Update web app entry point

- Rename 'app' -> 'web'
- Return shiny app object in entry point function

2 files changed, 580 insertions, 0 deletions

diff --git a/inst/web/scripts/data.R b/inst/web/scripts/data.R
new file mode 100644
index 0000000..8f8694c
--- /dev/null
+++ b/inst/web/scripts/data.R
@@ -0,0 +1,253 @@
+# Main logic block for data-related interactions.
+data_logic <- function(input, output, react_values) {
+  # Initialize a data frame to hold the datasets.
+  react_values$data_table <- data.frame(
+    Name = character(0),
+    `Time units` = character(0),
+    `Case counts` = character(0),
+    check.names = FALSE
+  )
+
+  manual_entry(input, output, react_values)
+  upload_data(input, output, react_values)
+  load_samples(input, output, react_values)
+  render_data_table(output, react_values)
+  render_plot(input, output, react_values, "Days")
+  render_plot(input, output, react_values, "Weeks")
+  delete_data(input, react_values)
+  export_data(output, react_values)
+}
+
+# Convert the input case counts string to an integer vector.
+tokenize_counts <- function(counts_str) {
+  suppressWarnings(as.integer(unlist(strsplit(trimws(counts_str), ","))))
+}
+
+# Render the plots for daily and weekly data when the data table is updated.
+render_plot <- function(input, output, react_values, time_units) {
+  observe({
+    datasets <- react_values$data_table[
+      which(react_values$data_table[["Time units"]] == time_units),
+    ]
+
+    data_plot <- plotly::plot_ly(type = "scatter", mode = "lines")
+    if (nrow(datasets) > 0) {
+      for (i in seq_len(nrow(datasets))) {
+        counts <- tokenize_counts(datasets[i, 3])
+        data_plot <- plotly::add_trace(data_plot,
+          x = seq_along(counts) - 1, y = counts, name = datasets[i, 1]
+        )
+      }
+    }
+
+    plot_title <- paste(
+      if (time_units == "Days") "Daily" else "Weekly", "case counts"
+    )
+
+    data_plot <- plotly::layout(data_plot, title = plot_title,
+      xaxis = list(title = time_units), yaxis = list(title = "Cases")
+    )
+
+    data_plot <- plotly::config(data_plot, displaylogo = FALSE,
+      toImageButtonOptions = list(
+        filename = paste0("Rnaught_data_", tolower(time_units), "_plot")
+      )
+    )
+
+    output[[paste0("data_plot_", tolower(time_units))]] <-
+      plotly::renderPlotly(data_plot)
+  })
+}
+
+# Validate and add manually-entered datasets.
+manual_entry <- function(input, output, react_values) {
+  observeEvent(input$data_bulk, {
+    validate_data(input, output, react_values, "data_area")
+  })
+}
+
+# Validate and add datasets from a CSV file.
+upload_data <- function(input, output, react_values) {
+  observeEvent(input$data_upload, {
+    validate_data(input, output, react_values, "data_upload")
+  })
+}
+
+# Validate datasets and update the data table.
+validate_data <- function(input, output, react_values, data_source) {
+  tryCatch(
+    {
+      if (data_source == "data_area") {
+        datasets <- read.csv(text = input$data_area, header = FALSE, sep = ",")
+      } else if (data_source == "data_upload") {
+        datasets <- read.csv(
+          file = input$data_upload$datapath, header = FALSE, sep = ","
+        )
+      }
+
+      names <- trimws(datasets[, 1])
+      units <- trimws(datasets[, 2])
+      counts <- apply(data.frame(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,
+          "Each row must begin with a non-blank dataset name.<br>"
+        )
+      } else {
+        if (length(unique(names)) != length(names)) {
+          warning_text <- paste0(warning_text,
+            "The rows contain duplicate dataset names.<br>"
+          )
+        }
+        if (any(names %in% react_values$data_table[, 1])) {
+          warning_text <- paste0(warning_text,
+            "The rows contain dataset names which already exist.<br>"
+          )
+        }
+      }
+
+      # Ensure the second entry in each row is a time unit equal to
+      # "Days" or "Weeks".
+      if (!all(units %in% c("Days", "Weeks"))) {
+        warning_text <- paste0(warning_text,
+          "The second entry in each row must be either 'Days' or 'Weeks'.<br>"
+        )
+      }
+
+      # Ensure the counts in each row have at least one non-negative integer.
+      if (any(counts == "")) {
+        warning_text <- paste0(warning_text,
+          "Each row must contain at least one non-negative integer.<br>"
+        )
+      }
+
+      output[[paste0(data_source, "_warn")]] <- renderUI(HTML(warning_text))
+
+      if (warning_text == "") {
+        # Add the new datasets to the data table.
+        new_rows <- data.frame(names, units, counts)
+        colnames(new_rows) <- c("Name", "Time units", "Case counts")
+        react_values$data_table <- rbind(react_values$data_table, new_rows)
+
+        # Evaluate all existing estimators on the new datasets and update the
+        # corresponding columns in the estimates table.
+        update_estimates_cols(new_rows, react_values)
+
+        showNotification("Datasets added successfully.", duration = 3)
+      }
+    },
+    error = function(e) {
+      output[[paste0(data_source, "_warn")]] <- renderText(
+        "The input does not match the required format."
+      )
+    }
+  )
+}
+
+# Load sample datasets.
+load_samples <- function(input, output, react_values) {
+  observeEvent(input$data_samples, {
+    names <- c()
+    units <- c()
+    counts <- c()
+
+    # COVID-19 Canada, March 2020 (weekly).
+    if (input$covid_canada) {
+      names <- c(names, "COVID-19 Canada 2020/03/03 - 2020/03/31")
+      units <- c(units, "Weeks")
+      counts <- c(counts, toString(Rnaught::COVIDCanada[seq(41, 69, 7), 2]))
+    }
+    # COVID-19 Ontario, March 2020 (weekly).
+    if (input$covid_ontario) {
+      names <- c(names, "COVID-19 Ontario 2020/03/03 - 2020/03/31")
+      units <- c(units, "Weeks")
+      counts <- c(counts,
+        toString(Rnaught::COVIDCanadaPT[seq(10176, 10204, 7), 3])
+      )
+    }
+
+    if (length(names) == 0) {
+      output$data_samples_warn <- renderText(
+        "At least one sample dataset must be selected."
+      )
+    } else if (any(names %in% react_values$data_table[, 1])) {
+      output$data_samples_warn <- renderText(
+        "At least one of the selected dataset names already exist."
+      )
+    } else {
+      output$data_samples_warn <- renderText("")
+
+      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 sample datasets and update the
+      # corresponding columns in the estimates table.
+      update_estimates_cols(new_rows, react_values)
+
+      showNotification("Datasets added successfully.", duration = 3)
+    }
+  })
+}
+
+# Render the data table when new datasets are added.
+render_data_table <- function(output, react_values) {
+  observe({
+    output$data_table <- DT::renderDataTable(
+      react_values$data_table, rownames = FALSE
+    )
+  })
+}
+
+# Delete rows in the data table and the corresponding columns in the estimates
+# table.
+delete_data <- function(input, react_values) {
+  observeEvent(input$data_delete, {
+    rows_selected <- input$data_table_rows_selected
+    react_values$data_table <- react_values$data_table[-rows_selected, ]
+    react_values$estimates_table <-
+      react_values$estimates_table[, -(rows_selected + 2)]
+  })
+}
+
+# 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 columns to the estimates table.
+update_estimates_cols <- function(datasets, react_values) {
+  new_cols <- data.frame(
+    matrix(nrow = nrow(react_values$estimates_table), ncol = nrow(datasets))
+  )
+  colnames(new_cols) <- datasets[, 1]
+
+  if (nrow(new_cols) > 0) {
+    for (row in seq_len(nrow(new_cols))) {
+      estimator <- react_values$estimators[[row]]
+      for (col in seq_len(ncol(new_cols))) {
+        new_cols[row, col] <- eval_estimator(estimator, datasets[col, ])
+      }
+    }
+  }
+
+  react_values$estimates_table <- cbind(
+    react_values$estimates_table, new_cols
+  )
+}
diff --git a/inst/web/scripts/estimators.R b/inst/web/scripts/estimators.R
new file mode 100644
index 0000000..7c457ea
--- /dev/null
+++ b/inst/web/scripts/estimators.R
@@ -0,0 +1,327 @@
+# Main logic block for estimator-related interactions.
+estimators_logic <- function(input, output, react_values) {
+  # Initialize a data frame to hold estimates.
+  react_values$estimates_table <- data.frame(
+    Estimator = character(0),
+    `Serial interval` = character(0),
+    check.names = FALSE
+
+  )
+  # Initialize a list to hold added estimators.
+  react_values$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
+      )
+      return()
+    }
+  }
+
+  # Add the new estimator to the list of estimators.
+  react_values$estimators[[num_estimators + 1]] <- new_estimator
+
+  showNotification("Estimator added successfully.", duration = 3)
+
+  # Evaluate the new estimator on all existing datasets and create a new row in
+  # the estimates table.
+  update_estimates_row(new_estimator, react_values)
+}
+
+# Ensure serial intervals are specified as positive numbers.
+validate_mu <- function(method, input, output) {
+  mu <- suppressWarnings(as.numeric(trimws(input[[paste0("mu_", method)]])))
+  if (is.na(mu) || mu <= 0) {
+    output[[paste0("mu_", method, "_warn")]] <- renderText(
+      "The serial interval must be a positive number."
+    )
+    return(NULL)
+  }
+  output[[paste0("mu_", method, "_warn")]] <- renderText("")
+  mu
+}
+
+# Incidence Decay (ID).
+add_id <- function(input, output, react_values) {
+  observeEvent(input$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$kappa)
+    kappa <- if (kappa == "") 20 else suppressWarnings(as.numeric(kappa))
+
+    if (is.na(kappa) || kappa < 1) {
+      output$kappa_warn <- renderText(
+        "The maximum prior must be a number greater than or equal to 1."
+      )
+    } 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 row to the estimates table when a new estimator is added.
+update_estimates_row <- function(estimator, react_values) {
+  dataset_rows <- seq_len(nrow(react_values$data_table))
+  estimates <- c()
+
+  if (nrow(react_values$data_table) > 0) {
+    estimates <- dataset_rows
+    for (row in dataset_rows) {
+      estimate <- eval_estimator(estimator, react_values$data_table[row, ])
+      estimates[row] <- estimate
+    }
+  }
+
+  new_row <- data.frame(
+    t(c(estimator_name(estimator), estimator_mu_text(estimator), estimates))
+  )
+  colnames(new_row) <- colnames(react_values$estimates_table)
+
+  react_values$estimates_table <- rbind(
+    react_values$estimates_table, new_row
+  )
+}
+
+# Evaluate the specified estimator on the given dataset.
+eval_estimator <- function(estimator, dataset) {
+  cases <- as.integer(unlist(strsplit(dataset[, 3], ",")))
+
+  tryCatch(
+    {
+      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$pmf), 2)
+          mu_units <- if (dataset[, 2] == "Days") "day(s)" else "week(s)"
+          estimate <- paste0(
+            round(estimate$r0, 2), " (SI = ", estimated_mu, " ", mu_units, ")"
+          )
+        } else {
+          mu <- convert_mu_units(dataset[, 2], estimator$mu_units, estimator$mu)
+          estimate <- round(Rnaught::wp(cases, mu), 2)
+        }
+      }
+
+      return(estimate)
+    }, error = function(e) {
+      showNotification(
+        paste0(toString(e),
+          " [Estimator: ", sub(" .*", "", estimator_name(estimator)),
+          ", Dataset: ", dataset[, 1], "]"
+        ), duration = 6
+      )
+      return("—")
+    }
+  )
+}
+
+# Create the name of an estimator to be added to the first column of the
+# estimates table.
+estimator_name <- function(estimator) {
+  if (estimator$method == "id") {
+    return("ID")
+  } else if (estimator$method == "idea") {
+    return("IDEA")
+  } else if (estimator$method == "seq_bayes") {
+    return(paste0("seqB", " (&#954; = ", estimator$kappa, ")"))
+  } else if (estimator$method == "wp") {
+    if (is.na(estimator$mu)) {
+      return(paste0("WP (", estimator$grid_length, ", ",
+        round(estimator$max_shape, 3), ", ", round(estimator$max_scale, 3), ")"
+      ))
+    } else {
+      return("WP")
+    }
+  }
+}
+
+# Create the text to be displayed for the serial interval in the second column
+# of the estimates table.
+estimator_mu_text <- function(estimator) {
+  if (is.na(estimator$mu)) {
+    return("—")
+  }
+  mu_units <- if (estimator$mu_units == "Days") "day(s)" else "week(s)"
+  paste(estimator$mu, 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,
+      escape = FALSE, rownames = FALSE,
+      options = list(
+        columnDefs = list(list(className = "dt-left", targets = "_all"))
+      ),
+    )
+  })
+}
+
+# Delete rows from the estimates table and the corresponding estimators.
+delete_estimators <- function(input, react_values) {
+  observeEvent(input$estimators_delete, {
+    rows_selected <- input$estimates_table_rows_selected
+    react_values$estimators <- react_values$estimators[-rows_selected]
+    react_values$estimates_table <-
+      react_values$estimates_table[-rows_selected, ]
+  })
+}
+
+# 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("&#954;", "kappa", obj)
+  obj
+}