+++ /dev/null
-# 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
- )
-}
git.nmode.ca / Rnaught / blobdiff
