From 91465cd1193400053a48cca196d3fd777183c82c Mon Sep 17 00:00:00 2001 From: Naeem Model Date: Mon, 24 Jul 2023 21:49:40 +0000 Subject: Adjust indent level and enforce 80-character line limit --- R/server.R | 509 +++++++++++++++++++++++++++++++++---------------------------- 1 file changed, 279 insertions(+), 230 deletions(-) (limited to 'R/server.R') 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$method, "
(μ = ", estimator$mu, " ", tolower(estimator$mu_units), ")"))) - return(new_est_table) + colnames(new_est_table) <- c(colnames(est_table), shiny::HTML( + paste0(estimator$method, "
(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), "
(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), "
(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(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) } -- cgit v1.2.3