### CSES DATA PLAYGROUND # Date: November 10th, 2025 # Author: Robert Vidigal, PhD # Purpose: CSES Shiny Data Playground based on LAPOP Lab Data Playground # Prev file: ./shiny_preprocessing.R # Machine: Windows OS # Status: On-going # # ----------------------------------------------------------------------- ### Data In: # 1. cses_shiny_data.rda # 2. cses_variable_labels.csv # 3. cses_labs.rds # 4. and fonts from /wwww/ ### Data Out: N/A # # ----------------------------------------------------------------------- options(shiny.useragg = TRUE) # speed it up # # ----------------------------------------------------------------------- # Packages loading # # ----------------------------------------------------------------------- library(lapop); library(bslib); library(htmltools); require(bsplus) suppressPackageStartupMessages(library(dplyr)) library(tidyr); library(stringr); library(haven) require(shiny); library(shinyWidgets); require(shinyjs); require(ggtext) suppressPackageStartupMessages(library(Hmisc, exclude = c("src", "summarize", "units", "format.pval"))) lapop_fonts() # LAPOP GRAPH STYLE # IMD CSES Data (only preselected variables) # # ----------------------------------------------------------------------- # RDA FILE BEST COMPRESSION FOR RSHINY load(file="./cses_shiny_data.rda"); # Labels data (for DP display) vars_labels <- read.csv("./cses_variable_labels.csv", encoding = "latin1") # Labs vector (for DP display outcomes versus secondary vars that include macro vars) labs <- readRDS("./cses_labs.rds") labs_sec <- readRDS("./cses_labs_sec.rds") load(file="./world.rda") # Dropping Demographics (OLD, ALLOW USERS TO USE BOTH RAW AND RECODE DEMOG VARS) #drop_demoglabs <- c("IMD2001_2", "IMD2002", "IMD2003", "IMD2006", "IMD2007") # Demographics #labs_sec <- labs[ !(unname(labs) %in% drop_demoglabs) ] # # ----------------------------------------------------------------------- # Error handling function (so app does not break easily) # # ----------------------------------------------------------------------- Error<-function(x){ tryCatch(x,error=function(e) return(FALSE)) } # CSES election-year vector for TS waves_total = c("1996", "1997", "1998", "1999", "2000", "2001", "2002", "2003", "2004", "2005", "2006", "2007", "2008", "2009", "2010", "2011", "2012", "2013", "2014", "2015", "2016", "2017", "2018", "2019", "2020", "2021") # Escape stuff that ggtext/markdown treats specially sanitize_for_ggtext <- function(x) { x <- as.character(x) # HTML specials x <- gsub("&", "&", x, fixed = TRUE) x <- gsub("<", "<", x, fixed = TRUE) x <- gsub(">", ">", x, fixed = TRUE) # Markdown link/emphasis/backtick x <- gsub("\\[", "[", x) x <- gsub("\\]", "]", x) x <- gsub("\\(", "(", x) x <- gsub("\\)", ")", x) x <- gsub("\\*", "*", x) x <- gsub("_", "_", x) x <- gsub("`", "`", x) x } # # ----------------------------------------------------------------------- # Helper function for TS # # ----------------------------------------------------------------------- # (handle missing values at end or middle of series) omit_na_edges <- function(df) { # Find which rows have NA values na_rows <- apply(df, 1, function(row) any(is.na(row))) # Find the first and last non-NA row first_non_na <- which(!na_rows)[1] last_non_na <- which(!na_rows)[length(which(!na_rows))] # Subset df to only include rows between the first and last non-NA rows df_clean <- df[first_non_na:last_non_na, ] return(df_clean) } # # ----------------------------------------------------------------------- # Custom weighted averages & CIs, much faster than survey_mean() etc # # ----------------------------------------------------------------------- weighted.ttest.ci <- function(x, weights) { nx <- length(x) vx <- Hmisc::wtd.var(x, weights, normwt = TRUE, na.rm = TRUE) # Weighted variance mx <- weighted.mean(x, weights, na.rm = TRUE) # Weighted mean stderr <- sqrt(vx/nx) tstat <- mx/stderr ## not mx - mu cint <- qt(1 - 0.05/2, nx - 1) cint <- tstat + c(-cint, cint) confint = cint * stderr result = data.frame(prop = mx, lb = confint[1], ub = confint[2]) return(result) } # # ----------------------------------------------------------------------- # Helper for missing country-year by outcome_var # # ----------------------------------------------------------------------- get_missing_combinations <- function(data, outcome_var, wave_var, selected_waves, selected_countries) { # Convert wave values to string using haven labels data <- data %>% mutate(wave_str = as.character(haven::as_factor(.data[[wave_var]]))) # Build the full country-wave grid all_combos <- expand.grid( pais_nam = selected_countries, wave = selected_waves, stringsAsFactors = FALSE ) # Subset only relevant countries data <- data %>% filter(pais_nam %in% selected_countries) # Summarize: how many valid (non-NA and not 0) values exist per combo summary <- data %>% group_by(pais_nam, wave = wave_str) %>% summarise( n_valid = sum(!is.na(.data[[outcome_var]]) & .data[[outcome_var]] != 0), .groups = "drop" ) # Merge and detect missing missing <- all_combos %>% left_join(summary, by = c("pais_nam", "wave")) %>% filter(is.na(n_valid) | n_valid == 0) %>% select(pais_nam, wave) return(missing) } # # ----------------------------------------------------------------------- # Helper function for mover plot (weighting and handling NAs) # # ----------------------------------------------------------------------- process_data <- function(data, outcome_var, recode_range, group_var, var_label, weight_var) { if (is.null(group_var)) { return(NULL) } processed_data <- data %>% drop_na(!!sym(outcome_var)) %>% mutate(outcome_rec = case_when( is.na(!!sym(outcome_var)) ~ NA_real_, !!sym(outcome_var) >= recode_range[1] & !!sym(outcome_var) <= recode_range[2] ~ 100, TRUE ~ 0 )) %>% group_by(vallabel = haven::as_factor(haven::zap_missing(!!sym(group_var)))) %>% summarise_at(vars("outcome_rec"), list(~weighted.ttest.ci(., !!sym(weight_var)))) %>% unnest_wider(col = "outcome_rec") %>% mutate( varlabel = var_label, proplabel = paste0(round(prop), "%") ) %>% drop_na(.) return(processed_data) } # # ----------------------------------------------------------------------- # BOOTSTRAP THEME # # ----------------------------------------------------------------------- cses_theme <- bs_theme( version = 5, bootswatch = "cosmo", bg = "#ffffff", fg = "#212529", primary = "#C4722A", secondary = "#C4722A", success = "#28a745", info = "#0066cc", warning = "#dc3545", danger = "#dc3545", #base_font = font_google("Open Sans"), #heading_font = font_google("Roboto Slab"), #code_font = font_google("Fira Mono"), #font_scale = 1 ) # # ----------------------------------------------------------------------- # HOVER POP-UP FOR LEFTSIDE MENU # # ----------------------------------------------------------------------- info_badge <- function(text, title, content) { bsplus::bs_embed_popover( tags$span(text, tags$span(icon("info-circle"), class = "me-1", style = "color:#C4722A;")), title = title, content = content, placement = "right", trigger = "click", container = "body" ) } # # ----------------------------------------------------------------------- # N-SIZE FUNCTION TO PULL COUNTRY-YEAR COMBOS (CHATGPT) # # ----------------------------------------------------------------------- get_sample_counts <- function( data, outcome_var, wave_var = "wave", country_var = "pais_nam", selected_waves = NULL, selected_countries = NULL, complete_grid = FALSE ) { df <- data if (!is.null(selected_waves)) df <- dplyr::filter(df, .data[[wave_var]] %in% selected_waves) if (!is.null(selected_countries)) df <- dplyr::filter(df, .data[[country_var]] %in% selected_countries) df <- dplyr::filter(df, !is.na(.data[[outcome_var]])) per_wave <- df |> dplyr::count(wave = .data[[wave_var]], name = "n") |> dplyr::arrange(wave) per_country <- df |> dplyr::count(pais = .data[[country_var]], name = "n") |> dplyr::arrange(pais) per_country_wave <- df |> dplyr::count(pais = .data[[country_var]], wave = .data[[wave_var]], name = "n") |> dplyr::arrange(pais, wave) if (complete_grid) { all_waves <- if (!is.null(selected_waves)) selected_waves else sort(unique(data[[wave_var]])) all_countries <- if (!is.null(selected_countries)) selected_countries else sort(unique(data[[country_var]])) per_country_wave <- per_country_wave |> tidyr::complete(pais = all_countries, wave = all_waves, fill = list(n = 0)) |> dplyr::arrange(pais, wave) per_country <- per_country_wave |> dplyr::group_by(pais) |> dplyr::summarise(n = sum(n), .groups = "drop") |> dplyr::arrange(pais) per_wave <- per_country_wave |> dplyr::group_by(wave) |> dplyr::summarise(n = sum(n), .groups = "drop") |> dplyr::arrange(wave) } list( overall = nrow(df), per_wave = per_wave, per_country = per_country, per_country_wave = per_country_wave ) } # # ----------------------------------------------------------------------- # # ----------------------------------------------------------------------- # # ----------------------------------------------------------------------- # # ----------------------------------------------------------------------- # Creating User Interface UI! # # ----------------------------------------------------------------------- ui <- fluidPage( useShinyjs(), theme = cses_theme, tags$h2("CSES Data Playground", style = "color: #C4722A; font-weight: bold; font-size: 36px;"), sidebarLayout( # ----- Sidebar panel for inputs sidebarPanel(width = 3, selectInput("variable", "Outcome", labs[order(names(labs))], selected = "IMD3010"), # Default picks most recent module pickerInput(inputId = "module", label = tagList(info_badge("Module", HTML("Please select which CSES Modules to be available in the analysis. Then, select which countries and years below."), "Module")), choices = sort(levels(as_factor(cses_shiny_data$IMD1008_MOD)[!is.na(cses_shiny_data$IMD1008_MOD)])), selected = c("MODULE 5"), options = list(`actions-box` = TRUE), multiple = TRUE), # ----- COUNTRY pickerInput(inputId = "pais", label = "Countries", #tagList(info_badge("Countries", #HTML("Please select which countries to be included in the analysis."), #"Countries")), choices = sort(levels(as_factor(cses_shiny_data$pais)[!is.na(cses_shiny_data$pais)])), options = list(`actions-box` = TRUE), multiple = TRUE), # ----- WAVE pickerInput(inputId = "wave", label = "Years", #tagList(info_badge("Years", # HTML("Please select which years to be included in the analysis."), # "Years")), choices = c("1996" = "1996", "1997" = "1997", "1998" = "1998", "1999" = "1999", "2000" = "2000", "2001" = "2001", "2002" = "2002", "2003" = "2003", "2004" = "2004", "2005" = "2005", "2006" = "2006", "2007" = "2007", "2008" = "2008", "2009" = "2009", "2010" = "2010", "2011" = "2011", "2012" = "2012", "2013" = "2013", "2014" = "2014", "2015" = "2015", "2016" = "2016", "2017" = "2017", "2018" = "2018", "2019" = "2019", "2020" = "2020", "2021" = "2021"), options = list(`actions-box` = TRUE), multiple = TRUE), # ----- WEIGHT selection radio buttons bsplus::use_bs_popover(), radioButtons( inputId = "weight_type", label = tagList(info_badge("Weights", HTML("Further information on weights is available in Part 6 of CSES Module 4."), "Weights")), # For a link, add: # HTML('Further information on weights is available in Part 6 of CSES Module 4.
Open doc') choiceValues = c("no_weight", "weight_demographic", "weight_sample"), choiceNames = list( info_badge("Unweighted", "No weights applied. Raw proportions/percentages.", "Unweighted"), info_badge("Demographic weight", "Post-stratification targets.", "Demographic weight"), info_badge("Sample weight", "Design/selection probability weights.", "Sample weight") ), selected = "no_weight"), # This fixes a formatting issue with checkboxGroupInput() below tags$head( tags$style( HTML(" .checkbox-inline { margin-left: 0px; margin-right: 10px; } .checkbox-inline+.checkbox-inline { margin-left: 0px; margin-right: 10px; } .shiny-notification { width: 615px !important; /* max width */ max-height: 140px; /* max height */ word-wrap: break-word; white-space: normal; overflow-y: auto; /* scrollbar */ right: 330px !important; /* shift away from right edge */ box-sizing: border-box; font-size: 14px; }"))), # This triggers the "Generate" button tags$script(HTML(" Shiny.addCustomMessageHandler('clickGenerateButton', function(message) { $('#go').click(); }); ")), # This makes the slider input to allow only integers for CSES years tags$style(type = "text/css", ".irs-grid-pol.small {height: 0px;}"), # Make popovers white + wire TRUE hover with JavaScript tags$style(HTML(" .popover { --bs-popover-bg: #ffffff; --bs-popover-border-color: #dddddd; --bs-popover-header-bg: #ffffff; --bs-popover-header-color: #212529; --bs-popover-body-color: #212529; border-color: #dddddd; } .popover .popover-header, .popover .popover-body { background-color: #ffffff; color: #212529; } ")), tags$script(HTML(" (function() { function upgradeDataAttr(el){ // Force manual disable if (el.getAttribute('data-toggle') === 'popover') { el.setAttribute('data-bs-toggle','popover'); el.removeAttribute('data-toggle'); } } function getPopover(el){ // Force manual trigger so we fully control hover behavior return bootstrap.Popover.getOrCreateInstance(el, { container: 'body', trigger: 'manual', html: true, sanitize: false }); } function addHoverBehavior(el){ var timer = null; var inst = getPopover(el); function startHide(delay){ if (timer) clearTimeout(timer); timer = setTimeout(function(){ inst.hide(); }, delay); } function cancelHide(){ if (timer) { clearTimeout(timer); timer = null; } } // Show on hover el.addEventListener('mouseenter', function(){ cancelHide(); inst.show(); // Auto-dismiss after 10s (unless user is hovering the popover) startHide(10000); }); // Hide shortly after leaving the icon (unless pointer is on the popover) el.addEventListener('mouseleave', function(){ // small delay to allow moving into the popover setTimeout(function(){ var pop = document.getElementById(el.getAttribute('aria-describedby')); if (!pop || !pop.matches(':hover')) inst.hide(); }, 150); }); // Keep open while hovering the popover; hide when leaving it el.addEventListener('shown.bs.popover', function(){ var pop = document.getElementById(el.getAttribute('aria-describedby')); if (!pop) return; pop.addEventListener('mouseenter', cancelHide); pop.addEventListener('mouseleave', function(){ startHide(150); // quick close after leaving the box }); }); // Prevent click toggling from fighting our hover logic el.addEventListener('click', function(e){ e.preventDefault(); }); } document.addEventListener('DOMContentLoaded', function(){ document.querySelectorAll('[data-bs-toggle=\"popover\"], [data-toggle=\"popover\"]').forEach(function(el){ upgradeDataAttr(el); getPopover(el); // ensure BS5 instance exists addHoverBehavior(el); // wire hover behavior + auto-dismiss }); }); })(); ")), # Show recode slider only for TS, CC, and mover plots (not for histogram) conditionalPanel( 'input.tabs == "Time Series" | input.tabs == "Cross Country" | input.tabs == "World Map" | input.tabs == "Breakdown"', uiOutput("sliderUI"), # Mean Value toggle # checkboxInput("use_mean", "Mean value", FALSE), ), # Add additional breakdown variable in mover plot conditionalPanel( 'input.tabs == "Breakdown"', selectInput("variable_sec", label = tagList( info_badge("Subgroup for analysis", HTML("Optionally split the Breakdown plot by another subgroup from the dataset. Select None to disable."), "Secondary Variable")), c("None" = "None", labs_sec[order(names(labs_sec))])), checkboxGroupInput("demog", "Demographic Variables", c("Gender" = "gendermc", "Age" = "age", "Income" = "wealth", "Education" = "edre", "Urban/Rural" = "ur"), selected = c("gendermc", "age", "edre"), inline = TRUE)), # Include button in UI (disabled) #actionButton("go", "Generate") tags$div( style = "display: none;", actionButton("go", "Generate"))), # Main panel for displaying outputs ---- # # ----------------------------------------------------------------------- mainPanel( # Output: Formatted text for caption ---- h3(textOutput("caption")), h5(textOutput("wording")), h5(textOutput("response")), tabsetPanel(id = "tabs", tabPanel("Histogram", plotOutput("hist")), tabPanel("Time Series", plotOutput("ts")), tabPanel("Cross Country", plotOutput("cc")), tabPanel("Breakdown", plotOutput("mover")), tabPanel("World Map", plotOutput("map"))), br(), fluidRow(column(12, tags$div(style = "margin-top:-15px"), downloadButton(outputId = "downloadPlot", label = "Download Figure"), downloadButton(outputId = "downloadTable", label = "Download Table"), tags$div(style = "height:10px"), uiOutput("ns_card"), #uiOutput("missing_warning_card"), ) ) ) ) ) # # ----------------------------------------------------------------------- # # ----------------------------------------------------------------------- # # ----------------------------------------------------------------------- # # ----------------------------------------------------------------------- # Define SERVER logic # # ----------------------------------------------------------------------- # The server function will be called when each client (browser) loads the app. server <- function(input, output, session) { observe({ req(input$variable) if (!input$variable %in% names(cses_shiny_data)) { showNotification("Selected variable not found in data!", type = "error") } if (!input$weight_type %in% names(cses_shiny_data)) { showNotification("Selected weight column not found!", type = "error") } }) # Triggers "go" between server and ui to generate default plots observe({ if (!is.null(input$module) && !is.null(input$pais) && !is.null(input$wave)) { isolate({ session$sendCustomMessage("clickGenerateButton", list()) }) } }) # Check the number of selected variables for breakdown observeEvent(input$demog, { if (length(input$demog) > 3) { # Show a warning message showNotification(HTML("You should only select a maximum of 3 demographic variables to plot."), type = "warning") } }) # MAKE IT REACTIVE # # ----------------------------------------------------------------------- formulaText <- reactive({ paste(input$variable) }) outcome <- reactive({ input$variable }) wave <- reactive({ input$wave }) outcome_code <- reactive({ vars_labels$column_name[which(vars_labels$column_name == paste(outcome()))] }) outcome_lab <- reactive({ vars_labels$question_short_en[which(vars_labels$column_name == paste(outcome()))] }) variable_sec <- reactive({ input$variable_sec }) variable_sec_lab <- reactive({ vars_labels$question_short_en[which(vars_labels$column_name == paste(variable_sec()))] }) sliderParams <- reactiveValues(valuex = c(1, 1)) # Reactive: Filter dataset based on selected module(s) # # ----------------------------------------------------------------------- filtered_data <- reactive({ req(input$module) cses_shiny_data %>% dplyr::filter(IMD1008_MOD %in% input$module) }) # OLD CODE THAT WOULD FORCE PRESELECTION, BUT IT BREAKS THE APP WITH FULL DATASET # Observe changes in module input to update wave and pais observeEvent(filtered_data(), { data <- filtered_data() wave_choices <- sort(unique(data$wave)) pais_choices <- sort(unique(data$pais)) updatePickerInput( session = session, inputId = "wave", choices = wave_choices, selected = wave_choices # you can leave this empty if no preselection ) # # updatePickerInput( # session = session, # inputId = "pais", # choices = pais_choices, # selected = pais_choices # ) }) all_waves <- sort(unique(cses_shiny_data$wave)) all_paises <- sort(unique(cses_shiny_data$pais)) observeEvent(input$module, { req(input$module) # Filter for the selected module valid <- dplyr::filter(cses_shiny_data, IMD1008_MOD %in% input$module) valid_waves <- sort(unique(valid$wave)) valid_paises <- sort(unique(valid$pais)) wave_disabled <- !(all_waves %in% valid_waves) pais_disabled <- !(all_paises %in% valid_paises) # 🔹 Automatically select *all valid waves* when a module is chosen shinyWidgets::updatePickerInput( session, "wave", choices = all_waves, selected = valid_waves, # <-- changed line choicesOpt = list( disabled = wave_disabled, style = ifelse(wave_disabled, "color:#999;", "") ) ) # 🔹 Automatically select *all valid countries* too (optional) shinyWidgets::updatePickerInput( session, "pais", choices = all_paises, selected = valid_paises, # <-- changed line choicesOpt = list( disabled = pais_disabled, style = ifelse(pais_disabled, "color:#999;", "") ) ) }) # Set default recode slider values: # # ----------------------------------------------------------------------- # 2-point: 1-1 # 3-point: 3-3 # 4-point: 1-2 # 5-point: 4-5 # 6-point: 3-3 # 7-point: 5-7 # 10-point: 8-10 # ALL OTHER: MEAN # UPDATE SLIDER DEFAULTS AND MEAN BEHAVIOR # ----------------------------------------------------------------------- observeEvent({ list(input$variable, input$use_mean) }, { # compute numeric vector safely xvals <- suppressWarnings(as.numeric(cses_shiny_data[[formulaText()]])) maxval <- max(xvals, na.rm = TRUE) # --- DEFAULT RECODE RANGES --- if (maxval == 1) { sliderParams$valuex <- c(1, 1) } else if (maxval == 2) { sliderParams$valuex <- c(1, 1) } else if (maxval == 3) { sliderParams$valuex <- c(3, 3) } else if (maxval == 4) { sliderParams$valuex <- c(1, 2) } else if (maxval == 5) { sliderParams$valuex <- c(4, 5) } else if (maxval == 6) { sliderParams$valuex <- c(3, 3) } else if (maxval == 7) { sliderParams$valuex <- c(5, 7) } else if (maxval == 10) { sliderParams$valuex <- c(8, 10) } else { mean_val <- mean(xvals, na.rm = TRUE) sliderParams$valuex <- c(mean_val, mean_val) } # --- IF USER SELECTED "USE MEAN VALUE" --- if (isTRUE(input$use_mean)) { mean_val <- mean(xvals, na.rm = TRUE) sliderParams$valuex <- c(mean_val, mean_val) } # force slider update updateSliderInput( session, inputId = "recode", value = sliderParams$valuex ) }) # RECODE SLIDER # # ----------------------------------------------------------------------- output$sliderUI <- renderUI({ sliderInput( inputId = "recode", label = tagList( info_badge( "Which values do you want to graph?", HTML("Please select which outcome values to be displayed."), "Which values do you want to graph?" ) ), min = min(as.numeric(cses_shiny_data[[formulaText()]]), na.rm = TRUE), max = max(as.numeric(cses_shiny_data[[formulaText()]]), na.rm = TRUE), value = sliderParams$valuex, step = 1 ) }) # Filtering data based on user's selection (dff) dff <- eventReactive(input$go, ignoreNULL = FALSE, { cses_shiny_data %>% dplyr::filter(as_factor(wave) %in% input$wave) %>% # year dplyr::filter(pais_nam %in% input$pais) # country }) # Rendering var caption based on user's var selection cap <- renderText({ vars_labels$question_short_en[which(vars_labels$column_name == formulaText())] }) output$caption <- renderText({ cap() }) # Rendering variable code + wording based on user's var selection word <- renderText({ paste0(toupper(vars_labels$column_name[which(vars_labels$column_name == formulaText())]), ". ", vars_labels$question_en[which(vars_labels$column_name == formulaText())]) }) output$wording <- renderText({ word() }) # Rendering ROs based on user's var selection resp <- renderText({ vars_labels$responses_en_rec[which(vars_labels$column_name == formulaText())] }) output$response <- renderText({ resp() }) # Rendering variable_sec ROs resp_sec <- renderText({ vars_labels$responses_en_rec[which(vars_labels$column_name == input$variable_sec)] }) output$response_sec <- renderText({ resp_sec() }) # Rendering User selected recode value(s) slider_values <- renderText({ if(input$recode[1] == input$recode[2]) { paste0("(value: ", unique(input$recode), ")") } else { paste0("(range: ", paste(input$recode, collapse = " to "), ")") } }) output$selected_values <- renderText({ slider_values() }) # Toggle recode slider # # ----------------------------------------------------------------------- observe({ if (isTRUE(input$use_mean)) { shinyjs::disable("recode") } else { shinyjs::enable("recode") } }) # # ----------------------------------------------------------------------- # N-SIZE CARD # # ----------------------------------------------------------------------- output$ns_card <- renderUI({ req(dff(), outcome(), input$wave, input$pais) selected_waves <- as.character(input$wave) selected_countries <- as.character(input$pais) ns <- get_sample_counts( data = dff(), outcome_var = outcome(), wave_var = "wave", country_var = "pais_nam", # adjust if your helper uses a different input col selected_waves = selected_waves, selected_countries = selected_countries ) # If absolutely no non-missing data, show a gentle note if (is.null(ns$overall) || ns$overall == 0) { return(tags$div( style = "border:2px solid #17a2b8; border-radius:8px; padding:14px; background:#e9f7ff; margin-bottom:20px;", HTML(paste0("ℹī¸ Ns for ", outcome(), ": No non-missing observations in the current selection.")) )) } # Expect columns: ns$per_wave (wave, n) and ns$per_country_wave (pais, wave, n) pCW <- ns$per_country_wave # If your helper names the country column differently, change "pais" below # Control whether to show zeros show_zeros <- FALSE # Order waves nicely waves <- unique(pCW$wave) # If waves are numeric-like but char, coerce to numeric for sorting (silently) suppressWarnings({ wave_num <- suppressWarnings(as.numeric(as.character(waves))) if (all(!is.na(wave_num))) waves <- waves[order(wave_num)] else waves <- sort(waves) }) # Create a quick lookup for total N per wave per_wave_tbl <- ns$per_wave |> dplyr::mutate(wave_chr = as.character(wave)) |> dplyr::select(wave_chr, n) # Build one
block per wave blocks <- lapply(seq_along(waves), function(i) { w <- waves[i] w_chr <- as.character(w) wt <- per_wave_tbl$n[match(w_chr, per_wave_tbl$wave_chr)] wt <- ifelse(is.na(wt), 0, wt) rows <- pCW |> dplyr::filter(as.character(wave) == w_chr) if (!show_zeros) rows <- dplyr::filter(rows, n > 0) rows <- dplyr::arrange(rows, dplyr::desc(n), .by_group = FALSE) items <- lapply(seq_len(nrow(rows)), function(j) { n_j <- format(rows$n[j], big.mark = ",") is_zero <- isTRUE(rows$n[j] == 0) li_style <- if (is_zero) "color:#6c757d;" else NULL # country column is "pais" as returned by the helper tags$li( tags$span(HTML(paste0("", rows$pais[j], ": N=", n_j))), style = li_style ) }) tags$details( open = (i == 1), # first year open by default class = "ns-year", tags$summary( HTML(paste0("", w_chr, " — Total N=", format(wt, big.mark=","))) ), tags$ul(items) ) }) tags$div( style = "border:2px solid #17a2b8; border-radius:8px; padding:14px; background:#e9f7ff; margin-bottom:20px; max-height:180px; overflow-y:auto;", # Title + grand total tags$div( HTML(paste0( "📊 Sample sizes (non-missing ", outcome(), ")
", "Total across selection: ", format(ns$overall, big.mark = ",") )), style = "margin-bottom:6px;" ), tags$hr(style="margin:8px 0;"), # Small CSS polish for the dropdowns tags$style(HTML(" details.ns-year { margin-bottom: 8px; } details > summary { cursor: pointer; list-style: none; } details > summary::-webkit-details-marker { display: none; } ")), blocks ) }) # ----------------------------------------------------------------------- # SOURCE INFO WITH ACTUAL DATA AVAILABILITY (not just user selections) # ----------------------------------------------------------------------- source_info_both <- reactive({ req(dff(), outcome(), input$wave, input$pais, input$module) # Reuse your helper to get Ns ns <- get_sample_counts( data = dff(), outcome_var = outcome(), wave_var = "wave", country_var = "pais_nam", selected_waves = input$wave, selected_countries = input$pais ) # Extract actual combinations with nonzero N valid_combos <- ns$per_country_wave %>% dplyr::filter(n > 0) # Actual waves and countries that have data valid_waves <- sort(unique(valid_combos$wave)) valid_countries <- sort(unique(valid_combos$pais)) selected_module = input$module # Get abbreviations for these countries (match order) pais_abbr <- cses_shiny_data %>% dplyr::filter(pais_nam %in% valid_countries) %>% distinct(pais_nam, pais_lab) %>% arrange(match(pais_nam, valid_countries)) %>% pull(pais_lab) paste0( "Source: CSES Data Playground\n\n", str_wrap(paste0("CSES ", selected_module, " - Years: ", paste(valid_waves, collapse = ", "), ". Countries: ", paste(pais_abbr, collapse = ", ") ), 130), "\n\n", str_wrap(paste0(word(), " ", resp()), 130) ) }) # ----------------------------------------------------------------------- source_info_pais <- reactive({ req(dff(), outcome(), input$wave, input$pais) ns <- get_sample_counts( data = dff(), outcome_var = outcome(), wave_var = "wave", country_var = "pais_nam", selected_waves = input$wave, selected_countries = input$pais ) valid_combos <- ns$per_country_wave %>% dplyr::filter(n > 0) valid_countries <- sort(unique(valid_combos$pais)) pais_abbr <- cses_shiny_data %>% dplyr::filter(pais_nam %in% valid_countries) %>% distinct(pais_nam, pais_lab) %>% arrange(match(pais_nam, valid_countries)) %>% pull(pais_lab) paste0( "Source: CSES Data Playground\n", "Countries: ", str_wrap(paste(pais_abbr, collapse = ", "), 130), "\n\n", str_wrap(paste0(word(), " ", resp()), 130) ) }) # ----------------------------------------------------------------------- source_info_wave <- reactive({ req(dff(), outcome(), input$wave, input$pais, input$module) ns <- get_sample_counts( data = dff(), outcome_var = outcome(), wave_var = "wave", country_var = "pais_nam", selected_waves = input$wave, selected_countries = input$pais ) valid_combos <- ns$per_country_wave %>% dplyr::filter(n > 0) valid_waves <- sort(unique(valid_combos$wave)) selected_module = input$module paste0( "Source: CSES Data Playground\n", "CSES ", selected_module, " - Years: ", str_wrap(paste(valid_waves, collapse = ", "), 130), "\n\n", str_wrap(paste0(word(), " ", resp()), 130) ) }) # # ----------------------------------------------------------------------- # PLOTS # # ----------------------------------------------------------------------- # Histogram # # ----------------------------------------------------------------------- # must break into data event, graph event, and renderPlot to get download to work histd <- reactive({ req(dff(), input$variable, input$weight_type) if (!input$variable %in% names(dff()) || !input$weight_type %in% names(dff())) { return(NULL) } tryCatch({ dff() %>% drop_na(!!sym(input$variable), !!sym(input$weight_type)) %>% group_by(cat = haven::as_factor(!!sym(input$variable))) %>% summarise(w = sum(!!sym(input$weight_type), na.rm = TRUE)) %>% mutate( prop = w / sum(w) * 100, proplabel = paste0(round(prop), "%"), cat = str_wrap(as.character(cat), width = 25) ) }, error = function(e) { NULL }) }) histg <- reactive({lapop_hist(histd(), ymax = ifelse(any(histd()$prop > 90), 110, 100), source_info = "Source: CSES Data Playground")}) output$hist <- renderPlot({ req(dff(), nrow(dff()) > 0, input$variable, input$variable %in% names(dff())) return(histg()) }) # Time-series # # ----------------------------------------------------------------------- tsd <- reactive({ dta_ts <- Error( dff() %>% drop_na(!!sym(outcome()), !!sym(input$weight_type)) %>% mutate(outcome_rec = case_when( !!sym(outcome()) >= input$recode[1] & !!sym(outcome()) <= input$recode[2] ~ 100, TRUE ~ 0 )) %>% group_by(wave = as.character(as_factor(wave))) %>% summarise_at( vars("outcome_rec"), list(~weighted.ttest.ci(., !!sym(input$weight_type))) ) %>% unnest_wider(col = "outcome_rec") %>% mutate(proplabel = paste0(round(prop), "%")) %>% dplyr::filter(prop != 0) ) validate( need(dta_ts, "Error: no data available. Please verify that this question was asked in this country/year combination.") ) dta_ts <- merge(dta_ts, data.frame(wave = as.character(waves_total), empty = 1), by = "wave", all.y = TRUE) %>% dplyr::filter(!is.na(prop)) # TO EXCLUDE YEARS NOT IN THE SELECTION # THEN YEARS ARE NOT SEQUENTIAL return(omit_na_edges(dta_ts)) }) tsg <- reactive({lapop_ts(tsd(), ymax = ifelse(any(tsd()$prop > 85, na.rm = TRUE), 110, 100), #label_vjust = -1.5, label_vjust = ifelse(any(tsd()$prop > 80, na.rm = TRUE), -1.1, -1.5), source_info = "Source: CSES Data Playground", subtitle = "% in selected category", ci_type = "errorbar") }) output$ts <- renderPlot({ return(tsg()) }) # Cross Country # # ----------------------------------------------------------------------- # define macro (aggregate-level) variables continuous_vars <- c("IMD3001_TS", "IMD5054_2", "IMD5057_1", "IMD5035", "IMD5056_2", "IMD5055_1", "IMD5053_1", "IMD5052_2") ccd <- reactive({ var_sel <- outcome() rec_min <- input$recode[1] rec_max <- input$recode[2] # CASE 1: Continuous macro-level variable (mean = TRUE) if (var_sel %in% continuous_vars) { curr_outcome <- sym(var_sel) dta_cc <- dff() %>% # Apply recode range first mutate( tmp_val = as.numeric(!!curr_outcome), tmp_val = ifelse(tmp_val >= rec_min & tmp_val <= rec_max, tmp_val, NA_real_) ) %>% group_by(vallabel = pais_lab) %>% summarise( prop = mean(tmp_val, na.rm = TRUE) ) %>% ungroup() %>% mutate( lb = NA_real_, ub = NA_real_, proplabel = ifelse(is.na(prop), "", sprintf("%.2f", prop)) ) %>% filter(!is.na(prop)) # CASE 2: Regular individual-level variable (weighted proportion) } else { dta_cc <- dff() %>% drop_na(!!sym(var_sel), !!sym(input$weight_type)) %>% mutate(outcome_rec = case_when( !!sym(var_sel) >= rec_min & !!sym(var_sel) <= rec_max ~ 100, TRUE ~ 0 )) %>% group_by(vallabel = pais_lab) %>% summarise_at( vars("outcome_rec"), list(~weighted.ttest.ci(., !!sym(input$weight_type))) ) %>% unnest_wider(col = "outcome_rec") %>% filter(prop != 0) %>% mutate(proplabel = paste0(round(prop), "%")) } validate( need(nrow(dta_cc) > 0, "Error: no data available. Please verify that this question was asked in this country/year combination.") ) return(dta_cc) }) ccg <- reactive({ lapop_cc( ccd(), sort = "hi-lo", subtitle = ifelse(outcome() %in% continuous_vars, "Countries (within selected range)", "% in selected category"), ymax = ifelse(outcome() %in% continuous_vars, 6, ifelse(any(ccd()$prop > 90, na.rm = TRUE), 110, 100)), source_info = "Source: CSES Data Playground" ) }) output$cc <- renderPlot({ ccg() }) # World Map # # ----------------------------------------------------------------------- mapd <- reactive({ var_sel <- outcome() rec_min <- input$recode[1] rec_max <- input$recode[2] continuous_vars <- c("IMD3001_TS", "IMD5054_2", "IMD5057_1", "IMD5035", "IMD5056_2", "IMD5055_1", "IMD5053_1", "IMD5052_2") req(input$module) # --- NEW: allow only one module at a time --- validate( need( length(input$module) == 1, "Please select only ONE module to display a map." ) ) # CASE 1: Continuous macro variable (mean values) if (var_sel %in% continuous_vars) { dta_map <- dff() %>% mutate( tmp_val = as.numeric(.data[[var_sel]]), tmp_val = ifelse(tmp_val >= rec_min & tmp_val <= rec_max, tmp_val, NA_real_) ) %>% group_by(pais_lab = pais_lab) %>% # IMPORTANT: must exist in your dataset summarise( value = mean(tmp_val, na.rm = TRUE), .groups = "drop" ) %>% filter(!is.na(value)) } else { # CASE 2: Categorical / proportion variables dta_map <- dff() %>% drop_na(.data[[var_sel]], .data[[input$weight_type]]) %>% mutate(outcome_rec = case_when( .data[[var_sel]] >= rec_min & .data[[var_sel]] <= rec_max ~ 100, TRUE ~ 0 )) %>% group_by(pais_lab = pais_lab) %>% summarise_at( vars("outcome_rec"), list(~weighted.ttest.ci(., .data[[input$weight_type]])) ) %>% unnest_wider(col = "outcome_rec") %>% filter(prop > 0) %>% rename(value = prop) } validate( need(nrow(dta_map) > 0, "Error: no map data available for this country/year/variable selection.") ) return(dta_map) }) mapg <- reactive({ lapop_map( mapd(), survey = "CSES", source_info = "\nSource: CSES Data Playground" ) }) output$map <- renderPlot({ mapg() }) # Breakdown # # ----------------------------------------------------------------------- # Use function for each demographic breakdown variable secdf <- eventReactive(input$go, ignoreNULL = FALSE, { if (input$variable_sec == "None") { NULL } else if (variable_sec() == outcome()) { showNotification(HTML("You cannot break the outcome variable by itself."), type = "error") NULL } else { process_data( data = dff(), outcome_var = outcome(), recode_range = input$recode, group_var = input$variable_sec, weight_var = input$weight_type, var_label = stringr::str_wrap(variable_sec_lab(), width = 25) ) } }) genderdf <- eventReactive(input$go, ignoreNULL = FALSE, { if ("gendermc" %in% input$demog) { process_data( data = dff(), outcome_var = outcome(), recode_range = input$recode, weight_var = input$weight_type, group_var = "gendermc", var_label = "Gender" ) } else { NULL } }) wealthdf <- eventReactive(input$go, ignoreNULL = FALSE, { if ("wealth" %in% input$demog) { process_data( data = dff(), outcome_var = outcome(), recode_range = input$recode, weight_var = input$weight_type, group_var = "wealthf", var_label = "Wealth" ) } else { NULL } }) eddf <- eventReactive(input$go, ignoreNULL = FALSE, { if ("edre" %in% input$demog) { process_data( data = dff(), outcome_var = outcome(), recode_range = input$recode, weight_var = input$weight_type, group_var = "edrerf", var_label = "Education" ) } else { NULL } }) agedf <- eventReactive(input$go, ignoreNULL = FALSE, { if ("age" %in% input$demog) { process_data( data = dff(), outcome_var = outcome(), recode_range = input$recode, weight_var = input$weight_type, group_var = "age", var_label = "Age" ) } else { NULL } }) urdf <- eventReactive(input$go, ignoreNULL = FALSE, { if ("ur" %in% input$demog) { process_data( data = dff(), outcome_var = outcome(), recode_range = input$recode, weight_var = input$weight_type, group_var = "ur", var_label = "Place of\nResidence" ) } else { NULL } }) # Combine demographic data frames into one df moverd <- reactive({ req(dff(), input$recode, input$weight_type) dta_mover <- Error(rbind( if (input$variable_sec != "None" && input$variable_sec != outcome()) { process_data( data = dff(), outcome_var = outcome(), recode_range = input$recode, group_var = input$variable_sec, weight_var = input$weight_type, var_label = str_wrap(variable_sec_lab(), width = 25) ) }, if ("gendermc" %in% input$demog) { process_data(dff(), outcome(), input$recode, "gendermc", "Gender", input$weight_type) }, if ("age" %in% input$demog) { process_data(dff(), outcome(), input$recode, "age", "Age", input$weight_type) }, if ("wealth" %in% input$demog) { process_data(dff(), outcome(), input$recode, "wealthf", "Wealth", input$weight_type) }, if ("edre" %in% input$demog) { process_data(dff(), outcome(), input$recode, "edrerf", "Education", input$weight_type) }, if ("ur" %in% input$demog) { process_data(dff(), outcome(), input$recode, "ur", "Place of\nResidence", input$weight_type) } )) validate( need(dta_mover, "Error: no data available. Please verify that this question was asked in this country/year combination") ) dta_mover$vallabel <- as.character(dta_mover$vallabel) return(dta_mover) }) moverg <- reactive({ moverg <- lapop_mover(moverd(), subtitle = "% in selected category", ymax = ifelse(any(moverd()$prop > 90, na.rm = TRUE), 119, ifelse(any(moverd()$prop > 80, na.rm = TRUE), 109, 100)), source_info = "Source: CSES Data Playground") return(moverg) }) output$mover <- renderPlot({ return(moverg()) }) # # ----------------------------------------------------------------------- # DOWNLOAD SECTION # # ----------------------------------------------------------------------- # Download Plot # # ----------------------------------------------------------------------- output$downloadPlot <- downloadHandler( filename = function(file) { weight_suffix <- switch(input$weight_type, # Add weight type to plot export "no_weight" = "unweighted", "weight_demographic" = "demogweighted", "weight_sample" = "sampleweighted") ifelse(input$tabs == "Histogram", paste0("hist_", outcome(), "_", weight_suffix, ".svg"), ifelse(input$tabs == "Time Series", paste0("ts_", outcome(), "_", weight_suffix, ".svg"), ifelse(input$tabs == "Cross Country", paste0("cc_", outcome(), "_", weight_suffix, ".svg"), ifelse(input$tabs == "World Map", paste0("map_", outcome(), "_", weight_suffix, ".svg"), paste0("mover_", outcome(), "_", weight_suffix, ".svg"))))) # Add plot type to file export }, content = function(file) { if(input$tabs == "Histogram") { title_text <- isolate(cap()) word_text <- isolate(word()) hist_to_save <- lapop_hist(histd(), main_title = title_text, subtitle = "% in selected category ", ymax = ifelse(any(histd()$prop > 90), 110, 100), source_info = source_info_both() ) lapop_save(hist_to_save, file) showNotification(HTML("Histogram plot download complete ✓ "), type = "message") } else if (input$tabs == "Time Series") { title_text <- isolate(cap()) subtitle_text <- slider_values() ts_to_save <- lapop_ts(tsd(), main_title = title_text, subtitle = paste0("% in selected category ", subtitle_text), ymax = ifelse(any(tsd()$prop > 88, na.rm = TRUE), 110, 100), label_vjust = ifelse(any(tsd()$prop > 80, na.rm = TRUE), -1.1, -1.5), source_info = source_info_pais() ) lapop_save(ts_to_save, file) showNotification(HTML("Time series plot download complete ✓ "), type = "message") } else if (input$tabs == "Cross Country") { title_text <- isolate(cap()) subtitle_text <- slider_values() cc_to_save <- lapop_cc(ccd(), sort = "hi-lo", main_title = title_text, subtitle = paste0("% in selected category ", subtitle_text), ymax = ifelse(any(ccd()$prop > 90, na.rm = TRUE), 110, 100), label_angle = 90, source_info = source_info_wave() ) lapop_save(cc_to_save, file) showNotification(HTML("Cross country plot download complete ✓ "), type = "message") } else if (input$tabs == "World Map") { title_text <- isolate(cap()) subtitle_text <- slider_values() map_to_save <- lapop_map(mapd(), main_title = title_text, subtitle = paste0("% in selected category ", subtitle_text), source_info = paste0("\n", source_info_both()), survey = "CSES" ) lapop_save(map_to_save, file) showNotification(HTML("Map plot download complete ✓ "), type = "message") } else { title_text <- isolate(cap()) subtitle_text <- slider_values() word_text <- isolate(word()) mover_to_save <- lapop_mover( moverd(), main_title = title_text, subtitle = paste0("% in selected category ", subtitle_text), ymax = ifelse(any(moverd()$prop > 90, na.rm = TRUE), 119, ifelse(any(moverd()$prop > 80, na.rm = TRUE), 109, 100)), source_info = source_info_both() ) lapop_save(mover_to_save, file) showNotification(HTML("Break down plot download complete ✓ "), type = "message") } } ) # DOWNLOAD TABLE # ----------------------------------------------------------------------- output$downloadTable <- downloadHandler( filename = function(file) { weight_suffix <- switch(input$weight_type, # Add weight type to file export "no_weight" = "unweighted", "weight_demographic" = "demogweighted", "weight_sample" = "sampleweighted") ifelse(input$tabs == "Histogram", paste0("hist_", outcome(), "_", weight_suffix, ".csv"), ifelse(input$tabs == "Time Series", paste0("ts_", outcome(), "_", weight_suffix,".csv"), ifelse(input$tabs == "Cross Country", paste0("cc_", outcome(), "_", weight_suffix, ".csv"), ifelse(input$tabs == "World Map", paste0("map_", outcome(), "_", weight_suffix, ".csv"), paste0("mover_", outcome(), "_", weight_suffix, ".csv"))))) }, content = function(file) { if(input$tabs == "Histogram") { write.csv(histd(), file, row.names=F) showNotification(HTML("Histogram file download complete ✓ "), type = "message") } else if (input$tabs == "Time Series") { write.csv(tsd(), file, row.names=F) showNotification(HTML("Time series file download complete ✓ "), type = "message") } else if (input$tabs == "Cross Country") { write.csv(ccd(), file, row.names=F) showNotification(HTML("Cross country file download complete ✓ "), type = "message") } else if (input$tabs == "World Map") { write.csv(mapd(), file, row.names=F) showNotification(HTML("Map file download complete ✓ "), type = "message") } else { write.csv(moverd(), file, row.names=F) showNotification(HTML("Break down file download complete ✓ "), type = "message") } } ) } # RUN APP # # ----------------------------------------------------------------------- shinyApp(ui, server) # # ----------------------------------------------------------------------- # END # # -----------------------------------------------------------------------