cleaning up app structure

.gitignore

@@ -1 +1,2 @@
 .env
+.Rproj.user

BICS_polarization_shiny.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

app.Rmd

@@ -96,13 +96,20 @@ ui <- fluidPage(
   sidebarLayout(
     sidebarPanel(
       width = 3,
-      selectInput("model", "Partisan Differences to Incorporate:",
-        choices = c("Manual", "Contacts", "Mask Usage", "Vaccination", "All Partisan Differences", "Homogenous Behavior", "Ideal Homogenous Behavior")
+      selectInput("model", "Model Scenario:",
+        choices = c(
+          "No Partisan Heterogeneity (Baseline)" = "Homogenous Behavior",
+          "All Partisans Adopt Best Behavior (Hypothetical)" = "Ideal Homogenous Behavior",
+          "Partisans Differ in Contact Rates" = "Contacts",
+          "Partisans Differ in Mask Usage" = "Mask Usage",
+          "Partisans Differ in Vaccination" = "Vaccination",
+          "All Partisan Differences Combined" = "All Partisan Differences",
+          "Manual Parameter Entry" = "Manual"
+        )
       ),
       selectInput("plots_to_display", "Plot to Show:",
         choices = c("Prevalence", "Incidence", "Infection Sources", "Proportion Dead", "Contacts", "Population Size", "Proportion of Contacts with a Mask", "Contact Homophily", "Multi-Panel Epidemic Outcomes", "Homophily Sensitivity", "Multi-Panel Homophily Sensitivity", "Population Composition Sensitivity", "Multi-Panel Population Composition Sensitivity")
       ),
-      sliderInput("time", "Simulation Time (days):", value = 365, min = 365, max = 1000, step = 365, ticks = FALSE),
       conditionalPanel(
         condition = "input.model != 'Homogenous Behavior' && input.plot_to_display != 'Homophily Sensitivity' && input.plot_to_display != 'Multi-Panel Homophily Sensitivity'",
         uiOutput("beta_a_slider")
@@ -160,6 +167,7 @@ ui <- fluidPage(
       shinyjs::hidden(
         div(
           id = "advanced_options",
+          sliderInput("time", "Simulation Time (days):", value = 365, min = 365, max = 1000, step = 365, ticks = FALSE),
           numericInput("frac_a", "Proportion of Population Republican:", value = 0.33333333, min = 0, max = 1, step = 0.1),
           numericInput("frac_b", "Proportion of Population Democrat:", value = 0.33333333, min = 0, max = 1, step = 0.1),
           numericInput("cmax_a", "Upper Bound for Contacts (Republicans):", value = no_zeta_contacts_r, min = 0, max = 100, step = .1),
@@ -192,28 +200,28 @@ ui <- fluidPage(
           numericInput("gamma", "Waning Immunity Rate:", value = 1 / 182.5, min = 0, max = 1, step = 0.01),
           numericInput("trans_p", "Transmission Probability", value = 0.05, min = 0, max = 1, step = 0.01),
           numericInput("rho", "recovery rate", value = 0.1, min = 0, max = 1, step = .01),
-          numericInput("kappa", "Probability of Transmission for Protected", value = 0.7, min = 0, max = 1, step = .1)
-        )
-      ),
-      selectInput("show_scenario_customization", "Scenario Customization",
-        choices = c("Hide", "Show")
-      ),
-      shinyjs::hidden(
-        div(
-          id = "scenario_options",
+          numericInput("kappa", "Probability of Transmission for Protected", value = 0.7, min = 0, max = 1, step = .1),
+          hr(),
+          h5("Scenario Customization"),
           checkboxInput("republican_zeta", "Republicans Respond to Death", value = FALSE),
           checkboxInput("null_combines_mortality", "The Null Averages Partisan-Specific Death Rates", value = FALSE),
-          checkboxInput("partisan_mortality", "Incorporate Age-Specific Mortality Rates For Partisans", value = TRUE),
+          checkboxInput("partisan_mortality", "Incorporate Age-Specific Mortality Rates For Partisans", value = FALSE),
           conditionalPanel(
             condition = "input.model != 'Manual'",
             selectInput("locale", "Choose a Population:",
-              choices = c("Default", "Berkeley", "Seattle", "Philadelphia", "Wyoming")
+              choices = list(
+                "Default" = "Default",
+                "Cities (2025)" = c("Berkeley", "Seattle", "Philadelphia"),
+                "States (2025)" = c("Wyoming")
+              )
             )
           )
         )
       ),
       actionButton("information", "The Purpose of this App"),
-      actionButton("vaccine_information", "How We Incorporate Vaccination")
+      actionButton("vaccine_information", "How We Incorporate Vaccination"),
+      actionButton("data_source", "Where Does the Data Come From?"),
+      actionButton("population_source", "Where Does Population Data Come From?")
     ),
     mainPanel(
       plotOutput("sirPlot", width = "110%", height = "800px"), # make plot bigger
@@ -239,15 +247,47 @@ server <- function(input, output, session) {
     shinyalert(title = "Implementing Vaccination", text = "In order to see the effects of vaccination, ensure that the vaccination start day occures within your simulation time window.", type = "info")
   })
 
+  # data source popup
+  observeEvent(input$data_source, {
+    shinyalert(
+      title = "Data Source: Berkeley Interpersonal Contact Study",
+      text = HTML("The Berkeley Interpersonal Contact Study (BICS) collected data on contact rates and health behaviors during the COVID-19 pandemic (April 2020 - May 2021) through nationally representative surveys.<br><br>
+<b>What was measured:</b><br>
+• <b>Daily contacts:</b> Number of in-person contacts the day before the survey<br>
+• <b>Mask usage:</b> Whether respondents wore masks during contacts<br>
+• <b>Vaccination:</b> Self-reported COVID-19 vaccination status (Wave 6, May 2021)<br><br>
+<b>Political affiliation:</b> Respondents self-identified as Democrat, Republican, or Independent upon enrollment in the survey panel.<br><br>
+<b>Reference:</b> Feehan & Mahmud (2021), <i>Quantifying interpersonal contact in the United States during the spread of COVID-19</i>"),
+      type = "info",
+      html = TRUE,
+      size = "m"
+    )
+  })
+
+  # population data source popup
+  observeEvent(input$population_source, {
+    shinyalert(
+      title = "Population Data Source",
+      text = HTML("Population sizes are from the U.S. Census Bureau. Partisan composition for cities and states is derived from voter registration data.<br><br>
+<b>References:</b><br>
+U.S. Census Bureau. (2024). <i>American Community Survey 1-year estimates</i>. https://data.census.gov/<br><br>
+USAFacts. (2025, August 29). <i>How many voters have a party affiliation?</i> https://usafacts.org/articles/how-many-voters-have-a-party-affiliation/"),
+      type = "info",
+      html = TRUE,
+      size = "m"
+    )
+  })
+
   # opening pop up screen
   observeEvent(input$information, {
     shinyalert(
       title = "The purpose of this app",
       text = HTML("This app demonstrates how political differences in behavior can affect the spread of a COVID-19-like disease. Key features:<br><br>
-  1. Compare models:<br>
-     - Homogenous Behavior: No partisan differences<br>
-     - Specialized models: Vary contacts, mask usage, or vaccination<br>
-     - All Partisan Differences model: Incorporates all differences<br><br>
+  1. Compare model scenarios:<br>
+     - No Partisan Heterogeneity: Baseline with uniform behavior<br>
+     - All Partisans Adopt Best Behavior: Hypothetical best-case scenario<br>
+     - Partisan difference models: Vary contacts, mask usage, or vaccination<br>
+     - All Partisan Differences Combined: Incorporates all observed differences<br><br>
   2. Explore various metrics:<br>
      - Infection rates<br>
      - Death rates<br>
@@ -255,7 +295,7 @@ server <- function(input, output, session) {
      - Social mixing patterns (homophily)<br><br>
   3. Customize your view:<br>
      - Toggle between party lines<br>
-     - Compare against the Homogenous Behavior<br><br>
+     - Compare against the baseline (no heterogeneity)<br><br>
   Use these tools to understand how partisan behaviors can shape both group-specific and overall pandemic outcomes.<br><br>
   Click 'Next' to begin exploring."),
       closeOnEsc = FALSE,
@@ -335,13 +375,6 @@ server <- function(input, output, session) {
     }
   })
 
-  observeEvent(input$show_scenario_customization, {
-    if (input$show_scenario_customization == "Show") {
-      shinyjs::show("scenario_options")
-    } else {
-      shinyjs::hide("scenario_options")
-    }
-  })
 
   output$beta_a_slider <- renderUI({
     sliderInput("beta_a", "Republican Homophily:",
@@ -922,7 +955,7 @@ server <- function(input, output, session) {
 
     plots_to_display <- input$plots_to_display
     plot_title_size <- input$title_size
-    # defining the Homogenous Behavior line types
+    # defining the baseline line types
     includes_null_lines <- c("Total" = "solid", "Null" = "dashed")
     no_null_lines <- c("Total" = "solid")
 
@@ -935,7 +968,7 @@ server <- function(input, output, session) {
         current_params$cmax_b * (current_params$N0 * current_params$frac_b) +
         current_params$cmax_c * (current_params$N0 * (1 - (current_params$frac_a + current_params$frac_b)))) / current_params$N0
 
-    print(paste("the Homogenous Behavior contacts are", null_three_party_contacts))
+    print(paste("the baseline contacts are", null_three_party_contacts))
 
     null_model_three_party <- sir_three_group_pu(
       frac_a = current_params$frac_a,
@@ -1494,7 +1527,7 @@ server <- function(input, output, session) {
       } +
       labs(
         title = if (input$show_title) "Prevalence" else element_blank(),
-        subtitle = if (input$show_subtitle) paste0("Compared to the Homogenous Behavior, the peak of the pandemic comes ", crossing_time_diff, " days sooner and is ", peak_three_increase, "% taller. Total cases increase by ", outbreak_size_percentage_increase, "%.\nThe proportion of Republican population infected is ", rep_versus_dem_prop_infected_three, "% larger than the proportion Democrat population infected.") else element_blank(),
+        subtitle = if (input$show_subtitle) paste0("Compared to the baseline (no heterogeneity), the peak comes ", crossing_time_diff, " days sooner and is ", peak_three_increase, "% taller. Total cases increase by ", outbreak_size_percentage_increase, "%.\nThe proportion of Republican population infected is ", rep_versus_dem_prop_infected_three, "% larger than the proportion Democrat population infected.") else element_blank(),
         x = "Time (days)",
         y = element_blank(),
         color = "Partisan Prevalence",
@@ -1532,7 +1565,7 @@ server <- function(input, output, session) {
       coord_cartesian(xlim = if (input$zoom_plot) c(75, max(sim_three$time)) else c(0, max(sim_three$time)))
 
     table_data_three <- data.frame(
-      Model = c("Homogenous Behavior", "Current Model"),
+      Model = c("Baseline (No Heterogeneity)", "Current Model"),
       Outbreak_Size = c(null_outbreak_size_three, current_outbreak_size_three),
       Peak = c(round(peak_null_three_proportion, digits = 3), round(peak_three_proportion, digits = 3)),
       Days_Until_Peak = c(crossing_time_null_three_party, crossing_time_three)
@@ -1772,7 +1805,7 @@ server <- function(input, output, session) {
       } +
       labs(
         title = if (input$show_title) "Cumulative Proportion Deceased" else element_blank(),
-        subtitle = if (input$show_subtitle) paste0("In this simulation that starts with ", format(current_params$N0, big.mark = ","), " people: Republicans deaths exceed Democrat death by ", diff_deaths_three, "%.\nThe inflection point comes at day ", inflection_point_three, " in the current model compared to ", inflection_point_null_three, " in the Homogenous Behavior.") else element_blank(),
+        subtitle = if (input$show_subtitle) paste0("In this simulation that starts with ", format(current_params$N0, big.mark = ","), " people: Republican deaths exceed Democrat deaths by ", diff_deaths_three, "%.\nThe inflection point comes at day ", inflection_point_three, " in the current model compared to ", inflection_point_null_three, " in the baseline.") else element_blank(),
         x = "Time (days)",
         y = element_blank(),
         color = "Partisan Deaths",
@@ -2157,12 +2190,12 @@ server <- function(input, output, session) {
       coord_cartesian(xlim = c(0, max(sim_three$time))) +
       geom_text(
         data = sim_three %>% filter(time == max(time)),
-        aes(x = time, y = null_total_prop_dead, label = "Homogenous"),
+        aes(x = time, y = null_total_prop_dead, label = "Baseline"),
         nudge_y = -0.002, hjust = 0.5, color = "gray", size = 4
       ) +
       geom_text(
         data = sim_three %>% filter(time == median(time)),
-        aes(x = time, y = total_prop_dead, label = "Heterogenous"),
+        aes(x = time, y = total_prop_dead, label = "Heterogeneous"),
         nudge_y = 0.002, hjust = 0.4, color = "black", size = 4
       ) +
       {