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Diffstat (limited to 'inst/web/scripts/data.R')
| -rw-r--r-- | inst/web/scripts/data.R | 253 |
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 + ) +} |