nmode's Git Repositories - Rnaught/blob - inst/app/scripts/data.R

   1 # Main logic block for data-related interactions.
   2 data_logic <- function(input, output, react_values) {
   3   # Initialize a data frame to hold the datasets.
   4   react_values$data_table <- data.frame(
   5     Name = character(0),
   6     `Time units` = character(0),
   7     `Case counts` = character(0),
   8     check.names = FALSE
   9   )
  10
  11   manual_entry(input, output, react_values)
  12   upload_data(input, output, react_values)
  13   load_samples(input, output, react_values)
  14   render_data_table(output, react_values)
  15   render_plot(input, output, react_values, "Days")
  16   render_plot(input, output, react_values, "Weeks")
  17   delete_data(input, react_values)
  18   export_data(output, react_values)
  19 }
  20
  21 # Convert the input case counts string to an integer vector.
  22 tokenize_counts <- function(counts_str) {
  23   suppressWarnings(as.integer(unlist(strsplit(trimws(counts_str), ","))))
  24 }
  25
  26 # Render the plots for daily and weekly data when the data table is updated.
  27 render_plot <- function(input, output, react_values, time_units) {
  28   observe({
  29     datasets <- react_values$data_table[
  30       which(react_values$data_table[["Time units"]] == time_units),
  31     ]
  32
  33     data_plot <- plotly::plot_ly(type = "scatter", mode = "lines")
  34     if (nrow(datasets) > 0) {
  35       for (i in seq_len(nrow(datasets))) {
  36         counts <- tokenize_counts(datasets[i, 3])
  37         data_plot <- plotly::add_trace(data_plot,
  38           x = seq_along(counts) - 1, y = counts, name = datasets[i, 1]
  39         )
  40       }
  41     }
  42
  43     plot_title <- paste(
  44       if (time_units == "Days") "Daily" else "Weekly", "case counts"
  45     )
  46
  47     data_plot <- plotly::layout(data_plot, title = plot_title,
  48       xaxis = list(title = time_units), yaxis = list(title = "Cases")
  49     )
  50
  51     data_plot <- plotly::config(data_plot, displaylogo = FALSE,
  52       toImageButtonOptions = list(
  53         filename = paste0("Rnaught_data_", tolower(time_units), "_plot")
  54       )
  55     )
  56
  57     output[[paste0("data_plot_", tolower(time_units))]] <-
  58       plotly::renderPlotly(data_plot)
  59   })
  60 }
  61
  62 # Validate and add manually-entered datasets.
  63 manual_entry <- function(input, output, react_values) {
  64   observeEvent(input$data_bulk, {
  65     validate_data(input, output, react_values, "data_area")
  66   })
  67 }
  68
  69 # Validate and add datasets from a CSV file.
  70 upload_data <- function(input, output, react_values) {
  71   observeEvent(input$data_upload, {
  72     validate_data(input, output, react_values, "data_upload")
  73   })
  74 }
  75
  76 # Validate datasets and update the data table.
  77 validate_data <- function(input, output, react_values, data_source) {
  78   tryCatch(
  79     {
  80       if (data_source == "data_area") {
  81         datasets <- read.csv(text = input$data_area, header = FALSE, sep = ",")
  82       } else if (data_source == "data_upload") {
  83         datasets <- read.csv(
  84           file = input$data_upload$datapath, header = FALSE, sep = ","
  85         )
  86       }
  87
  88       names <- trimws(datasets[, 1])
  89       units <- trimws(datasets[, 2])
  90       counts <- apply(data.frame(datasets[, 3:ncol(datasets)]), 1,
  91         function(row) {
  92           row <- suppressWarnings(as.integer(row))
  93           toString(row[!is.na(row) & row >= 0])
  94         }
  95       )
  96
  97       warning_text <- ""
  98
  99       # Ensure the dataset names are neither blank nor duplicates.
 100       if (anyNA(names) || any(names == "")) {
 101         warning_text <- paste0(warning_text,
 102           "Each row must begin with a non-blank dataset name.<br>"
 103         )
 104       } else {
 105         if (length(unique(names)) != length(names)) {
 106           warning_text <- paste0(warning_text,
 107             "The rows contain duplicate dataset names.<br>"
 108           )
 109         }
 110         if (any(names %in% react_values$data_table[, 1])) {
 111           warning_text <- paste0(warning_text,
 112             "The rows contain dataset names which already exist.<br>"
 113           )
 114         }
 115       }
 116
 117       # Ensure the second entry in each row is a time unit equal to
 118       # "Days" or "Weeks".
 119       if (!all(units %in% c("Days", "Weeks"))) {
 120         warning_text <- paste0(warning_text,
 121           "The second entry in each row must be either 'Days' or 'Weeks'.<br>"
 122         )
 123       }
 124
 125       # Ensure the counts in each row have at least one non-negative integer.
 126       if (any(counts == "")) {
 127         warning_text <- paste0(warning_text,
 128           "Each row must contain at least one non-negative integer.<br>"
 129         )
 130       }
 131
 132       output[[paste0(data_source, "_warn")]] <- renderUI(HTML(warning_text))
 133
 134       if (warning_text == "") {
 135         # Add the new datasets to the data table.
 136         new_rows <- data.frame(names, units, counts)
 137         colnames(new_rows) <- c("Name", "Time units", "Case counts")
 138         react_values$data_table <- rbind(react_values$data_table, new_rows)
 139
 140         # Evaluate all existing estimators on the new datasets and update the
 141         # corresponding columns in the estimates table.
 142         update_estimates_cols(new_rows, react_values)
 143
 144         showNotification("Datasets added successfully.",
 145           duration = 3, id = "notify-success"
 146         )
 147       }
 148     },
 149     error = function(e) {
 150       output[[paste0(data_source, "_warn")]] <- renderText(
 151         "The input does not match the required format."
 152       )
 153     }
 154   )
 155 }
 156
 157 # Load sample datasets.
 158 load_samples <- function(input, output, react_values) {
 159   observeEvent(input$data_samples, {
 160     names <- c()
 161     units <- c()
 162     counts <- c()
 163
 164     # COVID-19 Canada, March 2020 (weekly).
 165     if (input$covid_canada) {
 166       names <- c(names, "COVID-19 Canada 2020/03/03 - 2020/03/31")
 167       units <- c(units, "Weeks")
 168       counts <- c(counts, toString(Rnaught::COVIDCanada[seq(41, 69, 7), 2]))
 169     }
 170     # COVID-19 Ontario, March 2020 (weekly).
 171     if (input$covid_ontario) {
 172       names <- c(names, "COVID-19 Ontario 2020/03/03 - 2020/03/31")
 173       units <- c(units, "Weeks")
 174       counts <- c(counts,
 175         toString(Rnaught::COVIDCanadaPT[seq(10176, 10204, 7), 3])
 176       )
 177     }
 178
 179     if (length(names) == 0) {
 180       output$data_samples_warn <- renderText(
 181         "At least one sample dataset must be selected."
 182       )
 183     } else if (any(names %in% react_values$data_table[, 1])) {
 184       output$data_samples_warn <- renderText(
 185         "At least one of the selected dataset names already exist."
 186       )
 187     } else {
 188       output$data_samples_warn <- renderText("")
 189
 190       new_rows <- data.frame(names, units, counts)
 191       colnames(new_rows) <- c("Name", "Time units", "Case counts")
 192       react_values$data_table <- rbind(react_values$data_table, new_rows)
 193
 194       # Evaluate all existing estimators on the sample datasets and update the
 195       # corresponding columns in the estimates table.
 196       update_estimates_cols(new_rows, react_values)
 197
 198       showNotification("Datasets added successfully.",
 199         duration = 3, id = "notify-success"
 200       )
 201     }
 202   })
 203 }
 204
 205 # Render the data table when new datasets are added.
 206 render_data_table <- function(output, react_values) {
 207   observe({
 208     output$data_table <- DT::renderDataTable(
 209       react_values$data_table, rownames = FALSE
 210     )
 211   })
 212 }
 213
 214 # Delete rows in the data table and the corresponding columns in the estimates
 215 # table.
 216 delete_data <- function(input, react_values) {
 217   observeEvent(input$data_delete, {
 218     rows_selected <- input$data_table_rows_selected
 219     react_values$data_table <- react_values$data_table[-rows_selected, ]
 220     react_values$estimates_table <-
 221       react_values$estimates_table[, -(rows_selected + 2)]
 222   })
 223 }
 224
 225 # Export data table as a CSV file.
 226 export_data <- function(output, react_values) {
 227   output$data_export <- downloadHandler(
 228     filename = function() {
 229       paste0("Rnaught_data_", format(Sys.time(), "%y-%m-%d_%H-%M-%S"), ".csv")
 230     },
 231     content = function(file) {
 232       write.csv(react_values$data_table, file, row.names = FALSE)
 233     }
 234   )
 235 }
 236
 237 # When new datasets are added, evaluate all existing estimators on them and
 238 # add new columns to the estimates table.
 239 update_estimates_cols <- function(datasets, react_values) {
 240   new_cols <- data.frame(
 241     matrix(nrow = nrow(react_values$estimates_table), ncol = nrow(datasets))
 242   )
 243   colnames(new_cols) <- datasets[, 1]
 244
 245   if (nrow(new_cols) > 0) {
 246     for (row in seq_len(nrow(new_cols))) {
 247       estimator <- react_values$estimators[[row]]
 248       for (col in seq_len(ncol(new_cols))) {
 249         new_cols[row, col] <- eval_estimator(estimator, datasets[col, ])
 250       }
 251     }
 252   }
 253
 254   react_values$estimates_table <- cbind(
 255     react_values$estimates_table, new_cols
 256   )
 257 }