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)
}
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({
- 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",
- xlab = input$data_units, ylab = "Cases", cex.lab = 1.5,
- xlim = c(0, max(length(counts) - 1, 1)), ylim = c(0, max(counts, 1))
- )
- )
- } else {
- output$data_plot <- renderPlot(
- plot(NULL, xlim = c(0, 10), ylim = c(0, 10),
- xlab = input$data_units, ylab = "Cases", cex.lab = 1.5
+ datasets <- react_values$data_table[
+ which(react_values$data_table[["Time units"]] == time_units),
+ ]
+
+ data_plot <- plotly::plot_ly(type = "scatter", mode = "lines")
+ if (nrow(datasets) > 0) {
+ for (i in seq_len(nrow(datasets))) {
+ counts <- tokenize_counts(datasets[i, 3])
+ data_plot <- plotly::add_trace(data_plot,
+ x = seq_along(counts) - 1, y = counts, name = datasets[i, 1]
)
- )
+ }
}
- })
-}
-# Add a single dataset to the existing table.
-single_entry <- function(input, output, react_values) {
- observeEvent(input$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 = ",")
-
- 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, 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$data_table, new_rows)
+
+ # Evaluate all existing estimators on the sample datasets and update the
+ # corresponding columns in the estimates table.
+ update_estimates_cols(new_rows, react_values)
+
+ showNotification("Datasets added successfully.",
+ duration = 3, id = "notify-success"
+ )
+ }
})
}
# Render the data table when new datasets are added.
-render_data <- function(output, react_values) {
+render_data_table <- function(output, react_values) {
observe({
- output$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, {
- 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)]
})
}
}
# 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
)
}
git.nmode.ca / Rnaught / blobdiff
