#' @importFrom methods is
#' @importFrom utils read.csv write.csv
server <- function(input, output) {
- # Hide the sidebar if the 'About' tab is active.
- shiny::observeEvent(input$tabset, {
- if (input$tabset == "About") {
- shinyjs::hideElement(selector="#sidebar")
- shinyjs::removeCssClass("main", "col-sm-8")
- shinyjs::addCssClass("main", "col-sm-12")
- } else {
- shinyjs::showElement(selector="#sidebar")
- shinyjs::removeCssClass("main", "col-sm-12")
- shinyjs::addCssClass("main", "col-sm-8")
- }
- })
-
- 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$est_table)
+ if (is.na(max_scale) || max_scale < 1 / grid_length) {
+ output$gridScaleWarn <- 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$method, "<br/>(μ = ", estimator$mu, " ", tolower(estimator$mu_units), ")")))
- return(new_est_table)
+ colnames(new_est_table) <- c(colnames(est_table), shiny::HTML(
+ paste0(estimator$method, "<br/>(μ = ", 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/>(μ = ", 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/>(μ = ",
+ 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(seq_bayes(unlist(dataset[3]), mu = serial,
+ kappa = estimator$kappa), 2)
+ # Incidence Decay
+ else if (estimator$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)
}
git.nmode.ca / Rnaught / blobdiff
