actually "Republican"
+          Qoptimized$pi_star_point <- list(k1 = Qoptimized$pi_star_point$k2, 
+                                           k2 = Qoptimized$pi_star_point$k1)
+          Qoptimized$pi_star_se <- list(k1 = Qoptimized$pi_star_se$k2, 
+                                        k2 = Qoptimized$pi_star_se$k1)
         }
       }
       
       incProgress(0.8, detail = "Finalizing results...")
-      return(Qoptimized)
+      
+      # Store in the reactiveValues cache
+      cachedResults$data[[label]] <- Qoptimized
+      
+      # Update the choice list for previous results
+      updateSelectInput(session, "previousResults", 
+                        choices = names(cachedResults$data),
+                        selected = label)
     })
   })
   
+  # Reactive to pick the result the user wants to display
+  selectedResult <- reactive({
+    validate(
+      need(input$previousResults != "", "No result computed or selected yet.")
+    )
+    cachedResults$data[[input$previousResults]]
+  })
+  
   # Render strategy plot
   output$strategy_plot <- renderPlot({
-    req(result())
+    req(selectedResult())
     factor_name <- input$factor
-    pi_star_list <- result()$pi_star_point
-    pi_star_se_list <- result()$pi_star_se
+    pi_star_list <- selectedResult()$pi_star_point
+    pi_star_se_list <- selectedResult()$pi_star_se
     plot_factor(pi_star_list, pi_star_se_list, factor_name)
   })
   
   # Render Q value
   output$q_value <- renderText({
-    req(result())
-    q_point <- result()$Q_point_mEst
-    q_se <- result()$Q_se_mEst
+    req(selectedResult())
+    q_point <- selectedResult()$Q_point_mEst
+    q_se <- selectedResult()$Q_se_mEst
     paste("Estimated Q Value: ", sprintf("%.3f ± %.3f", q_point, 1.96 * q_se))
   })
+  
+  # Show which set of parameters (label) is currently selected
+  output$selection_summary <- renderText({
+    input$previousResults
+  })
 }
 
 # Run the app