X-Git-Url: https://git.nmode.ca/Rnaught/blobdiff_plain/1a97bcbfb014a8c4906c0161c9a397efbe9420d1..2d34b71c7a8da7fd0fac59b934145286b2be7b1f:/inst/app/scripts/data.R diff --git a/inst/app/scripts/data.R b/inst/app/scripts/data.R index eebd14f..8f8694c 100644 --- 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 ) - 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) } @@ -21,180 +23,198 @@ tokenize_counts <- function(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({ - 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 = ",") - - 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 = "
", - "Each row must begin with a non-blank dataset name." - ) - } else { - if (length(unique(names)) != length(names)) { - warning_text <- paste0(warning_text, sep = "
", - "The rows contain duplicate dataset names." - ) - } - if (any(names %in% react_values$data_table[, 1])) { - warning_text <- paste0(warning_text, sep = "
", - "The rows contain dataset names which already exist." - ) - } + names <- trimws(datasets[, 1]) + units <- trimws(datasets[, 2]) + counts <- apply(data.frame(datasets[, 3:ncol(datasets)]), 1, + function(row) { + row <- suppressWarnings(as.integer(row)) + toString(row[!is.na(row) & row >= 0]) } + ) + + warning_text <- "" - # Ensure the second entry in each row is a time unit equal to - # "Days" or "Weeks". - if (!all(units %in% c("Days", "Weeks"))) { - warning_text <- paste0(warning_text, sep = "
", - "The second entry in each row must be either 'Days' or 'Weeks'." + # Ensure the dataset names are neither blank nor duplicates. + if (anyNA(names) || any(names == "")) { + warning_text <- paste0(warning_text, + "Each row must begin with a non-blank dataset name.
" + ) + } else { + if (length(unique(names)) != length(names)) { + warning_text <- paste0(warning_text, + "The rows contain duplicate dataset names.
" ) } - - # Ensure the counts in each row have at least one non-negative integer. - if (any(counts == "")) { - warning_text <- paste0(warning_text, sep = "
", - "Each row must contain at least one non-negative integer." + if (any(names %in% react_values$data_table[, 1])) { + warning_text <- paste0(warning_text, + "The rows contain dataset names which already exist.
" ) } + } + + # 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'.
" + ) + } - output$data_area_warn <- renderUI(HTML(warning_text)) + # Ensure the counts in each row have at least one non-negative integer. + if (any(counts == "")) { + warning_text <- paste0(warning_text, + "Each row must contain at least one non-negative integer.
" + ) + } - if (warning_text == "") { - # Add the new datasets to the data table. - new_rows <- data.frame(names, units, counts) - colnames(new_rows) <- c("Name", "Time units", "Case counts") - react_values$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$data_table, new_rows) + + # Evaluate all existing estimators on the sample datasets and update the + # corresponding columns in the estimates table. + update_estimates_cols(new_rows, react_values) + + showNotification("Datasets added successfully.", duration = 3) + } }) } # Render the data table when new datasets are added. -render_data <- function(output, react_values) { +render_data_table <- function(output, react_values) { observe({ - output$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)] }) } @@ -211,26 +231,23 @@ export_data <- function(output, react_values) { } # 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 ) }