From dce01ac19a0298388089ce4297a44ef2aa2c4c46 Mon Sep 17 00:00:00 2001
From: Naeem Model <me@nmode.ca>
Date: Thu, 9 May 2024 20:22:45 +0000
Subject: Revamp Shiny app

---
 inst/app/scripts/data.R       | 236 ++++++++++++++++++++++++++++++++
 inst/app/scripts/estimators.R | 305 ++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 541 insertions(+)
 create mode 100644 inst/app/scripts/data.R
 create mode 100644 inst/app/scripts/estimators.R

(limited to 'inst/app/scripts')

diff --git a/inst/app/scripts/data.R b/inst/app/scripts/data.R
new file mode 100644
index 0000000..06cb256
--- /dev/null
+++ b/inst/app/scripts/data.R
@@ -0,0 +1,236 @@
+# Main logic block for data-related interactions.
+data_logic <- function(input, output, react_values) {
+  # Initialize a data frame to hold the datasets.
+  react_values$data_table <- data.frame(
+    Name = character(0),
+    `Time units` = character(0),
+    `Case counts` = character(0),
+    check.names = FALSE
+  )
+
+  render_plot(input, output)
+  single_entry(input, output, react_values)
+  bulk_entry(input, output, react_values)
+  render_data(output, react_values)
+  delete_data(input, react_values)
+  export_data(output, react_values)
+}
+
+# Convert the input case counts string to an integer vector.
+tokenize_counts <- function(counts_str) {
+  suppressWarnings(as.integer(unlist(strsplit(trimws(counts_str), ","))))
+}
+
+# Render the preview plot for single entry data.
+render_plot <- function(input, output) {
+  observe({
+    counts <- tokenize_counts(input$data_counts)
+    if (length(counts) > 0 && !anyNA(counts) && all(counts >= 0)) {
+      output$data_plot <- renderPlot(
+        plot(seq_along(counts) - 1, counts, type = "o", pch = 16, col = "red",
+          xlab = input$data_units, ylab = "Cases", cex.lab = 1.5,
+          xlim = c(0, max(length(counts) - 1, 1)), ylim = c(0, max(counts, 1))
+        )
+      )
+    } else {
+      output$data_plot <- renderPlot(
+        plot(NULL, xlim = c(0, 10), ylim = c(0, 10),
+          xlab = input$data_units, ylab = "Cases", cex.lab = 1.5
+        )
+      )
+    }
+  })
+}
+
+# Add a single dataset to the existing table.
+single_entry <- function(input, output, react_values) {
+  observeEvent(input$data_single, {
+    valid <- TRUE
+
+    # Ensure the dataset name is neither blank nor a duplicate.
+    name <- trimws(input$data_name)
+    if (name == "") {
+      output$data_name_warn <- renderText("The dataset name cannot be blank.")
+      valid <- FALSE
+    } else if (name %in% react_values$data_table[, 1]) {
+      output$data_name_warn <- renderText(
+        "There is already a dataset with the specified name."
+      )
+      valid <- FALSE
+    } else {
+      output$data_name_warn <- renderText("")
+    }
+
+    # Ensure the case counts are specified as a comma-separated of one or more
+    # non-negative integers.
+    counts <- tokenize_counts(input$data_counts)
+    if (length(counts) == 0) {
+      output$data_counts_warn <- renderText("Case counts cannot be blank.")
+      valid <- FALSE
+    } else if (anyNA(counts) || any(counts < 0)) {
+      output$data_counts_warn <- renderText(
+        "Case counts can only contain non-negative integers."
+      )
+      valid <- FALSE
+    } else {
+      output$data_counts_warn <- renderText("")
+    }
+
+    if (valid) {
+      # Add the new dataset to the data table.
+      new_row <- data.frame(name, input$data_units, toString(counts))
+      colnames(new_row) <- c("Name", "Time units", "Case counts")
+      react_values$data_table <- rbind(react_values$data_table, new_row)
+
+      # Evaluate all existing estimators on the new dataset and update the
+      # corresponding row in the estimates table.
+      update_estimates_rows(new_row, react_values)
+
+      showNotification("Dataset added successfully.",
+        duration = 3, id = "notify-success"
+      )
+    }
+  })
+}
+
+# Add multiple datasets to the existing table.
+bulk_entry <- function(input, output, react_values) {
+  observeEvent(input$data_bulk, {
+    tryCatch(
+      {
+        datasets <- read.csv(text = input$data_area, header = FALSE, sep = ",")
+
+        names <- trimws(datasets[, 1])
+        units <- trimws(datasets[, 2])
+        counts <- apply(datasets[, 3:ncol(datasets)], 1,
+          function(row) {
+            row <- suppressWarnings(as.integer(row))
+            toString(row[!is.na(row) & row >= 0])
+          }
+        )
+
+        warning_text <- ""
+
+        # Ensure the dataset names are neither blank nor duplicates.
+        if (anyNA(names) || any(names == "")) {
+          warning_text <- paste0(warning_text, sep = "<br>",
+            "Each row must begin with a non-blank dataset name."
+          )
+        } else {
+          if (length(unique(names)) != length(names)) {
+            warning_text <- paste0(warning_text, sep = "<br>",
+              "The rows contain duplicate dataset names."
+            )
+          }
+          if (any(names %in% react_values$data_table[, 1])) {
+            warning_text <- paste0(warning_text, sep = "<br>",
+              "The rows contain dataset names which already exist."
+            )
+          }
+        }
+
+        # Ensure the second entry in each row is a time unit equal to
+        # "Days" or "Weeks".
+        if (!all(units %in% c("Days", "Weeks"))) {
+          warning_text <- paste0(warning_text, sep = "<br>",
+            "The second entry in each row must be either 'Days' or 'Weeks'."
+          )
+        }
+
+        # Ensure the counts in each row have at least one non-negative integer.
+        if (any(counts == "")) {
+          warning_text <- paste0(warning_text, sep = "<br>",
+            "Each row must contain at least one non-negative integer."
+          )
+        }
+
+        output$data_area_warn <- renderUI(HTML(warning_text))
+
+        if (warning_text == "") {
+          # Add the new datasets to the data table.
+          new_rows <- data.frame(names, units, counts)
+          colnames(new_rows) <- c("Name", "Time units", "Case counts")
+          react_values$data_table <- rbind(react_values$data_table, new_rows)
+
+          # Evaluate all existing estimators on the new dataset and update the
+          # corresponding row in the estimates table.
+          update_estimates_rows(new_rows, react_values)
+
+          showNotification("Datasets added successfully.",
+            duration = 3, id = "notify-success"
+          )
+        }
+      },
+      error = function(e) {
+        output$data_area_warn <- renderText(
+          "The input does not match the required format."
+        )
+      }
+    )
+  })
+}
+
+# Render the data table when new datasets are added.
+render_data <- function(output, react_values) {
+  observe({
+    output$data_table <- DT::renderDataTable(react_values$data_table)
+  })
+}
+
+# Delete rows in the data table,
+# and the corresponding rows in the estimates table.
+delete_data <- function(input, react_values) {
+  observeEvent(input$data_delete, {
+    new_table <- react_values$data_table[-input$data_table_rows_selected, ]
+    if (nrow(new_table) > 0) {
+      rownames(new_table) <- seq_len(nrow(new_table))
+    }
+    react_values$data_table <- new_table
+
+    if (ncol(react_values$estimates_table) == 1) {
+      react_values$estimates_table <- data.frame(
+        Datasets = react_values$data_table[, 1]
+      )
+    } else {
+      react_values$estimates_table <-
+        react_values$estimates_table[-input$data_table_rows_selected, ]
+    }
+  })
+}
+
+# Export data table as a CSV file.
+export_data <- function(output, react_values) {
+  output$data_export <- downloadHandler(
+    filename = function() {
+      paste0("Rnaught_data_", format(Sys.time(), "%y-%m-%d_%H-%M-%S"), ".csv")
+    },
+    content = function(file) {
+      write.csv(react_values$data_table, file, row.names = FALSE)
+    }
+  )
+}
+
+# When new datasets are added, evaluate all existing estimators on them and
+# add new rows to the estimates table.
+update_estimates_rows <- function(datasets, react_values) {
+  new_rows <- data.frame(
+    matrix(nrow = nrow(datasets), ncol = ncol(react_values$estimates_table))
+  )
+  colnames(new_rows) <- colnames(react_values$estimates_table)
+
+  for (row in seq_len(nrow(datasets))) {
+    new_rows[row, 1] <- datasets[row, 1]
+
+    if (length(react_values$estimators) > 0) {
+      for (col in 2:ncol(react_values$estimates_table)) {
+        new_rows[row, col] <- eval_estimator(
+          react_values$estimators[[col - 1]], datasets[row, ]
+        )
+      }
+    }
+  }
+
+  react_values$estimates_table <- rbind(
+    react_values$estimates_table, new_rows
+  )
+}
diff --git a/inst/app/scripts/estimators.R b/inst/app/scripts/estimators.R
new file mode 100644
index 0000000..171d197
--- /dev/null
+++ b/inst/app/scripts/estimators.R
@@ -0,0 +1,305 @@
+estimators_logic <- function(input, output, react_values) {
+  # Initialize a data frame to hold estimates.
+  react_values$estimates_table <- data.frame(Dataset = character(0))
+  # Initialize a list to hold added estimators.
+  react_values$estimators <- list()
+
+  add_id(input, output, react_values)
+  add_idea(input, output, react_values)
+  add_seq_bayes(input, output, react_values)
+  add_wp(input, output, react_values)
+
+  render_estimates(output, react_values)
+  delete_estimators(input, react_values)
+  export_estimates(output, react_values)
+}
+
+# If an estimator is added, ensure it is not a duplicate and add it to the list
+# of estimators. This function should be called at the end of each
+# estimator-specific 'add' function, after validating their parameters.
+add_estimator <- function(method, new_estimator, output, react_values) {
+  num_estimators <- length(react_values$estimators)
+
+  # Check whether the new estimator is a duplicate, and warn if so.
+  for (i in seq_len(num_estimators)) {
+    if (identical(new_estimator, react_values$estimators[[i]])) {
+      showNotification("Error: This estimator has already been added.",
+        duration = 3, id = "notify-error"
+      )
+      return()
+    }
+  }
+
+  # Add the new estimator to the list of estimators.
+  react_values$estimators[[num_estimators + 1]] <- new_estimator
+
+  showNotification("Estimator added successfully.",
+    duration = 3, id = "notify-success"
+  )
+
+  # Evaluate all the new estimator on all existing datasets and create a new
+  # column in the estimates table.
+  update_estimates_col(new_estimator, react_values)
+}
+
+# Ensure serial intervals are specified as positive numbers.
+validate_mu <- function(method, input, output) {
+  mu <- suppressWarnings(as.numeric(trimws(input[[paste0("mu_", method)]])))
+  if (is.na(mu) || mu <= 0) {
+    output[[paste0("mu_", method, "_warn")]] <- renderText(
+      "The serial interval must be a positive number."
+    )
+    return(NULL)
+  }
+  output[[paste0("mu_", method, "_warn")]] <- renderText("")
+  mu
+}
+
+# Incidence Decay (ID).
+add_id <- function(input, output, react_values) {
+  observeEvent(input$add_id, {
+    mu <- validate_mu("id", input, output)
+    if (!is.null(mu)) {
+      new_estimator <- list(
+        method = "id", mu = mu, mu_units = input$mu_id_units
+      )
+      add_estimator("id", new_estimator, output, react_values)
+    }
+  })
+}
+
+# Incidence Decay and Exponential Adjustment (IDEA).
+add_idea <- function(input, output, react_values) {
+  observeEvent(input$add_idea, {
+    mu <- validate_mu("idea", input, output)
+    if (!is.null(mu)) {
+      new_estimator <- list(
+        method = "idea", mu = mu, mu_units = input$mu_idea_units
+      )
+      add_estimator("idea", new_estimator, output, react_values)
+    }
+  })
+}
+
+# Sequential Bayes (seqB).
+add_seq_bayes <- function(input, output, react_values) {
+  observeEvent(input$add_seq_bayes, {
+    mu <- validate_mu("seq_bayes", input, output)
+
+    kappa <- trimws(input$kappa)
+    kappa <- if (kappa == "") 20 else suppressWarnings(as.numeric(kappa))
+
+    if (is.na(kappa) || kappa <= 0) {
+      output$kappa_warn <- renderText(
+        "The maximum prior must be a positive number."
+      )
+    } else if (!is.null(mu)) {
+      output$kappa_warn <- renderText("")
+      new_estimator <- list(
+        method = "seq_bayes", mu = mu,
+        mu_units = input$mu_seq_bayes_units, kappa = kappa
+      )
+      add_estimator("seq_bayes", new_estimator, output, react_values)
+    }
+  })
+}
+
+# White and Pagano (WP).
+add_wp <- function(input, output, react_values) {
+  observeEvent(input$add_wp, {
+    if (input$wp_mu_known == "Yes") {
+      mu <- validate_mu("wp", input, output)
+      if (!is.null(mu)) {
+        new_estimator <- list(method = "wp",
+          mu = mu, mu_units = input$mu_wp_units
+        )
+        add_estimator("wp", new_estimator, output, react_values)
+      }
+    } else {
+      grid_length <- trimws(input$grid_length)
+      max_shape <- trimws(input$max_shape)
+      max_scale <- trimws(input$max_scale)
+
+      suppressWarnings({
+        grid_length <- if (grid_length == "") 100 else as.numeric(grid_length)
+        max_shape <- if (max_shape == "") 10 else as.numeric(max_shape)
+        max_scale <- if (max_scale == "") 10 else as.numeric(max_scale)
+      })
+
+      valid <- TRUE
+
+      if (is.na(grid_length) || grid_length <= 0) {
+        output$grid_length_warn <- renderText(
+          "The grid length must be a positive integer."
+        )
+        valid <- FALSE
+      } else {
+        output$grid_length_warn <- renderText("")
+      }
+
+      if (is.na(max_shape) || max_shape <= 0) {
+        output$max_shape_warn <- renderText(
+          "The maximum shape must be a positive number."
+        )
+        valid <- FALSE
+      } else {
+        output$max_shape_warn <- renderText("")
+      }
+
+      if (is.na(max_scale) || max_scale <= 0) {
+        output$max_scale_warn <- renderText(
+          "The maximum scale must be a positive number."
+        )
+        valid <- FALSE
+      } else {
+        output$max_scale_warn <- renderText("")
+      }
+
+      if (valid) {
+        new_estimator <- list(method = "wp", mu = NA, grid_length = grid_length,
+          max_shape = max_shape, max_scale = max_scale
+        )
+        add_estimator("wp", new_estimator, output, react_values)
+      }
+    }
+  })
+}
+
+# Convert an estimator's specified serial interval to match the time units of
+# the given dataset.
+convert_mu_units <- function(data_units, estimator_units, mu) {
+  if (data_units == "Days" && estimator_units == "Weeks") {
+    return(mu * 7)
+  } else if (data_units == "Weeks" && estimator_units == "Days") {
+    return(mu / 7)
+  }
+  mu
+}
+
+# Add a column to the estimates table when a new estimator is added.
+update_estimates_col <- function(estimator, react_values) {
+  dataset_rows <- seq_len(nrow(react_values$data_table))
+  estimates <- dataset_rows
+
+  for (row in dataset_rows) {
+    estimate <- eval_estimator(estimator, react_values$data_table[row, ])
+    estimates[row] <- estimate
+  }
+
+  estimates <- data.frame(estimates)
+  colnames(estimates) <- estimates_col_name(estimates, estimator)
+
+  react_values$estimates_table <- cbind(
+    react_values$estimates_table, estimates
+  )
+}
+
+# Evaluate the specified estimator on the given dataset.
+eval_estimator <- function(estimator, dataset) {
+  cases <- as.integer(unlist(strsplit(dataset[, 3], ",")))
+
+  if (estimator$method == "id") {
+    mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+    estimate <- round(Rnaught::id(cases, mu), 2)
+  } else if (estimator$method == "idea") {
+    mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+    estimate <- round(Rnaught::idea(cases, mu), 2)
+  } else if (estimator$method == "seq_bayes") {
+    mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+    estimate <- round(Rnaught::seq_bayes(cases, mu, estimator$kappa), 2)
+  } else if (estimator$method == "wp") {
+    if (is.na(estimator$mu)) {
+      estimate <- Rnaught::wp(cases, serial = TRUE,
+        grid_length = estimator$grid_length,
+        max_shape = estimator$max_shape, max_scale = estimator$max_scale
+      )
+      estimated_mu <- round(sum(estimate$supp * estimate$pmf), 2)
+      estimate <- paste0(round(estimate$r0, 2), " (&#956; = ", estimated_mu,
+        " ", tolower(dataset[, 2]), ")"
+      )
+    } else {
+      mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+      estimate <- round(Rnaught::wp(cases, mu), 2)
+    }
+  }
+
+  return(estimate)
+}
+
+# Create the column name of an estimator when it is
+# added to the estimates table.
+estimates_col_name <- function(estimates, estimator) {
+  if (estimator$method == "id") {
+    return(paste0("ID", " (&#956; = ", estimator$mu, " ",
+      tolower(estimator$mu_units), ")"
+    ))
+  } else if (estimator$method == "idea") {
+    return(paste0("IDEA", " (&#956; = ", estimator$mu, " ",
+      tolower(estimator$mu_units), ")"
+    ))
+  } else if (estimator$method == "seq_bayes") {
+    return(paste0("seqB", " (&#956; = ", estimator$mu, " ",
+      tolower(estimator$mu_units), ", &#954; = ", estimator$kappa, ")"
+    ))
+  } else if (estimator$method == "wp") {
+    if (is.na(estimator$mu)) {
+      return(paste0("WP (", estimator$grid_length, ", ",
+        round(estimator$max_shape, 3), ", ", round(estimator$max_scale, 3), ")"
+      ))
+    } else {
+      return(paste0("WP", " (&#956; = ", estimator$mu, " ",
+        tolower(estimator$mu_units), ")"
+      ))
+    }
+  }
+}
+
+# Render the estimates table whenever it is updated.
+render_estimates <- function(output, react_values) {
+  observe({
+    output$estimates_table <- DT::renderDataTable(react_values$estimates_table,
+      selection = list(target = "column", selectable = c(0)),
+      escape = FALSE, rownames = FALSE,
+      options = list(
+        columnDefs = list(list(className = "dt-left", targets = "_all"))
+      ),
+    )
+  })
+}
+
+# Delete columns from the estimates table,
+# as well as the corresponding estimators.
+delete_estimators <- function(input, react_values) {
+  observeEvent(input$estimators_delete, {
+    cols_selected <- input$estimates_table_columns_selected
+    react_values$estimators <- react_values$estimators[-cols_selected]
+    react_values$estimates_table[, cols_selected + 1] <- NULL
+  })
+}
+
+# Export estimates table as a CSV file.
+export_estimates <- function(output, react_values) {
+  output$estimates_export <- downloadHandler(
+    filename = function() {
+      paste0(
+        "Rnaught_estimates_", format(Sys.time(), "%y-%m-%d_%H-%M-%S"), ".csv"
+      )
+    },
+    content = function(file) {
+      output_table <- data.frame(
+        lapply(react_values$estimates_table, sub_entity)
+      )
+      colnames(output_table) <- sub_entity(
+        colnames(react_values$estimates_table)
+      )
+      write.csv(output_table, file, row.names = FALSE)
+    }
+  )
+}
+
+# Substitute HTML entity codes with natural names.
+sub_entity <- function(obj) {
+  obj <- gsub("&#956;", "mu", obj)
+  obj <- gsub("&#954;", "kappa", obj)
+  obj
+}
-- 
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