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speed improvements
This commit is contained in:
17
R/classes.R
17
R/classes.R
@ -360,14 +360,15 @@ print.mic <- function(x, ...) {
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n_total <- x %>% length()
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x <- x[!is.na(x)]
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n <- x %>% length()
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cat("Class 'mic': ", n, " isolates\n", sep = '')
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cat('\n')
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cat('<NA> ', n_total - n, '\n')
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cat('\n')
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tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
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cnt <- tbl %>% pull(y)
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names(cnt) <- tbl %>% pull(x)
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print(cnt)
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cat("Class 'mic'\n")
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cat(n, " results (missing: ", n_total - n, ' = ', percent((n_total - n) / n_total, force_zero = TRUE), ')\n', sep = "")
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if (n > 0) {
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cat('\n')
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tibble(MIC = x, y = 1) %>%
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group_by(MIC) %>%
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summarise(n = sum(y)) %>%
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base::print.data.frame(row.names = FALSE)
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}
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}
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#' @exportMethod summary.mic
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@ -314,11 +314,11 @@ first_isolate <- function(tbl,
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if (col_keyantibiotics != '') {
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if (info == TRUE) {
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if (type == 'keyantibiotics') {
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cat('Comparing key antibiotics for first weighted isolates (')
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cat('Key antibiotics for first weighted isolates will be compared (')
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if (ignore_I == FALSE) {
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cat('NOT ')
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}
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cat('ignoring I)...\n')
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cat('ignoring I).')
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}
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if (type == 'points') {
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cat(paste0('Comparing antibiotics for first weighted isolates (using points threshold of '
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@ -523,7 +523,6 @@ key_antibiotics_equal <- function(x,
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points_threshold = 2,
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info = FALSE) {
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# x is active row, y is lag
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type <- type[1]
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if (length(x) != length(y)) {
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@ -532,73 +531,75 @@ key_antibiotics_equal <- function(x,
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result <- logical(length(x))
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if (info == TRUE) {
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p <- dplyr::progress_estimated(length(x))
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}
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for (i in 1:length(x)) {
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if (type == "keyantibiotics") {
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if (ignore_I == TRUE) {
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# evaluation using regular expression will treat '?' as any character
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# so I is actually ignored then
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x <- gsub('I', '?', x, ignore.case = TRUE)
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y <- gsub('I', '?', y, ignore.case = TRUE)
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}
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for (i in 1:length(x)) {
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result[i] <- grepl(x = x[i],
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pattern = y[i],
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ignore.case = TRUE) |
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grepl(x = y[i],
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pattern = x[i],
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ignore.case = TRUE)
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}
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return(result)
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} else {
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if (info == TRUE) {
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p$tick()$print()
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p <- dplyr::progress_estimated(length(x))
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}
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if (is.na(x[i])) {
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x[i] <- ''
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}
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if (is.na(y[i])) {
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y[i] <- ''
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}
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for (i in 1:length(x)) {
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if (nchar(x[i]) != nchar(y[i])) {
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if (info == TRUE) {
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p$tick()$print()
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}
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result[i] <- FALSE
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if (is.na(x[i])) {
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x[i] <- ''
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}
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if (is.na(y[i])) {
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y[i] <- ''
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}
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} else if (x[i] == '' & y[i] == '') {
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if (nchar(x[i]) != nchar(y[i])) {
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result[i] <- TRUE
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result[i] <- FALSE
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} else {
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} else if (x[i] == '' & y[i] == '') {
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x2 <- strsplit(x[i], "")[[1]]
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y2 <- strsplit(y[i], "")[[1]]
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if (type == 'points') {
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# count points for every single character:
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# - no change is 0 points
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# - I <-> S|R is 0.5 point
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# - S|R <-> R|S is 1 point
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# use the levels of as.rsi (S = 1, I = 2, R = 3)
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suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
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suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
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points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
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result[i] <- ((points / 2) >= points_threshold)
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} else if (type == 'keyantibiotics') {
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# check if key antibiotics are exactly the same
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# also possible to ignore I, so only S <-> R and S <-> R are counted
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if (ignore_I == TRUE) {
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valid_chars <- c('S', 's', 'R', 'r')
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} else {
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valid_chars <- c('S', 's', 'I', 'i', 'R', 'r')
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}
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# remove invalid values (like "-", NA) on both locations
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x2[which(!x2 %in% valid_chars)] <- '?'
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x2[which(!y2 %in% valid_chars)] <- '?'
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y2[which(!x2 %in% valid_chars)] <- '?'
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y2[which(!y2 %in% valid_chars)] <- '?'
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result[i] <- all(x2 == y2)
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result[i] <- TRUE
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} else {
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stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
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x2 <- strsplit(x[i], "")[[1]]
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y2 <- strsplit(y[i], "")[[1]]
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if (type == 'points') {
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# count points for every single character:
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# - no change is 0 points
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# - I <-> S|R is 0.5 point
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# - S|R <-> R|S is 1 point
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# use the levels of as.rsi (S = 1, I = 2, R = 3)
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suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
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suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
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points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
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result[i] <- ((points / 2) >= points_threshold)
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} else {
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stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
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}
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}
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}
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if (info == TRUE) {
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cat('\n')
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}
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result
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}
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if (info == TRUE) {
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cat('\n')
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}
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result
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}
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4
R/freq.R
4
R/freq.R
@ -151,7 +151,9 @@ frequency_tbl <- function(x,
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dots <- base::eval(base::substitute(base::alist(...)))
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ndots <- length(dots)
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if (ndots > 0 & ndots < 10) {
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if (NROW(x) == 0) {
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x <- NA
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} else if (ndots > 0 & ndots < 10) {
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cols <- as.character(dots)
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if (!all(cols %in% colnames(x))) {
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stop("one or more columns not found: `", paste(cols, collapse = "`, `"), '`', call. = FALSE)
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@ -41,6 +41,7 @@ globalVariables(c('abname',
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'labs',
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'median',
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'mic',
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'MIC',
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'microorganisms',
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'mocode',
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'molis',
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258
R/resistance.R
258
R/resistance.R
@ -24,9 +24,11 @@
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#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}.
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#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
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#' @param interpretation antimicrobial interpretation
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#' @param info \emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}
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#' @param warning \emph{DEPRECATED} show a warning when the available amount of isolates is below \code{minimum}
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#' @details \strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
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#'
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#' All return values are calculated using hybrid evaluation (i.e. using C++), which makes these functions 60-65 times faster than in \code{AMR} v0.2.0 and below. The \code{rsi} function is available for backwards compatibility and deprecated. It now uses the \code{resistance} and \code{susceptibility} functions internally, based on the \code{interpretation} parameter.
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#' The functions \code{resistance}, \code{susceptibility} and \code{n_rsi} calculate using hybrid evaluation (i.e. using C++), which makes these functions 25-30 times faster than the old \code{rsi} function. This function is still available for backwards compatibility but is deprecated.
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#' \if{html}{
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#' \cr
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#' To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
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@ -90,6 +92,29 @@
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#' genus == "Helicobacter") %>%
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#' summarise(p = susceptibility(amox, metr), # amoxicillin with metronidazole
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#' n = n_rsi(amox, metr))
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#'
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#'
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#' # How fast is this hybrid evaluation in C++ compared to R?
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#' # In other words: how is the speed improvement of the new `resistance` compared to old `rsi`?
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#'
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#' library(microbenchmark)
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#' df <- septic_patients %>% group_by(hospital_id, bactid) # 317 groups with sizes 1 to 167
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#'
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#' microbenchmark(old_IR = df %>% summarise(p = rsi(amox, minimum = 0, interpretation = "IR")),
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#' new_IR = df %>% summarise(p = resistance(amox, minimum = 0)),
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#' old_S = df %>% summarise(p = rsi(amox, minimum = 0, interpretation = "S")),
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#' new_S = df %>% summarise(p = susceptibility(amox, minimum = 0)),
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#' times = 5,
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#' unit = "s")
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#'
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#' # Unit: seconds
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#' # expr min lq mean median uq max neval
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#' # old_IR 1.95600230 1.96096857 1.97981537 1.96823318 2.00645711 2.00741568 5
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#' # new_IR 0.06872808 0.06984932 0.07162866 0.06987306 0.07050094 0.07919192 5
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#' # old_S 1.68893579 1.69024888 1.72461867 1.69785934 1.70428796 1.84176137 5
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#' # new_S 0.06737037 0.06838167 0.07431906 0.07745364 0.07827224 0.08011738 5
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#'
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#' # The old function took roughly 2 seconds, the new ones take 0.07 seconds.
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#' }
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resistance <- function(ab,
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include_I = TRUE,
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@ -109,7 +134,11 @@ resistance <- function(ab,
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stop('`as_percent` must be logical', call. = FALSE)
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}
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x <- as.integer(as.rsi(ab))
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if (!is.rsi(ab)) {
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x <- as.rsi(ab)
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} else {
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x <- ab
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}
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total <- .Call(`_AMR_rsi_calc_total`, x)
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if (total < minimum) {
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return(NA)
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@ -144,16 +173,22 @@ susceptibility <- function(ab1,
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stop('`as_percent` must be logical', call. = FALSE)
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}
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if (!is.rsi(ab1)) {
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ab1 <- as.rsi(ab1)
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}
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if (!is.null(ab2)) {
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if (NCOL(ab2) > 1) {
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stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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x <- apply(X = data.frame(ab1 = as.integer(as.rsi(ab1)),
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ab2 = as.integer(as.rsi(ab2))),
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if (!is.rsi(ab2)) {
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ab2 <- as.rsi(ab2)
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}
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x <- apply(X = data.frame(ab1 = as.integer(ab1),
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ab2 = as.integer(ab2)),
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MARGIN = 1,
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FUN = min)
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} else {
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x <- as.integer(as.rsi(ab1))
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x <- ab1
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}
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total <- .Call(`_AMR_rsi_calc_total`, x)
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if (total < minimum) {
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@ -174,42 +209,221 @@ n_rsi <- function(ab1, ab2 = NULL) {
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if (NCOL(ab1) > 1) {
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stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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if (!is.rsi(ab1)) {
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ab1 <- as.rsi(ab1)
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}
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if (!is.null(ab2)) {
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if (NCOL(ab2) > 1) {
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stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
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}
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x <- apply(X = data.frame(ab1 = as.integer(as.rsi(ab1)),
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ab2 = as.integer(as.rsi(ab2))),
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if (!is.rsi(ab2)) {
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ab2 <- as.rsi(ab2)
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}
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x <- apply(X = data.frame(ab1 = as.integer(ab1),
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ab2 = as.integer(ab2)),
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MARGIN = 1,
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FUN = min)
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} else {
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x <- as.integer(as.rsi(ab1))
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x <- ab1
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}
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.Call(`_AMR_rsi_calc_total`, x)
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}
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#' @rdname resistance
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#' @export
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rsi <- function(ab1,
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ab2 = NULL,
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interpretation = "IR",
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ab2 = NA,
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interpretation = 'IR',
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minimum = 30,
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as_percent = FALSE) {
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warning("'rsi' is deprecated. Use 'resistance' or 'susceptibility' instead.", call. = FALSE)
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if (interpretation %in% c('IR', 'RI')) {
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resistance(ab = ab1, include_I = TRUE, minimum = minimum, as_percent = as_percent)
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} else if (interpretation == 'R') {
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resistance(ab = ab1, include_I = FALSE, minimum = minimum, as_percent = as_percent)
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} else if (interpretation %in% c('IS', 'SI')) {
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susceptibility(ab1 = ab1, ab2 = ab2, include_I = TRUE, minimum = minimum, as_percent = as_percent)
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} else if (interpretation == 'S') {
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susceptibility(ab1 = ab1, ab2 = ab2, include_I = FALSE, minimum = minimum, as_percent = as_percent)
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as_percent = FALSE,
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info = FALSE,
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warning = TRUE) {
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ab1.name <- deparse(substitute(ab1))
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if (ab1.name %like% '.[$].') {
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ab1.name <- unlist(strsplit(ab1.name, "$", fixed = TRUE))
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ab1.name <- ab1.name[length(ab1.name)]
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}
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if (!ab1.name %like% '^[a-z]{3,4}$') {
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ab1.name <- 'rsi1'
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}
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if (length(ab1) == 1 & is.character(ab1)) {
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stop('`ab1` must be a vector of antibiotic interpretations.',
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'\n Try rsi(', ab1, ', ...) instead of rsi("', ab1, '", ...)', call. = FALSE)
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}
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ab2.name <- deparse(substitute(ab2))
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if (ab2.name %like% '.[$].') {
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ab2.name <- unlist(strsplit(ab2.name, "$", fixed = TRUE))
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ab2.name <- ab2.name[length(ab2.name)]
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}
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if (!ab2.name %like% '^[a-z]{3,4}$') {
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ab2.name <- 'rsi2'
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}
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if (length(ab2) == 1 & is.character(ab2)) {
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stop('`ab2` must be a vector of antibiotic interpretations.',
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'\n Try rsi(', ab2, ', ...) instead of rsi("', ab2, '", ...)', call. = FALSE)
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}
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interpretation <- paste(interpretation, collapse = "")
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ab1 <- as.rsi(ab1)
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ab2 <- as.rsi(ab2)
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tbl <- tibble(rsi1 = ab1, rsi2 = ab2)
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colnames(tbl) <- c(ab1.name, ab2.name)
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if (length(ab2) == 1) {
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r <- rsi_df(tbl = tbl,
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ab = ab1.name,
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interpretation = interpretation,
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minimum = minimum,
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as_percent = FALSE,
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info = info,
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warning = warning)
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} else {
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stop('invalid `interpretation`')
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if (length(ab1) != length(ab2)) {
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stop('`ab1` (n = ', length(ab1), ') and `ab2` (n = ', length(ab2), ') must be of same length.', call. = FALSE)
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}
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if (!interpretation %in% c('S', 'IS', 'SI')) {
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warning('`interpretation` not set to S or I/S, albeit analysing a combination therapy.', call. = FALSE)
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}
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r <- rsi_df(tbl = tbl,
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ab = c(ab1.name, ab2.name),
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interpretation = interpretation,
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minimum = minimum,
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as_percent = FALSE,
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info = info,
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warning = warning)
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}
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if (as_percent == TRUE) {
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percent(r, force_zero = TRUE)
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} else {
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r
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}
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}
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#' @importFrom dplyr %>% filter_at vars any_vars all_vars
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#' @noRd
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rsi_df <- function(tbl,
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ab,
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interpretation = 'IR',
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minimum = 30,
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as_percent = FALSE,
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info = TRUE,
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warning = TRUE) {
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# in case tbl$interpretation already exists:
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interpretations_to_check <- paste(interpretation, collapse = "")
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# validate:
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if (min(grepl('^[a-z]{3,4}$', ab)) == 0 &
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min(grepl('^rsi[1-2]$', ab)) == 0) {
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for (i in 1:length(ab)) {
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ab[i] <- paste0('rsi', i)
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}
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}
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if (!grepl('^(S|SI|IS|I|IR|RI|R){1}$', interpretations_to_check)) {
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stop('Invalid `interpretation`; must be "S", "SI", "I", "IR", or "R".')
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}
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if ('is_ic' %in% colnames(tbl)) {
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if (n_distinct(tbl$is_ic) > 1 & warning == TRUE) {
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warning('Dataset contains isolates from the Intensive Care. Exclude them from proper epidemiological analysis.')
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}
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}
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# transform when checking for different results
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if (interpretations_to_check %in% c('SI', 'IS')) {
|
||||
for (i in 1:length(ab)) {
|
||||
tbl[which(tbl[, ab[i]] == 'I'), ab[i]] <- 'S'
|
||||
}
|
||||
interpretations_to_check <- 'S'
|
||||
}
|
||||
if (interpretations_to_check %in% c('RI', 'IR')) {
|
||||
for (i in 1:length(ab)) {
|
||||
tbl[which(tbl[, ab[i]] == 'I'), ab[i]] <- 'R'
|
||||
}
|
||||
interpretations_to_check <- 'R'
|
||||
}
|
||||
|
||||
# get fraction
|
||||
if (length(ab) == 1) {
|
||||
numerator <- tbl %>%
|
||||
filter(pull(., ab[1]) == interpretations_to_check) %>%
|
||||
nrow()
|
||||
|
||||
denominator <- tbl %>%
|
||||
filter(pull(., ab[1]) %in% c("S", "I", "R")) %>%
|
||||
nrow()
|
||||
|
||||
} else if (length(ab) == 2) {
|
||||
if (interpretations_to_check != 'S') {
|
||||
warning('`interpretation` not set to S or I/S, albeit analysing a combination therapy.', call. = FALSE)
|
||||
}
|
||||
numerator <- tbl %>%
|
||||
filter_at(vars(ab[1], ab[2]),
|
||||
any_vars(. == interpretations_to_check)) %>%
|
||||
filter_at(vars(ab[1], ab[2]),
|
||||
all_vars(. %in% c("S", "R", "I"))) %>%
|
||||
nrow()
|
||||
|
||||
denominator <- tbl %>%
|
||||
filter_at(vars(ab[1], ab[2]),
|
||||
all_vars(. %in% c("S", "R", "I"))) %>%
|
||||
nrow()
|
||||
|
||||
} else if (length(ab) == 3) {
|
||||
if (interpretations_to_check != 'S') {
|
||||
warning('`interpretation` not set to S or I/S, albeit analysing a combination therapy.', call. = FALSE)
|
||||
}
|
||||
numerator <- tbl %>%
|
||||
filter_at(vars(ab[1], ab[2], ab[3]),
|
||||
any_vars(. == interpretations_to_check)) %>%
|
||||
filter_at(vars(ab[1], ab[2], ab[3]),
|
||||
all_vars(. %in% c("S", "R", "I"))) %>%
|
||||
nrow()
|
||||
|
||||
denominator <- tbl %>%
|
||||
filter_at(vars(ab[1], ab[2], ab[3]),
|
||||
all_vars(. %in% c("S", "R", "I"))) %>%
|
||||
nrow()
|
||||
|
||||
} else {
|
||||
stop('Maximum of 3 drugs allowed.')
|
||||
}
|
||||
|
||||
# build text part
|
||||
if (info == TRUE) {
|
||||
cat('n =', denominator)
|
||||
info.txt1 <- percent(denominator / nrow(tbl))
|
||||
if (denominator == 0) {
|
||||
info.txt1 <- 'none'
|
||||
}
|
||||
info.txt2 <- gsub(',', ' and',
|
||||
ab %>%
|
||||
abname(tolower = TRUE) %>%
|
||||
toString(), fixed = TRUE)
|
||||
info.txt2 <- gsub('rsi1 and rsi2', 'these two drugs', info.txt2, fixed = TRUE)
|
||||
info.txt2 <- gsub('rsi1', 'this drug', info.txt2, fixed = TRUE)
|
||||
cat(paste0(' (of ', nrow(tbl), ' in total; ', info.txt1, ' tested on ', info.txt2, ')\n'))
|
||||
}
|
||||
|
||||
# calculate and format
|
||||
y <- numerator / denominator
|
||||
if (as_percent == TRUE) {
|
||||
y <- percent(y, force_zero = TRUE)
|
||||
}
|
||||
|
||||
if (denominator < minimum) {
|
||||
if (warning == TRUE) {
|
||||
warning(paste0('TOO FEW ISOLATES OF ', toString(ab), ' (n = ', denominator, ', n < ', minimum, '); NO RESULT.'))
|
||||
}
|
||||
y <- NA
|
||||
}
|
||||
|
||||
# output
|
||||
y
|
||||
}
|
||||
|
||||
|
||||
#' Predict antimicrobial resistance
|
||||
#'
|
||||
#' Create a prediction model to predict antimicrobial resistance for the next years on statistical solid ground. Standard errors (SE) will be returned as columns \code{se_min} and \code{se_max}. See Examples for a real live example.
|
||||
|
||||
Reference in New Issue
Block a user