Create app_v2.R

warmup/app_v2.R

@@ -0,0 +1,482 @@
+# setwd("~/Dropbox/OptimizingSI/Analysis/ono")
+# install.packages("~/Documents/strategize-software/strategize", repos = NULL, type = "source", force = FALSE)
+
+# =============================================================================
+#  app_ono.R
+#  Async, navigation‑friendly Shiny demo for strategize‑Ono
+#  ---------------------------------------------------------------------------
+#  * Heavy strategize jobs run in a background R session via future/promises.
+#  * UI stays responsive; you can browse old results while a new run crunches.
+#  * STARTUP‑SAFE and INPUT‑SAFE:
+#      • req(input$case_type) prevents length‑zero error.
+#      • Reactive inputs are captured (isolated) *before* the future() call,
+#        fixing “Can't access reactive value outside reactive consumer.”
+# =============================================================================
+
+options(error = NULL)
+
+library(shiny)
+library(ggplot2)
+library(strategize)
+library(dplyr)
+
+# ---- Async helpers ----------------------------------------------------------
+library(promises)
+library(future)      ; plan(multisession)   # 1 worker per core
+library(shinyjs)
+
+# =============================================================================
+#  Custom plotting function (unchanged)
+# =============================================================================
+plot_factor <- function(pi_star_list,
+                        pi_star_se_list,
+                        factor_name,
+                        zStar = 1.96,
+                        n_strategies = 1L) {
+  
+  probs  <- lapply(pi_star_list, function(x) x[[factor_name]])
+  ses    <- lapply(pi_star_se_list, function(x) x[[factor_name]])
+  levels <- names(probs[[1]])
+  
+  df <- do.call(rbind, lapply(seq_len(n_strategies), function(i) {
+    data.frame(
+      Strategy    = if (n_strategies == 1) "Optimal"
+      else c("Democrat", "Republican")[i],
+      Level       = levels,
+      Probability = probs[[i]]
+    )
+  }))
+  
+  df$Level_num <- as.numeric(as.factor(df$Level))
+  df$x_dodged  <- if (n_strategies == 1)
+    df$Level_num
+  else
+    df$Level_num + ifelse(df$Strategy == "Democrat", -0.05, 0.05)
+  
+  ggplot(df, aes(x = x_dodged, y = Probability, color = Strategy)) +
+    geom_segment(aes(x = x_dodged, xend = x_dodged, 
+                     y = 0, yend = Probability), size = 0.3) +
+    geom_point(size = 2.5) +
+    geom_text(aes(label = sprintf("%.2f", Probability)),
+              vjust = -0.7, size = 3) +
+    scale_x_continuous(breaks  = unique(df$Level_num),
+                       labels  = unique(df$Level),
+                       limits  = c(min(df$x_dodged) - 0.20,
+                                   max(df$x_dodged) + 0.20)) +
+    labs(title    = "Optimal Distribution for:",
+         subtitle = sprintf("*%s*",
+                            gsub(factor_name, pattern = "\\.", replace = " ")),
+         x = "Level",
+         y = "Probability") +
+    theme_minimal(base_size = 18) +
+    theme(legend.position  = "none",
+          legend.title     = element_blank(),
+          panel.grid.major = element_blank(),
+          panel.grid.minor = element_blank(),
+          axis.line        = element_line(color = "black", size = 0.5),
+          axis.text.x      = element_text(angle = 45, hjust = 1,
+                                          margin = margin(r = 10))) +
+    scale_color_manual(values = c(Democrat   = "#89cff0",
+                                  Republican = "red",
+                                  Optimal    = "black"))
+}
+
+# =============================================================================
+#  UI (identical to previous async version—only shinyjs::useShinyjs() added)
+# =============================================================================
+ui <- fluidPage(
+  useShinyjs(),
+  
+  titlePanel("Exploring strategize with the candidate choice conjoint data"),
+  
+  tags$p(
+    style = "text-align: left; margin-top: -10px;",
+    tags$a(href   = "https://strategizelab.org/",
+           target = "_blank",
+           title  = "strategizelab.org",
+           style  = "color: #337ab7; text-decoration: none;",
+           "strategizelab.org ",
+           icon("external-link", style = "font-size: 12px;"))
+  ),
+  
+  # ---- Share button (unchanged) --------------------------------------------
+  tags$div(
+    style = "text-align: left; margin: 0.5em 0 0.5em 0em;",
+    HTML('
+        <button id="share-button" 
+                style="
+                  display: inline-flex;
+                  align-items: center;
+                  justify-content: center;
+                  gap: 8px; 
+                  padding: 5px 10px;
+                  font-size: 16px;
+                  font-weight: normal;
+                  color: #000;
+                  background-color: #fff;
+                  border: 1px solid #ddd;
+                  border-radius: 6px;
+                  cursor: pointer;
+                  box-shadow: 0 1.5px 0 #000;
+                ">
+          <svg width="18" height="18" viewBox="0 0 24 24" fill="none" 
+               stroke="currentColor" stroke-width="2" stroke-linecap="round" 
+               stroke-linejoin="round">
+            <circle cx="18" cy="5" r="3"></circle>
+            <circle cx="6" cy="12" r="3"></circle>
+            <circle cx="18" cy="19" r="3"></circle>
+            <line x1="8.59" y1="13.51" x2="15.42" y2="17.49"></line>
+            <line x1="15.41" y1="6.51" x2="8.59" y2="10.49"></line>
+          </svg>
+          <strong>Share</strong>
+        </button>
+      '),
+    tags$script(
+      HTML("
+        (function() {
+          const shareBtn = document.getElementById('share-button');
+          function toast() {
+            const n = document.createElement('div');
+            n.innerText = 'Copied to clipboard';
+            Object.assign(n.style, {
+              position:'fixed',bottom:'20px',right:'20px',
+              background:'rgba(0,0,0,0.8)',color:'#fff',
+              padding:'8px 12px',borderRadius:'4px',zIndex:9999});
+            document.body.appendChild(n); setTimeout(()=>n.remove(),2000);
+          }
+          shareBtn.addEventListener('click', ()=>{
+            const url = window.location.href;
+            if (navigator.share) {
+              navigator.share({title:document.title||'Link',url})
+                       .catch(()=>{});
+            } else if (navigator.clipboard) {
+              navigator.clipboard.writeText(url).then(toast);
+            } else {
+              const ta = document.createElement('textarea');
+              ta.value=url; document.body.appendChild(ta); ta.select();
+              try{document.execCommand('copy'); toast();}
+              catch(e){alert('Copy this link:\\n'+url);} ta.remove();
+            }
+          });
+        })();")
+    )
+  ),
+  
+  sidebarLayout(
+    sidebarPanel(
+      h4("Analysis Options"),
+      radioButtons("case_type", "Case Type:",
+                   choices = c("Average", "Adversarial"),
+                   selected = "Average"),
+      conditionalPanel(
+        condition = "input.case_type == 'Average'",
+        selectInput("respondent_group", "Respondent Group:",
+                    choices = c("All", "Democrat", "Independent", "Republican"),
+                    selected = "Democrat")
+      ),
+      numericInput("lambda_input", "Lambda (regularization):",
+                   value = 0.01, min = 1e-6, max = 10, step = 0.01),
+      actionButton("compute", "Compute Results", class = "btn-primary"),
+      div(id = "status_text",
+          style = "margin-top:6px; font-style:italic; color:#555;"), 
+      hr(),
+      h4("Visualization"),
+      selectInput("factor", "Select Factor to Display:", choices = NULL),
+      br(),
+      selectInput("previousResults", "View Previous Results:", choices = NULL),
+      hr(),
+      h5("Instructions:"),
+      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("5. Use 'View Previous Results' to toggle among past computations.")
+    ),
+    
+    mainPanel(
+      tabsetPanel(
+        tabPanel("Optimal Strategy Plot",
+                 plotOutput("strategy_plot", height = "600px")),
+        tabPanel("Q Value",
+                 verbatimTextOutput("q_value"),
+                 p("Q represents the estimated outcome under the optimal strategy,",
+                   "with 95% confidence interval.")),
+        tabPanel("About",
+                 h3("About this page"),
+                 p("This page app explores the ",
+                   a("strategize R package",
+                     href = "https://github.com/cjerzak/strategize-software/",
+                     target = "_blank"),
+                   " using Ono forced conjoint experimental data.",
+                   "It computes optimal strategies for Average (optimizing for a respondent",
+                   "group) and Adversarial (optimizing for both parties in competition) cases",
+                   "on the fly."),
+                 p(strong("Average Case:"), "Optimizes candidate characteristics for a",
+                   "selected respondent group."),
+                 p(strong("Adversarial Case:"), "Finds equilibrium strategies for Democrats",
+                   "and Republicans."),
+                 p(strong("More information:"),
+                   a("strategizelab.org", href = "https://strategizelab.org",
+                     target = "_blank"))
+        )
+      ),
+      br(),
+      wellPanel(
+        h4("Currently Selected Computation:"),
+        verbatimTextOutput("selection_summary")
+      )
+    )
+  )
+)
+
+# =============================================================================
+#  SERVER
+# =============================================================================
+server <- function(input, output, session) {
+  
+  # ---- Data load (unchanged) -----------------------------------------------
+  load("Processed_OnoData.RData")
+  Primary2016 <- read.csv("PrimaryCandidates2016 - Sheet1.csv")
+  
+  # ---- Reactive stores ------------------------------------------------------
+  cachedResults <- reactiveValues(data = list())
+  runningFlags  <- reactiveValues(active = list())
+  
+  # ---- Factor dropdown updater ---------------------------------------------
+  observe({
+    req(input$case_type)
+    if (input$case_type == "Average") {
+      factors <- setdiff(colnames(FACTOR_MAT_FULL), "Office")
+    } else {
+      factors <- setdiff(colnames(FACTOR_MAT_FULL),
+                         c("Office", "Party.affiliation", "Party.competition"))
+    }
+    updateSelectInput(session, "factor",
+                      choices  = factors,
+                      selected = factors[1])
+  })
+  
+  # ===========================================================================
+  #  Compute Results button
+  # ===========================================================================
+  observeEvent(input$compute, {
+    
+    ## ---- CAPTURE reactive inputs ------------------------------------------
+    case_type        <- isolate(input$case_type)
+    respondent_group <- isolate(input$respondent_group)
+    my_lambda        <- isolate(input$lambda_input)
+    
+    label <- if (case_type == "Average") {
+      paste0("Case=Average, Group=", respondent_group,
+             ", Lambda=", my_lambda)
+    } else {
+      paste0("Case=Adversarial, Lambda=", my_lambda)
+    }
+    
+    runningFlags$active[[label]] <- TRUE
+    cachedResults$data[[label]]  <- NULL
+    updateSelectInput(session, "previousResults",
+                      choices  = names(cachedResults$data),
+                      selected = label)
+    shinyjs::html("status_text", "")
+    shinyjs::html("status_text", "submitting…") # Immediately show “submitting…”
+    shinyjs::delay(2000, shinyjs::html("status_text", "submitted")) # Two‑second later switch to “submitted”
+    shinyjs::disable("compute")
+    showNotification(sprintf("Job '%s' submitted …", label),
+                     type = "message", duration = 3)
+    
+    ## ---- FUTURE -----------------------------------------------------------
+    future({
+      
+      strategize_start <- Sys.time()
+      
+      # --------------- shared hyper‑params ----------------------------------
+      params <- list(
+        nSGD         = 1000L,
+        batch_size   = 50L,
+        penalty_type = "KL",
+        nFolds       = 3L,
+        use_optax    = TRUE,
+        compute_se   = FALSE,
+        conf_level   = 0.95,
+        conda_env    = "strategize",
+        conda_env_required = TRUE
+      )
+      
+      if (case_type == "Average") {
+        # ---------- Average case --------------------------------------------
+        indices <- if (respondent_group == "All") {
+          which(my_data$Office == "President")
+        } else {
+          which(my_data_FULL$R_Partisanship == respondent_group &
+                  my_data$Office == "President")
+        }
+        
+        FACTOR_MAT <- FACTOR_MAT_FULL[indices,
+                                      !colnames(FACTOR_MAT_FULL) %in%
+                                        c("Office", "Party.affiliation", "Party.competition")]
+        Yobs               <- Yobs_FULL[indices]
+        X                  <- X_FULL[indices, ]
+        pair_id            <- pair_id_FULL[indices]
+        assignmentProbList <- assignmentProbList_FULL[colnames(FACTOR_MAT)]
+        
+        Qoptimized <- strategize(
+          Y = Yobs,
+          W = FACTOR_MAT,
+          X = X,
+          pair_id = pair_id,
+          p_list  = assignmentProbList[colnames(FACTOR_MAT)],
+          lambda  = my_lambda,
+          diff    = TRUE,
+          adversarial      = FALSE,
+          use_regularization = TRUE,
+          K       = 1L,
+          nSGD    = params$nSGD,
+          penalty_type = params$penalty_type,
+          folds   = params$nFolds,
+          use_optax = params$use_optax,
+          compute_se = params$compute_se,
+          conf_level = params$conf_level,
+          conda_env  = params$conda_env,
+          conda_env_required = params$conda_env_required
+        )
+        Qoptimized$n_strategies <- 1L
+        
+      } else {
+        # ---------- Adversarial case ----------------------------------------
+        DROP <- c("Office", "Party.affiliation", "Party.competition")
+        FACTOR_MAT <- FACTOR_MAT_FULL[, !colnames(FACTOR_MAT_FULL) %in% DROP]
+        assignmentProbList <- assignmentProbList_FULL[!names(assignmentProbList_FULL) %in% DROP]
+        
+        # Build Primary slates
+        FactorOptions <- apply(FACTOR_MAT, 2, table)
+        prior_alpha   <- 10
+        Primary_D     <- Primary2016[Primary2016$Party == "Democratic",
+                                     colnames(FACTOR_MAT)]
+        Primary_R     <- Primary2016[Primary2016$Party == "Republican",
+                                     colnames(FACTOR_MAT)]
+        slate_fun <- function(df) {
+          lapply(colnames(df), function(col) {
+            post <- FactorOptions[[col]]; post[] <- prior_alpha
+            emp  <- table(df[[col]]); emp <- emp[names(emp) != "Unclear"]
+            post[names(emp)] <- post[names(emp)] + emp
+            prop.table(post)
+          }) |> setNames(colnames(df))
+        }
+        slate_list <- list(Democratic = slate_fun(Primary_D),
+                           Republican = slate_fun(Primary_R))
+        
+        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")]
+        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", "Republican",
+                 ifelse(my_data_red$Party.affiliation == "Democratic Party","Democrat","Independent"))
+        
+        assignmentProbList <- assignmentProbList_FULL[colnames(FACTOR_MAT)]
+        slate_list$Democratic <- slate_list$Democratic[names(assignmentProbList)]
+        slate_list$Republican <- slate_list$Republican[names(assignmentProbList)]
+        
+        Qoptimized <- strategize(
+          Y = Yobs,
+          W = FACTOR_MAT,
+          X = NULL,
+          p_list = assignmentProbList,
+          slate_list = slate_list,
+          varcov_cluster_variable = cluster_var,
+          competing_group_variable_respondent = my_data_red$R_Partisanship,
+          competing_group_variable_candidate  = my_data_red$Party.affiliation_clean,
+          competing_group_competition_variable_candidate =
+            my_data_red$Party.competition,
+          pair_id          = pair_id,
+          respondent_id    = my_data_red$respondentIndex,
+          respondent_task_id = my_data_red$task,
+          profile_order    = my_data_red$profile,
+          lambda           = my_lambda,
+          diff             = TRUE,
+          use_regularization = TRUE,
+          force_gaussian   = FALSE,
+          adversarial      = TRUE,
+          K       = 1L,
+          nMonte_adversarial = 20L,
+          nSGD    = params$nSGD,
+          penalty_type = params$penalty_type,
+          learning_rate_max = 0.001,
+          use_optax  = params$use_optax,
+          compute_se = params$compute_se,
+          conf_level = params$conf_level,
+          conda_env  = params$conda_env,
+          conda_env_required = params$conda_env_required
+        )
+        Qoptimized$n_strategies <- 2L
+      }
+      
+      Qoptimized$runtime_seconds <-
+        as.numeric(difftime(Sys.time(), strategize_start, units = "secs"))
+      Qoptimized[c("pi_star_point", "pi_star_se", "Q_point",
+                   "Q_se", "n_strategies", "runtime_seconds")]
+    }) %...>%   # success handler
+      (function(res) {
+        cachedResults$data[[label]]  <- res
+        runningFlags$active[[label]] <- FALSE
+        updateSelectInput(session, "previousResults",
+                          choices  = names(cachedResults$data),
+                          selected = label)
+        shinyjs::html("status_text", "complete!")
+        shinyjs::enable("compute")
+        showNotification(sprintf("Job '%s' finished (%.1f s).",
+                                 label, res$runtime_seconds),
+                         type = "message", duration = 6)
+      }) %...!%   # error handler
+      (function(err) {
+        runningFlags$active[[label]] <- FALSE
+        cachedResults$data[[label]]  <- NULL
+        shinyjs::html("status_text", "error – see log")
+        shinyjs::enable("compute")
+        showNotification(paste("Error in", label, ":", err$message),
+                         type = "error", duration = 8)
+      })
+    
+    NULL  # return value of observeEvent
+  })
+  
+  # ---- Helper: fetch selected result or show waiting msg -------------------
+  selectedResult <- reactive({
+    lbl <- input$previousResults ; req(lbl)
+    if (isTRUE(runningFlags$active[[lbl]]))
+      validate("Computation is still running – please wait…")
+    res <- cachedResults$data[[lbl]]
+    validate(need(!is.null(res), "No finished result selected."))
+    res
+  })
+  
+  # ---- Outputs -------------------------------------------------------------
+  output$strategy_plot <- renderPlot({
+    res <- selectedResult()
+    plot_factor(res$pi_star_point, res$pi_star_se,
+                factor_name  = input$factor,
+                n_strategies = res$n_strategies)
+  })
+  
+  output$q_value <- renderText({
+    res <- selectedResult()
+    q_pt <- res$Q_point; q_se <- res$Q_se
+    txt  <- if (length(q_se) && q_se > 0)
+      sprintf("Estimated Q Value: %.3f ± %.3f", q_pt, 1.96*q_se)
+    else sprintf("Estimated Q Value: %.3f", q_pt)
+    sprintf("%s (Runtime: %.2f s)", txt, res$runtime_seconds)
+  })
+  
+  output$selection_summary <- renderText({ input$previousResults })
+}
+
+# =============================================================================
+#  Run the app
+# =============================================================================
+shinyApp(ui, server)