Adjust indent level and enforce 80-character line limit

1 files changed, 279 insertions, 230 deletions

diff --git a/R/server.R b/R/server.R
index e8d2970..ed5ecd7 100644
--- a/R/server.R
+++ b/R/server.R
@@ -1,263 +1,312 @@
 #' @importFrom methods is
 #' @importFrom utils read.csv write.csv
 server <- function(input, output) {
-    reactive <- shiny::reactiveValues(
-        data_table=data.frame(Name=character(0), `Reporting Frequency`=character(0), `Case Counts`=numeric(0), check.names=FALSE),
-        est_table=data.frame(Dataset=character(0)),
-        estimators=list()
-    )
-
-    # Validate and add datasets when button is clicked.
-    # Also evaluate the new datasets on existing estimators.
-    shiny::observeEvent(input$addData, {
-        # Option 1: Manual entry.
-        if (input$dataInputMethod == 1) {
-            checks_passed <- TRUE
-
-            # Ensure the dataset name is not blank.
-            if (grepl("^\\s*$", input$dataName)) {
-                output$dataNameWarn <- shiny::renderText("Error: The dataset name cannot be blank.")
-                checks_passed <- FALSE
-            }
-            # Ensure the dataset name is not a duplicate.
-            else if (input$dataName %in% reactive$data_table[,1]) {
-                output$dataNameWarn <- shiny::renderText("Error: There is already a dataset with the specified name.")
-                checks_passed <- FALSE
-            }
-            else
-                output$dataNameWarn <- shiny::renderText("")
-
-            # Ensure the case counts consist only of non-negative integers, separated by commas.
-            counts <- as.numeric(unlist(strsplit(input$dataCounts, split=",")))
-            if (any(is.na(counts)) || any(counts <= 0) || any(counts %% 1 != 0)) {
-                output$dataCountsWarn <- shiny::renderText("Error: The list of case counts should only contain non-negative integers, separated by commas.")
-                checks_passed <- FALSE
-            }
-            # Ensure the case counts contain at least two entries.
-            else if (length(counts) < 2) {
-                output$dataCountsWarn <- shiny::renderText("Error: The list of case counts should contain at least two entries.")
-                checks_passed <- FALSE
-            }
-            else
-                output$dataCountsWarn <- shiny::renderText("")
+  reactive <- shiny::reactiveValues(
+    data_table = data.frame(Name = character(0),
+                            `Reporting Frequency` = character(0),
+                            `Case Counts` = numeric(0), check.names = FALSE),
+    est_table = data.frame(Dataset = character(0)),
+    estimators = list()
+  )
+
+  # Validate and add datasets when button is clicked.
+  # Also evaluate the new datasets on existing estimators.
+  shiny::observeEvent(input$addData, {
+    # Option 1: Manual entry.
+    if (input$dataInputMethod == 1) {
+      checks_passed <- TRUE
+
+      # Ensure the dataset name is not blank.
+      if (grepl("^\\s*$", input$dataName)) {
+        output$dataNameWarn <- shiny::renderText(
+          "Error: The dataset name cannot be blank.")
+        checks_passed <- FALSE
+      }
+      # Ensure the dataset name is not a duplicate.
+      else if (input$dataName %in% reactive$data_table[,1]) {
+        output$dataNameWarn <- shiny::renderText(
+          "Error: There is already a dataset with the specified name.")
+        checks_passed <- FALSE
+      }
+      else
+        output$dataNameWarn <- shiny::renderText("")
+
+      # Ensure the case counts consist only of positive integers, separated by
+      # commas.
+      counts <- as.numeric(unlist(strsplit(input$dataCounts, split = ",")))
+      if (any(is.na(counts)) || any(counts <= 0) || any(counts %% 1 != 0)) {
+        output$dataCountsWarn <- shiny::renderText("Error: The list of case
+          counts should only contain positive integers, separated by commas.")
+        checks_passed <- FALSE
+      }
+      # Ensure the case counts contain at least two entries.
+      else if (length(counts) < 2) {
+        output$dataCountsWarn <- shiny::renderText(
+          "Error: The list of case counts should contain at least two entries.")
+        checks_passed <- FALSE
+      }
+      else
+        output$dataCountsWarn <- shiny::renderText("")
       
-            if (checks_passed)
-                d <- data.frame(input$dataName, input$dataUnits, t(counts))
-        }
+      if (checks_passed)
+        d <- data.frame(input$dataName, input$dataUnits, t(counts))
+    }
 
-        else {
-            checks_passed <- FALSE
-
-            # Option 2: Upload .csv
-            if (input$dataInputMethod == 2)
-                d <- try(read.csv(input$dataUpload$datapath, header=FALSE))
-            # Option 3: Paste .csv
-            else
-                d <- try(read.csv(text=input$dataPaste, header=FALSE))
-
-            if (is(d, "try-error"))
-                output$dataCSVWarn <- shiny::renderText("Error reading file.")
-            else if (ncol(d) < 4 || anyNA(d[,1]) || anyNA(sapply(d[,3:4], as.numeric)) || !all(trimws(d[,2]) %in% c("Daily", "Weekly")))
-                output$dataCSVWarn <- shiny::renderText("Error: The provided .csv file does not match the required format.")
-            else if (length(intersect(reactive$data_table[,1], d[,1])) > 0)
-                output$dataCSVWarn <- shiny::renderText("Error: The provided .csv file contains dataset names which already exist.")
-            else if (length(unique(d[,1])) != length(d[,1]))
-                output$dataCSVWarn <- shiny::renderText("Error: The provided .csv file contains duplicate dataset names.")
-            else {
-                output$dataCSVWarn <- shiny::renderText("")
-                checks_passed <- TRUE
-            }
-        }
+    else {
+      checks_passed <- FALSE
 
-        if (checks_passed) {
-            d[,3:ncol(d)] <- apply(d[,3:ncol(d)], 2, as.numeric)
-            d[,3] <- data.frame(I(lapply(split(d[,3:ncol(d)], 1:nrow(d)), function(x) x[!is.na(x)])))
-            d <- d[,1:3]
-            d[,2] <- trimws(d[,2])
-            colnames(d) <- c("Name", "Reporting Frequency", "Case Counts")
-            reactive$data_table <- rbind(reactive$data_table, d)
-            reactive$est_table <- update_est_row(input, output, d, reactive$estimators, reactive$est_table)
-        }
-    })
-  
-    output$dataTable <- shiny::renderDataTable(reactive$data_table, escape=FALSE)
-    output$estTable <- shiny::renderDataTable(reactive$est_table, escape=FALSE)
+      # Option 2: Upload .csv
+      if (input$dataInputMethod == 2)
+        d <- try(read.csv(input$dataUpload$datapath, header = FALSE))
+      # Option 3: Paste .csv
+      else
+        d <- try(read.csv(text = input$dataPaste, header = FALSE))
+
+      if (is(d, "try-error"))
+        output$dataCSVWarn <- shiny::renderText("Error reading file.")
+      else if (ncol(d) < 4 || anyNA(d[,1]) || anyNA(sapply(d[,3:4], as.numeric))
+               || !all(trimws(d[,2]) %in% c("Daily", "Weekly")))
+        output$dataCSVWarn <- shiny::renderText(
+          "Error: The provided .csv file does not match the required format.")
+      else if (length(intersect(reactive$data_table[,1], d[,1])) > 0)
+        output$dataCSVWarn <- shiny::renderText("Error: The provided .csv file
+          contains dataset names which already exist.")
+      else if (length(unique(d[,1])) != length(d[,1]))
+        output$dataCSVWarn <- shiny::renderText(
+          "Error: The provided .csv file contains duplicate dataset names.")
+      else {
+        output$dataCSVWarn <- shiny::renderText("")
+        checks_passed <- TRUE
+      }
+    }
+
+    if (checks_passed) {
+      d[,3:ncol(d)] <- apply(d[,3:ncol(d)], 2, as.numeric)
+      d[,3] <- data.frame(I(lapply(split(d[,3:ncol(d)], 1:nrow(d)),
+                                   function(x) x[!is.na(x)])))
+      d <- d[,1:3]
+      d[,2] <- trimws(d[,2])
+      colnames(d) <- c("Name", "Reporting Frequency", "Case Counts")
+      reactive$data_table <- rbind(reactive$data_table, d)
+      reactive$est_table <- update_est_row(input, output, d,
+        reactive$estimators, reactive$est_table)
+    }
+  })
   
-    # Download table of estimates as a .csv file.
-    output$downloadEst <- shiny::downloadHandler(
-        filename=function() { paste0("Rnaught-", Sys.Date(), ".csv") },
-        content=function(file) { write.csv(reactive$est_table, file) }
-    )
-
-    shiny::observeEvent(input$addWP, {
-        if (input$serialWPKnown == 1) {
-            serial <- validate_serial(input, output, "serialWPInput", "serialWPWarn")
-            if (!is.na(serial)) {
-                reactive$estimators[[length(reactive$estimators)+1]] <- list(method="WP", mu=serial, search=list(B=100, shape.max=10, scale.max=10), mu_units=input$serialWPUnits)
-                reactive$est_table <- update_est_col(input, output, reactive$data_table, reactive$estimators[[length(reactive$estimators)]], reactive$est_table)
-            }
-        }
-        else {
-            checks_passed <- TRUE
-
-            grid_length <- as.numeric(input$gridLengthInput)
-            max_shape <- as.numeric(input$gridShapeInput)
-            max_scale <- as.numeric(input$gridScaleInput)
-
-            if (is.na(grid_length) || grid_length <= 0 || grid_length %% 1 != 0) {
-                output$gridLengthWarn <- shiny::renderText("Error: The grid size must be a positive integer.")
-                output$gridShapeWarn <- shiny::renderText("")
-                output$gridScaleWarn <- shiny::renderText("")
-                checks_passed <- FALSE
-            }
-            else {
-                output$gridLengthWarn <- shiny::renderText("")
-
-            if (is.na(max_shape) || max_shape < 1 / grid_length) {
-                output$gridShapeWarn <- shiny::renderText("Error: The maximum shape must be at least the reciprocal of the grid length.")
-                checks_passed <- FALSE
-            }
-            else
-                output$gridShapeWarn <- shiny::renderText("")
-
-            if (is.na(max_scale) || max_scale < 1 / grid_length) {
-                output$gridShapeWarn <- shiny::renderText("Error: The maximum scale must be at least the reciprocal of the grid length.")
-                checks_passed <- FALSE
-            }
-            else
-                output$gridScaleWarn <- shiny::renderText("")
-            }
-
-            if (checks_passed) {
-                reactive$estimators[[length(reactive$estimators)+1]] <- list(method="WP", mu=NA, search=list(B=grid_length, shape.max=max_shape, scale.max=max_scale), mu_units=input$serialWPUnits)
-                reactive$est_table <- update_est_col(input, output, reactive$data_table, reactive$estimators[[length(reactive$estimators)]], reactive$est_table)
-            }  
-        }
-    })
-
-    shiny::observeEvent(input$addseqB, {
-        serial <- validate_serial(input, output, "serialseqBInput", "serialseqBWarn")
-        checks_passed <- !is.na(serial)
-    
-        kappa <- as.numeric(input$kappaInput)
-        if (is.na(kappa) || kappa <= 0) {
-            output$kappaWarn <- shiny::renderText("Error: The maximum value must be a positive number.")
-            checks_passed <- FALSE
+  output$dataTable <- shiny::renderDataTable(reactive$data_table,
+                                             escape = FALSE)
+  output$estTable <- shiny::renderDataTable(reactive$est_table,
+                                            escape = FALSE)
+
+  # Download table of estimates as a .csv file.
+  output$downloadEst <- shiny::downloadHandler(
+    filename = function() { paste0("Rnaught-", Sys.Date(), ".csv") },
+    content = function(file) { write.csv(reactive$est_table, file) }
+  )
+
+  shiny::observeEvent(input$addWP, {
+    if (input$serialWPKnown == 1) {
+      serial <- validate_serial(input, output, "serialWPInput", "serialWPWarn")
+      if (!is.na(serial)) {
+        reactive$estimators[[length(reactive$estimators) + 1]] <- list(
+          method = "WP", mu = serial, mu_units = input$serialWPUnits,
+          search = list(B = 100, shape.max = 10, scale.max = 10))
+        reactive$est_table <- update_est_col(input, output, reactive$data_table,
+          reactive$estimators[[length(reactive$estimators)]],
+          reactive$est_table)
+      }
+    }
+    else {
+      checks_passed <- TRUE
+
+      grid_length <- as.numeric(input$gridLengthInput)
+      max_shape <- as.numeric(input$gridShapeInput)
+      max_scale <- as.numeric(input$gridScaleInput)
+
+      if (is.na(grid_length) || grid_length <= 0 || grid_length %% 1 != 0) {
+        output$gridLengthWarn <- shiny::renderText(
+          "Error: The grid size must be a positive integer.")
+        output$gridShapeWarn <- shiny::renderText("")
+        output$gridScaleWarn <- shiny::renderText("")
+        checks_passed <- FALSE
+      }
+      else {
+        output$gridLengthWarn <- shiny::renderText("")
+
+        if (is.na(max_shape) || max_shape < 1 / grid_length) {
+          output$gridShapeWarn <- shiny::renderText("Error: The maximum shape
+            must be at least the reciprocal of the grid length.")
+          checks_passed <- FALSE
         }
         else
-            output$kappaWarn <- shiny::renderText("")
-    
-        if (checks_passed) {
-            reactive$estimators[[length(reactive$estimators)+1]] <- list(method="seqB", mu=serial, kappa=kappa, mu_units=input$serialseqBUnits)
-            reactive$est_table <- update_est_col(input, output, reactive$data_table, reactive$estimators[[length(reactive$estimators)]], reactive$est_table)
-        }
-    })
+          output$gridShapeWarn <- shiny::renderText("")
 
-    shiny::observeEvent(input$addID, {
-        serial <- validate_serial(input, output, "serialIDInput", "serialIDWarn")
-        if (!is.na(serial)) {
-            reactive$estimators[[length(reactive$estimators)+1]] <- list(method="ID", mu=serial, mu_units=input$serialIDUnits)
-            reactive$est_table <- update_est_col(input, output, reactive$data_table, reactive$estimators[[length(reactive$estimators)]], reactive$est_table)
+        if (is.na(max_scale) || max_scale < 1 / grid_length) {
+          output$gridShapeWarn <- shiny::renderText("Error: The maximum scale
+            must be at least the reciprocal of the grid length.")
+          checks_passed <- FALSE
         }
-    })
+        else
+          output$gridScaleWarn <- shiny::renderText("")
+      }
 
-    shiny::observeEvent(input$addIDEA, {
-        serial <- validate_serial(input, output, "serialIDEAInput", "serialIDEAWarn")
-        if (!is.na(serial)) {
-            reactive$estimators[[length(reactive$estimators)+1]] <- list(method="IDEA", mu=serial, mu_units=input$serialIDEAUnits)
-            reactive$est_table <- update_est_col(input, output, reactive$data_table, reactive$estimators[[length(reactive$estimators)]], reactive$est_table)
-        }
-    })
-}
+      if (checks_passed) {
+        reactive$estimators[[length(reactive$estimators) + 1]] <- list(
+          method = "WP", mu = NA, mu_units = input$serialWPUnits,
+          search = list(B = grid_length, shape.max = max_shape,
+                        scale.max = max_scale))
+        reactive$est_table <- update_est_col(input, output, reactive$data_table,
+          reactive$estimators[[length(reactive$estimators)]],
+          reactive$est_table)
+      }  
+    }
+  })
 
-validate_serial <- function(input, output, serialInputId, serialWarnId) {
-    serial <- as.numeric(input[[serialInputId]])
-    if (is.na(serial) || serial <= 0) {
-        output[[serialWarnId]] <- shiny::renderText("Error: The mean serial interval should be a non-negative number.")
-        serial <- NA
+  shiny::observeEvent(input$addseqB, {
+    serial <- validate_serial(input, output, "serialseqBInput",
+                              "serialseqBWarn")
+    checks_passed <- !is.na(serial)
+
+    kappa <- as.numeric(input$kappaInput)
+    if (is.na(kappa) || kappa <= 0) {
+      output$kappaWarn <- shiny::renderText(
+        "Error: The maximum value must be a positive number.")
+      checks_passed <- FALSE
     }
     else
-        output[[serialWarnId]] <- shiny::renderText("") # Clear warning text.
-    
-    return(serial)
+      output$kappaWarn <- shiny::renderText("")
+
+    if (checks_passed) {
+      reactive$estimators[[length(reactive$estimators) + 1]] <- list(
+        method="seqB", mu = serial, kappa = kappa,
+        mu_units = input$serialseqBUnits)
+      reactive$est_table <- update_est_col(input, output, reactive$data_table,
+        reactive$estimators[[length(reactive$estimators)]], reactive$est_table)
+    }
+  })
+
+  shiny::observeEvent(input$addID, {
+    serial <- validate_serial(input, output, "serialIDInput", "serialIDWarn")
+    if (!is.na(serial)) {
+      reactive$estimators[[length(reactive$estimators) + 1]] <- list(
+        method = "ID", mu = serial, mu_units = input$serialIDUnits)
+      reactive$est_table <- update_est_col(input, output, reactive$data_table,
+        reactive$estimators[[length(reactive$estimators)]], reactive$est_table)
+    }
+  })
+
+  shiny::observeEvent(input$addIDEA, {
+    serial <- validate_serial(input, output, "serialIDEAInput",
+                              "serialIDEAWarn")
+    if (!is.na(serial)) {
+      reactive$estimators[[length(reactive$estimators) + 1]] <- list(
+        method = "IDEA", mu = serial, mu_units = input$serialIDEAUnits)
+      reactive$est_table <- update_est_col(input, output, reactive$data_table,
+        reactive$estimators[[length(reactive$estimators)]], reactive$est_table)
+    }
+  })
 }
 
+validate_serial <- function(input, output, serialInputId, serialWarnId) {
+  serial <- as.numeric(input[[serialInputId]])
+  if (is.na(serial) || serial <= 0) {
+    output[[serialWarnId]] <- shiny::renderText(
+      "Error: The mean serial interval should be a positive number.")
+    serial <- NA
+  }
+  else
+    output[[serialWarnId]] <- shiny::renderText("") # Clear warning text.
+
+  return(serial)
+}
+
+# Create a new column in the estimator table when a new estimator is added.
 update_est_col <- function(input, output, datasets, estimator, est_table) {
-    if (nrow(datasets) == 0)
-        new_est_table <- data.frame(matrix(nrow=0, ncol=ncol(est_table)+1))
-    else {
-        estimates <- rep(NA, nrow(datasets))
+  if (nrow(datasets) == 0)
+    new_est_table <- data.frame(matrix(nrow = 0, ncol = ncol(est_table) + 1))
+  else {
+    estimates <- rep(NA, nrow(datasets))
 
     for (row in 1:nrow(datasets))
-        estimates[row] <- eval_estimator(input, output, estimator, datasets[row,])
+      estimates[row] <- eval_estimator(input, output, estimator, datasets[row,])
 
-        if (nrow(est_table) == 0)
-            new_est_table <- cbind(datasets[,1], estimates)
-        else
-            new_est_table <- cbind(est_table, estimates)
-    }
+      if (nrow(est_table) == 0)
+        new_est_table <- cbind(datasets[,1], estimates)
+      else
+        new_est_table <- cbind(est_table, estimates)
+  }
 
-    colnames(new_est_table) <- c(colnames(est_table), shiny::HTML(paste0(estimator$method, "<br/>(&mu; = ", estimator$mu, " ", tolower(estimator$mu_units), ")")))
-    return(new_est_table)
+  colnames(new_est_table) <- c(colnames(est_table), shiny::HTML(
+    paste0(estimator$method, "<br/>(&mu; = ", estimator$mu, " ",
+           tolower(estimator$mu_units), ")")))
+
+  return(new_est_table)
 }
-  
+
+# Create a new row in the estimator table when new datasets are added.
 update_est_row <- function(input, output, datasets, estimators, est_table) {
-    if (length(estimators) == 0) {
-        if (nrow(est_table) == 0)
-            new_est_table <- data.frame(datasets[,1])
-        else
-            new_est_table <- data.frame(c(est_table[,1], datasets[,1]))
+  if (length(estimators) == 0) {
+    if (nrow(est_table) == 0)
+      new_est_table <- data.frame(datasets[,1])
+    else
+      new_est_table <- data.frame(c(est_table[,1], datasets[,1]))
 
-        colnames(new_est_table) <- colnames(est_table)
-    }
-    else {
-        new_est_table <- data.frame(matrix(nrow=nrow(datasets), ncol=length(estimators)))
+    colnames(new_est_table) <- colnames(est_table)
+  }
+  else {
+    new_est_table <- data.frame(matrix(nrow = nrow(datasets),
+                                       ncol = length(estimators)))
 
-        for (row in 1:nrow(datasets))
-            for (col in 1:length(estimators))
-                new_est_table[row, col] <- eval_estimator(input, output, estimators[[col]], datasets[row,])
+    for (row in 1:nrow(datasets))
+      for (col in 1:length(estimators))
+        new_est_table[row, col] <- eval_estimator(input, output,
+          estimators[[col]], datasets[row,])
 
-        new_est_table <- cbind(datasets[,1], new_est_table)
-        colnames(new_est_table) <- colnames(est_table)
-        new_est_table <- rbind(est_table, new_est_table)
-    }
+    new_est_table <- cbind(datasets[,1], new_est_table)
+    colnames(new_est_table) <- colnames(est_table)
+    new_est_table <- rbind(est_table, new_est_table)
+  }
 
-    return(new_est_table)
+  return(new_est_table)
 }
 
+# Evaluate an estimator on a given dataset.
 eval_estimator <- function(input, output, estimator, dataset) {
-    # Adjust serial interval to match time unit of case counts.
-    serial <- estimator$mu
-    if (estimator$mu_units == "Days" && dataset[2] == "Weekly")
-        serial <- serial / 7
-    else if (estimator$mu_units == "Weeks" && dataset[2] == "Daily")
-        serial <- serial * 7
-
-    # White and Panago
-    if (estimator$method == "WP") {
-        estimate <- WP(unlist(dataset[3]), mu=serial, search=estimator$search)
-
-        if (!is.na(estimator$mu))
-            estimate <- round(estimate$Rhat, 2)
-        # Display the estimated mean of the serial distribution if mu was not specified.
-        else {
-            if (dataset[2] == "Daily")
-                mu_units <- "days"
-            else
-                mu_units <- "weeks"
-            MSI <- sum(estimate$SD$supp * estimate$SD$pmf)
-            estimate <- shiny::HTML(paste0(round(estimate$Rhat, 2), "<br/>(&mu; = ", round(MSI, 2), " ", mu_units, ")"))
-        }
+  # Adjust serial interval to match time unit of case counts.
+  serial <- estimator$mu
+  if (estimator$mu_units == "Days" && dataset[2] == "Weekly")
+    serial <- serial / 7
+  else if (estimator$mu_units == "Weeks" && dataset[2] == "Daily")
+    serial <- serial * 7
+
+  # White and Panago
+  if (estimator$method == "WP") {
+    estimate <- WP(unlist(dataset[3]), mu = serial, search = estimator$search)
+
+    if (!is.na(estimator$mu))
+      estimate <- round(estimate$Rhat, 2)
+    # Display the estimated mean of the serial distribution if mu was not
+    # specified.
+    else {
+      if (dataset[2] == "Daily")
+        mu_units <- "days"
+      else
+        mu_units <- "weeks"
+      MSI <- sum(estimate$SD$supp * estimate$SD$pmf)
+      estimate <- shiny::HTML(paste0(round(estimate$Rhat, 2), "<br/>(&mu; = ",
+                                     round(MSI, 2), " ", mu_units, ")"))
     }
-    # Sequential Bayes
-    else if (estimator$method == "seqB")
-        estimate <- round(seqB(unlist(dataset[3]), mu=serial, kappa=estimator$kappa)$Rhat, 2)
-    # Incidence Decay
-    else if (estimator$method == "ID")
-        estimate <- round(ID(unlist(dataset[3]), mu=serial), 2)
-    # Incidence Decay with Exponential Adjustement
-        else if (estimator$method == "IDEA")
-        estimate <- round(IDEA(unlist(dataset[3]), mu=serial), 2)
-
-    return(estimate)
+  }
+  # Sequential Bayes
+  else if (estimator$method == "seqB")
+    estimate <- round(seqB(unlist(dataset[3]), mu = serial,
+                           kappa = estimator$kappa)$Rhat, 2)
+  # Incidence Decay
+  else if (estimator$method == "ID")
+    estimate <- round(ID(unlist(dataset[3]), mu = serial), 2)
+  # Incidence Decay with Exponential Adjustement
+  else if (estimator$method == "IDEA")
+    estimate <- round(IDEA(unlist(dataset[3]), mu = serial), 2)
+
+  return(estimate)
 }