From 2e827e2fc54d30833cbb02909e042258d84ceae9 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 28 Apr 2025 17:28:23 -0400
Subject: [PATCH 01/47] add label select on upload, initial filter to duplicate
 table, and minor changes

---
 inst/shiny-app/CiteSource/app.R | 353 +++++++++++++++++++++++---------
 1 file changed, 252 insertions(+), 101 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index b01c2f79..c5b99dfb 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -33,7 +33,20 @@ ui <- shiny::navbarPage("CiteSource",
         font-weight: 500;
         line-height: 1.2;
         color: #23395B;
-      }
+        }
+    blockquote {
+          border-left: 5px solid #008080; /* Use theme primary color for border */
+          background-color: #f8f9fa;    /* A very light grey background */
+          padding: 10px 20px;           /* Add padding */
+          margin-bottom: 1rem;          /* Add space below */
+          color: #212529;              /* Ensure text color contrasts well */
+        }
+        summary strong {
+          font-size: 1.15rem; /* Adjust size similar to h5, tweak as needed */
+          /* font-weight: bold; /* Handled by strong, but can be explicit */
+          /* Optional: Adjust vertical alignment slightly if needed */
+           vertical-align: middle;
+        }
     "))
     )
   ),
@@ -63,9 +76,17 @@ ui <- shiny::navbarPage("CiteSource",
         title = "Use Cases",
         htmltools::includeMarkdown("www/use-cases.md")
       ),
+      # User Guide
+      shiny::tabPanel(
+        title = "User Guide",
+        # Load the external Markdown file
+        htmltools::includeMarkdown("www/user_guide.md")
+      ), # End tabPanel,
+      
       widths = c(2, 10)
-    )
-  ),
+    ) # End navlistPanel
+  ), # End Home tabPanel
+  
   shiny::tabPanel(
     "File upload",
     shiny::fluidRow(
@@ -73,24 +94,28 @@ ui <- shiny::navbarPage("CiteSource",
         12,
         # Sidebar layout with input and output definitions ----
         shiny::sidebarLayout(
-          shiny::sidebarPanel( # Input: Select a file ----
+          shiny::sidebarPanel( 
             shiny::h5("Step 1: Upload your citation files"),
+            
             shiny::fileInput("file", "",
-              multiple = TRUE,
-              accept = c(".ris", ".txt", ".bib")
+                             multiple = TRUE,
+                             accept = c(".ris", ".txt", ".bib")
             ),
-            shiny::p(
-              HTML("NOTE: OVID citations may be incompatible.<br> 
-                   Import and export them using citation software<br>
-                  before uploading for accurate metadata mapping."),
-              style = "font-size: 85%; color: black;"
+            
+            shiny::selectInput("upload_label_select",
+                               label = "Set Label for Uploaded File(s):",
+                               choices = c("search", "screened", "final"), 
+                               selected = "search" 
             ),
+
             shiny::hr(),
-            shiny::h5("OR: Re-upload an .ris or .csv exported from CiteSource"),
+            shiny::br(),
+            shiny::h5("Re-upload an .ris or .csv exported from CiteSource"),
             shiny::fileInput("file_reimport", "",
-              multiple = TRUE,
-              accept = c(".ris", ".csv")
-            )
+                             multiple = TRUE,
+                             accept = c(".ris", ".csv")
+            ),
+            shiny::helpText("Re-uploading skips deduplication (Steps 3 & 4) and uses the metadata already present in the exported file. Proceed directly to 'Visualise' or 'Tables'.", style="font-size: 85%;"),
           ),
           # Main panel for displaying outputs ----
           shiny::mainPanel(
@@ -109,8 +134,9 @@ ui <- shiny::navbarPage("CiteSource",
         "Automated deduplication",
         br(),
         shiny::h5("Step 3: Deduplicate"),
-        shiny::p("Click the button below to detect and remove duplicates automatically"),
-
+        shiny::p(shiny::p("CiteSource uses ASySD to identify duplicates."),
+        shiny::p(shiny::p("Dedplication takes place *within* each file (Internal Deduplication) and *across* all uploaded files (External Deduplication).")),
+        ),
         # Action button: identify duplicates in uploaded dataset
         shinyWidgets::actionBttn(
           "identify_dups", "Find duplicates",
@@ -126,7 +152,7 @@ ui <- shiny::navbarPage("CiteSource",
         "Manual deduplication",
         br(),
         shiny::h5("Step 4: Review potential duplicates manually"),
-        shiny::p("The following records were identified as potential duplicates. Potential duplicates are combined into a single row with metadata fields for each record represented (ex. Title 1 & Title 2). Click any row to indicate that the records in that row ARE duplicates. Once all duplicates are identified you can click the button 'Remove additional duplicates' and then proceed to the visualizations."),
+        shiny::p("The following records were identified as potential duplicate pairs. Pairs are combined into a single row with metadata fields for each record represented (ex. Title 1 & Title 2). Click any row to indicate that the records in that row ARE duplicates. Once all duplicates are identified you can click the button 'Remove additional duplicates' and then proceed to the visualizations."),
         shiny::textOutput("Manual_pretext"),
         shiny::br(),
 
@@ -318,7 +344,6 @@ ui <- shiny::navbarPage("CiteSource",
           
           shiny::tabPanel(
             "Review individual records",
-            shiny::div("Note that the record table will take a long time to create if you include more than a few hundred references ... so you might want to filter your data first."),
             shiny::br(),
             shinyWidgets::actionBttn(
               "generateRecordTable", "Generate the table",
@@ -327,7 +352,54 @@ ui <- shiny::navbarPage("CiteSource",
               color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
             
             shiny::br(),
-            shiny::br(" "),
+            
+            shiny::wellPanel(
+              style = "background-color: #f0f8ff; border-color: #bce8f1; margin-top: 15px; margin-bottom: 15px; padding: 15px;", # Style
+              shiny::tags$h5(shiny::icon("info-circle", lib = "font-awesome"), " Using the Interactive Record Table", style = "margin-top: 0; color: #31708f;"), # Title
+              shiny::tags$p("After clicking 'Generate the table', you can explore the records using these features:"), # Introduction
+              
+              # Performance Note
+              shiny::tags$p(
+                style = "margin-bottom: 12px;", # Add space below this paragraph
+                # Using exclamation-triangle icon for warning
+                shiny::icon("exclamation-triangle", lib = "font-awesome"), shiny::tags$strong(" Performance Note:"),
+                " The record table may take a long time to generate if you include more than a few hundred references. Consider filtering your data first using the sidebar selectors before generating."
+              ),
+              
+              # Instruction 1: Expand/Collapse
+              shiny::tags$p(
+                style = "margin-bottom: 12px;",
+                shiny::icon("expand", lib = "font-awesome"), shiny::tags$strong(" Expand/Collapse Row:"),
+                " Click the ", shiny::tags$code(HTML("&oplus;")), " symbol in a row to view the full APA reference. Click ", shiny::tags$code(HTML("&CircleMinus;")), " to hide it again."
+              ),
+              
+              # Instruction 2: Single Sort
+              shiny::tags$p(
+                style = "margin-bottom: 12px;",
+                shiny::icon("sort", lib = "font-awesome"), shiny::tags$strong(" Sort by Single Column:"),
+                " Click any column header (like 'Citation' or a source name) to sort the table by that column's values. Click the header again to reverse the sort order."
+              ),
+              
+              # Instruction 3: Multi Sort
+              shiny::tags$p(
+                style = "margin-bottom: 12px;",
+                shiny::icon("bars", lib = "font-awesome"), shiny::tags$strong(" Sort by Multiple Columns:"),
+                " Click the primary column header you want to sort by. Then, hold down the ", shiny::tags$strong("Shift"), " key on your keyboard and click a second column header. You can repeat this for more sorting levels."
+              ),
+              
+              # Instruction 4: Filter/Search
+              shiny::tags$p(
+                style = "margin-bottom: 12px;",
+                shiny::icon("filter", lib = "font-awesome"), shiny::tags$strong(" Filter/Search:"),
+                " Type into the search box located at the top-right of the table to dynamically filter records based on any information displayed."
+              ),
+              
+              # Instruction 5: Download (No bottom margin needed on the last item)
+              shiny::tags$p(
+                shiny::icon("download", lib = "font-awesome"), shiny::tags$strong(" Download Data:"),
+                " Click the 'Download CSV' button (located above the table, next to 'Print') to save the data currently shown in the table (including applied filters) as a CSV file."
+              )
+            ),
             DT::dataTableOutput("reviewTab")
           )
         )
@@ -342,7 +414,8 @@ ui <- shiny::navbarPage("CiteSource",
         12,
         shiny::mainPanel(
           shiny::h5("Step 7: Export citations"),
-          shiny::h6("Note that you can only download the data after you have run the deduplication. Also, you are only able to re-upload CSV and RIS files to continue with CiteSource, so please use these formats if you want that option."),
+          shiny::h6("Data is only availabel after deduplication. CiteSource currently only supports re-upload of CSV and RIS files"),
+          shiny::h6("CiteSource currently only supports re-upload of CSV and RIS files"),
           shiny::downloadButton("downloadCsv", "Download csv"),
           shiny::downloadButton("downloadRis", "Download RIS"),
           shiny::downloadButton("downloadBib", "Download BibTex")
@@ -356,15 +429,11 @@ ui <- shiny::navbarPage("CiteSource",
 # Define server logic to read selected file ----
 server <- function(input, output, session) {
   rv <- shiny::reactiveValues()
-  rv$df <- data.frame()
-  #for original uploads
-  rv$upload_df <- data.frame()
-  #for reimported data
-  rv$latest_unique <- data.frame()
-  #for potential duplicates/manual dedup
-  rv$pairs_to_check <- data.frame()
-  #for removed records
-  rv$pairs_removed <- data.frame()
+  rv$df <- data.frame() 
+  rv$upload_df <- data.frame()#for original uploads
+  rv$latest_unique <- data.frame() #for reimported data
+  rv$pairs_to_check <- data.frame()#for potential duplicates/manual dedup
+  rv$pairs_removed <- data.frame()#for removed records
   
   #### Upload files tab section ------
   # upload on click
@@ -372,60 +441,97 @@ server <- function(input, output, session) {
     shiny::validate(need(input$file != "", "Select your bibliographic file to upload..."))
     if (is.null(input$file)) {
       return(NULL)
-    } 
-    else {
-      # upload files one-by-one
+    } else {
+      # --- NEW: Read the selected label from the dropdown ---
+      selected_label <- input$upload_label_select
+      # Ensure it's not NULL or empty if that's possible
+      if (is.null(selected_label) || selected_label == "") {
+        warning("No label selected for upload. Defaulting to 'search'.")
+        selected_label <- "search" # Use the default from the selectInput if needed
+      }
+      
+      # Get file paths and names (as before)
       path_list <- input$file$datapath
+      file_names <- input$file$name
       
-      # Increment the upload number
+      # Generate suggested source names (as before)
+      suggested_source <- stringr::str_replace_all(file_names, "\\.(ris|bib|txt)$", "")
+      
+      # --- MODIFIED: Create label vector using the selected label ---
+      # Repeat the single selected label for each file being uploaded in this batch
+      labels_for_upload <- rep(selected_label, length(path_list))
+      # Keep strings empty unless you add a similar input for them
+      empty_strings <- rep("", length(path_list))
+      
+      # Increment upload number (as before)
       if (is.null(rv$upload_number)) {
         rv$upload_number <- 1
       } else {
         rv$upload_number <- rv$upload_number + 1
       }
       
-      suggested_source <- stringr::str_replace_all(input$file$name, "\\.(ris|bib|txt)$", "")
+      # --- MODIFIED: Call read_citations with the selected labels ---
+      # Use tryCatch for better error handling during file reading
+      upload_df <- tryCatch({
+        CiteSource::read_citations(
+          files = path_list,
+          cite_sources = suggested_source,
+          cite_labels = labels_for_upload, # Use the label from the dropdown
+          cite_strings = empty_strings,    # Keep strings empty
+          only_key_fields = FALSE
+        )
+      }, error = function(e) {
+        shiny::showNotification(paste("Error reading file(s):", e$message), type = "error", duration = 10)
+        return(NULL) # Return NULL on error
+      })
       
-      empty_strings <- rep("", length(input$file$datapath))
+      # Stop processing if read_citations failed
+      if(is.null(upload_df) || nrow(upload_df) == 0) {
+        # Optionally clear the file input if reading failed completely for all files
+        # resetFileInput("file") # You would need a function like this using shinyjs
+        return(NULL)
+      }
       
-      # Read the uploaded citations
-      upload_df <- CiteSource::read_citations(
-        files = path_list,
-        cite_sources = suggested_source,
-        cite_labels = empty_strings,
-        cite_strings = empty_strings
+      # --- MODIFIED: Create the summary df with the selected label ---
+      # Ensure df has columns corresponding to the uploaded files in this batch
+      df <- data.frame(
+        file.datapath = path_list[1:length(suggested_source)], # Match length just in case
+        file.name = file_names[1:length(suggested_source)],
+        suggested_source = suggested_source,
+        label = labels_for_upload[1:length(suggested_source)], # Assign the label chosen in the dropdown
+        string = empty_strings[1:length(suggested_source)],    # Keep strings empty
+        stringsAsFactors = FALSE # Good practice
       )
       
-      # Summarize the number of records by citation source
+      # Summarize record counts from the successfully read data (as before)
       upload_length <- upload_df %>%
         dplyr::group_by(cite_source) %>%
         dplyr::count(name = "records") %>%
+        dplyr::ungroup() %>% # Ungroup after counting
         dplyr::rename(source = cite_source)
       
-      # Create a data frame summarizing the uploaded files
-      df <- data.frame(
-        "file" = input$file,
-        "suggested_source" = suggested_source,
-        "label" = empty_strings,
-        "string" = empty_strings
-      )
-      
-      # Join upload_df with df to match on cite_source
-      upload_df <- dplyr::left_join(upload_df, df, by = c("cite_source" = "suggested_source")) %>%
-        dplyr::select(-label, -string) %>%
-        dplyr::select(cite_source, cite_label, cite_string, dplyr::everything())
+      # Update the summary data frame df with record counts (as before)
+      # Join counts back to the summary df for display
+      df <- dplyr::left_join(df, upload_length, by = c("suggested_source" = "source")) %>%
+        dplyr::select(file.datapath, file.name, records, source = suggested_source, label, string) # Reorder/rename for consistency
       
-      # Ensure required columns are present in upload_df
-      required_cols <- c("title", "doi", "label", "isbn", "source", "year", "journal", "pages", "volume", "number", "abstract")
-      upload_df[required_cols[!(required_cols %in% colnames(upload_df))]] <- NA
+      # Ensure required columns are present in upload_df (label is now cite_label)
+      # No need to join the label/string from df to upload_df anymore, it came from read_citations
+      required_cols <- c("cite_source", "cite_label", "cite_string", "title", "doi", "isbn", "year", "journal", "pages", "volume", "number", "abstract")
+      # Add missing required columns as NA
+      missing_cols <- setdiff(required_cols, colnames(upload_df))
+      if(length(missing_cols) > 0) {
+        upload_df[missing_cols] <- NA
+      }
+      # Select a reasonable default order including new cite_* columns
+      upload_df <- upload_df %>%
+        dplyr::select(dplyr::any_of(c("cite_source", "cite_label", "cite_string")), dplyr::everything())
       
-      # Update the summary data frame df with record counts
-      df <- dplyr::left_join(upload_length, df, by = c("source" = "suggested_source")) %>%
-        dplyr::select(file.datapath, file.name, records, source, label, string)
       
-      # Append the results to the reactive values
+      # Append the results to the reactive values (as before)
       rv$df <- dplyr::bind_rows(rv$df, df)
       rv$upload_df <- dplyr::bind_rows(rv$upload_df, upload_df)
+      
     }
   })
   
@@ -574,23 +680,28 @@ server <- function(input, output, session) {
     n_unique_records <- nrow(dedup_results$unique)
     n_pairs_manual <- nrow(rv$pairs_to_check)
     
-    message <- if (n_pairs_manual > 0) {
-      paste(
-        "From the", n_citations, "records, that were uploaded, there were", n_distinct_records, 
-        "distinct records identified after internal source deduplication. 
-        Of these distinct records, there were", n_unique_records, "unique records.
-        Head to the manual deduplication tab to check", n_pairs_manual, "potential duplicates."
+    # Title for the alert
+    alert_title <- "Automated Deduplication Complete"
+    
+    # Text for the alert body
+    if (n_pairs_manual > 0) {
+      alert_text <- sprintf(
+        "Process Summary:\n - Started with %d uploaded records.\n - Found %d distinct records (after removing duplicates within each source file).\n - Found %d unique records (after removing duplicates across source files).\n\nAction Required:\n%d potential duplicate pairs require your review. Please go to the 'Manual deduplication' sub-tab.",
+        n_citations, n_distinct_records, n_unique_records, n_pairs_manual
       )
     } else {
-      paste(
-        "From the", n_citations, "records, that were uploaded, there were", n_distinct_records, 
-        "distinct records identified after internal source deduplication. 
-        There were no potential duplicates identifid for manual review. 
-        You can proceed to the visualization tab."
+      alert_text <- sprintf(
+        "Process Summary:\n - Started with %d uploaded records.\n - Found %d distinct records (after removing duplicates withi* each source file).\n - Found %d unique records (after removing duplicates across source files).\n\nNo potential duplicates were flagged for manual review.\n\nNext Step:\nYou can now proceed to the 'Visualise' tab.",
+        n_citations, n_distinct_records, n_unique_records
       )
     }
     
-    shinyalert::shinyalert("Auto-deduplication complete", message, type = "success")
+    # Show the alert
+    shinyalert::shinyalert(title = alert_title, 
+                           text = alert_text, 
+                           type = "success",
+                           size = "l")
+    
   })
 
   ## Manual deduplication -----
@@ -629,11 +740,17 @@ server <- function(input, output, session) {
 
       })
 
-  observe({
-   shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
-                      choices = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)],
-                      selected = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)])
-  })
+    observe({
+      all_cols <- names(rv$pairs_to_check)
+      base_cols <- unique(gsub("(1|2)$", "", all_cols)) # Remove "1" or "2" suffix
+      
+      # Initial selection
+      initial_base_selection <- c("author", "title", "year", "journal", "abstract", "doi", "pages")
+      
+      shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
+                                      choices = base_cols,
+                                      selected = initial_base_selection)
+    })
   
   
   # if no manual dedup, proceed to visualisations
@@ -659,18 +776,40 @@ server <- function(input, output, session) {
   # Output: manual dedup datatable
   manual_dedup_data <- reactive({
     
-    data <- rv$pairs_to_check[,1:36]
-    data <- data[,c(paste0(input$manual_dedup_cols))]
+    data <- rv$pairs_to_check[, 1:36]
+    selected_cols <- input$manual_dedup_cols
+    
+    # Define the desired base order
+    core_col_order <- c("author", "title", "year", "journal", "abstract","doi", "pages","volume","number","source","label","string")
+    
+    # Create the desired interleaved order of columns to select
+    desired_table_order <- character(0)
+    for (base_col in core_col_order) {
+      col1 <- paste0(base_col, "1")
+      col2 <- paste0(base_col, "2")
+      desired_table_order <- c(desired_table_order, col1, col2)
+    }
+    
+    # Intersect with the actual and selected columns to maintain order and presence
+    cols_to_show <- intersect(desired_table_order, colnames(data))
+    cols_to_show <- intersect(cols_to_show, paste0(selected_cols, rep(c("1", "2"), each = length(selected_cols))))
+    
+    ordered_data <- data %>%
+      dplyr::select(any_of(cols_to_show))
     
+    # Define match_cols INSIDE the reactive expression
     match_cols <- c("title", "author", "doi", "volume",
                     "pages", "number", "year", "abstract", "journal", "isbn")
     
+    ordered_data <- ordered_data %>% dplyr::select(-any_of(match_cols))
     
-    data <- data %>% dplyr::select(-any_of(match_cols))
-   match_number_cols <- rv$pairs_to_check[,c(paste0(match_cols))]
-   
-   data <- cbind(data,match_number_cols)
-   
+    # Add the match_number_cols at the end (if they exist)
+    match_number_cols_to_add <- intersect(paste0(match_cols), colnames(data))
+    if (length(match_number_cols_to_add) > 0) {
+      ordered_data <- cbind(ordered_data, data[, match_number_cols_to_add])
+    }
+    
+    ordered_data
   })
   output$manual_dedup_dt <- DT::renderDataTable({
     
@@ -679,7 +818,7 @@ server <- function(input, output, session) {
     format_cols <- c(
       "title1", "author1", "doi1", "volume1",
       "pages1", "number1", "year1", "abstract1", "journal1", "isbn1",
-      "title2", "author2", "doi2", "volume2",
+      "title2", "author2", "doi2", "volume2", 
       "pages2", "number2", "year2", "abstract2", "journal2", "isbn2"
     )
     
@@ -687,23 +826,33 @@ server <- function(input, output, session) {
     shinyjs::useShinyjs()
     
     datatable(data,
+              extensions = c('FixedHeader', 'FixedColumns'),
               options = list(
-                pageLength = 100, info = FALSE,
-                               lengthMenu = list(c(100, -1), c("100", "All")),
-                columnDefs =
+                fixedHeader = TRUE,                   
+                fixedColumns = list(leftColumns = 2), 
+                scrollX = TRUE,
+                pageLength = 100,
+                info = FALSE,
+                lengthMenu = list(c(100, -1), c("100", "All")),
+                columnDefs = list(
                   list(
-                    list(visible = FALSE, 
-                         targets = columns2hide),
-                    list(
-                      targets = "_all",
-                      render = JS(
-                        "function(data, type, row, meta) {",
-                        "return type === 'display' && data != null && data.length > 25 ?",
-                        "'<span title=\"' + data + '\">' + data.substr(0, 25) + '...</span>' : data;",
-                        "}"
-                      )
+                    targets = "_all",
+                    render = JS(
+                      "function(data, type, row, meta) {",
+                      "  if (type === 'display' && data != null && typeof data === 'string' && data.length > 25) {",
+                      "    var rawData = data;",
+                      "    return '<span title=\"' + rawData + '\">' + rawData.substr(0, 25) + '...</span>';",
+                      "  } else {",
+                      "    return data;",
+                      "  }",
+                      "}"
                     )
+                  ),
+                  list(
+                    visible = FALSE,
+                    targets = columns2hide
                   )
+                ) 
               ))
     # ) %>%
     #   DT::formatStyle(
@@ -731,7 +880,7 @@ server <- function(input, output, session) {
 
   #### Visualise tab ####
   
-  # Reactive expression to filter the data for visualization
+  # Reactive expression to filter the data for visualization 
   unique_filtered_visual <- shiny::reactive({
     sources <- input$sources_visual 
     sources <- ifelse(sources == "_blank_", "unknown", sources)
@@ -741,7 +890,9 @@ server <- function(input, output, session) {
     labels <- ifelse(labels == "_blank_", "unknown", labels)
     
     out <- rv$latest_unique %>%
+      # Group by the ID representing unique/duplicate groups
       dplyr::group_by(duplicate_id) %>%
+      # Separate comma-separated values into individual rows
       tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
       dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
       dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%

From ec92826627d885f8b5ab532fc9857e61820b2b1f Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 29 Apr 2025 09:36:47 -0400
Subject: [PATCH 02/47] add record type as standard export field

---
 R/export.R | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/R/export.R b/R/export.R
index b3b93741..fcaa187e 100644
--- a/R/export.R
+++ b/R/export.R
@@ -96,7 +96,8 @@ export_ris <- function(citations, filename = "citations.ris", source_field = "DB
       string_field, "cite_string", TRUE,
       label_field, "cite_label", TRUE,
       "C1", "duplicate_id", TRUE,
-      "C2", "record_ids", TRUE
+      "C2", "record_ids", TRUE,
+      "TY", "type", TRUE
     ),
     synthesisr_code_lookup %>% dplyr::filter(.data$ris_synthesisr)
   ) %>% dplyr::distinct(.data$code, .keep_all = TRUE) # Remove fields from synthesisr specification used for CiteSource metadata

From 97a4bcdf5039cf7a8678afd7e744ebacf255db96 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 29 Apr 2025 09:37:39 -0400
Subject: [PATCH 03/47] update description and news for version to 0.1.1

---
 DESCRIPTION | 2 +-
 NEWS.md     | 7 +++++++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 2eb7a38a..b37c8739 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: CiteSource
 Title: Analyze the Utility of Information Sources and Retrieval Methodologies for Evidence Synthesis
-Version: 0.0.1
+Version: 0.1.1
 Date: 2023-06-22
 Authors@R: c(
     person("Trevor", "Riley", , "trevor.riley@noaa.gov", role = c("aut", "cre"),
diff --git a/NEWS.md b/NEWS.md
index 3c812e2c..2f85f30c 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -8,3 +8,10 @@
 
 - Integrated new dedup function into R shiny app
 
+# CiteSource 0.1.1
+
+- Added new functions which allow creation of tables and plots based on deduplicated (reimported) data. 
+
+- Updated shiny functionality, look and feel, and documentation
+
+- Added new vignettes 
\ No newline at end of file

From c741a3afaf765f7426430f68bf9049025c9556b1 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 29 Apr 2025 10:29:46 -0400
Subject: [PATCH 04/47] Updated documentation for shiny

---
 inst/shiny-app/CiteSource/www/about.md     |  80 ++++++++++++++-
 inst/shiny-app/CiteSource/www/use-cases.md | 113 ++++++++++++++++-----
 2 files changed, 160 insertions(+), 33 deletions(-)

diff --git a/inst/shiny-app/CiteSource/www/about.md b/inst/shiny-app/CiteSource/www/about.md
index ac710c33..a1b12fe2 100644
--- a/inst/shiny-app/CiteSource/www/about.md
+++ b/inst/shiny-app/CiteSource/www/about.md
@@ -1,9 +1,79 @@
-<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" align="right"/>
+## About CiteSource
 
-CiteSource was developed in order to provide researchers the ability to examine the utility and efficacy of literature resources and search methodologies. The idea behind CiteSource is simply allowing users to deduplicate citation records, while maintaining customizable metadata about the citation.
+<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
 
-Customizable metadata can include anything from a resource name (ex.Web of Science, LENS.org, PubMed), a method (database search, handsearching, citation chasing/ snowballing), a variation used within a method (WoS string #1, Wos string #2, WoS string #3), a research phase (search, TI/AB screening, Full-text Screening), or a unique group of citations (benchmarking articles, articles from a previous review, articles with a specific author affiliation).
+CiteSource is an R package and accompanying Shiny web application designed to support evidence data-driven decision-making during search strategy development. CiteSource also allows users to analyze and report on the impact of information sources and search methods.
 
-The CiteSource deduplication process is better described as record merging process due to the fact that the customizable metadata from duplicate records is maintained through the creation of a single, primary record. Beyond the merging of customizable metadata, the primary record is created by using the most complete metadata available between duplicate records (currently DOI and Abstract fields).
+CiteSource was developed as part of the [Evidence Synthesis Hackathon](https://www.eshackathon.org/) initiative.
 
-Once records are deduplicated, users are able to easily create plots and tables in order to answer specific questions or to simply explore the data to an effort to develop new hypotheses. Examples of analysis may include how many unique records a specific source contributed or how traditional methods of searching fare against a new AI discovery tool in discovering relevant articles. Users may want to understand the overlap in records between two different search strings or evaluate the impact of including Google Scholar in a review. Before searching, a user may even develop a targeted search to better understand the topical coverage across databases that they intend to search, and once the search has been developed, how particular source, string, or method performed in discovering benchmarking articles.
+---
+
+### Key Features:
+
+<details>
+  <summary><strong>Flexible Metadata for Provenance Tracking:</strong></summary>
+
+> * A core strength of CiteSource is its ability to assign and retain custom metadata to track the *provenance* of each citation – precisely where and how it was found. Users can tag records using three key fields:
+>     * `cite_source`: Identify the origin database ('Web of Science', 'Scopus'), platform ('Lens.org'), or the specific search method used ('Citation Searching', 'String_1').
+>     * `cite_label`: Track citations through screening phases using standardized terms: `search` (for initial results, benchmarks), `screened` (for records passing title/abstract review), and `final` (for records included in the synthesis after full-text review).
+>     * `cite_string`: Add further detail, such as variations in search string syntax tested ('String_1a_truncation'), specific supplementary methods ('Handsearching_JournalX'), or other custom categories relevant to analysis.
+> * This detailed tagging enables rigorous analysis of the performance and contribution of each component of your overall search strategy.
+
+</details>
+
+<details>
+  <summary><strong>Advanced Deduplication & Intelligent Merging:</strong></summary>
+
+> * CiteSource employs the [`ASySD` (Automated Systematic Search Deduplicator) R package](https://github.com/camaradesuk/ASySD) to perform robust identification and merging of duplicate records.
+> * It conducts both *internal deduplication* (identifying duplicates within a single uploaded file/source, resulting in *distinct* records) and *external deduplication* (identifying duplicates across all uploaded files/sources, resulting in the set of *unique* citations).
+> * The process uses *intelligent merging*: custom metadata tags (`source`, `label`, `string`) from all identified duplicates are combined onto the primary record, preserving the full discovery history.
+> * The most complete bibliographic data (prioritizing DOI, Abstract) across duplicates is retained in the primary record.
+> * An optional *manual review* stage presents potential duplicates that fall below the automatic matching threshold, allowing users to confirm or reject merges for maximum accuracy.
+
+</details>
+
+<details>
+  <summary><strong>Data-Driven Analysis & Visualization:</strong></summary>
+
+> * Once deduplication is complete, CiteSource offers a suite of analysis and visualization tools designed specifically to speed up the *iterative process* of developing, testing, and validating search strategies:
+>     * Visualize Overlap: Use interactive **Heatmaps** (pairwise overlap) and **Upset Plots** (multi-set intersections) to understand shared and unique records across sources, labels, or strings.
+>     * Track Phase Progression: Employ the **Phase Analysis plot** (bar chart) to see the flow of unique and duplicate records through screening stages (`search` -> `screened` -> `final`).
+>     * Generate Summary Tables: Access quantitative insights via automated tables detailing:
+>         * Initial Record counts (showing the impact of internal deduplication).
+>         * Record Summaries (detailing unique/overlapping records contributed by each source/method).
+>         * Precision/Sensitivity calculations (evaluating source/method performance against the `final` included set).
+>         * A detailed, interactive **Record Level Table** for quickly examining and linking to citations .
+
+</details>
+
+<details>
+  <summary><strong>Enhanced Reporting & Transparent Export:</strong></summary>
+
+> * CiteSource facilitates *transparent reporting* of search methods and results, aligning with guidelines like PRISMA.
+> * Export your final, deduplicated dataset in standard bibliographic formats (`.csv`, `.ris`, `.bib`).
+> * The custom metadata is embedded directly into standard fields within the export files (e.g., using C1, C2, C7, C8, DB fields in `.ris` format), providing a clear and reproducible audit trail for your methodology.
+
+</details>
+
+---
+
+### Why use CiteSource for Evidence Synthesis?
+
+CiteSource is built for anyone involved in evidence synthesis. It helps you:
+
+* **Evaluate and optimize** information source selection based on unique record contributions.
+* **Refine and optimize** search strings by quickly testing variations.
+* **Analyze and report** the added value of different search methods, including supplementary searching techniques.
+* **Perform benchmark testing** to ensure key articles are captured by your strategy.
+* **Increase transparency and effectiveness** of your search strategy and processess through built-in tables for reporting.
+* **Save time** during the iterative search development.
+
+### What Other Applications does CiteSource Serve ?
+
+* Training in evidence synthesis search methods - MLIS classroom use for skill/knowledge development.
+* Methods research & development - large-scale methods testing, quick/live updates to analysis.
+* Library collection development - analyzing coverage of new databases compared to current subscriptions.
+
+---
+
+*CiteSource is available both as this interactive Shiny application and as a full R package with detailed vignettes. For more information on the R package, visit the [CiteSource Website](https://www.eshackathon.org/CiteSource/).*
\ No newline at end of file
diff --git a/inst/shiny-app/CiteSource/www/use-cases.md b/inst/shiny-app/CiteSource/www/use-cases.md
index 5617fa4c..ddf90516 100644
--- a/inst/shiny-app/CiteSource/www/use-cases.md
+++ b/inst/shiny-app/CiteSource/www/use-cases.md
@@ -1,50 +1,107 @@
-<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" align="right"/>
+## CiteSource Use Cases: Overview
 
+<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
 
-**Source & Method Analysis**
+CiteSource provides a suite of tools to support data-driven decision-making throughout the evidence synthesis process. Its applications generally fall into two main categories: **Optimizing Search Strategies** (typically during protocol development and iterative searching) and **Analyzing Search Impact** (often after screening is complete, for reporting and methodological insights). Additional applications extend to training and resource management.
 
-When teams are selecting databases for inclusion in a review it can be difficult to determine the best resources and determine the ROI in terms of the time it takes to apply searches. This is especially true in environmental or social science research, where research is often cross-disciplinary. By tracking where/how each citation was found, the evidence synthesis community could in turn track the efficacy of various databases and identify the most relevant resources as it relates to their research topic. This idea can be extended to search string comparison as well as strategy and methodology comparison and hedge validation.
+---
 
--Database/Platform/Index
--Methodology
--Search string/strategy
--Hedge validation
+### I. Optimizing Search Strategies
 
-Examples:
-Databases/Database: GreenFile vs. CAB Direct vs. Aquatic Sciences and Fisheries Abstracts (ASFA) vs. Water Resources Abstracts
-Platform/Indexes: Web of Science- Science Citation Index Expanded (YR-YR) vs. Core Collection vs. "ALL Databases" (YR-YR)
-Search Engine/Database: Google scholar vs. Web of Science
-Methodology/Methodology: Database search vs. citation chasing vs. novel discovery tools
-String/strategy: ASFA string 1 vs. ASFA string 2 vs. ASFA string 3
+CiteSource enables researchers to move beyond reliance on experience or potentially outdated guidance by providing empirical data specific to their project *during* the search development phase.
 
+<details>
+  <summary><strong>Information Source/Method Selection & Optimization</strong></summary>
 
+> Choosing the most effective and efficient set of databases, platforms, or indexes (e.g., Web of Science, Scopus, ASFA, Dimensions, OATD) can be challenging, especially for interdisciplinary topics where overlap and unique contributions are unknown. CiteSource addresses this by allowing users to empirically compare potential sources *before* committing significant time. After uploading initial search results and tagging them using the `cite_source` field (e.g., `Web of Science`, `Scopus`), deduplication the overlapping and unique records across sources and methods. This analysis enables informed, data-driven decisions about which sources and methods provide the best return on investment and helps optimize the selection, potentially reducing redundancy. Key CiteSource features used include:
+> * Tagging records with `cite_source` metadata.
+> * Robust internal and external deduplication (using `ASySD`).
+> * Visualization of overlap using interactive **Heatmaps** and **Upset Plots**.
+> * Quick analysis of individual citations using the interactive **Record Level Table**
 
-**Stage/Topic Analysis**
+</details>
 
-Once the title and absract screening has been complete OR once the final papers the final literature has been selected, users can analyze the contributions of each Source/Method to better understand its impact on the review. By using the "Source" data along with the "label" data, users can analyze the number of overlapping/unique records from each source or method and how each one fared during the screening proces. This stage analysis can also include benchmark testing as users are able to analyze how various search strings fared in capturing known literature, this ability to quickly assess search strings provides users with the ability to iterate on these strings as it relates to their findings.
+<details>
+  <summary><strong>Search String Development & Optimization</strong></summary>
 
-Examples: 
-Databases/Database & (Stage Analysis): GreenFile (Search, Screen, Final) Aquatic Sciences and Fisheries Abstracts (Search, Screen, Final)
-Databases/Database & (Topic Analysis): GreenFile (life history, genetics, stock dynamics) Aquatic Sciences and Fisheries Abstracts (life history, genetics, stock dynamics)
-Benchmark Testing: (Search Stage): String #1 vs. String #2 vs. Benchmarking articles
+> Developing effective search strings is an iterative process involving testing terms, syntax variations, Boolean logic, proximity operators, field codes, etc. Comparing the impact of these subtle changes across potentially multiple databases is time-consuming. CiteSource assists by streamlining the analysis of string effectiveness. Users can upload results from different string variations, tag them using `cite_source` and `cite_string` (e.g., `String_1`, `String_2_proximity`), and visualize the impact on retrieval after deduplication. This allows for rapid assessment of how changes affect results, speeding up refinement for an optimal balance of sensitivity and precision and helping identify errors in logic or syntax. CiteSource facilitates this via:
+> * Tagging result sets with `cite_source` and `cite_string` metadata.
+> * Deduplication to compare results accurately.
+> * Visualization of unique/overlapping records retrieved by different strings using **Upset Plots**.
+> * Quick examination of individual citations using the interactive **Record Level Table**.
 
+</details>
 
+<details>
+  <summary><strong>Benchmark Testing</strong></summary>
 
-**Metadata Enhancement**
+> Ensuring a search strategy retrieves known, key relevant articles (benchmark or reference articles) is crucial for assessing sensitivity. CiteSource facilitates this by comparing search results against a predefined benchmark set. After uploading search results and the benchmark set (tagging each appropriately using `cite_source`, `cite_string`, and `cite_label`), deduplication allows for direct comparison. This provides a quantitative assessment of how well different strings or sources capture the benchmark articles, highlighting potential weaknesses or indexing gaps. While benchmark sets should be used cautiously due to potential bias, this process aids refinement and is useful for updates or developing standardized protocols. Key functionalities include:
+> * Tagging benchmark sets and search results distinctly using `cite_source` and `cite_label`.
+> * Deduplication to identify matches between search results and the benchmark set.
+> * Using **Upset Plots** to visualize captured vs. missed benchmark articles across different strings/sources.
+> * Investigating specific missed articles using the interactive **Record Level Table**.
 
-Beyond the ability to analyze and track an idividual citations' Source/Methods. The functions behind CiteSource provides users with the ability to create a single record that uses prefered metadata in selecte metadata fields. Selection is based on metadata attributes (filled or empty, length). This function alone will help to resolve many hours of metadata entry and ensure that each record is as complete as possible. At some point in the future users may be able to select Source data as prefered over these rules.
+</details>
 
-Basic logic for metadata selection (Filled/Empty + Length)
+<details>
+  <summary><strong>Efficient Iterative Testing (Overarching Benefit)</strong></summary>
 
-**IF** metadata from one record’s DOI field contains text and the others’ do not **THEN** the metadata from the record with text will be used for the merge record
+> The entire process of testing variations in sources, strings, and methods is inherently iterative. CiteSource significantly compresses this cycle by providing rapid analysis and visualization (**Heatmaps**, **Upset Plots**, **Summary Tables**) immediately after deduplication. This drastic reduction in the time needed to evaluate the impact of each iteration saves researcher time and allows for more thorough testing and validation, leading to a more optimized and well-documented strategy.
 
-**IF** metadata from one record’s ABSTRACT field contains more text and the other records with ABSTRACT text **THEN** the metadata from the record with more text will be used for the merge record
+</details>
 
+---
 
+### II. Analyzing Search Impact (Post-Screening / Reporting)
 
-**Library Collection Development**
+After screening is complete, CiteSource analyzes the *actual* contribution of different search components to the final set of included studies and enhances reporting.
 
-As the community continues to apply this analysis to resources, this information would be extremely valuable to researchers and librarians outside of the Evidence Synthesis community. From the library's perspective this tool could have a substantial impact on collection development decisions and resource recommendations. 
+<details>
+  <summary><strong>Analyzing Information Source & Search Method Contribution</strong></summary>
 
-Examples:
-Database Coverage: Compare coverage of databases on specific research topics - What percent of results are duplicated in current subscriptions? What percent are unique? 
\ No newline at end of file
+> Understanding which sources or methods were most effective in identifying the studies ultimately included in the synthesis is crucial for methodological reflection and reporting. CiteSource enables this analysis by tracking records through screening phases. By tagging records with `cite_source`/`cite_string` and progressively updating the `cite_label` (`search` -> `screened` -> `final`), users can quantify the "true impact" or ROI of each component. This identifies high-yield sources/methods versus those contributing mostly irrelevant records, providing valuable data for reporting and future strategy refinement. Analysis tools include:
+> * Tracking records using `cite_source`, `cite_string`, and `cite_label` tags.
+> * Visualizing the flow through screening stages with the **Bar Chart (Phase Analysis Plot)**.
+> * Quantifying performance using the **Precision/Sensitivity Table** (calculating precision and recall against the `final` set).
+> * Examining contributions at each stage using the **Record Summary Table**.
+
+</details>
+
+<details>
+  <summary><strong>Enhanced Reporting & Transparency</strong></summary>
+
+> Reporting guidelines like PRISMA require transparent and detailed documentation of the search process. CiteSource directly supports this by generating clear outputs and ensuring provenance is maintained. The plots and tables offer visual and quantitative summaries of the search process, outcomes, and source/method contributions. Furthermore, exporting the final dataset embeds the custom metadata tags (`cite_source`, `cite_label`, `cite_string`) into standard bibliographic fields (e.g., C1, C2, C7, C8, DB in `.ris` format), providing a clear, reproducible audit trail. This enhances transparency and allows reviewers/readers to scrutinize the methodology effectively. Key outputs for reporting include:
+> * Ready-to-use **plots** (**Heatmaps**, **Upset Plots**, **Bar Charts**).
+> * Summary **tables** (**Initial Record**, **Record Summary**, **Precision/Sensitivity**, **Record Level**).
+> * Exported datasets (`.csv`, `.ris`, `.bib`) with embedded provenance metadata.
+
+</details>
+
+---
+
+### III. Broader Applications
+
+Beyond individual reviews, CiteSource has wider utility:
+
+<details>
+  <summary><strong>Training & Education</strong></summary>
+
+> CiteSource serves as an effective training tool for evidence synthesis methods. Its interactive visualizations provide a hands-on way for students and early-career researchers to understand abstract concepts like database overlap, string variation impacts, and benchmark testing. Instructors can use it to demonstrate best practices in real-time, building practical skills and competence in systematic searching.
+
+</details>
+
+<details>
+  <summary><strong>Library Collection Development</strong></summary>
+
+> Librarians can leverage CiteSource to support collection development decisions. By analyzing search results from institutional researchers or targeted test searches, they can gain empirical data on database coverage and overlap for specific research topics. This helps justify subscription costs, compare existing resources with potential new ones, and make effective recommendations based on demonstrated value and uniqueness.
+
+</details>
+
+<details>
+  <summary><strong>Methodological Research</strong></summary>
+
+> CiteSource facilitates methodological research on searching itself. When researchers use the tool and report their quantitative findings on source/method performance (e.g., precision/sensitivity, unique contributions), they contribute valuable empirical data to the wider evidence synthesis community. Aggregating such findings across studies can inform the development and refinement of evidence-based search guidelines and best practices, potentially supporting "Studies Within A Review" (SWAR) focused on search methodology.
+
+</details>
+
+---
\ No newline at end of file

From ecddf17fc714b3af44fdc37247ce8391ac434b8e Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 29 Apr 2025 11:18:28 -0400
Subject: [PATCH 05/47] remove font-awesome icons

---
 inst/shiny-app/CiteSource/app.R | 15 +++++++--------
 1 file changed, 7 insertions(+), 8 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index dffcf515..56668a4f 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -354,48 +354,47 @@ ui <- shiny::navbarPage("CiteSource",
             
             shiny::wellPanel(
               style = "background-color: #f0f8ff; border-color: #bce8f1; margin-top: 15px; margin-bottom: 15px; padding: 15px;", # Style
-              shiny::tags$h5(shiny::icon("info-circle", lib = "font-awesome"), " Using the Interactive Record Table", style = "margin-top: 0; color: #31708f;"), # Title
+              shiny::tags$h5(" Using the Interactive Record Table", style = "margin-top: 0; color: #31708f;"), # Title
               shiny::tags$p("After clicking 'Generate the table', you can explore the records using these features:"), # Introduction
               
               # Performance Note
               shiny::tags$p(
                 style = "margin-bottom: 12px;", # Add space below this paragraph
-                # Using exclamation-triangle icon for warning
-                shiny::icon("exclamation-triangle", lib = "font-awesome"), shiny::tags$strong(" Performance Note:"),
+                 shiny::tags$strong(" Performance Note:"),
                 " The record table may take a long time to generate if you include more than a few hundred references. Consider filtering your data first using the sidebar selectors before generating."
               ),
               
               # Instruction 1: Expand/Collapse
               shiny::tags$p(
                 style = "margin-bottom: 12px;",
-                shiny::icon("expand", lib = "font-awesome"), shiny::tags$strong(" Expand/Collapse Row:"),
+                shiny::tags$strong(" Expand/Collapse Row:"),
                 " Click the ", shiny::tags$code(HTML("&oplus;")), " symbol in a row to view the full APA reference. Click ", shiny::tags$code(HTML("&CircleMinus;")), " to hide it again."
               ),
               
               # Instruction 2: Single Sort
               shiny::tags$p(
                 style = "margin-bottom: 12px;",
-                shiny::icon("sort", lib = "font-awesome"), shiny::tags$strong(" Sort by Single Column:"),
+                shiny::tags$strong(" Sort by Single Column:"),
                 " Click any column header (like 'Citation' or a source name) to sort the table by that column's values. Click the header again to reverse the sort order."
               ),
               
               # Instruction 3: Multi Sort
               shiny::tags$p(
                 style = "margin-bottom: 12px;",
-                shiny::icon("bars", lib = "font-awesome"), shiny::tags$strong(" Sort by Multiple Columns:"),
+                shiny::tags$strong(" Sort by Multiple Columns:"),
                 " Click the primary column header you want to sort by. Then, hold down the ", shiny::tags$strong("Shift"), " key on your keyboard and click a second column header. You can repeat this for more sorting levels."
               ),
               
               # Instruction 4: Filter/Search
               shiny::tags$p(
                 style = "margin-bottom: 12px;",
-                shiny::icon("filter", lib = "font-awesome"), shiny::tags$strong(" Filter/Search:"),
+                shiny::tags$strong(" Filter/Search:"),
                 " Type into the search box located at the top-right of the table to dynamically filter records based on any information displayed."
               ),
               
               # Instruction 5: Download (No bottom margin needed on the last item)
               shiny::tags$p(
-                shiny::icon("download", lib = "font-awesome"), shiny::tags$strong(" Download Data:"),
+                shiny::tags$strong(" Download Data:"),
                 " Click the 'Download CSV' button (located above the table, next to 'Print') to save the data currently shown in the table (including applied filters) as a CSV file."
               )
             ),

From 25ee213348a2487cc3b8faa22f46de90894959f7 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 29 Apr 2025 11:26:28 -0400
Subject: [PATCH 06/47] add user guide markdown file

---
 inst/shiny-app/CiteSource/www/user_guide.md | 108 ++++++++++++++++++++
 1 file changed, 108 insertions(+)
 create mode 100644 inst/shiny-app/CiteSource/www/user_guide.md

diff --git a/inst/shiny-app/CiteSource/www/user_guide.md b/inst/shiny-app/CiteSource/www/user_guide.md
new file mode 100644
index 00000000..487a22ad
--- /dev/null
+++ b/inst/shiny-app/CiteSource/www/user_guide.md
@@ -0,0 +1,108 @@
+## CiteSource User Guide
+
+<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
+
+> CiteSource has a number of applications. This guide walks users through the step-wise process of uploading, deduplicating and analyzing data > within the shiny application. For step by step instructions for running CiteSource in R, [check out our vignettes](https://www.eshackathon.org/CiteSource/articles/)
+---
+
+### Using CiteSource: Step-by-Step
+
+<details>
+  <summary><strong>Step 1: File Upload, Labeling, & Re-importing</strong></summary>
+
+> **Standard Upload:**
+>
+> * Navigate to the 'File upload' tab.
+> * Use the 'Set Label for Uploaded File(s)' dropdown to select the appropriate stage for the file(s) you are about to upload (e.g., `search`, `screened`, or `final`). This label helps organize records, especially for phase analysis and some summary tables.
+> * Click the file input area ('Browse...') to select one or more citation files from your computer. Supported formats are `.ris`, `.bib`, and `.txt`.
+> * The label you selected will be applied to all citation records within the file(s) uploaded in that specific action.
+>
+> **Re-importing Previously Processed Data:**
+>
+> * If you have previously exported data from CiteSource as a `.ris` or `.csv` file (these exported files contain special `cite_` columns), you can re-upload this file directly.
+> * On the 'File upload' tab, use the 'OR: Re-upload an .ris or .csv exported from CiteSource' file input.
+> * This bypasses the initial upload processing and deduplication steps (Steps 3 & 4), allowing you to proceed directly to the 'Visualise' and 'Tables' tabs with your previously processed data.
+>
+> * **NOTE**: Raw citation exports from some platforms (e.g.OVID) may be incompatible due to abnormal .ris field use or structuring. If you are having issues, please be sure to try importing them using citation software (e.g. Zotero, EndNote) and exporting them before uploading to CiteSource.*
+
+</details>
+
+<details>
+  <summary><strong>Step 2: Review Uploads & Edit</strong></summary>
+
+> * After uploading citations, a summary table appears in the main panel showing each file, its detected record count, and the assigned source name, label, and string.
+> * To correct the auto-assigned source name, or to change the label or string for *all* records from a specific file after upload, you can double-click the corresponding cell in the table and type the new value.
+
+</details>
+
+<details>
+  <summary><strong>Step 3: Automated Deduplication</strong></summary>
+
+> * Navigate to the 'Deduplicate' tab and ensure you are on the 'Automated deduplication' sub-tab.
+> * Click the 'Find duplicates' button.
+> * CiteSource will process all the records you've uploaded. It compares metadata fields (like DOI, title, authors, journal, year, volume, pages) to identify potential duplicates both *within* the same source file (internal deduplication) and *across* different source files (external deduplication).
+> * A pop-up message will summarize the results, indicating the number of unique records found and if any potential duplicates require manual review.
+
+</details>
+
+<details>
+  <summary><strong>Step 4: Manual Deduplication (If Needed)</strong></summary>
+
+> * If the summary message from Step 3 indicates potential duplicates need review, or if you want to manually inspect potential matches, go to the 'Manual deduplication' sub-tab.
+> * Pairs of records identified as potential duplicates are displayed. Each row represents a pair, showing selected metadata side-by-side (e.g., Title 1 vs. Title 2).
+> * Use the 'Choose columns' filter dropdown (filter icon) above the table to select which metadata fields (e.g., author, year, abstract) you want to see for comparison.
+> * Carefully review each pair. If you determine a pair represents the *same* underlying citation, click on that row to select it.
+> * After selecting all rows that are true duplicates, click the 'Remove additional duplicates' button (this button only appears after you select at least one row). This merges the selected pairs, keeping only one unique record with combined metadata.
+> * If you finish reviewing or decide no manual merging is needed, click 'Go to visualisations'.
+
+</details>
+
+<details>
+  <summary><strong>Step 5: Visualise Overlap</strong></summary>
+
+> * Navigate to the 'Visualise' tab.
+> * Use the sidebar controls to tailor the analysis:
+>     * **Choose comparison type:** Select whether you want to compare overlap based on 'sources' (original files/databases), 'labels' (e.g., search vs screened), or 'strings' (if used).
+>     * **Filter data:** Select specific sources, labels, or strings to include in the visualizations.
+> * Explore the generated plots:
+>     * **Heatmap:** This matrix shows pairwise overlap. Each cell represents the number of citations shared between two groups (the groups depend on your chosen comparison type). Darker cells indicate higher overlap. Hover over cells to see exact counts. It helps quickly identify pairs with significant commonality.
+>     * **Upset Plot:** This plot visualizes intersections among multiple groups simultaneously. The large bottom bar chart shows the number of citations unique to specific combinations of groups (e.g., found only in Source A, or found in both Source A and B but not C). The smaller top bar chart shows the total number of unique citations in each individual group. It's excellent for understanding complex overlap patterns involving more than two groups.
+>     * **Phase Analysis Plot:** This plot is most useful when comparing by 'labels' representing stages (e.g., `search`, `screened`, `final`). It shows the total number of records at each stage, broken down into those that are unique (first identified at that stage) versus those that were already found in a previous stage (duplicates relative to earlier stages). It helps visualize the yield and deduplication effectiveness across a review workflow.
+> * Use the 'Download' buttons above each plot to save them as image files.
+
+</details>
+
+<details>
+  <summary><strong>Step 6: Summary Tables & Record Review</strong></summary>
+
+> * Navigate to the 'Tables' tab.
+> * Use the sidebar filters (Sources, Labels, Strings) to select the subset of data you want summarized.
+> * Generate specific summary tables by clicking the corresponding 'Generate...' button:
+>     * **Initial Records Table:** Provides a high-level count based on the earliest phase (typically records labeled `search`). Shows the total uploaded records for that phase. This table distinguishes between the number of uploaded records andduplicates found *within* the each source file. 
+>     * **Detailed Record Table:** Breaks down the citation counts by individual source/method (within your selected filters). For each set of records, it shows how many citations were unique to that set and how many were also found in other sets. This helps identify which sources/methods contributed the most unique records and which have a high level of overlap.
+>     * **Precision/Sensitivity Table:** Calculates performance metrics, requiring data labeled as `final` to be present and selected. It compares each source, method, or search string against this 'final' set. 'Precision' tells you what proportion of records retrieved by a source were actually relevant ('final' records). 'Sensitivity' (or Recall) tells you what proportion of all relevant ('final') records were found by that specific source. Useful for evaluating search strategy performance.
+>     * **Review individual records:** Click 'Generate the table' on the "Review individual records" sub-tab to view the detailed, deduplicated citation list. This table may take a while to load if you have a large number of records. 
+>
+> **Using the Interactive Record Table:**
+>
+>     * **Expand/Collapse Row:** Click the `⊕` symbol in a row to view the full APA reference. Click `⊖` to hide it again.
+>     * **Sort by Single Column:** Click any column header (like 'Citation' or a source name) to sort the table by that column's values. Click the header again to reverse the sort order.
+>     * **Sort by Multiple Columns:** Click the primary column header you want to sort by. Then, hold down the **Shift** key on your keyboard and click a second column header. You can repeat this for more sorting levels.
+>     * **Filter/Search:** Type into the search box located at the top-right of the table to dynamically filter records based on any information displayed.
+>     * **Download Data:** Click the 'Download CSV' button (located above the table, next to 'Print') to save the data currently shown in the table (including applied filters) as a CSV file.
+
+</details>
+
+<details>
+  <summary><strong>Step 7: Export Results</strong></summary>
+
+> * Navigate to the 'Export' tab.
+> * This tab becomes active after you have run the deduplication process (Step 3).
+> * Click the button corresponding to your desired file format: 'Download csv', 'Download RIS', or 'Download BibTex'.
+> * The custom metadata is embedded directly into fields within the export files (e.g., using C1, C2, C7, C8, DB fields in `.ris` format)
+> * This will save the final dataset of unique citations (after both automated and any manual deduplication). 
+> * **Note:** Only `.csv` and `.ris` files can be re-imported later.
+
+</details>
+
+---
\ No newline at end of file

From 72f461246c7b5f28e58b250593ba976d672349a6 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 29 Apr 2025 12:39:45 -0400
Subject: [PATCH 07/47] fixed upload table bug

---
 inst/shiny-app/CiteSource/app.R | 69 ++++++++++++++++++++++++++-------
 1 file changed, 55 insertions(+), 14 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 56668a4f..3b338e73 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -620,28 +620,69 @@ server <- function(input, output, session) {
 
   # when file upload table is edited, edit reactive value upload df
   shiny::observeEvent(input$tbl_out_cell_edit, {
-    # make sure not blank to avoid blanks in output
-
-    info <- input$tbl_out_cell_edit
+    # 1. Get Edit Information
+    info <- input$tbl_out_cell_edit 
     val <- info$value
-
+    # 2. Handle blank input
     if (val == "") {
       val <- NA
     }
-
+    # 3. Update the Summary DataFrame (rv$df)
     rv$df[info$row, info$col + 1] <- val
     
-    # get rownames for file
+    # 4. Find Corresponding Rows in the Main DataFrame (rv$upload_df) using cite_source
+    
+    # 4a. Get the 'source' value from the edited row in rv$df
+    # Assumes the column named 'source' holds the relevant identifier in rv$df
+    # Check rv$df structure if this column name is different.
+    source_value_from_edited_row <- rv$df[info$row, "source"] # Use column name "source"
+    
+    # Check if the source value is valid before proceeding
+    if (is.null(source_value_from_edited_row) || is.na(source_value_from_edited_row)) {
+      warning(paste("Could not find valid source value in edited row:", info$row))
+      return() # Stop processing if the source identifier is missing
+    }
+    
+    # 4b. Calculate row ranges for each source in rv$upload_df 
+    # Group by 'cite_source' instead of 'file.datapath'
     row_indexes <- rv$upload_df %>%
       dplyr::mutate(rowname = dplyr::row_number()) %>%
-      dplyr::group_by(file.datapath) %>%
-      dplyr::summarise(min_row = dplyr::first(rowname), max_row = dplyr::last(rowname))
-
-    rows <- row_indexes[row_indexes$file.datapath == rv$df[info$row, 1], 2:3]
-    col <- paste0("cite_", names(rv$df[info$col + 1]))
-    file <- rv$df[info$row, 1]
-
-    rv$upload_df[c(rows$min_row:rows$max_row), col] <- val
+      dplyr::group_by(cite_source) %>% # *** CHANGED HERE ***
+      dplyr::summarise(
+        min_row = dplyr::first(rowname), 
+        max_row = dplyr::last(rowname),
+        .groups = 'drop' # Good practice to add .groups='drop'
+      )
+    
+    # 4c. Look up the min/max rows for the edited source
+    # Filter row_indexes using 'cite_source' and the value obtained from rv$df
+    rows <- row_indexes[row_indexes$cite_source == source_value_from_edited_row, ] # *** CHANGED HERE ***
+    
+    # Check if rows were found
+    if (nrow(rows) == 0 || is.na(rows$min_row) || is.na(rows$max_row)) {
+      warning(paste("Could not find row indices in rv$upload_df for source:", source_value_from_edited_row))
+      # Decide how to handle: maybe the source was edited TO something not in rv$upload_df?
+      # For now, just stop to prevent error. Depending on desired behavior, might need adjustment.
+      return() 
+    }
+    
+    # 5. Determine Target Column Name in rv$upload_df (No change needed in principle)
+    # This part should be okay, as it maps rv$df column names ('source', 'label', 'string')
+    # to rv$upload_df column names ('cite_source', 'cite_label', 'cite_string')
+    col_name_in_df <- names(rv$df)[info$col + 1] # Safer way to get name
+    col <- paste0("cite_", col_name_in_df) 
+    
+    # Add a check to ensure the target column exists in rv$upload_df
+    if (!col %in% names(rv$upload_df)) {
+      warning(paste("Target column", col, "not found in rv$upload_df"))
+      return() # Stop if the target column doesn't exist
+    }
+    
+    # 6. Update the Main DataFrame (rv$upload_df) (No change in logic, but context changed)
+    # Now uses row indices derived via cite_source
+    # Added safety checks above should prevent errors here if rows/col were invalid
+    rv$upload_df[c(rows$min_row:rows$max_row), col] <- val 
+    
   })
 
 # Deduplication tab -----------------

From d4bd82dd4d97e636dca6370d2f2ebf2c3ce9f812 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 30 Apr 2025 13:16:16 -0400
Subject: [PATCH 08/47] corrected the shiny app link to the main branch shiny
 instead of dev

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 638fd5fa..931d34f0 100644
--- a/README.md
+++ b/README.md
@@ -22,7 +22,7 @@ CiteSource was created under [the General Public License (>=v3)](https://www.gnu
 
 **Shiny Web Application**
 
-Whether you know R or not, we want you to be able to use CiteSource! Check out our [CiteSource Shiny App!](https://litrev.shinyapps.io/CiteSource_latest/) 
+Whether you know R or not, we want you to be able to use CiteSource! Check out our [CiteSource Shiny App!](https://litrev.shinyapps.io/CiteSource/) 
 
 ## Features
 **Customizable Metadata Tags**

From 7fca2296de0d2f0f68749613eea2bf40a0079c49 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 30 Apr 2025 16:09:30 -0400
Subject: [PATCH 09/47] updated table edit functionality for quicker bulk
 entry.

---
 inst/shiny-app/CiteSource/app.R | 1247 +++++++++++++++----------------
 1 file changed, 601 insertions(+), 646 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 3b338e73..514d9dd2 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -15,423 +15,401 @@ shiny::tags$head(shiny::tags$style(
 ))
 
 columns2hide <- c("title", "author", "doi", "volume",
-                "pages", "number", "year", "abstract", "journal", "isbn")
+                  "pages", "number", "year", "abstract", "journal", "isbn")
 
 
 # Define UI for data upload app ----
 ui <- shiny::navbarPage("CiteSource",
-  id = "tabs",
-  header = shiny::tagList(
-    shinybusy::add_busy_spinner(spin = "circle"),
-    shinyjs::useShinyjs(),
-    #header text colour
-    tags$head(
-      tags$style(HTML("
+                        id = "tabs",
+                        header = shiny::tagList(
+                          shinybusy::add_busy_spinner(spin = "circle"),
+                          shinyjs::useShinyjs(),
+                          #header text colour
+                          tags$head(
+                            tags$style(HTML("
       h6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {
         margin-top: 0;
         margin-bottom: .5rem;
         font-weight: 500;
         line-height: 1.2;
         color: #23395B;
-        }
-    blockquote {
-          border-left: 5px solid #008080; /* Use theme primary color for border */
-          background-color: #f8f9fa;    /* A very light grey background */
-          padding: 10px 20px;           /* Add padding */
-          margin-bottom: 1rem;          /* Add space below */
-          color: #212529;              /* Ensure text color contrasts well */
-        }
-        summary strong {
-          font-size: 1.15rem; /* Adjust size similar to h5, tweak as needed */
-          /* font-weight: bold; /* Handled by strong, but can be explicit */
-          /* Optional: Adjust vertical alignment slightly if needed */
-           vertical-align: middle;
-        }
+      }
     "))
-    )
-  ),
-  theme = bslib::bs_theme(
-    bg = "rgb(251, 251, 251)",
-    primary = "#008080",
-    secondary = "#CBF7ED",
-    success = "#23395B",
-    info = "#82D173",
-    warning = "#FFC07F",
-    danger = "#008080",
-    font_scale = NULL,
-    bootswatch = "cerulean",
-    fg = "#000",
-    input_bg = "#E0E0E0",  # Set the background color for input boxes
-    input_border_color = "#23395B"  # Set the border color for input boxes
-  ),
-  # Home tab
-  shiny::tabPanel(
-    "Home",
-    shiny::navlistPanel(
-      shiny::tabPanel(
-        title = "About",
-        htmltools::includeMarkdown("www/about.md")
-      ),
-      shiny::tabPanel(
-        title = "Use Cases",
-        htmltools::includeMarkdown("www/use-cases.md")
-      ),
-      # User Guide
-      shiny::tabPanel(
-        title = "User Guide",
-        # Load the external Markdown file
-        htmltools::includeMarkdown("www/user_guide.md")
-      ), # End tabPanel,
-      
-      widths = c(2, 10)
-    ) # End navlistPanel
-  ), # End Home tabPanel
-  
-  shiny::tabPanel(
-    "File upload",
-    shiny::fluidRow(
-      shiny::column(
-        12,
-        # Sidebar layout with input and output definitions ----
-        shiny::sidebarLayout(
-          shiny::sidebarPanel( 
-            shiny::h5("Step 1: Upload your citation files"),
-            
-            shiny::fileInput("file", "",
-                             multiple = TRUE,
-                             accept = c(".ris", ".txt", ".bib")
-            ),
-            shiny::selectInput("upload_label_select",
-                               label = "Set Label for Uploaded File(s):",
-                               choices = c("search", "screened", "final"), 
-                               selected = "search" 
-            ),
-
-            shiny::hr(),
-            shiny::br(),
-            shiny::h5("Re-upload an .ris or .csv exported from CiteSource"),
-            shiny::fileInput("file_reimport", "",
-                             multiple = TRUE,
-                             accept = c(".ris", ".csv")
-            ),
-            shiny::helpText("Re-uploading skips deduplication (Steps 3 & 4) and uses the metadata already present in the exported file. Proceed directly to 'Visualise' or 'Tables'.", style="font-size: 85%;"),
-          ),
-          # Main panel for displaying outputs ----
-          shiny::mainPanel(
-            shiny::h5("Step 2: Double click the row to edit sources, labels, and strings"),
-            # Output: Data file ----
-            DT::dataTableOutput("tbl_out")
-          )
-        )
-      )
-    )
-  ),
-  shiny::tabPanel(
-    "Deduplicate",
-    shiny::tabsetPanel(
-      shiny::tabPanel(
-        "Automated deduplication",
-        br(),
-        shiny::h5("Step 3: Deduplicate"),
-        shiny::p(shiny::p("CiteSource uses ASySD to identify duplicates."),
-        shiny::p(shiny::p("Dedplication takes place *within* each file (Internal Deduplication) and *across* all uploaded files (External Deduplication).")),
-        ),
-        # Action button: identify duplicates in uploaded dataset
-        shinyWidgets::actionBttn(
-          "identify_dups", "Find duplicates",
-          style = "jelly",
-          color = "primary",
-          icon = shiny::icon("search")
-        ) %>% htmltools::tagAppendAttributes(style = "background-color: #008080; margin-right: 20px"),
-
-        # Output: datatable of deduplication results
-        DT::dataTableOutput("dedup_results")
-      ),
-      shiny::tabPanel(
-        "Manual deduplication",
-        br(),
-        shiny::h5("Step 4: Review potential duplicates manually"),
-        shiny::p("The following records were identified as potential duplicate pairs. Pairs are combined into a single row with metadata fields for each record represented (ex. Title 1 & Title 2). Click any row to indicate that the records in that row ARE duplicates. Once all duplicates are identified you can click the button 'Remove additional duplicates' and then proceed to the visualizations."),
-        shiny::textOutput("Manual_pretext"),
-        shiny::br(),
-
-        
-        # Button
-        shinyWidgets::actionBttn(
-          inputId = "nomanualdedup",
-          label = "Go to visualisations",
-          style = "jelly",
-          icon = shiny::icon("arrow-right"),
-          color = "primary"
-        ) %>% htmltools::tagAppendAttributes(style = "background-color: #82D173"),
-        br(),
-        shinyWidgets::actionBttn(
-          inputId = "manualdedupsubmit",
-          label = "Remove additional duplicates",
-          style = "jelly",
-          icon = shiny::icon("reply"),
-          color = "primary" # Hide the button initially
-        ) %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-        
-        shinyWidgets::dropdown(
-          
-          tags$h3("Select columns to display"),
-
-          shinyWidgets::pickerInput(
-            inputId = "manual_dedup_cols",
-            label = "Choose columns",
-            choices = NULL,
-            selected = NULL,
-            multiple = TRUE,
-            options = list(
-              `live-search` = TRUE,
-              `actions-box` = TRUE,
-              style = "btn-primary")
-          ), 
-          icon = icon("filter"),
-          inline =TRUE,
-          status = "danger", width = "600px",
-          tooltip = shinyWidgets::tooltipOptions(title = "Select columns to display")),
-    
-        DT::DTOutput("manual_dedup_dt"),
-        tags$style(HTML(".table.dataTable tbody td.active, .table.dataTable tbody tr.active td {
+                          )
+                        ),
+                        theme = bslib::bs_theme(
+                          bg = "rgb(251, 251, 251)",
+                          primary = "#008080",
+                          secondary = "#CBF7ED",
+                          success = "#23395B",
+                          info = "#82D173",
+                          warning = "#FFC07F",
+                          danger = "#008080",
+                          font_scale = NULL,
+                          bootswatch = "cerulean",
+                          fg = "#000",
+                          input_bg = "#E0E0E0",  # Set the background color for input boxes
+                          input_border_color = "#23395B"  # Set the border color for input boxes
+                        ),
+                        # Home tab
+                        shiny::tabPanel(
+                          "Home",
+                          shiny::navlistPanel(
+                            shiny::tabPanel(
+                              title = "About",
+                              htmltools::includeMarkdown("www/about.md")
+                            ),
+                            shiny::tabPanel(
+                              title = "Use Cases",
+                              htmltools::includeMarkdown("www/use-cases.md")
+                            ),
+                            # User Guide
+                            shiny::tabPanel(
+                              title = "User Guide",
+                              # Load the external Markdown file
+                              htmltools::includeMarkdown("www/user_guide.md")
+                            ),
+                            widths = c(2, 10)
+                          )
+                        ),
+                        shiny::tabPanel(
+                          "File upload",
+                          shiny::fluidRow(
+                            shiny::column(
+                              12,
+                              # Sidebar layout with input and output definitions ----
+                              shiny::sidebarLayout(
+                                shiny::sidebarPanel( # Input: Select a file ----
+                                                     shiny::h5("Step 1: Upload your citation files"),
+                                                     shiny::fileInput("file", "",
+                                                                      multiple = TRUE,
+                                                                      accept = c(".ris", ".txt", ".bib")
+                                                     ),
+                                                     shiny::hr(),
+                                                     shiny::h5("OR: Re-upload an .ris or .csv exported from CiteSource"),
+                                                     shiny::fileInput("file_reimport", "",
+                                                                      multiple = TRUE,
+                                                                      accept = c(".ris", ".csv")
+                                                     )
+                                ),
+                                # Main panel for displaying outputs ----
+                                shiny::mainPanel(
+                                  shiny::h5("Step 2: Double click on a column to edit sources, labels, and strings. Use *Ctrl+Enter* to save edits, one column at a time"),
+                                  # Output: Data file ----
+                                  DT::dataTableOutput("tbl_out")
+                                )
+                              )
+                            )
+                          )
+                        ),
+                        shiny::tabPanel(
+                          "Deduplicate",
+                          shiny::tabsetPanel(
+                            shiny::tabPanel(
+                              "Automated deduplication",
+                              br(),
+                              shiny::h5("Step 3: Deduplicate"),
+                              shiny::p("Click the button below to detect and remove duplicates automatically"),
+                              
+                              # Action button: identify duplicates in uploaded dataset
+                              shinyWidgets::actionBttn(
+                                "identify_dups", "Find duplicates",
+                                style = "jelly",
+                                color = "primary",
+                                icon = shiny::icon("search")
+                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #008080; margin-right: 20px"),
+                              
+                              # Output: datatable of deduplication results
+                              DT::dataTableOutput("dedup_results")
+                            ),
+                            shiny::tabPanel(
+                              "Manual deduplication",
+                              br(),
+                              shiny::h5("Step 4: Review potential duplicates manually"),
+                              shiny::p("The following records were identified as potential duplicates. Potential duplicates are combined into a single row with metadata fields for each record represented (ex. Title 1 & Title 2). Click any row to indicate that the records in that row ARE duplicates. Once all duplicates are identified you can click the button 'Remove additional duplicates' and then proceed to the visualizations."),
+                              shiny::textOutput("Manual_pretext"),
+                              shiny::br(),
+                              
+                              
+                              # Button
+                              shinyWidgets::actionBttn(
+                                inputId = "nomanualdedup",
+                                label = "Go to visualisations",
+                                style = "jelly",
+                                icon = shiny::icon("arrow-right"),
+                                color = "primary"
+                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #82D173"),
+                              br(),
+                              shinyWidgets::actionBttn(
+                                inputId = "manualdedupsubmit",
+                                label = "Remove additional duplicates",
+                                style = "jelly",
+                                icon = shiny::icon("reply"),
+                                color = "primary" # Hide the button initially
+                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                              
+                              shinyWidgets::dropdown(
+                                
+                                tags$h3("Select columns to display"),
+                                
+                                shinyWidgets::pickerInput(
+                                  inputId = "manual_dedup_cols",
+                                  label = "Choose columns",
+                                  choices = NULL,
+                                  selected = NULL,
+                                  multiple = TRUE,
+                                  options = list(
+                                    `live-search` = TRUE,
+                                    `actions-box` = TRUE,
+                                    style = "btn-primary")
+                                ), 
+                                icon = icon("filter"),
+                                inline =TRUE,
+                                status = "danger", width = "600px",
+                                tooltip = shinyWidgets::tooltipOptions(title = "Select columns to display")),
+                              
+                              DT::DTOutput("manual_dedup_dt"),
+                              tags$style(HTML(".table.dataTable tbody td.active, .table.dataTable tbody tr.active td {
             background-color: #CBF7ED!important; color: black!important}")),
-        
-      )
-    )
-  ),
-  shiny::tabPanel(
-    "Visualise",
-    
-    # Sidebar layout with input and output definitions ----
-    shiny::sidebarLayout(
-      
-      # Sidebar panel for inputs ----
-      shiny::sidebarPanel(
-        width = 3,
-        id = "sidebar",
-        shiny::h5("Step 5: Visualise overlap"),
-        shinyWidgets::prettyRadioButtons(
-          inputId = "comp_type",
-          label = "Choose a comparison",
-          inline = TRUE,
-          choices = c(
-            "sources",
-            "labels", "strings"
-          ),
-          status = "primary"
-        ),
-        selectInput(
-          inputId = "sources_visual",
-          "Sources to include",
-          list(),
-          multiple = TRUE,
-          selectize = TRUE
-        ),
-        selectInput(
-          inputId = "labels_visual",
-          "Labels to include",
-          list(),
-          multiple = TRUE,
-          selectize = TRUE
-        ),
-        selectInput(
-          inputId = "strings_visual",
-          "Strings to include",
-          list(),
-          multiple = TRUE,
-          selectize = TRUE
-        )
-      ),
-      
-      # Main panel for displaying outputs ----
-      shiny::mainPanel(
-        shiny::tabsetPanel(
-          shiny::tabPanel(
-            "Plot overlap as a heatmap matrix",
-            shiny::downloadButton("downloadHeatPlot"),
-            plotly::plotlyOutput("plotgraph1")
-          ),
-          shiny::tabPanel(
-            "Plot overlap as an upset plot",
-            shiny::downloadButton("downloadUpsetPlot"),
-            shiny::plotOutput("plotgraph2")
-          ),
-          shiny::tabPanel(
-            "Phase Analysis",  # New Tab for Phase Analysis
-            shiny::downloadButton("downloadPhasePlot"),
-            shiny::plotOutput("phasePlot")
-          )
-        )
-      )
-    )
-  ),
-  
-  shiny::tabPanel(
-    "Tables",
-    
-    shiny::sidebarLayout(
-      
-      shiny::sidebarPanel(
-        id = "sidebar",
-        width = 3,
-        shiny::h5("Step 6: Summary tables"),
-        selectInput(
-          inputId = "sources_tables",
-          "Sources to include",
-          list(),
-          multiple = TRUE,
-          selectize = TRUE
-        ),
-        selectInput(
-          inputId = "labels_tables",
-          "Labels to include",
-          list(),
-          multiple = TRUE,
-          selectize = TRUE
-        ),
-        selectInput(
-          inputId = "strings_tables",
-          "Strings to include",
-          list(),
-          multiple = TRUE,
-          selectize = TRUE
-        )
-      ),
-      
-      shiny::mainPanel(
-        shiny::tabsetPanel(
-          
-          shiny::tabPanel(
-            "Initial Records Table",
-            shiny::div("View the initial record counts and deduplication results."),
-            shinyWidgets::actionBttn(
-              "generateInitialRecordTable", "Generate Initial Records Table",
-              style = "jelly",
-              icon = shiny::icon("table"),
-              color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-            shiny::br(),
-            shiny::br(),
-            gt::gt_output("initialRecordTab")
-          ),
-          
-          shiny::tabPanel(
-            "Detailed Record Table",
-            shiny::div("Summary of unique and non-unique records by source."),
-            shinyWidgets::actionBttn(
-              "generateDetailedRecordTable", "Generate Detailed Record Table",
-              style = "jelly",
-              icon = shiny::icon("table"),
-              color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-            shiny::br(),
-            shiny::br(),
-            gt::gt_output("summaryRecordTab")
-          ),
-          
-          shiny::tabPanel(
-            "Precision/Sensitivity Table",
-            shiny::div("Precision and Sensitivity of records across screening phases."),
-            shinyWidgets::actionBttn(
-              "generatePrecisionTable", "Generate Precision/Sensitivity Table",
-              style = "jelly",
-              icon = shiny::icon("table"),
-              color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-            shiny::br(),
-            shiny::br(),
-            gt::gt_output("summaryPrecTab")
-          ),
-          
-          shiny::tabPanel(
-            "Review individual records",
-            shiny::br(),
-            shinyWidgets::actionBttn(
-              "generateRecordTable", "Generate the table",
-              style = "jelly",
-              icon = shiny::icon("table"),
-              color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-            
-            shiny::br(),
-            
-            shiny::wellPanel(
-              style = "background-color: #f0f8ff; border-color: #bce8f1; margin-top: 15px; margin-bottom: 15px; padding: 15px;", # Style
-              shiny::tags$h5(" Using the Interactive Record Table", style = "margin-top: 0; color: #31708f;"), # Title
-              shiny::tags$p("After clicking 'Generate the table', you can explore the records using these features:"), # Introduction
-              
-              # Performance Note
-              shiny::tags$p(
-                style = "margin-bottom: 12px;", # Add space below this paragraph
-                 shiny::tags$strong(" Performance Note:"),
-                " The record table may take a long time to generate if you include more than a few hundred references. Consider filtering your data first using the sidebar selectors before generating."
-              ),
-              
-              # Instruction 1: Expand/Collapse
-              shiny::tags$p(
-                style = "margin-bottom: 12px;",
-                shiny::tags$strong(" Expand/Collapse Row:"),
-                " Click the ", shiny::tags$code(HTML("&oplus;")), " symbol in a row to view the full APA reference. Click ", shiny::tags$code(HTML("&CircleMinus;")), " to hide it again."
-              ),
-              
-              # Instruction 2: Single Sort
-              shiny::tags$p(
-                style = "margin-bottom: 12px;",
-                shiny::tags$strong(" Sort by Single Column:"),
-                " Click any column header (like 'Citation' or a source name) to sort the table by that column's values. Click the header again to reverse the sort order."
-              ),
-              
-              # Instruction 3: Multi Sort
-              shiny::tags$p(
-                style = "margin-bottom: 12px;",
-                shiny::tags$strong(" Sort by Multiple Columns:"),
-                " Click the primary column header you want to sort by. Then, hold down the ", shiny::tags$strong("Shift"), " key on your keyboard and click a second column header. You can repeat this for more sorting levels."
-              ),
-              
-              # Instruction 4: Filter/Search
-              shiny::tags$p(
-                style = "margin-bottom: 12px;",
-                shiny::tags$strong(" Filter/Search:"),
-                " Type into the search box located at the top-right of the table to dynamically filter records based on any information displayed."
-              ),
-              
-              # Instruction 5: Download (No bottom margin needed on the last item)
-              shiny::tags$p(
-                shiny::tags$strong(" Download Data:"),
-                " Click the 'Download CSV' button (located above the table, next to 'Print') to save the data currently shown in the table (including applied filters) as a CSV file."
-              )
-            ),
-            DT::dataTableOutput("reviewTab")
-          )
-        )
-      )
-    )
-  ),
-    
-  shiny::tabPanel(
-    "Export",
-    shiny::fluidRow(
-      shiny::column(
-        12,
-        shiny::mainPanel(
-          shiny::h5("Step 7: Export citations"),
-          shiny::h6("Data is only availabel after deduplication. CiteSource currently only supports re-upload of CSV and RIS files"),
-          shiny::h6("CiteSource currently only supports re-upload of CSV and RIS files"),
-          shiny::downloadButton("downloadCsv", "Download csv"),
-          shiny::downloadButton("downloadRis", "Download RIS"),
-          shiny::downloadButton("downloadBib", "Download BibTex")
-        )
-      )
-    )
-  )
+                              
+                            )
+                          )
+                        ),
+                        shiny::tabPanel(
+                          "Visualise",
+                          
+                          # Sidebar layout with input and output definitions ----
+                          shiny::sidebarLayout(
+                            
+                            # Sidebar panel for inputs ----
+                            shiny::sidebarPanel(
+                              width = 3,
+                              id = "sidebar",
+                              shiny::h5("Step 5: Visualise overlap"),
+                              shinyWidgets::prettyRadioButtons(
+                                inputId = "comp_type",
+                                label = "Choose a comparison",
+                                inline = TRUE,
+                                choices = c(
+                                  "sources",
+                                  "labels", "strings"
+                                ),
+                                status = "primary"
+                              ),
+                              selectInput(
+                                inputId = "sources_visual",
+                                "Sources to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              ),
+                              selectInput(
+                                inputId = "labels_visual",
+                                "Labels to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              ),
+                              selectInput(
+                                inputId = "strings_visual",
+                                "Strings to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              )
+                            ),
+                            
+                            # Main panel for displaying outputs ----
+                            shiny::mainPanel(
+                              shiny::tabsetPanel(
+                                shiny::tabPanel(
+                                  "Plot overlap as a heatmap matrix",
+                                  shiny::downloadButton("downloadHeatPlot"),
+                                  plotly::plotlyOutput("plotgraph1")
+                                ),
+                                shiny::tabPanel(
+                                  "Plot overlap as an upset plot",
+                                  shiny::downloadButton("downloadUpsetPlot"),
+                                  shiny::plotOutput("plotgraph2")
+                                ),
+                                shiny::tabPanel(
+                                  "Phase Analysis",  # New Tab for Phase Analysis
+                                  shiny::downloadButton("downloadPhasePlot"),
+                                  shiny::plotOutput("phasePlot")
+                                )
+                              )
+                            )
+                          )
+                        ),
+                        
+                        shiny::tabPanel(
+                          "Tables",
+                          
+                          shiny::sidebarLayout(
+                            
+                            shiny::sidebarPanel(
+                              id = "sidebar",
+                              width = 3,
+                              shiny::h5("Step 6: Summary tables"),
+                              selectInput(
+                                inputId = "sources_tables",
+                                "Sources to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              ),
+                              selectInput(
+                                inputId = "labels_tables",
+                                "Labels to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              ),
+                              selectInput(
+                                inputId = "strings_tables",
+                                "Strings to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              )
+                            ),
+                            
+                            shiny::mainPanel(
+                              shiny::tabsetPanel(
+                                
+                                shiny::tabPanel(
+                                  "Initial Records Table",
+                                  shiny::div("View the initial record counts and deduplication results."),
+                                  shinyWidgets::actionBttn(
+                                    "generateInitialRecordTable", "Generate Initial Records Table",
+                                    style = "jelly",
+                                    icon = shiny::icon("table"),
+                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                                  shiny::br(),
+                                  shiny::br(),
+                                  gt::gt_output("initialRecordTab")
+                                ),
+                                
+                                shiny::tabPanel(
+                                  "Detailed Record Table",
+                                  shiny::div("Summary of unique and non-unique records by source."),
+                                  shinyWidgets::actionBttn(
+                                    "generateDetailedRecordTable", "Generate Detailed Record Table",
+                                    style = "jelly",
+                                    icon = shiny::icon("table"),
+                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                                  shiny::br(),
+                                  shiny::br(),
+                                  gt::gt_output("summaryRecordTab")
+                                ),
+                                
+                                shiny::tabPanel(
+                                  "Precision/Sensitivity Table",
+                                  shiny::div("Precision and Sensitivity of records across screening phases."),
+                                  shinyWidgets::actionBttn(
+                                    "generatePrecisionTable", "Generate Precision/Sensitivity Table",
+                                    style = "jelly",
+                                    icon = shiny::icon("table"),
+                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                                  shiny::br(),
+                                  shiny::br(),
+                                  gt::gt_output("summaryPrecTab")
+                                ),
+                                
+                                shiny::tabPanel(
+                                  "Review individual records",
+                                  shiny::br(),
+                                  shinyWidgets::actionBttn(
+                                    "generateRecordTable", "Generate the table",
+                                    style = "jelly",
+                                    icon = shiny::icon("table"),
+                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                                  
+                                  shiny::br(),
+                                  
+                                  shiny::wellPanel(
+                                    style = "background-color: #f0f8ff; border-color: #bce8f1; margin-top: 15px; margin-bottom: 15px; padding: 15px;", # Style
+                                    shiny::tags$h5(" Using the Interactive Record Table", style = "margin-top: 0; color: #31708f;"), # Title
+                                    shiny::tags$p("After clicking 'Generate the table', you can explore the records using these features:"), # Introduction
+                                    
+                                    # Performance Note
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;", # Add space below this paragraph
+                                      shiny::tags$strong(" Performance Note:"),
+                                      " The record table may take a long time to generate if you include more than a few hundred references. Consider filtering your data first using the sidebar selectors before generating."
+                                    ),
+                                    
+                                    # Instruction 1: Expand/Collapse
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;",
+                                      shiny::tags$strong(" Expand/Collapse Row:"),
+                                      " Click the ", shiny::tags$code(HTML("&oplus;")), " symbol in a row to view the full APA reference. Click ", shiny::tags$code(HTML("&CircleMinus;")), " to hide it again."
+                                    ),
+                                    
+                                    # Instruction 2: Single Sort
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;",
+                                      shiny::tags$strong(" Sort by Single Column:"),
+                                      " Click any column header (like 'Citation' or a source name) to sort the table by that column's values. Click the header again to reverse the sort order."
+                                    ),
+                                    
+                                    # Instruction 3: Multi Sort
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;",
+                                      shiny::tags$strong(" Sort by Multiple Columns:"),
+                                      " Click the primary column header you want to sort by. Then, hold down the ", shiny::tags$strong("Shift"), " key on your keyboard and click a second column header. You can repeat this for more sorting levels."
+                                    ),
+                                    
+                                    # Instruction 4: Filter/Search
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;",
+                                      shiny::tags$strong(" Filter/Search:"),
+                                      " Type into the search box located at the top-right of the table to dynamically filter records based on any information displayed."
+                                    ),
+                                    
+                                    # Instruction 5: Download (No bottom margin needed on the last item)
+                                    shiny::tags$p(
+                                      shiny::tags$strong(" Download Data:"),
+                                      " Click the 'Download CSV' button (located above the table, next to 'Print') to save the data currently shown in the table (including applied filters) as a CSV file."
+                                    )
+                                  ),
+                                  DT::dataTableOutput("reviewTab")
+                                )
+                              )
+                            )
+                          )
+                        ),
+                        
+                        shiny::tabPanel(
+                          "Export",
+                          shiny::fluidRow(
+                            shiny::column(
+                              12,
+                              shiny::mainPanel(
+                                shiny::h5("Step 7: Export citations"),
+                                shiny::h6("Note that you can only download the data after you have run the deduplication. Also, you are only able to re-upload CSV and RIS files to continue with CiteSource, so please use these formats if you want that option."),
+                                shiny::downloadButton("downloadCsv", "Download csv"),
+                                shiny::downloadButton("downloadRis", "Download RIS"),
+                                shiny::downloadButton("downloadBib", "Download BibTex")
+                              )
+                            )
+                          )
+                        )
 )
 
 
 # Define server logic to read selected file ----
 server <- function(input, output, session) {
   rv <- shiny::reactiveValues()
-  rv$df <- data.frame() 
-  rv$upload_df <- data.frame()#for original uploads
-  rv$latest_unique <- data.frame() #for reimported data
-  rv$pairs_to_check <- data.frame()#for potential duplicates/manual dedup
-  rv$pairs_removed <- data.frame()#for removed records
+  rv$df <- data.frame()
+  #for original uploads
+  rv$upload_df <- data.frame()
+  #for reimported data
+  rv$latest_unique <- data.frame()
+  #for potential duplicates/manual dedup
+  rv$pairs_to_check <- data.frame()
+  #for removed records
+  rv$pairs_removed <- data.frame()
   
   #### Upload files tab section ------
   # upload on click
@@ -439,97 +417,62 @@ server <- function(input, output, session) {
     shiny::validate(need(input$file != "", "Select your bibliographic file to upload..."))
     if (is.null(input$file)) {
       return(NULL)
-    } else {
-      # --- NEW: Read the selected label from the dropdown ---
-      selected_label <- input$upload_label_select
-      # Ensure it's not NULL or empty if that's possible
-      if (is.null(selected_label) || selected_label == "") {
-        warning("No label selected for upload. Defaulting to 'search'.")
-        selected_label <- "search" # Use the default from the selectInput if needed
-      }
-      
-      # Get file paths and names (as before)
+    } 
+    else {
+      # upload files one-by-one
       path_list <- input$file$datapath
-      file_names <- input$file$name
-      
-      # Generate suggested source names (as before)
-      suggested_source <- stringr::str_replace_all(file_names, "\\.(ris|bib|txt)$", "")
       
-      # --- MODIFIED: Create label vector using the selected label ---
-      # Repeat the single selected label for each file being uploaded in this batch
-      labels_for_upload <- rep(selected_label, length(path_list))
-      # Keep strings empty unless you add a similar input for them
-      empty_strings <- rep("", length(path_list))
-      
-      # Increment upload number (as before)
+      # Increment the upload number
       if (is.null(rv$upload_number)) {
         rv$upload_number <- 1
       } else {
         rv$upload_number <- rv$upload_number + 1
       }
       
-      # --- MODIFIED: Call read_citations with the selected labels ---
-      # Use tryCatch for better error handling during file reading
-      upload_df <- tryCatch({
-        CiteSource::read_citations(
-          files = path_list,
-          cite_sources = suggested_source,
-          cite_labels = labels_for_upload, # Use the label from the dropdown
-          cite_strings = empty_strings,    # Keep strings empty
-          only_key_fields = FALSE
-        )
-      }, error = function(e) {
-        shiny::showNotification(paste("Error reading file(s):", e$message), type = "error", duration = 10)
-        return(NULL) # Return NULL on error
-      })
+      suggested_source <- stringr::str_replace_all(input$file$name, "\\.(ris|bib|txt)$", "")
       
-      # Stop processing if read_citations failed
-      if(is.null(upload_df) || nrow(upload_df) == 0) {
-        # Optionally clear the file input if reading failed completely for all files
-        # resetFileInput("file") # You would need a function like this using shinyjs
-        return(NULL)
-      }
+      empty_strings <- rep("", length(input$file$datapath))
       
-      # --- MODIFIED: Create the summary df with the selected label ---
-      # Ensure df has columns corresponding to the uploaded files in this batch
-      df <- data.frame(
-        file.datapath = path_list[1:length(suggested_source)], # Match length just in case
-        file.name = file_names[1:length(suggested_source)],
-        suggested_source = suggested_source,
-        label = labels_for_upload[1:length(suggested_source)], # Assign the label chosen in the dropdown
-        string = empty_strings[1:length(suggested_source)],    # Keep strings empty
-        stringsAsFactors = FALSE # Good practice
+      # Read the uploaded citations
+      upload_df <- CiteSource::read_citations(
+        files = path_list,
+        cite_sources = suggested_source,
+        cite_labels = empty_strings,
+        cite_strings = empty_strings,
+        only_key_fields = FALSE
+        
       )
       
-      # Summarize record counts from the successfully read data (as before)
+      # Summarize the number of records by citation source
       upload_length <- upload_df %>%
         dplyr::group_by(cite_source) %>%
         dplyr::count(name = "records") %>%
-        dplyr::ungroup() %>% # Ungroup after counting
         dplyr::rename(source = cite_source)
       
-      # Update the summary data frame df with record counts (as before)
-      # Join counts back to the summary df for display
-      df <- dplyr::left_join(df, upload_length, by = c("suggested_source" = "source")) %>%
-        dplyr::select(file.datapath, file.name, records, source = suggested_source, label, string) # Reorder/rename for consistency
+      # Create a data frame summarizing the uploaded files
+      df <- data.frame(
+        "file" = input$file,
+        "suggested_source" = suggested_source,
+        "label" = empty_strings,
+        "string" = empty_strings
+      )
       
-      # Ensure required columns are present in upload_df (label is now cite_label)
-      # No need to join the label/string from df to upload_df anymore, it came from read_citations
-      required_cols <- c("cite_source", "cite_label", "cite_string", "title", "doi", "isbn", "year", "journal", "pages", "volume", "number", "abstract")
-      # Add missing required columns as NA
-      missing_cols <- setdiff(required_cols, colnames(upload_df))
-      if(length(missing_cols) > 0) {
-        upload_df[missing_cols] <- NA
-      }
-      # Select a reasonable default order including new cite_* columns
-      upload_df <- upload_df %>%
-        dplyr::select(dplyr::any_of(c("cite_source", "cite_label", "cite_string")), dplyr::everything())
+      # Join upload_df with df to match on cite_source
+      upload_df <- dplyr::left_join(upload_df, df, by = c("cite_source" = "suggested_source")) %>%
+        dplyr::select(-label, -string) %>%
+        dplyr::select(cite_source, cite_label, cite_string, dplyr::everything())
       
+      # Ensure required columns are present in upload_df
+      required_cols <- c("title", "doi", "label", "isbn", "source", "year", "journal", "pages", "volume", "number", "abstract")
+      upload_df[required_cols[!(required_cols %in% colnames(upload_df))]] <- NA
       
-      # Append the results to the reactive values (as before)
+      # Update the summary data frame df with record counts
+      df <- dplyr::left_join(upload_length, df, by = c("source" = "suggested_source")) %>%
+        dplyr::select(file.datapath, file.name, records, source, label, string)
+      
+      # Append the results to the reactive values
       rv$df <- dplyr::bind_rows(rv$df, df)
       rv$upload_df <- dplyr::bind_rows(rv$upload_df, upload_df)
-      
     }
   })
   
@@ -537,18 +480,25 @@ server <- function(input, output, session) {
   ## display summary input table - summary of files added
   output$tbl_out <- DT::renderDataTable({
     if (is.null(input$file_reimport)) {
-      DT::datatable(rv$df,
-                    editable = TRUE,
-                    options = list(paging = FALSE,
-                                   searching = FALSE,
-                                   columnDefs = list(list(visible = FALSE, targets = c(0)))),
-                    rownames = FALSE)
+      DT::datatable(
+        rv$df,
+        options = list(
+          paging = FALSE,
+          searching = FALSE,
+          columnDefs = list(list(visible = FALSE, targets = c(0))) 
+        ),
+        editable = list(
+          target = 'column',
+          disable = list(columns = c(1, 2)) 
+        ),
+        rownames = FALSE
+      )
     }
   })
   
   shiny::observeEvent(input$file_reimport, {
     file_extension <- tolower(tools::file_ext(input$file_reimport$datapath))
-
+    
     if (file_extension == "csv") {
       rv$latest_unique <- reimport_csv(input$file_reimport$datapath)
     } else if (file_extension == "ris") {
@@ -559,13 +509,13 @@ server <- function(input, output, session) {
     
     rv$n_unique <- count_unique(rv$latest_unique)
     
-  shinyalert::shinyalert("Re-import successful",
-                         paste("Imported", nrow(rv$latest_unique), "citations. You can now proceed to visualisation and tables."),
-                         type = "success"
-  )
-  
+    shinyalert::shinyalert("Re-import successful",
+                           paste("Imported", nrow(rv$latest_unique), "citations. You can now proceed to visualisation and tables."),
+                           type = "success"
+    )
+    
   })
-
+  
   ## Update filters
   shiny::observe({
     if (nrow(rv$latest_unique) > 0) {
@@ -611,82 +561,137 @@ server <- function(input, output, session) {
       shiny::updateSelectInput(inputId = "sources_visual", choices = sources, selected = sources)
       shiny::updateSelectInput(inputId = "labels_visual", choices = labels, selected = labels)
       shiny::updateSelectInput(inputId = "strings_visual", choices = strings, selected = strings)
-
+      
       shiny::updateSelectInput(inputId = "sources_tables", choices = sources, selected = sources)
       shiny::updateSelectInput(inputId = "labels_tables", choices = labels, selected = labels)
       shiny::updateSelectInput(inputId = "strings_tables", choices = strings, selected = strings)
     }
   })
-
-  # when file upload table is edited, edit reactive value upload df
+  
+  # Robust Observer for Cell Edits in tbl_out
   shiny::observeEvent(input$tbl_out_cell_edit, {
-    # 1. Get Edit Information
-    info <- input$tbl_out_cell_edit 
-    val <- info$value
-    # 2. Handle blank input
-    if (val == "") {
-      val <- NA
-    }
-    # 3. Update the Summary DataFrame (rv$df)
-    rv$df[info$row, info$col + 1] <- val
-    
-    # 4. Find Corresponding Rows in the Main DataFrame (rv$upload_df) using cite_source
+    # This observer handles edits made to the summary table (rv$df)
+    # and propagates relevant changes (source, label, string)
+    # to the corresponding records in the detailed table (rv$upload_df).
     
-    # 4a. Get the 'source' value from the edited row in rv$df
-    # Assumes the column named 'source' holds the relevant identifier in rv$df
-    # Check rv$df structure if this column name is different.
-    source_value_from_edited_row <- rv$df[info$row, "source"] # Use column name "source"
+    info <- input$tbl_out_cell_edit
     
-    # Check if the source value is valid before proceeding
-    if (is.null(source_value_from_edited_row) || is.na(source_value_from_edited_row)) {
-      warning(paste("Could not find valid source value in edited row:", info$row))
-      return() # Stop processing if the source identifier is missing
+    # Ensure rv$df and rv$upload_df are valid data frames before proceeding
+    if (!is.data.frame(rv$df) || nrow(rv$df) == 0) {
+      # Silently return if summary data isn't ready (e.g., during initial load)
+      return()
     }
-    
-    # 4b. Calculate row ranges for each source in rv$upload_df 
-    # Group by 'cite_source' instead of 'file.datapath'
-    row_indexes <- rv$upload_df %>%
-      dplyr::mutate(rowname = dplyr::row_number()) %>%
-      dplyr::group_by(cite_source) %>% # *** CHANGED HERE ***
-      dplyr::summarise(
-        min_row = dplyr::first(rowname), 
-        max_row = dplyr::last(rowname),
-        .groups = 'drop' # Good practice to add .groups='drop'
-      )
-    
-    # 4c. Look up the min/max rows for the edited source
-    # Filter row_indexes using 'cite_source' and the value obtained from rv$df
-    rows <- row_indexes[row_indexes$cite_source == source_value_from_edited_row, ] # *** CHANGED HERE ***
-    
-    # Check if rows were found
-    if (nrow(rows) == 0 || is.na(rows$min_row) || is.na(rows$max_row)) {
-      warning(paste("Could not find row indices in rv$upload_df for source:", source_value_from_edited_row))
-      # Decide how to handle: maybe the source was edited TO something not in rv$upload_df?
-      # For now, just stop to prevent error. Depending on desired behavior, might need adjustment.
-      return() 
+    if (!is.data.frame(rv$upload_df)) {
+      # Log warning if detailed data structure is missing, but allow proceeding
+      # if only rv$df needs update (though propagation will fail later)
+      warning("rv$upload_df is not a valid data frame. Edits cannot be propagated.")
+      # Depending on desired behavior, could 'return()' here too.
     }
     
-    # 5. Determine Target Column Name in rv$upload_df (No change needed in principle)
-    # This part should be okay, as it maps rv$df column names ('source', 'label', 'string')
-    # to rv$upload_df column names ('cite_source', 'cite_label', 'cite_string')
-    col_name_in_df <- names(rv$df)[info$col + 1] # Safer way to get name
-    col <- paste0("cite_", col_name_in_df) 
+    # Get column names from the summary data frame
+    df_col_names <- names(rv$df)
     
-    # Add a check to ensure the target column exists in rv$upload_df
-    if (!col %in% names(rv$upload_df)) {
-      warning(paste("Target column", col, "not found in rv$upload_df"))
-      return() # Stop if the target column doesn't exist
-    }
+    # Determine the number of edits reported in this event
+    n_edits <- length(info$row)
     
-    # 6. Update the Main DataFrame (rv$upload_df) (No change in logic, but context changed)
-    # Now uses row indices derived via cite_source
-    # Added safety checks above should prevent errors here if rows/col were invalid
-    rv$upload_df[c(rows$min_row:rows$max_row), col] <- val 
+    # Process each reported edit individually
+    for (i in 1:n_edits) {
+      # Extract information for the current (i-th) edit
+      target_row_df <- as.integer(info$row[i]) # 1-based row index for rv$df
+      target_col_dt <- as.integer(info$col[i]) # 0-based column index from DT
+      target_col_df <- target_col_dt + 1       # Convert to 1-based R index for rv$df
+      val <- if (is.list(info$value)) info$value[[i]] else info$value[i] # Handle list/vector values
+      
+      # Convert blank input ("") to logical NA
+      if (length(val) == 1 && !is.na(val) && val == "") {
+        val <- NA
+      }
+      
+      # --- Validate indices against rv$df ---
+      if (target_row_df <= 0 || target_row_df > nrow(rv$df) ||
+          target_col_df <= 0 || target_col_df > ncol(rv$df)) {
+        warning(paste("Invalid row/column index received from DT edit. Row:",
+                      target_row_df, "Col:", target_col_df, ". Skipping this edit."))
+        next # Skip to the next edit
+      }
+      
+      # --- 1. Update rv$df (the summary table data) ---
+      # Use tryCatch to handle potential errors during assignment (e.g., type mismatch)
+      tryCatch({
+        rv$df[target_row_df, target_col_df] <- val
+      }, error = function(e) {
+        warning(paste("Error updating rv$df[", target_row_df, ",", target_col_df, "]:", e$message))
+        # Continue to the next edit even if this one failed
+        next
+      })
+      
+      # --- 2. Propagate change to rv$upload_df (if applicable) ---
+      
+      # Get the file.datapath associated with the edited row in rv$df
+      # This assumes column 1 of rv$df is 'file.datapath'
+      if (df_col_names[1] != "file.datapath") {
+        warning("Column 1 of rv$df is not 'file.datapath'. Cannot link edits to rv$upload_df.")
+        next # Skip propagation for this edit
+      }
+      edited_datapath <- rv$df[[target_row_df, 1]]
+      
+      # Check if the datapath is valid for lookup
+      if (is.na(edited_datapath) || edited_datapath == "") {
+        # Don't warn every time, might be expected if datapath is missing
+        next # Cannot link without a valid datapath
+      }
+      
+      # Check if rv$upload_df is ready for update
+      if (nrow(rv$upload_df) == 0 || !"file.datapath" %in% names(rv$upload_df)) {
+        # Silently skip if detailed data isn't ready or lacks the key column
+        next
+      }
+      
+      # Find rows in rv$upload_df matching the datapath
+      target_rows_upload_idx <- which(rv$upload_df$file.datapath == edited_datapath)
+      
+      if (length(target_rows_upload_idx) == 0) {
+        # No matching rows found in detailed data, nothing to propagate
+        next
+      }
+      
+      # Determine the target column name in rv$upload_df based on the edited column in rv$df
+      col_name_df <- df_col_names[target_col_df] # Name of edited column in summary table
+      col_name_upload <- NULL # Target column name in detailed table
+      
+      # Define the mapping for propagation
+      if (col_name_df == "source") {
+        col_name_upload <- "cite_source"
+      } else if (col_name_df == "label") {
+        col_name_upload <- "cite_label"
+      } else if (col_name_df == "string") {
+        col_name_upload <- "cite_string"
+      } else {
+        # If the edited column (e.g., 'records') shouldn't be propagated, skip
+        next
+      }
+      
+      # Check if the target column exists in rv$upload_df
+      if (!col_name_upload %in% names(rv$upload_df)) {
+        warning(paste("Target column '", col_name_upload, "' not found in rv$upload_df. Cannot propagate edit."))
+        next
+      }
+      
+      # Perform the update on all matching rows in the detailed data frame
+      tryCatch({
+        rv$upload_df[target_rows_upload_idx, col_name_upload] <- val
+      }, error = function(e) {
+        warning(paste("Error updating rv$upload_df rows for datapath", edited_datapath,
+                      "Column:", col_name_upload, ":", e$message))
+        # Continue to the next edit even if propagation failed
+      })
+      
+    } # End FOR loop iterating through edits reported by DT
     
   })
-
-# Deduplication tab -----------------
-
+  
+  # Deduplication tab -----------------
+  
   # when dedup button clicked, deduplicate
   shiny::observeEvent(input$identify_dups, {
     if (nrow(rv$upload_df) == 0) {
@@ -715,34 +720,24 @@ server <- function(input, output, session) {
     
     # Generate a summary message based on deduplication results
     n_citations <- nrow(rv$upload_df)
-    n_distinct_records <- nrow(rv$n_unique)
-    n_unique_records <- nrow(dedup_results$unique)
+    n_unique_records <- nrow(rv$n_unique)  # Changed variable name to avoid conflict
     n_pairs_manual <- nrow(rv$pairs_to_check)
     
-    # Title for the alert
-    alert_title <- "Automated Deduplication Complete"
-    
-    # Text for the alert body
-    if (n_pairs_manual > 0) {
-      alert_text <- sprintf(
-        "Process Summary:\n - Started with %d uploaded records.\n - Found %d distinct records (after removing duplicates within each source file).\n - Found %d unique records (after removing duplicates across source files).\n\nAction Required:\n%d potential duplicate pairs require your review. Please go to the 'Manual deduplication' sub-tab.",
-        n_citations, n_distinct_records, n_unique_records, n_pairs_manual
+    message <- if (n_pairs_manual > 0) {
+      paste(
+        "From a total of", n_citations, "citations added, there are", n_unique_records, 
+        "unique citations. Head to the manual deduplication tab to check", n_pairs_manual, "potential duplicates."
       )
     } else {
-      alert_text <- sprintf(
-        "Process Summary:\n - Started with %d uploaded records.\n - Found %d distinct records (after removing duplicates withi* each source file).\n - Found %d unique records (after removing duplicates across source files).\n\nNo potential duplicates were flagged for manual review.\n\nNext Step:\nYou can now proceed to the 'Visualise' tab.",
-        n_citations, n_distinct_records, n_unique_records
+      paste(
+        "From a total of", n_citations, "citations added, there are", n_unique_records, 
+        "unique citations. There are no potential duplicates for manual review. You can proceed to the visualization tab."
       )
     }
     
-    # Show the alert
-    shinyalert::shinyalert(title = alert_title, 
-                           text = alert_text, 
-                           type = "success",
-                           size = "l")
-    
+    shinyalert::shinyalert("Auto-deduplication complete", message, type = "success")
   })
-
+  
   ## Manual deduplication -----
   
   # Action button: remove manually selected duplicates [merged two segments into one]
@@ -765,8 +760,8 @@ server <- function(input, output, session) {
     
   })
   
-    observeEvent(input$completed_manual_dedup,{
-      
+  observeEvent(input$completed_manual_dedup,{
+    
     # provide shiny alert
     shinyalert::shinyalert("Manual deduplication complete",
                            paste(
@@ -776,20 +771,14 @@ server <- function(input, output, session) {
                            ),
                            type = "success"
     )
-
-      })
-
-    observe({
-      all_cols <- names(rv$pairs_to_check)
-      base_cols <- unique(gsub("(1|2)$", "", all_cols)) # Remove "1" or "2" suffix
-      
-      # Initial selection
-      initial_base_selection <- c("author", "title", "year", "journal", "abstract", "doi", "pages")
-      
-      shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
-                                      choices = base_cols,
-                                      selected = initial_base_selection)
-    })
+    
+  })
+  
+  observe({
+    shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
+                                    choices = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)],
+                                    selected = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)])
+  })
   
   
   # if no manual dedup, proceed to visualisations
@@ -801,7 +790,7 @@ server <- function(input, output, session) {
       selected = "Visualise"
     )
   })
-
+  
   # if rows selected in manual dedup, make buttom appear
   observe({
     selected_rows <- input$manual_dedup_dt_rows_selected
@@ -815,40 +804,18 @@ server <- function(input, output, session) {
   # Output: manual dedup datatable
   manual_dedup_data <- reactive({
     
-    data <- rv$pairs_to_check[, 1:36]
-    selected_cols <- input$manual_dedup_cols
-    
-    # Define the desired base order
-    core_col_order <- c("author", "title", "year", "journal", "abstract","doi", "pages","volume","number","source","label","string")
+    data <- rv$pairs_to_check[,1:36]
+    data <- data[,c(paste0(input$manual_dedup_cols))]
     
-    # Create the desired interleaved order of columns to select
-    desired_table_order <- character(0)
-    for (base_col in core_col_order) {
-      col1 <- paste0(base_col, "1")
-      col2 <- paste0(base_col, "2")
-      desired_table_order <- c(desired_table_order, col1, col2)
-    }
-    
-    # Intersect with the actual and selected columns to maintain order and presence
-    cols_to_show <- intersect(desired_table_order, colnames(data))
-    cols_to_show <- intersect(cols_to_show, paste0(selected_cols, rep(c("1", "2"), each = length(selected_cols))))
-    
-    ordered_data <- data %>%
-      dplyr::select(any_of(cols_to_show))
-    
-    # Define match_cols INSIDE the reactive expression
     match_cols <- c("title", "author", "doi", "volume",
                     "pages", "number", "year", "abstract", "journal", "isbn")
     
-    ordered_data <- ordered_data %>% dplyr::select(-any_of(match_cols))
     
-    # Add the match_number_cols at the end (if they exist)
-    match_number_cols_to_add <- intersect(paste0(match_cols), colnames(data))
-    if (length(match_number_cols_to_add) > 0) {
-      ordered_data <- cbind(ordered_data, data[, match_number_cols_to_add])
-    }
+    data <- data %>% dplyr::select(-any_of(match_cols))
+    match_number_cols <- rv$pairs_to_check[,c(paste0(match_cols))]
+    
+    data <- cbind(data,match_number_cols)
     
-    ordered_data
   })
   output$manual_dedup_dt <- DT::renderDataTable({
     
@@ -857,7 +824,7 @@ server <- function(input, output, session) {
     format_cols <- c(
       "title1", "author1", "doi1", "volume1",
       "pages1", "number1", "year1", "abstract1", "journal1", "isbn1",
-      "title2", "author2", "doi2", "volume2", 
+      "title2", "author2", "doi2", "volume2",
       "pages2", "number2", "year2", "abstract2", "journal2", "isbn2"
     )
     
@@ -865,33 +832,23 @@ server <- function(input, output, session) {
     shinyjs::useShinyjs()
     
     datatable(data,
-              extensions = c('FixedHeader', 'FixedColumns'),
               options = list(
-                fixedHeader = TRUE,                   
-                fixedColumns = list(leftColumns = 2), 
-                scrollX = TRUE,
-                pageLength = 100,
-                info = FALSE,
+                pageLength = 100, info = FALSE,
                 lengthMenu = list(c(100, -1), c("100", "All")),
-                columnDefs = list(
+                columnDefs =
                   list(
-                    targets = "_all",
-                    render = JS(
-                      "function(data, type, row, meta) {",
-                      "  if (type === 'display' && data != null && typeof data === 'string' && data.length > 25) {",
-                      "    var rawData = data;",
-                      "    return '<span title=\"' + rawData + '\">' + rawData.substr(0, 25) + '...</span>';",
-                      "  } else {",
-                      "    return data;",
-                      "  }",
-                      "}"
+                    list(visible = FALSE, 
+                         targets = columns2hide),
+                    list(
+                      targets = "_all",
+                      render = JS(
+                        "function(data, type, row, meta) {",
+                        "return type === 'display' && data != null && data.length > 25 ?",
+                        "'<span title=\"' + data + '\">' + data.substr(0, 25) + '...</span>' : data;",
+                        "}"
+                      )
                     )
-                  ),
-                  list(
-                    visible = FALSE,
-                    targets = columns2hide
                   )
-                ) 
               ))
     # ) %>%
     #   DT::formatStyle(
@@ -906,20 +863,20 @@ server <- function(input, output, session) {
     #     color = JS("value >= 0.95 ? 'white' : null")
     #   )
   })
-
-
+  
+  
   # ASySD manual dedup pre text 
   output$Manual_pretext <- shiny::renderText({
-
+    
     paste(nrow(rv$pairs_to_check), "pairs of citations require manual deduplication. Review the pairs in the table
         below.")
-
+    
   })
-
-
+  
+  
   #### Visualise tab ####
   
-  # Reactive expression to filter the data for visualization 
+  # Reactive expression to filter the data for visualization
   unique_filtered_visual <- shiny::reactive({
     sources <- input$sources_visual 
     sources <- ifelse(sources == "_blank_", "unknown", sources)
@@ -929,9 +886,7 @@ server <- function(input, output, session) {
     labels <- ifelse(labels == "_blank_", "unknown", labels)
     
     out <- rv$latest_unique %>%
-      # Group by the ID representing unique/duplicate groups
       dplyr::group_by(duplicate_id) %>%
-      # Separate comma-separated values into individual rows
       tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
       dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
       dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
@@ -1093,7 +1048,7 @@ server <- function(input, output, session) {
   
   # Rendering the precision and sensitivity table
   output$summaryPrecTab <- gt::render_gt({
-
+    
     unique_citations <- unique_filtered_table()
     
     # The table is only for phase comparison, include "final" in labels for comparison
@@ -1109,13 +1064,13 @@ server <- function(input, output, session) {
   
   # Rendering the record-level table
   output$reviewTab <- DT::renderDataTable({
-   
-     if (nrow(rv$latest_unique) == 0) {
+    
+    if (nrow(rv$latest_unique) == 0) {
       shinyalert::shinyalert("Data needed",
                              "Please import and deduplicate your citations first.",
                              type = "error"
       )
-       shiny::req(FALSE)
+      shiny::req(FALSE)
     }
     
     citations <- unique_filtered_table()
@@ -1124,9 +1079,9 @@ server <- function(input, output, session) {
   }) %>% shiny::bindEvent(input$generateRecordTable)
   
   
-
+  
   #### Export tab ####
-
+  
   # # Downloadable bibtex ----
   # Downloadable bibtex ----
   output$downloadCsv <- shiny::downloadHandler(
@@ -1150,7 +1105,7 @@ server <- function(input, output, session) {
       export_bib(rv$latest_unique, file)
     }
   )
-
+  
   output$downloadRis <- shiny::downloadHandler(
     filename = function() {
       paste("data-", Sys.Date(), ".ris", sep = "")

From 620178ea1cc1df6d5b7ec900e0d69f61c2d31ad7 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 09:59:15 -0400
Subject: [PATCH 10/47] testing for phase plot

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 1328 +++++++++++++++++++++++++
 inst/shiny-app/CiteSource/app.R       |  300 ++++--
 2 files changed, 1540 insertions(+), 88 deletions(-)
 create mode 100644 inst/shiny-app/CiteSource/CopyOfapp.R

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
new file mode 100644
index 00000000..07280679
--- /dev/null
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -0,0 +1,1328 @@
+library(DT)
+library(CiteSource)
+library(dplyr)
+
+# Set background colour
+shiny::tags$head(shiny::tags$style(
+  shiny::HTML('
+                     #sidebar {
+                        background-color: #ffffff;
+                    }
+
+                    body, label, input, button, select {
+                      font-family: "Arial";
+                    }')
+))
+
+columns2hide <- c("title", "author", "doi", "volume",
+                  "pages", "number", "year", "abstract", "journal", "isbn")
+
+
+# Define UI for data upload app ----
+ui <- shiny::navbarPage("CiteSource",
+                        id = "tabs",
+                        header = shiny::tagList(
+                          shinybusy::add_busy_spinner(spin = "circle"),
+                          shinyjs::useShinyjs(),
+                          #header text colour
+                          tags$head(
+                            tags$style(HTML("
+      h6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {
+        margin-top: 0;
+        margin-bottom: .5rem;
+        font-weight: 500;
+        line-height: 1.2;
+        color: #23395B;
+      }
+    "))
+                          )
+                        ),
+                        theme = bslib::bs_theme(
+                          bg = "rgb(251, 251, 251)",
+                          primary = "#008080",
+                          secondary = "#CBF7ED",
+                          success = "#23395B",
+                          info = "#82D173",
+                          warning = "#FFC07F",
+                          danger = "#008080",
+                          font_scale = NULL,
+                          bootswatch = "cerulean",
+                          fg = "#000",
+                          input_bg = "#E0E0E0",  # Set the background color for input boxes
+                          input_border_color = "#23395B"  # Set the border color for input boxes
+                        ),
+                        # Home tab
+                        shiny::tabPanel(
+                          "Home",
+                          shiny::navlistPanel(
+                            shiny::tabPanel(
+                              title = "About",
+                              htmltools::includeMarkdown("www/about.md")
+                            ),
+                            shiny::tabPanel(
+                              title = "Use Cases",
+                              htmltools::includeMarkdown("www/use-cases.md")
+                            ),
+                            # User Guide
+                            shiny::tabPanel(
+                              title = "User Guide",
+                              # Load the external Markdown file
+                              htmltools::includeMarkdown("www/user_guide.md")
+                            ),
+                            widths = c(2, 10)
+                          )
+                        ),
+                        shiny::tabPanel(
+                          "File upload",
+                          shiny::fluidRow(
+                            shiny::column(
+                              12,
+                              # Sidebar layout with input and output definitions ----
+                              shiny::sidebarLayout(
+                                shiny::sidebarPanel( # Input: Select a file ----
+                                                     shiny::h5("Step 1: Upload your citation files"),
+                                                     shiny::fileInput("file", "",
+                                                                      multiple = TRUE,
+                                                                      accept = c(".ris", ".txt", ".bib")
+                                                     ),
+                                                     shiny::hr(),
+                                                     shiny::h5("OR: Re-upload an .ris or .csv exported from CiteSource"),
+                                                     shiny::fileInput("file_reimport", "",
+                                                                      multiple = TRUE,
+                                                                      accept = c(".ris", ".csv")
+                                                     )
+                                ),
+                                # Main panel for displaying outputs ----
+                                shiny::mainPanel(
+                                  shiny::h5("Step 2: Double click on a column to edit sources, labels, and strings. Use *Ctrl+Enter* to save edits, one column at a time"),
+                                  # Output: Data file ----
+                                  DT::dataTableOutput("tbl_out")
+                                )
+                              )
+                            )
+                          )
+                        ),
+                        shiny::tabPanel(
+                          "Deduplicate",
+                          shiny::tabsetPanel(
+                            shiny::tabPanel(
+                              "Automated deduplication",
+                              br(),
+                              shiny::h5("Step 3: Deduplicate"),
+                              shiny::p("Click the button below to detect and remove duplicates automatically"),
+                              
+                              # Action button: identify duplicates in uploaded dataset
+                              shinyWidgets::actionBttn(
+                                "identify_dups", "Find duplicates",
+                                style = "jelly",
+                                color = "primary",
+                                icon = shiny::icon("search")
+                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #008080; margin-right: 20px"),
+                              
+                              # Output: datatable of deduplication results
+                              DT::dataTableOutput("dedup_results")
+                            ),
+                            shiny::tabPanel(
+                              "Manual deduplication",
+                              br(),
+                              shiny::h5("Step 4: Review potential duplicates manually"),
+                              shiny::p("The following records were identified as potential duplicates. Potential duplicates are combined into a single row with metadata fields for each record represented (ex. Title 1 & Title 2). Click any row to indicate that the records in that row ARE duplicates. Once all duplicates are identified you can click the button 'Remove additional duplicates' and then proceed to the visualizations."),
+                              shiny::textOutput("Manual_pretext"),
+                              shiny::br(),
+                              
+                              
+                              # Button
+                              shinyWidgets::actionBttn(
+                                inputId = "nomanualdedup",
+                                label = "Go to visualisations",
+                                style = "jelly",
+                                icon = shiny::icon("arrow-right"),
+                                color = "primary"
+                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #82D173"),
+                              br(),
+                              shinyWidgets::actionBttn(
+                                inputId = "manualdedupsubmit",
+                                label = "Remove additional duplicates",
+                                style = "jelly",
+                                icon = shiny::icon("reply"),
+                                color = "primary" # Hide the button initially
+                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                              
+                              shinyWidgets::dropdown(
+                                
+                                tags$h3("Select columns to display"),
+                                
+                                shinyWidgets::pickerInput(
+                                  inputId = "manual_dedup_cols",
+                                  label = "Choose columns",
+                                  choices = NULL,
+                                  selected = NULL,
+                                  multiple = TRUE,
+                                  options = list(
+                                    `live-search` = TRUE,
+                                    `actions-box` = TRUE,
+                                    style = "btn-primary")
+                                ), 
+                                icon = icon("filter"),
+                                inline =TRUE,
+                                status = "danger", width = "600px",
+                                tooltip = shinyWidgets::tooltipOptions(title = "Select columns to display")),
+                              
+                              DT::DTOutput("manual_dedup_dt"),
+                              tags$style(HTML(".table.dataTable tbody td.active, .table.dataTable tbody tr.active td {
+            background-color: #CBF7ED!important; color: black!important}")),
+                              
+                            )
+                          )
+                        ),
+                        shiny::tabPanel(
+                          "Visualise",
+                          
+                          # Sidebar layout with input and output definitions ----
+                          shiny::sidebarLayout(
+                            
+                            # Sidebar panel for inputs ----
+                            shiny::sidebarPanel(
+                              width = 3,
+                              id = "sidebar",
+                              shiny::h5("Step 5: Visualise overlap"),
+                              shinyWidgets::prettyRadioButtons(
+                                inputId = "comp_type",
+                                label = "Choose a comparison",
+                                inline = TRUE,
+                                choices = c(
+                                  "sources",
+                                  "labels", "strings"
+                                ),
+                                status = "primary"
+                              ),
+                              selectInput(
+                                inputId = "sources_visual",
+                                "Sources to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              ),
+                              selectInput(
+                                inputId = "labels_visual",
+                                "Labels to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              ),
+                              selectInput(
+                                inputId = "strings_visual",
+                                "Strings to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              )
+                            ),
+                            
+                            # Main panel for displaying outputs ----
+                            shiny::mainPanel(
+                              shiny::tabsetPanel(
+                                shiny::tabPanel(
+                                  "Plot overlap as a heatmap matrix",
+                                  shiny::downloadButton("downloadHeatPlot"),
+                                  plotly::plotlyOutput("plotgraph1")
+                                ),
+                                shiny::tabPanel(
+                                  "Plot overlap as an upset plot",
+                                  shiny::downloadButton("downloadUpsetPlot"),
+                                  shiny::plotOutput("plotgraph2")
+                                ),
+                                shiny::tabPanel(
+                                  "Phase Analysis",  # New Tab for Phase Analysis
+                                  shiny::downloadButton("downloadPhasePlot"),
+                                  shiny::plotOutput("phasePlot")
+                                )
+                              )
+                            )
+                          )
+                        ),
+                        
+                        shiny::tabPanel(
+                          "Tables",
+                          
+                          shiny::sidebarLayout(
+                            
+                            shiny::sidebarPanel(
+                              id = "sidebar",
+                              width = 3,
+                              shiny::h5("Step 6: Summary tables"),
+                              selectInput(
+                                inputId = "sources_tables",
+                                "Sources to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              ),
+                              selectInput(
+                                inputId = "labels_tables",
+                                "Labels to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              ),
+                              selectInput(
+                                inputId = "strings_tables",
+                                "Strings to include",
+                                list(),
+                                multiple = TRUE,
+                                selectize = TRUE
+                              )
+                            ),
+                            
+                            shiny::mainPanel(
+                              shiny::tabsetPanel(
+                                
+                                shiny::tabPanel(
+                                  "Initial Records Table",
+                                  shiny::div("View the initial record counts and deduplication results."),
+                                  shinyWidgets::actionBttn(
+                                    "generateInitialRecordTable", "Generate Initial Records Table",
+                                    style = "jelly",
+                                    icon = shiny::icon("table"),
+                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                                  shiny::br(),
+                                  shiny::br(),
+                                  gt::gt_output("initialRecordTab")
+                                ),
+                                
+                                shiny::tabPanel(
+                                  "Detailed Record Table",
+                                  shiny::div("Summary of unique and non-unique records by source."),
+                                  shinyWidgets::actionBttn(
+                                    "generateDetailedRecordTable", "Generate Detailed Record Table",
+                                    style = "jelly",
+                                    icon = shiny::icon("table"),
+                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                                  shiny::br(),
+                                  shiny::br(),
+                                  gt::gt_output("summaryRecordTab")
+                                ),
+                                
+                                shiny::tabPanel(
+                                  "Precision/Sensitivity Table",
+                                  shiny::div("Precision and Sensitivity of records across screening phases."),
+                                  shinyWidgets::actionBttn(
+                                    "generatePrecisionTable", "Generate Precision/Sensitivity Table",
+                                    style = "jelly",
+                                    icon = shiny::icon("table"),
+                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                                  shiny::br(),
+                                  shiny::br(),
+                                  gt::gt_output("summaryPrecTab")
+                                ),
+                                
+                                shiny::tabPanel(
+                                  "Review individual records",
+                                  shiny::br(),
+                                  shinyWidgets::actionBttn(
+                                    "generateRecordTable", "Generate the table",
+                                    style = "jelly",
+                                    icon = shiny::icon("table"),
+                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
+                                  
+                                  shiny::br(),
+                                  
+                                  shiny::wellPanel(
+                                    style = "background-color: #f0f8ff; border-color: #bce8f1; margin-top: 15px; margin-bottom: 15px; padding: 15px;", # Style
+                                    shiny::tags$h5(" Using the Interactive Record Table", style = "margin-top: 0; color: #31708f;"), # Title
+                                    shiny::tags$p("After clicking 'Generate the table', you can explore the records using these features:"), # Introduction
+                                    
+                                    # Performance Note
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;", # Add space below this paragraph
+                                      shiny::tags$strong(" Performance Note:"),
+                                      " The record table may take a long time to generate if you include more than a few hundred references. Consider filtering your data first using the sidebar selectors before generating."
+                                    ),
+                                    
+                                    # Instruction 1: Expand/Collapse
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;",
+                                      shiny::tags$strong(" Expand/Collapse Row:"),
+                                      " Click the ", shiny::tags$code(HTML("&oplus;")), " symbol in a row to view the full APA reference. Click ", shiny::tags$code(HTML("&CircleMinus;")), " to hide it again."
+                                    ),
+                                    
+                                    # Instruction 2: Single Sort
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;",
+                                      shiny::tags$strong(" Sort by Single Column:"),
+                                      " Click any column header (like 'Citation' or a source name) to sort the table by that column's values. Click the header again to reverse the sort order."
+                                    ),
+                                    
+                                    # Instruction 3: Multi Sort
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;",
+                                      shiny::tags$strong(" Sort by Multiple Columns:"),
+                                      " Click the primary column header you want to sort by. Then, hold down the ", shiny::tags$strong("Shift"), " key on your keyboard and click a second column header. You can repeat this for more sorting levels."
+                                    ),
+                                    
+                                    # Instruction 4: Filter/Search
+                                    shiny::tags$p(
+                                      style = "margin-bottom: 12px;",
+                                      shiny::tags$strong(" Filter/Search:"),
+                                      " Type into the search box located at the top-right of the table to dynamically filter records based on any information displayed."
+                                    ),
+                                    
+                                    # Instruction 5: Download (No bottom margin needed on the last item)
+                                    shiny::tags$p(
+                                      shiny::tags$strong(" Download Data:"),
+                                      " Click the 'Download CSV' button (located above the table, next to 'Print') to save the data currently shown in the table (including applied filters) as a CSV file."
+                                    )
+                                  ),
+                                  DT::dataTableOutput("reviewTab")
+                                )
+                              )
+                            )
+                          )
+                        ),
+                        
+                        shiny::tabPanel(
+                          "Export",
+                          shiny::fluidRow(
+                            shiny::column(
+                              12,
+                              shiny::mainPanel(
+                                shiny::h5("Step 7: Export citations"),
+                                shiny::h6("Note that you can only download the data after you have run the deduplication. Also, you are only able to re-upload CSV and RIS files to continue with CiteSource, so please use these formats if you want that option."),
+                                shiny::downloadButton("downloadCsv", "Download csv"),
+                                shiny::downloadButton("downloadRis", "Download RIS"),
+                                shiny::downloadButton("downloadBib", "Download BibTex")
+                              )
+                            )
+                          )
+                        )
+)
+
+
+# Define server logic to read selected file ----
+server <- function(input, output, session) {
+  rv <- shiny::reactiveValues()
+  rv$df <- data.frame()
+  rv$upload_df <- data.frame()#for original uploads
+  rv$latest_unique <- data.frame()#for reimported data
+  rv$pairs_to_check <- data.frame()#for potential duplicates/manual dedup
+  rv$pairs_removed <- data.frame()#for removed records
+  rv$n_unique = NULL # Initialize n_unique
+  
+  #### Upload files tab section ------
+  # upload on click
+  shiny::observeEvent(input$file, {
+    shiny::validate(need(input$file != "", "Select your bibliographic file to upload..."))
+    if (is.null(input$file)) {
+      return(NULL)
+    } 
+    else {
+      # upload files one-by-one
+      path_list <- input$file$datapath
+      
+      # Increment the upload number
+      if (is.null(rv$upload_number)) {
+        rv$upload_number <- 1
+      } else {
+        rv$upload_number <- rv$upload_number + 1
+      }
+      
+      suggested_source <- stringr::str_replace_all(input$file$name, "\\.(ris|bib|txt)$", "")
+      
+      empty_strings <- rep("", length(input$file$datapath))
+      
+      # Read the uploaded citations
+      upload_df <- CiteSource::read_citations(
+        files = path_list,
+        cite_sources = suggested_source,
+        cite_labels = empty_strings,
+        cite_strings = empty_strings,
+        only_key_fields = FALSE
+        
+      )
+      
+      # Summarize the number of records by citation source
+      upload_length <- upload_df %>%
+        dplyr::group_by(cite_source) %>%
+        dplyr::count(name = "records") %>%
+        dplyr::rename(source = cite_source)
+      
+      # Create a data frame summarizing the uploaded files
+      df <- data.frame(
+        "file" = input$file,
+        "suggested_source" = suggested_source,
+        "label" = empty_strings,
+        "string" = empty_strings
+      )
+      
+      # Join upload_df with df to match on cite_source
+      upload_df <- dplyr::left_join(upload_df, df, by = c("cite_source" = "suggested_source")) %>%
+        dplyr::select(-label, -string) %>%
+        dplyr::select(cite_source, cite_label, cite_string, dplyr::everything())
+      
+      # Ensure required columns are present in upload_df
+      required_cols <- c("title", "doi", "label", "isbn", "source", "year", "journal", "pages", "volume", "number", "abstract")
+      upload_df[required_cols[!(required_cols %in% colnames(upload_df))]] <- NA
+      
+      # Update the summary data frame df with record counts
+      df <- dplyr::left_join(upload_length, df, by = c("source" = "suggested_source")) %>%
+        dplyr::select(file.datapath, file.name, records, source, label, string)
+      
+      # Append the results to the reactive values
+      rv$df <- dplyr::bind_rows(rv$df, df)
+      rv$upload_df <- dplyr::bind_rows(rv$upload_df, upload_df)
+    }
+  })
+  
+  
+  ## display summary input table - summary of files added
+  output$tbl_out <- DT::renderDataTable({
+    if (is.null(input$file_reimport)) {
+      DT::datatable(
+        rv$df,
+        options = list(
+          paging = FALSE,
+          searching = FALSE,
+          columnDefs = list(list(visible = FALSE, targets = c(0))) 
+        ),
+        editable = list(
+          target = 'column',
+          disable = list(columns = c(1, 2)) 
+        ),
+        rownames = FALSE
+      )
+    }
+  })
+  
+  shiny::observeEvent(input$file_reimport, {
+    file_extension <- tolower(tools::file_ext(input$file_reimport$datapath))
+    
+    if (file_extension == "csv") {
+      rv$latest_unique <- reimport_csv(input$file_reimport$datapath)
+    } else if (file_extension == "ris") {
+      rv$latest_unique <- reimport_ris(input$file_reimport$datapath)
+    } else {
+      warning("Invalid file extension, needs to be .ris or .csv")
+    }
+    
+    rv$n_unique <- count_unique(rv$latest_unique)
+    
+    shinyalert::shinyalert("Re-import successful",
+                           paste("Imported", nrow(rv$latest_unique), "citations. You can now proceed to visualisation and tables."),
+                           type = "success"
+    )
+    
+  })
+  
+  ## Update filters
+  shiny::observe({
+    if (nrow(rv$latest_unique) > 0) {
+      
+      # Handle cite_source
+      sources <- rv$latest_unique$cite_source
+      if (all(is.na(sources) | sources == "")) {
+        sources <- NULL  # Leave it as NULL or NA
+      } else {
+        sources <- unique(sources[!is.na(sources) & sources != ""]) %>%
+          stringr::str_split(", ") %>%
+          unlist() %>%
+          unique() %>%
+          sort()
+      }
+      
+      # Handle cite_label
+      labels <- rv$latest_unique$cite_label
+      if (all(is.na(labels) | labels == "")) {
+        labels <- NULL  # Leave it as NULL or NA
+      } else {
+        labels <- unique(labels[!is.na(labels) & labels != ""]) %>%
+          stringr::str_split(", ") %>%
+          unlist() %>%
+          unique() %>%
+          sort()
+        
+      }
+      
+      # Handle cite_string
+      strings <- rv$latest_unique$cite_string
+      if (all(is.na(strings) | strings == "")) {
+        strings <- NULL  # Leave it as NULL or NA
+      } else {
+        strings <- unique(strings[!is.na(strings) & strings != ""]) %>%
+          stringr::str_split(", ") %>%
+          unlist() %>%
+          unique() %>%
+          sort()
+      }
+      
+      # Update select inputs
+      shiny::updateSelectInput(inputId = "sources_visual", choices = sources, selected = sources)
+      shiny::updateSelectInput(inputId = "labels_visual", choices = labels, selected = labels)
+      shiny::updateSelectInput(inputId = "strings_visual", choices = strings, selected = strings)
+      
+      shiny::updateSelectInput(inputId = "sources_tables", choices = sources, selected = sources)
+      shiny::updateSelectInput(inputId = "labels_tables", choices = labels, selected = labels)
+      shiny::updateSelectInput(inputId = "strings_tables", choices = strings, selected = strings)
+    }
+  })
+  
+  # Robust Observer for Cell Edits in tbl_out
+  shiny::observeEvent(input$tbl_out_cell_edit, {
+    # This observer handles edits made to the summary table (rv$df)
+    # and propagates relevant changes (source, label, string)
+    # to the corresponding records in the detailed table (rv$upload_df).
+    
+    info <- input$tbl_out_cell_edit
+    
+    # Ensure rv$df and rv$upload_df are valid data frames before proceeding
+    if (!is.data.frame(rv$df) || nrow(rv$df) == 0) {
+      # Silently return if summary data isn't ready (e.g., during initial load)
+      return()
+    }
+    if (!is.data.frame(rv$upload_df)) {
+      # Log warning if detailed data structure is missing, but allow proceeding
+      # if only rv$df needs update (though propagation will fail later)
+      warning("rv$upload_df is not a valid data frame. Edits cannot be propagated.")
+      # Depending on desired behavior, could 'return()' here too.
+    }
+    
+    # Get column names from the summary data frame
+    df_col_names <- names(rv$df)
+    
+    # Determine the number of edits reported in this event
+    n_edits <- length(info$row)
+    
+    # Process each reported edit individually
+    for (i in 1:n_edits) {
+      # Extract information for the current (i-th) edit
+      target_row_df <- as.integer(info$row[i]) # 1-based row index for rv$df
+      target_col_dt <- as.integer(info$col[i]) # 0-based column index from DT
+      target_col_df <- target_col_dt + 1       # Convert to 1-based R index for rv$df
+      val <- if (is.list(info$value)) info$value[[i]] else info$value[i] # Handle list/vector values
+      
+      # Convert blank input ("") to logical NA
+      if (length(val) == 1 && !is.na(val) && val == "") {
+        val <- NA
+      }
+      
+      # --- Validate indices against rv$df ---
+      if (target_row_df <= 0 || target_row_df > nrow(rv$df) ||
+          target_col_df <= 0 || target_col_df > ncol(rv$df)) {
+        warning(paste("Invalid row/column index received from DT edit. Row:",
+                      target_row_df, "Col:", target_col_df, ". Skipping this edit."))
+        next # Skip to the next edit
+      }
+      
+      # --- 1. Update rv$df (the summary table data) ---
+      # Use tryCatch to handle potential errors during assignment (e.g., type mismatch)
+      tryCatch({
+        rv$df[target_row_df, target_col_df] <- val
+      }, error = function(e) {
+        warning(paste("Error updating rv$df[", target_row_df, ",", target_col_df, "]:", e$message))
+        # Continue to the next edit even if this one failed
+        next
+      })
+      
+      # --- 2. Propagate change to rv$upload_df (if applicable) ---
+      
+      # Get the file.datapath associated with the edited row in rv$df
+      # This assumes column 1 of rv$df is 'file.datapath'
+      if (df_col_names[1] != "file.datapath") {
+        warning("Column 1 of rv$df is not 'file.datapath'. Cannot link edits to rv$upload_df.")
+        next # Skip propagation for this edit
+      }
+      edited_datapath <- rv$df[[target_row_df, 1]]
+      
+      # Check if the datapath is valid for lookup
+      if (is.na(edited_datapath) || edited_datapath == "") {
+        # Don't warn every time, might be expected if datapath is missing
+        next # Cannot link without a valid datapath
+      }
+      
+      # Check if rv$upload_df is ready for update
+      if (nrow(rv$upload_df) == 0 || !"file.datapath" %in% names(rv$upload_df)) {
+        # Silently skip if detailed data isn't ready or lacks the key column
+        next
+      }
+      
+      # Find rows in rv$upload_df matching the datapath
+      target_rows_upload_idx <- which(rv$upload_df$file.datapath == edited_datapath)
+      
+      if (length(target_rows_upload_idx) == 0) {
+        # No matching rows found in detailed data, nothing to propagate
+        next
+      }
+      
+      # Determine the target column name in rv$upload_df based on the edited column in rv$df
+      col_name_df <- df_col_names[target_col_df] # Name of edited column in summary table
+      col_name_upload <- NULL # Target column name in detailed table
+      
+      # Define the mapping for propagation
+      if (col_name_df == "source") {
+        col_name_upload <- "cite_source"
+      } else if (col_name_df == "label") {
+        col_name_upload <- "cite_label"
+      } else if (col_name_df == "string") {
+        col_name_upload <- "cite_string"
+      } else {
+        # If the edited column (e.g., 'records') shouldn't be propagated, skip
+        next
+      }
+      
+      # Check if the target column exists in rv$upload_df
+      if (!col_name_upload %in% names(rv$upload_df)) {
+        warning(paste("Target column '", col_name_upload, "' not found in rv$upload_df. Cannot propagate edit."))
+        next
+      }
+      
+      # Perform the update on all matching rows in the detailed data frame
+      tryCatch({
+        rv$upload_df[target_rows_upload_idx, col_name_upload] <- val
+      }, error = function(e) {
+        warning(paste("Error updating rv$upload_df rows for datapath", edited_datapath,
+                      "Column:", col_name_upload, ":", e$message))
+        # Continue to the next edit even if propagation failed
+      })
+      
+    } # End FOR loop iterating through edits reported by DT
+    
+  })
+  
+  # Deduplication tab -----------------
+  
+  # when dedup button clicked, deduplicate
+  shiny::observeEvent(input$identify_dups, {
+    if (nrow(rv$upload_df) == 0) {
+      if (nrow(rv$latest_unique) > 0) {
+        shinyalert::shinyalert("Deduplications already complete",
+                               "You have reimported a dataset that has already been deduplicated. In that case, further deduplication is not possible here, but would need to take place outside the app.",
+                               type = "error"
+        )
+      } else {
+        shinyalert::shinyalert("Data needed",
+                               "Please import your citations first.",
+                               type = "error"
+        )
+      }
+      return()  # Early return to stop further execution
+    }
+    
+    # Assign unique IDs to avoid issues with manual deduplication
+    rv$upload_df <- rv$upload_df %>% dplyr::mutate(record_id = as.character(1000 + dplyr::row_number()))
+    
+    # Perform deduplication
+    dedup_results <- CiteSource::dedup_citations(rv$upload_df, manual = TRUE, show_unknown_tags = TRUE)
+    rv$pairs_to_check <- dedup_results$manual_dedup
+    rv$latest_unique <- dedup_results$unique
+    rv$n_unique <- count_unique(rv$latest_unique)  # Generate the n_unique data
+    
+    # Generate a summary message based on deduplication results
+    n_citations <- nrow(rv$upload_df)
+    n_unique_records <- nrow(rv$n_unique)  # Changed variable name to avoid conflict
+    n_pairs_manual <- nrow(rv$pairs_to_check)
+    
+    message <- if (n_pairs_manual > 0) {
+      paste(
+        "From a total of", n_citations, "citations added, there are", n_unique_records, 
+        "unique citations. Head to the manual deduplication tab to check", n_pairs_manual, "potential duplicates."
+      )
+    } else {
+      paste(
+        "From a total of", n_citations, "citations added, there are", n_unique_records, 
+        "unique citations. There are no potential duplicates for manual review. You can proceed to the visualization tab."
+      )
+    }
+    
+    shinyalert::shinyalert("Auto-deduplication complete", message, type = "success")
+  })
+  
+  ## Manual deduplication -----
+  
+  # Action button: remove manually selected duplicates [merged two segments into one]
+  # remove manually selected duplicates 
+  observeEvent(input$manualdedupsubmit,{
+    
+    rv$pairs_removed <- rv$pairs_to_check[input$manual_dedup_dt_rows_selected,]
+    rv$pairs_to_check <- rv$pairs_to_check[-input$manual_dedup_dt_rows_selected,]
+    
+    if(nrow(rv$pairs_removed) < 1){
+      shinyalert("Oops!", "You haven't selected any duplicate pairs to remove.", type = "error")
+      return()
+    }
+    
+    after <- dedup_citations_add_manual(rv$latest_unique,
+                                        additional_pairs = rv$pairs_removed)
+    
+    # update latest unique df reactive value
+    rv$latest_unique <- after
+    
+  })
+  
+  observeEvent(input$completed_manual_dedup,{
+    
+    # provide shiny alert
+    shinyalert::shinyalert("Manual deduplication complete",
+                           paste(
+                             "From a total of", nrow(rv$upload_df), "citations uploaded, there are", nrow(rv$latest_unique),
+                             "unique citations after automated and manual deduplication.
+                           Compare citations across sources, labels, and strings in the visualisation tab"
+                           ),
+                           type = "success"
+    )
+    
+  })
+  
+  observe({
+    shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
+                                    choices = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)],
+                                    selected = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)])
+  })
+  
+  
+  # if no manual dedup, proceed to visualisations
+  shiny::observeEvent(input$nomanualdedup, {
+    
+    shiny::updateNavbarPage(
+      session = session,
+      inputId = "tabs",
+      selected = "Visualise"
+    )
+  })
+  
+  # if rows selected in manual dedup, make buttom appear
+  observe({
+    selected_rows <- input$manual_dedup_dt_rows_selected
+    if (length(selected_rows) > 0) {
+      shinyjs::show("manualdedupsubmit")  # Show the button when rows are selected
+    } else {
+      shinyjs::hide("manualdedupsubmit")  # Hide the button when no rows are selected
+    }
+  })
+  
+  # Output: manual dedup datatable
+  manual_dedup_data <- reactive({
+    
+    data <- rv$pairs_to_check[,1:36]
+    data <- data[,c(paste0(input$manual_dedup_cols))]
+    
+    match_cols <- c("title", "author", "doi", "volume",
+                    "pages", "number", "year", "abstract", "journal", "isbn")
+    
+    
+    data <- data %>% dplyr::select(-any_of(match_cols))
+    match_number_cols <- rv$pairs_to_check[,c(paste0(match_cols))]
+    
+    data <- cbind(data,match_number_cols)
+    
+  })
+  output$manual_dedup_dt <- DT::renderDataTable({
+    
+    data <- manual_dedup_data()
+    
+    format_cols <- c(
+      "title1", "author1", "doi1", "volume1",
+      "pages1", "number1", "year1", "abstract1", "journal1", "isbn1",
+      "title2", "author2", "doi2", "volume2",
+      "pages2", "number2", "year2", "abstract2", "journal2", "isbn2"
+    )
+    
+    format_cols <- intersect(format_cols, colnames(data))
+    shinyjs::useShinyjs()
+    
+    datatable(data,
+              options = list(
+                pageLength = 100, info = FALSE,
+                lengthMenu = list(c(100, -1), c("100", "All")),
+                columnDefs =
+                  list(
+                    list(visible = FALSE, 
+                         targets = columns2hide),
+                    list(
+                      targets = "_all",
+                      render = JS(
+                        "function(data, type, row, meta) {",
+                        "return type === 'display' && data != null && data.length > 25 ?",
+                        "'<span title=\"' + data + '\">' + data.substr(0, 25) + '...</span>' : data;",
+                        "}"
+                      )
+                    )
+                  )
+              ))
+  })
+  
+  
+  # ASySD manual dedup pre text 
+  output$Manual_pretext <- shiny::renderText({
+    
+    paste(nrow(rv$pairs_to_check), "pairs of citations require manual deduplication. Review the pairs in the table
+        below.")
+    
+  })
+  
+  
+  #### Visualise tab ####
+  
+  # --- Heatmap and Upset Plot Code ---
+  # Note: These plots WILL use unique_filtered_visual() which applies filters.
+  unique_filtered_visual <- shiny::reactive({
+    # Require rv$latest_unique to be a valid, non-empty data frame
+    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+    
+    sources <- input$sources_visual
+    sources <- ifelse(sources == "_blank_", "unknown", sources)
+    strings <- input$strings_visual
+    strings <- ifelse(strings == "_blank_", "unknown", strings)
+    labels <- input$labels_visual
+    labels <- ifelse(labels == "_blank_", "unknown", labels)
+    
+    # Define potential columns to separate and summarise
+    cols_to_process <- c("record_ids", "cite_label", "cite_source", "cite_string")
+    # Find which of these columns actually exist in the data
+    existing_cols <- intersect(cols_to_process, names(rv$latest_unique))
+    # Ensure essential columns for filtering exist
+    shiny::req("cite_source" %in% names(rv$latest_unique),
+               "cite_label" %in% names(rv$latest_unique),
+               "cite_string" %in% names(rv$latest_unique))
+    
+    # Need a local copy to modify within the reactive expression
+    current_latest_unique <- rv$latest_unique
+    
+    # Ensure existing_cols are character type before separate_rows
+    for (col in existing_cols) {
+      if (!is.character(current_latest_unique[[col]])) {
+        current_latest_unique[[col]] <- as.character(current_latest_unique[[col]])
+      }
+    }
+    
+    out <- current_latest_unique %>%
+      dplyr::group_by(duplicate_id) %>%
+      # Separate only the columns that exist
+      { if (length(existing_cols) > 0) tidyr::separate_rows(., tidyselect::all_of(existing_cols), sep = ", ") else . } %>%
+      # Filter based on existing columns
+      dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
+      dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
+      dplyr::filter(length(labels) == 0 | cite_label %in% labels) %>%
+      # Summarise only the columns that exist
+      dplyr::summarise(dplyr::across(tidyselect::all_of(existing_cols),
+                                     # Get unique non-NA values before pasting
+                                     ~ trimws(paste(na.omit(unique(.)), collapse = ', '))),
+                       .groups = "drop")
+    
+    return(out)
+  }) # End unique_filtered_visual reactive
+  
+  
+  # --- Heatmap Plot ---
+  plotHeat <- shiny::reactive({
+    # Requires unique_filtered_visual() to run successfully
+    shiny::req(unique_filtered_visual())
+    # Use tryCatch for robustness
+    tryCatch({
+      source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
+      # Ensure plot_source_overlap_heatmap exists and is callable
+      if (exists("plot_source_overlap_heatmap") && is.function(plot_source_overlap_heatmap)) {
+        plot_source_overlap_heatmap(source_comparison, cells = stringr::str_sub(input$comp_type, end = -2))
+      } else {
+        warning("Function 'plot_source_overlap_heatmap' not found.")
+        NULL
+      }
+    }, error = function(e) {
+      shiny::showNotification(paste("Error generating heatmap data:", e$message), type = "warning")
+      NULL # Return NULL on error
+    })
+  })
+  
+  output$plotgraph1 <- plotly::renderPlotly({
+    # Check for data before attempting to plot
+    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+    # Plotting depends on plotHeat() which depends on unique_filtered_visual()
+    heatmap_plot <- plotHeat()
+    shiny::req(heatmap_plot) # Require the plot object itself to be non-NULL
+    heatmap_plot
+  })
+  
+  output$downloadHeatPlot <- shiny::downloadHandler(
+    filename = function() { paste("heatmap", ".png", sep = "") },
+    content = function(file) {
+      # Ensure data for plotting exists
+      heatmap_plot_obj <- plotHeat()
+      shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0, heatmap_plot_obj)
+      if (inherits(heatmap_plot_obj, "ggplot")) {
+        ggplot2::ggsave(filename = file, plot = heatmap_plot_obj, device = "png", width = 8, height = 6)
+      } else if (inherits(heatmap_plot_obj, "plotly")) {
+        tryCatch({
+          plotly::save_image(heatmap_plot_obj, file, width = 800, height = 600)
+        }, error = function(e){
+          shiny::showNotification("Error saving plotly heatmap. Ensure 'kaleido' or 'orca' is installed.", type="error")
+        })
+      } else {
+        png(file, width = 800, height = 600)
+        print(heatmap_plot_obj)
+        dev.off()
+      }
+    }
+  )
+  
+  # --- Upset Plot ---
+  plotUpset <- shiny::reactive({
+    # Requires unique_filtered_visual() to run successfully
+    shiny::req(unique_filtered_visual())
+    # Use tryCatch for robustness
+    tryCatch({
+      source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
+      # Ensure plot_source_overlap_upset exists and is callable
+      if (exists("plot_source_overlap_upset") && is.function(plot_source_overlap_upset)) {
+        plot_source_overlap_upset(source_comparison, groups = stringr::str_sub(input$comp_type, end = -2), decreasing = c(TRUE, TRUE))
+      } else {
+        warning("Function 'plot_source_overlap_upset' not found.")
+        NULL
+      }
+    }, error = function(e) {
+      shiny::showNotification(paste("Error generating upset plot data:", e$message), type = "warning")
+      NULL # Return NULL on error
+    })
+  })
+  
+  output$plotgraph2 <- shiny::renderPlot({
+    # Check for data before attempting to plot
+    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+    # Plotting depends on plotUpset() which depends on unique_filtered_visual()
+    upset_plot <- plotUpset()
+    shiny::req(upset_plot) # Require the plot object itself to be non-NULL
+    print(upset_plot) # Upset plot usually needs print()
+  })
+  
+  output$downloadUpsetPlot <- shiny::downloadHandler(
+    filename = function() { paste("upset", ".png", sep = "") },
+    content = function(file) {
+      # Ensure data for plotting exists
+      upset_plot_obj <- plotUpset()
+      shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0, upset_plot_obj)
+      if (inherits(upset_plot_obj, "ggplot") || inherits(upset_plot_obj, "grob")) {
+        ggplot2::ggsave(filename = file, plot = upset_plot_obj, device = "png", width = 10, height = 6)
+      } else {
+        png(file, width = 1000, height = 600)
+        print(upset_plot_obj)
+        dev.off()
+      }
+    }
+  )
+  
+  
+  # --- Phase Analysis Plot Section ---
+  
+  # Observer to calculate n_unique based on rv$latest_unique
+  # Includes pre-processing step to fill missing sources for later phases
+  shiny::observe({
+    # Check if rv$latest_unique is valid before proceeding
+    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
+      
+      # --- Pre-processing Step ---
+      tryCatch({
+        # Convert blank strings to NA for easier handling
+        data_to_process <- rv$latest_unique %>%
+          dplyr::mutate(dplyr::across(tidyselect::any_of(c("cite_source", "cite_label", "cite_string")),
+                                      ~ dplyr::na_if(trimws(as.character(.)), ""))) # Ensure character
+        
+        # Find the source(s) for each duplicate_id (handle multiple sources)
+        source_lookup <- data_to_process %>%
+          dplyr::filter(!is.na(cite_source)) %>%
+          dplyr::group_by(duplicate_id) %>%
+          # Paste unique non-NA sources
+          dplyr::summarise(source_to_fill = paste(unique(na.omit(cite_source)), collapse = ", "), .groups = "drop") %>%
+          dplyr::filter(source_to_fill != "") # Ensure we don't have blank results
+        
+        # Join the lookup table back and fill NA sources
+        processed_data <- dplyr::left_join(data_to_process, source_lookup, by = "duplicate_id") %>%
+          dplyr::mutate(
+            # Fill cite_source only if it's NA AND we found a source_to_fill
+            cite_source = ifelse(is.na(cite_source) & !is.na(source_to_fill), source_to_fill, cite_source)
+          ) %>%
+          dplyr::select(-source_to_fill) # Remove the temporary column
+        
+        # --- Call Original count_unique ---
+        # Now call the original count_unique function on the pre-processed data
+        # Ensure the original count_unique function is available
+        if (exists("count_unique") && is.function(count_unique)) {
+          # Use the ORIGINAL count_unique function
+          rv$n_unique <- count_unique(processed_data)
+        } else {
+          stop("Original 'count_unique' function not found.")
+        }
+        
+      }, error = function(e) {
+        shiny::showNotification(paste("Error during pre-processing or count_unique:", e$message), type = "error")
+        rv$n_unique <- NULL # Set to NULL on error
+      })
+    } else {
+      # If rv$latest_unique is not valid, set n_unique to NULL
+      rv$n_unique <- NULL
+    }
+  }) # Triggered whenever rv$latest_unique changes
+  
+  
+  # Render the Phase Analysis plot
+  output$phasePlot <- shiny::renderPlot({
+    # Require that rv$n_unique has been calculated and is valid
+    shiny::req(!is.null(rv$n_unique),
+               is.data.frame(rv$n_unique),
+               nrow(rv$n_unique) > 0)
+    
+    # --- Apply visualization filters to rv$n_unique ---
+    sources_filt <- input$sources_visual
+    # REMOVED _blank_ mapping for sources
+    strings_filt <- input$strings_visual
+    strings_filt <- if (!is.null(strings_filt) && "_blank_" %in% strings_filt) {
+      c(setdiff(strings_filt, "_blank_"), NA_character_, "")
+    } else { strings_filt }
+    labels_filt <- input$labels_visual
+    labels_filt <- if (!is.null(labels_filt) && "_blank_" %in% labels_filt) {
+      c(setdiff(labels_filt, "_blank_"), NA_character_, "")
+    } else { labels_filt }
+    
+    # Filter the calculated rv$n_unique data for plotting sequentially
+    # rv$n_unique should have separated rows from original count_unique
+    plot_data <- rv$n_unique
+    
+    # Apply source filter (Simple %in% should work now)
+    if (!is.null(sources_filt) && length(sources_filt) > 0 && "cite_source" %in% names(plot_data)) {
+      # Exclude _blank_ selection if present, as NAs should be filled
+      actual_sources_selected <- setdiff(sources_filt, "_blank_")
+      if(length(actual_sources_selected) > 0) {
+        # Filter rows where cite_source is exactly one of the selected sources
+        # (original count_unique separates rows)
+        plot_data <- plot_data %>% dplyr::filter(.data$cite_source %in% actual_sources_selected)
+      } else {
+        # If only _blank_ was selected (or sources_filt is empty after removing _blank_)
+        # show nothing, as pre-processing should have removed blanks/NAs
+        plot_data <- plot_data[0,]
+      }
+    }
+    # Apply string filter
+    if (!is.null(strings_filt) && length(strings_filt) > 0 && "cite_string" %in% names(plot_data)) {
+      # Use %in% which handles NA correctly if NA is in strings_filt
+      plot_data <- plot_data %>% dplyr::filter(.data$cite_string %in% strings_filt)
+    }
+    # Apply label filter
+    if (!is.null(labels_filt) && length(labels_filt) > 0 && "cite_label" %in% names(plot_data)) {
+      # Use %in% which handles NA correctly if NA is in labels_filt
+      plot_data <- plot_data %>% dplyr::filter(.data$cite_label %in% labels_filt)
+    }
+    
+    # Require that the filtered data still has rows
+    shiny::req(nrow(plot_data) > 0)
+    
+    # Generate the plot using the *filtered* data
+    # Use the ORIGINAL plot_contributions function (that counts ROWS)
+    # Ensure the original plot_contributions function is available
+    if (exists("plot_contributions") && is.function(plot_contributions)) {
+      plot_contributions(
+        data = plot_data,          # Use the filtered data for plotting
+        center = TRUE,
+        bar_order = c("search", "screened", "final"),
+        color_order = c("unique", "duplicated")
+        # Default facets = cite_source is used
+      )
+    } else {
+      warning("Original 'plot_contributions' function not found.")
+      NULL
+    }
+  })
+  
+  # Download handler uses the same plotting logic (including filtering)
+  output$downloadPhasePlot <- shiny::downloadHandler(
+    filename = function() {
+      paste("phase_analysis", ".png", sep = "")
+    },
+    content = function(file) {
+      # Require that rv$n_unique has been calculated and is valid
+      shiny::req(!is.null(rv$n_unique),
+                 is.data.frame(rv$n_unique),
+                 nrow(rv$n_unique) > 0)
+      
+      # --- Apply visualization filters to rv$n_unique ---
+      sources_filt <- input$sources_visual
+      # REMOVED _blank_ mapping for sources
+      strings_filt <- input$strings_visual
+      strings_filt <- if (!is.null(strings_filt) && "_blank_" %in% strings_filt) {
+        c(setdiff(strings_filt, "_blank_"), NA_character_, "")
+      } else { strings_filt }
+      labels_filt <- input$labels_visual
+      labels_filt <- if (!is.null(labels_filt) && "_blank_" %in% labels_filt) {
+        c(setdiff(labels_filt, "_blank_"), NA_character_, "")
+      } else { labels_filt }
+      
+      # Filter the calculated rv$n_unique data for plotting sequentially
+      plot_data <- rv$n_unique
+      
+      # Apply source filter
+      if (!is.null(sources_filt) && length(sources_filt) > 0 && "cite_source" %in% names(plot_data)) {
+        actual_sources_selected <- setdiff(sources_filt, "_blank_")
+        if(length(actual_sources_selected) > 0) {
+          plot_data <- plot_data %>% dplyr::filter(.data$cite_source %in% actual_sources_selected)
+        } else {
+          plot_data <- plot_data[0,]
+        }
+      }
+      # Apply string filter
+      if (!is.null(strings_filt) && length(strings_filt) > 0 && "cite_string" %in% names(plot_data)) {
+        plot_data <- plot_data %>% dplyr::filter(.data$cite_string %in% strings_filt)
+      }
+      # Apply label filter
+      if (!is.null(labels_filt) && length(labels_filt) > 0 && "cite_label" %in% names(plot_data)) {
+        plot_data <- plot_data %>% dplyr::filter(.data$cite_label %in% labels_filt)
+      }
+      
+      # Require that the filtered data still has rows
+      shiny::req(nrow(plot_data) > 0)
+      
+      # Generate the plot again for saving, using filtered data
+      # Use the ORIGINAL plot_contributions function
+      if (exists("plot_contributions") && is.function(plot_contributions)) {
+        phase_plot <- plot_contributions(
+          data = plot_data, # Use filtered data
+          center = TRUE,
+          bar_order = c("search", "screened", "final"),
+          color_order = c("unique", "duplicated")
+          # Default facets = cite_source is used
+        )
+        # Save the plot to the file
+        ggplot2::ggsave(filename = file, plot = phase_plot, device = "png", width = 8, height = 6)
+      } else {
+        warning("Original 'plot_contributions' function not found. Cannot save plot.")
+        file.create(file) # Create an empty file as placeholder
+      }
+    }
+  )
+  
+  
+  
+  #### Table tab ####
+  
+  # Event reactive for filtering the data used in the record table and summary table
+  unique_filtered_table <- shiny::eventReactive(
+    c(input$generateRecordTable, input$generateSummaryTable, 
+      input$sources_tables, input$strings_tables, input$labels_tables,
+      input$generateInitialRecordTable, input$generateDetailedRecordTable, 
+      input$generatePrecisionTable),
+    {
+      sources <- input$sources_tables 
+      sources <- ifelse(sources == "_blank_", "unknown", sources)
+      strings <- input$strings_tables
+      strings <- ifelse(strings == "_blank_", "unknown", strings)
+      labels <- input$labels_tables
+      labels <- ifelse(labels == "_blank_", "unknown", labels)
+      
+      rv$latest_unique %>%
+        dplyr::group_by(duplicate_id) %>%
+        tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
+        dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
+        dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
+        dplyr::filter(length(labels) == 0 | cite_label %in% labels) %>%
+        dplyr::mutate(across(c(record_ids, cite_label, cite_source, cite_string), ~ trimws(paste(na.omit(.), collapse = ', ')))) %>%
+        unique() %>%
+        dplyr::ungroup()
+    }
+  )
+  
+  # Rendering the initial record table
+  output$initialRecordTab <- gt::render_gt({
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error"
+      )
+      shiny::req(FALSE)
+    }
+    
+    unique_citations <- unique_filtered_table()
+    initial_records <- calculate_initial_records(unique_citations, "search")
+    create_initial_record_table(initial_records)
+  }) %>% shiny::bindEvent(input$generateInitialRecordTable)
+  
+  # Rendering the detailed record table
+  output$summaryRecordTab <- gt::render_gt({
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error"
+      )
+      shiny::req(FALSE)
+    }
+    
+    unique_citations <- unique_filtered_table()
+    detailed_count <- calculate_detailed_records(unique_citations, rv$n_unique, "search")
+    create_detailed_record_table(detailed_count)
+  }) %>% shiny::bindEvent(input$generateDetailedRecordTable)
+  
+  # Rendering the precision and sensitivity table
+  output$summaryPrecTab <- gt::render_gt({
+    
+    unique_citations <- unique_filtered_table()
+    
+    # The table is only for phase comparison, include "final" in labels for comparison
+    if (!any(stringr::str_detect(tolower(unique_citations$cite_label), "final"))) {
+      shiny::req(FALSE)
+    }
+    
+    unique_citations <- unique_filtered_table()
+    phase_counts <- calculate_phase_records(unique_citations, n_unique, "cite_source")
+    create_precision_sensitivity_table(phase_counts)
+  }) %>% shiny::bindEvent(input$generatePrecisionTable)
+  
+  
+  # Rendering the record-level table
+  output$reviewTab <- DT::renderDataTable({
+    
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error"
+      )
+      shiny::req(FALSE)
+    }
+    
+    citations <- unique_filtered_table()
+    citations$source <- citations$cite_source
+    record_level_table(citations = citations, return = "DT")
+  }) %>% shiny::bindEvent(input$generateRecordTable)
+  
+  
+  
+  #### Export tab ####
+  
+  # # Downloadable bibtex ----
+  # Downloadable bibtex ----
+  output$downloadCsv <- shiny::downloadHandler(
+    filename = function() {
+      paste("data-", Sys.Date(), ".csv", sep = "")
+    },
+    content = function(file) {
+      if (nrow(rv$latest_unique) > 0) {
+        write.csv(rv$latest_unique, file)
+      } else {
+        stop("No data to download!")
+        shiny::req(FALSE)
+      }
+    }
+  )
+  output$downloadBib <- shiny::downloadHandler(
+    filename = function() {
+      paste("data-", Sys.Date(), ".bib", sep = "")
+    },
+    content = function(file) {
+      export_bib(rv$latest_unique, file)
+    }
+  )
+  
+  output$downloadRis <- shiny::downloadHandler(
+    filename = function() {
+      paste("data-", Sys.Date(), ".ris", sep = "")
+    },
+    content = function(file) {
+      export_ris(rv$latest_unique, file)
+    }
+  )
+}
+
+# Create Shiny app ----
+shiny::shinyApp(ui, server)
diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 514d9dd2..d260f42a 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -402,14 +402,11 @@ ui <- shiny::navbarPage("CiteSource",
 server <- function(input, output, session) {
   rv <- shiny::reactiveValues()
   rv$df <- data.frame()
-  #for original uploads
-  rv$upload_df <- data.frame()
-  #for reimported data
-  rv$latest_unique <- data.frame()
-  #for potential duplicates/manual dedup
-  rv$pairs_to_check <- data.frame()
-  #for removed records
-  rv$pairs_removed <- data.frame()
+  rv$upload_df <- data.frame()#for original uploads
+  rv$latest_unique <- data.frame()#for reimported data
+  rv$pairs_to_check <- data.frame()#for potential duplicates/manual dedup
+  rv$pairs_removed <- data.frame()#for removed records
+  rv$n_unique = NULL # Initialize n_unique
   
   #### Upload files tab section ------
   # upload on click
@@ -850,18 +847,6 @@ server <- function(input, output, session) {
                     )
                   )
               ))
-    # ) %>%
-    #   DT::formatStyle(
-    #     columns = format_cols,
-    #     backgroundColor = DT::styleInterval(c(0.95, 1), c("white", "#82d173", "#82d173")),
-    #     target = "row"
-    #   ) %>%
-    #   DT::formatStyle(
-    #     columns = format_cols,
-    #     backgroundColor = DT::styleInterval(c(0.95, 1), c("white", "#82d173", "#82d173")),
-    #     target = "cell",
-    #     color = JS("value >= 0.95 ? 'white' : null")
-    #   )
   })
   
   
@@ -876,118 +861,257 @@ server <- function(input, output, session) {
   
   #### Visualise tab ####
   
-  # Reactive expression to filter the data for visualization
+    # --- Heatmap and Upset Plot Code ---
+  # Note: These plots WILL use unique_filtered_visual() which applies filters.
+  # Define unique_filtered_visual() here if it's only used for these plots.
   unique_filtered_visual <- shiny::reactive({
-    sources <- input$sources_visual 
+    # Require rv$latest_unique to be a valid, non-empty data frame
+    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+
+    sources <- input$sources_visual
     sources <- ifelse(sources == "_blank_", "unknown", sources)
     strings <- input$strings_visual
     strings <- ifelse(strings == "_blank_", "unknown", strings)
-    labels <- input$labels_visual 
+    labels <- input$labels_visual
     labels <- ifelse(labels == "_blank_", "unknown", labels)
-    
-    out <- rv$latest_unique %>%
+
+    # Define potential columns to separate and summarise
+    cols_to_process <- c("record_ids", "cite_label", "cite_source", "cite_string")
+    # Find which of these columns actually exist in the data
+    existing_cols <- intersect(cols_to_process, names(rv$latest_unique))
+    # Ensure essential columns for filtering exist
+    shiny::req("cite_source" %in% names(rv$latest_unique),
+               "cite_label" %in% names(rv$latest_unique),
+               "cite_string" %in% names(rv$latest_unique))
+
+    # The dplyr chain should now only run on valid data
+    # Need a local copy to modify within the reactive expression
+    current_latest_unique <- rv$latest_unique
+
+    # Ensure existing_cols are character type before separate_rows
+    for (col in existing_cols) {
+        if (!is.character(current_latest_unique[[col]])) {
+            current_latest_unique[[col]] <- as.character(current_latest_unique[[col]])
+        }
+    }
+
+    out <- current_latest_unique %>%
       dplyr::group_by(duplicate_id) %>%
-      tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
+      # Separate only the columns that exist
+      { if (length(existing_cols) > 0) tidyr::separate_rows(., tidyselect::all_of(existing_cols), sep = ", ") else . } %>%
+      # Filter based on existing columns
       dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
       dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
       dplyr::filter(length(labels) == 0 | cite_label %in% labels) %>%
-      dplyr::summarise(across(c(record_ids, cite_label, cite_source, cite_string), ~ trimws(paste(na.omit(.), collapse = ', ')))) %>%
-      dplyr::ungroup()
-  })
-  
-  # Heatmap plot
+      # Summarise only the columns that exist
+      dplyr::summarise(dplyr::across(tidyselect::all_of(existing_cols),
+                                     # Get unique non-NA values before pasting
+                                     ~ trimws(paste(na.omit(unique(.)), collapse = ', '))),
+                       .groups = "drop")
+
+    return(out)
+  }) # End unique_filtered_visual reactive
+
+
+  # --- Heatmap Plot ---
   plotHeat <- shiny::reactive({
-    source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
-    plot_source_overlap_heatmap(source_comparison, cells = stringr::str_sub(input$comp_type, end = -2))
+    # Requires unique_filtered_visual() to run successfully
+    shiny::req(unique_filtered_visual())
+    # Use tryCatch for robustness
+    tryCatch({
+        source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
+        plot_source_overlap_heatmap(source_comparison, cells = stringr::str_sub(input$comp_type, end = -2))
+    }, error = function(e) {
+        shiny::showNotification(paste("Error generating heatmap data:", e$message), type = "warning")
+        NULL # Return NULL on error
+    })
   })
-  
+
   output$plotgraph1 <- plotly::renderPlotly({
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
-    }
-    
-    print(plotHeat())
+    # Check for data before attempting to plot
+    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+    # Plotting depends on plotHeat() which depends on unique_filtered_visual()
+    heatmap_plot <- plotHeat()
+    shiny::req(heatmap_plot) # Require the plot object itself to be non-NULL
+    # Assuming plot_source_overlap_heatmap returns a plotly object or ggplot object
+    # If it returns ggplot, wrap in ggplotly()
+    # print(heatmap_plot) # Use print for base R, direct object for ggplot/plotly
+    heatmap_plot
   })
-  
+
   output$downloadHeatPlot <- shiny::downloadHandler(
-    filename = function() {
-      paste("heatmap", ".png", sep = "")
-    },
+    filename = function() { paste("heatmap", ".png", sep = "") },
     content = function(file) {
-      png(file)
-      print(plotHeat())
-      dev.off()
+      # Ensure data for plotting exists
+      heatmap_plot_obj <- plotHeat()
+      shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0, heatmap_plot_obj)
+
+      # Use ggsave for ggplot objects. If plotly, use orca or kaleido via plotly::save_image
+      # Assuming ggplot for now, adjust if it's plotly or base R
+      if (inherits(heatmap_plot_obj, "ggplot")) {
+          ggplot2::ggsave(filename = file, plot = heatmap_plot_obj, device = "png", width = 8, height = 6)
+      } else if (inherits(heatmap_plot_obj, "plotly")) {
+          # Requires kaleido package: reticulate::install_miniconda(); reticulate::conda_install('r-reticulate', 'python-kaleido'); reticulate::conda_install('r-reticulate', 'plotly', channel = 'plotly')
+          # Or use orca (older method)
+          tryCatch({
+              plotly::save_image(heatmap_plot_obj, file, width = 800, height = 600) # Adjust size as needed
+          }, error = function(e){
+              shiny::showNotification("Error saving plotly heatmap. Ensure 'kaleido' or 'orca' is installed.", type="error")
+          })
+      } else {
+         # Fallback for base R or other plot types
+         png(file, width = 800, height = 600) # Adjust size
+         print(heatmap_plot_obj)
+         dev.off()
+      }
     }
   )
-  
-  # Upset plot
+
+  # --- Upset Plot ---
   plotUpset <- shiny::reactive({
-    source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
-    plot_source_overlap_upset(source_comparison, groups = stringr::str_sub(input$comp_type, end = -2), decreasing = c(TRUE, TRUE))
+     # Requires unique_filtered_visual() to run successfully
+    shiny::req(unique_filtered_visual())
+    # Use tryCatch for robustness
+    tryCatch({
+        source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
+        plot_source_overlap_upset(source_comparison, groups = stringr::str_sub(input$comp_type, end = -2), decreasing = c(TRUE, TRUE))
+    }, error = function(e) {
+        shiny::showNotification(paste("Error generating upset plot data:", e$message), type = "warning")
+        NULL # Return NULL on error
+    })
   })
-  
+
   output$plotgraph2 <- shiny::renderPlot({
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
-    }
-    print(plotUpset())
+    # Check for data before attempting to plot
+    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+    # Plotting depends on plotUpset() which depends on unique_filtered_visual()
+    upset_plot <- plotUpset()
+    shiny::req(upset_plot) # Require the plot object itself to be non-NULL
+    print(upset_plot) # Upset plot usually needs print()
   })
-  
+
   output$downloadUpsetPlot <- shiny::downloadHandler(
-    filename = function() {
-      paste("upset", ".png", sep = "")
-    },
+    filename = function() { paste("upset", ".png", sep = "") },
     content = function(file) {
-      png(file)
-      print(plotUpset())
-      dev.off()
+      # Ensure data for plotting exists
+      upset_plot_obj <- plotUpset()
+      shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0, upset_plot_obj)
+      # Use ggsave for ggplot objects, otherwise use standard png/print
+      # Assuming plot_source_overlap_upset returns ggplot or grid object
+      if (inherits(upset_plot_obj, "ggplot") || inherits(upset_plot_obj, "grob")) {
+          ggplot2::ggsave(filename = file, plot = upset_plot_obj, device = "png", width = 10, height = 6) # Upset plots often wider
+      } else {
+         # Fallback for base R or other plot types
+         png(file, width = 1000, height = 600) # Adjust size
+         print(upset_plot_obj)
+         dev.off()
+      }
     }
   )
-  
-  # Phase Analysis plot
-  # Update n_unique based on the filtered data
-  rv$n_unique <- shiny::reactive({
-    filtered_data <- unique_filtered_visual()
-    count_unique(filtered_data) 
-  })
-  
+
+
+  # --- Phase Analysis Plot Section ---
+
+  # Observer to calculate n_unique based DIRECTLY on rv$latest_unique
+  # This calculates the TRUE unique/duplicate status across the whole dataset
+  shiny::observe({
+    # Check if rv$latest_unique is valid before proceeding
+    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
+        # Use tryCatch around count_unique
+        tryCatch({
+            # Calculate n_unique from the complete deduplicated data
+            rv$n_unique <- count_unique(rv$latest_unique)
+        }, error = function(e_count) {
+            shiny::showNotification(paste("Error calculating unique counts:", e_count$message), type = "error")
+            rv$n_unique <- NULL # Set to NULL on error
+        })
+    } else {
+        # If rv$latest_unique is not valid, set n_unique to NULL
+        rv$n_unique <- NULL
+    }
+  }) # Triggered whenever rv$latest_unique changes
+
+
+  # Render the Phase Analysis plot
   output$phasePlot <- shiny::renderPlot({
-    unique_citations <- unique_filtered_visual()
-    rv$n_unique<- count_unique(unique_citations)
+    # Require that rv$n_unique has been calculated and is valid
+    shiny::req(!is.null(rv$n_unique),
+               is.data.frame(rv$n_unique),
+               nrow(rv$n_unique) > 0)
+
+    # --- Apply visualization filters to rv$n_unique ---
+    # Get current filter selections
+    sources_filt <- input$sources_visual
+    sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
+    strings_filt <- input$strings_visual
+    strings_filt <- ifelse(strings_filt == "_blank_", "unknown", strings_filt)
+    labels_filt <- input$labels_visual
+    labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
+
+    # Filter the calculated rv$n_unique data for plotting
+    # Note: rv$n_unique has one row per record *per source* after count_unique
+    plot_data <- rv$n_unique %>%
+      # Use checks for robustness in case columns don't exist in rv$n_unique
+      dplyr::filter(length(sources_filt) == 0 | ( "cite_source" %in% names(.) && cite_source %in% sources_filt)) %>%
+      dplyr::filter(length(strings_filt) == 0 | ( "cite_string" %in% names(.) && cite_string %in% strings_filt)) %>%
+      dplyr::filter(length(labels_filt) == 0 | ( "cite_label" %in% names(.) && cite_label %in% labels_filt))
+
+    # Require that the filtered data still has rows
+    shiny::req(nrow(plot_data) > 0)
+
+    # Generate the plot using the *filtered* data
     plot_contributions(
-      data = rv$n_unique,
+      data = plot_data,          # Use the filtered data for plotting
       center = TRUE,
       bar_order = c("search", "screened", "final"),
       color_order = c("unique", "duplicated")
+      # Default facets = cite_source is used
     )
   })
-  
+
+  # Download handler uses the same plotting logic (including filtering)
   output$downloadPhasePlot <- shiny::downloadHandler(
     filename = function() {
       paste("phase_analysis", ".png", sep = "")
     },
     content = function(file) {
-      png(file)
-      print(
-        plot_contributions(
-          data = rv$n_unique,
+      # Require that rv$n_unique has been calculated and is valid
+      shiny::req(!is.null(rv$n_unique),
+                 is.data.frame(rv$n_unique),
+                 nrow(rv$n_unique) > 0)
+
+      # --- Apply visualization filters to rv$n_unique ---
+      sources_filt <- input$sources_visual
+      sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
+      strings_filt <- input$strings_visual
+      strings_filt <- ifelse(strings_filt == "_blank_", "unknown", strings_filt)
+      labels_filt <- input$labels_visual
+      labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
+
+      plot_data <- rv$n_unique %>%
+        # Use checks for robustness
+        dplyr::filter(length(sources_filt) == 0 | ( "cite_source" %in% names(.) && cite_source %in% sources_filt)) %>%
+        dplyr::filter(length(strings_filt) == 0 | ( "cite_string" %in% names(.) && cite_string %in% strings_filt)) %>%
+        dplyr::filter(length(labels_filt) == 0 | ( "cite_label" %in% names(.) && cite_label %in% labels_filt))
+
+
+      # Require that the filtered data still has rows
+      shiny::req(nrow(plot_data) > 0)
+
+      # Generate the plot again for saving, using filtered data
+      phase_plot <- plot_contributions(
+          data = plot_data, # Use filtered data
           center = TRUE,
           bar_order = c("search", "screened", "final"),
           color_order = c("unique", "duplicated")
+          # Default facets = cite_source is used
         )
-      )
-      dev.off()
+      # Save the plot to the file
+      ggplot2::ggsave(filename = file, plot = phase_plot, device = "png", width = 8, height = 6)
     }
   )
   
+  
   #### Table tab ####
   
   # Event reactive for filtering the data used in the record table and summary table

From eb6bb4dd9d8691dfbd016325d32f5451127a740c Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 10:28:12 -0400
Subject: [PATCH 11/47] remove some comments

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 465 +++++++++++---------------
 1 file changed, 192 insertions(+), 273 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index 07280679..2a345ce0 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -861,11 +861,10 @@ server <- function(input, output, session) {
   
   #### Visualise tab ####
   
-  # --- Heatmap and Upset Plot Code ---
-  # Note: These plots WILL use unique_filtered_visual() which applies filters.
+  # Reactive expression to filter the data for visualization (used for Heatmap and Upset)
   unique_filtered_visual <- shiny::reactive({
-    # Require rv$latest_unique to be a valid, non-empty data frame
-    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+    # Ensure latest_unique has data before attempting filtering
+    shiny::req(nrow(rv$latest_unique) > 0)
     
     sources <- input$sources_visual
     sources <- ifelse(sources == "_blank_", "unknown", sources)
@@ -874,328 +873,248 @@ server <- function(input, output, session) {
     labels <- input$labels_visual
     labels <- ifelse(labels == "_blank_", "unknown", labels)
     
-    # Define potential columns to separate and summarise
-    cols_to_process <- c("record_ids", "cite_label", "cite_source", "cite_string")
-    # Find which of these columns actually exist in the data
-    existing_cols <- intersect(cols_to_process, names(rv$latest_unique))
-    # Ensure essential columns for filtering exist
-    shiny::req("cite_source" %in% names(rv$latest_unique),
-               "cite_label" %in% names(rv$latest_unique),
-               "cite_string" %in% names(rv$latest_unique))
+    out <- rv$latest_unique %>%
+      # Filter rows where *any* of the required sources/labels/strings are present before summarizing
+      dplyr::filter(
+        (length(sources) == 0 | stringr::str_detect(cite_source, paste0("\\b(", paste(sources, collapse="|"), ")\\b"))) &
+          (length(strings) == 0 | stringr::str_detect(cite_string, paste0("\\b(", paste(strings, collapse="|"), ")\\b"))) &
+          (length(labels) == 0 | stringr::str_detect(cite_label, paste0("\\b(", paste(labels, collapse="|"), ")\\b")))
+      ) %>%
+      # Now group and summarize
+      dplyr::group_by(duplicate_id) %>%
+      {
+        df_grouped <- .
+        df_separated <- df_grouped %>%
+          tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
+          dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
+          dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
+          dplyr::filter(length(labels) == 0 | cite_label %in% labels)
+        
+        # Check if df_separated is empty for this group before summarizing
+        if(nrow(df_separated) == 0) {
+          # Return a row with the group id and NAs or empty strings if nothing matches filters
+          tibble::tibble(duplicate_id = dplyr::first(df_grouped$duplicate_id),
+                         record_ids = "", cite_label = "", cite_source = "", cite_string = "")
+        } else {
+          df_separated %>%
+            dplyr::summarise(across(c(record_ids, cite_label, cite_source, cite_string), ~ trimws(paste(unique(na.omit(.)), collapse = ', '))))
+        }
+      } %>%
+      dplyr::ungroup()
     
-    # Need a local copy to modify within the reactive expression
-    current_latest_unique <- rv$latest_unique
+    # Ensure the output has the same columns even if empty
+    if (nrow(out) == 0) {
+      out <- tibble::tibble(duplicate_id=character(), record_ids=character(), cite_label=character(), cite_source=character(), cite_string=character()) # Adjust columns as needed
+    }
+    # Add back other columns from rv$latest_unique if needed by downstream functions
+    dplyr::left_join(out, dplyr::select(rv$latest_unique, -any_of(c("record_ids", "cite_label", "cite_source", "cite_string"))), by = "duplicate_id")
     
-    # Ensure existing_cols are character type before separate_rows
-    for (col in existing_cols) {
-      if (!is.character(current_latest_unique[[col]])) {
-        current_latest_unique[[col]] <- as.character(current_latest_unique[[col]])
-      }
+  })
+  
+  # REACTIVE for Phase Plot Data
+  unique_separated_phase <- shiny::reactive({
+    # Require rv$latest_unique to have data
+    shiny::req(nrow(rv$latest_unique) > 0)
+    
+    # Get filter inputs
+    sources_filt <- input$sources_visual
+    sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
+    labels_filt <- input$labels_visual
+    labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
+    
+    # Start with the base unique data, select columns needed
+    df <- rv$latest_unique %>%
+      dplyr::select(duplicate_id, cite_source, cite_label) # Add record_ids if needed
+    
+    # Separate rows for source and label
+    df_long <- df %>%
+      tidyr::separate_rows(cite_source, sep = ",\\s*") %>%
+      tidyr::separate_rows(cite_label, sep = ",\\s*") %>%
+      dplyr::filter(!is.na(cite_source) & cite_source != "", # Ensure no blank/NA sources/labels after separation
+                    !is.na(cite_label) & cite_label != "")
+    
+    # Apply filtering based on selected sources and labels for the plot
+    df_filtered <- df_long %>%
+      dplyr::filter(length(sources_filt) == 0 | cite_source %in% sources_filt) %>%
+      dplyr::filter(length(labels_filt) == 0 | cite_label %in% labels_filt)
+    
+    # Check if filtering resulted in empty data
+    if (nrow(df_filtered) == 0) {
+      # Return an empty tibble with correct columns/types if no data matches
+      return(tibble::tibble(
+        duplicate_id = character(),
+        cite_source = character(),
+        cite_label = character(),
+        type = factor(levels = c("unique", "duplicated"))
+      ))
     }
     
-    out <- current_latest_unique %>%
-      dplyr::group_by(duplicate_id) %>%
-      # Separate only the columns that exist
-      { if (length(existing_cols) > 0) tidyr::separate_rows(., tidyselect::all_of(existing_cols), sep = ", ") else . } %>%
-      # Filter based on existing columns
-      dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
-      dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
-      dplyr::filter(length(labels) == 0 | cite_label %in% labels) %>%
-      # Summarise only the columns that exist
-      dplyr::summarise(dplyr::across(tidyselect::all_of(existing_cols),
-                                     # Get unique non-NA values before pasting
-                                     ~ trimws(paste(na.omit(unique(.)), collapse = ', '))),
-                       .groups = "drop")
+    # Calculate 'type' based on uniqueness *within* each phase (label) for the *filtered* set
+    # A record is 'unique' in a phase if it's found in only 1 source within that phase in the filtered data
+    df_typed <- df_filtered %>%
+      dplyr::group_by(duplicate_id, cite_label) %>%
+      dplyr::mutate(n_sources_in_phase = dplyr::n_distinct(cite_source)) %>%
+      dplyr::ungroup() %>%
+      dplyr::mutate(
+        type = factor(
+          ifelse(n_sources_in_phase == 1, "unique", "duplicated"),
+          levels = c("unique", "duplicated")
+        )
+      ) %>%
+      # Select relevant columns needed by plot_contributions
+      dplyr::select(duplicate_id, cite_source, cite_label, type)
     
-    return(out)
-  }) # End unique_filtered_visual reactive
+    # Return the long data frame, ensuring distinct rows
+    dplyr::distinct(df_typed)
+  })# End Phase Plot Reactive
   
   
-  # --- Heatmap Plot ---
+  # Heatmap plot (uses unique_filtered_visual)
   plotHeat <- shiny::reactive({
-    # Requires unique_filtered_visual() to run successfully
-    shiny::req(unique_filtered_visual())
-    # Use tryCatch for robustness
-    tryCatch({
-      source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
-      # Ensure plot_source_overlap_heatmap exists and is callable
-      if (exists("plot_source_overlap_heatmap") && is.function(plot_source_overlap_heatmap)) {
-        plot_source_overlap_heatmap(source_comparison, cells = stringr::str_sub(input$comp_type, end = -2))
-      } else {
-        warning("Function 'plot_source_overlap_heatmap' not found.")
-        NULL
-      }
-    }, error = function(e) {
-      shiny::showNotification(paste("Error generating heatmap data:", e$message), type = "warning")
-      NULL # Return NULL on error
-    })
+    # Add check if data is available
+    data_vis <- unique_filtered_visual()
+    shiny::req(nrow(data_vis) > 0)
+    source_comparison <- compare_sources(data_vis, comp_type = input$comp_type)
+    plot_source_overlap_heatmap(source_comparison, cells = stringr::str_sub(input$comp_type, end = -2))
   })
   
   output$plotgraph1 <- plotly::renderPlotly({
-    # Check for data before attempting to plot
-    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
-    # Plotting depends on plotHeat() which depends on unique_filtered_visual()
-    heatmap_plot <- plotHeat()
-    shiny::req(heatmap_plot) # Require the plot object itself to be non-NULL
-    heatmap_plot
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error"
+      )
+      shiny::req(FALSE)
+    }
+    # Add check specific to filtered data for this plot
+    shiny::req(nrow(unique_filtered_visual()) > 0, cancelOutput = TRUE)
+    print(plotHeat())
   })
   
   output$downloadHeatPlot <- shiny::downloadHandler(
-    filename = function() { paste("heatmap", ".png", sep = "") },
+    filename = function() {
+      paste("heatmap", ".png", sep = "")
+    },
     content = function(file) {
-      # Ensure data for plotting exists
-      heatmap_plot_obj <- plotHeat()
-      shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0, heatmap_plot_obj)
-      if (inherits(heatmap_plot_obj, "ggplot")) {
-        ggplot2::ggsave(filename = file, plot = heatmap_plot_obj, device = "png", width = 8, height = 6)
-      } else if (inherits(heatmap_plot_obj, "plotly")) {
-        tryCatch({
-          plotly::save_image(heatmap_plot_obj, file, width = 800, height = 600)
-        }, error = function(e){
-          shiny::showNotification("Error saving plotly heatmap. Ensure 'kaleido' or 'orca' is installed.", type="error")
-        })
-      } else {
-        png(file, width = 800, height = 600)
-        print(heatmap_plot_obj)
-        dev.off()
-      }
+      # Add check specific to filtered data for this plot
+      shiny::req(nrow(unique_filtered_visual()) > 0)
+      # Use a non-interactive device like png
+      grDevices::png(file, width = 800, height = 600) # Adjust size as needed
+      # Ensure the plot is printed
+      print(plotHeat())
+      grDevices::dev.off()
     }
   )
   
-  # --- Upset Plot ---
+  # Upset plot (uses unique_filtered_visual)
   plotUpset <- shiny::reactive({
-    # Requires unique_filtered_visual() to run successfully
-    shiny::req(unique_filtered_visual())
-    # Use tryCatch for robustness
-    tryCatch({
-      source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
-      # Ensure plot_source_overlap_upset exists and is callable
-      if (exists("plot_source_overlap_upset") && is.function(plot_source_overlap_upset)) {
-        plot_source_overlap_upset(source_comparison, groups = stringr::str_sub(input$comp_type, end = -2), decreasing = c(TRUE, TRUE))
-      } else {
-        warning("Function 'plot_source_overlap_upset' not found.")
-        NULL
-      }
-    }, error = function(e) {
-      shiny::showNotification(paste("Error generating upset plot data:", e$message), type = "warning")
-      NULL # Return NULL on error
-    })
+    # Add check if data is available
+    data_vis <- unique_filtered_visual()
+    shiny::req(nrow(data_vis) > 0)
+    source_comparison <- compare_sources(data_vis, comp_type = input$comp_type)
+    plot_source_overlap_upset(source_comparison, groups = stringr::str_sub(input$comp_type, end = -2), decreasing = c(TRUE, TRUE))
   })
   
   output$plotgraph2 <- shiny::renderPlot({
-    # Check for data before attempting to plot
-    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
-    # Plotting depends on plotUpset() which depends on unique_filtered_visual()
-    upset_plot <- plotUpset()
-    shiny::req(upset_plot) # Require the plot object itself to be non-NULL
-    print(upset_plot) # Upset plot usually needs print()
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error"
+      )
+      shiny::req(FALSE)
+    }
+    # Add check specific to filtered data for this plot
+    shiny::req(nrow(unique_filtered_visual()) > 0, cancelOutput = TRUE)
+    print(plotUpset())
   })
   
   output$downloadUpsetPlot <- shiny::downloadHandler(
-    filename = function() { paste("upset", ".png", sep = "") },
+    filename = function() {
+      paste("upset", ".png", sep = "")
+    },
     content = function(file) {
-      # Ensure data for plotting exists
-      upset_plot_obj <- plotUpset()
-      shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0, upset_plot_obj)
-      if (inherits(upset_plot_obj, "ggplot") || inherits(upset_plot_obj, "grob")) {
-        ggplot2::ggsave(filename = file, plot = upset_plot_obj, device = "png", width = 10, height = 6)
-      } else {
-        png(file, width = 1000, height = 600)
-        print(upset_plot_obj)
-        dev.off()
-      }
+      # Add check specific to filtered data for this plot
+      shiny::req(nrow(unique_filtered_visual()) > 0)
+      # Use png for static plot
+      grDevices::png(file, width = 800, height = 600) # Adjust size as needed
+      # plotUpset() returns a complex plot object (often ggplot/patchwork) that needs printing
+      print(plotUpset())
+      grDevices::dev.off()
     }
   )
   
+  # Phase Analysis plot (uses unique_separated_phase reactive)
+  # Keep rv$n_unique definition as is for potential use in table functions,
+  # but note it relies on the potentially problematic unique_filtered_visual().
+  # If table functions are updated or don't rely on this specific structure,
+  # this reactive could be revisited or removed.
+  rv$n_unique <- shiny::reactive({
+    # Original logic based on unique_filtered_visual and count_unique
+    # Ensure unique_filtered_visual() is not empty
+    ufv <- unique_filtered_visual()
+    shiny::req(nrow(ufv) > 0)
+    count_unique(ufv) # Pass the wide, summarized, filtered data
+  })
   
-  # --- Phase Analysis Plot Section ---
-  
-  # Observer to calculate n_unique based on rv$latest_unique
-  # Includes pre-processing step to fill missing sources for later phases
-  shiny::observe({
-    # Check if rv$latest_unique is valid before proceeding
-    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
-      
-      # --- Pre-processing Step ---
-      tryCatch({
-        # Convert blank strings to NA for easier handling
-        data_to_process <- rv$latest_unique %>%
-          dplyr::mutate(dplyr::across(tidyselect::any_of(c("cite_source", "cite_label", "cite_string")),
-                                      ~ dplyr::na_if(trimws(as.character(.)), ""))) # Ensure character
-        
-        # Find the source(s) for each duplicate_id (handle multiple sources)
-        source_lookup <- data_to_process %>%
-          dplyr::filter(!is.na(cite_source)) %>%
-          dplyr::group_by(duplicate_id) %>%
-          # Paste unique non-NA sources
-          dplyr::summarise(source_to_fill = paste(unique(na.omit(cite_source)), collapse = ", "), .groups = "drop") %>%
-          dplyr::filter(source_to_fill != "") # Ensure we don't have blank results
-        
-        # Join the lookup table back and fill NA sources
-        processed_data <- dplyr::left_join(data_to_process, source_lookup, by = "duplicate_id") %>%
-          dplyr::mutate(
-            # Fill cite_source only if it's NA AND we found a source_to_fill
-            cite_source = ifelse(is.na(cite_source) & !is.na(source_to_fill), source_to_fill, cite_source)
-          ) %>%
-          dplyr::select(-source_to_fill) # Remove the temporary column
-        
-        # --- Call Original count_unique ---
-        # Now call the original count_unique function on the pre-processed data
-        # Ensure the original count_unique function is available
-        if (exists("count_unique") && is.function(count_unique)) {
-          # Use the ORIGINAL count_unique function
-          rv$n_unique <- count_unique(processed_data)
-        } else {
-          stop("Original 'count_unique' function not found.")
-        }
-        
-      }, error = function(e) {
-        shiny::showNotification(paste("Error during pre-processing or count_unique:", e$message), type = "error")
-        rv$n_unique <- NULL # Set to NULL on error
-      })
-    } else {
-      # If rv$latest_unique is not valid, set n_unique to NULL
-      rv$n_unique <- NULL
-    }
-  }) # Triggered whenever rv$latest_unique changes
-  
-  
-  # Render the Phase Analysis plot
+  # Modified phase plot output
   output$phasePlot <- shiny::renderPlot({
-    # Require that rv$n_unique has been calculated and is valid
-    shiny::req(!is.null(rv$n_unique),
-               is.data.frame(rv$n_unique),
-               nrow(rv$n_unique) > 0)
-    
-    # --- Apply visualization filters to rv$n_unique ---
-    sources_filt <- input$sources_visual
-    # REMOVED _blank_ mapping for sources
-    strings_filt <- input$strings_visual
-    strings_filt <- if (!is.null(strings_filt) && "_blank_" %in% strings_filt) {
-      c(setdiff(strings_filt, "_blank_"), NA_character_, "")
-    } else { strings_filt }
-    labels_filt <- input$labels_visual
-    labels_filt <- if (!is.null(labels_filt) && "_blank_" %in% labels_filt) {
-      c(setdiff(labels_filt, "_blank_"), NA_character_, "")
-    } else { labels_filt }
-    
-    # Filter the calculated rv$n_unique data for plotting sequentially
-    # rv$n_unique should have separated rows from original count_unique
-    plot_data <- rv$n_unique
-    
-    # Apply source filter (Simple %in% should work now)
-    if (!is.null(sources_filt) && length(sources_filt) > 0 && "cite_source" %in% names(plot_data)) {
-      # Exclude _blank_ selection if present, as NAs should be filled
-      actual_sources_selected <- setdiff(sources_filt, "_blank_")
-      if(length(actual_sources_selected) > 0) {
-        # Filter rows where cite_source is exactly one of the selected sources
-        # (original count_unique separates rows)
-        plot_data <- plot_data %>% dplyr::filter(.data$cite_source %in% actual_sources_selected)
-      } else {
-        # If only _blank_ was selected (or sources_filt is empty after removing _blank_)
-        # show nothing, as pre-processing should have removed blanks/NAs
-        plot_data <- plot_data[0,]
-      }
-    }
-    # Apply string filter
-    if (!is.null(strings_filt) && length(strings_filt) > 0 && "cite_string" %in% names(plot_data)) {
-      # Use %in% which handles NA correctly if NA is in strings_filt
-      plot_data <- plot_data %>% dplyr::filter(.data$cite_string %in% strings_filt)
-    }
-    # Apply label filter
-    if (!is.null(labels_filt) && length(labels_filt) > 0 && "cite_label" %in% names(plot_data)) {
-      # Use %in% which handles NA correctly if NA is in labels_filt
-      plot_data <- plot_data %>% dplyr::filter(.data$cite_label %in% labels_filt)
+    # Initial check if any data has been processed
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error")
+      shiny::req(FALSE, cancelOutput = TRUE)
     }
     
-    # Require that the filtered data still has rows
-    shiny::req(nrow(plot_data) > 0)
+    # Use the reactive data specifically prepared for this plot
+    plot_data <- unique_separated_phase()
     
-    # Generate the plot using the *filtered* data
-    # Use the ORIGINAL plot_contributions function (that counts ROWS)
-    # Ensure the original plot_contributions function is available
-    if (exists("plot_contributions") && is.function(plot_contributions)) {
-      plot_contributions(
-        data = plot_data,          # Use the filtered data for plotting
-        center = TRUE,
-        bar_order = c("search", "screened", "final"),
-        color_order = c("unique", "duplicated")
-        # Default facets = cite_source is used
-      )
-    } else {
-      warning("Original 'plot_contributions' function not found.")
-      NULL
+    # Check if the prepared data is empty after filtering
+    if (nrow(plot_data) == 0) {
+      # Optionally, display a message instead of just cancelling
+      plot.new()
+      mtext("No data matches the selected filters for Phase Analysis.")
+      shiny::req(FALSE, cancelOutput = TRUE)
     }
+    
+    # Call plot_contributions with the prepared data
+    CiteSource::plot_contributions( # Explicitly call from CiteSource if needed
+      data = plot_data, # Use the new reactive data
+      center = TRUE,
+      bar_order = c("search", "screened", "final"), # Keep or make dynamic?
+      color_order = c("unique", "duplicated")
+    )
   })
   
-  # Download handler uses the same plotting logic (including filtering)
+  # Pphase plot download
   output$downloadPhasePlot <- shiny::downloadHandler(
     filename = function() {
       paste("phase_analysis", ".png", sep = "")
     },
     content = function(file) {
-      # Require that rv$n_unique has been calculated and is valid
-      shiny::req(!is.null(rv$n_unique),
-                 is.data.frame(rv$n_unique),
-                 nrow(rv$n_unique) > 0)
-      
-      # --- Apply visualization filters to rv$n_unique ---
-      sources_filt <- input$sources_visual
-      # REMOVED _blank_ mapping for sources
-      strings_filt <- input$strings_visual
-      strings_filt <- if (!is.null(strings_filt) && "_blank_" %in% strings_filt) {
-        c(setdiff(strings_filt, "_blank_"), NA_character_, "")
-      } else { strings_filt }
-      labels_filt <- input$labels_visual
-      labels_filt <- if (!is.null(labels_filt) && "_blank_" %in% labels_filt) {
-        c(setdiff(labels_filt, "_blank_"), NA_character_, "")
-      } else { labels_filt }
+      # Use the NEW reactive data
+      plot_data <- unique_separated_phase()
       
-      # Filter the calculated rv$n_unique data for plotting sequentially
-      plot_data <- rv$n_unique
-      
-      # Apply source filter
-      if (!is.null(sources_filt) && length(sources_filt) > 0 && "cite_source" %in% names(plot_data)) {
-        actual_sources_selected <- setdiff(sources_filt, "_blank_")
-        if(length(actual_sources_selected) > 0) {
-          plot_data <- plot_data %>% dplyr::filter(.data$cite_source %in% actual_sources_selected)
-        } else {
-          plot_data <- plot_data[0,]
-        }
-      }
-      # Apply string filter
-      if (!is.null(strings_filt) && length(strings_filt) > 0 && "cite_string" %in% names(plot_data)) {
-        plot_data <- plot_data %>% dplyr::filter(.data$cite_string %in% strings_filt)
-      }
-      # Apply label filter
-      if (!is.null(labels_filt) && length(labels_filt) > 0 && "cite_label" %in% names(plot_data)) {
-        plot_data <- plot_data %>% dplyr::filter(.data$cite_label %in% labels_filt)
+      # Add check if data is empty
+      if (nrow(plot_data) == 0) {
+        # Stop execution or create a plot with a message
+        stop("No data available to plot based on current filters.")
       }
       
-      # Require that the filtered data still has rows
-      shiny::req(nrow(plot_data) > 0)
-      
-      # Generate the plot again for saving, using filtered data
-      # Use the ORIGINAL plot_contributions function
-      if (exists("plot_contributions") && is.function(plot_contributions)) {
-        phase_plot <- plot_contributions(
-          data = plot_data, # Use filtered data
+      grDevices::png(file, width = 800, height = 600) # Adjust size
+      print(
+        CiteSource::plot_contributions( # Explicitly call from CiteSource if needed
+          data = plot_data, # Use the new reactive data
           center = TRUE,
           bar_order = c("search", "screened", "final"),
           color_order = c("unique", "duplicated")
-          # Default facets = cite_source is used
         )
-        # Save the plot to the file
-        ggplot2::ggsave(filename = file, plot = phase_plot, device = "png", width = 8, height = 6)
-      } else {
-        warning("Original 'plot_contributions' function not found. Cannot save plot.")
-        file.create(file) # Create an empty file as placeholder
-      }
+      )
+      grDevices::dev.off()
     }
   )
   
   
-  
   #### Table tab ####
   
   # Event reactive for filtering the data used in the record table and summary table

From fdcdb9d1293a39abc4a782c9f084d679bf4ad501 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 10:38:20 -0400
Subject: [PATCH 12/47] hide 'unkown' source values as default in plots

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 86 +++++++++++++++++----------
 1 file changed, 55 insertions(+), 31 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index 2a345ce0..b61708cd 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -515,55 +515,79 @@ server <- function(input, output, session) {
   
   ## Update filters
   shiny::observe({
-    if (nrow(rv$latest_unique) > 0) {
+    # Make sure rv$latest_unique is populated and is a data frame
+    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
       
-      # Handle cite_source
-      sources <- rv$latest_unique$cite_source
-      if (all(is.na(sources) | sources == "")) {
-        sources <- NULL  # Leave it as NULL or NA
-      } else {
-        sources <- unique(sources[!is.na(sources) & sources != ""]) %>%
-          stringr::str_split(", ") %>%
+      # --- Handle cite_source ---
+      sources_raw <- rv$latest_unique$cite_source
+      sources_choices <- NULL # Initialize choices list
+      
+      if (!all(is.na(sources_raw) | sources_raw == "")) {
+        sources_choices <- sources_raw[!is.na(sources_raw) & sources_raw != ""] %>%
+          stringr::str_split(",\\s*") %>% # Use regex for robustness
           unlist() %>%
           unique() %>%
           sort()
       }
       
-      # Handle cite_label
-      labels <- rv$latest_unique$cite_label
-      if (all(is.na(labels) | labels == "")) {
-        labels <- NULL  # Leave it as NULL or NA
-      } else {
-        labels <- unique(labels[!is.na(labels) & labels != ""]) %>%
-          stringr::str_split(", ") %>%
+      # *** Calculate the default selection ONLY for sources_visual (exclude "unknown") ***
+      sources_visual_selected_default <- NULL
+      if (!is.null(sources_choices)) {
+        sources_visual_selected_default <- sources_choices[sources_choices != "unknown"]
+        # Handle edge case where only "unknown" was present
+        if (length(sources_visual_selected_default) == 0 && "unknown" %in% sources_choices) {
+          sources_visual_selected_default <- NULL
+        }
+      }
+      # For sources_tables, the default remains all available choices (sources_choices)
+      
+      
+      # --- Handle cite_label ---
+      labels_raw <- rv$latest_unique$cite_label
+      labels_choices <- NULL
+      if (!all(is.na(labels_raw) | labels_raw == "")) {
+        labels_choices <- unique(labels_raw[!is.na(labels_raw) & labels_raw != ""]) %>%
+          stringr::str_split(",\\s*") %>%
           unlist() %>%
           unique() %>%
           sort()
-        
       }
+      labels_selected_default <- labels_choices # Default: select all valid labels
       
-      # Handle cite_string
-      strings <- rv$latest_unique$cite_string
-      if (all(is.na(strings) | strings == "")) {
-        strings <- NULL  # Leave it as NULL or NA
-      } else {
-        strings <- unique(strings[!is.na(strings) & strings != ""]) %>%
-          stringr::str_split(", ") %>%
+      # --- Handle cite_string ---
+      strings_raw <- rv$latest_unique$cite_string
+      strings_choices <- NULL
+      if (!all(is.na(strings_raw) | strings_raw == "")) {
+        strings_choices <- unique(strings_raw[!is.na(strings_raw) & strings_raw != ""]) %>%
+          stringr::str_split(",\\s*") %>%
           unlist() %>%
           unique() %>%
           sort()
       }
+      strings_selected_default <- strings_choices # Default: select all valid strings
+      
       
-      # Update select inputs
-      shiny::updateSelectInput(inputId = "sources_visual", choices = sources, selected = sources)
-      shiny::updateSelectInput(inputId = "labels_visual", choices = labels, selected = labels)
-      shiny::updateSelectInput(inputId = "strings_visual", choices = strings, selected = strings)
+      # --- Update select inputs ---
+      # Use the specific default (excluding "unknown") for sources_visual
+      shiny::updateSelectInput(session, inputId = "sources_visual", choices = sources_choices, selected = sources_visual_selected_default)
+      shiny::updateSelectInput(session, inputId = "labels_visual", choices = labels_choices, selected = labels_selected_default)
+      shiny::updateSelectInput(session, inputId = "strings_visual", choices = strings_choices, selected = strings_selected_default)
       
-      shiny::updateSelectInput(inputId = "sources_tables", choices = sources, selected = sources)
-      shiny::updateSelectInput(inputId = "labels_tables", choices = labels, selected = labels)
-      shiny::updateSelectInput(inputId = "strings_tables", choices = strings, selected = strings)
+      # Use the original default (all choices) for sources_tables
+      shiny::updateSelectInput(session, inputId = "sources_tables", choices = sources_choices, selected = sources_choices) # Reverted to selecting all
+      shiny::updateSelectInput(session, inputId = "labels_tables", choices = labels_choices, selected = labels_selected_default)
+      shiny::updateSelectInput(session, inputId = "strings_tables", choices = strings_choices, selected = strings_selected_default)
+      
+    } else {
+      # Optional: Clear the inputs if rv$latest_unique is empty or not a data frame
+      shiny::updateSelectInput(session, inputId = "sources_visual", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "labels_visual", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "strings_visual", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "sources_tables", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "labels_tables", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "strings_tables", choices = character(0), selected = character(0))
     }
-  })
+  }) # End update filters observe
   
   # Robust Observer for Cell Edits in tbl_out
   shiny::observeEvent(input$tbl_out_cell_edit, {

From b8c727e55e8bad662a28470a583f8ac6809a7240 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 12:46:14 -0400
Subject: [PATCH 13/47] fix the detailed records table

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 146 +++++++++++++++++++++++---
 1 file changed, 133 insertions(+), 13 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index b61708cd..a07de8bf 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -300,7 +300,7 @@ ui <- shiny::navbarPage("CiteSource",
                                     color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
                                   shiny::br(),
                                   shiny::br(),
-                                  gt::gt_output("summaryRecordTab")
+                                  gt::gt_output("detailedRecordTab")
                                 ),
                                 
                                 shiny::tabPanel(
@@ -1110,7 +1110,7 @@ server <- function(input, output, session) {
     )
   })
   
-  # Pphase plot download
+  # Phase plot download
   output$downloadPhasePlot <- shiny::downloadHandler(
     filename = function() {
       paste("phase_analysis", ".png", sep = "")
@@ -1166,6 +1166,122 @@ server <- function(input, output, session) {
         dplyr::ungroup()
     }
   )
+  detailed_table_data <- reactive({
+    # Require base data to proceed
+    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+    
+    # Get current filter selections from the UI
+    sources_filt <- input$sources_tables
+    sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
+    labels_filt <- input$labels_tables
+    labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
+    strings_filt <- input$strings_tables
+    strings_filt <- ifelse(strings_filt == "_blank_", "unknown", strings_filt)
+    
+    # Create filter patterns for labels and strings (can still pre-filter these)
+    labels_pattern <- if (length(labels_filt) > 0) paste0("\\b(", paste(labels_filt, collapse = "|"), ")\\b") else NULL
+    strings_pattern <- if (length(strings_filt) > 0) paste0("\\b(", paste(strings_filt, collapse = "|"), ")\\b") else NULL
+    
+    # Apply initial filters for labels and strings only
+    df_filtered_wide <- rv$latest_unique %>%
+      dplyr::filter(
+        (is.null(labels_pattern) || stringr::str_detect(cite_label, labels_pattern)),
+        (is.null(strings_pattern) || stringr::str_detect(cite_string, strings_pattern))
+      )
+    
+    # Define the structure for returning an empty result
+    empty_result_df <- tibble::tibble(
+      Source = character(), `Records Imported` = integer(), `Distinct Records` = integer(),
+      `Unique Records` = integer(), `Non-unique Records` = integer(),
+      `Source Contribution %` = character(), `Source Unique Contribution %` = character(),
+      `Source Unique %` = character() )
+    
+    # Return empty structure if no records match initial filters
+    if (nrow(df_filtered_wide) == 0) { return(empty_result_df) }
+    
+    # Separate only the cite_source column for source-level aggregation
+    df_long_source <- df_filtered_wide %>%
+      dplyr::select(duplicate_id, cite_source) %>%
+      tidyr::separate_rows(cite_source, sep = ",\\s*") %>%
+      dplyr::filter(!is.na(cite_source) & cite_source != "") # Filter out blank sources
+    
+    # *** Apply the source filter AFTER separating sources ***
+    df_long_source_filtered <- df_long_source %>%
+      dplyr::filter(length(sources_filt) == 0 | cite_source %in% sources_filt)
+    
+    # Return empty if no valid sources remain after filtering
+    if (nrow(df_long_source_filtered) == 0) { return(empty_result_df) }
+    
+    # --- All subsequent calculations use df_long_source_filtered ---
+    
+    # Calculate 'Records Imported' and 'Distinct Records' per source
+    source_base_counts <- df_long_source_filtered %>% # Use filtered data
+      dplyr::group_by(cite_source) %>%
+      dplyr::summarise(`Records Imported` = dplyr::n(), `Distinct Records` = dplyr::n_distinct(duplicate_id), .groups = 'drop')
+    
+    # Calculate how many sources each distinct record appears in (within the filtered set)
+    # Use the df_long_source_filtered data here too
+    record_source_counts <- df_long_source_filtered %>%
+      dplyr::group_by(duplicate_id) %>%
+      dplyr::summarise(n_sources = dplyr::n_distinct(cite_source), .groups = 'drop')
+    
+    # Identify records unique to one source within the filtered set
+    # Use the df_long_source_filtered data here too
+    unique_record_sources <- df_long_source_filtered %>%
+      dplyr::inner_join(record_source_counts, by = "duplicate_id") %>%
+      dplyr::filter(n_sources == 1) %>%
+      dplyr::distinct(duplicate_id, cite_source) # Get the source for unique records
+    
+    # Count the number of unique records per source
+    source_unique_counts <- unique_record_sources %>%
+      dplyr::group_by(cite_source) %>%
+      dplyr::summarise(`Unique Records` = dplyr::n(), .groups = 'drop')
+    
+    # Combine base counts with unique counts
+    detailed_counts_per_source <- source_base_counts %>%
+      dplyr::left_join(source_unique_counts, by = "cite_source") %>%
+      dplyr::mutate(`Unique Records` = tidyr::replace_na(`Unique Records`, 0)) %>%
+      dplyr::mutate(`Non-unique Records` = `Distinct Records` - `Unique Records`)
+    
+    # Calculate totals consistently from the *filtered* data
+    total_records_imported <- sum(detailed_counts_per_source$`Records Imported`, na.rm = TRUE)
+    total_distinct_records <- dplyr::n_distinct(df_long_source_filtered$duplicate_id) # Use filtered data
+    total_unique_records <- sum(detailed_counts_per_source$`Unique Records`, na.rm = TRUE)
+    total_nonunique_records <- sum(detailed_counts_per_source$`Non-unique Records`, na.rm = TRUE)
+    
+    # Prepare for percentage calculation (avoid division by zero)
+    total_distinct_divisor <- ifelse(total_distinct_records == 0, 1, total_distinct_records)
+    total_unique_divisor <- ifelse(total_unique_records == 0, 1, total_unique_records)
+    
+    # Calculate and format percentages
+    detailed_counts_final <- detailed_counts_per_source %>%
+      dplyr::mutate(
+        perc_contr = `Distinct Records` / total_distinct_divisor,
+        perc_unique_contr = `Unique Records` / total_unique_divisor,
+        perc_source_unique = ifelse(`Distinct Records` == 0, 0, `Unique Records` / `Distinct Records`) ) %>%
+      dplyr::mutate(
+        `Source Contribution %` = scales::percent(perc_contr, accuracy = 0.1),
+        `Source Unique Contribution %` = scales::percent(perc_unique_contr, accuracy = 0.1),
+        `Source Unique %` = scales::percent(perc_source_unique, accuracy = 0.1) ) %>%
+      dplyr::select( # Select and order final columns
+        cite_source, `Records Imported`, `Distinct Records`, `Unique Records`,
+        `Non-unique Records`, `Source Contribution %`,
+        `Source Unique Contribution %`, `Source Unique %` )
+    
+    # Create the total row
+    total_row <- tibble::tibble(
+      cite_source = "Total", `Records Imported` = total_records_imported,
+      `Distinct Records` = total_distinct_records, `Unique Records` = total_unique_records,
+      `Non-unique Records` = total_nonunique_records, `Source Contribution %` = NA_character_,
+      `Source Unique Contribution %` = NA_character_,
+      `Source Unique %` = scales::percent(total_unique_records / total_distinct_divisor, accuracy = 0.1) )
+    
+    # Add total row and rename source column
+    detailed_counts_final <- dplyr::bind_rows(detailed_counts_final, total_row) %>%
+      dplyr::rename(Source = cite_source)
+    
+    return(detailed_counts_final)
+  })
   
   # Rendering the initial record table
   output$initialRecordTab <- gt::render_gt({
@@ -1183,18 +1299,22 @@ server <- function(input, output, session) {
   }) %>% shiny::bindEvent(input$generateInitialRecordTable)
   
   # Rendering the detailed record table
-  output$summaryRecordTab <- gt::render_gt({
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
+  output$detailedRecordTab <- gt::render_gt({
+    # Check if base data is loaded
+    if (!is.data.frame(rv$latest_unique) || nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed", "Please import and deduplicate your citations first.", type = "error")
+      shiny::req(FALSE) # Stop execution
     }
-    
-    unique_citations <- unique_filtered_table()
-    detailed_count <- calculate_detailed_records(unique_citations, rv$n_unique, "search")
-    create_detailed_record_table(detailed_count)
+    # Get the data from the new reactive
+    table_data <- detailed_table_data()
+    # Check if the reactive returned any data (e.g., after filtering)
+    shiny::validate(
+      shiny::need(is.data.frame(table_data) && nrow(table_data) > 0,
+                  "No records match the current filter selections for the Detailed Record Table.")
+    )
+    # Pass the prepared data frame to the formatting function
+    create_detailed_record_table(table_data)
+    # Bind to the same button trigger
   }) %>% shiny::bindEvent(input$generateDetailedRecordTable)
   
   # Rendering the precision and sensitivity table

From 005242e52b0ac3ac7c88d15ffc66e0d80beb5208 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 14:19:02 -0400
Subject: [PATCH 14/47] update plot export

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 71 ++++++++++++++++++---------
 1 file changed, 47 insertions(+), 24 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index a07de8bf..a3e51196 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -1017,16 +1017,22 @@ server <- function(input, output, session) {
   
   output$downloadHeatPlot <- shiny::downloadHandler(
     filename = function() {
-      paste("heatmap", ".png", sep = "")
+      paste("heatmap-overlap", Sys.Date(), ".png", sep = "") # Added date for uniqueness
     },
     content = function(file) {
-      # Add check specific to filtered data for this plot
+      # Ensure data is available
       shiny::req(nrow(unique_filtered_visual()) > 0)
-      # Use a non-interactive device like png
-      grDevices::png(file, width = 800, height = 600) # Adjust size as needed
-      # Ensure the plot is printed
-      print(plotHeat())
-      grDevices::dev.off()
+      
+      # Generate the plot object
+      heat_plot_obj <- plotHeat()
+      
+      # Check if plot object was created successfully
+      if (!is.null(heat_plot_obj)) {
+        # Save the ggplot object directly using ggsave
+        ggplot2::ggsave(filename = file, plot = heat_plot_obj, device = "png", width = 10, height = 8, dpi = 300) # Adjust size/dpi as needed
+      } else {
+        stop("Failed to generate heatmap plot for download.")
+      }
     }
   )
   
@@ -1054,16 +1060,27 @@ server <- function(input, output, session) {
   
   output$downloadUpsetPlot <- shiny::downloadHandler(
     filename = function() {
-      paste("upset", ".png", sep = "")
+      paste("upset-overlap", Sys.Date(), ".png", sep = "") # Added date
     },
     content = function(file) {
-      # Add check specific to filtered data for this plot
+      # Ensure data is available
       shiny::req(nrow(unique_filtered_visual()) > 0)
-      # Use png for static plot
-      grDevices::png(file, width = 800, height = 600) # Adjust size as needed
-      # plotUpset() returns a complex plot object (often ggplot/patchwork) that needs printing
-      print(plotUpset())
-      grDevices::dev.off()
+      
+      # Generate the plot object (we need it to print)
+      upset_plot_obj <- plotUpset() # plotUpset() returns the plot object
+      
+      # Check if plot object was created successfully
+      if (!is.null(upset_plot_obj)) {
+        # Use png device for static plot
+        grDevices::png(file, width = 1200, height = 800, res = 100) # Adjust size/resolution
+        # Print the plot object to the device
+        print(upset_plot_obj)
+        # The device is automatically closed by downloadHandler
+        grDevices::dev.off() # Explicitly close device for clarity/safety
+      } else {
+        stop("Failed to generate upset plot for download.")
+
+      }
     }
   )
   
@@ -1113,7 +1130,7 @@ server <- function(input, output, session) {
   # Phase plot download
   output$downloadPhasePlot <- shiny::downloadHandler(
     filename = function() {
-      paste("phase_analysis", ".png", sep = "")
+      paste("phase-analysis", Sys.Date(), ".png", sep = "") # Added date
     },
     content = function(file) {
       # Use the NEW reactive data
@@ -1125,16 +1142,22 @@ server <- function(input, output, session) {
         stop("No data available to plot based on current filters.")
       }
       
-      grDevices::png(file, width = 800, height = 600) # Adjust size
-      print(
-        CiteSource::plot_contributions( # Explicitly call from CiteSource if needed
-          data = plot_data, # Use the new reactive data
-          center = TRUE,
-          bar_order = c("search", "screened", "final"),
-          color_order = c("unique", "duplicated")
-        )
+      # Generate the plot object itself
+      phase_plot_obj <- CiteSource::plot_contributions( # Explicitly call from CiteSource if needed
+        data = plot_data, # Use the prepared reactive data
+        center = TRUE,
+        bar_order = c("search", "screened", "final"), # Make dynamic if needed
+        color_order = c("unique", "duplicated")
       )
-      grDevices::dev.off()
+      
+      # Check if plot object was created successfully
+      if (!is.null(phase_plot_obj)) {
+        grDevices::png(file, width = 1000, height = 700, res=100) # Adjust size/resolution
+        print(phase_plot_obj) # Print the generated plot object
+        grDevices::dev.off() # Explicitly close device
+      } else {
+        stop("Failed to generate phase plot for download.")
+      }
     }
   )
   

From 9d69b05c2bb20418cfdf20ff523e0b3b9498a971 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 14:45:20 -0400
Subject: [PATCH 15/47] remove problematic legend count

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index a3e51196..e31e4bfd 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -1097,7 +1097,7 @@ server <- function(input, output, session) {
     count_unique(ufv) # Pass the wide, summarized, filtered data
   })
   
-  # Modified phase plot output
+  # Phase plot output
   output$phasePlot <- shiny::renderPlot({
     # Initial check if any data has been processed
     if (nrow(rv$latest_unique) == 0) {
@@ -1123,7 +1123,8 @@ server <- function(input, output, session) {
       data = plot_data, # Use the new reactive data
       center = TRUE,
       bar_order = c("search", "screened", "final"), # Keep or make dynamic?
-      color_order = c("unique", "duplicated")
+      color_order = c("unique", "duplicated"),
+      totals_in_legend = FALSE #legend total needs an update
     )
   })
   
@@ -1147,7 +1148,8 @@ server <- function(input, output, session) {
         data = plot_data, # Use the prepared reactive data
         center = TRUE,
         bar_order = c("search", "screened", "final"), # Make dynamic if needed
-        color_order = c("unique", "duplicated")
+        color_order = c("unique", "duplicated"),
+        totals_in_legend = FALSE
       )
       
       # Check if plot object was created successfully

From 331ee25ad30c6f47a6275b40a79a99269ab704f2 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 14:45:56 -0400
Subject: [PATCH 16/47] set legend count to FALSE until fixed

---
 R/plots.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/R/plots.R b/R/plots.R
index 338f5dab..458f1754 100644
--- a/R/plots.R
+++ b/R/plots.R
@@ -260,7 +260,7 @@ cite_source <- cite_label <- type <- NULL
 #'
 plot_contributions <- function(data, facets = cite_source, bars = cite_label, color = type, 
                                center = FALSE, bar_order = "keep", facet_order = "keep", 
-                               color_order = "keep", totals_in_legend = TRUE) {
+                               color_order = "keep", totals_in_legend = FALSE) {
   
   bars <- rlang::enquo(bars)
   color <- rlang::enquo(color)

From a0362f24c2d0c6c34aae9b8a7d9a7ab1f89b97e9 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 14:54:15 -0400
Subject: [PATCH 17/47] corrected OR statement

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index e31e4bfd..0e8dcf7f 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -1210,8 +1210,8 @@ server <- function(input, output, session) {
     # Apply initial filters for labels and strings only
     df_filtered_wide <- rv$latest_unique %>%
       dplyr::filter(
-        (is.null(labels_pattern) || stringr::str_detect(cite_label, labels_pattern)),
-        (is.null(strings_pattern) || stringr::str_detect(cite_string, strings_pattern))
+        (is.null(labels_pattern) | stringr::str_detect(cite_label, labels_pattern)),
+        (is.null(strings_pattern) | stringr::str_detect(cite_string, strings_pattern))
       )
     
     # Define the structure for returning an empty result

From 004eb5f54b42359a4dc2b768727a523a62d7ddfd Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 14:59:10 -0400
Subject: [PATCH 18/47] remove left over script

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index 0e8dcf7f..47eb26a1 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -118,9 +118,6 @@ ui <- shiny::navbarPage("CiteSource",
                                 color = "primary",
                                 icon = shiny::icon("search")
                               ) %>% htmltools::tagAppendAttributes(style = "background-color: #008080; margin-right: 20px"),
-                              
-                              # Output: datatable of deduplication results
-                              DT::dataTableOutput("dedup_results")
                             ),
                             shiny::tabPanel(
                               "Manual deduplication",

From 278d98c311ee87e4fe8f8b9a8285219b741ecc95 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 14:59:59 -0400
Subject: [PATCH 19/47] removed unused upload number

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 9 +--------
 1 file changed, 1 insertion(+), 8 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index 47eb26a1..d165be5e 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -415,14 +415,7 @@ server <- function(input, output, session) {
     else {
       # upload files one-by-one
       path_list <- input$file$datapath
-      
-      # Increment the upload number
-      if (is.null(rv$upload_number)) {
-        rv$upload_number <- 1
-      } else {
-        rv$upload_number <- rv$upload_number + 1
-      }
-      
+
       suggested_source <- stringr::str_replace_all(input$file$name, "\\.(ris|bib|txt)$", "")
       
       empty_strings <- rep("", length(input$file$datapath))

From e024ebf397fc1b4bd3c0f76d7033b49fcffe4796 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 15:01:35 -0400
Subject: [PATCH 20/47] remove unused observe event

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 15 +--------------
 1 file changed, 1 insertion(+), 14 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index d165be5e..d73d3248 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -770,20 +770,7 @@ server <- function(input, output, session) {
     rv$latest_unique <- after
     
   })
-  
-  observeEvent(input$completed_manual_dedup,{
-    
-    # provide shiny alert
-    shinyalert::shinyalert("Manual deduplication complete",
-                           paste(
-                             "From a total of", nrow(rv$upload_df), "citations uploaded, there are", nrow(rv$latest_unique),
-                             "unique citations after automated and manual deduplication.
-                           Compare citations across sources, labels, and strings in the visualisation tab"
-                           ),
-                           type = "success"
-    )
-    
-  })
+
   
   observe({
     shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",

From 58158cc0d5cfafd3d2415d0269fd0dc8e382cef9 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 15:39:43 -0400
Subject: [PATCH 21/47] removed uncesary null initialization of n_unique
 reactive

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
index d73d3248..ffa1fcae 100644
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ b/inst/shiny-app/CiteSource/CopyOfapp.R
@@ -403,7 +403,6 @@ server <- function(input, output, session) {
   rv$latest_unique <- data.frame()#for reimported data
   rv$pairs_to_check <- data.frame()#for potential duplicates/manual dedup
   rv$pairs_removed <- data.frame()#for removed records
-  rv$n_unique = NULL # Initialize n_unique
   
   #### Upload files tab section ------
   # upload on click
@@ -415,9 +414,7 @@ server <- function(input, output, session) {
     else {
       # upload files one-by-one
       path_list <- input$file$datapath
-
       suggested_source <- stringr::str_replace_all(input$file$name, "\\.(ris|bib|txt)$", "")
-      
       empty_strings <- rep("", length(input$file$datapath))
       
       # Read the uploaded citations

From 8a71848052d05e0932a1b51b72c2ea4ed390231a Mon Sep 17 00:00:00 2001
From: GitHub Actions <actions@github.com>
Date: Fri, 2 May 2025 19:44:47 +0000
Subject: [PATCH 22/47] Documentation

---
 man/plot_contributions.Rd | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/man/plot_contributions.Rd b/man/plot_contributions.Rd
index ef9f945d..c4bd3835 100644
--- a/man/plot_contributions.Rd
+++ b/man/plot_contributions.Rd
@@ -13,7 +13,7 @@ plot_contributions(
   bar_order = "keep",
   facet_order = "keep",
   color_order = "keep",
-  totals_in_legend = TRUE
+  totals_in_legend = FALSE
 )
 }
 \arguments{

From 35f65a22da1b56bc6dff3bf37a6a3e3f77aedccd Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 15:48:42 -0400
Subject: [PATCH 23/47] updated app - removed copy app for testing

---
 inst/shiny-app/CiteSource/CopyOfapp.R | 1390 -------------------------
 inst/shiny-app/CiteSource/app.R       |  704 ++++++++-----
 2 files changed, 425 insertions(+), 1669 deletions(-)
 delete mode 100644 inst/shiny-app/CiteSource/CopyOfapp.R

diff --git a/inst/shiny-app/CiteSource/CopyOfapp.R b/inst/shiny-app/CiteSource/CopyOfapp.R
deleted file mode 100644
index ffa1fcae..00000000
--- a/inst/shiny-app/CiteSource/CopyOfapp.R
+++ /dev/null
@@ -1,1390 +0,0 @@
-library(DT)
-library(CiteSource)
-library(dplyr)
-
-# Set background colour
-shiny::tags$head(shiny::tags$style(
-  shiny::HTML('
-                     #sidebar {
-                        background-color: #ffffff;
-                    }
-
-                    body, label, input, button, select {
-                      font-family: "Arial";
-                    }')
-))
-
-columns2hide <- c("title", "author", "doi", "volume",
-                  "pages", "number", "year", "abstract", "journal", "isbn")
-
-
-# Define UI for data upload app ----
-ui <- shiny::navbarPage("CiteSource",
-                        id = "tabs",
-                        header = shiny::tagList(
-                          shinybusy::add_busy_spinner(spin = "circle"),
-                          shinyjs::useShinyjs(),
-                          #header text colour
-                          tags$head(
-                            tags$style(HTML("
-      h6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {
-        margin-top: 0;
-        margin-bottom: .5rem;
-        font-weight: 500;
-        line-height: 1.2;
-        color: #23395B;
-      }
-    "))
-                          )
-                        ),
-                        theme = bslib::bs_theme(
-                          bg = "rgb(251, 251, 251)",
-                          primary = "#008080",
-                          secondary = "#CBF7ED",
-                          success = "#23395B",
-                          info = "#82D173",
-                          warning = "#FFC07F",
-                          danger = "#008080",
-                          font_scale = NULL,
-                          bootswatch = "cerulean",
-                          fg = "#000",
-                          input_bg = "#E0E0E0",  # Set the background color for input boxes
-                          input_border_color = "#23395B"  # Set the border color for input boxes
-                        ),
-                        # Home tab
-                        shiny::tabPanel(
-                          "Home",
-                          shiny::navlistPanel(
-                            shiny::tabPanel(
-                              title = "About",
-                              htmltools::includeMarkdown("www/about.md")
-                            ),
-                            shiny::tabPanel(
-                              title = "Use Cases",
-                              htmltools::includeMarkdown("www/use-cases.md")
-                            ),
-                            # User Guide
-                            shiny::tabPanel(
-                              title = "User Guide",
-                              # Load the external Markdown file
-                              htmltools::includeMarkdown("www/user_guide.md")
-                            ),
-                            widths = c(2, 10)
-                          )
-                        ),
-                        shiny::tabPanel(
-                          "File upload",
-                          shiny::fluidRow(
-                            shiny::column(
-                              12,
-                              # Sidebar layout with input and output definitions ----
-                              shiny::sidebarLayout(
-                                shiny::sidebarPanel( # Input: Select a file ----
-                                                     shiny::h5("Step 1: Upload your citation files"),
-                                                     shiny::fileInput("file", "",
-                                                                      multiple = TRUE,
-                                                                      accept = c(".ris", ".txt", ".bib")
-                                                     ),
-                                                     shiny::hr(),
-                                                     shiny::h5("OR: Re-upload an .ris or .csv exported from CiteSource"),
-                                                     shiny::fileInput("file_reimport", "",
-                                                                      multiple = TRUE,
-                                                                      accept = c(".ris", ".csv")
-                                                     )
-                                ),
-                                # Main panel for displaying outputs ----
-                                shiny::mainPanel(
-                                  shiny::h5("Step 2: Double click on a column to edit sources, labels, and strings. Use *Ctrl+Enter* to save edits, one column at a time"),
-                                  # Output: Data file ----
-                                  DT::dataTableOutput("tbl_out")
-                                )
-                              )
-                            )
-                          )
-                        ),
-                        shiny::tabPanel(
-                          "Deduplicate",
-                          shiny::tabsetPanel(
-                            shiny::tabPanel(
-                              "Automated deduplication",
-                              br(),
-                              shiny::h5("Step 3: Deduplicate"),
-                              shiny::p("Click the button below to detect and remove duplicates automatically"),
-                              
-                              # Action button: identify duplicates in uploaded dataset
-                              shinyWidgets::actionBttn(
-                                "identify_dups", "Find duplicates",
-                                style = "jelly",
-                                color = "primary",
-                                icon = shiny::icon("search")
-                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #008080; margin-right: 20px"),
-                            ),
-                            shiny::tabPanel(
-                              "Manual deduplication",
-                              br(),
-                              shiny::h5("Step 4: Review potential duplicates manually"),
-                              shiny::p("The following records were identified as potential duplicates. Potential duplicates are combined into a single row with metadata fields for each record represented (ex. Title 1 & Title 2). Click any row to indicate that the records in that row ARE duplicates. Once all duplicates are identified you can click the button 'Remove additional duplicates' and then proceed to the visualizations."),
-                              shiny::textOutput("Manual_pretext"),
-                              shiny::br(),
-                              
-                              
-                              # Button
-                              shinyWidgets::actionBttn(
-                                inputId = "nomanualdedup",
-                                label = "Go to visualisations",
-                                style = "jelly",
-                                icon = shiny::icon("arrow-right"),
-                                color = "primary"
-                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #82D173"),
-                              br(),
-                              shinyWidgets::actionBttn(
-                                inputId = "manualdedupsubmit",
-                                label = "Remove additional duplicates",
-                                style = "jelly",
-                                icon = shiny::icon("reply"),
-                                color = "primary" # Hide the button initially
-                              ) %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-                              
-                              shinyWidgets::dropdown(
-                                
-                                tags$h3("Select columns to display"),
-                                
-                                shinyWidgets::pickerInput(
-                                  inputId = "manual_dedup_cols",
-                                  label = "Choose columns",
-                                  choices = NULL,
-                                  selected = NULL,
-                                  multiple = TRUE,
-                                  options = list(
-                                    `live-search` = TRUE,
-                                    `actions-box` = TRUE,
-                                    style = "btn-primary")
-                                ), 
-                                icon = icon("filter"),
-                                inline =TRUE,
-                                status = "danger", width = "600px",
-                                tooltip = shinyWidgets::tooltipOptions(title = "Select columns to display")),
-                              
-                              DT::DTOutput("manual_dedup_dt"),
-                              tags$style(HTML(".table.dataTable tbody td.active, .table.dataTable tbody tr.active td {
-            background-color: #CBF7ED!important; color: black!important}")),
-                              
-                            )
-                          )
-                        ),
-                        shiny::tabPanel(
-                          "Visualise",
-                          
-                          # Sidebar layout with input and output definitions ----
-                          shiny::sidebarLayout(
-                            
-                            # Sidebar panel for inputs ----
-                            shiny::sidebarPanel(
-                              width = 3,
-                              id = "sidebar",
-                              shiny::h5("Step 5: Visualise overlap"),
-                              shinyWidgets::prettyRadioButtons(
-                                inputId = "comp_type",
-                                label = "Choose a comparison",
-                                inline = TRUE,
-                                choices = c(
-                                  "sources",
-                                  "labels", "strings"
-                                ),
-                                status = "primary"
-                              ),
-                              selectInput(
-                                inputId = "sources_visual",
-                                "Sources to include",
-                                list(),
-                                multiple = TRUE,
-                                selectize = TRUE
-                              ),
-                              selectInput(
-                                inputId = "labels_visual",
-                                "Labels to include",
-                                list(),
-                                multiple = TRUE,
-                                selectize = TRUE
-                              ),
-                              selectInput(
-                                inputId = "strings_visual",
-                                "Strings to include",
-                                list(),
-                                multiple = TRUE,
-                                selectize = TRUE
-                              )
-                            ),
-                            
-                            # Main panel for displaying outputs ----
-                            shiny::mainPanel(
-                              shiny::tabsetPanel(
-                                shiny::tabPanel(
-                                  "Plot overlap as a heatmap matrix",
-                                  shiny::downloadButton("downloadHeatPlot"),
-                                  plotly::plotlyOutput("plotgraph1")
-                                ),
-                                shiny::tabPanel(
-                                  "Plot overlap as an upset plot",
-                                  shiny::downloadButton("downloadUpsetPlot"),
-                                  shiny::plotOutput("plotgraph2")
-                                ),
-                                shiny::tabPanel(
-                                  "Phase Analysis",  # New Tab for Phase Analysis
-                                  shiny::downloadButton("downloadPhasePlot"),
-                                  shiny::plotOutput("phasePlot")
-                                )
-                              )
-                            )
-                          )
-                        ),
-                        
-                        shiny::tabPanel(
-                          "Tables",
-                          
-                          shiny::sidebarLayout(
-                            
-                            shiny::sidebarPanel(
-                              id = "sidebar",
-                              width = 3,
-                              shiny::h5("Step 6: Summary tables"),
-                              selectInput(
-                                inputId = "sources_tables",
-                                "Sources to include",
-                                list(),
-                                multiple = TRUE,
-                                selectize = TRUE
-                              ),
-                              selectInput(
-                                inputId = "labels_tables",
-                                "Labels to include",
-                                list(),
-                                multiple = TRUE,
-                                selectize = TRUE
-                              ),
-                              selectInput(
-                                inputId = "strings_tables",
-                                "Strings to include",
-                                list(),
-                                multiple = TRUE,
-                                selectize = TRUE
-                              )
-                            ),
-                            
-                            shiny::mainPanel(
-                              shiny::tabsetPanel(
-                                
-                                shiny::tabPanel(
-                                  "Initial Records Table",
-                                  shiny::div("View the initial record counts and deduplication results."),
-                                  shinyWidgets::actionBttn(
-                                    "generateInitialRecordTable", "Generate Initial Records Table",
-                                    style = "jelly",
-                                    icon = shiny::icon("table"),
-                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-                                  shiny::br(),
-                                  shiny::br(),
-                                  gt::gt_output("initialRecordTab")
-                                ),
-                                
-                                shiny::tabPanel(
-                                  "Detailed Record Table",
-                                  shiny::div("Summary of unique and non-unique records by source."),
-                                  shinyWidgets::actionBttn(
-                                    "generateDetailedRecordTable", "Generate Detailed Record Table",
-                                    style = "jelly",
-                                    icon = shiny::icon("table"),
-                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-                                  shiny::br(),
-                                  shiny::br(),
-                                  gt::gt_output("detailedRecordTab")
-                                ),
-                                
-                                shiny::tabPanel(
-                                  "Precision/Sensitivity Table",
-                                  shiny::div("Precision and Sensitivity of records across screening phases."),
-                                  shinyWidgets::actionBttn(
-                                    "generatePrecisionTable", "Generate Precision/Sensitivity Table",
-                                    style = "jelly",
-                                    icon = shiny::icon("table"),
-                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-                                  shiny::br(),
-                                  shiny::br(),
-                                  gt::gt_output("summaryPrecTab")
-                                ),
-                                
-                                shiny::tabPanel(
-                                  "Review individual records",
-                                  shiny::br(),
-                                  shinyWidgets::actionBttn(
-                                    "generateRecordTable", "Generate the table",
-                                    style = "jelly",
-                                    icon = shiny::icon("table"),
-                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-                                  
-                                  shiny::br(),
-                                  
-                                  shiny::wellPanel(
-                                    style = "background-color: #f0f8ff; border-color: #bce8f1; margin-top: 15px; margin-bottom: 15px; padding: 15px;", # Style
-                                    shiny::tags$h5(" Using the Interactive Record Table", style = "margin-top: 0; color: #31708f;"), # Title
-                                    shiny::tags$p("After clicking 'Generate the table', you can explore the records using these features:"), # Introduction
-                                    
-                                    # Performance Note
-                                    shiny::tags$p(
-                                      style = "margin-bottom: 12px;", # Add space below this paragraph
-                                      shiny::tags$strong(" Performance Note:"),
-                                      " The record table may take a long time to generate if you include more than a few hundred references. Consider filtering your data first using the sidebar selectors before generating."
-                                    ),
-                                    
-                                    # Instruction 1: Expand/Collapse
-                                    shiny::tags$p(
-                                      style = "margin-bottom: 12px;",
-                                      shiny::tags$strong(" Expand/Collapse Row:"),
-                                      " Click the ", shiny::tags$code(HTML("&oplus;")), " symbol in a row to view the full APA reference. Click ", shiny::tags$code(HTML("&CircleMinus;")), " to hide it again."
-                                    ),
-                                    
-                                    # Instruction 2: Single Sort
-                                    shiny::tags$p(
-                                      style = "margin-bottom: 12px;",
-                                      shiny::tags$strong(" Sort by Single Column:"),
-                                      " Click any column header (like 'Citation' or a source name) to sort the table by that column's values. Click the header again to reverse the sort order."
-                                    ),
-                                    
-                                    # Instruction 3: Multi Sort
-                                    shiny::tags$p(
-                                      style = "margin-bottom: 12px;",
-                                      shiny::tags$strong(" Sort by Multiple Columns:"),
-                                      " Click the primary column header you want to sort by. Then, hold down the ", shiny::tags$strong("Shift"), " key on your keyboard and click a second column header. You can repeat this for more sorting levels."
-                                    ),
-                                    
-                                    # Instruction 4: Filter/Search
-                                    shiny::tags$p(
-                                      style = "margin-bottom: 12px;",
-                                      shiny::tags$strong(" Filter/Search:"),
-                                      " Type into the search box located at the top-right of the table to dynamically filter records based on any information displayed."
-                                    ),
-                                    
-                                    # Instruction 5: Download (No bottom margin needed on the last item)
-                                    shiny::tags$p(
-                                      shiny::tags$strong(" Download Data:"),
-                                      " Click the 'Download CSV' button (located above the table, next to 'Print') to save the data currently shown in the table (including applied filters) as a CSV file."
-                                    )
-                                  ),
-                                  DT::dataTableOutput("reviewTab")
-                                )
-                              )
-                            )
-                          )
-                        ),
-                        
-                        shiny::tabPanel(
-                          "Export",
-                          shiny::fluidRow(
-                            shiny::column(
-                              12,
-                              shiny::mainPanel(
-                                shiny::h5("Step 7: Export citations"),
-                                shiny::h6("Note that you can only download the data after you have run the deduplication. Also, you are only able to re-upload CSV and RIS files to continue with CiteSource, so please use these formats if you want that option."),
-                                shiny::downloadButton("downloadCsv", "Download csv"),
-                                shiny::downloadButton("downloadRis", "Download RIS"),
-                                shiny::downloadButton("downloadBib", "Download BibTex")
-                              )
-                            )
-                          )
-                        )
-)
-
-
-# Define server logic to read selected file ----
-server <- function(input, output, session) {
-  rv <- shiny::reactiveValues()
-  rv$df <- data.frame()
-  rv$upload_df <- data.frame()#for original uploads
-  rv$latest_unique <- data.frame()#for reimported data
-  rv$pairs_to_check <- data.frame()#for potential duplicates/manual dedup
-  rv$pairs_removed <- data.frame()#for removed records
-  
-  #### Upload files tab section ------
-  # upload on click
-  shiny::observeEvent(input$file, {
-    shiny::validate(need(input$file != "", "Select your bibliographic file to upload..."))
-    if (is.null(input$file)) {
-      return(NULL)
-    } 
-    else {
-      # upload files one-by-one
-      path_list <- input$file$datapath
-      suggested_source <- stringr::str_replace_all(input$file$name, "\\.(ris|bib|txt)$", "")
-      empty_strings <- rep("", length(input$file$datapath))
-      
-      # Read the uploaded citations
-      upload_df <- CiteSource::read_citations(
-        files = path_list,
-        cite_sources = suggested_source,
-        cite_labels = empty_strings,
-        cite_strings = empty_strings,
-        only_key_fields = FALSE
-        
-      )
-      
-      # Summarize the number of records by citation source
-      upload_length <- upload_df %>%
-        dplyr::group_by(cite_source) %>%
-        dplyr::count(name = "records") %>%
-        dplyr::rename(source = cite_source)
-      
-      # Create a data frame summarizing the uploaded files
-      df <- data.frame(
-        "file" = input$file,
-        "suggested_source" = suggested_source,
-        "label" = empty_strings,
-        "string" = empty_strings
-      )
-      
-      # Join upload_df with df to match on cite_source
-      upload_df <- dplyr::left_join(upload_df, df, by = c("cite_source" = "suggested_source")) %>%
-        dplyr::select(-label, -string) %>%
-        dplyr::select(cite_source, cite_label, cite_string, dplyr::everything())
-      
-      # Ensure required columns are present in upload_df
-      required_cols <- c("title", "doi", "label", "isbn", "source", "year", "journal", "pages", "volume", "number", "abstract")
-      upload_df[required_cols[!(required_cols %in% colnames(upload_df))]] <- NA
-      
-      # Update the summary data frame df with record counts
-      df <- dplyr::left_join(upload_length, df, by = c("source" = "suggested_source")) %>%
-        dplyr::select(file.datapath, file.name, records, source, label, string)
-      
-      # Append the results to the reactive values
-      rv$df <- dplyr::bind_rows(rv$df, df)
-      rv$upload_df <- dplyr::bind_rows(rv$upload_df, upload_df)
-    }
-  })
-  
-  
-  ## display summary input table - summary of files added
-  output$tbl_out <- DT::renderDataTable({
-    if (is.null(input$file_reimport)) {
-      DT::datatable(
-        rv$df,
-        options = list(
-          paging = FALSE,
-          searching = FALSE,
-          columnDefs = list(list(visible = FALSE, targets = c(0))) 
-        ),
-        editable = list(
-          target = 'column',
-          disable = list(columns = c(1, 2)) 
-        ),
-        rownames = FALSE
-      )
-    }
-  })
-  
-  shiny::observeEvent(input$file_reimport, {
-    file_extension <- tolower(tools::file_ext(input$file_reimport$datapath))
-    
-    if (file_extension == "csv") {
-      rv$latest_unique <- reimport_csv(input$file_reimport$datapath)
-    } else if (file_extension == "ris") {
-      rv$latest_unique <- reimport_ris(input$file_reimport$datapath)
-    } else {
-      warning("Invalid file extension, needs to be .ris or .csv")
-    }
-    
-    rv$n_unique <- count_unique(rv$latest_unique)
-    
-    shinyalert::shinyalert("Re-import successful",
-                           paste("Imported", nrow(rv$latest_unique), "citations. You can now proceed to visualisation and tables."),
-                           type = "success"
-    )
-    
-  })
-  
-  ## Update filters
-  shiny::observe({
-    # Make sure rv$latest_unique is populated and is a data frame
-    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
-      
-      # --- Handle cite_source ---
-      sources_raw <- rv$latest_unique$cite_source
-      sources_choices <- NULL # Initialize choices list
-      
-      if (!all(is.na(sources_raw) | sources_raw == "")) {
-        sources_choices <- sources_raw[!is.na(sources_raw) & sources_raw != ""] %>%
-          stringr::str_split(",\\s*") %>% # Use regex for robustness
-          unlist() %>%
-          unique() %>%
-          sort()
-      }
-      
-      # *** Calculate the default selection ONLY for sources_visual (exclude "unknown") ***
-      sources_visual_selected_default <- NULL
-      if (!is.null(sources_choices)) {
-        sources_visual_selected_default <- sources_choices[sources_choices != "unknown"]
-        # Handle edge case where only "unknown" was present
-        if (length(sources_visual_selected_default) == 0 && "unknown" %in% sources_choices) {
-          sources_visual_selected_default <- NULL
-        }
-      }
-      # For sources_tables, the default remains all available choices (sources_choices)
-      
-      
-      # --- Handle cite_label ---
-      labels_raw <- rv$latest_unique$cite_label
-      labels_choices <- NULL
-      if (!all(is.na(labels_raw) | labels_raw == "")) {
-        labels_choices <- unique(labels_raw[!is.na(labels_raw) & labels_raw != ""]) %>%
-          stringr::str_split(",\\s*") %>%
-          unlist() %>%
-          unique() %>%
-          sort()
-      }
-      labels_selected_default <- labels_choices # Default: select all valid labels
-      
-      # --- Handle cite_string ---
-      strings_raw <- rv$latest_unique$cite_string
-      strings_choices <- NULL
-      if (!all(is.na(strings_raw) | strings_raw == "")) {
-        strings_choices <- unique(strings_raw[!is.na(strings_raw) & strings_raw != ""]) %>%
-          stringr::str_split(",\\s*") %>%
-          unlist() %>%
-          unique() %>%
-          sort()
-      }
-      strings_selected_default <- strings_choices # Default: select all valid strings
-      
-      
-      # --- Update select inputs ---
-      # Use the specific default (excluding "unknown") for sources_visual
-      shiny::updateSelectInput(session, inputId = "sources_visual", choices = sources_choices, selected = sources_visual_selected_default)
-      shiny::updateSelectInput(session, inputId = "labels_visual", choices = labels_choices, selected = labels_selected_default)
-      shiny::updateSelectInput(session, inputId = "strings_visual", choices = strings_choices, selected = strings_selected_default)
-      
-      # Use the original default (all choices) for sources_tables
-      shiny::updateSelectInput(session, inputId = "sources_tables", choices = sources_choices, selected = sources_choices) # Reverted to selecting all
-      shiny::updateSelectInput(session, inputId = "labels_tables", choices = labels_choices, selected = labels_selected_default)
-      shiny::updateSelectInput(session, inputId = "strings_tables", choices = strings_choices, selected = strings_selected_default)
-      
-    } else {
-      # Optional: Clear the inputs if rv$latest_unique is empty or not a data frame
-      shiny::updateSelectInput(session, inputId = "sources_visual", choices = character(0), selected = character(0))
-      shiny::updateSelectInput(session, inputId = "labels_visual", choices = character(0), selected = character(0))
-      shiny::updateSelectInput(session, inputId = "strings_visual", choices = character(0), selected = character(0))
-      shiny::updateSelectInput(session, inputId = "sources_tables", choices = character(0), selected = character(0))
-      shiny::updateSelectInput(session, inputId = "labels_tables", choices = character(0), selected = character(0))
-      shiny::updateSelectInput(session, inputId = "strings_tables", choices = character(0), selected = character(0))
-    }
-  }) # End update filters observe
-  
-  # Robust Observer for Cell Edits in tbl_out
-  shiny::observeEvent(input$tbl_out_cell_edit, {
-    # This observer handles edits made to the summary table (rv$df)
-    # and propagates relevant changes (source, label, string)
-    # to the corresponding records in the detailed table (rv$upload_df).
-    
-    info <- input$tbl_out_cell_edit
-    
-    # Ensure rv$df and rv$upload_df are valid data frames before proceeding
-    if (!is.data.frame(rv$df) || nrow(rv$df) == 0) {
-      # Silently return if summary data isn't ready (e.g., during initial load)
-      return()
-    }
-    if (!is.data.frame(rv$upload_df)) {
-      # Log warning if detailed data structure is missing, but allow proceeding
-      # if only rv$df needs update (though propagation will fail later)
-      warning("rv$upload_df is not a valid data frame. Edits cannot be propagated.")
-      # Depending on desired behavior, could 'return()' here too.
-    }
-    
-    # Get column names from the summary data frame
-    df_col_names <- names(rv$df)
-    
-    # Determine the number of edits reported in this event
-    n_edits <- length(info$row)
-    
-    # Process each reported edit individually
-    for (i in 1:n_edits) {
-      # Extract information for the current (i-th) edit
-      target_row_df <- as.integer(info$row[i]) # 1-based row index for rv$df
-      target_col_dt <- as.integer(info$col[i]) # 0-based column index from DT
-      target_col_df <- target_col_dt + 1       # Convert to 1-based R index for rv$df
-      val <- if (is.list(info$value)) info$value[[i]] else info$value[i] # Handle list/vector values
-      
-      # Convert blank input ("") to logical NA
-      if (length(val) == 1 && !is.na(val) && val == "") {
-        val <- NA
-      }
-      
-      # --- Validate indices against rv$df ---
-      if (target_row_df <= 0 || target_row_df > nrow(rv$df) ||
-          target_col_df <= 0 || target_col_df > ncol(rv$df)) {
-        warning(paste("Invalid row/column index received from DT edit. Row:",
-                      target_row_df, "Col:", target_col_df, ". Skipping this edit."))
-        next # Skip to the next edit
-      }
-      
-      # --- 1. Update rv$df (the summary table data) ---
-      # Use tryCatch to handle potential errors during assignment (e.g., type mismatch)
-      tryCatch({
-        rv$df[target_row_df, target_col_df] <- val
-      }, error = function(e) {
-        warning(paste("Error updating rv$df[", target_row_df, ",", target_col_df, "]:", e$message))
-        # Continue to the next edit even if this one failed
-        next
-      })
-      
-      # --- 2. Propagate change to rv$upload_df (if applicable) ---
-      
-      # Get the file.datapath associated with the edited row in rv$df
-      # This assumes column 1 of rv$df is 'file.datapath'
-      if (df_col_names[1] != "file.datapath") {
-        warning("Column 1 of rv$df is not 'file.datapath'. Cannot link edits to rv$upload_df.")
-        next # Skip propagation for this edit
-      }
-      edited_datapath <- rv$df[[target_row_df, 1]]
-      
-      # Check if the datapath is valid for lookup
-      if (is.na(edited_datapath) || edited_datapath == "") {
-        # Don't warn every time, might be expected if datapath is missing
-        next # Cannot link without a valid datapath
-      }
-      
-      # Check if rv$upload_df is ready for update
-      if (nrow(rv$upload_df) == 0 || !"file.datapath" %in% names(rv$upload_df)) {
-        # Silently skip if detailed data isn't ready or lacks the key column
-        next
-      }
-      
-      # Find rows in rv$upload_df matching the datapath
-      target_rows_upload_idx <- which(rv$upload_df$file.datapath == edited_datapath)
-      
-      if (length(target_rows_upload_idx) == 0) {
-        # No matching rows found in detailed data, nothing to propagate
-        next
-      }
-      
-      # Determine the target column name in rv$upload_df based on the edited column in rv$df
-      col_name_df <- df_col_names[target_col_df] # Name of edited column in summary table
-      col_name_upload <- NULL # Target column name in detailed table
-      
-      # Define the mapping for propagation
-      if (col_name_df == "source") {
-        col_name_upload <- "cite_source"
-      } else if (col_name_df == "label") {
-        col_name_upload <- "cite_label"
-      } else if (col_name_df == "string") {
-        col_name_upload <- "cite_string"
-      } else {
-        # If the edited column (e.g., 'records') shouldn't be propagated, skip
-        next
-      }
-      
-      # Check if the target column exists in rv$upload_df
-      if (!col_name_upload %in% names(rv$upload_df)) {
-        warning(paste("Target column '", col_name_upload, "' not found in rv$upload_df. Cannot propagate edit."))
-        next
-      }
-      
-      # Perform the update on all matching rows in the detailed data frame
-      tryCatch({
-        rv$upload_df[target_rows_upload_idx, col_name_upload] <- val
-      }, error = function(e) {
-        warning(paste("Error updating rv$upload_df rows for datapath", edited_datapath,
-                      "Column:", col_name_upload, ":", e$message))
-        # Continue to the next edit even if propagation failed
-      })
-      
-    } # End FOR loop iterating through edits reported by DT
-    
-  })
-  
-  # Deduplication tab -----------------
-  
-  # when dedup button clicked, deduplicate
-  shiny::observeEvent(input$identify_dups, {
-    if (nrow(rv$upload_df) == 0) {
-      if (nrow(rv$latest_unique) > 0) {
-        shinyalert::shinyalert("Deduplications already complete",
-                               "You have reimported a dataset that has already been deduplicated. In that case, further deduplication is not possible here, but would need to take place outside the app.",
-                               type = "error"
-        )
-      } else {
-        shinyalert::shinyalert("Data needed",
-                               "Please import your citations first.",
-                               type = "error"
-        )
-      }
-      return()  # Early return to stop further execution
-    }
-    
-    # Assign unique IDs to avoid issues with manual deduplication
-    rv$upload_df <- rv$upload_df %>% dplyr::mutate(record_id = as.character(1000 + dplyr::row_number()))
-    
-    # Perform deduplication
-    dedup_results <- CiteSource::dedup_citations(rv$upload_df, manual = TRUE, show_unknown_tags = TRUE)
-    rv$pairs_to_check <- dedup_results$manual_dedup
-    rv$latest_unique <- dedup_results$unique
-    rv$n_unique <- count_unique(rv$latest_unique)  # Generate the n_unique data
-    
-    # Generate a summary message based on deduplication results
-    n_citations <- nrow(rv$upload_df)
-    n_unique_records <- nrow(rv$n_unique)  # Changed variable name to avoid conflict
-    n_pairs_manual <- nrow(rv$pairs_to_check)
-    
-    message <- if (n_pairs_manual > 0) {
-      paste(
-        "From a total of", n_citations, "citations added, there are", n_unique_records, 
-        "unique citations. Head to the manual deduplication tab to check", n_pairs_manual, "potential duplicates."
-      )
-    } else {
-      paste(
-        "From a total of", n_citations, "citations added, there are", n_unique_records, 
-        "unique citations. There are no potential duplicates for manual review. You can proceed to the visualization tab."
-      )
-    }
-    
-    shinyalert::shinyalert("Auto-deduplication complete", message, type = "success")
-  })
-  
-  ## Manual deduplication -----
-  
-  # Action button: remove manually selected duplicates [merged two segments into one]
-  # remove manually selected duplicates 
-  observeEvent(input$manualdedupsubmit,{
-    
-    rv$pairs_removed <- rv$pairs_to_check[input$manual_dedup_dt_rows_selected,]
-    rv$pairs_to_check <- rv$pairs_to_check[-input$manual_dedup_dt_rows_selected,]
-    
-    if(nrow(rv$pairs_removed) < 1){
-      shinyalert("Oops!", "You haven't selected any duplicate pairs to remove.", type = "error")
-      return()
-    }
-    
-    after <- dedup_citations_add_manual(rv$latest_unique,
-                                        additional_pairs = rv$pairs_removed)
-    
-    # update latest unique df reactive value
-    rv$latest_unique <- after
-    
-  })
-
-  
-  observe({
-    shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
-                                    choices = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)],
-                                    selected = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)])
-  })
-  
-  
-  # if no manual dedup, proceed to visualisations
-  shiny::observeEvent(input$nomanualdedup, {
-    
-    shiny::updateNavbarPage(
-      session = session,
-      inputId = "tabs",
-      selected = "Visualise"
-    )
-  })
-  
-  # if rows selected in manual dedup, make buttom appear
-  observe({
-    selected_rows <- input$manual_dedup_dt_rows_selected
-    if (length(selected_rows) > 0) {
-      shinyjs::show("manualdedupsubmit")  # Show the button when rows are selected
-    } else {
-      shinyjs::hide("manualdedupsubmit")  # Hide the button when no rows are selected
-    }
-  })
-  
-  # Output: manual dedup datatable
-  manual_dedup_data <- reactive({
-    
-    data <- rv$pairs_to_check[,1:36]
-    data <- data[,c(paste0(input$manual_dedup_cols))]
-    
-    match_cols <- c("title", "author", "doi", "volume",
-                    "pages", "number", "year", "abstract", "journal", "isbn")
-    
-    
-    data <- data %>% dplyr::select(-any_of(match_cols))
-    match_number_cols <- rv$pairs_to_check[,c(paste0(match_cols))]
-    
-    data <- cbind(data,match_number_cols)
-    
-  })
-  output$manual_dedup_dt <- DT::renderDataTable({
-    
-    data <- manual_dedup_data()
-    
-    format_cols <- c(
-      "title1", "author1", "doi1", "volume1",
-      "pages1", "number1", "year1", "abstract1", "journal1", "isbn1",
-      "title2", "author2", "doi2", "volume2",
-      "pages2", "number2", "year2", "abstract2", "journal2", "isbn2"
-    )
-    
-    format_cols <- intersect(format_cols, colnames(data))
-    shinyjs::useShinyjs()
-    
-    datatable(data,
-              options = list(
-                pageLength = 100, info = FALSE,
-                lengthMenu = list(c(100, -1), c("100", "All")),
-                columnDefs =
-                  list(
-                    list(visible = FALSE, 
-                         targets = columns2hide),
-                    list(
-                      targets = "_all",
-                      render = JS(
-                        "function(data, type, row, meta) {",
-                        "return type === 'display' && data != null && data.length > 25 ?",
-                        "'<span title=\"' + data + '\">' + data.substr(0, 25) + '...</span>' : data;",
-                        "}"
-                      )
-                    )
-                  )
-              ))
-  })
-  
-  
-  # ASySD manual dedup pre text 
-  output$Manual_pretext <- shiny::renderText({
-    
-    paste(nrow(rv$pairs_to_check), "pairs of citations require manual deduplication. Review the pairs in the table
-        below.")
-    
-  })
-  
-  
-  #### Visualise tab ####
-  
-  # Reactive expression to filter the data for visualization (used for Heatmap and Upset)
-  unique_filtered_visual <- shiny::reactive({
-    # Ensure latest_unique has data before attempting filtering
-    shiny::req(nrow(rv$latest_unique) > 0)
-    
-    sources <- input$sources_visual
-    sources <- ifelse(sources == "_blank_", "unknown", sources)
-    strings <- input$strings_visual
-    strings <- ifelse(strings == "_blank_", "unknown", strings)
-    labels <- input$labels_visual
-    labels <- ifelse(labels == "_blank_", "unknown", labels)
-    
-    out <- rv$latest_unique %>%
-      # Filter rows where *any* of the required sources/labels/strings are present before summarizing
-      dplyr::filter(
-        (length(sources) == 0 | stringr::str_detect(cite_source, paste0("\\b(", paste(sources, collapse="|"), ")\\b"))) &
-          (length(strings) == 0 | stringr::str_detect(cite_string, paste0("\\b(", paste(strings, collapse="|"), ")\\b"))) &
-          (length(labels) == 0 | stringr::str_detect(cite_label, paste0("\\b(", paste(labels, collapse="|"), ")\\b")))
-      ) %>%
-      # Now group and summarize
-      dplyr::group_by(duplicate_id) %>%
-      {
-        df_grouped <- .
-        df_separated <- df_grouped %>%
-          tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
-          dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
-          dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
-          dplyr::filter(length(labels) == 0 | cite_label %in% labels)
-        
-        # Check if df_separated is empty for this group before summarizing
-        if(nrow(df_separated) == 0) {
-          # Return a row with the group id and NAs or empty strings if nothing matches filters
-          tibble::tibble(duplicate_id = dplyr::first(df_grouped$duplicate_id),
-                         record_ids = "", cite_label = "", cite_source = "", cite_string = "")
-        } else {
-          df_separated %>%
-            dplyr::summarise(across(c(record_ids, cite_label, cite_source, cite_string), ~ trimws(paste(unique(na.omit(.)), collapse = ', '))))
-        }
-      } %>%
-      dplyr::ungroup()
-    
-    # Ensure the output has the same columns even if empty
-    if (nrow(out) == 0) {
-      out <- tibble::tibble(duplicate_id=character(), record_ids=character(), cite_label=character(), cite_source=character(), cite_string=character()) # Adjust columns as needed
-    }
-    # Add back other columns from rv$latest_unique if needed by downstream functions
-    dplyr::left_join(out, dplyr::select(rv$latest_unique, -any_of(c("record_ids", "cite_label", "cite_source", "cite_string"))), by = "duplicate_id")
-    
-  })
-  
-  # REACTIVE for Phase Plot Data
-  unique_separated_phase <- shiny::reactive({
-    # Require rv$latest_unique to have data
-    shiny::req(nrow(rv$latest_unique) > 0)
-    
-    # Get filter inputs
-    sources_filt <- input$sources_visual
-    sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
-    labels_filt <- input$labels_visual
-    labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
-    
-    # Start with the base unique data, select columns needed
-    df <- rv$latest_unique %>%
-      dplyr::select(duplicate_id, cite_source, cite_label) # Add record_ids if needed
-    
-    # Separate rows for source and label
-    df_long <- df %>%
-      tidyr::separate_rows(cite_source, sep = ",\\s*") %>%
-      tidyr::separate_rows(cite_label, sep = ",\\s*") %>%
-      dplyr::filter(!is.na(cite_source) & cite_source != "", # Ensure no blank/NA sources/labels after separation
-                    !is.na(cite_label) & cite_label != "")
-    
-    # Apply filtering based on selected sources and labels for the plot
-    df_filtered <- df_long %>%
-      dplyr::filter(length(sources_filt) == 0 | cite_source %in% sources_filt) %>%
-      dplyr::filter(length(labels_filt) == 0 | cite_label %in% labels_filt)
-    
-    # Check if filtering resulted in empty data
-    if (nrow(df_filtered) == 0) {
-      # Return an empty tibble with correct columns/types if no data matches
-      return(tibble::tibble(
-        duplicate_id = character(),
-        cite_source = character(),
-        cite_label = character(),
-        type = factor(levels = c("unique", "duplicated"))
-      ))
-    }
-    
-    # Calculate 'type' based on uniqueness *within* each phase (label) for the *filtered* set
-    # A record is 'unique' in a phase if it's found in only 1 source within that phase in the filtered data
-    df_typed <- df_filtered %>%
-      dplyr::group_by(duplicate_id, cite_label) %>%
-      dplyr::mutate(n_sources_in_phase = dplyr::n_distinct(cite_source)) %>%
-      dplyr::ungroup() %>%
-      dplyr::mutate(
-        type = factor(
-          ifelse(n_sources_in_phase == 1, "unique", "duplicated"),
-          levels = c("unique", "duplicated")
-        )
-      ) %>%
-      # Select relevant columns needed by plot_contributions
-      dplyr::select(duplicate_id, cite_source, cite_label, type)
-    
-    # Return the long data frame, ensuring distinct rows
-    dplyr::distinct(df_typed)
-  })# End Phase Plot Reactive
-  
-  
-  # Heatmap plot (uses unique_filtered_visual)
-  plotHeat <- shiny::reactive({
-    # Add check if data is available
-    data_vis <- unique_filtered_visual()
-    shiny::req(nrow(data_vis) > 0)
-    source_comparison <- compare_sources(data_vis, comp_type = input$comp_type)
-    plot_source_overlap_heatmap(source_comparison, cells = stringr::str_sub(input$comp_type, end = -2))
-  })
-  
-  output$plotgraph1 <- plotly::renderPlotly({
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
-    }
-    # Add check specific to filtered data for this plot
-    shiny::req(nrow(unique_filtered_visual()) > 0, cancelOutput = TRUE)
-    print(plotHeat())
-  })
-  
-  output$downloadHeatPlot <- shiny::downloadHandler(
-    filename = function() {
-      paste("heatmap-overlap", Sys.Date(), ".png", sep = "") # Added date for uniqueness
-    },
-    content = function(file) {
-      # Ensure data is available
-      shiny::req(nrow(unique_filtered_visual()) > 0)
-      
-      # Generate the plot object
-      heat_plot_obj <- plotHeat()
-      
-      # Check if plot object was created successfully
-      if (!is.null(heat_plot_obj)) {
-        # Save the ggplot object directly using ggsave
-        ggplot2::ggsave(filename = file, plot = heat_plot_obj, device = "png", width = 10, height = 8, dpi = 300) # Adjust size/dpi as needed
-      } else {
-        stop("Failed to generate heatmap plot for download.")
-      }
-    }
-  )
-  
-  # Upset plot (uses unique_filtered_visual)
-  plotUpset <- shiny::reactive({
-    # Add check if data is available
-    data_vis <- unique_filtered_visual()
-    shiny::req(nrow(data_vis) > 0)
-    source_comparison <- compare_sources(data_vis, comp_type = input$comp_type)
-    plot_source_overlap_upset(source_comparison, groups = stringr::str_sub(input$comp_type, end = -2), decreasing = c(TRUE, TRUE))
-  })
-  
-  output$plotgraph2 <- shiny::renderPlot({
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
-    }
-    # Add check specific to filtered data for this plot
-    shiny::req(nrow(unique_filtered_visual()) > 0, cancelOutput = TRUE)
-    print(plotUpset())
-  })
-  
-  output$downloadUpsetPlot <- shiny::downloadHandler(
-    filename = function() {
-      paste("upset-overlap", Sys.Date(), ".png", sep = "") # Added date
-    },
-    content = function(file) {
-      # Ensure data is available
-      shiny::req(nrow(unique_filtered_visual()) > 0)
-      
-      # Generate the plot object (we need it to print)
-      upset_plot_obj <- plotUpset() # plotUpset() returns the plot object
-      
-      # Check if plot object was created successfully
-      if (!is.null(upset_plot_obj)) {
-        # Use png device for static plot
-        grDevices::png(file, width = 1200, height = 800, res = 100) # Adjust size/resolution
-        # Print the plot object to the device
-        print(upset_plot_obj)
-        # The device is automatically closed by downloadHandler
-        grDevices::dev.off() # Explicitly close device for clarity/safety
-      } else {
-        stop("Failed to generate upset plot for download.")
-
-      }
-    }
-  )
-  
-  # Phase Analysis plot (uses unique_separated_phase reactive)
-  # Keep rv$n_unique definition as is for potential use in table functions,
-  # but note it relies on the potentially problematic unique_filtered_visual().
-  # If table functions are updated or don't rely on this specific structure,
-  # this reactive could be revisited or removed.
-  rv$n_unique <- shiny::reactive({
-    # Original logic based on unique_filtered_visual and count_unique
-    # Ensure unique_filtered_visual() is not empty
-    ufv <- unique_filtered_visual()
-    shiny::req(nrow(ufv) > 0)
-    count_unique(ufv) # Pass the wide, summarized, filtered data
-  })
-  
-  # Phase plot output
-  output$phasePlot <- shiny::renderPlot({
-    # Initial check if any data has been processed
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error")
-      shiny::req(FALSE, cancelOutput = TRUE)
-    }
-    
-    # Use the reactive data specifically prepared for this plot
-    plot_data <- unique_separated_phase()
-    
-    # Check if the prepared data is empty after filtering
-    if (nrow(plot_data) == 0) {
-      # Optionally, display a message instead of just cancelling
-      plot.new()
-      mtext("No data matches the selected filters for Phase Analysis.")
-      shiny::req(FALSE, cancelOutput = TRUE)
-    }
-    
-    # Call plot_contributions with the prepared data
-    CiteSource::plot_contributions( # Explicitly call from CiteSource if needed
-      data = plot_data, # Use the new reactive data
-      center = TRUE,
-      bar_order = c("search", "screened", "final"), # Keep or make dynamic?
-      color_order = c("unique", "duplicated"),
-      totals_in_legend = FALSE #legend total needs an update
-    )
-  })
-  
-  # Phase plot download
-  output$downloadPhasePlot <- shiny::downloadHandler(
-    filename = function() {
-      paste("phase-analysis", Sys.Date(), ".png", sep = "") # Added date
-    },
-    content = function(file) {
-      # Use the NEW reactive data
-      plot_data <- unique_separated_phase()
-      
-      # Add check if data is empty
-      if (nrow(plot_data) == 0) {
-        # Stop execution or create a plot with a message
-        stop("No data available to plot based on current filters.")
-      }
-      
-      # Generate the plot object itself
-      phase_plot_obj <- CiteSource::plot_contributions( # Explicitly call from CiteSource if needed
-        data = plot_data, # Use the prepared reactive data
-        center = TRUE,
-        bar_order = c("search", "screened", "final"), # Make dynamic if needed
-        color_order = c("unique", "duplicated"),
-        totals_in_legend = FALSE
-      )
-      
-      # Check if plot object was created successfully
-      if (!is.null(phase_plot_obj)) {
-        grDevices::png(file, width = 1000, height = 700, res=100) # Adjust size/resolution
-        print(phase_plot_obj) # Print the generated plot object
-        grDevices::dev.off() # Explicitly close device
-      } else {
-        stop("Failed to generate phase plot for download.")
-      }
-    }
-  )
-  
-  
-  #### Table tab ####
-  
-  # Event reactive for filtering the data used in the record table and summary table
-  unique_filtered_table <- shiny::eventReactive(
-    c(input$generateRecordTable, input$generateSummaryTable, 
-      input$sources_tables, input$strings_tables, input$labels_tables,
-      input$generateInitialRecordTable, input$generateDetailedRecordTable, 
-      input$generatePrecisionTable),
-    {
-      sources <- input$sources_tables 
-      sources <- ifelse(sources == "_blank_", "unknown", sources)
-      strings <- input$strings_tables
-      strings <- ifelse(strings == "_blank_", "unknown", strings)
-      labels <- input$labels_tables
-      labels <- ifelse(labels == "_blank_", "unknown", labels)
-      
-      rv$latest_unique %>%
-        dplyr::group_by(duplicate_id) %>%
-        tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
-        dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
-        dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
-        dplyr::filter(length(labels) == 0 | cite_label %in% labels) %>%
-        dplyr::mutate(across(c(record_ids, cite_label, cite_source, cite_string), ~ trimws(paste(na.omit(.), collapse = ', ')))) %>%
-        unique() %>%
-        dplyr::ungroup()
-    }
-  )
-  detailed_table_data <- reactive({
-    # Require base data to proceed
-    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
-    
-    # Get current filter selections from the UI
-    sources_filt <- input$sources_tables
-    sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
-    labels_filt <- input$labels_tables
-    labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
-    strings_filt <- input$strings_tables
-    strings_filt <- ifelse(strings_filt == "_blank_", "unknown", strings_filt)
-    
-    # Create filter patterns for labels and strings (can still pre-filter these)
-    labels_pattern <- if (length(labels_filt) > 0) paste0("\\b(", paste(labels_filt, collapse = "|"), ")\\b") else NULL
-    strings_pattern <- if (length(strings_filt) > 0) paste0("\\b(", paste(strings_filt, collapse = "|"), ")\\b") else NULL
-    
-    # Apply initial filters for labels and strings only
-    df_filtered_wide <- rv$latest_unique %>%
-      dplyr::filter(
-        (is.null(labels_pattern) | stringr::str_detect(cite_label, labels_pattern)),
-        (is.null(strings_pattern) | stringr::str_detect(cite_string, strings_pattern))
-      )
-    
-    # Define the structure for returning an empty result
-    empty_result_df <- tibble::tibble(
-      Source = character(), `Records Imported` = integer(), `Distinct Records` = integer(),
-      `Unique Records` = integer(), `Non-unique Records` = integer(),
-      `Source Contribution %` = character(), `Source Unique Contribution %` = character(),
-      `Source Unique %` = character() )
-    
-    # Return empty structure if no records match initial filters
-    if (nrow(df_filtered_wide) == 0) { return(empty_result_df) }
-    
-    # Separate only the cite_source column for source-level aggregation
-    df_long_source <- df_filtered_wide %>%
-      dplyr::select(duplicate_id, cite_source) %>%
-      tidyr::separate_rows(cite_source, sep = ",\\s*") %>%
-      dplyr::filter(!is.na(cite_source) & cite_source != "") # Filter out blank sources
-    
-    # *** Apply the source filter AFTER separating sources ***
-    df_long_source_filtered <- df_long_source %>%
-      dplyr::filter(length(sources_filt) == 0 | cite_source %in% sources_filt)
-    
-    # Return empty if no valid sources remain after filtering
-    if (nrow(df_long_source_filtered) == 0) { return(empty_result_df) }
-    
-    # --- All subsequent calculations use df_long_source_filtered ---
-    
-    # Calculate 'Records Imported' and 'Distinct Records' per source
-    source_base_counts <- df_long_source_filtered %>% # Use filtered data
-      dplyr::group_by(cite_source) %>%
-      dplyr::summarise(`Records Imported` = dplyr::n(), `Distinct Records` = dplyr::n_distinct(duplicate_id), .groups = 'drop')
-    
-    # Calculate how many sources each distinct record appears in (within the filtered set)
-    # Use the df_long_source_filtered data here too
-    record_source_counts <- df_long_source_filtered %>%
-      dplyr::group_by(duplicate_id) %>%
-      dplyr::summarise(n_sources = dplyr::n_distinct(cite_source), .groups = 'drop')
-    
-    # Identify records unique to one source within the filtered set
-    # Use the df_long_source_filtered data here too
-    unique_record_sources <- df_long_source_filtered %>%
-      dplyr::inner_join(record_source_counts, by = "duplicate_id") %>%
-      dplyr::filter(n_sources == 1) %>%
-      dplyr::distinct(duplicate_id, cite_source) # Get the source for unique records
-    
-    # Count the number of unique records per source
-    source_unique_counts <- unique_record_sources %>%
-      dplyr::group_by(cite_source) %>%
-      dplyr::summarise(`Unique Records` = dplyr::n(), .groups = 'drop')
-    
-    # Combine base counts with unique counts
-    detailed_counts_per_source <- source_base_counts %>%
-      dplyr::left_join(source_unique_counts, by = "cite_source") %>%
-      dplyr::mutate(`Unique Records` = tidyr::replace_na(`Unique Records`, 0)) %>%
-      dplyr::mutate(`Non-unique Records` = `Distinct Records` - `Unique Records`)
-    
-    # Calculate totals consistently from the *filtered* data
-    total_records_imported <- sum(detailed_counts_per_source$`Records Imported`, na.rm = TRUE)
-    total_distinct_records <- dplyr::n_distinct(df_long_source_filtered$duplicate_id) # Use filtered data
-    total_unique_records <- sum(detailed_counts_per_source$`Unique Records`, na.rm = TRUE)
-    total_nonunique_records <- sum(detailed_counts_per_source$`Non-unique Records`, na.rm = TRUE)
-    
-    # Prepare for percentage calculation (avoid division by zero)
-    total_distinct_divisor <- ifelse(total_distinct_records == 0, 1, total_distinct_records)
-    total_unique_divisor <- ifelse(total_unique_records == 0, 1, total_unique_records)
-    
-    # Calculate and format percentages
-    detailed_counts_final <- detailed_counts_per_source %>%
-      dplyr::mutate(
-        perc_contr = `Distinct Records` / total_distinct_divisor,
-        perc_unique_contr = `Unique Records` / total_unique_divisor,
-        perc_source_unique = ifelse(`Distinct Records` == 0, 0, `Unique Records` / `Distinct Records`) ) %>%
-      dplyr::mutate(
-        `Source Contribution %` = scales::percent(perc_contr, accuracy = 0.1),
-        `Source Unique Contribution %` = scales::percent(perc_unique_contr, accuracy = 0.1),
-        `Source Unique %` = scales::percent(perc_source_unique, accuracy = 0.1) ) %>%
-      dplyr::select( # Select and order final columns
-        cite_source, `Records Imported`, `Distinct Records`, `Unique Records`,
-        `Non-unique Records`, `Source Contribution %`,
-        `Source Unique Contribution %`, `Source Unique %` )
-    
-    # Create the total row
-    total_row <- tibble::tibble(
-      cite_source = "Total", `Records Imported` = total_records_imported,
-      `Distinct Records` = total_distinct_records, `Unique Records` = total_unique_records,
-      `Non-unique Records` = total_nonunique_records, `Source Contribution %` = NA_character_,
-      `Source Unique Contribution %` = NA_character_,
-      `Source Unique %` = scales::percent(total_unique_records / total_distinct_divisor, accuracy = 0.1) )
-    
-    # Add total row and rename source column
-    detailed_counts_final <- dplyr::bind_rows(detailed_counts_final, total_row) %>%
-      dplyr::rename(Source = cite_source)
-    
-    return(detailed_counts_final)
-  })
-  
-  # Rendering the initial record table
-  output$initialRecordTab <- gt::render_gt({
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
-    }
-    
-    unique_citations <- unique_filtered_table()
-    initial_records <- calculate_initial_records(unique_citations, "search")
-    create_initial_record_table(initial_records)
-  }) %>% shiny::bindEvent(input$generateInitialRecordTable)
-  
-  # Rendering the detailed record table
-  output$detailedRecordTab <- gt::render_gt({
-    # Check if base data is loaded
-    if (!is.data.frame(rv$latest_unique) || nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed", "Please import and deduplicate your citations first.", type = "error")
-      shiny::req(FALSE) # Stop execution
-    }
-    # Get the data from the new reactive
-    table_data <- detailed_table_data()
-    # Check if the reactive returned any data (e.g., after filtering)
-    shiny::validate(
-      shiny::need(is.data.frame(table_data) && nrow(table_data) > 0,
-                  "No records match the current filter selections for the Detailed Record Table.")
-    )
-    # Pass the prepared data frame to the formatting function
-    create_detailed_record_table(table_data)
-    # Bind to the same button trigger
-  }) %>% shiny::bindEvent(input$generateDetailedRecordTable)
-  
-  # Rendering the precision and sensitivity table
-  output$summaryPrecTab <- gt::render_gt({
-    
-    unique_citations <- unique_filtered_table()
-    
-    # The table is only for phase comparison, include "final" in labels for comparison
-    if (!any(stringr::str_detect(tolower(unique_citations$cite_label), "final"))) {
-      shiny::req(FALSE)
-    }
-    
-    unique_citations <- unique_filtered_table()
-    phase_counts <- calculate_phase_records(unique_citations, n_unique, "cite_source")
-    create_precision_sensitivity_table(phase_counts)
-  }) %>% shiny::bindEvent(input$generatePrecisionTable)
-  
-  
-  # Rendering the record-level table
-  output$reviewTab <- DT::renderDataTable({
-    
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
-    }
-    
-    citations <- unique_filtered_table()
-    citations$source <- citations$cite_source
-    record_level_table(citations = citations, return = "DT")
-  }) %>% shiny::bindEvent(input$generateRecordTable)
-  
-  
-  
-  #### Export tab ####
-  
-  # # Downloadable bibtex ----
-  # Downloadable bibtex ----
-  output$downloadCsv <- shiny::downloadHandler(
-    filename = function() {
-      paste("data-", Sys.Date(), ".csv", sep = "")
-    },
-    content = function(file) {
-      if (nrow(rv$latest_unique) > 0) {
-        write.csv(rv$latest_unique, file)
-      } else {
-        stop("No data to download!")
-        shiny::req(FALSE)
-      }
-    }
-  )
-  output$downloadBib <- shiny::downloadHandler(
-    filename = function() {
-      paste("data-", Sys.Date(), ".bib", sep = "")
-    },
-    content = function(file) {
-      export_bib(rv$latest_unique, file)
-    }
-  )
-  
-  output$downloadRis <- shiny::downloadHandler(
-    filename = function() {
-      paste("data-", Sys.Date(), ".ris", sep = "")
-    },
-    content = function(file) {
-      export_ris(rv$latest_unique, file)
-    }
-  )
-}
-
-# Create Shiny app ----
-shiny::shinyApp(ui, server)
diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index d260f42a..ddf833c5 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -118,9 +118,6 @@ ui <- shiny::navbarPage("CiteSource",
                                 color = "primary",
                                 icon = shiny::icon("search")
                               ) %>% htmltools::tagAppendAttributes(style = "background-color: #008080; margin-right: 20px"),
-                              
-                              # Output: datatable of deduplication results
-                              DT::dataTableOutput("dedup_results")
                             ),
                             shiny::tabPanel(
                               "Manual deduplication",
@@ -300,7 +297,7 @@ ui <- shiny::navbarPage("CiteSource",
                                     color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
                                   shiny::br(),
                                   shiny::br(),
-                                  gt::gt_output("summaryRecordTab")
+                                  gt::gt_output("detailedRecordTab")
                                 ),
                                 
                                 shiny::tabPanel(
@@ -406,7 +403,6 @@ server <- function(input, output, session) {
   rv$latest_unique <- data.frame()#for reimported data
   rv$pairs_to_check <- data.frame()#for potential duplicates/manual dedup
   rv$pairs_removed <- data.frame()#for removed records
-  rv$n_unique = NULL # Initialize n_unique
   
   #### Upload files tab section ------
   # upload on click
@@ -418,16 +414,7 @@ server <- function(input, output, session) {
     else {
       # upload files one-by-one
       path_list <- input$file$datapath
-      
-      # Increment the upload number
-      if (is.null(rv$upload_number)) {
-        rv$upload_number <- 1
-      } else {
-        rv$upload_number <- rv$upload_number + 1
-      }
-      
       suggested_source <- stringr::str_replace_all(input$file$name, "\\.(ris|bib|txt)$", "")
-      
       empty_strings <- rep("", length(input$file$datapath))
       
       # Read the uploaded citations
@@ -515,55 +502,79 @@ server <- function(input, output, session) {
   
   ## Update filters
   shiny::observe({
-    if (nrow(rv$latest_unique) > 0) {
+    # Make sure rv$latest_unique is populated and is a data frame
+    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
       
-      # Handle cite_source
-      sources <- rv$latest_unique$cite_source
-      if (all(is.na(sources) | sources == "")) {
-        sources <- NULL  # Leave it as NULL or NA
-      } else {
-        sources <- unique(sources[!is.na(sources) & sources != ""]) %>%
-          stringr::str_split(", ") %>%
+      # --- Handle cite_source ---
+      sources_raw <- rv$latest_unique$cite_source
+      sources_choices <- NULL # Initialize choices list
+      
+      if (!all(is.na(sources_raw) | sources_raw == "")) {
+        sources_choices <- sources_raw[!is.na(sources_raw) & sources_raw != ""] %>%
+          stringr::str_split(",\\s*") %>% # Use regex for robustness
           unlist() %>%
           unique() %>%
           sort()
       }
       
-      # Handle cite_label
-      labels <- rv$latest_unique$cite_label
-      if (all(is.na(labels) | labels == "")) {
-        labels <- NULL  # Leave it as NULL or NA
-      } else {
-        labels <- unique(labels[!is.na(labels) & labels != ""]) %>%
-          stringr::str_split(", ") %>%
+      # *** Calculate the default selection ONLY for sources_visual (exclude "unknown") ***
+      sources_visual_selected_default <- NULL
+      if (!is.null(sources_choices)) {
+        sources_visual_selected_default <- sources_choices[sources_choices != "unknown"]
+        # Handle edge case where only "unknown" was present
+        if (length(sources_visual_selected_default) == 0 && "unknown" %in% sources_choices) {
+          sources_visual_selected_default <- NULL
+        }
+      }
+      # For sources_tables, the default remains all available choices (sources_choices)
+      
+      
+      # --- Handle cite_label ---
+      labels_raw <- rv$latest_unique$cite_label
+      labels_choices <- NULL
+      if (!all(is.na(labels_raw) | labels_raw == "")) {
+        labels_choices <- unique(labels_raw[!is.na(labels_raw) & labels_raw != ""]) %>%
+          stringr::str_split(",\\s*") %>%
           unlist() %>%
           unique() %>%
           sort()
-        
       }
+      labels_selected_default <- labels_choices # Default: select all valid labels
       
-      # Handle cite_string
-      strings <- rv$latest_unique$cite_string
-      if (all(is.na(strings) | strings == "")) {
-        strings <- NULL  # Leave it as NULL or NA
-      } else {
-        strings <- unique(strings[!is.na(strings) & strings != ""]) %>%
-          stringr::str_split(", ") %>%
+      # --- Handle cite_string ---
+      strings_raw <- rv$latest_unique$cite_string
+      strings_choices <- NULL
+      if (!all(is.na(strings_raw) | strings_raw == "")) {
+        strings_choices <- unique(strings_raw[!is.na(strings_raw) & strings_raw != ""]) %>%
+          stringr::str_split(",\\s*") %>%
           unlist() %>%
           unique() %>%
           sort()
       }
+      strings_selected_default <- strings_choices # Default: select all valid strings
+      
       
-      # Update select inputs
-      shiny::updateSelectInput(inputId = "sources_visual", choices = sources, selected = sources)
-      shiny::updateSelectInput(inputId = "labels_visual", choices = labels, selected = labels)
-      shiny::updateSelectInput(inputId = "strings_visual", choices = strings, selected = strings)
+      # --- Update select inputs ---
+      # Use the specific default (excluding "unknown") for sources_visual
+      shiny::updateSelectInput(session, inputId = "sources_visual", choices = sources_choices, selected = sources_visual_selected_default)
+      shiny::updateSelectInput(session, inputId = "labels_visual", choices = labels_choices, selected = labels_selected_default)
+      shiny::updateSelectInput(session, inputId = "strings_visual", choices = strings_choices, selected = strings_selected_default)
       
-      shiny::updateSelectInput(inputId = "sources_tables", choices = sources, selected = sources)
-      shiny::updateSelectInput(inputId = "labels_tables", choices = labels, selected = labels)
-      shiny::updateSelectInput(inputId = "strings_tables", choices = strings, selected = strings)
+      # Use the original default (all choices) for sources_tables
+      shiny::updateSelectInput(session, inputId = "sources_tables", choices = sources_choices, selected = sources_choices) # Reverted to selecting all
+      shiny::updateSelectInput(session, inputId = "labels_tables", choices = labels_choices, selected = labels_selected_default)
+      shiny::updateSelectInput(session, inputId = "strings_tables", choices = strings_choices, selected = strings_selected_default)
+      
+    } else {
+      # Optional: Clear the inputs if rv$latest_unique is empty or not a data frame
+      shiny::updateSelectInput(session, inputId = "sources_visual", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "labels_visual", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "strings_visual", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "sources_tables", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "labels_tables", choices = character(0), selected = character(0))
+      shiny::updateSelectInput(session, inputId = "strings_tables", choices = character(0), selected = character(0))
     }
-  })
+  }) # End update filters observe
   
   # Robust Observer for Cell Edits in tbl_out
   shiny::observeEvent(input$tbl_out_cell_edit, {
@@ -757,19 +768,6 @@ server <- function(input, output, session) {
     
   })
   
-  observeEvent(input$completed_manual_dedup,{
-    
-    # provide shiny alert
-    shinyalert::shinyalert("Manual deduplication complete",
-                           paste(
-                             "From a total of", nrow(rv$upload_df), "citations uploaded, there are", nrow(rv$latest_unique),
-                             "unique citations after automated and manual deduplication.
-                           Compare citations across sources, labels, and strings in the visualisation tab"
-                           ),
-                           type = "success"
-    )
-    
-  })
   
   observe({
     shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
@@ -861,253 +859,281 @@ server <- function(input, output, session) {
   
   #### Visualise tab ####
   
-    # --- Heatmap and Upset Plot Code ---
-  # Note: These plots WILL use unique_filtered_visual() which applies filters.
-  # Define unique_filtered_visual() here if it's only used for these plots.
+  # Reactive expression to filter the data for visualization (used for Heatmap and Upset)
   unique_filtered_visual <- shiny::reactive({
-    # Require rv$latest_unique to be a valid, non-empty data frame
-    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
-
+    # Ensure latest_unique has data before attempting filtering
+    shiny::req(nrow(rv$latest_unique) > 0)
+    
     sources <- input$sources_visual
     sources <- ifelse(sources == "_blank_", "unknown", sources)
     strings <- input$strings_visual
     strings <- ifelse(strings == "_blank_", "unknown", strings)
     labels <- input$labels_visual
     labels <- ifelse(labels == "_blank_", "unknown", labels)
-
-    # Define potential columns to separate and summarise
-    cols_to_process <- c("record_ids", "cite_label", "cite_source", "cite_string")
-    # Find which of these columns actually exist in the data
-    existing_cols <- intersect(cols_to_process, names(rv$latest_unique))
-    # Ensure essential columns for filtering exist
-    shiny::req("cite_source" %in% names(rv$latest_unique),
-               "cite_label" %in% names(rv$latest_unique),
-               "cite_string" %in% names(rv$latest_unique))
-
-    # The dplyr chain should now only run on valid data
-    # Need a local copy to modify within the reactive expression
-    current_latest_unique <- rv$latest_unique
-
-    # Ensure existing_cols are character type before separate_rows
-    for (col in existing_cols) {
-        if (!is.character(current_latest_unique[[col]])) {
-            current_latest_unique[[col]] <- as.character(current_latest_unique[[col]])
+    
+    out <- rv$latest_unique %>%
+      # Filter rows where *any* of the required sources/labels/strings are present before summarizing
+      dplyr::filter(
+        (length(sources) == 0 | stringr::str_detect(cite_source, paste0("\\b(", paste(sources, collapse="|"), ")\\b"))) &
+          (length(strings) == 0 | stringr::str_detect(cite_string, paste0("\\b(", paste(strings, collapse="|"), ")\\b"))) &
+          (length(labels) == 0 | stringr::str_detect(cite_label, paste0("\\b(", paste(labels, collapse="|"), ")\\b")))
+      ) %>%
+      # Now group and summarize
+      dplyr::group_by(duplicate_id) %>%
+      {
+        df_grouped <- .
+        df_separated <- df_grouped %>%
+          tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
+          dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
+          dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
+          dplyr::filter(length(labels) == 0 | cite_label %in% labels)
+        
+        # Check if df_separated is empty for this group before summarizing
+        if(nrow(df_separated) == 0) {
+          # Return a row with the group id and NAs or empty strings if nothing matches filters
+          tibble::tibble(duplicate_id = dplyr::first(df_grouped$duplicate_id),
+                         record_ids = "", cite_label = "", cite_source = "", cite_string = "")
+        } else {
+          df_separated %>%
+            dplyr::summarise(across(c(record_ids, cite_label, cite_source, cite_string), ~ trimws(paste(unique(na.omit(.)), collapse = ', '))))
         }
+      } %>%
+      dplyr::ungroup()
+    
+    # Ensure the output has the same columns even if empty
+    if (nrow(out) == 0) {
+      out <- tibble::tibble(duplicate_id=character(), record_ids=character(), cite_label=character(), cite_source=character(), cite_string=character()) # Adjust columns as needed
     }
-
-    out <- current_latest_unique %>%
-      dplyr::group_by(duplicate_id) %>%
-      # Separate only the columns that exist
-      { if (length(existing_cols) > 0) tidyr::separate_rows(., tidyselect::all_of(existing_cols), sep = ", ") else . } %>%
-      # Filter based on existing columns
-      dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
-      dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
-      dplyr::filter(length(labels) == 0 | cite_label %in% labels) %>%
-      # Summarise only the columns that exist
-      dplyr::summarise(dplyr::across(tidyselect::all_of(existing_cols),
-                                     # Get unique non-NA values before pasting
-                                     ~ trimws(paste(na.omit(unique(.)), collapse = ', '))),
-                       .groups = "drop")
-
-    return(out)
-  }) # End unique_filtered_visual reactive
-
-
-  # --- Heatmap Plot ---
+    # Add back other columns from rv$latest_unique if needed by downstream functions
+    dplyr::left_join(out, dplyr::select(rv$latest_unique, -any_of(c("record_ids", "cite_label", "cite_source", "cite_string"))), by = "duplicate_id")
+    
+  })
+  
+  # REACTIVE for Phase Plot Data
+  unique_separated_phase <- shiny::reactive({
+    # Require rv$latest_unique to have data
+    shiny::req(nrow(rv$latest_unique) > 0)
+    
+    # Get filter inputs
+    sources_filt <- input$sources_visual
+    sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
+    labels_filt <- input$labels_visual
+    labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
+    
+    # Start with the base unique data, select columns needed
+    df <- rv$latest_unique %>%
+      dplyr::select(duplicate_id, cite_source, cite_label) # Add record_ids if needed
+    
+    # Separate rows for source and label
+    df_long <- df %>%
+      tidyr::separate_rows(cite_source, sep = ",\\s*") %>%
+      tidyr::separate_rows(cite_label, sep = ",\\s*") %>%
+      dplyr::filter(!is.na(cite_source) & cite_source != "", # Ensure no blank/NA sources/labels after separation
+                    !is.na(cite_label) & cite_label != "")
+    
+    # Apply filtering based on selected sources and labels for the plot
+    df_filtered <- df_long %>%
+      dplyr::filter(length(sources_filt) == 0 | cite_source %in% sources_filt) %>%
+      dplyr::filter(length(labels_filt) == 0 | cite_label %in% labels_filt)
+    
+    # Check if filtering resulted in empty data
+    if (nrow(df_filtered) == 0) {
+      # Return an empty tibble with correct columns/types if no data matches
+      return(tibble::tibble(
+        duplicate_id = character(),
+        cite_source = character(),
+        cite_label = character(),
+        type = factor(levels = c("unique", "duplicated"))
+      ))
+    }
+    
+    # Calculate 'type' based on uniqueness *within* each phase (label) for the *filtered* set
+    # A record is 'unique' in a phase if it's found in only 1 source within that phase in the filtered data
+    df_typed <- df_filtered %>%
+      dplyr::group_by(duplicate_id, cite_label) %>%
+      dplyr::mutate(n_sources_in_phase = dplyr::n_distinct(cite_source)) %>%
+      dplyr::ungroup() %>%
+      dplyr::mutate(
+        type = factor(
+          ifelse(n_sources_in_phase == 1, "unique", "duplicated"),
+          levels = c("unique", "duplicated")
+        )
+      ) %>%
+      # Select relevant columns needed by plot_contributions
+      dplyr::select(duplicate_id, cite_source, cite_label, type)
+    
+    # Return the long data frame, ensuring distinct rows
+    dplyr::distinct(df_typed)
+  })# End Phase Plot Reactive
+  
+  
+  # Heatmap plot (uses unique_filtered_visual)
   plotHeat <- shiny::reactive({
-    # Requires unique_filtered_visual() to run successfully
-    shiny::req(unique_filtered_visual())
-    # Use tryCatch for robustness
-    tryCatch({
-        source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
-        plot_source_overlap_heatmap(source_comparison, cells = stringr::str_sub(input$comp_type, end = -2))
-    }, error = function(e) {
-        shiny::showNotification(paste("Error generating heatmap data:", e$message), type = "warning")
-        NULL # Return NULL on error
-    })
+    # Add check if data is available
+    data_vis <- unique_filtered_visual()
+    shiny::req(nrow(data_vis) > 0)
+    source_comparison <- compare_sources(data_vis, comp_type = input$comp_type)
+    plot_source_overlap_heatmap(source_comparison, cells = stringr::str_sub(input$comp_type, end = -2))
   })
-
+  
   output$plotgraph1 <- plotly::renderPlotly({
-    # Check for data before attempting to plot
-    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
-    # Plotting depends on plotHeat() which depends on unique_filtered_visual()
-    heatmap_plot <- plotHeat()
-    shiny::req(heatmap_plot) # Require the plot object itself to be non-NULL
-    # Assuming plot_source_overlap_heatmap returns a plotly object or ggplot object
-    # If it returns ggplot, wrap in ggplotly()
-    # print(heatmap_plot) # Use print for base R, direct object for ggplot/plotly
-    heatmap_plot
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error"
+      )
+      shiny::req(FALSE)
+    }
+    # Add check specific to filtered data for this plot
+    shiny::req(nrow(unique_filtered_visual()) > 0, cancelOutput = TRUE)
+    print(plotHeat())
   })
-
+  
   output$downloadHeatPlot <- shiny::downloadHandler(
-    filename = function() { paste("heatmap", ".png", sep = "") },
+    filename = function() {
+      paste("heatmap-overlap", Sys.Date(), ".png", sep = "") # Added date for uniqueness
+    },
     content = function(file) {
-      # Ensure data for plotting exists
-      heatmap_plot_obj <- plotHeat()
-      shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0, heatmap_plot_obj)
-
-      # Use ggsave for ggplot objects. If plotly, use orca or kaleido via plotly::save_image
-      # Assuming ggplot for now, adjust if it's plotly or base R
-      if (inherits(heatmap_plot_obj, "ggplot")) {
-          ggplot2::ggsave(filename = file, plot = heatmap_plot_obj, device = "png", width = 8, height = 6)
-      } else if (inherits(heatmap_plot_obj, "plotly")) {
-          # Requires kaleido package: reticulate::install_miniconda(); reticulate::conda_install('r-reticulate', 'python-kaleido'); reticulate::conda_install('r-reticulate', 'plotly', channel = 'plotly')
-          # Or use orca (older method)
-          tryCatch({
-              plotly::save_image(heatmap_plot_obj, file, width = 800, height = 600) # Adjust size as needed
-          }, error = function(e){
-              shiny::showNotification("Error saving plotly heatmap. Ensure 'kaleido' or 'orca' is installed.", type="error")
-          })
+      # Ensure data is available
+      shiny::req(nrow(unique_filtered_visual()) > 0)
+      
+      # Generate the plot object
+      heat_plot_obj <- plotHeat()
+      
+      # Check if plot object was created successfully
+      if (!is.null(heat_plot_obj)) {
+        # Save the ggplot object directly using ggsave
+        ggplot2::ggsave(filename = file, plot = heat_plot_obj, device = "png", width = 10, height = 8, dpi = 300) # Adjust size/dpi as needed
       } else {
-         # Fallback for base R or other plot types
-         png(file, width = 800, height = 600) # Adjust size
-         print(heatmap_plot_obj)
-         dev.off()
+        stop("Failed to generate heatmap plot for download.")
       }
     }
   )
-
-  # --- Upset Plot ---
+  
+  # Upset plot (uses unique_filtered_visual)
   plotUpset <- shiny::reactive({
-     # Requires unique_filtered_visual() to run successfully
-    shiny::req(unique_filtered_visual())
-    # Use tryCatch for robustness
-    tryCatch({
-        source_comparison <- compare_sources(unique_filtered_visual(), comp_type = input$comp_type)
-        plot_source_overlap_upset(source_comparison, groups = stringr::str_sub(input$comp_type, end = -2), decreasing = c(TRUE, TRUE))
-    }, error = function(e) {
-        shiny::showNotification(paste("Error generating upset plot data:", e$message), type = "warning")
-        NULL # Return NULL on error
-    })
+    # Add check if data is available
+    data_vis <- unique_filtered_visual()
+    shiny::req(nrow(data_vis) > 0)
+    source_comparison <- compare_sources(data_vis, comp_type = input$comp_type)
+    plot_source_overlap_upset(source_comparison, groups = stringr::str_sub(input$comp_type, end = -2), decreasing = c(TRUE, TRUE))
   })
-
+  
   output$plotgraph2 <- shiny::renderPlot({
-    # Check for data before attempting to plot
-    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
-    # Plotting depends on plotUpset() which depends on unique_filtered_visual()
-    upset_plot <- plotUpset()
-    shiny::req(upset_plot) # Require the plot object itself to be non-NULL
-    print(upset_plot) # Upset plot usually needs print()
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error"
+      )
+      shiny::req(FALSE)
+    }
+    # Add check specific to filtered data for this plot
+    shiny::req(nrow(unique_filtered_visual()) > 0, cancelOutput = TRUE)
+    print(plotUpset())
   })
-
+  
   output$downloadUpsetPlot <- shiny::downloadHandler(
-    filename = function() { paste("upset", ".png", sep = "") },
+    filename = function() {
+      paste("upset-overlap", Sys.Date(), ".png", sep = "") # Added date
+    },
     content = function(file) {
-      # Ensure data for plotting exists
-      upset_plot_obj <- plotUpset()
-      shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0, upset_plot_obj)
-      # Use ggsave for ggplot objects, otherwise use standard png/print
-      # Assuming plot_source_overlap_upset returns ggplot or grid object
-      if (inherits(upset_plot_obj, "ggplot") || inherits(upset_plot_obj, "grob")) {
-          ggplot2::ggsave(filename = file, plot = upset_plot_obj, device = "png", width = 10, height = 6) # Upset plots often wider
+      # Ensure data is available
+      shiny::req(nrow(unique_filtered_visual()) > 0)
+      
+      # Generate the plot object (we need it to print)
+      upset_plot_obj <- plotUpset() # plotUpset() returns the plot object
+      
+      # Check if plot object was created successfully
+      if (!is.null(upset_plot_obj)) {
+        # Use png device for static plot
+        grDevices::png(file, width = 1200, height = 800, res = 100) # Adjust size/resolution
+        # Print the plot object to the device
+        print(upset_plot_obj)
+        # The device is automatically closed by downloadHandler
+        grDevices::dev.off() # Explicitly close device for clarity/safety
       } else {
-         # Fallback for base R or other plot types
-         png(file, width = 1000, height = 600) # Adjust size
-         print(upset_plot_obj)
-         dev.off()
+        stop("Failed to generate upset plot for download.")
+        
       }
     }
   )
-
-
-  # --- Phase Analysis Plot Section ---
-
-  # Observer to calculate n_unique based DIRECTLY on rv$latest_unique
-  # This calculates the TRUE unique/duplicate status across the whole dataset
-  shiny::observe({
-    # Check if rv$latest_unique is valid before proceeding
-    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
-        # Use tryCatch around count_unique
-        tryCatch({
-            # Calculate n_unique from the complete deduplicated data
-            rv$n_unique <- count_unique(rv$latest_unique)
-        }, error = function(e_count) {
-            shiny::showNotification(paste("Error calculating unique counts:", e_count$message), type = "error")
-            rv$n_unique <- NULL # Set to NULL on error
-        })
-    } else {
-        # If rv$latest_unique is not valid, set n_unique to NULL
-        rv$n_unique <- NULL
-    }
-  }) # Triggered whenever rv$latest_unique changes
-
-
-  # Render the Phase Analysis plot
+  
+  # Phase Analysis plot (uses unique_separated_phase reactive)
+  # Keep rv$n_unique definition as is for potential use in table functions,
+  # but note it relies on the potentially problematic unique_filtered_visual().
+  # If table functions are updated or don't rely on this specific structure,
+  # this reactive could be revisited or removed.
+  rv$n_unique <- shiny::reactive({
+    # Original logic based on unique_filtered_visual and count_unique
+    # Ensure unique_filtered_visual() is not empty
+    ufv <- unique_filtered_visual()
+    shiny::req(nrow(ufv) > 0)
+    count_unique(ufv) # Pass the wide, summarized, filtered data
+  })
+  
+  # Phase plot output
   output$phasePlot <- shiny::renderPlot({
-    # Require that rv$n_unique has been calculated and is valid
-    shiny::req(!is.null(rv$n_unique),
-               is.data.frame(rv$n_unique),
-               nrow(rv$n_unique) > 0)
-
-    # --- Apply visualization filters to rv$n_unique ---
-    # Get current filter selections
-    sources_filt <- input$sources_visual
-    sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
-    strings_filt <- input$strings_visual
-    strings_filt <- ifelse(strings_filt == "_blank_", "unknown", strings_filt)
-    labels_filt <- input$labels_visual
-    labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
-
-    # Filter the calculated rv$n_unique data for plotting
-    # Note: rv$n_unique has one row per record *per source* after count_unique
-    plot_data <- rv$n_unique %>%
-      # Use checks for robustness in case columns don't exist in rv$n_unique
-      dplyr::filter(length(sources_filt) == 0 | ( "cite_source" %in% names(.) && cite_source %in% sources_filt)) %>%
-      dplyr::filter(length(strings_filt) == 0 | ( "cite_string" %in% names(.) && cite_string %in% strings_filt)) %>%
-      dplyr::filter(length(labels_filt) == 0 | ( "cite_label" %in% names(.) && cite_label %in% labels_filt))
-
-    # Require that the filtered data still has rows
-    shiny::req(nrow(plot_data) > 0)
-
-    # Generate the plot using the *filtered* data
-    plot_contributions(
-      data = plot_data,          # Use the filtered data for plotting
+    # Initial check if any data has been processed
+    if (nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed",
+                             "Please import and deduplicate your citations first.",
+                             type = "error")
+      shiny::req(FALSE, cancelOutput = TRUE)
+    }
+    
+    # Use the reactive data specifically prepared for this plot
+    plot_data <- unique_separated_phase()
+    
+    # Check if the prepared data is empty after filtering
+    if (nrow(plot_data) == 0) {
+      # Optionally, display a message instead of just cancelling
+      plot.new()
+      mtext("No data matches the selected filters for Phase Analysis.")
+      shiny::req(FALSE, cancelOutput = TRUE)
+    }
+    
+    # Call plot_contributions with the prepared data
+    CiteSource::plot_contributions( # Explicitly call from CiteSource if needed
+      data = plot_data, # Use the new reactive data
       center = TRUE,
-      bar_order = c("search", "screened", "final"),
-      color_order = c("unique", "duplicated")
-      # Default facets = cite_source is used
+      bar_order = c("search", "screened", "final"), # Keep or make dynamic?
+      color_order = c("unique", "duplicated"),
+      totals_in_legend = FALSE #legend total needs an update
     )
   })
-
-  # Download handler uses the same plotting logic (including filtering)
+  
+  # Phase plot download
   output$downloadPhasePlot <- shiny::downloadHandler(
     filename = function() {
-      paste("phase_analysis", ".png", sep = "")
+      paste("phase-analysis", Sys.Date(), ".png", sep = "") # Added date
     },
     content = function(file) {
-      # Require that rv$n_unique has been calculated and is valid
-      shiny::req(!is.null(rv$n_unique),
-                 is.data.frame(rv$n_unique),
-                 nrow(rv$n_unique) > 0)
-
-      # --- Apply visualization filters to rv$n_unique ---
-      sources_filt <- input$sources_visual
-      sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
-      strings_filt <- input$strings_visual
-      strings_filt <- ifelse(strings_filt == "_blank_", "unknown", strings_filt)
-      labels_filt <- input$labels_visual
-      labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
-
-      plot_data <- rv$n_unique %>%
-        # Use checks for robustness
-        dplyr::filter(length(sources_filt) == 0 | ( "cite_source" %in% names(.) && cite_source %in% sources_filt)) %>%
-        dplyr::filter(length(strings_filt) == 0 | ( "cite_string" %in% names(.) && cite_string %in% strings_filt)) %>%
-        dplyr::filter(length(labels_filt) == 0 | ( "cite_label" %in% names(.) && cite_label %in% labels_filt))
-
-
-      # Require that the filtered data still has rows
-      shiny::req(nrow(plot_data) > 0)
-
-      # Generate the plot again for saving, using filtered data
-      phase_plot <- plot_contributions(
-          data = plot_data, # Use filtered data
-          center = TRUE,
-          bar_order = c("search", "screened", "final"),
-          color_order = c("unique", "duplicated")
-          # Default facets = cite_source is used
-        )
-      # Save the plot to the file
-      ggplot2::ggsave(filename = file, plot = phase_plot, device = "png", width = 8, height = 6)
+      # Use the NEW reactive data
+      plot_data <- unique_separated_phase()
+      
+      # Add check if data is empty
+      if (nrow(plot_data) == 0) {
+        # Stop execution or create a plot with a message
+        stop("No data available to plot based on current filters.")
+      }
+      
+      # Generate the plot object itself
+      phase_plot_obj <- CiteSource::plot_contributions( # Explicitly call from CiteSource if needed
+        data = plot_data, # Use the prepared reactive data
+        center = TRUE,
+        bar_order = c("search", "screened", "final"), # Make dynamic if needed
+        color_order = c("unique", "duplicated"),
+        totals_in_legend = FALSE
+      )
+      
+      # Check if plot object was created successfully
+      if (!is.null(phase_plot_obj)) {
+        grDevices::png(file, width = 1000, height = 700, res=100) # Adjust size/resolution
+        print(phase_plot_obj) # Print the generated plot object
+        grDevices::dev.off() # Explicitly close device
+      } else {
+        stop("Failed to generate phase plot for download.")
+      }
     }
   )
   
@@ -1139,6 +1165,122 @@ server <- function(input, output, session) {
         dplyr::ungroup()
     }
   )
+  detailed_table_data <- reactive({
+    # Require base data to proceed
+    shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
+    
+    # Get current filter selections from the UI
+    sources_filt <- input$sources_tables
+    sources_filt <- ifelse(sources_filt == "_blank_", "unknown", sources_filt)
+    labels_filt <- input$labels_tables
+    labels_filt <- ifelse(labels_filt == "_blank_", "unknown", labels_filt)
+    strings_filt <- input$strings_tables
+    strings_filt <- ifelse(strings_filt == "_blank_", "unknown", strings_filt)
+    
+    # Create filter patterns for labels and strings (can still pre-filter these)
+    labels_pattern <- if (length(labels_filt) > 0) paste0("\\b(", paste(labels_filt, collapse = "|"), ")\\b") else NULL
+    strings_pattern <- if (length(strings_filt) > 0) paste0("\\b(", paste(strings_filt, collapse = "|"), ")\\b") else NULL
+    
+    # Apply initial filters for labels and strings only
+    df_filtered_wide <- rv$latest_unique %>%
+      dplyr::filter(
+        (is.null(labels_pattern) | stringr::str_detect(cite_label, labels_pattern)),
+        (is.null(strings_pattern) | stringr::str_detect(cite_string, strings_pattern))
+      )
+    
+    # Define the structure for returning an empty result
+    empty_result_df <- tibble::tibble(
+      Source = character(), `Records Imported` = integer(), `Distinct Records` = integer(),
+      `Unique Records` = integer(), `Non-unique Records` = integer(),
+      `Source Contribution %` = character(), `Source Unique Contribution %` = character(),
+      `Source Unique %` = character() )
+    
+    # Return empty structure if no records match initial filters
+    if (nrow(df_filtered_wide) == 0) { return(empty_result_df) }
+    
+    # Separate only the cite_source column for source-level aggregation
+    df_long_source <- df_filtered_wide %>%
+      dplyr::select(duplicate_id, cite_source) %>%
+      tidyr::separate_rows(cite_source, sep = ",\\s*") %>%
+      dplyr::filter(!is.na(cite_source) & cite_source != "") # Filter out blank sources
+    
+    # *** Apply the source filter AFTER separating sources ***
+    df_long_source_filtered <- df_long_source %>%
+      dplyr::filter(length(sources_filt) == 0 | cite_source %in% sources_filt)
+    
+    # Return empty if no valid sources remain after filtering
+    if (nrow(df_long_source_filtered) == 0) { return(empty_result_df) }
+    
+    # --- All subsequent calculations use df_long_source_filtered ---
+    
+    # Calculate 'Records Imported' and 'Distinct Records' per source
+    source_base_counts <- df_long_source_filtered %>% # Use filtered data
+      dplyr::group_by(cite_source) %>%
+      dplyr::summarise(`Records Imported` = dplyr::n(), `Distinct Records` = dplyr::n_distinct(duplicate_id), .groups = 'drop')
+    
+    # Calculate how many sources each distinct record appears in (within the filtered set)
+    # Use the df_long_source_filtered data here too
+    record_source_counts <- df_long_source_filtered %>%
+      dplyr::group_by(duplicate_id) %>%
+      dplyr::summarise(n_sources = dplyr::n_distinct(cite_source), .groups = 'drop')
+    
+    # Identify records unique to one source within the filtered set
+    # Use the df_long_source_filtered data here too
+    unique_record_sources <- df_long_source_filtered %>%
+      dplyr::inner_join(record_source_counts, by = "duplicate_id") %>%
+      dplyr::filter(n_sources == 1) %>%
+      dplyr::distinct(duplicate_id, cite_source) # Get the source for unique records
+    
+    # Count the number of unique records per source
+    source_unique_counts <- unique_record_sources %>%
+      dplyr::group_by(cite_source) %>%
+      dplyr::summarise(`Unique Records` = dplyr::n(), .groups = 'drop')
+    
+    # Combine base counts with unique counts
+    detailed_counts_per_source <- source_base_counts %>%
+      dplyr::left_join(source_unique_counts, by = "cite_source") %>%
+      dplyr::mutate(`Unique Records` = tidyr::replace_na(`Unique Records`, 0)) %>%
+      dplyr::mutate(`Non-unique Records` = `Distinct Records` - `Unique Records`)
+    
+    # Calculate totals consistently from the *filtered* data
+    total_records_imported <- sum(detailed_counts_per_source$`Records Imported`, na.rm = TRUE)
+    total_distinct_records <- dplyr::n_distinct(df_long_source_filtered$duplicate_id) # Use filtered data
+    total_unique_records <- sum(detailed_counts_per_source$`Unique Records`, na.rm = TRUE)
+    total_nonunique_records <- sum(detailed_counts_per_source$`Non-unique Records`, na.rm = TRUE)
+    
+    # Prepare for percentage calculation (avoid division by zero)
+    total_distinct_divisor <- ifelse(total_distinct_records == 0, 1, total_distinct_records)
+    total_unique_divisor <- ifelse(total_unique_records == 0, 1, total_unique_records)
+    
+    # Calculate and format percentages
+    detailed_counts_final <- detailed_counts_per_source %>%
+      dplyr::mutate(
+        perc_contr = `Distinct Records` / total_distinct_divisor,
+        perc_unique_contr = `Unique Records` / total_unique_divisor,
+        perc_source_unique = ifelse(`Distinct Records` == 0, 0, `Unique Records` / `Distinct Records`) ) %>%
+      dplyr::mutate(
+        `Source Contribution %` = scales::percent(perc_contr, accuracy = 0.1),
+        `Source Unique Contribution %` = scales::percent(perc_unique_contr, accuracy = 0.1),
+        `Source Unique %` = scales::percent(perc_source_unique, accuracy = 0.1) ) %>%
+      dplyr::select( # Select and order final columns
+        cite_source, `Records Imported`, `Distinct Records`, `Unique Records`,
+        `Non-unique Records`, `Source Contribution %`,
+        `Source Unique Contribution %`, `Source Unique %` )
+    
+    # Create the total row
+    total_row <- tibble::tibble(
+      cite_source = "Total", `Records Imported` = total_records_imported,
+      `Distinct Records` = total_distinct_records, `Unique Records` = total_unique_records,
+      `Non-unique Records` = total_nonunique_records, `Source Contribution %` = NA_character_,
+      `Source Unique Contribution %` = NA_character_,
+      `Source Unique %` = scales::percent(total_unique_records / total_distinct_divisor, accuracy = 0.1) )
+    
+    # Add total row and rename source column
+    detailed_counts_final <- dplyr::bind_rows(detailed_counts_final, total_row) %>%
+      dplyr::rename(Source = cite_source)
+    
+    return(detailed_counts_final)
+  })
   
   # Rendering the initial record table
   output$initialRecordTab <- gt::render_gt({
@@ -1156,18 +1298,22 @@ server <- function(input, output, session) {
   }) %>% shiny::bindEvent(input$generateInitialRecordTable)
   
   # Rendering the detailed record table
-  output$summaryRecordTab <- gt::render_gt({
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
+  output$detailedRecordTab <- gt::render_gt({
+    # Check if base data is loaded
+    if (!is.data.frame(rv$latest_unique) || nrow(rv$latest_unique) == 0) {
+      shinyalert::shinyalert("Data needed", "Please import and deduplicate your citations first.", type = "error")
+      shiny::req(FALSE) # Stop execution
     }
-    
-    unique_citations <- unique_filtered_table()
-    detailed_count <- calculate_detailed_records(unique_citations, rv$n_unique, "search")
-    create_detailed_record_table(detailed_count)
+    # Get the data from the new reactive
+    table_data <- detailed_table_data()
+    # Check if the reactive returned any data (e.g., after filtering)
+    shiny::validate(
+      shiny::need(is.data.frame(table_data) && nrow(table_data) > 0,
+                  "No records match the current filter selections for the Detailed Record Table.")
+    )
+    # Pass the prepared data frame to the formatting function
+    create_detailed_record_table(table_data)
+    # Bind to the same button trigger
   }) %>% shiny::bindEvent(input$generateDetailedRecordTable)
   
   # Rendering the precision and sensitivity table

From f4066f318c00f31923e48820f6f727b7ec249456 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 2 May 2025 16:30:14 -0400
Subject: [PATCH 24/47] add CS gif

---
 inst/shiny-app/CiteSource/app.R             |   2 +-
 inst/shiny-app/CiteSource/www/CS.gif        | Bin 0 -> 1504632 bytes
 inst/shiny-app/CiteSource/www/about.md      |   3 ++-
 inst/shiny-app/CiteSource/www/use-cases.md  |   2 +-
 inst/shiny-app/CiteSource/www/user_guide.md |   2 +-
 5 files changed, 5 insertions(+), 4 deletions(-)
 create mode 100644 inst/shiny-app/CiteSource/www/CS.gif

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index ddf833c5..8b14063a 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -24,7 +24,7 @@ ui <- shiny::navbarPage("CiteSource",
                         header = shiny::tagList(
                           shinybusy::add_busy_spinner(spin = "circle"),
                           shinyjs::useShinyjs(),
-                          #header text colour
+                          #header text color
                           tags$head(
                             tags$style(HTML("
       h6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {
diff --git a/inst/shiny-app/CiteSource/www/CS.gif b/inst/shiny-app/CiteSource/www/CS.gif
new file mode 100644
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diff --git a/inst/shiny-app/CiteSource/www/about.md b/inst/shiny-app/CiteSource/www/about.md
index a1b12fe2..5fd3fe85 100644
--- a/inst/shiny-app/CiteSource/www/about.md
+++ b/inst/shiny-app/CiteSource/www/about.md
@@ -1,6 +1,7 @@
 ## About CiteSource
 
-<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
+<img src="CS.gif" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
+
 
 CiteSource is an R package and accompanying Shiny web application designed to support evidence data-driven decision-making during search strategy development. CiteSource also allows users to analyze and report on the impact of information sources and search methods.
 
diff --git a/inst/shiny-app/CiteSource/www/use-cases.md b/inst/shiny-app/CiteSource/www/use-cases.md
index ddf90516..a66e1d09 100644
--- a/inst/shiny-app/CiteSource/www/use-cases.md
+++ b/inst/shiny-app/CiteSource/www/use-cases.md
@@ -1,6 +1,6 @@
 ## CiteSource Use Cases: Overview
 
-<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
+<img src="CS.gif" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
 
 CiteSource provides a suite of tools to support data-driven decision-making throughout the evidence synthesis process. Its applications generally fall into two main categories: **Optimizing Search Strategies** (typically during protocol development and iterative searching) and **Analyzing Search Impact** (often after screening is complete, for reporting and methodological insights). Additional applications extend to training and resource management.
 
diff --git a/inst/shiny-app/CiteSource/www/user_guide.md b/inst/shiny-app/CiteSource/www/user_guide.md
index 487a22ad..01197c29 100644
--- a/inst/shiny-app/CiteSource/www/user_guide.md
+++ b/inst/shiny-app/CiteSource/www/user_guide.md
@@ -1,6 +1,6 @@
 ## CiteSource User Guide
 
-<img src="https://user-images.githubusercontent.com/89118428/155393065-780381a0-ff77-45d3-b2ee-40332ef72064.png" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
+<img src="CS.gif" width="200" style="float: right; margin-left: 20px; margin-bottom: 10px;"/>
 
 > CiteSource has a number of applications. This guide walks users through the step-wise process of uploading, deduplicating and analyzing data > within the shiny application. For step by step instructions for running CiteSource in R, [check out our vignettes](https://www.eshackathon.org/CiteSource/articles/)
 ---

From a71781f7113f27a50bee5d6da8ba0c2f27a74409 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 5 May 2025 10:07:41 -0400
Subject: [PATCH 25/47] fix - link open in new tab

---
 R/tables.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/R/tables.R b/R/tables.R
index befee1d2..1036623e 100644
--- a/R/tables.R
+++ b/R/tables.R
@@ -585,8 +585,8 @@ generate_apa_reference <- function(authors, year, title, source, volume, issue,
       dplyr::rowwise() %>%
       dplyr::mutate(
         reference = glue::glue("
-                              {glue::glue_collapse(initialed_names, ', ', last = ' & ')} ({year}). {nNA(title, '.')} {nNA(source, pre = '<i>', '</i>')}{nNA(volume, pre = '<i>, ', '</i>')}{nNA(issue, pre = '(', ')')}. {nNA(link, pre = '<a href = \"', '\">')}{nNA(link, '</a>')}
-                                                     ")
+                               {glue::glue_collapse(initialed_names, ', ', last = ' & ')} ({year}). {nNA(title, '.')} {nNA(source, pre = '<i>', '</i>')}{nNA(volume, pre = '<i>, ', '</i>')}{nNA(issue, pre = '(', ')')}. {nNA(link, pre = '<a href=\"', '\" target=\"_blank\" rel=\"noopener noreferrer\">')}{nNA(link, '</a>')}
+                                     ")
       ) %>%
       dplyr::pull(.data$reference)
   } else {

From dc4649eb609ca2d076c956d5c0e2851eb9d8dbec Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 5 May 2025 10:49:13 -0400
Subject: [PATCH 26/47] Better UX on dedup table, less columns, better order,
 and easier drop down selection.

---
 inst/shiny-app/CiteSource/app.R | 49 ++++++++++++++++++++++++++-------
 1 file changed, 39 insertions(+), 10 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 8b14063a..3af96de2 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -2,7 +2,7 @@ library(DT)
 library(CiteSource)
 library(dplyr)
 
-# Set background colour
+# Set background color
 shiny::tags$head(shiny::tags$style(
   shiny::HTML('
                      #sidebar {
@@ -767,12 +767,17 @@ server <- function(input, output, session) {
     rv$latest_unique <- after
     
   })
-  
-  
   observe({
+    all_cols <- names(rv$pairs_to_check)
+    base_cols <- unique(gsub("(1|2)$", "", all_cols)) # Remove "1" or "2" suffix
+    
+    # Initial selection
+    initial_base_selection <- c("year", "author", "title", "journal", "abstract",
+                                "doi", "pages", "source", "label")
+
     shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
-                                    choices = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)],
-                                    selected = names(rv$pairs_to_check)[c(1,2,4,5,7,8,10,11,13,14,16,17,19,20,22,23,26,27,28,29,31,32,33,34,35,36)])
+                                    choices = base_cols,
+                                    selected = initial_base_selection)
   })
   
   
@@ -800,16 +805,39 @@ server <- function(input, output, session) {
   manual_dedup_data <- reactive({
     
     data <- rv$pairs_to_check[,1:36]
-    data <- data[,c(paste0(input$manual_dedup_cols))]
+    selected_cols <- input$manual_dedup_cols
+
+    # Define the desired base order
+    core_col_order <- c("author", "title", "year", "journal", "abstract","doi", "pages","volume","number","source","label","string")
+    # Create the desired interleaved order of columns to select
+    
+    desired_table_order <- character(0)
+    for (base_col in core_col_order) {
+      col1 <- paste0(base_col, "1")
+      col2 <- paste0(base_col, "2")
+      desired_table_order <- c(desired_table_order, col1, col2)
+    }
+
+    # Intersect with the actual and selected columns to maintain order and presence
+    cols_to_show <- intersect(desired_table_order, colnames(data))
+    cols_to_show <- intersect(cols_to_show, paste0(selected_cols, rep(c("1", "2"), each = length(selected_cols))))
+    
+    ordered_data <- data %>%
+      dplyr::select(any_of(cols_to_show))
     
+    # Define match_cols INSIDE the reactive expression
     match_cols <- c("title", "author", "doi", "volume",
                     "pages", "number", "year", "abstract", "journal", "isbn")
     
     
-    data <- data %>% dplyr::select(-any_of(match_cols))
-    match_number_cols <- rv$pairs_to_check[,c(paste0(match_cols))]
+    # Add the match_number_cols at the end (if they exist)
     
-    data <- cbind(data,match_number_cols)
+    match_number_cols_to_add <- intersect(paste0(match_cols), colnames(data))
+    if (length(match_number_cols_to_add) > 0) {
+      ordered_data <- cbind(ordered_data, data[, match_number_cols_to_add])
+    }
+
+    ordered_data
     
   })
   output$manual_dedup_dt <- DT::renderDataTable({
@@ -828,7 +856,8 @@ server <- function(input, output, session) {
     
     datatable(data,
               options = list(
-                pageLength = 100, info = FALSE,
+                pageLength = 100, 
+                info = FALSE,
                 lengthMenu = list(c(100, -1), c("100", "All")),
                 columnDefs =
                   list(

From 349ba4bdf0787dc51e0c0245494433fefdfeff78 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 5 May 2025 15:10:28 -0400
Subject: [PATCH 27/47] added extra_merge_fields = "cite_string" back to
 correct manual dedup

---
 R/dedup.R | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/R/dedup.R b/R/dedup.R
index d32ccaeb..25445768 100644
--- a/R/dedup.R
+++ b/R/dedup.R
@@ -95,8 +95,9 @@ dedup_citations_add_manual <- function(unique_citations, additional_pairs) {
   unique_citations$source = unique_citations$cite_source
   unique_citations$label = unique_citations$cite_label
   
-  dedup_results <- ASySD::dedup_citations_add_manual(unique_citations, additional_pairs = additional_pairs
-                                                     )
+  dedup_results <- ASySD::dedup_citations_add_manual(unique_citations, 
+                                                     additional_pairs = additional_pairs, 
+                                                     extra_merge_fields = "cite_string")
 
   dedup_results$cite_source <- dedup_results$source
   dedup_results$cite_label <- dedup_results$label

From 1a4b21bcd8e64fc9107e68bf2b80f88a29f7bb68 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 6 May 2025 10:01:05 -0400
Subject: [PATCH 28/47] initiate renv in order to test working local version on
 shinyapps.io dev version.

---
 .Rbuildignore      |    2 +
 .Rprofile          |    1 +
 renv.lock          | 2515 ++++++++++++++++++++++++++++++++++++++++++++
 renv/.gitignore    |    7 +
 renv/activate.R    | 1220 +++++++++++++++++++++
 renv/settings.json |   19 +
 6 files changed, 3764 insertions(+)
 create mode 100644 .Rprofile
 create mode 100644 renv.lock
 create mode 100644 renv/.gitignore
 create mode 100644 renv/activate.R
 create mode 100644 renv/settings.json

diff --git a/.Rbuildignore b/.Rbuildignore
index 82ecd93d..1182ad1d 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -1,3 +1,5 @@
+^renv$
+^renv\.lock$
 ^.*\.Rproj$
 ^\.Rproj\.user$
 ^LICENSE\.md$
diff --git a/.Rprofile b/.Rprofile
new file mode 100644
index 00000000..81b960f5
--- /dev/null
+++ b/.Rprofile
@@ -0,0 +1 @@
+source("renv/activate.R")
diff --git a/renv.lock b/renv.lock
new file mode 100644
index 00000000..88f2a47e
--- /dev/null
+++ b/renv.lock
@@ -0,0 +1,2515 @@
+{
+  "R": {
+    "Version": "4.2.2",
+    "Repositories": [
+      {
+        "Name": "CRAN",
+        "URL": "https://packagemanager.posit.co/cran/latest"
+      }
+    ]
+  },
+  "Packages": {
+    "ASySD": {
+      "Package": "ASySD",
+      "Version": "0.4.5",
+      "Source": "GitHub",
+      "RemoteType": "github",
+      "RemoteHost": "api.github.com",
+      "RemoteUsername": "camaradesuk",
+      "RemoteRepo": "ASySD",
+      "RemoteRef": "master",
+      "RemoteSha": "912c1fe68e86f54b29bccb92a1b9340faf01a973",
+      "Requirements": [
+        "R",
+        "RecordLinkage",
+        "RefManageR",
+        "XML",
+        "bibliometrix",
+        "dplyr",
+        "glue",
+        "igraph",
+        "keys",
+        "parallel",
+        "parallelly",
+        "progressr",
+        "rlang",
+        "shiny",
+        "shinyjs",
+        "shinythemes",
+        "synthesisr",
+        "tidyr",
+        "utf8"
+      ],
+      "Hash": "6092ed2b576a2695ce2698644d256618"
+    },
+    "DBI": {
+      "Package": "DBI",
+      "Version": "1.2.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "methods"
+      ],
+      "Hash": "164809cd72e1d5160b4cb3aa57f510fe"
+    },
+    "DT": {
+      "Package": "DT",
+      "Version": "0.33",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "crosstalk",
+        "htmltools",
+        "htmlwidgets",
+        "httpuv",
+        "jquerylib",
+        "jsonlite",
+        "magrittr",
+        "promises"
+      ],
+      "Hash": "64ff3427f559ce3f2597a4fe13255cb6"
+    },
+    "KernSmooth": {
+      "Package": "KernSmooth",
+      "Version": "2.23-20",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "stats"
+      ],
+      "Hash": "8dcfa99b14c296bc9f1fd64d52fd3ce7"
+    },
+    "MASS": {
+      "Package": "MASS",
+      "Version": "7.3-58.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "grDevices",
+        "graphics",
+        "methods",
+        "stats",
+        "utils"
+      ],
+      "Hash": "762e1804143a332333c054759f89a706"
+    },
+    "Matrix": {
+      "Package": "Matrix",
+      "Version": "1.5-1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "graphics",
+        "grid",
+        "lattice",
+        "methods",
+        "stats",
+        "utils"
+      ],
+      "Hash": "539dc0c0c05636812f1080f473d2c177"
+    },
+    "R6": {
+      "Package": "R6",
+      "Version": "2.5.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R"
+      ],
+      "Hash": "470851b6d5d0ac559e9d01bb352b4021"
+    },
+    "RColorBrewer": {
+      "Package": "RColorBrewer",
+      "Version": "1.1-3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R"
+      ],
+      "Hash": "45f0398006e83a5b10b72a90663d8d8c"
+    },
+    "RSQLite": {
+      "Package": "RSQLite",
+      "Version": "2.3.6",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "DBI",
+        "R",
+        "bit64",
+        "blob",
+        "cpp11",
+        "memoise",
+        "methods",
+        "pkgconfig",
+        "plogr",
+        "rlang"
+      ],
+      "Hash": "ae4a925e0f6bb1b7e5fa96b739c5221a"
+    },
+    "Rcpp": {
+      "Package": "Rcpp",
+      "Version": "1.0.10",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "methods",
+        "utils"
+      ],
+      "Hash": "e749cae40fa9ef469b6050959517453c"
+    },
+    "RecordLinkage": {
+      "Package": "RecordLinkage",
+      "Version": "0.4-12.4",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "DBI",
+        "R",
+        "RSQLite",
+        "ada",
+        "data.table",
+        "e1071",
+        "evd",
+        "ff",
+        "ipred",
+        "methods",
+        "nnet",
+        "rpart",
+        "stats",
+        "xtable"
+      ],
+      "Hash": "14657f3793f185e271b7293d311afe8a"
+    },
+    "RefManageR": {
+      "Package": "RefManageR",
+      "Version": "1.4.0",
+      "Source": "GitHub",
+      "RemoteType": "github",
+      "RemoteHost": "api.github.com",
+      "RemoteRepo": "RefManageR",
+      "RemoteUsername": "ropensci",
+      "RemoteRef": "HEAD",
+      "RemoteSha": "261980940c5c2200522b40f065c1ff8461bdf7cc",
+      "Requirements": [
+        "R",
+        "bibtex",
+        "httr",
+        "jsonlite",
+        "lubridate",
+        "methods",
+        "plyr",
+        "stringr",
+        "tools",
+        "utils",
+        "xml2"
+      ],
+      "Hash": "baaadb0efff610639788fcacea4ee77f"
+    },
+    "SQUAREM": {
+      "Package": "SQUAREM",
+      "Version": "2021.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R"
+      ],
+      "Hash": "0cf10dab0d023d5b46a5a14387556891"
+    },
+    "SnowballC": {
+      "Package": "SnowballC",
+      "Version": "0.7.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "46da3912f69e3e6258a033802c4af32e"
+    },
+    "UpSetR": {
+      "Package": "UpSetR",
+      "Version": "1.4.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "ggplot2",
+        "grDevices",
+        "gridExtra",
+        "methods",
+        "plyr",
+        "scales",
+        "stats",
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+      "Version": "2.0.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "cbff1b666c6fa6d21202f07e2318d4f1"
+    },
+    "rentrez": {
+      "Package": "rentrez",
+      "Version": "1.2.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "XML",
+        "httr",
+        "jsonlite"
+      ],
+      "Hash": "90e6d8dc1b2fe96d0753a94943cf604d"
+    },
+    "renv": {
+      "Package": "renv",
+      "Version": "1.0.7",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "utils"
+      ],
+      "Hash": "397b7b2a265bc5a7a06852524dabae20"
+    },
+    "rlang": {
+      "Package": "rlang",
+      "Version": "1.1.6",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "utils"
+      ],
+      "Hash": "892124978869b74935dc3934c42bfe5a"
+    },
+    "rmarkdown": {
+      "Package": "rmarkdown",
+      "Version": "2.29",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "bslib",
+        "evaluate",
+        "fontawesome",
+        "htmltools",
+        "jquerylib",
+        "jsonlite",
+        "knitr",
+        "methods",
+        "tinytex",
+        "tools",
+        "utils",
+        "xfun",
+        "yaml"
+      ],
+      "Hash": "df99277f63d01c34e95e3d2f06a79736"
+    },
+    "rpart": {
+      "Package": "rpart",
+      "Version": "4.1.19",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "grDevices",
+        "graphics",
+        "stats"
+      ],
+      "Hash": "b3c892a81783376cc2204af0f5805a80"
+    },
+    "rprojroot": {
+      "Package": "rprojroot",
+      "Version": "2.0.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R"
+      ],
+      "Hash": "1de7ab598047a87bba48434ba35d497d"
+    },
+    "rscopus": {
+      "Package": "rscopus",
+      "Version": "0.6.6",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "dplyr",
+        "glue",
+        "httr",
+        "jsonlite",
+        "plyr",
+        "stats",
+        "tidyr",
+        "tools",
+        "utils"
+      ],
+      "Hash": "f61cb29ba3a155b720f8adaad30afefb"
+    },
+    "sass": {
+      "Package": "sass",
+      "Version": "0.4.9",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R6",
+        "fs",
+        "htmltools",
+        "rappdirs",
+        "rlang"
+      ],
+      "Hash": "d53dbfddf695303ea4ad66f86e99b95d"
+    },
+    "scales": {
+      "Package": "scales",
+      "Version": "1.4.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "R6",
+        "RColorBrewer",
+        "cli",
+        "farver",
+        "glue",
+        "labeling",
+        "lifecycle",
+        "rlang",
+        "viridisLite"
+      ],
+      "Hash": "c5bba8f0d1df8c4b9538a40570798d9b"
+    },
+    "shape": {
+      "Package": "shape",
+      "Version": "1.4.6.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "grDevices",
+        "graphics",
+        "stats"
+      ],
+      "Hash": "5c47e84dc0a3ca761ae1d307889e796d"
+    },
+    "shiny": {
+      "Package": "shiny",
+      "Version": "1.10.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "R6",
+        "bslib",
+        "cachem",
+        "commonmark",
+        "crayon",
+        "fastmap",
+        "fontawesome",
+        "glue",
+        "grDevices",
+        "htmltools",
+        "httpuv",
+        "jsonlite",
+        "later",
+        "lifecycle",
+        "methods",
+        "mime",
+        "promises",
+        "rlang",
+        "sourcetools",
+        "tools",
+        "utils",
+        "withr",
+        "xtable"
+      ],
+      "Hash": "4b4477baa9a939c5577e5ddb4bf01f28"
+    },
+    "shinyWidgets": {
+      "Package": "shinyWidgets",
+      "Version": "0.9.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "bslib",
+        "grDevices",
+        "htmltools",
+        "jsonlite",
+        "rlang",
+        "sass",
+        "shiny"
+      ],
+      "Hash": "2356d33ce915ad415b1fe9a476c99a7d"
+    },
+    "shinyalert": {
+      "Package": "shinyalert",
+      "Version": "3.1.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "htmltools",
+        "shiny",
+        "uuid"
+      ],
+      "Hash": "45f99f3173e5513e2dec78dc4d63f9f0"
+    },
+    "shinybusy": {
+      "Package": "shinybusy",
+      "Version": "0.3.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "htmltools",
+        "htmlwidgets",
+        "jsonlite",
+        "shiny"
+      ],
+      "Hash": "7dc8feb4207741ff581422a04fc8f3ea"
+    },
+    "shinyjs": {
+      "Package": "shinyjs",
+      "Version": "2.1.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "digest",
+        "jsonlite",
+        "shiny"
+      ],
+      "Hash": "802e4786b353a4bb27116957558548d5"
+    },
+    "shinythemes": {
+      "Package": "shinythemes",
+      "Version": "1.2.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "shiny"
+      ],
+      "Hash": "30f0ebc41feba25691073626ff5e2cf4"
+    },
+    "sourcetools": {
+      "Package": "sourcetools",
+      "Version": "0.1.7-1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R"
+      ],
+      "Hash": "5f5a7629f956619d519205ec475fe647"
+    },
+    "stringdist": {
+      "Package": "stringdist",
+      "Version": "0.9.12",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "parallel"
+      ],
+      "Hash": "f360720fa3feb7db9d4133b31ebb067f"
+    },
+    "stringi": {
+      "Package": "stringi",
+      "Version": "1.8.4",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "stats",
+        "tools",
+        "utils"
+      ],
+      "Hash": "39e1144fd75428983dc3f63aa53dfa91"
+    },
+    "stringr": {
+      "Package": "stringr",
+      "Version": "1.5.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cli",
+        "glue",
+        "lifecycle",
+        "magrittr",
+        "rlang",
+        "stringi",
+        "vctrs"
+      ],
+      "Hash": "960e2ae9e09656611e0b8214ad543207"
+    },
+    "survival": {
+      "Package": "survival",
+      "Version": "3.4-0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "Matrix",
+        "R",
+        "graphics",
+        "methods",
+        "splines",
+        "stats",
+        "utils"
+      ],
+      "Hash": "04411ae66ab4659230c067c32966fc20"
+    },
+    "synthesisr": {
+      "Package": "synthesisr",
+      "Version": "0.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "stringdist"
+      ],
+      "Hash": "01e48771d6acc493dc76e5eb77079c1e"
+    },
+    "sys": {
+      "Package": "sys",
+      "Version": "3.4.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "3a1be13d68d47a8cd0bfd74739ca1555"
+    },
+    "systemfonts": {
+      "Package": "systemfonts",
+      "Version": "1.0.4",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cpp11"
+      ],
+      "Hash": "90b28393209827327de889f49935140a"
+    },
+    "testthat": {
+      "Package": "testthat",
+      "Version": "3.2.3",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "R6",
+        "brio",
+        "callr",
+        "cli",
+        "desc",
+        "digest",
+        "evaluate",
+        "jsonlite",
+        "lifecycle",
+        "magrittr",
+        "methods",
+        "pkgload",
+        "praise",
+        "processx",
+        "ps",
+        "rlang",
+        "utils",
+        "waldo",
+        "withr"
+      ],
+      "Hash": "42f889439ccb14c55fc3d75c9c755056"
+    },
+    "textshaping": {
+      "Package": "textshaping",
+      "Version": "0.3.6",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cpp11",
+        "systemfonts"
+      ],
+      "Hash": "1ab6223d3670fac7143202cb6a2d43d5"
+    },
+    "tibble": {
+      "Package": "tibble",
+      "Version": "3.2.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "fansi",
+        "lifecycle",
+        "magrittr",
+        "methods",
+        "pillar",
+        "pkgconfig",
+        "rlang",
+        "utils",
+        "vctrs"
+      ],
+      "Hash": "a84e2cc86d07289b3b6f5069df7a004c"
+    },
+    "tidyr": {
+      "Package": "tidyr",
+      "Version": "1.3.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cli",
+        "cpp11",
+        "dplyr",
+        "glue",
+        "lifecycle",
+        "magrittr",
+        "purrr",
+        "rlang",
+        "stringr",
+        "tibble",
+        "tidyselect",
+        "utils",
+        "vctrs"
+      ],
+      "Hash": "915fb7ce036c22a6a33b5a8adb712eb1"
+    },
+    "tidyselect": {
+      "Package": "tidyselect",
+      "Version": "1.2.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cli",
+        "glue",
+        "lifecycle",
+        "rlang",
+        "vctrs",
+        "withr"
+      ],
+      "Hash": "829f27b9c4919c16b593794a6344d6c0"
+    },
+    "tidytext": {
+      "Package": "tidytext",
+      "Version": "0.4.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "Matrix",
+        "R",
+        "cli",
+        "dplyr",
+        "generics",
+        "janeaustenr",
+        "lifecycle",
+        "methods",
+        "purrr",
+        "rlang",
+        "stringr",
+        "tibble",
+        "tokenizers",
+        "vctrs"
+      ],
+      "Hash": "612125521ebc22fd028182761211b5d9"
+    },
+    "timechange": {
+      "Package": "timechange",
+      "Version": "0.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cpp11"
+      ],
+      "Hash": "c5f3c201b931cd6474d17d8700ccb1c8"
+    },
+    "tinytex": {
+      "Package": "tinytex",
+      "Version": "0.51",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "xfun"
+      ],
+      "Hash": "d44e2fcd2e4e076f0aac540208559d1d"
+    },
+    "tokenizers": {
+      "Package": "tokenizers",
+      "Version": "0.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "Rcpp",
+        "SnowballC",
+        "stringi"
+      ],
+      "Hash": "76d35ebfaaf291e08c15696c9f2ec96d"
+    },
+    "tzdb": {
+      "Package": "tzdb",
+      "Version": "0.4.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cpp11"
+      ],
+      "Hash": "f561504ec2897f4d46f0c7657e488ae1"
+    },
+    "utf8": {
+      "Package": "utf8",
+      "Version": "1.2.4",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R"
+      ],
+      "Hash": "62b65c52671e6665f803ff02954446e9"
+    },
+    "uuid": {
+      "Package": "uuid",
+      "Version": "1.2-1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R"
+      ],
+      "Hash": "34e965e62a41fcafb1ca60e9b142085b"
+    },
+    "vctrs": {
+      "Package": "vctrs",
+      "Version": "0.6.5",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cli",
+        "glue",
+        "lifecycle",
+        "rlang"
+      ],
+      "Hash": "c03fa420630029418f7e6da3667aac4a"
+    },
+    "viridisLite": {
+      "Package": "viridisLite",
+      "Version": "0.4.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R"
+      ],
+      "Hash": "c826c7c4241b6fc89ff55aaea3fa7491"
+    },
+    "vroom": {
+      "Package": "vroom",
+      "Version": "1.6.5",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "bit64",
+        "cli",
+        "cpp11",
+        "crayon",
+        "glue",
+        "hms",
+        "lifecycle",
+        "methods",
+        "progress",
+        "rlang",
+        "stats",
+        "tibble",
+        "tidyselect",
+        "tzdb",
+        "vctrs",
+        "withr"
+      ],
+      "Hash": "390f9315bc0025be03012054103d227c"
+    },
+    "waldo": {
+      "Package": "waldo",
+      "Version": "0.6.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cli",
+        "diffobj",
+        "glue",
+        "methods",
+        "rlang"
+      ],
+      "Hash": "52f574062a7b66e56926988c3fbdb3b7"
+    },
+    "whisker": {
+      "Package": "whisker",
+      "Version": "0.4.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "c6abfa47a46d281a7d5159d0a8891e88"
+    },
+    "withr": {
+      "Package": "withr",
+      "Version": "3.0.2",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "grDevices",
+        "graphics"
+      ],
+      "Hash": "cc2d62c76458d425210d1eb1478b30b4"
+    },
+    "xfun": {
+      "Package": "xfun",
+      "Version": "0.52",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "grDevices",
+        "stats",
+        "tools"
+      ],
+      "Hash": "652ce36fe7d57688e6786819b09d9190"
+    },
+    "xml2": {
+      "Package": "xml2",
+      "Version": "1.3.6",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "cli",
+        "methods",
+        "rlang"
+      ],
+      "Hash": "1d0336142f4cd25d8d23cd3ba7a8fb61"
+    },
+    "xtable": {
+      "Package": "xtable",
+      "Version": "1.8-4",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Requirements": [
+        "R",
+        "stats",
+        "utils"
+      ],
+      "Hash": "b8acdf8af494d9ec19ccb2481a9b11c2"
+    },
+    "yaml": {
+      "Package": "yaml",
+      "Version": "2.3.8",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "29240487a071f535f5e5d5a323b7afbd"
+    },
+    "zip": {
+      "Package": "zip",
+      "Version": "2.3.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "fcc4bd8e6da2d2011eb64a5e5cc685ab"
+    }
+  }
+}
diff --git a/renv/.gitignore b/renv/.gitignore
new file mode 100644
index 00000000..0ec0cbba
--- /dev/null
+++ b/renv/.gitignore
@@ -0,0 +1,7 @@
+library/
+local/
+cellar/
+lock/
+python/
+sandbox/
+staging/
diff --git a/renv/activate.R b/renv/activate.R
new file mode 100644
index 00000000..d13f9932
--- /dev/null
+++ b/renv/activate.R
@@ -0,0 +1,1220 @@
+
+local({
+
+  # the requested version of renv
+  version <- "1.0.7"
+  attr(version, "sha") <- NULL
+
+  # the project directory
+  project <- Sys.getenv("RENV_PROJECT")
+  if (!nzchar(project))
+    project <- getwd()
+
+  # use start-up diagnostics if enabled
+  diagnostics <- Sys.getenv("RENV_STARTUP_DIAGNOSTICS", unset = "FALSE")
+  if (diagnostics) {
+    start <- Sys.time()
+    profile <- tempfile("renv-startup-", fileext = ".Rprof")
+    utils::Rprof(profile)
+    on.exit({
+      utils::Rprof(NULL)
+      elapsed <- signif(difftime(Sys.time(), start, units = "auto"), digits = 2L)
+      writeLines(sprintf("- renv took %s to run the autoloader.", format(elapsed)))
+      writeLines(sprintf("- Profile: %s", profile))
+      print(utils::summaryRprof(profile))
+    }, add = TRUE)
+  }
+
+  # figure out whether the autoloader is enabled
+  enabled <- local({
+
+    # first, check config option
+    override <- getOption("renv.config.autoloader.enabled")
+    if (!is.null(override))
+      return(override)
+
+    # if we're being run in a context where R_LIBS is already set,
+    # don't load -- presumably we're being run as a sub-process and
+    # the parent process has already set up library paths for us
+    rcmd <- Sys.getenv("R_CMD", unset = NA)
+    rlibs <- Sys.getenv("R_LIBS", unset = NA)
+    if (!is.na(rlibs) && !is.na(rcmd))
+      return(FALSE)
+
+    # next, check environment variables
+    # TODO: prefer using the configuration one in the future
+    envvars <- c(
+      "RENV_CONFIG_AUTOLOADER_ENABLED",
+      "RENV_AUTOLOADER_ENABLED",
+      "RENV_ACTIVATE_PROJECT"
+    )
+
+    for (envvar in envvars) {
+      envval <- Sys.getenv(envvar, unset = NA)
+      if (!is.na(envval))
+        return(tolower(envval) %in% c("true", "t", "1"))
+    }
+
+    # enable by default
+    TRUE
+
+  })
+
+  # bail if we're not enabled
+  if (!enabled) {
+
+    # if we're not enabled, we might still need to manually load
+    # the user profile here
+    profile <- Sys.getenv("R_PROFILE_USER", unset = "~/.Rprofile")
+    if (file.exists(profile)) {
+      cfg <- Sys.getenv("RENV_CONFIG_USER_PROFILE", unset = "TRUE")
+      if (tolower(cfg) %in% c("true", "t", "1"))
+        sys.source(profile, envir = globalenv())
+    }
+
+    return(FALSE)
+
+  }
+
+  # avoid recursion
+  if (identical(getOption("renv.autoloader.running"), TRUE)) {
+    warning("ignoring recursive attempt to run renv autoloader")
+    return(invisible(TRUE))
+  }
+
+  # signal that we're loading renv during R startup
+  options(renv.autoloader.running = TRUE)
+  on.exit(options(renv.autoloader.running = NULL), add = TRUE)
+
+  # signal that we've consented to use renv
+  options(renv.consent = TRUE)
+
+  # load the 'utils' package eagerly -- this ensures that renv shims, which
+  # mask 'utils' packages, will come first on the search path
+  library(utils, lib.loc = .Library)
+
+  # unload renv if it's already been loaded
+  if ("renv" %in% loadedNamespaces())
+    unloadNamespace("renv")
+
+  # load bootstrap tools   
+  `%||%` <- function(x, y) {
+    if (is.null(x)) y else x
+  }
+  
+  catf <- function(fmt, ..., appendLF = TRUE) {
+  
+    quiet <- getOption("renv.bootstrap.quiet", default = FALSE)
+    if (quiet)
+      return(invisible())
+  
+    msg <- sprintf(fmt, ...)
+    cat(msg, file = stdout(), sep = if (appendLF) "\n" else "")
+  
+    invisible(msg)
+  
+  }
+  
+  header <- function(label,
+                     ...,
+                     prefix = "#",
+                     suffix = "-",
+                     n = min(getOption("width"), 78))
+  {
+    label <- sprintf(label, ...)
+    n <- max(n - nchar(label) - nchar(prefix) - 2L, 8L)
+    if (n <= 0)
+      return(paste(prefix, label))
+  
+    tail <- paste(rep.int(suffix, n), collapse = "")
+    paste0(prefix, " ", label, " ", tail)
+  
+  }
+  
+  heredoc <- function(text, leave = 0) {
+  
+    # remove leading, trailing whitespace
+    trimmed <- gsub("^\\s*\\n|\\n\\s*$", "", text)
+  
+    # split into lines
+    lines <- strsplit(trimmed, "\n", fixed = TRUE)[[1L]]
+  
+    # compute common indent
+    indent <- regexpr("[^[:space:]]", lines)
+    common <- min(setdiff(indent, -1L)) - leave
+    paste(substring(lines, common), collapse = "\n")
+  
+  }
+  
+  startswith <- function(string, prefix) {
+    substring(string, 1, nchar(prefix)) == prefix
+  }
+  
+  bootstrap <- function(version, library) {
+  
+    friendly <- renv_bootstrap_version_friendly(version)
+    section <- header(sprintf("Bootstrapping renv %s", friendly))
+    catf(section)
+  
+    # attempt to download renv
+    catf("- Downloading renv ... ", appendLF = FALSE)
+    withCallingHandlers(
+      tarball <- renv_bootstrap_download(version),
+      error = function(err) {
+        catf("FAILED")
+        stop("failed to download:\n", conditionMessage(err))
+      }
+    )
+    catf("OK")
+    on.exit(unlink(tarball), add = TRUE)
+  
+    # now attempt to install
+    catf("- Installing renv  ... ", appendLF = FALSE)
+    withCallingHandlers(
+      status <- renv_bootstrap_install(version, tarball, library),
+      error = function(err) {
+        catf("FAILED")
+        stop("failed to install:\n", conditionMessage(err))
+      }
+    )
+    catf("OK")
+  
+    # add empty line to break up bootstrapping from normal output
+    catf("")
+  
+    return(invisible())
+  }
+  
+  renv_bootstrap_tests_running <- function() {
+    getOption("renv.tests.running", default = FALSE)
+  }
+  
+  renv_bootstrap_repos <- function() {
+  
+    # get CRAN repository
+    cran <- getOption("renv.repos.cran", "https://cloud.r-project.org")
+  
+    # check for repos override
+    repos <- Sys.getenv("RENV_CONFIG_REPOS_OVERRIDE", unset = NA)
+    if (!is.na(repos)) {
+  
+      # check for RSPM; if set, use a fallback repository for renv
+      rspm <- Sys.getenv("RSPM", unset = NA)
+      if (identical(rspm, repos))
+        repos <- c(RSPM = rspm, CRAN = cran)
+  
+      return(repos)
+  
+    }
+  
+    # check for lockfile repositories
+    repos <- tryCatch(renv_bootstrap_repos_lockfile(), error = identity)
+    if (!inherits(repos, "error") && length(repos))
+      return(repos)
+  
+    # retrieve current repos
+    repos <- getOption("repos")
+  
+    # ensure @CRAN@ entries are resolved
+    repos[repos == "@CRAN@"] <- cran
+  
+    # add in renv.bootstrap.repos if set
+    default <- c(FALLBACK = "https://cloud.r-project.org")
+    extra <- getOption("renv.bootstrap.repos", default = default)
+    repos <- c(repos, extra)
+  
+    # remove duplicates that might've snuck in
+    dupes <- duplicated(repos) | duplicated(names(repos))
+    repos[!dupes]
+  
+  }
+  
+  renv_bootstrap_repos_lockfile <- function() {
+  
+    lockpath <- Sys.getenv("RENV_PATHS_LOCKFILE", unset = "renv.lock")
+    if (!file.exists(lockpath))
+      return(NULL)
+  
+    lockfile <- tryCatch(renv_json_read(lockpath), error = identity)
+    if (inherits(lockfile, "error")) {
+      warning(lockfile)
+      return(NULL)
+    }
+  
+    repos <- lockfile$R$Repositories
+    if (length(repos) == 0)
+      return(NULL)
+  
+    keys <- vapply(repos, `[[`, "Name", FUN.VALUE = character(1))
+    vals <- vapply(repos, `[[`, "URL", FUN.VALUE = character(1))
+    names(vals) <- keys
+  
+    return(vals)
+  
+  }
+  
+  renv_bootstrap_download <- function(version) {
+  
+    sha <- attr(version, "sha", exact = TRUE)
+  
+    methods <- if (!is.null(sha)) {
+  
+      # attempting to bootstrap a development version of renv
+      c(
+        function() renv_bootstrap_download_tarball(sha),
+        function() renv_bootstrap_download_github(sha)
+      )
+  
+    } else {
+  
+      # attempting to bootstrap a release version of renv
+      c(
+        function() renv_bootstrap_download_tarball(version),
+        function() renv_bootstrap_download_cran_latest(version),
+        function() renv_bootstrap_download_cran_archive(version)
+      )
+  
+    }
+  
+    for (method in methods) {
+      path <- tryCatch(method(), error = identity)
+      if (is.character(path) && file.exists(path))
+        return(path)
+    }
+  
+    stop("All download methods failed")
+  
+  }
+  
+  renv_bootstrap_download_impl <- function(url, destfile) {
+  
+    mode <- "wb"
+  
+    # https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17715
+    fixup <-
+      Sys.info()[["sysname"]] == "Windows" &&
+      substring(url, 1L, 5L) == "file:"
+  
+    if (fixup)
+      mode <- "w+b"
+  
+    args <- list(
+      url      = url,
+      destfile = destfile,
+      mode     = mode,
+      quiet    = TRUE
+    )
+  
+    if ("headers" %in% names(formals(utils::download.file)))
+      args$headers <- renv_bootstrap_download_custom_headers(url)
+  
+    do.call(utils::download.file, args)
+  
+  }
+  
+  renv_bootstrap_download_custom_headers <- function(url) {
+  
+    headers <- getOption("renv.download.headers")
+    if (is.null(headers))
+      return(character())
+  
+    if (!is.function(headers))
+      stopf("'renv.download.headers' is not a function")
+  
+    headers <- headers(url)
+    if (length(headers) == 0L)
+      return(character())
+  
+    if (is.list(headers))
+      headers <- unlist(headers, recursive = FALSE, use.names = TRUE)
+  
+    ok <-
+      is.character(headers) &&
+      is.character(names(headers)) &&
+      all(nzchar(names(headers)))
+  
+    if (!ok)
+      stop("invocation of 'renv.download.headers' did not return a named character vector")
+  
+    headers
+  
+  }
+  
+  renv_bootstrap_download_cran_latest <- function(version) {
+  
+    spec <- renv_bootstrap_download_cran_latest_find(version)
+    type  <- spec$type
+    repos <- spec$repos
+  
+    baseurl <- utils::contrib.url(repos = repos, type = type)
+    ext <- if (identical(type, "source"))
+      ".tar.gz"
+    else if (Sys.info()[["sysname"]] == "Windows")
+      ".zip"
+    else
+      ".tgz"
+    name <- sprintf("renv_%s%s", version, ext)
+    url <- paste(baseurl, name, sep = "/")
+  
+    destfile <- file.path(tempdir(), name)
+    status <- tryCatch(
+      renv_bootstrap_download_impl(url, destfile),
+      condition = identity
+    )
+  
+    if (inherits(status, "condition"))
+      return(FALSE)
+  
+    # report success and return
+    destfile
+  
+  }
+  
+  renv_bootstrap_download_cran_latest_find <- function(version) {
+  
+    # check whether binaries are supported on this system
+    binary <-
+      getOption("renv.bootstrap.binary", default = TRUE) &&
+      !identical(.Platform$pkgType, "source") &&
+      !identical(getOption("pkgType"), "source") &&
+      Sys.info()[["sysname"]] %in% c("Darwin", "Windows")
+  
+    types <- c(if (binary) "binary", "source")
+  
+    # iterate over types + repositories
+    for (type in types) {
+      for (repos in renv_bootstrap_repos()) {
+  
+        # retrieve package database
+        db <- tryCatch(
+          as.data.frame(
+            utils::available.packages(type = type, repos = repos),
+            stringsAsFactors = FALSE
+          ),
+          error = identity
+        )
+  
+        if (inherits(db, "error"))
+          next
+  
+        # check for compatible entry
+        entry <- db[db$Package %in% "renv" & db$Version %in% version, ]
+        if (nrow(entry) == 0)
+          next
+  
+        # found it; return spec to caller
+        spec <- list(entry = entry, type = type, repos = repos)
+        return(spec)
+  
+      }
+    }
+  
+    # if we got here, we failed to find renv
+    fmt <- "renv %s is not available from your declared package repositories"
+    stop(sprintf(fmt, version))
+  
+  }
+  
+  renv_bootstrap_download_cran_archive <- function(version) {
+  
+    name <- sprintf("renv_%s.tar.gz", version)
+    repos <- renv_bootstrap_repos()
+    urls <- file.path(repos, "src/contrib/Archive/renv", name)
+    destfile <- file.path(tempdir(), name)
+  
+    for (url in urls) {
+  
+      status <- tryCatch(
+        renv_bootstrap_download_impl(url, destfile),
+        condition = identity
+      )
+  
+      if (identical(status, 0L))
+        return(destfile)
+  
+    }
+  
+    return(FALSE)
+  
+  }
+  
+  renv_bootstrap_download_tarball <- function(version) {
+  
+    # if the user has provided the path to a tarball via
+    # an environment variable, then use it
+    tarball <- Sys.getenv("RENV_BOOTSTRAP_TARBALL", unset = NA)
+    if (is.na(tarball))
+      return()
+  
+    # allow directories
+    if (dir.exists(tarball)) {
+      name <- sprintf("renv_%s.tar.gz", version)
+      tarball <- file.path(tarball, name)
+    }
+  
+    # bail if it doesn't exist
+    if (!file.exists(tarball)) {
+  
+      # let the user know we weren't able to honour their request
+      fmt <- "- RENV_BOOTSTRAP_TARBALL is set (%s) but does not exist."
+      msg <- sprintf(fmt, tarball)
+      warning(msg)
+  
+      # bail
+      return()
+  
+    }
+  
+    catf("- Using local tarball '%s'.", tarball)
+    tarball
+  
+  }
+  
+  renv_bootstrap_download_github <- function(version) {
+  
+    enabled <- Sys.getenv("RENV_BOOTSTRAP_FROM_GITHUB", unset = "TRUE")
+    if (!identical(enabled, "TRUE"))
+      return(FALSE)
+  
+    # prepare download options
+    pat <- Sys.getenv("GITHUB_PAT")
+    if (nzchar(Sys.which("curl")) && nzchar(pat)) {
+      fmt <- "--location --fail --header \"Authorization: token %s\""
+      extra <- sprintf(fmt, pat)
+      saved <- options("download.file.method", "download.file.extra")
+      options(download.file.method = "curl", download.file.extra = extra)
+      on.exit(do.call(base::options, saved), add = TRUE)
+    } else if (nzchar(Sys.which("wget")) && nzchar(pat)) {
+      fmt <- "--header=\"Authorization: token %s\""
+      extra <- sprintf(fmt, pat)
+      saved <- options("download.file.method", "download.file.extra")
+      options(download.file.method = "wget", download.file.extra = extra)
+      on.exit(do.call(base::options, saved), add = TRUE)
+    }
+  
+    url <- file.path("https://api.github.com/repos/rstudio/renv/tarball", version)
+    name <- sprintf("renv_%s.tar.gz", version)
+    destfile <- file.path(tempdir(), name)
+  
+    status <- tryCatch(
+      renv_bootstrap_download_impl(url, destfile),
+      condition = identity
+    )
+  
+    if (!identical(status, 0L))
+      return(FALSE)
+  
+    renv_bootstrap_download_augment(destfile)
+  
+    return(destfile)
+  
+  }
+  
+  # Add Sha to DESCRIPTION. This is stop gap until #890, after which we
+  # can use renv::install() to fully capture metadata.
+  renv_bootstrap_download_augment <- function(destfile) {
+    sha <- renv_bootstrap_git_extract_sha1_tar(destfile)
+    if (is.null(sha)) {
+      return()
+    }
+  
+    # Untar
+    tempdir <- tempfile("renv-github-")
+    on.exit(unlink(tempdir, recursive = TRUE), add = TRUE)
+    untar(destfile, exdir = tempdir)
+    pkgdir <- dir(tempdir, full.names = TRUE)[[1]]
+  
+    # Modify description
+    desc_path <- file.path(pkgdir, "DESCRIPTION")
+    desc_lines <- readLines(desc_path)
+    remotes_fields <- c(
+      "RemoteType: github",
+      "RemoteHost: api.github.com",
+      "RemoteRepo: renv",
+      "RemoteUsername: rstudio",
+      "RemotePkgRef: rstudio/renv",
+      paste("RemoteRef: ", sha),
+      paste("RemoteSha: ", sha)
+    )
+    writeLines(c(desc_lines[desc_lines != ""], remotes_fields), con = desc_path)
+  
+    # Re-tar
+    local({
+      old <- setwd(tempdir)
+      on.exit(setwd(old), add = TRUE)
+  
+      tar(destfile, compression = "gzip")
+    })
+    invisible()
+  }
+  
+  # Extract the commit hash from a git archive. Git archives include the SHA1
+  # hash as the comment field of the tarball pax extended header
+  # (see https://www.kernel.org/pub/software/scm/git/docs/git-archive.html)
+  # For GitHub archives this should be the first header after the default one
+  # (512 byte) header.
+  renv_bootstrap_git_extract_sha1_tar <- function(bundle) {
+  
+    # open the bundle for reading
+    # We use gzcon for everything because (from ?gzcon)
+    # > Reading from a connection which does not supply a 'gzip' magic
+    # > header is equivalent to reading from the original connection
+    conn <- gzcon(file(bundle, open = "rb", raw = TRUE))
+    on.exit(close(conn))
+  
+    # The default pax header is 512 bytes long and the first pax extended header
+    # with the comment should be 51 bytes long
+    # `52 comment=` (11 chars) + 40 byte SHA1 hash
+    len <- 0x200 + 0x33
+    res <- rawToChar(readBin(conn, "raw", n = len)[0x201:len])
+  
+    if (grepl("^52 comment=", res)) {
+      sub("52 comment=", "", res)
+    } else {
+      NULL
+    }
+  }
+  
+  renv_bootstrap_install <- function(version, tarball, library) {
+  
+    # attempt to install it into project library
+    dir.create(library, showWarnings = FALSE, recursive = TRUE)
+    output <- renv_bootstrap_install_impl(library, tarball)
+  
+    # check for successful install
+    status <- attr(output, "status")
+    if (is.null(status) || identical(status, 0L))
+      return(status)
+  
+    # an error occurred; report it
+    header <- "installation of renv failed"
+    lines <- paste(rep.int("=", nchar(header)), collapse = "")
+    text <- paste(c(header, lines, output), collapse = "\n")
+    stop(text)
+  
+  }
+  
+  renv_bootstrap_install_impl <- function(library, tarball) {
+  
+    # invoke using system2 so we can capture and report output
+    bin <- R.home("bin")
+    exe <- if (Sys.info()[["sysname"]] == "Windows") "R.exe" else "R"
+    R <- file.path(bin, exe)
+  
+    args <- c(
+      "--vanilla", "CMD", "INSTALL", "--no-multiarch",
+      "-l", shQuote(path.expand(library)),
+      shQuote(path.expand(tarball))
+    )
+  
+    system2(R, args, stdout = TRUE, stderr = TRUE)
+  
+  }
+  
+  renv_bootstrap_platform_prefix <- function() {
+  
+    # construct version prefix
+    version <- paste(R.version$major, R.version$minor, sep = ".")
+    prefix <- paste("R", numeric_version(version)[1, 1:2], sep = "-")
+  
+    # include SVN revision for development versions of R
+    # (to avoid sharing platform-specific artefacts with released versions of R)
+    devel <-
+      identical(R.version[["status"]],   "Under development (unstable)") ||
+      identical(R.version[["nickname"]], "Unsuffered Consequences")
+  
+    if (devel)
+      prefix <- paste(prefix, R.version[["svn rev"]], sep = "-r")
+  
+    # build list of path components
+    components <- c(prefix, R.version$platform)
+  
+    # include prefix if provided by user
+    prefix <- renv_bootstrap_platform_prefix_impl()
+    if (!is.na(prefix) && nzchar(prefix))
+      components <- c(prefix, components)
+  
+    # build prefix
+    paste(components, collapse = "/")
+  
+  }
+  
+  renv_bootstrap_platform_prefix_impl <- function() {
+  
+    # if an explicit prefix has been supplied, use it
+    prefix <- Sys.getenv("RENV_PATHS_PREFIX", unset = NA)
+    if (!is.na(prefix))
+      return(prefix)
+  
+    # if the user has requested an automatic prefix, generate it
+    auto <- Sys.getenv("RENV_PATHS_PREFIX_AUTO", unset = NA)
+    if (is.na(auto) && getRversion() >= "4.4.0")
+      auto <- "TRUE"
+  
+    if (auto %in% c("TRUE", "True", "true", "1"))
+      return(renv_bootstrap_platform_prefix_auto())
+  
+    # empty string on failure
+    ""
+  
+  }
+  
+  renv_bootstrap_platform_prefix_auto <- function() {
+  
+    prefix <- tryCatch(renv_bootstrap_platform_os(), error = identity)
+    if (inherits(prefix, "error") || prefix %in% "unknown") {
+  
+      msg <- paste(
+        "failed to infer current operating system",
+        "please file a bug report at https://github.com/rstudio/renv/issues",
+        sep = "; "
+      )
+  
+      warning(msg)
+  
+    }
+  
+    prefix
+  
+  }
+  
+  renv_bootstrap_platform_os <- function() {
+  
+    sysinfo <- Sys.info()
+    sysname <- sysinfo[["sysname"]]
+  
+    # handle Windows + macOS up front
+    if (sysname == "Windows")
+      return("windows")
+    else if (sysname == "Darwin")
+      return("macos")
+  
+    # check for os-release files
+    for (file in c("/etc/os-release", "/usr/lib/os-release"))
+      if (file.exists(file))
+        return(renv_bootstrap_platform_os_via_os_release(file, sysinfo))
+  
+    # check for redhat-release files
+    if (file.exists("/etc/redhat-release"))
+      return(renv_bootstrap_platform_os_via_redhat_release())
+  
+    "unknown"
+  
+  }
+  
+  renv_bootstrap_platform_os_via_os_release <- function(file, sysinfo) {
+  
+    # read /etc/os-release
+    release <- utils::read.table(
+      file             = file,
+      sep              = "=",
+      quote            = c("\"", "'"),
+      col.names        = c("Key", "Value"),
+      comment.char     = "#",
+      stringsAsFactors = FALSE
+    )
+  
+    vars <- as.list(release$Value)
+    names(vars) <- release$Key
+  
+    # get os name
+    os <- tolower(sysinfo[["sysname"]])
+  
+    # read id
+    id <- "unknown"
+    for (field in c("ID", "ID_LIKE")) {
+      if (field %in% names(vars) && nzchar(vars[[field]])) {
+        id <- vars[[field]]
+        break
+      }
+    }
+  
+    # read version
+    version <- "unknown"
+    for (field in c("UBUNTU_CODENAME", "VERSION_CODENAME", "VERSION_ID", "BUILD_ID")) {
+      if (field %in% names(vars) && nzchar(vars[[field]])) {
+        version <- vars[[field]]
+        break
+      }
+    }
+  
+    # join together
+    paste(c(os, id, version), collapse = "-")
+  
+  }
+  
+  renv_bootstrap_platform_os_via_redhat_release <- function() {
+  
+    # read /etc/redhat-release
+    contents <- readLines("/etc/redhat-release", warn = FALSE)
+  
+    # infer id
+    id <- if (grepl("centos", contents, ignore.case = TRUE))
+      "centos"
+    else if (grepl("redhat", contents, ignore.case = TRUE))
+      "redhat"
+    else
+      "unknown"
+  
+    # try to find a version component (very hacky)
+    version <- "unknown"
+  
+    parts <- strsplit(contents, "[[:space:]]")[[1L]]
+    for (part in parts) {
+  
+      nv <- tryCatch(numeric_version(part), error = identity)
+      if (inherits(nv, "error"))
+        next
+  
+      version <- nv[1, 1]
+      break
+  
+    }
+  
+    paste(c("linux", id, version), collapse = "-")
+  
+  }
+  
+  renv_bootstrap_library_root_name <- function(project) {
+  
+    # use project name as-is if requested
+    asis <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT_ASIS", unset = "FALSE")
+    if (asis)
+      return(basename(project))
+  
+    # otherwise, disambiguate based on project's path
+    id <- substring(renv_bootstrap_hash_text(project), 1L, 8L)
+    paste(basename(project), id, sep = "-")
+  
+  }
+  
+  renv_bootstrap_library_root <- function(project) {
+  
+    prefix <- renv_bootstrap_profile_prefix()
+  
+    path <- Sys.getenv("RENV_PATHS_LIBRARY", unset = NA)
+    if (!is.na(path))
+      return(paste(c(path, prefix), collapse = "/"))
+  
+    path <- renv_bootstrap_library_root_impl(project)
+    if (!is.null(path)) {
+      name <- renv_bootstrap_library_root_name(project)
+      return(paste(c(path, prefix, name), collapse = "/"))
+    }
+  
+    renv_bootstrap_paths_renv("library", project = project)
+  
+  }
+  
+  renv_bootstrap_library_root_impl <- function(project) {
+  
+    root <- Sys.getenv("RENV_PATHS_LIBRARY_ROOT", unset = NA)
+    if (!is.na(root))
+      return(root)
+  
+    type <- renv_bootstrap_project_type(project)
+    if (identical(type, "package")) {
+      userdir <- renv_bootstrap_user_dir()
+      return(file.path(userdir, "library"))
+    }
+  
+  }
+  
+  renv_bootstrap_validate_version <- function(version, description = NULL) {
+  
+    # resolve description file
+    #
+    # avoid passing lib.loc to `packageDescription()` below, since R will
+    # use the loaded version of the package by default anyhow. note that
+    # this function should only be called after 'renv' is loaded
+    # https://github.com/rstudio/renv/issues/1625
+    description <- description %||% packageDescription("renv")
+  
+    # check whether requested version 'version' matches loaded version of renv
+    sha <- attr(version, "sha", exact = TRUE)
+    valid <- if (!is.null(sha))
+      renv_bootstrap_validate_version_dev(sha, description)
+    else
+      renv_bootstrap_validate_version_release(version, description)
+  
+    if (valid)
+      return(TRUE)
+  
+    # the loaded version of renv doesn't match the requested version;
+    # give the user instructions on how to proceed
+    dev <- identical(description[["RemoteType"]], "github")
+    remote <- if (dev)
+      paste("rstudio/renv", description[["RemoteSha"]], sep = "@")
+    else
+      paste("renv", description[["Version"]], sep = "@")
+  
+    # display both loaded version + sha if available
+    friendly <- renv_bootstrap_version_friendly(
+      version = description[["Version"]],
+      sha     = if (dev) description[["RemoteSha"]]
+    )
+  
+    fmt <- heredoc("
+      renv %1$s was loaded from project library, but this project is configured to use renv %2$s.
+      - Use `renv::record(\"%3$s\")` to record renv %1$s in the lockfile.
+      - Use `renv::restore(packages = \"renv\")` to install renv %2$s into the project library.
+    ")
+    catf(fmt, friendly, renv_bootstrap_version_friendly(version), remote)
+  
+    FALSE
+  
+  }
+  
+  renv_bootstrap_validate_version_dev <- function(version, description) {
+    expected <- description[["RemoteSha"]]
+    is.character(expected) && startswith(expected, version)
+  }
+  
+  renv_bootstrap_validate_version_release <- function(version, description) {
+    expected <- description[["Version"]]
+    is.character(expected) && identical(expected, version)
+  }
+  
+  renv_bootstrap_hash_text <- function(text) {
+  
+    hashfile <- tempfile("renv-hash-")
+    on.exit(unlink(hashfile), add = TRUE)
+  
+    writeLines(text, con = hashfile)
+    tools::md5sum(hashfile)
+  
+  }
+  
+  renv_bootstrap_load <- function(project, libpath, version) {
+  
+    # try to load renv from the project library
+    if (!requireNamespace("renv", lib.loc = libpath, quietly = TRUE))
+      return(FALSE)
+  
+    # warn if the version of renv loaded does not match
+    renv_bootstrap_validate_version(version)
+  
+    # execute renv load hooks, if any
+    hooks <- getHook("renv::autoload")
+    for (hook in hooks)
+      if (is.function(hook))
+        tryCatch(hook(), error = warnify)
+  
+    # load the project
+    renv::load(project)
+  
+    TRUE
+  
+  }
+  
+  renv_bootstrap_profile_load <- function(project) {
+  
+    # if RENV_PROFILE is already set, just use that
+    profile <- Sys.getenv("RENV_PROFILE", unset = NA)
+    if (!is.na(profile) && nzchar(profile))
+      return(profile)
+  
+    # check for a profile file (nothing to do if it doesn't exist)
+    path <- renv_bootstrap_paths_renv("profile", profile = FALSE, project = project)
+    if (!file.exists(path))
+      return(NULL)
+  
+    # read the profile, and set it if it exists
+    contents <- readLines(path, warn = FALSE)
+    if (length(contents) == 0L)
+      return(NULL)
+  
+    # set RENV_PROFILE
+    profile <- contents[[1L]]
+    if (!profile %in% c("", "default"))
+      Sys.setenv(RENV_PROFILE = profile)
+  
+    profile
+  
+  }
+  
+  renv_bootstrap_profile_prefix <- function() {
+    profile <- renv_bootstrap_profile_get()
+    if (!is.null(profile))
+      return(file.path("profiles", profile, "renv"))
+  }
+  
+  renv_bootstrap_profile_get <- function() {
+    profile <- Sys.getenv("RENV_PROFILE", unset = "")
+    renv_bootstrap_profile_normalize(profile)
+  }
+  
+  renv_bootstrap_profile_set <- function(profile) {
+    profile <- renv_bootstrap_profile_normalize(profile)
+    if (is.null(profile))
+      Sys.unsetenv("RENV_PROFILE")
+    else
+      Sys.setenv(RENV_PROFILE = profile)
+  }
+  
+  renv_bootstrap_profile_normalize <- function(profile) {
+  
+    if (is.null(profile) || profile %in% c("", "default"))
+      return(NULL)
+  
+    profile
+  
+  }
+  
+  renv_bootstrap_path_absolute <- function(path) {
+  
+    substr(path, 1L, 1L) %in% c("~", "/", "\\") || (
+      substr(path, 1L, 1L) %in% c(letters, LETTERS) &&
+      substr(path, 2L, 3L) %in% c(":/", ":\\")
+    )
+  
+  }
+  
+  renv_bootstrap_paths_renv <- function(..., profile = TRUE, project = NULL) {
+    renv <- Sys.getenv("RENV_PATHS_RENV", unset = "renv")
+    root <- if (renv_bootstrap_path_absolute(renv)) NULL else project
+    prefix <- if (profile) renv_bootstrap_profile_prefix()
+    components <- c(root, renv, prefix, ...)
+    paste(components, collapse = "/")
+  }
+  
+  renv_bootstrap_project_type <- function(path) {
+  
+    descpath <- file.path(path, "DESCRIPTION")
+    if (!file.exists(descpath))
+      return("unknown")
+  
+    desc <- tryCatch(
+      read.dcf(descpath, all = TRUE),
+      error = identity
+    )
+  
+    if (inherits(desc, "error"))
+      return("unknown")
+  
+    type <- desc$Type
+    if (!is.null(type))
+      return(tolower(type))
+  
+    package <- desc$Package
+    if (!is.null(package))
+      return("package")
+  
+    "unknown"
+  
+  }
+  
+  renv_bootstrap_user_dir <- function() {
+    dir <- renv_bootstrap_user_dir_impl()
+    path.expand(chartr("\\", "/", dir))
+  }
+  
+  renv_bootstrap_user_dir_impl <- function() {
+  
+    # use local override if set
+    override <- getOption("renv.userdir.override")
+    if (!is.null(override))
+      return(override)
+  
+    # use R_user_dir if available
+    tools <- asNamespace("tools")
+    if (is.function(tools$R_user_dir))
+      return(tools$R_user_dir("renv", "cache"))
+  
+    # try using our own backfill for older versions of R
+    envvars <- c("R_USER_CACHE_DIR", "XDG_CACHE_HOME")
+    for (envvar in envvars) {
+      root <- Sys.getenv(envvar, unset = NA)
+      if (!is.na(root))
+        return(file.path(root, "R/renv"))
+    }
+  
+    # use platform-specific default fallbacks
+    if (Sys.info()[["sysname"]] == "Windows")
+      file.path(Sys.getenv("LOCALAPPDATA"), "R/cache/R/renv")
+    else if (Sys.info()[["sysname"]] == "Darwin")
+      "~/Library/Caches/org.R-project.R/R/renv"
+    else
+      "~/.cache/R/renv"
+  
+  }
+  
+  renv_bootstrap_version_friendly <- function(version, shafmt = NULL, sha = NULL) {
+    sha <- sha %||% attr(version, "sha", exact = TRUE)
+    parts <- c(version, sprintf(shafmt %||% " [sha: %s]", substring(sha, 1L, 7L)))
+    paste(parts, collapse = "")
+  }
+  
+  renv_bootstrap_exec <- function(project, libpath, version) {
+    if (!renv_bootstrap_load(project, libpath, version))
+      renv_bootstrap_run(version, libpath)
+  }
+  
+  renv_bootstrap_run <- function(version, libpath) {
+  
+    # perform bootstrap
+    bootstrap(version, libpath)
+  
+    # exit early if we're just testing bootstrap
+    if (!is.na(Sys.getenv("RENV_BOOTSTRAP_INSTALL_ONLY", unset = NA)))
+      return(TRUE)
+  
+    # try again to load
+    if (requireNamespace("renv", lib.loc = libpath, quietly = TRUE)) {
+      return(renv::load(project = getwd()))
+    }
+  
+    # failed to download or load renv; warn the user
+    msg <- c(
+      "Failed to find an renv installation: the project will not be loaded.",
+      "Use `renv::activate()` to re-initialize the project."
+    )
+  
+    warning(paste(msg, collapse = "\n"), call. = FALSE)
+  
+  }
+  
+  renv_json_read <- function(file = NULL, text = NULL) {
+  
+    jlerr <- NULL
+  
+    # if jsonlite is loaded, use that instead
+    if ("jsonlite" %in% loadedNamespaces()) {
+  
+      json <- tryCatch(renv_json_read_jsonlite(file, text), error = identity)
+      if (!inherits(json, "error"))
+        return(json)
+  
+      jlerr <- json
+  
+    }
+  
+    # otherwise, fall back to the default JSON reader
+    json <- tryCatch(renv_json_read_default(file, text), error = identity)
+    if (!inherits(json, "error"))
+      return(json)
+  
+    # report an error
+    if (!is.null(jlerr))
+      stop(jlerr)
+    else
+      stop(json)
+  
+  }
+  
+  renv_json_read_jsonlite <- function(file = NULL, text = NULL) {
+    text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n")
+    jsonlite::fromJSON(txt = text, simplifyVector = FALSE)
+  }
+  
+  renv_json_read_default <- function(file = NULL, text = NULL) {
+  
+    # find strings in the JSON
+    text <- paste(text %||% readLines(file, warn = FALSE), collapse = "\n")
+    pattern <- '["](?:(?:\\\\.)|(?:[^"\\\\]))*?["]'
+    locs <- gregexpr(pattern, text, perl = TRUE)[[1]]
+  
+    # if any are found, replace them with placeholders
+    replaced <- text
+    strings <- character()
+    replacements <- character()
+  
+    if (!identical(c(locs), -1L)) {
+  
+      # get the string values
+      starts <- locs
+      ends <- locs + attr(locs, "match.length") - 1L
+      strings <- substring(text, starts, ends)
+  
+      # only keep those requiring escaping
+      strings <- grep("[[\\]{}:]", strings, perl = TRUE, value = TRUE)
+  
+      # compute replacements
+      replacements <- sprintf('"\032%i\032"', seq_along(strings))
+  
+      # replace the strings
+      mapply(function(string, replacement) {
+        replaced <<- sub(string, replacement, replaced, fixed = TRUE)
+      }, strings, replacements)
+  
+    }
+  
+    # transform the JSON into something the R parser understands
+    transformed <- replaced
+    transformed <- gsub("{}", "`names<-`(list(), character())", transformed, fixed = TRUE)
+    transformed <- gsub("[[{]", "list(", transformed, perl = TRUE)
+    transformed <- gsub("[]}]", ")", transformed, perl = TRUE)
+    transformed <- gsub(":", "=", transformed, fixed = TRUE)
+    text <- paste(transformed, collapse = "\n")
+  
+    # parse it
+    json <- parse(text = text, keep.source = FALSE, srcfile = NULL)[[1L]]
+  
+    # construct map between source strings, replaced strings
+    map <- as.character(parse(text = strings))
+    names(map) <- as.character(parse(text = replacements))
+  
+    # convert to list
+    map <- as.list(map)
+  
+    # remap strings in object
+    remapped <- renv_json_read_remap(json, map)
+  
+    # evaluate
+    eval(remapped, envir = baseenv())
+  
+  }
+  
+  renv_json_read_remap <- function(json, map) {
+  
+    # fix names
+    if (!is.null(names(json))) {
+      lhs <- match(names(json), names(map), nomatch = 0L)
+      rhs <- match(names(map), names(json), nomatch = 0L)
+      names(json)[rhs] <- map[lhs]
+    }
+  
+    # fix values
+    if (is.character(json))
+      return(map[[json]] %||% json)
+  
+    # handle true, false, null
+    if (is.name(json)) {
+      text <- as.character(json)
+      if (text == "true")
+        return(TRUE)
+      else if (text == "false")
+        return(FALSE)
+      else if (text == "null")
+        return(NULL)
+    }
+  
+    # recurse
+    if (is.recursive(json)) {
+      for (i in seq_along(json)) {
+        json[i] <- list(renv_json_read_remap(json[[i]], map))
+      }
+    }
+  
+    json
+  
+  }
+
+  # load the renv profile, if any
+  renv_bootstrap_profile_load(project)
+
+  # construct path to library root
+  root <- renv_bootstrap_library_root(project)
+
+  # construct library prefix for platform
+  prefix <- renv_bootstrap_platform_prefix()
+
+  # construct full libpath
+  libpath <- file.path(root, prefix)
+
+  # run bootstrap code
+  renv_bootstrap_exec(project, libpath, version)
+
+  invisible()
+
+})
diff --git a/renv/settings.json b/renv/settings.json
new file mode 100644
index 00000000..ffdbb320
--- /dev/null
+++ b/renv/settings.json
@@ -0,0 +1,19 @@
+{
+  "bioconductor.version": null,
+  "external.libraries": [],
+  "ignored.packages": [],
+  "package.dependency.fields": [
+    "Imports",
+    "Depends",
+    "LinkingTo"
+  ],
+  "ppm.enabled": null,
+  "ppm.ignored.urls": [],
+  "r.version": null,
+  "snapshot.type": "implicit",
+  "use.cache": true,
+  "vcs.ignore.cellar": true,
+  "vcs.ignore.library": true,
+  "vcs.ignore.local": true,
+  "vcs.manage.ignores": true
+}

From 9fd0f1cd4e55ead947635a4493625efb39de2e78 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 6 May 2025 12:12:15 -0400
Subject: [PATCH 29/47] add debug script to text shinyapps.io version logs -
 this version works locally.

---
 inst/shiny-app/CiteSource/app.R | 54 +++++++++++++++++++++++++++++++++
 1 file changed, 54 insertions(+)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 3af96de2..fdca612a 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -900,6 +900,60 @@ server <- function(input, output, session) {
     labels <- input$labels_visual
     labels <- ifelse(labels == "_blank_", "unknown", labels)
     
+    # --- BEGIN DEBUGGING ---
+    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
+      print("CiteSource App Debug: Inspecting rv$latest_unique before separate_rows in unique_filtered_table")
+      
+      count_elements <- function(text_vector) {
+        sapply(text_vector, function(x) {
+          # Robustly handle NA and empty strings before splitting
+          char_x <- as.character(x)
+          if (is.na(char_x) || char_x == "" || char_x == "NA") return(0) # Or 1 if an empty string should count as one segment
+          length(stringr::str_split(char_x, pattern = ", ")[[1]])
+        })
+      }
+      
+      temp_debug_df <- rv$latest_unique %>%
+        dplyr::mutate(
+          n_record_ids = count_elements(record_ids),
+          n_cite_label = count_elements(cite_label),
+          n_cite_source = count_elements(cite_source),
+          n_cite_string = count_elements(cite_string)
+        ) %>%
+        dplyr::select(
+          # Try to include an identifier column if available
+          dplyr::any_of(c("duplicate_id", "record_id", "title")), 
+          record_ids, n_record_ids, 
+          cite_label, n_cite_label, 
+          cite_source, n_cite_source, 
+          cite_string, n_cite_string
+        ) %>%
+        dplyr::filter(
+          (n_record_ids > 0 | n_cite_label > 0 | n_cite_source > 0 | n_cite_string > 0) &
+            !(n_record_ids == n_cite_label & 
+                n_record_ids == n_cite_source & 
+                n_record_ids == n_cite_string)
+        )
+      
+      if (nrow(temp_debug_df) > 0) {
+        print("CiteSource App Debug: Problematic rows found in rv$latest_unique (item counts differ across columns):")
+        # Print first few problematic rows. Use as.data.frame for robust logging.
+        print(as.data.frame(head(temp_debug_df, 20))) 
+      } else {
+        print("CiteSource App Debug: No rows found in rv$latest_unique with inconsistent item counts upon this inspection.")
+      }
+      
+      # Optional: Print a few raw rows to see what's going into separate_rows
+      # print("CiteSource App Debug: First 5 rows of rv$latest_unique feeding into separate_rows:")
+      # print(as.data.frame(head(rv$latest_unique %>% 
+      #       dplyr::select(dplyr::any_of(c("duplicate_id", "record_id", "title")), 
+      #                     record_ids, cite_label, cite_source, cite_string), 5)))
+      
+    } else {
+      print("CiteSource App Debug: rv$latest_unique is empty or not a dataframe when unique_filtered_table is triggered.")
+    }
+    # --- END DEBUGGING ---
+    
     out <- rv$latest_unique %>%
       # Filter rows where *any* of the required sources/labels/strings are present before summarizing
       dplyr::filter(

From 3896a17a9f87bbff977f3ce5194b3a1c65075f47 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 6 May 2025 16:57:16 -0400
Subject: [PATCH 30/47] remove previous debug script

---
 inst/shiny-app/CiteSource/app.R | 54 ---------------------------------
 1 file changed, 54 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index fdca612a..3af96de2 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -900,60 +900,6 @@ server <- function(input, output, session) {
     labels <- input$labels_visual
     labels <- ifelse(labels == "_blank_", "unknown", labels)
     
-    # --- BEGIN DEBUGGING ---
-    if (is.data.frame(rv$latest_unique) && nrow(rv$latest_unique) > 0) {
-      print("CiteSource App Debug: Inspecting rv$latest_unique before separate_rows in unique_filtered_table")
-      
-      count_elements <- function(text_vector) {
-        sapply(text_vector, function(x) {
-          # Robustly handle NA and empty strings before splitting
-          char_x <- as.character(x)
-          if (is.na(char_x) || char_x == "" || char_x == "NA") return(0) # Or 1 if an empty string should count as one segment
-          length(stringr::str_split(char_x, pattern = ", ")[[1]])
-        })
-      }
-      
-      temp_debug_df <- rv$latest_unique %>%
-        dplyr::mutate(
-          n_record_ids = count_elements(record_ids),
-          n_cite_label = count_elements(cite_label),
-          n_cite_source = count_elements(cite_source),
-          n_cite_string = count_elements(cite_string)
-        ) %>%
-        dplyr::select(
-          # Try to include an identifier column if available
-          dplyr::any_of(c("duplicate_id", "record_id", "title")), 
-          record_ids, n_record_ids, 
-          cite_label, n_cite_label, 
-          cite_source, n_cite_source, 
-          cite_string, n_cite_string
-        ) %>%
-        dplyr::filter(
-          (n_record_ids > 0 | n_cite_label > 0 | n_cite_source > 0 | n_cite_string > 0) &
-            !(n_record_ids == n_cite_label & 
-                n_record_ids == n_cite_source & 
-                n_record_ids == n_cite_string)
-        )
-      
-      if (nrow(temp_debug_df) > 0) {
-        print("CiteSource App Debug: Problematic rows found in rv$latest_unique (item counts differ across columns):")
-        # Print first few problematic rows. Use as.data.frame for robust logging.
-        print(as.data.frame(head(temp_debug_df, 20))) 
-      } else {
-        print("CiteSource App Debug: No rows found in rv$latest_unique with inconsistent item counts upon this inspection.")
-      }
-      
-      # Optional: Print a few raw rows to see what's going into separate_rows
-      # print("CiteSource App Debug: First 5 rows of rv$latest_unique feeding into separate_rows:")
-      # print(as.data.frame(head(rv$latest_unique %>% 
-      #       dplyr::select(dplyr::any_of(c("duplicate_id", "record_id", "title")), 
-      #                     record_ids, cite_label, cite_source, cite_string), 5)))
-      
-    } else {
-      print("CiteSource App Debug: rv$latest_unique is empty or not a dataframe when unique_filtered_table is triggered.")
-    }
-    # --- END DEBUGGING ---
-    
     out <- rv$latest_unique %>%
       # Filter rows where *any* of the required sources/labels/strings are present before summarizing
       dplyr::filter(

From 14ee21e38ceef07a13926c4f2a15f2a8567d2e98 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 6 May 2025 17:01:19 -0400
Subject: [PATCH 31/47] removed renv.lock as not needed. consider adding again
 once stable

---
 .Rprofile     |    1 -
 R/sysdata.rda |  Bin 4649 -> 0 bytes
 renv.lock     | 2515 -------------------------------------------------
 3 files changed, 2516 deletions(-)
 delete mode 100644 .Rprofile
 delete mode 100644 R/sysdata.rda
 delete mode 100644 renv.lock

diff --git a/.Rprofile b/.Rprofile
deleted file mode 100644
index 81b960f5..00000000
--- a/.Rprofile
+++ /dev/null
@@ -1 +0,0 @@
-source("renv/activate.R")
diff --git a/R/sysdata.rda b/R/sysdata.rda
deleted file mode 100644
index d546b8145abe7a17b06f50712d7a6ac2f478144e..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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diff --git a/renv.lock b/renv.lock
deleted file mode 100644
index 88f2a47e..00000000
--- a/renv.lock
+++ /dev/null
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From ff9235871f3cd8aa3d79bc6a2482d8f852b9a3c0 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Tue, 6 May 2025 23:28:13 -0400
Subject: [PATCH 32/47] large overhaul of Shiny to conform with latest R
 version

---
 inst/shiny-app/CiteSource/app.R | 314 +++++++++++++++++++++++++-------
 1 file changed, 251 insertions(+), 63 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 3af96de2..870036d6 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -774,7 +774,7 @@ server <- function(input, output, session) {
     # Initial selection
     initial_base_selection <- c("year", "author", "title", "journal", "abstract",
                                 "doi", "pages", "source", "label")
-
+    
     shinyWidgets::updatePickerInput(session = session, "manual_dedup_cols",
                                     choices = base_cols,
                                     selected = initial_base_selection)
@@ -806,7 +806,7 @@ server <- function(input, output, session) {
     
     data <- rv$pairs_to_check[,1:36]
     selected_cols <- input$manual_dedup_cols
-
+    
     # Define the desired base order
     core_col_order <- c("author", "title", "year", "journal", "abstract","doi", "pages","volume","number","source","label","string")
     # Create the desired interleaved order of columns to select
@@ -817,7 +817,7 @@ server <- function(input, output, session) {
       col2 <- paste0(base_col, "2")
       desired_table_order <- c(desired_table_order, col1, col2)
     }
-
+    
     # Intersect with the actual and selected columns to maintain order and presence
     cols_to_show <- intersect(desired_table_order, colnames(data))
     cols_to_show <- intersect(cols_to_show, paste0(selected_cols, rep(c("1", "2"), each = length(selected_cols))))
@@ -836,7 +836,7 @@ server <- function(input, output, session) {
     if (length(match_number_cols_to_add) > 0) {
       ordered_data <- cbind(ordered_data, data[, match_number_cols_to_add])
     }
-
+    
     ordered_data
     
   })
@@ -890,52 +890,130 @@ server <- function(input, output, session) {
   
   # Reactive expression to filter the data for visualization (used for Heatmap and Upset)
   unique_filtered_visual <- shiny::reactive({
-    # Ensure latest_unique has data before attempting filtering
-    shiny::req(nrow(rv$latest_unique) > 0)
+    shiny::req(rv$latest_unique, is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
     
-    sources <- input$sources_visual
-    sources <- ifelse(sources == "_blank_", "unknown", sources)
-    strings <- input$strings_visual
-    strings <- ifelse(strings == "_blank_", "unknown", strings)
-    labels <- input$labels_visual
-    labels <- ifelse(labels == "_blank_", "unknown", labels)
+    data_in <- rv$latest_unique
     
-    out <- rv$latest_unique %>%
-      # Filter rows where *any* of the required sources/labels/strings are present before summarizing
-      dplyr::filter(
-        (length(sources) == 0 | stringr::str_detect(cite_source, paste0("\\b(", paste(sources, collapse="|"), ")\\b"))) &
-          (length(strings) == 0 | stringr::str_detect(cite_string, paste0("\\b(", paste(strings, collapse="|"), ")\\b"))) &
-          (length(labels) == 0 | stringr::str_detect(cite_label, paste0("\\b(", paste(labels, collapse="|"), ")\\b")))
-      ) %>%
-      # Now group and summarize
-      dplyr::group_by(duplicate_id) %>%
-      {
-        df_grouped <- .
-        df_separated <- df_grouped %>%
-          tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
-          dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
-          dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
-          dplyr::filter(length(labels) == 0 | cite_label %in% labels)
+    sources_selected_in_input <- input$sources_visual
+    sources_selected_in_input <- sources_selected_in_input[!is.na(sources_selected_in_input) & sources_selected_in_input != ""]
+    
+    labels_selected_in_input <- input$labels_visual
+    labels_selected_in_input <- labels_selected_in_input[!is.na(labels_selected_in_input) & labels_selected_in_input != ""]
+    
+    strings_selected_in_input <- input$strings_visual
+    strings_selected_in_input <- strings_selected_in_input[!is.na(strings_selected_in_input) & strings_selected_in_input != ""]
+    
+    # Step 1: Filter rows to ensure they contain at least one selected item from ANY category
+    # This pre-filters, but the main goal is to correctly process the strings in Step 2
+    data_candidate_rows <- data_in
+    
+    # Apply row-level filtering if selections are made.
+    # If a category (e.g. sources) has selections, row must match one of them.
+    # If a category has NO selections, that category doesn't restrict rows.
+    if (length(sources_selected_in_input) > 0) {
+      pattern <- paste0("\\b(", paste(sources_selected_in_input, collapse = "|"), ")\\b")
+      if ("cite_source" %in% names(data_candidate_rows) && is.character(data_candidate_rows$cite_source)) {
+        data_candidate_rows <- data_candidate_rows %>%
+          dplyr::filter(stringr::str_detect(as.character(cite_source), pattern))
+      } else { data_candidate_rows <- data_candidate_rows %>% dplyr::slice(0) } # No relevant column, empty
+    }
+    # Only proceed if rows remain
+    if(nrow(data_candidate_rows) == 0) return(rv$latest_unique %>% dplyr::slice(0))
+    
+    
+    if (length(labels_selected_in_input) > 0) {
+      pattern <- paste0("\\b(", paste(labels_selected_in_input, collapse = "|"), ")\\b")
+      if ("cite_label" %in% names(data_candidate_rows) && is.character(data_candidate_rows$cite_label)) {
+        data_candidate_rows <- data_candidate_rows %>%
+          dplyr::filter(stringr::str_detect(as.character(cite_label), pattern))
+      } else { data_candidate_rows <- data_candidate_rows %>% dplyr::slice(0) }
+    }
+    if(nrow(data_candidate_rows) == 0) return(rv$latest_unique %>% dplyr::slice(0))
+    
+    
+    if (length(strings_selected_in_input) > 0) {
+      pattern <- paste0("\\b(", paste(strings_selected_in_input, collapse = "|"), ")\\b")
+      if ("cite_string" %in% names(data_candidate_rows) && is.character(data_candidate_rows$cite_string)) {
+        data_candidate_rows <- data_candidate_rows %>%
+          dplyr::filter(stringr::str_detect(as.character(cite_string), pattern))
+      } else { data_candidate_rows <- data_candidate_rows %>% dplyr::slice(0) }
+    }
+    if(nrow(data_candidate_rows) == 0) return(rv$latest_unique %>% dplyr::slice(0))
+    
+    
+    # Step 2: Mutate the columns to only contain selected items
+    data_processed_cols <- data_candidate_rows %>%
+      dplyr::mutate(
+        cite_source = if ("cite_source" %in% names(.)) {
+          sapply(as.character(cite_source), function(cs_val) {
+            if (is.na(cs_val) || cs_val == "") return("") 
+            items <- stringr::str_split(cs_val, ",\\s*")[[1]]
+            items <- items[!is.na(items) & items != ""]
+            
+            items_to_keep <- if (length(sources_selected_in_input) > 0) {
+              items[items %in% sources_selected_in_input]
+            } else { # If no specific sources were selected by user, keep all original (valid) items for this row
+              items 
+            }
+            paste(unique(items_to_keep), collapse = ", ") # Ensure unique items are pasted
+          }, USE.NAMES = FALSE)
+        } else { NA_character_ },
         
-        # Check if df_separated is empty for this group before summarizing
-        if(nrow(df_separated) == 0) {
-          # Return a row with the group id and NAs or empty strings if nothing matches filters
-          tibble::tibble(duplicate_id = dplyr::first(df_grouped$duplicate_id),
-                         record_ids = "", cite_label = "", cite_source = "", cite_string = "")
-        } else {
-          df_separated %>%
-            dplyr::summarise(across(c(record_ids, cite_label, cite_source, cite_string), ~ trimws(paste(unique(na.omit(.)), collapse = ', '))))
-        }
-      } %>%
-      dplyr::ungroup()
+        cite_label = if ("cite_label" %in% names(.)) {
+          sapply(as.character(cite_label), function(cl_val) {
+            if (is.na(cl_val) || cl_val == "") return("")
+            items <- stringr::str_split(cl_val, ",\\s*")[[1]]
+            items <- items[!is.na(items) & items != ""]
+            items_to_keep <- if (length(labels_selected_in_input) > 0) {
+              items[items %in% labels_selected_in_input]
+            } else {
+              items
+            }
+            paste(unique(items_to_keep), collapse = ", ")
+          }, USE.NAMES = FALSE)
+        } else { NA_character_ },
+        
+        cite_string = if ("cite_string" %in% names(.)) {
+          sapply(as.character(cite_string), function(cstr_val) {
+            if (is.na(cstr_val) || cstr_val == "") return("")
+            items <- stringr::str_split(cstr_val, ",\\s*")[[1]]
+            items <- items[!is.na(items) & items != ""]
+            items_to_keep <- if (length(strings_selected_in_input) > 0) {
+              items[items %in% strings_selected_in_input]
+            } else {
+              items
+            }
+            paste(unique(items_to_keep), collapse = ", ")
+          }, USE.NAMES = FALSE)
+        } else { NA_character_ }
+      )
     
-    # Ensure the output has the same columns even if empty
-    if (nrow(out) == 0) {
-      out <- tibble::tibble(duplicate_id=character(), record_ids=character(), cite_label=character(), cite_source=character(), cite_string=character()) # Adjust columns as needed
+    # Step 3: Filter out rows where the column relevant to comp_type became empty
+    # This ensures that if comp_type is "sources", only rows with non-empty cite_source are passed.
+    data_final <- data_processed_cols
+    current_comp_type <- input$comp_type # Get the comparison type
+    
+    if (current_comp_type == "sources") {
+      if ("cite_source" %in% names(data_final)) {
+        data_final <- data_final %>% dplyr::filter(!is.na(cite_source) & cite_source != "")
+      } else { # If cite_source column doesn't exist, return empty
+        return(rv$latest_unique %>% dplyr::slice(0))
+      }
+    } else if (current_comp_type == "labels") {
+      if ("cite_label" %in% names(data_final)) {
+        data_final <- data_final %>% dplyr::filter(!is.na(cite_label) & cite_label != "")
+      } else {
+        return(rv$latest_unique %>% dplyr::slice(0))
+      }
+    } else if (current_comp_type == "strings") {
+      if ("cite_string" %in% names(data_final)) {
+        data_final <- data_final %>% dplyr::filter(!is.na(cite_string) & cite_string != "")
+      } else {
+        return(rv$latest_unique %>% dplyr::slice(0))
+      }
     }
-    # Add back other columns from rv$latest_unique if needed by downstream functions
-    dplyr::left_join(out, dplyr::select(rv$latest_unique, -any_of(c("record_ids", "cite_label", "cite_source", "cite_string"))), by = "duplicate_id")
     
+    return(data_final)
   })
   
   # REACTIVE for Phase Plot Data
@@ -1170,30 +1248,140 @@ server <- function(input, output, session) {
   #### Table tab ####
   
   # Event reactive for filtering the data used in the record table and summary table
+  # In your server function, replace the current unique_filtered_table eventReactive
+  
   unique_filtered_table <- shiny::eventReactive(
-    c(input$generateRecordTable, input$generateSummaryTable, 
+    c(input$generateRecordTable,
       input$sources_tables, input$strings_tables, input$labels_tables,
-      input$generateInitialRecordTable, input$generateDetailedRecordTable, 
+      input$generateInitialRecordTable, input$generateDetailedRecordTable,
       input$generatePrecisionTable),
     {
-      sources <- input$sources_tables 
-      sources <- ifelse(sources == "_blank_", "unknown", sources)
-      strings <- input$strings_tables
-      strings <- ifelse(strings == "_blank_", "unknown", strings)
-      labels <- input$labels_tables
-      labels <- ifelse(labels == "_blank_", "unknown", labels)
+      shiny::req(rv$latest_unique, is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
       
-      rv$latest_unique %>%
-        dplyr::group_by(duplicate_id) %>%
-        tidyr::separate_rows(c(record_ids, cite_label, cite_source, cite_string), sep=", ") %>%
-        dplyr::filter(length(sources) == 0 | cite_source %in% sources) %>%
-        dplyr::filter(length(strings) == 0 | cite_string %in% strings) %>%
-        dplyr::filter(length(labels) == 0 | cite_label %in% labels) %>%
-        dplyr::mutate(across(c(record_ids, cite_label, cite_source, cite_string), ~ trimws(paste(na.omit(.), collapse = ', ')))) %>%
-        unique() %>%
-        dplyr::ungroup()
-    }
-  )
+      data_in <- rv$latest_unique
+      
+      # Get current selections from table filters
+      sources_sel_tbl <- input$sources_tables
+      sources_sel_tbl <- sources_sel_tbl[!is.na(sources_sel_tbl) & sources_sel_tbl != ""] # Clean selections
+      
+      labels_sel_tbl <- input$labels_tables
+      labels_sel_tbl <- labels_sel_tbl[!is.na(labels_sel_tbl) & labels_sel_tbl != ""]
+      
+      strings_sel_tbl <- input$strings_tables
+      strings_sel_tbl <- strings_sel_tbl[!is.na(strings_sel_tbl) & strings_sel_tbl != ""]
+      
+      # Step 1: Filter rows based on selections.
+      # A row must match selections in each category that HAS selections.
+      data_candidate_rows <- data_in
+      
+      if (length(sources_sel_tbl) > 0) { # Only filter by source if sources are selected in the input
+        pattern <- paste0("\\b(", paste(sources_sel_tbl, collapse = "|"), ")\\b")
+        if ("cite_source" %in% names(data_candidate_rows) && is.character(data_candidate_rows$cite_source)) {
+          data_candidate_rows <- data_candidate_rows %>%
+            dplyr::filter(stringr::str_detect(as.character(cite_source), pattern))
+        } else { # If cite_source column is missing, no rows can match this criteria
+          data_candidate_rows <- data_candidate_rows %>% dplyr::slice(0) 
+        }
+      }
+      # If no rows remain after source filtering (and sources were selected), return empty
+      if(nrow(data_candidate_rows) == 0 && length(sources_sel_tbl) > 0) return(rv$latest_unique %>% dplyr::slice(0))
+      
+      
+      if (length(labels_sel_tbl) > 0) { # Only filter by label if labels are selected
+        pattern <- paste0("\\b(", paste(labels_sel_tbl, collapse = "|"), ")\\b")
+        if ("cite_label" %in% names(data_candidate_rows) && is.character(data_candidate_rows$cite_label)) {
+          data_candidate_rows <- data_candidate_rows %>%
+            dplyr::filter(stringr::str_detect(as.character(cite_label), pattern))
+        } else {
+          data_candidate_rows <- data_candidate_rows %>% dplyr::slice(0)
+        }
+      }
+      if(nrow(data_candidate_rows) == 0 && length(labels_sel_tbl) > 0) return(rv$latest_unique %>% dplyr::slice(0))
+      
+      
+      if (length(strings_sel_tbl) > 0) { # Only filter by string if strings are selected
+        pattern <- paste0("\\b(", paste(strings_sel_tbl, collapse = "|"), ")\\b")
+        if ("cite_string" %in% names(data_candidate_rows) && is.character(data_candidate_rows$cite_string)) {
+          data_candidate_rows <- data_candidate_rows %>%
+            dplyr::filter(stringr::str_detect(as.character(cite_string), pattern))
+        } else {
+          data_candidate_rows <- data_candidate_rows %>% dplyr::slice(0)
+        }
+      }
+      # If, after all row filtering, no candidates remain, return an empty frame
+      if(nrow(data_candidate_rows) == 0) return(rv$latest_unique %>% dplyr::slice(0))
+      
+      
+      # Step 2: For the remaining rows, mutate their cite_source, cite_label, cite_string
+      # to only contain the items that were actually selected in the input filters.
+      data_processed_cols <- data_candidate_rows %>%
+        dplyr::mutate(
+          cite_source = if ("cite_source" %in% names(.)) {
+            sapply(as.character(cite_source), function(cs_val) {
+              if (is.na(cs_val) || cs_val == "") return("") 
+              items <- stringr::str_split(cs_val, ",\\s*")[[1]]
+              items <- items[!is.na(items) & items != ""] # Clean items
+              
+              # If user selected specific sources for tables, filter by that selection
+              # Otherwise (if input$sources_tables was empty), keep all original items for this record
+              items_to_keep <- if (length(sources_sel_tbl) > 0) {
+                items[items %in% sources_sel_tbl]
+              } else {
+                items 
+              }
+              paste(unique(items_to_keep), collapse = ", ") # Ensure unique items are pasted
+            }, USE.NAMES = FALSE) # Prevent sapply from naming the vector
+          } else { NA_character_ }, # Column didn't exist
+          
+          cite_label = if ("cite_label" %in% names(.)) {
+            sapply(as.character(cite_label), function(cl_val) {
+              if (is.na(cl_val) || cl_val == "") return("")
+              items <- stringr::str_split(cl_val, ",\\s*")[[1]]
+              items <- items[!is.na(items) & items != ""]
+              items_to_keep <- if (length(labels_sel_tbl) > 0) {
+                items[items %in% labels_sel_tbl]
+              } else {
+                items
+              }
+              paste(unique(items_to_keep), collapse = ", ")
+            }, USE.NAMES = FALSE)
+          } else { NA_character_ },
+          
+          cite_string = if ("cite_string" %in% names(.)) {
+            sapply(as.character(cite_string), function(cstr_val) {
+              if (is.na(cstr_val) || cstr_val == "") return("")
+              items <- stringr::str_split(cstr_val, ",\\s*")[[1]]
+              items <- items[!is.na(items) & items != ""]
+              items_to_keep <- if (length(strings_sel_tbl) > 0) {
+                items[items %in% strings_sel_tbl]
+              } else {
+                items
+              }
+              paste(unique(items_to_keep), collapse = ", ")
+            }, USE.NAMES = FALSE)
+          } else { NA_character_ }
+        ) # End mutate
+      
+      # Step 3: Filter out rows that might have become "empty" in all key identifier fields
+      # (cite_source, cite_label, cite_string) after the transformation.
+      # This prevents passing rows that are no longer meaningful based on these common categorizations.
+      data_final <- data_processed_cols %>%
+        dplyr::filter(
+          !( (is.na(cite_source) | cite_source == "") &
+               (is.na(cite_label)  | cite_label  == "") &
+               (is.na(cite_string) | cite_string == "") )
+        )
+      
+      # If, after all processing, the dataframe is empty, return an empty version
+      # of the original structure to avoid downstream errors with missing columns.
+      if(nrow(data_final) == 0) {
+        return(rv$latest_unique %>% dplyr::slice(0))
+      }
+      
+      return(data_final)
+    } # End eventReactive logic
+  ) # End unique_filtered_table
+  
   detailed_table_data <- reactive({
     # Require base data to proceed
     shiny::req(is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)

From 421b71551c541f38894deba4bb5bc749cb8906fd Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 7 May 2025 10:23:34 -0400
Subject: [PATCH 33/47] enhanced count for edge cases where duplication exists
 in screened or final data

---
 R/new_count_and_table.R | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/R/new_count_and_table.R b/R/new_count_and_table.R
index 70215c5c..11059413 100644
--- a/R/new_count_and_table.R
+++ b/R/new_count_and_table.R
@@ -315,15 +315,19 @@ calculate_phase_records <- function(unique_citations, n_unique, db_colname) {
   total_distinct_records <- dplyr::n_distinct(unique_citations$duplicate_id)
   
   # Split the cite_label column and count any occurrence of "screened" and "final"
+  # Updated for edge cases where a citation is duplicated within the screened set
   total_screened <- unique_citations %>%
     tidyr::separate_rows(cite_label, sep = ",\\s*") %>%
     dplyr::filter(cite_label == "screened") %>%
-    nrow()
+    # Count the number of distinct duplicate_ids that remain
+    dplyr::n_distinct(duplicate_id)
   
+  # Updated for edge cases where a citation is duplicated within the screened set (should never happen)
   total_final <- unique_citations %>%
     tidyr::separate_rows(cite_label, sep = ",\\s*") %>%
     dplyr::filter(cite_label == "final") %>%
-    nrow()
+    # Count the number of distinct duplicate_ids that remain
+    dplyr::n_distinct(duplicate_id)
   
   # Step 2: Proceed with the regular calculation for distinct records by source
   distinct_count <- unique_citations %>%

From 4a249ffe5476afb7cedb2b7bc9e6e293c88daf00 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 7 May 2025 11:05:30 -0400
Subject: [PATCH 34/47] add ability to change number of records in table

---
 R/tables.R | 63 +++++++++++++++++++++++++++++++-----------------------
 1 file changed, 36 insertions(+), 27 deletions(-)

diff --git a/R/tables.R b/R/tables.R
index 1036623e..0e953917 100644
--- a/R/tables.R
+++ b/R/tables.R
@@ -106,47 +106,56 @@ record_level_table <- function(citations, include = "sources", include_empty = T
         )
       )
 
-      citations %>%
-        dplyr::select(-"duplicate_id", -"reference") %>%
-        cbind(" " = "&oplus;", .) %>%
-        DT::datatable(
-          escape = FALSE,
-          extensions = "Buttons",
-          options = list(
+      # Data for DT: selected columns from citations, with an added details control column
+      data_for_dt <- citations %>%
+        dplyr::select(-"duplicate_id", -"reference") %>% # Keeps "citation", "html_reference", and the renamed source indicator columns
+        cbind(" " = "&oplus;", .) # Adds a column named " " at the beginning for details control
+
+      DT::datatable(
+        data_for_dt, # Use the prepared data
+        escape = FALSE,
+        extensions = "Buttons",
+        options = list(
+          dom = "Blfrtip", # Added 'l' for length changing input. Common placement: Buttons, length, filter, processing, table, info, pagination.
+          pageLength = 10,   # Sets the initial number of rows displayed.
+          lengthMenu = list(c(10, 25, 50, 100, -1), c('10', '25', '50', '100', 'All')), # Choices for "Show X entries" dropdown. -1 means "All".
             columnDefs = list(
               list(visible = FALSE, targets = c(0, 3:(3 + ncol(to_display)))),
               list(orderable = FALSE, className = "details-control", targets = 1)
             ),
-            dom = "Bfrtip",
             buttons =
               list("print", list(
                 extend = "csv", filename = "CiteSource_record_summary",
                 text = "Download csv",
-                exportOptions = list(columns = c(0, 2:(3 + ncol(to_display))))
+                # Adjust exportOptions columns based on the actual structure of data_for_dt and what you want to export
+                exportOptions = list(columns = c(1, 2, 4:(3 + (ncol(data_for_dt) - 3))))
               ))
           ), container = sketch,
-          callback = DT::JS("
-              table.column(1).nodes().to$().css({cursor: 'pointer'});
-              var format = function(d) {
-                return '<div style=\"background-color:#eee; padding: .5em;\">' +
-                        d[3];
-              };
-              table.on('click', 'td.details-control', function() {
-                var td = $(this), row = table.row(td.closest('tr'));
-                if (row.child.isShown()) {
-                  row.child.hide();
-                  td.html('&oplus;');
-                } else {
-                  row.child(format(row.data())).show();
-                  td.html('&CircleMinus;');
-                }
-              });")
+        callback = DT::JS(paste0("
+          table.column(0).nodes().to$().css({cursor: 'pointer'}); // Assumes column 0 is the details-control
+          var format = function(d) {
+            // 'd' is the data array for the row. d[2] should be 'html_reference'
+            // (0-indexed: col 0 is ' ', col 1 is 'citation', col 2 is 'html_reference').
+            return '<div style=\"background-color:#eee; padding: .5em;\">' +
+                   d[2] + // This should be html_reference
+                   '</div>';
+          };
+          table.on('click', 'td.details-control', function() {
+            var td = $(this), row = table.row(td.closest('tr'));
+            if (row.child.isShown()) {
+              row.child.hide();
+              td.html('&oplus;');
+            } else {
+              row.child(format(row.data())).show();
+              td.html('&CircleMinus;'); // Unicode for circled minus
+            }
+          });"))
         )
     }
   } else {
     citations %>%
-      dplyr::select(1:3, dplyr::matches(" "), -"html_reference") %>%
-      dplyr::rename_with(stringr::str_trim)
+      dplyr::select(1:3, dplyr::matches(" "), -"html_reference") %>% # Selects duplicate_id, citation, reference, and space-suffixed columns
+      dplyr::rename_with(stringr::str_trim) # Remove trailing spaces from column names
   }
 }
 

From 22e807cf3d7880d78856944644eb9163e2066651 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 7 May 2025 12:06:04 -0400
Subject: [PATCH 35/47] add R/sysdata.rda file

---
 R/sysdata.rda | Bin 0 -> 4649 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 R/sysdata.rda

diff --git a/R/sysdata.rda b/R/sysdata.rda
new file mode 100644
index 0000000000000000000000000000000000000000..d546b8145abe7a17b06f50712d7a6ac2f478144e
GIT binary patch
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From b679e1691752ada1a150af6e8740978e13cc2319 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 7 May 2025 14:19:49 -0400
Subject: [PATCH 36/47] fix html for citation display

---
 R/tables.R | 44 ++++++++++++++++++++------------------------
 1 file changed, 20 insertions(+), 24 deletions(-)

diff --git a/R/tables.R b/R/tables.R
index 0e953917..c1df7fe4 100644
--- a/R/tables.R
+++ b/R/tables.R
@@ -107,12 +107,11 @@ record_level_table <- function(citations, include = "sources", include_empty = T
       )
 
       # Data for DT: selected columns from citations, with an added details control column
-      data_for_dt <- citations %>%
+      citations %>%
         dplyr::select(-"duplicate_id", -"reference") %>% # Keeps "citation", "html_reference", and the renamed source indicator columns
-        cbind(" " = "&oplus;", .) # Adds a column named " " at the beginning for details control
+        cbind(" " = "&oplus;", .)%>%
 
       DT::datatable(
-        data_for_dt, # Use the prepared data
         escape = FALSE,
         extensions = "Buttons",
         options = list(
@@ -127,29 +126,26 @@ record_level_table <- function(citations, include = "sources", include_empty = T
               list("print", list(
                 extend = "csv", filename = "CiteSource_record_summary",
                 text = "Download csv",
-                # Adjust exportOptions columns based on the actual structure of data_for_dt and what you want to export
-                exportOptions = list(columns = c(1, 2, 4:(3 + (ncol(data_for_dt) - 3))))
+                # Adjust exportOptions columns based on the actual structure of citations data and what you want to export
+                exportOptions = list(columns = c(1, 2, 4:(3 + (ncol(citations) - 3))))
               ))
           ), container = sketch,
-        callback = DT::JS(paste0("
-          table.column(0).nodes().to$().css({cursor: 'pointer'}); // Assumes column 0 is the details-control
-          var format = function(d) {
-            // 'd' is the data array for the row. d[2] should be 'html_reference'
-            // (0-indexed: col 0 is ' ', col 1 is 'citation', col 2 is 'html_reference').
-            return '<div style=\"background-color:#eee; padding: .5em;\">' +
-                   d[2] + // This should be html_reference
-                   '</div>';
-          };
-          table.on('click', 'td.details-control', function() {
-            var td = $(this), row = table.row(td.closest('tr'));
-            if (row.child.isShown()) {
-              row.child.hide();
-              td.html('&oplus;');
-            } else {
-              row.child(format(row.data())).show();
-              td.html('&CircleMinus;'); // Unicode for circled minus
-            }
-          });"))
+        callback = DT::JS("
+              table.column(1).nodes().to$().css({cursor: 'pointer'});
+              var format = function(d) {
+                return '<div style=\"background-color:#eee; padding: .5em;\">' +
+                        d[3];
+              };
+              table.on('click', 'td.details-control', function() {
+                var td = $(this), row = table.row(td.closest('tr'));
+                if (row.child.isShown()) {
+                  row.child.hide();
+                  td.html('&oplus;');
+                } else {
+                  row.child(format(row.data())).show();
+                  td.html('&CircleMinus;');
+                }
+              });")
         )
     }
   } else {

From 7f1d857ff9b899df9598915ef677c7b1e898ad74 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 7 May 2025 14:20:51 -0400
Subject: [PATCH 37/47] autofill labels with search

---
 inst/shiny-app/CiteSource/app.R | 35 +++++++++++++++++----------------
 1 file changed, 18 insertions(+), 17 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 870036d6..868c31b9 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -18,7 +18,7 @@ columns2hide <- c("title", "author", "doi", "volume",
                   "pages", "number", "year", "abstract", "journal", "isbn")
 
 
-# Define UI for data upload app ----
+# ---- Define UI ----
 ui <- shiny::navbarPage("CiteSource",
                         id = "tabs",
                         header = shiny::tagList(
@@ -51,7 +51,7 @@ ui <- shiny::navbarPage("CiteSource",
                           input_bg = "#E0E0E0",  # Set the background color for input boxes
                           input_border_color = "#23395B"  # Set the border color for input boxes
                         ),
-                        # Home tab
+                        # Home tab ----
                         shiny::tabPanel(
                           "Home",
                           shiny::navlistPanel(
@@ -77,7 +77,7 @@ ui <- shiny::navbarPage("CiteSource",
                           shiny::fluidRow(
                             shiny::column(
                               12,
-                              # Sidebar layout with input and output definitions ----
+                              # Sidebar layout ----
                               shiny::sidebarLayout(
                                 shiny::sidebarPanel( # Input: Select a file ----
                                                      shiny::h5("Step 1: Upload your citation files"),
@@ -127,7 +127,6 @@ ui <- shiny::navbarPage("CiteSource",
                               shiny::textOutput("Manual_pretext"),
                               shiny::br(),
                               
-                              
                               # Button
                               shinyWidgets::actionBttn(
                                 inputId = "nomanualdedup",
@@ -146,7 +145,7 @@ ui <- shiny::navbarPage("CiteSource",
                               ) %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
                               
                               shinyWidgets::dropdown(
-                                
+
                                 tags$h3("Select columns to display"),
                                 
                                 shinyWidgets::pickerInput(
@@ -175,7 +174,7 @@ ui <- shiny::navbarPage("CiteSource",
                         shiny::tabPanel(
                           "Visualise",
                           
-                          # Sidebar layout with input and output definitions ----
+                          # Sidebar layout ----
                           shiny::sidebarLayout(
                             
                             # Sidebar panel for inputs ----
@@ -189,7 +188,8 @@ ui <- shiny::navbarPage("CiteSource",
                                 inline = TRUE,
                                 choices = c(
                                   "sources",
-                                  "labels", "strings"
+                                  "labels", 
+                                  "strings"
                                 ),
                                 status = "primary"
                               ),
@@ -331,7 +331,7 @@ ui <- shiny::navbarPage("CiteSource",
                                     
                                     # Performance Note
                                     shiny::tags$p(
-                                      style = "margin-bottom: 12px;", # Add space below this paragraph
+                                      style = "margin-bottom: 12px;",
                                       shiny::tags$strong(" Performance Note:"),
                                       " The record table may take a long time to generate if you include more than a few hundred references. Consider filtering your data first using the sidebar selectors before generating."
                                     ),
@@ -364,7 +364,7 @@ ui <- shiny::navbarPage("CiteSource",
                                       " Type into the search box located at the top-right of the table to dynamically filter records based on any information displayed."
                                     ),
                                     
-                                    # Instruction 5: Download (No bottom margin needed on the last item)
+                                    # Instruction 5: Download
                                     shiny::tags$p(
                                       shiny::tags$strong(" Download Data:"),
                                       " Click the 'Download CSV' button (located above the table, next to 'Print') to save the data currently shown in the table (including applied filters) as a CSV file."
@@ -415,13 +415,14 @@ server <- function(input, output, session) {
       # upload files one-by-one
       path_list <- input$file$datapath
       suggested_source <- stringr::str_replace_all(input$file$name, "\\.(ris|bib|txt)$", "")
+      suggested_label <- rep("search", length(input$file$datapath))
       empty_strings <- rep("", length(input$file$datapath))
       
       # Read the uploaded citations
       upload_df <- CiteSource::read_citations(
         files = path_list,
         cite_sources = suggested_source,
-        cite_labels = empty_strings,
+        cite_labels = suggested_label,
         cite_strings = empty_strings,
         only_key_fields = FALSE
         
@@ -437,7 +438,7 @@ server <- function(input, output, session) {
       df <- data.frame(
         "file" = input$file,
         "suggested_source" = suggested_source,
-        "label" = empty_strings,
+        "label" = suggested_label,
         "string" = empty_strings
       )
       
@@ -1466,17 +1467,17 @@ server <- function(input, output, session) {
     total_nonunique_records <- sum(detailed_counts_per_source$`Non-unique Records`, na.rm = TRUE)
     
     # Prepare for percentage calculation (avoid division by zero)
-    total_distinct_divisor <- ifelse(total_distinct_records == 0, 1, total_distinct_records)
+    total_distinct_divisor <- ifelse(total_distinct_records == 0, 1, total_distinct_records) 
     total_unique_divisor <- ifelse(total_unique_records == 0, 1, total_unique_records)
     
     # Calculate and format percentages
     detailed_counts_final <- detailed_counts_per_source %>%
       dplyr::mutate(
-        perc_contr = `Distinct Records` / total_distinct_divisor,
+        perc_contr = `Distinct Records` / total_distinct_divisor, #incorrect percent testing 5/7 TR
         perc_unique_contr = `Unique Records` / total_unique_divisor,
         perc_source_unique = ifelse(`Distinct Records` == 0, 0, `Unique Records` / `Distinct Records`) ) %>%
       dplyr::mutate(
-        `Source Contribution %` = scales::percent(perc_contr, accuracy = 0.1),
+        `Source Contribution %` = scales::percent(perc_contr, accuracy = 0.1), #incorrect percent testing 5/7 TR
         `Source Unique Contribution %` = scales::percent(perc_unique_contr, accuracy = 0.1),
         `Source Unique %` = scales::percent(perc_source_unique, accuracy = 0.1) ) %>%
       dplyr::select( # Select and order final columns
@@ -1510,7 +1511,7 @@ server <- function(input, output, session) {
     }
     
     unique_citations <- unique_filtered_table()
-    initial_records <- calculate_initial_records(unique_citations, "search")
+    initial_records <- calculate_initial_records(unique_citations, "search") #incorrect percent testing 5/7 TR should be the same as detailed record table total records imported
     create_initial_record_table(initial_records)
   }) %>% shiny::bindEvent(input$generateInitialRecordTable)
   
@@ -1533,7 +1534,7 @@ server <- function(input, output, session) {
     # Bind to the same button trigger
   }) %>% shiny::bindEvent(input$generateDetailedRecordTable)
   
-  # Rendering the precision and sensitivity table
+  # Rendering the precision and sensitivity table ----
   output$summaryPrecTab <- gt::render_gt({
     
     unique_citations <- unique_filtered_table()
@@ -1549,7 +1550,7 @@ server <- function(input, output, session) {
   }) %>% shiny::bindEvent(input$generatePrecisionTable)
   
   
-  # Rendering the record-level table
+  # Rendering the record-level table ----
   output$reviewTab <- DT::renderDataTable({
     
     if (nrow(rv$latest_unique) == 0) {

From c20c1f02f0604e3ccb1270c27ab63f295e017c06 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 7 May 2025 17:05:26 -0400
Subject: [PATCH 38/47] simplified view rows and export

---
 R/tables.R | 63 +++++++++++++++++++++++++++---------------------------
 1 file changed, 32 insertions(+), 31 deletions(-)

diff --git a/R/tables.R b/R/tables.R
index c1df7fe4..9d6f40f2 100644
--- a/R/tables.R
+++ b/R/tables.R
@@ -25,32 +25,32 @@
 record_level_table <- function(citations, include = "sources", include_empty = TRUE, return = c("tibble", "DT"), indicator_presence = NULL, indicator_absence = NULL) {
   
   if (!is.data.frame(citations) || nrow(citations) == 0) stop("Citations must be a tibble and cannot have 0 entries")
-
+  
   if (is.null(indicator_absence)) {
     indicator_absence <- switch(return[1],
-      tibble = FALSE,
-      DT = "&#x2717;"
+                                tibble = FALSE,
+                                DT = "&#x2717;"
     )
   }
   if (is.null(indicator_presence)) {
     indicator_presence <- switch(return[1],
-      tibble = TRUE,
-      DT = "&#10004;"
+                                 tibble = TRUE,
+                                 DT = "&#10004;"
     )
   }
   
   sources <- compare_sources(citations, comp_type = include)
-
+  
   if (nrow(sources) == 0) {
     warning("Citations provided contain no information on ", include, ". NA will be displayed, but check whether you intended to do a different comparison")
-  sources <- tibble::tibble(duplicate_id = citations$duplicate_id)
-   sources[[paste0(stringr::str_sub(include, 1, -2), "__NA")]] <- TRUE
+    sources <- tibble::tibble(duplicate_id = citations$duplicate_id)
+    sources[[paste0(stringr::str_sub(include, 1, -2), "__NA")]] <- TRUE
   }
   
   if (!include_empty == TRUE) {
     citations <- citations %>% dplyr::filter(.data$duplicate_id %in% sources$duplicate_id)
   }
-
+  
   if (! "url" %in% colnames(citations)) {
     citations$url <- NA_character_
   }
@@ -65,17 +65,17 @@ record_level_table <- function(citations, include = "sources", include_empty = T
     dplyr::arrange(stringr::str_extract(.data$author, "^.*?,"), .data$citation) %>%
     dplyr::select("duplicate_id", "citation", "reference", "html_reference") %>%
     dplyr::left_join(sources, by = "duplicate_id")
-
+  
   indicator_presence <- as.character(indicator_presence)
   indicator_absence <- as.character(indicator_absence)
-
+  
   to_display <- citations %>%
     dplyr::select(-(1:4)) %>%
     dplyr::mutate(dplyr::across(dplyr::everything(), ~ ifelse(.x, indicator_presence, indicator_absence))) %>%
     dplyr::rename_with(~ paste0(.x, " ")) # Add space to keep column names unique
-
+  
   citations <- dplyr::bind_cols(citations, to_display)
-
+  
   if (return[1] == "DT") {
     if (!rlang::is_installed("DT")) {
       warning('DT can only be returned when the DT package is installed. Please run install.packages("DT")')
@@ -88,7 +88,7 @@ record_level_table <- function(citations, include = "sources", include_empty = T
           stringr::str_remove(glue::glue("^{type}__"))
         list(type = type %>% stringr::str_to_title(), values = values)
       }) %>% purrr::transpose()
-
+      
       sketch <- htmltools::tags$table(
         class = "display",
         htmltools::tags$thead(
@@ -105,32 +105,33 @@ record_level_table <- function(citations, include = "sources", include_empty = T
           htmltools::tags$td(colspan = 4 + length(unlist(headings$values)), htmltools::HTML("Click on the &oplus; to view the full reference"))
         )
       )
-
-      # Data for DT: selected columns from citations, with an added details control column
+      
       citations %>%
-        dplyr::select(-"duplicate_id", -"reference") %>% # Keeps "citation", "html_reference", and the renamed source indicator columns
-        cbind(" " = "&oplus;", .)%>%
-
-      DT::datatable(
-        escape = FALSE,
-        extensions = "Buttons",
-        options = list(
-          dom = "Blfrtip", # Added 'l' for length changing input. Common placement: Buttons, length, filter, processing, table, info, pagination.
-          pageLength = 10,   # Sets the initial number of rows displayed.
-          lengthMenu = list(c(10, 25, 50, 100, -1), c('10', '25', '50', '100', 'All')), # Choices for "Show X entries" dropdown. -1 means "All".
+        dplyr::select(-"duplicate_id", -"reference") %>%
+        cbind(" " = "&oplus;", .) %>%
+        DT::datatable(
+          escape = FALSE,
+          extensions = "Buttons",
+          options = list(
+            pageLength = 10,
+            lengthMenu = list(c(10, 25, 50, 100, -1), c('10', '25', '50', '100', 'All')),
             columnDefs = list(
               list(visible = FALSE, targets = c(0, 3:(3 + ncol(to_display)))),
               list(orderable = FALSE, className = "details-control", targets = 1)
             ),
+            dom = "lBfrtip",
             buttons =
               list("print", list(
                 extend = "csv", filename = "CiteSource_record_summary",
                 text = "Download csv",
-                # Adjust exportOptions columns based on the actual structure of citations data and what you want to export
-                exportOptions = list(columns = c(1, 2, 4:(3 + (ncol(citations) - 3))))
+                exportOptions = list(
+                  columns = c(0, 2:(3 + ncol(to_display))),
+                  modifier = list(page = "all")
+                )
+                
               ))
           ), container = sketch,
-        callback = DT::JS("
+          callback = DT::JS("
               table.column(1).nodes().to$().css({cursor: 'pointer'});
               var format = function(d) {
                 return '<div style=\"background-color:#eee; padding: .5em;\">' +
@@ -150,8 +151,8 @@ record_level_table <- function(citations, include = "sources", include_empty = T
     }
   } else {
     citations %>%
-      dplyr::select(1:3, dplyr::matches(" "), -"html_reference") %>% # Selects duplicate_id, citation, reference, and space-suffixed columns
-      dplyr::rename_with(stringr::str_trim) # Remove trailing spaces from column names
+      dplyr::select(1:3, dplyr::matches(" "), -"html_reference") %>%
+      dplyr::rename_with(stringr::str_trim)
   }
 }
 

From e10e3c5352620f147e7688432a89b5874e9784e6 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 7 May 2025 17:06:04 -0400
Subject: [PATCH 39/47] removed initial record table duplicative with detailed
 record table

---
 inst/shiny-app/CiteSource/app.R | 40 +++++----------------------------
 1 file changed, 5 insertions(+), 35 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 868c31b9..73365c44 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -273,20 +273,6 @@ ui <- shiny::navbarPage("CiteSource",
                             
                             shiny::mainPanel(
                               shiny::tabsetPanel(
-                                
-                                shiny::tabPanel(
-                                  "Initial Records Table",
-                                  shiny::div("View the initial record counts and deduplication results."),
-                                  shinyWidgets::actionBttn(
-                                    "generateInitialRecordTable", "Generate Initial Records Table",
-                                    style = "jelly",
-                                    icon = shiny::icon("table"),
-                                    color = "primary") %>% htmltools::tagAppendAttributes(style = "background-color: #23395B"),
-                                  shiny::br(),
-                                  shiny::br(),
-                                  gt::gt_output("initialRecordTab")
-                                ),
-                                
                                 shiny::tabPanel(
                                   "Detailed Record Table",
                                   shiny::div("Summary of unique and non-unique records by source."),
@@ -1249,12 +1235,12 @@ server <- function(input, output, session) {
   #### Table tab ####
   
   # Event reactive for filtering the data used in the record table and summary table
-  # In your server function, replace the current unique_filtered_table eventReactive
-  
   unique_filtered_table <- shiny::eventReactive(
     c(input$generateRecordTable,
-      input$sources_tables, input$strings_tables, input$labels_tables,
-      input$generateInitialRecordTable, input$generateDetailedRecordTable,
+      input$sources_tables, 
+      input$strings_tables, 
+      input$labels_tables,
+      input$generateDetailedRecordTable,
       input$generatePrecisionTable),
     {
       shiny::req(rv$latest_unique, is.data.frame(rv$latest_unique), nrow(rv$latest_unique) > 0)
@@ -1499,22 +1485,7 @@ server <- function(input, output, session) {
     
     return(detailed_counts_final)
   })
-  
-  # Rendering the initial record table
-  output$initialRecordTab <- gt::render_gt({
-    if (nrow(rv$latest_unique) == 0) {
-      shinyalert::shinyalert("Data needed",
-                             "Please import and deduplicate your citations first.",
-                             type = "error"
-      )
-      shiny::req(FALSE)
-    }
-    
-    unique_citations <- unique_filtered_table()
-    initial_records <- calculate_initial_records(unique_citations, "search") #incorrect percent testing 5/7 TR should be the same as detailed record table total records imported
-    create_initial_record_table(initial_records)
-  }) %>% shiny::bindEvent(input$generateInitialRecordTable)
-  
+ 
   # Rendering the detailed record table
   output$detailedRecordTab <- gt::render_gt({
     # Check if base data is loaded
@@ -1570,7 +1541,6 @@ server <- function(input, output, session) {
   
   #### Export tab ####
   
-  # # Downloadable bibtex ----
   # Downloadable bibtex ----
   output$downloadCsv <- shiny::downloadHandler(
     filename = function() {

From 1fa6d8705d623a0dc62dd7aa3da2ef86af4dc8f1 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Wed, 7 May 2025 17:26:07 -0400
Subject: [PATCH 40/47] fix detailed record source contribution

---
 inst/shiny-app/CiteSource/app.R | 120 ++++++++++++++++++--------------
 1 file changed, 66 insertions(+), 54 deletions(-)

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 73365c44..731b0d10 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -1381,105 +1381,117 @@ server <- function(input, output, session) {
     strings_filt <- input$strings_tables
     strings_filt <- ifelse(strings_filt == "_blank_", "unknown", strings_filt)
     
-    # Create filter patterns for labels and strings (can still pre-filter these)
-    labels_pattern <- if (length(labels_filt) > 0) paste0("\\b(", paste(labels_filt, collapse = "|"), ")\\b") else NULL
-    strings_pattern <- if (length(strings_filt) > 0) paste0("\\b(", paste(strings_filt, collapse = "|"), ")\\b") else NULL
+    # Create filter patterns (ensure robust handling of empty/NA filter values)
+    labels_filt_cleaned <- labels_filt[!is.na(labels_filt) & labels_filt != ""]
+    labels_pattern <- if (length(labels_filt_cleaned) > 0) paste0("\\b(", paste(labels_filt_cleaned, collapse = "|"), ")\\b") else NULL
+    
+    strings_filt_cleaned <- strings_filt[!is.na(strings_filt) & strings_filt != ""]
+    strings_pattern <- if (length(strings_filt_cleaned) > 0) paste0("\\b(", paste(strings_filt_cleaned, collapse = "|"), ")\\b") else NULL
     
     # Apply initial filters for labels and strings only
     df_filtered_wide <- rv$latest_unique %>%
       dplyr::filter(
-        (is.null(labels_pattern) | stringr::str_detect(cite_label, labels_pattern)),
-        (is.null(strings_pattern) | stringr::str_detect(cite_string, strings_pattern))
+        (is.null(labels_pattern) | stringr::str_detect(as.character(cite_label), labels_pattern)),
+        (is.null(strings_pattern) | stringr::str_detect(as.character(cite_string), strings_pattern))
       )
     
-    # Define the structure for returning an empty result
-    empty_result_df <- tibble::tibble(
+    empty_result_df <- tibble::tibble( # Define structure for empty returns
       Source = character(), `Records Imported` = integer(), `Distinct Records` = integer(),
       `Unique Records` = integer(), `Non-unique Records` = integer(),
       `Source Contribution %` = character(), `Source Unique Contribution %` = character(),
       `Source Unique %` = character() )
     
-    # Return empty structure if no records match initial filters
     if (nrow(df_filtered_wide) == 0) { return(empty_result_df) }
     
-    # Separate only the cite_source column for source-level aggregation
+    # Separate cite_source column
     df_long_source <- df_filtered_wide %>%
-      dplyr::select(duplicate_id, cite_source) %>%
+      dplyr::select(duplicate_id, cite_source, cite_label, cite_string) %>% 
       tidyr::separate_rows(cite_source, sep = ",\\s*") %>%
-      dplyr::filter(!is.na(cite_source) & cite_source != "") # Filter out blank sources
+      dplyr::mutate(cite_source = trimws(cite_source)) %>%
+      dplyr::filter(!is.na(cite_source) & cite_source != "")
     
-    # *** Apply the source filter AFTER separating sources ***
+    # Apply source filter
+    sources_filt_cleaned <- sources_filt[!is.na(sources_filt) & sources_filt != ""]
     df_long_source_filtered <- df_long_source %>%
-      dplyr::filter(length(sources_filt) == 0 | cite_source %in% sources_filt)
+      dplyr::filter(length(sources_filt_cleaned) == 0 | cite_source %in% sources_filt_cleaned)
     
-    # Return empty if no valid sources remain after filtering
     if (nrow(df_long_source_filtered) == 0) { return(empty_result_df) }
     
-    # --- All subsequent calculations use df_long_source_filtered ---
-    
     # Calculate 'Records Imported' and 'Distinct Records' per source
-    source_base_counts <- df_long_source_filtered %>% # Use filtered data
+    source_base_counts <- df_long_source_filtered %>%
       dplyr::group_by(cite_source) %>%
-      dplyr::summarise(`Records Imported` = dplyr::n(), `Distinct Records` = dplyr::n_distinct(duplicate_id), .groups = 'drop')
+      dplyr::summarise(
+        `Records Imported` = dplyr::n(),
+        `Distinct Records` = dplyr::n_distinct(duplicate_id), # 'Distinct Records' for this source
+        .groups = 'drop'
+      )
     
-    # Calculate how many sources each distinct record appears in (within the filtered set)
-    # Use the df_long_source_filtered data here too
+    if (nrow(source_base_counts) == 0) { return(empty_result_df) } # Ensure source_base_counts has rows for sum below
+    
+    # Calculate 'Unique Records' per source
     record_source_counts <- df_long_source_filtered %>%
       dplyr::group_by(duplicate_id) %>%
-      dplyr::summarise(n_sources = dplyr::n_distinct(cite_source), .groups = 'drop')
+      dplyr::summarise(n_sources_for_id = dplyr::n_distinct(cite_source), .groups = 'drop')
     
-    # Identify records unique to one source within the filtered set
-    # Use the df_long_source_filtered data here too
-    unique_record_sources <- df_long_source_filtered %>%
+    unique_record_sources <- df_long_source_filtered %>% # Records found in only one of the *currently filtered* sources
       dplyr::inner_join(record_source_counts, by = "duplicate_id") %>%
-      dplyr::filter(n_sources == 1) %>%
-      dplyr::distinct(duplicate_id, cite_source) # Get the source for unique records
+      dplyr::filter(n_sources_for_id == 1) %>%
+      dplyr::distinct(duplicate_id, cite_source) # Get the source for these unique records
     
-    # Count the number of unique records per source
     source_unique_counts <- unique_record_sources %>%
       dplyr::group_by(cite_source) %>%
-      dplyr::summarise(`Unique Records` = dplyr::n(), .groups = 'drop')
+      dplyr::summarise(`Unique Records` = dplyr::n_distinct(duplicate_id), .groups = 'drop')
     
-    # Combine base counts with unique counts
+    # Combine counts
     detailed_counts_per_source <- source_base_counts %>%
       dplyr::left_join(source_unique_counts, by = "cite_source") %>%
-      dplyr::mutate(`Unique Records` = tidyr::replace_na(`Unique Records`, 0)) %>%
-      dplyr::mutate(`Non-unique Records` = `Distinct Records` - `Unique Records`)
+      dplyr::mutate(
+        `Unique Records` = tidyr::replace_na(`Unique Records`, 0),
+        `Non-unique Records` = `Distinct Records` - `Unique Records`
+      )
     
-    # Calculate totals consistently from the *filtered* data
-    total_records_imported <- sum(detailed_counts_per_source$`Records Imported`, na.rm = TRUE)
-    total_distinct_records <- dplyr::n_distinct(df_long_source_filtered$duplicate_id) # Use filtered data
-    total_unique_records <- sum(detailed_counts_per_source$`Unique Records`, na.rm = TRUE)
-    total_nonunique_records <- sum(detailed_counts_per_source$`Non-unique Records`, na.rm = TRUE)
+    # Denominator for 'Source Contribution %' should be the sum of 'Distinct Records' from each source.
+    denominator_source_contribution <- sum(detailed_counts_per_source$`Distinct Records`, na.rm = TRUE)
+    denominator_source_contribution_safe <- ifelse(denominator_source_contribution == 0, 1, denominator_source_contribution)
     
-    # Prepare for percentage calculation (avoid division by zero)
-    total_distinct_divisor <- ifelse(total_distinct_records == 0, 1, total_distinct_records) 
-    total_unique_divisor <- ifelse(total_unique_records == 0, 1, total_unique_records)
+    # Denominator for 'Source Unique Contribution %' (total unique records overall from *these filtered sources*)
+    total_overall_unique_records = dplyr::n_distinct(unique_record_sources$duplicate_id) # Count unique IDs from records that are unique to some source
+    denominator_source_unique_contribution_safe <- ifelse(total_overall_unique_records == 0, 1, total_overall_unique_records)
     
-    # Calculate and format percentages
+    
+    # Calculate percentages
     detailed_counts_final <- detailed_counts_per_source %>%
       dplyr::mutate(
-        perc_contr = `Distinct Records` / total_distinct_divisor, #incorrect percent testing 5/7 TR
-        perc_unique_contr = `Unique Records` / total_unique_divisor,
-        perc_source_unique = ifelse(`Distinct Records` == 0, 0, `Unique Records` / `Distinct Records`) ) %>%
+        perc_contr = `Distinct Records` / denominator_source_contribution_safe, # MODIFIED DENOMINATOR
+        perc_unique_contr = `Unique Records` / denominator_source_unique_contribution_safe,
+        perc_source_unique = ifelse(`Distinct Records` == 0, 0, `Unique Records` / `Distinct Records`)
+      ) %>%
       dplyr::mutate(
-        `Source Contribution %` = scales::percent(perc_contr, accuracy = 0.1), #incorrect percent testing 5/7 TR
+        `Source Contribution %` = scales::percent(perc_contr, accuracy = 0.1),
         `Source Unique Contribution %` = scales::percent(perc_unique_contr, accuracy = 0.1),
-        `Source Unique %` = scales::percent(perc_source_unique, accuracy = 0.1) ) %>%
-      dplyr::select( # Select and order final columns
+        `Source Unique %` = scales::percent(perc_source_unique, accuracy = 0.1)
+      ) %>%
+      dplyr::select( 
         cite_source, `Records Imported`, `Distinct Records`, `Unique Records`,
         `Non-unique Records`, `Source Contribution %`,
-        `Source Unique Contribution %`, `Source Unique %` )
+        `Source Unique Contribution %`, `Source Unique %`
+      )
+    
+    # --- TOTAL ROW ---
+    # 'Total Distinct Records' in the total row should be the overall number of unique duplicate_ids found.
+    overall_total_distinct_records <- dplyr::n_distinct(df_long_source_filtered$duplicate_id)
     
-    # Create the total row
     total_row <- tibble::tibble(
-      cite_source = "Total", `Records Imported` = total_records_imported,
-      `Distinct Records` = total_distinct_records, `Unique Records` = total_unique_records,
-      `Non-unique Records` = total_nonunique_records, `Source Contribution %` = NA_character_,
-      `Source Unique Contribution %` = NA_character_,
-      `Source Unique %` = scales::percent(total_unique_records / total_distinct_divisor, accuracy = 0.1) )
+      cite_source = "Total",
+      `Records Imported` = sum(detailed_counts_final$`Records Imported`, na.rm = TRUE),
+      `Distinct Records` = overall_total_distinct_records, # Overall unique items
+      `Unique Records` = sum(detailed_counts_final$`Unique Records`, na.rm = TRUE), 
+      `Non-unique Records` = sum(detailed_counts_final$`Non-unique Records`, na.rm = TRUE),
+      `Source Contribution %` = scales::percent(1.0, accuracy = 0.1), # Sum of these per-source % should now be 100%
+      `Source Unique Contribution %` = if(total_overall_unique_records > 0) scales::percent(1.0, accuracy = 0.1) else scales::percent(0.0, accuracy = 0.1), # Sum of these should be 100% if any uniques
+      `Source Unique %` = scales::percent(sum(detailed_counts_final$`Unique Records`, na.rm = TRUE) / ifelse(overall_total_distinct_records == 0, 1, overall_total_distinct_records), accuracy = 0.1)
+    )
     
-    # Add total row and rename source column
     detailed_counts_final <- dplyr::bind_rows(detailed_counts_final, total_row) %>%
       dplyr::rename(Source = cite_source)
     

From bc5d9fa21305f97028c21aae7078d3bef0b0726b Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Fri, 9 May 2025 14:42:59 -0400
Subject: [PATCH 41/47] add flavicon

---
 inst/shiny-app/CiteSource/app.R           |  18 +++++++++---------
 inst/shiny-app/CiteSource/www/favicon.png | Bin 0 -> 63250 bytes
 2 files changed, 9 insertions(+), 9 deletions(-)
 create mode 100644 inst/shiny-app/CiteSource/www/favicon.png

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 731b0d10..a01605ab 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -24,17 +24,17 @@ ui <- shiny::navbarPage("CiteSource",
                         header = shiny::tagList(
                           shinybusy::add_busy_spinner(spin = "circle"),
                           shinyjs::useShinyjs(),
-                          #header text color
                           tags$head(
+                            tags$link(rel = "icon", type = "image/png", href = "www/favicon.png"),  # Add favicon
                             tags$style(HTML("
-      h6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {
-        margin-top: 0;
-        margin-bottom: .5rem;
-        font-weight: 500;
-        line-height: 1.2;
-        color: #23395B;
-      }
-    "))
+                              h6, .h6, h5, .h5, h4, .h4, h3, .h3, h2, .h2, h1, .h1 {
+                                margin-top: 0;
+                                margin-bottom: .5rem;
+                                font-weight: 500;
+                                line-height: 1.2;
+                                color: #23395B;
+                              }
+                            "))
                           )
                         ),
                         theme = bslib::bs_theme(
diff --git a/inst/shiny-app/CiteSource/www/favicon.png b/inst/shiny-app/CiteSource/www/favicon.png
new file mode 100644
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GIT binary patch
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literal 0
HcmV?d00001


From 612bdd4e3146f5583668f64237e8127acd7aa8bc Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 12 May 2025 09:43:59 -0400
Subject: [PATCH 42/47] add google analytics to dev app

---
 inst/shiny-app/CiteSource/app.R                 | 5 +++--
 inst/shiny-app/CiteSource/google-analytics.html | 9 +++++++++
 2 files changed, 12 insertions(+), 2 deletions(-)
 create mode 100644 inst/shiny-app/CiteSource/google-analytics.html

diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index 3fed1bf6..d600a6a2 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -3,8 +3,9 @@ library(CiteSource)
 library(dplyr)
 
 # Set background color
-shiny::tags$head(shiny::tags$style(
-  shiny::HTML('
+shiny::tags$head(
+  includeHTML("google-analytics.html"),
+  shiny::tags$style(shiny::HTML('
                      #sidebar {
                         background-color: #ffffff;
                     }
diff --git a/inst/shiny-app/CiteSource/google-analytics.html b/inst/shiny-app/CiteSource/google-analytics.html
new file mode 100644
index 00000000..f31c3d31
--- /dev/null
+++ b/inst/shiny-app/CiteSource/google-analytics.html
@@ -0,0 +1,9 @@
+<!-- Google tag (gtag.js) -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-FTH8FP1X54"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-FTH8FP1X54');
+</script>
\ No newline at end of file

From d07913707cffce56ff449b4bde8dc5c9daffd9f9 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 12 May 2025 10:02:37 -0400
Subject: [PATCH 43/47] add google analytics to main

---
 .../{google-analytics.html => google_analytics_dev.html} | 0
 inst/shiny-app/CiteSource/google_analytics_main.html     | 9 +++++++++
 2 files changed, 9 insertions(+)
 rename inst/shiny-app/CiteSource/{google-analytics.html => google_analytics_dev.html} (100%)
 create mode 100644 inst/shiny-app/CiteSource/google_analytics_main.html

diff --git a/inst/shiny-app/CiteSource/google-analytics.html b/inst/shiny-app/CiteSource/google_analytics_dev.html
similarity index 100%
rename from inst/shiny-app/CiteSource/google-analytics.html
rename to inst/shiny-app/CiteSource/google_analytics_dev.html
diff --git a/inst/shiny-app/CiteSource/google_analytics_main.html b/inst/shiny-app/CiteSource/google_analytics_main.html
new file mode 100644
index 00000000..eb4691bd
--- /dev/null
+++ b/inst/shiny-app/CiteSource/google_analytics_main.html
@@ -0,0 +1,9 @@
+<!-- Google tag (gtag.js) -->
+<script async src="https://www.googletagmanager.com/gtag/js?id=G-0XR2H5C57G"></script>
+<script>
+  window.dataLayer = window.dataLayer || [];
+  function gtag(){dataLayer.push(arguments);}
+  gtag('js', new Date());
+
+  gtag('config', 'G-0XR2H5C57G');
+</script>
\ No newline at end of file

From a097301b38b49964e0356d2702de2dc590f4d2a5 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 12 May 2025 10:03:44 -0400
Subject: [PATCH 44/47] add google analytics to app and deploy

---
 .github/workflows/document-and-deploy.yml | 12 ++++++++++--
 inst/shiny-app/CiteSource/app.R           | 16 ++++++++++++++--
 2 files changed, 24 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/document-and-deploy.yml b/.github/workflows/document-and-deploy.yml
index 03e25e5f..f1643c74 100644
--- a/.github/workflows/document-and-deploy.yml
+++ b/.github/workflows/document-and-deploy.yml
@@ -53,7 +53,11 @@ jobs:
           R -e "
             remotes::install_github('ESHackathon/CiteSource', force = TRUE);
             rsconnect::setAccountInfo(name=${{secrets.SHINY_LUKAS_ACCOUNT}}, token=${{secrets.SHINY_LUKAS_TOKEN}}, secret=${{secrets.SHINY_LUKAS_SECRET}}); 
-            rsconnect::deployApp(appName = 'CiteSource_latest', appDir = './inst/shiny-app/CiteSource', forceUpdate = TRUE)"   
+            rsconnect::deployApp(
+              appName = 'CiteSource_latest', 
+              appDir = './inst/shiny-app/CiteSource',
+              envVars = c(APP_ENV = "development"),
+              forceUpdate = TRUE)"   
 
       - name: Deploy stable version from main
         if: github.ref == 'refs/heads/main'
@@ -63,7 +67,11 @@ jobs:
           R -e "
             remotes::install_github('ESHackathon/CiteSource', force = TRUE);
             rsconnect::setAccountInfo(name=${{secrets.SHINY_LUKAS_ACCOUNT}}, token=${{secrets.SHINY_LUKAS_TOKEN}}, secret=${{secrets.SHINY_LUKAS_SECRET}}); 
-            rsconnect::deployApp(appName = 'CiteSource', appDir = './inst/shiny-app/CiteSource', forceUpdate = TRUE)"   
+            rsconnect::deployApp(
+              appName = 'CiteSource', 
+              appDir = './inst/shiny-app/CiteSource',
+              envVars = c(APP_ENV = "production"),
+              forceUpdate = TRUE)"   
 
       - name: Create pkgdown
         env:
diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index d600a6a2..a095e841 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -2,9 +2,21 @@ library(DT)
 library(CiteSource)
 library(dplyr)
 
-# Set background color
+app_environment <- Sys.getenv("APP_ENV")
+# Determine which Google Analytics file to include based on the environment
+ga_include_file <- NULL
+if (app_environment == "production") {
+  ga_include_file <- "google_analytics_main.html"
+} else if (app_environment == "development") {
+  ga_include_file <- "google_analytics_dev.html"
+}
+
 shiny::tags$head(
-  includeHTML("google-analytics.html"),
+  # Include the appropriate Google Analytics file if determined
+  if (!is.null(ga_include_file) && file.exists(ga_include_file)) {
+    includeHTML(ga_include_file)
+  },
+  # style
   shiny::tags$style(shiny::HTML('
                      #sidebar {
                         background-color: #ffffff;

From 1e2335d11f376aeb11ebb23f1ac353fc6f80cb04 Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 12 May 2025 10:33:15 -0400
Subject: [PATCH 45/47] fix: Correct env var quoting in shinyapps deploy steps

---
 .github/workflows/document-and-deploy.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/document-and-deploy.yml b/.github/workflows/document-and-deploy.yml
index f1643c74..dd81669e 100644
--- a/.github/workflows/document-and-deploy.yml
+++ b/.github/workflows/document-and-deploy.yml
@@ -56,7 +56,7 @@ jobs:
             rsconnect::deployApp(
               appName = 'CiteSource_latest', 
               appDir = './inst/shiny-app/CiteSource',
-              envVars = c(APP_ENV = "development"),
+              envVars = c(APP_ENV = 'development'),
               forceUpdate = TRUE)"   
 
       - name: Deploy stable version from main
@@ -70,7 +70,7 @@ jobs:
             rsconnect::deployApp(
               appName = 'CiteSource', 
               appDir = './inst/shiny-app/CiteSource',
-              envVars = c(APP_ENV = "production"),
+              envVars = c(APP_ENV = 'production'),
               forceUpdate = TRUE)"   
 
       - name: Create pkgdown

From c7c783720792bed766ff8347ae3659d0fb54dcea Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 12 May 2025 10:44:54 -0400
Subject: [PATCH 46/47] Attempt to fix deploy error by restructuring envVars

---
 .github/workflows/document-and-deploy.yml | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/document-and-deploy.yml b/.github/workflows/document-and-deploy.yml
index dd81669e..1406da35 100644
--- a/.github/workflows/document-and-deploy.yml
+++ b/.github/workflows/document-and-deploy.yml
@@ -53,10 +53,12 @@ jobs:
           R -e "
             remotes::install_github('ESHackathon/CiteSource', force = TRUE);
             rsconnect::setAccountInfo(name=${{secrets.SHINY_LUKAS_ACCOUNT}}, token=${{secrets.SHINY_LUKAS_TOKEN}}, secret=${{secrets.SHINY_LUKAS_SECRET}}); 
+            deploy_env_vars <- setNames('development', 'APP_ENV')
+            # Deploy the Shiny app
             rsconnect::deployApp(
-              appName = 'CiteSource_latest', 
+              appName = 'CiteSource_latest',
               appDir = './inst/shiny-app/CiteSource',
-              envVars = c(APP_ENV = 'development'),
+              envVars = deploy_env_vars,
               forceUpdate = TRUE)"   
 
       - name: Deploy stable version from main
@@ -67,10 +69,12 @@ jobs:
           R -e "
             remotes::install_github('ESHackathon/CiteSource', force = TRUE);
             rsconnect::setAccountInfo(name=${{secrets.SHINY_LUKAS_ACCOUNT}}, token=${{secrets.SHINY_LUKAS_TOKEN}}, secret=${{secrets.SHINY_LUKAS_SECRET}}); 
+            deploy_env_vars <- setNames('production', 'APP_ENV')
+            # Deploy the Shiny app
             rsconnect::deployApp(
-              appName = 'CiteSource', 
+              appName = 'CiteSource',
               appDir = './inst/shiny-app/CiteSource',
-              envVars = c(APP_ENV = 'production'),
+              envVars = deploy_env_vars,
               forceUpdate = TRUE)"   
 
       - name: Create pkgdown

From 2143779dbd6c218614c9bd7bf5dcb4519b1681cc Mon Sep 17 00:00:00 2001
From: Trevor Riley <tnriley@gmail.com>
Date: Mon, 12 May 2025 11:03:04 -0400
Subject: [PATCH 47/47] attempt new GA integration

---
 .github/workflows/document-and-deploy.yml |  6 --
 inst/shiny-app/CiteSource/app.R           | 79 +++++++++++++++++++----
 2 files changed, 66 insertions(+), 19 deletions(-)

diff --git a/.github/workflows/document-and-deploy.yml b/.github/workflows/document-and-deploy.yml
index 1406da35..402c7b9e 100644
--- a/.github/workflows/document-and-deploy.yml
+++ b/.github/workflows/document-and-deploy.yml
@@ -53,12 +53,9 @@ jobs:
           R -e "
             remotes::install_github('ESHackathon/CiteSource', force = TRUE);
             rsconnect::setAccountInfo(name=${{secrets.SHINY_LUKAS_ACCOUNT}}, token=${{secrets.SHINY_LUKAS_TOKEN}}, secret=${{secrets.SHINY_LUKAS_SECRET}}); 
-            deploy_env_vars <- setNames('development', 'APP_ENV')
-            # Deploy the Shiny app
             rsconnect::deployApp(
               appName = 'CiteSource_latest',
               appDir = './inst/shiny-app/CiteSource',
-              envVars = deploy_env_vars,
               forceUpdate = TRUE)"   
 
       - name: Deploy stable version from main
@@ -69,12 +66,9 @@ jobs:
           R -e "
             remotes::install_github('ESHackathon/CiteSource', force = TRUE);
             rsconnect::setAccountInfo(name=${{secrets.SHINY_LUKAS_ACCOUNT}}, token=${{secrets.SHINY_LUKAS_TOKEN}}, secret=${{secrets.SHINY_LUKAS_SECRET}}); 
-            deploy_env_vars <- setNames('production', 'APP_ENV')
-            # Deploy the Shiny app
             rsconnect::deployApp(
               appName = 'CiteSource',
               appDir = './inst/shiny-app/CiteSource',
-              envVars = deploy_env_vars,
               forceUpdate = TRUE)"   
 
       - name: Create pkgdown
diff --git a/inst/shiny-app/CiteSource/app.R b/inst/shiny-app/CiteSource/app.R
index a095e841..2aad160e 100644
--- a/inst/shiny-app/CiteSource/app.R
+++ b/inst/shiny-app/CiteSource/app.R
@@ -2,20 +2,7 @@ library(DT)
 library(CiteSource)
 library(dplyr)
 
-app_environment <- Sys.getenv("APP_ENV")
-# Determine which Google Analytics file to include based on the environment
-ga_include_file <- NULL
-if (app_environment == "production") {
-  ga_include_file <- "google_analytics_main.html"
-} else if (app_environment == "development") {
-  ga_include_file <- "google_analytics_dev.html"
-}
-
 shiny::tags$head(
-  # Include the appropriate Google Analytics file if determined
-  if (!is.null(ga_include_file) && file.exists(ga_include_file)) {
-    includeHTML(ga_include_file)
-  },
   # style
   shiny::tags$style(shiny::HTML('
                      #sidebar {
@@ -396,6 +383,9 @@ ui <- shiny::navbarPage("CiteSource",
 
 # Define server logic to read selected file ----
 server <- function(input, output, session) {
+  
+  # --- Reactive Values ---
+  # Used to store data that changes during the session
   rv <- shiny::reactiveValues()
   rv$df <- data.frame()
   rv$upload_df <- data.frame()#for original uploads
@@ -403,6 +393,69 @@ server <- function(input, output, session) {
   rv$pairs_to_check <- data.frame()#for potential duplicates/manual dedup
   rv$pairs_removed <- data.frame()#for removed records
   
+  # --- Google Analytics Integration ---
+  # Flag to ensure GA script is inserted only once per session
+  ga_script_inserted <- reactiveVal(FALSE)
+  
+  # Use observeEvent on session$clientData which becomes available early
+  observeEvent(session$clientData, {
+    # Only proceed if the script hasn't been inserted yet for this session
+    if (!ga_script_inserted()) {
+      # Get the application's path from the URL (e.g., /CiteSource_latest/)
+      app_path <- session$clientData$url_pathname
+      ga_include_file <- NULL # Variable to hold the GA HTML filename
+      
+      # --- Determine GA HTML filename based on the application path ---
+      # Check if the path ends with '_latest' or '_latest/' (case-insensitive)
+      if (grepl("_latest/?$", app_path, ignore.case = TRUE)) {
+        # Development version
+        message("GA: Detected DEV environment based on URL path: ", app_path) # Logging
+        # *** SET the DEV Google Analytics HTML filename ***
+        ga_include_file <- "google_analytics_dev.html" # file is in same directory as app.R
+        
+      }
+      # Check if the path corresponds to the production app name (e.g., /CiteSource/ or /CiteSource)
+      # Adjust '/CiteSource/?$' if your production app name is different
+      else if (grepl("/CiteSource/?$", app_path, ignore.case = TRUE)) {
+        # Production version
+        message("GA: Detected PROD environment based on URL path: ", app_path) # Logging
+        # *** SET the PROD Google Analytics HTML filename ***
+        ga_include_file <- "google_analytics_main.html" # file is in same directory as app.R
+        
+      } else {
+        # Path didn't match known patterns
+        message("GA: Could not determine environment from URL path: ", app_path) # Logging
+      }
+      
+      # --- Insert the GA HTML file content if a filename was determined and file exists ---
+      if (!is.null(ga_include_file) && nzchar(ga_include_file)) {
+        # Check if the determined file actually exists in the app directory
+        if (file.exists(ga_include_file)) {
+          # Insert the content of the HTML file into the document's <head>
+          insertUI(
+            selector = "head",     # Target the <head> tag
+            where = "beforeEnd", # Add the script at the end of the head's content
+            # Use includeHTML to read and insert the file content
+            ui = includeHTML(ga_include_file),
+            immediate = TRUE      # Attempt to insert as soon as possible
+          )
+          # Set the flag to TRUE to prevent this code running again for this session
+          ga_script_inserted(TRUE)
+          message("GA: Inserted script from file: ", ga_include_file) # Logging
+        } else {
+          # Log an error if the file is missing
+          message("GA Error: HTML file not found: ", ga_include_file)
+          # Optionally set the flag anyway to prevent repeated checks for missing file
+          ga_script_inserted(TRUE)
+        }
+      } else {
+        # If no file was determined (e.g., path didn't match), set flag to prevent re-check
+        ga_script_inserted(TRUE)
+      }
+    }
+  }, ignoreNULL = TRUE, once = FALSE) # Trigger when clientData is available, but flag prevents re-run
+  # --- End Google Analytics Integration ---
+  
   #### Upload files tab section ------
   # upload on click
   shiny::observeEvent(input$file, {