render_plot(input, output)
single_entry(input, output, react_values)
- bulk_entry(input, output, react_values)
+ manual_bulk_entry(input, output, react_values)
+ upload_data(input, output, react_values)
+ load_samples(input, output, react_values)
render_data(output, react_values)
delete_data(input, react_values)
export_data(output, react_values)
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",
+ plot(seq_along(counts) - 1, counts, type = "o", pch = 16, col = "black",
xlab = input$data_units, ylab = "Cases", cex.lab = 1.5,
xlim = c(0, max(length(counts) - 1, 1)), ylim = c(0, max(counts, 1))
)
})
}
-# Add multiple datasets to the existing table.
-bulk_entry <- function(input, output, react_values) {
+manual_bulk_entry <- function(input, output, react_values) {
observeEvent(input$data_bulk, {
- tryCatch(
- {
- datasets <- read.csv(text = input$data_area, header = FALSE, sep = ",")
+ validate_bulk_data(input, output, react_values, "data_area")
+ })
+}
- names <- trimws(datasets[, 1])
- units <- trimws(datasets[, 2])
- counts <- apply(datasets[, 3:ncol(datasets)], 1,
- function(row) {
- row <- suppressWarnings(as.integer(row))
- toString(row[!is.na(row) & row >= 0])
- }
- )
+upload_data <- function(input, output, react_values) {
+ observeEvent(input$data_upload, {
+ validate_bulk_data(input, output, react_values, "data_upload")
+ })
+}
- warning_text <- ""
+validate_bulk_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$datapath, header = FALSE, sep = ","
+ )
+ }
- # Ensure the dataset names are neither blank nor duplicates.
- if (anyNA(names) || any(names == "")) {
- warning_text <- paste0(warning_text, sep = "<br>",
- "Each row must begin with a non-blank dataset name."
- )
- } else {
- if (length(unique(names)) != length(names)) {
- warning_text <- paste0(warning_text, sep = "<br>",
- "The rows contain duplicate dataset names."
- )
- }
- if (any(names %in% react_values$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])
}
+ )
- # 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'."
+ 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>"
)
}
-
- # 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$data_table[, 1])) {
+ warning_text <- paste0(warning_text,
+ "The rows contain dataset names which already exist.<br>"
)
}
+ }
- output$data_area_warn <- renderUI(HTML(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,
+ "The second entry in each row must be either 'Days' or 'Weeks'.<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)
+ # 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>"
+ )
+ }
- # Evaluate all existing estimators on the new dataset and update the
- # corresponding row in the estimates table.
- update_estimates_rows(new_rows, react_values)
+ output[[paste0(data_source, "_warn")]] <- renderUI(HTML(warning_text))
- showNotification("Datasets added successfully.",
- duration = 3, id = "notify-success"
- )
- }
- },
- error = function(e) {
- output$data_area_warn <- renderText(
- "The input does not match the required format."
+ if (warning_text == "") {
+ # Add the new datasets to the data table.
+ new_rows <- data.frame(names, units, counts)
+ colnames(new_rows) <- c("Name", "Time units", "Case counts")
+ react_values$data_table <- rbind(react_values$data_table, new_rows)
+
+ # Evaluate all existing estimators on the new datasets and update the
+ # corresponding rows in the estimates table.
+ update_estimates_rows(new_rows, react_values)
+
+ showNotification("Datasets added successfully.",
+ duration = 3, id = "notify-success"
)
}
- )
+ },
+ error = function(e) {
+ output[[paste0(data_source, "_warn")]] <- renderText(
+ "The input does not match the required format."
+ )
+ }
+ )
+}
+
+# Load sample datasets.
+load_samples <- function(input, output, react_values) {
+ observeEvent(input$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 rows in the estimates table.
+ update_estimates_rows(new_rows, react_values)
+
+ showNotification("Datasets added successfully.",
+ duration = 3, id = "notify-success"
+ )
+ }
})
}
git.nmode.ca / Rnaught / blobdiff
