Update web app entry point

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

1 files changed, 253 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
+  )
+}