Update seq_bayes.html

[Rnaught] / inst / app / scripts / data.R

diff --git a/inst/app/scripts/data.R b/inst/app/scripts/data.R
index eebd14fd6ba4bd830c68915ffa5436ba85d5690d..8f8694c2957b276c1b8b2533383194ea003f6f74 100644 (file)
--- a/inst/app/scripts/data.R
+++ b/inst/app/scripts/data.R
@@ -8,10 +8,12 @@ data_logic <- function(input, output, react_values) {
     check.names = FALSE
   )
 
     check.names = FALSE
   )
 

-  render_plot(input, output)
-  single_entry(input, output, react_values)
-  bulk_entry(input, output, react_values)
-  render_data(output, react_values)
+  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)
 }
   delete_data(input, react_values)
   export_data(output, react_values)
 }


@@ -21,180 +23,198 @@ tokenize_counts <- function(counts_str) {
   suppressWarnings(as.integer(unlist(strsplit(trimws(counts_str), ","))))
 }
 
   suppressWarnings(as.integer(unlist(strsplit(trimws(counts_str), ","))))
 }
 

-# Render the preview plot for single entry data.
-render_plot <- function(input, output) {
+# Render the plots for daily and weekly data when the data table is updated.
+render_plot <- function(input, output, react_values, time_units) {

   observe({
   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 = "black",
-          xlab = input$data_units, ylab = "Cases", cex.lab = 1.5,
-          xlim = c(0, max(length(counts) - 1, 1)), ylim = c(0, max(counts, 1))
+    datasets <- react_values$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]

         )
         )

-      )
-    } 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("")
-    }
+    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")
+    )

 
 

-    # 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."
+    data_plot <- plotly::config(data_plot, displaylogo = FALSE,
+      toImageButtonOptions = list(
+        filename = paste0("Rnaught_data_", tolower(time_units), "_plot")

       )
       )

-      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)
+    output[[paste0("data_plot_", tolower(time_units))]] <-
+      plotly::renderPlotly(data_plot)
+  })
+}

 
 

-      # Evaluate all existing estimators on the new dataset and update the
-      # corresponding row in the estimates table.
-      update_estimates_rows(new_row, react_values)
+# 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")
+  })
+}

 
 

-      showNotification("Dataset added successfully.",
-        duration = 3, id = "notify-success"
-      )
-    }
+# 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")

   })
 }
 
   })
 }
 

-# Add multiple datasets to the existing table.
-bulk_entry <- function(input, output, react_values) {
-  observeEvent(input$data_bulk, {
-    tryCatch(
-      {
+# 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$data_area, header = FALSE, sep = ",")
         datasets <- read.csv(text = input$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])
-          }
+      } else if (data_source == "data_upload") {
+        datasets <- read.csv(
+          file = input$data_upload$datapath, header = FALSE, sep = ","

         )
         )

+      }

 
 

-        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."
-            )
-          }
+      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 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 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>"

           )
         }
           )
         }

-
-        # 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."
+        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>"
+        )
+      }

 
 

-        output$data_area_warn <- renderUI(HTML(warning_text))
+      # 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>"
+        )
+      }

 
 

-        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)
+      output[[paste0(data_source, "_warn")]] <- renderUI(HTML(warning_text))

 
 

-          # Evaluate all existing estimators on the new dataset and update the
-          # corresponding row in the estimates table.
-          update_estimates_rows(new_rows, react_values)
+      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)

 
 

-          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."
-        )
+        # 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 the data table when new datasets are added.

-render_data <- function(output, react_values) {
+render_data_table <- function(output, react_values) {

   observe({
   observe({

-    output$data_table <- DT::renderDataTable(react_values$data_table)
+    output$data_table <- DT::renderDataTable(
+      react_values$data_table, rownames = FALSE
+    )

   })
 }
 
   })
 }
 

-# Delete rows in the data table,
-# and the corresponding rows in the estimates table.
+# Delete rows in the data table and the corresponding columns in the estimates
+# table.

 delete_data <- function(input, react_values) {
   observeEvent(input$data_delete, {
 delete_data <- function(input, react_values) {
   observeEvent(input$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$data_table_rows_selected, ]
-    }
+    rows_selected <- input$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)]

   })
 }
 
   })
 }
 


@@ -211,26 +231,23 @@ export_data <- function(output, react_values) {
 }
 
 # When new datasets are added, evaluate all existing estimators on them and
 }
 
 # 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$estimates_table))
+# add new columns to the estimates table.
+update_estimates_cols <- function(datasets, react_values) {
+  new_cols <- data.frame(
+    matrix(nrow = nrow(react_values$estimates_table), ncol = nrow(datasets))

   )
   )

-  colnames(new_rows) <- colnames(react_values$estimates_table)
+  colnames(new_cols) <- datasets[, 1]

 
 

-  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$estimators[[col - 1]], datasets[row, ]
-        )
+  if (nrow(new_cols) > 0) {
+    for (row in seq_len(nrow(new_cols))) {
+      estimator <- react_values$estimators[[row]]
+      for (col in seq_len(ncol(new_cols))) {
+        new_cols[row, col] <- eval_estimator(estimator, datasets[col, ])

       }
     }
   }
 
       }
     }
   }
 

-  react_values$estimates_table <- rbind(
-    react_values$estimates_table, new_rows
+  react_values$estimates_table <- cbind(
+    react_values$estimates_table, new_cols

   )
 }
   )
 }