diff options
Diffstat (limited to 'inst/app/scripts/data.R')
| -rw-r--r-- | inst/app/scripts/data.R | 178 |
1 files changed, 65 insertions, 113 deletions
diff --git a/inst/app/scripts/data.R b/inst/app/scripts/data.R index 02b57c5..8f8694c 100644 --- a/inst/app/scripts/data.R +++ b/inst/app/scripts/data.R @@ -8,12 +8,12 @@ data_logic <- function(input, output, react_values) { check.names = FALSE ) - render_plot(input, output) - single_entry(input, output, react_values) - manual_bulk_entry(input, output, react_values) + manual_entry(input, output, react_values) upload_data(input, output, react_values) load_samples(input, output, react_values) - render_data(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) } @@ -23,91 +23,58 @@ 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$data_name_warn <- renderText("") - } - - # Ensure the case counts are specified as a comma-separated of one or more - # non-negative integers. - counts <- tokenize_counts(input$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." - ) - 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) + plot_title <- paste( + if (time_units == "Days") "Daily" else "Weekly", "case counts" + ) - # Evaluate all existing estimators on the new dataset and update the - # corresponding row in the estimates table. - update_estimates_rows(new_row, react_values) + data_plot <- plotly::layout(data_plot, title = plot_title, + xaxis = list(title = time_units), yaxis = list(title = "Cases") + ) - showNotification("Dataset added successfully.", - duration = 3, id = "notify-success" + 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) }) } -manual_bulk_entry <- function(input, output, react_values) { +# Validate and add manually-entered datasets. +manual_entry <- function(input, output, react_values) { observeEvent(input$data_bulk, { - validate_bulk_data(input, output, react_values, "data_area") + validate_data(input, output, react_values, "data_area") }) } +# Validate and add datasets from a CSV file. upload_data <- function(input, output, react_values) { observeEvent(input$data_upload, { - validate_bulk_data(input, output, react_values, "data_upload") + validate_data(input, output, react_values, "data_upload") }) } -validate_bulk_data <- function(input, output, react_values, data_source) { +# Validate datasets and update the data table. +validate_data <- function(input, output, react_values, data_source) { tryCatch( { if (data_source == "data_area") { @@ -171,12 +138,10 @@ validate_bulk_data <- function(input, output, react_values, data_source) { 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) + # corresponding columns in the estimates table. + update_estimates_cols(new_rows, react_values) - showNotification("Datasets added successfully.", - duration = 3, id = "notify-success" - ) + showNotification("Datasets added successfully.", duration = 3) } }, error = function(e) { @@ -225,41 +190,31 @@ load_samples <- function(input, output, react_values) { 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) + # corresponding columns in the estimates table. + update_estimates_cols(new_rows, react_values) - showNotification("Datasets added successfully.", - duration = 3, id = "notify-success" - ) + 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$estimates_table[, -(rows_selected + 2)] }) } @@ -276,26 +231,23 @@ export_data <- function(output, react_values) { } # When new datasets are added, evaluate all existing estimators on them and -# add new rows to the estimates table. -update_estimates_rows <- function(datasets, react_values) { - new_rows <- data.frame( - matrix(nrow = nrow(datasets), ncol = ncol(react_values$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) - - for (row in seq_len(nrow(datasets))) { - new_rows[row, 1] <- datasets[row, 1] + colnames(new_cols) <- datasets[, 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 ) } |