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speed improvements

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dr. M.S. (Matthijs) Berends 2018-07-15 22:56:41 +02:00
parent 8240959f38
commit 6eaf33baf3
13 changed files with 359 additions and 110 deletions
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2
DESCRIPTION
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@ -1,6 +1,6 @@
Package: AMR
Version: 0.2.0.9011
Date: 2018-07-13
Date: 2018-07-15
Title: Antimicrobial Resistance Analysis
Authors@R: c(
person(

4
NEWS.md
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@ -1,6 +1,6 @@
# 0.2.0.90xx (development version)
#### New
* **BREAKING**: `rsi_df` was removed in favour of new functions `resistance` and `susceptibility`. Now, all functions used to calculate resistance (`resistance` and `susceptibility`) or count isolates (`n_rsi`) use **hybrid evaluation**. This means calculations are not done in R directly but rather in C++ using the `Rcpp` package, making them 60 to 65 times faster. The function `rsi` still works, but is deprecated.
* **BREAKING**: `rsi_df` was removed in favour of new functions `resistance` and `susceptibility`. Now, all functions used to calculate resistance (`resistance` and `susceptibility`) or count isolates (`n_rsi`) use **hybrid evaluation**. This means calculations are not done in R directly but rather in C++ using the `Rcpp` package, making them 25 to 30 times faster. The function `rsi` still works, but is deprecated.
* Support for Addins menu in RStudio to quickly insert `%in%` or `%like%` (and give them keyboard shortcuts), or to view the datasets that come with this package
* For convience, new descriptive statistical functions `kurtosis` and `skewness` that are lacking in base R - they are generic functions and have support for vectors, data.frames and matrices
* Function `g.test` as added to perform the Χ<sup>2</sup> distributed [*G*-test](https://en.wikipedia.org/wiki/G-test), which use is the same as `chisq.test`
@ -23,6 +23,8 @@ ratio(c(772, 1611, 737), ratio = "1:2:1")
#### Changed
* Pretty printing for tibbles removed as it is not really the scope of this package
* Improved speed of key antibiotics comparison for determining first isolates
* Printing of class `mic` now shows all MIC values
* `%like%` now supports multiple patterns
* Frequency tables are now actual `data.frame`s with altered console printing to make it look like a frequency table. Because of this, the parameter `toConsole` is not longer needed.
* Small translational improvements to the `septic_patients` dataset

17
R/classes.R
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@ -360,14 +360,15 @@ print.mic <- function(x, ...) {
n_total <- x %>% length()
x <- x[!is.na(x)]
n <- x %>% length()
cat("Class 'mic': ", n, " isolates\n", sep = '')
cat('\n')
cat('<NA> ', n_total - n, '\n')
cat('\n')
tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
cnt <- tbl %>% pull(y)
names(cnt) <- tbl %>% pull(x)
print(cnt)
cat("Class 'mic'\n")
cat(n, " results (missing: ", n_total - n, ' = ', percent((n_total - n) / n_total, force_zero = TRUE), ')\n', sep = "")
if (n > 0) {
cat('\n')
tibble(MIC = x, y = 1) %>%
group_by(MIC) %>%
summarise(n = sum(y)) %>%
base::print.data.frame(row.names = FALSE)
}
}
#' @exportMethod summary.mic

115
R/first_isolates.R
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@ -314,11 +314,11 @@ first_isolate <- function(tbl,
if (col_keyantibiotics != '') {
if (info == TRUE) {
if (type == 'keyantibiotics') {
cat('Comparing key antibiotics for first weighted isolates (')
cat('Key antibiotics for first weighted isolates will be compared (')
if (ignore_I == FALSE) {
cat('NOT ')
}
cat('ignoring I)...\n')
cat('ignoring I).')
}
if (type == 'points') {
cat(paste0('Comparing antibiotics for first weighted isolates (using points threshold of '
@ -523,7 +523,6 @@ key_antibiotics_equal <- function(x,
points_threshold = 2,
info = FALSE) {
# x is active row, y is lag
type <- type[1]
if (length(x) != length(y)) {
@ -532,73 +531,75 @@ key_antibiotics_equal <- function(x,
result <- logical(length(x))
if (info == TRUE) {
p <- dplyr::progress_estimated(length(x))
}
for (i in 1:length(x)) {
if (type == "keyantibiotics") {
if (ignore_I == TRUE) {
# evaluation using regular expression will treat '?' as any character
# so I is actually ignored then
x <- gsub('I', '?', x, ignore.case = TRUE)
y <- gsub('I', '?', y, ignore.case = TRUE)
}
for (i in 1:length(x)) {
result[i] <- grepl(x = x[i],
pattern = y[i],
ignore.case = TRUE) |
grepl(x = y[i],
pattern = x[i],
ignore.case = TRUE)
}
return(result)
} else {
if (info == TRUE) {
p$tick()$print()
p <- dplyr::progress_estimated(length(x))
}
if (is.na(x[i])) {
x[i] <- ''
}
if (is.na(y[i])) {
y[i] <- ''
}
for (i in 1:length(x)) {
if (nchar(x[i]) != nchar(y[i])) {
if (info == TRUE) {
p$tick()$print()
}
result[i] <- FALSE
if (is.na(x[i])) {
x[i] <- ''
}
if (is.na(y[i])) {
y[i] <- ''
}
} else if (x[i] == '' & y[i] == '') {
if (nchar(x[i]) != nchar(y[i])) {
result[i] <- TRUE
result[i] <- FALSE
} else {
} else if (x[i] == '' & y[i] == '') {
x2 <- strsplit(x[i], "")[[1]]
y2 <- strsplit(y[i], "")[[1]]
if (type == 'points') {
# count points for every single character:
# - no change is 0 points
# - I <-> S|R is 0.5 point
# - S|R <-> R|S is 1 point
# use the levels of as.rsi (S = 1, I = 2, R = 3)
suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
result[i] <- ((points / 2) >= points_threshold)
} else if (type == 'keyantibiotics') {
# check if key antibiotics are exactly the same
# also possible to ignore I, so only S <-> R and S <-> R are counted
if (ignore_I == TRUE) {
valid_chars <- c('S', 's', 'R', 'r')
} else {
valid_chars <- c('S', 's', 'I', 'i', 'R', 'r')
}
# remove invalid values (like "-", NA) on both locations
x2[which(!x2 %in% valid_chars)] <- '?'
x2[which(!y2 %in% valid_chars)] <- '?'
y2[which(!x2 %in% valid_chars)] <- '?'
y2[which(!y2 %in% valid_chars)] <- '?'
result[i] <- all(x2 == y2)
result[i] <- TRUE
} else {
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
x2 <- strsplit(x[i], "")[[1]]
y2 <- strsplit(y[i], "")[[1]]
if (type == 'points') {
# count points for every single character:
# - no change is 0 points
# - I <-> S|R is 0.5 point
# - S|R <-> R|S is 1 point
# use the levels of as.rsi (S = 1, I = 2, R = 3)
suppressWarnings(x2 <- x2 %>% as.rsi() %>% as.double())
suppressWarnings(y2 <- y2 %>% as.rsi() %>% as.double())
points <- (x2 - y2) %>% abs() %>% sum(na.rm = TRUE)
result[i] <- ((points / 2) >= points_threshold)
} else {
stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
}
}
}
if (info == TRUE) {
cat('\n')
}
result
}
if (info == TRUE) {
cat('\n')
}
result
}

4
R/freq.R
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@ -151,7 +151,9 @@ frequency_tbl <- function(x,
dots <- base::eval(base::substitute(base::alist(...)))
ndots <- length(dots)
if (ndots > 0 & ndots < 10) {
if (NROW(x) == 0) {
x <- NA
} else if (ndots > 0 & ndots < 10) {
cols <- as.character(dots)
if (!all(cols %in% colnames(x))) {
stop("one or more columns not found: `", paste(cols, collapse = "`, `"), '`', call. = FALSE)

1
R/globals.R
View File

@ -41,6 +41,7 @@ globalVariables(c('abname',
'labs',
'median',
'mic',
'MIC',
'microorganisms',
'mocode',
'molis',

258
R/resistance.R
View File

@ -24,9 +24,11 @@
#' @param minimum minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}.
#' @param as_percent logical to indicate whether the output must be returned as percent (text), will else be a double
#' @param interpretation antimicrobial interpretation
#' @param info \emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}
#' @param warning \emph{DEPRECATED} show a warning when the available amount of isolates is below \code{minimum}
#' @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.
#'
#' 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.
#' 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.
#' \if{html}{
#' \cr
#' To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
@ -90,6 +92,29 @@
#' genus == "Helicobacter") %>%
#' summarise(p = susceptibility(amox, metr), # amoxicillin with metronidazole
#' n = n_rsi(amox, metr))
#'
#'
#' # How fast is this hybrid evaluation in C++ compared to R?
#' # In other words: how is the speed improvement of the new `resistance` compared to old `rsi`?
#'
#' library(microbenchmark)
#' df <- septic_patients %>% group_by(hospital_id, bactid) # 317 groups with sizes 1 to 167
#'
#' microbenchmark(old_IR = df %>% summarise(p = rsi(amox, minimum = 0, interpretation = "IR")),
#' new_IR = df %>% summarise(p = resistance(amox, minimum = 0)),
#' old_S = df %>% summarise(p = rsi(amox, minimum = 0, interpretation = "S")),
#' new_S = df %>% summarise(p = susceptibility(amox, minimum = 0)),
#' times = 5,
#' unit = "s")
#'
#' # Unit: seconds
#' # expr min lq mean median uq max neval
#' # old_IR 1.95600230 1.96096857 1.97981537 1.96823318 2.00645711 2.00741568 5
#' # new_IR 0.06872808 0.06984932 0.07162866 0.06987306 0.07050094 0.07919192 5
#' # old_S 1.68893579 1.69024888 1.72461867 1.69785934 1.70428796 1.84176137 5
#' # new_S 0.06737037 0.06838167 0.07431906 0.07745364 0.07827224 0.08011738 5
#'
#' # The old function took roughly 2 seconds, the new ones take 0.07 seconds.
#' }
resistance <- function(ab,
include_I = TRUE,
@ -109,7 +134,11 @@ resistance <- function(ab,
stop('`as_percent` must be logical', call. = FALSE)
}
x <- as.integer(as.rsi(ab))
if (!is.rsi(ab)) {
x <- as.rsi(ab)
} else {
x <- ab
}
total <- .Call(`_AMR_rsi_calc_total`, x)
if (total < minimum) {
return(NA)
@ -144,16 +173,22 @@ susceptibility <- function(ab1,
stop('`as_percent` must be logical', call. = FALSE)
}
if (!is.rsi(ab1)) {
ab1 <- as.rsi(ab1)
}
if (!is.null(ab2)) {
if (NCOL(ab2) > 1) {
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
}
x <- apply(X = data.frame(ab1 = as.integer(as.rsi(ab1)),
ab2 = as.integer(as.rsi(ab2))),
if (!is.rsi(ab2)) {
ab2 <- as.rsi(ab2)
}
x <- apply(X = data.frame(ab1 = as.integer(ab1),
ab2 = as.integer(ab2)),
MARGIN = 1,
FUN = min)
} else {
x <- as.integer(as.rsi(ab1))
x <- ab1
}
total <- .Call(`_AMR_rsi_calc_total`, x)
if (total < minimum) {
@ -174,42 +209,221 @@ n_rsi <- function(ab1, ab2 = NULL) {
if (NCOL(ab1) > 1) {
stop('`ab1` must be a vector of antimicrobial interpretations', call. = FALSE)
}
if (!is.rsi(ab1)) {
ab1 <- as.rsi(ab1)
}
if (!is.null(ab2)) {
if (NCOL(ab2) > 1) {
stop('`ab2` must be a vector of antimicrobial interpretations', call. = FALSE)
}
x <- apply(X = data.frame(ab1 = as.integer(as.rsi(ab1)),
ab2 = as.integer(as.rsi(ab2))),
if (!is.rsi(ab2)) {
ab2 <- as.rsi(ab2)
}
x <- apply(X = data.frame(ab1 = as.integer(ab1),
ab2 = as.integer(ab2)),
MARGIN = 1,
FUN = min)
} else {
x <- as.integer(as.rsi(ab1))
x <- ab1
}
.Call(`_AMR_rsi_calc_total`, x)
}
#' @rdname resistance
#' @export
rsi <- function(ab1,
ab2 = NULL,
interpretation = "IR",
ab2 = NA,
interpretation = 'IR',
minimum = 30,
as_percent = FALSE) {
warning("'rsi' is deprecated. Use 'resistance' or 'susceptibility' instead.", call. = FALSE)
if (interpretation %in% c('IR', 'RI')) {
resistance(ab = ab1, include_I = TRUE, minimum = minimum, as_percent = as_percent)
} else if (interpretation == 'R') {
resistance(ab = ab1, include_I = FALSE, minimum = minimum, as_percent = as_percent)
} else if (interpretation %in% c('IS', 'SI')) {
susceptibility(ab1 = ab1, ab2 = ab2, include_I = TRUE, minimum = minimum, as_percent = as_percent)
} else if (interpretation == 'S') {
susceptibility(ab1 = ab1, ab2 = ab2, include_I = FALSE, minimum = minimum, as_percent = as_percent)
as_percent = FALSE,
info = FALSE,
warning = TRUE) {
ab1.name <- deparse(substitute(ab1))
if (ab1.name %like% '.[$].') {
ab1.name <- unlist(strsplit(ab1.name, "$", fixed = TRUE))
ab1.name <- ab1.name[length(ab1.name)]
}
if (!ab1.name %like% '^[a-z]{3,4}$') {
ab1.name <- 'rsi1'
}
if (length(ab1) == 1 & is.character(ab1)) {
stop('`ab1` must be a vector of antibiotic interpretations.',
'\n Try rsi(', ab1, ', ...) instead of rsi("', ab1, '", ...)', call. = FALSE)
}
ab2.name <- deparse(substitute(ab2))
if (ab2.name %like% '.[$].') {
ab2.name <- unlist(strsplit(ab2.name, "$", fixed = TRUE))
ab2.name <- ab2.name[length(ab2.name)]
}
if (!ab2.name %like% '^[a-z]{3,4}$') {
ab2.name <- 'rsi2'
}
if (length(ab2) == 1 & is.character(ab2)) {
stop('`ab2` must be a vector of antibiotic interpretations.',
'\n Try rsi(', ab2, ', ...) instead of rsi("', ab2, '", ...)', call. = FALSE)
}
interpretation <- paste(interpretation, collapse = "")
ab1 <- as.rsi(ab1)
ab2 <- as.rsi(ab2)
tbl <- tibble(rsi1 = ab1, rsi2 = ab2)
colnames(tbl) <- c(ab1.name, ab2.name)
if (length(ab2) == 1) {
r <- rsi_df(tbl = tbl,
ab = ab1.name,
interpretation = interpretation,
minimum = minimum,
as_percent = FALSE,
info = info,
warning = warning)
} else {
stop('invalid `interpretation`')
if (length(ab1) != length(ab2)) {
stop('`ab1` (n = ', length(ab1), ') and `ab2` (n = ', length(ab2), ') must be of same length.', call. = FALSE)
}
if (!interpretation %in% c('S', 'IS', 'SI')) {
warning('`interpretation` not set to S or I/S, albeit analysing a combination therapy.', call. = FALSE)
}
r <- rsi_df(tbl = tbl,
ab = c(ab1.name, ab2.name),
interpretation = interpretation,
minimum = minimum,
as_percent = FALSE,
info = info,
warning = warning)
}
if (as_percent == TRUE) {
percent(r, force_zero = TRUE)
} else {
r
}
}
#' @importFrom dplyr %>% filter_at vars any_vars all_vars
#' @noRd
rsi_df <- function(tbl,
ab,
interpretation = 'IR',
minimum = 30,
as_percent = FALSE,
info = TRUE,
warning = TRUE) {
# in case tbl$interpretation already exists:
interpretations_to_check <- paste(interpretation, collapse = "")
# validate:
if (min(grepl('^[a-z]{3,4}$', ab)) == 0 &
min(grepl('^rsi[1-2]$', ab)) == 0) {
for (i in 1:length(ab)) {
ab[i] <- paste0('rsi', i)
}
}
if (!grepl('^(S|SI|IS|I|IR|RI|R){1}$', interpretations_to_check)) {
stop('Invalid `interpretation`; must be "S", "SI", "I", "IR", or "R".')
}
if ('is_ic' %in% colnames(tbl)) {
if (n_distinct(tbl$is_ic) > 1 & warning == TRUE) {
warning('Dataset contains isolates from the Intensive Care. Exclude them from proper epidemiological analysis.')
}
}
# transform when checking for different results
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.

4
README.md
View File

@ -25,7 +25,7 @@ This R package was created for academic research by PhD students of the Faculty
This R package contains functions to make **microbiological, epidemiological data analysis easier**. It allows the use of some new classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).
With `AMR` you can:
* Calculate the resistance (and even co-resistance) of microbial isolates with the `resistance` and `susceptibility` functions, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`). Our functions use expressions that are not evaluated by R, but by alternative C++ code that is dramatically faster and uses less memory. This is called *hybrid evaluation*.
* Calculate the resistance (and even co-resistance) of microbial isolates with the `resistance` and `susceptibility` functions, that can also be used with the `dplyr` package (e.g. in conjunction with `summarise`)
* Predict antimicrobial resistance for the nextcoming years with the `rsi_predict` function
* Apply [EUCAST rules to isolates](http://www.eucast.org/expert_rules_and_intrinsic_resistance/) with the `EUCAST_rules` function
* Identify first isolates of every patient [using guidelines from the CLSI](https://clsi.org/standards/products/microbiology/documents/m39/) (Clinical and Laboratory Standards Institute) with the `first_isolate` function
@ -41,6 +41,8 @@ And it contains:
With the `MDRO` function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues/new](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).
The functions to calculate microbial resistance use expressions that are not evaluated by R itself, but by alternative C++ code that is 25 to 30 times faster and uses less memory. This is called *hybrid evaluation*.
#### Read all changes and new functions in [NEWS.md](NEWS.md).
## How to get it?

33
man/resistance.Rd
View File

@ -14,8 +14,8 @@ susceptibility(ab1, ab2 = NULL, include_I = FALSE, minimum = 30,
n_rsi(ab1, ab2 = NULL)
rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
as_percent = FALSE)
rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
as_percent = FALSE, info = FALSE, warning = TRUE)
}
\arguments{
\item{ab, ab1, ab2}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
@ -27,6 +27,10 @@ rsi(ab1, ab2 = NULL, interpretation = "IR", minimum = 30,
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{interpretation}{antimicrobial interpretation}
\item{info}{\emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}}
\item{warning}{\emph{DEPRECATED} show a warning when the available amount of isolates is below \code{minimum}}
}
\value{
Double or, when \code{as_percent = TRUE}, a character.
@ -37,7 +41,7 @@ These functions can be used to calculate the (co-)resistance of microbial isolat
\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.
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.
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.
\if{html}{
\cr
To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
@ -98,6 +102,29 @@ my_table \%>\%
genus == "Helicobacter") \%>\%
summarise(p = susceptibility(amox, metr), # amoxicillin with metronidazole
n = n_rsi(amox, metr))
# How fast is this hybrid evaluation in C++ compared to R?
# In other words: how is the speed improvement of the new `resistance` compared to old `rsi`?
library(microbenchmark)
df <- septic_patients \%>\% group_by(hospital_id, bactid) # 317 groups with sizes 1 to 167
microbenchmark(old_IR = df \%>\% summarise(p = rsi(amox, minimum = 0, interpretation = "IR")),
new_IR = df \%>\% summarise(p = resistance(amox, minimum = 0)),
old_S = df \%>\% summarise(p = rsi(amox, minimum = 0, interpretation = "S")),
new_S = df \%>\% summarise(p = susceptibility(amox, minimum = 0)),
times = 5,
unit = "s")
# Unit: seconds
# expr min lq mean median uq max neval
# old_IR 1.95600230 1.96096857 1.97981537 1.96823318 2.00645711 2.00741568 5
# new_IR 0.06872808 0.06984932 0.07162866 0.06987306 0.07050094 0.07919192 5
# old_S 1.68893579 1.69024888 1.72461867 1.69785934 1.70428796 1.84176137 5
# new_S 0.06737037 0.06838167 0.07431906 0.07745364 0.07827224 0.08011738 5
# The old function took roughly 2 seconds, the new ones take 0.07 seconds.
}
}
\keyword{antibiotics}

12
src/RcppExports.cpp
View File

@ -6,36 +6,36 @@
using namespace Rcpp;
// rsi_calc_S
int rsi_calc_S(std::vector<double> x, bool include_I);
int rsi_calc_S(DoubleVector x, bool include_I);
RcppExport SEXP _AMR_rsi_calc_S(SEXP xSEXP, SEXP include_ISEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Rcpp::traits::input_parameter< std::vector<double> >::type x(xSEXP);
Rcpp::traits::input_parameter< DoubleVector >::type x(xSEXP);
Rcpp::traits::input_parameter< bool >::type include_I(include_ISEXP);
rcpp_result_gen = Rcpp::wrap(rsi_calc_S(x, include_I));
return rcpp_result_gen;
END_RCPP
}
// rsi_calc_R
int rsi_calc_R(std::vector<double> x, bool include_I);
int rsi_calc_R(DoubleVector x, bool include_I);
RcppExport SEXP _AMR_rsi_calc_R(SEXP xSEXP, SEXP include_ISEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Rcpp::traits::input_parameter< std::vector<double> >::type x(xSEXP);
Rcpp::traits::input_parameter< DoubleVector >::type x(xSEXP);
Rcpp::traits::input_parameter< bool >::type include_I(include_ISEXP);
rcpp_result_gen = Rcpp::wrap(rsi_calc_R(x, include_I));
return rcpp_result_gen;
END_RCPP
}
// rsi_calc_total
int rsi_calc_total(std::vector<double> x);
int rsi_calc_total(DoubleVector x);
RcppExport SEXP _AMR_rsi_calc_total(SEXP xSEXP) {
BEGIN_RCPP
Rcpp::RObject rcpp_result_gen;
Rcpp::RNGScope rcpp_rngScope_gen;
Rcpp::traits::input_parameter< std::vector<double> >::type x(xSEXP);
Rcpp::traits::input_parameter< DoubleVector >::type x(xSEXP);
rcpp_result_gen = Rcpp::wrap(rsi_calc_total(x));
return rcpp_result_gen;
END_RCPP

11
src/rsi_calc.cpp
View File

@ -1,12 +1,11 @@
#include <Rcpp.h>
#include <vector> // for std::vector
#include <functional> // for std::less, etc
#include <algorithm> // for count_if
using namespace Rcpp ;
using namespace Rcpp;
// [[Rcpp::export]]
int rsi_calc_S(std::vector<double> x, bool include_I) {
int rsi_calc_S(DoubleVector x, bool include_I) {
if (include_I == TRUE) {
return count_if(x.begin(), x.end(), bind2nd(std::less_equal<double>(), 2));
} else {
@ -15,7 +14,7 @@ int rsi_calc_S(std::vector<double> x, bool include_I) {
}
// [[Rcpp::export]]
int rsi_calc_R(std::vector<double> x, bool include_I) {
int rsi_calc_R(DoubleVector x, bool include_I) {
if (include_I == TRUE) {
return count_if(x.begin(), x.end(), bind2nd(std::greater_equal<double>(), 2));
} else {
@ -24,6 +23,6 @@ int rsi_calc_R(std::vector<double> x, bool include_I) {
}
// [[Rcpp::export]]
int rsi_calc_total(std::vector<double> x) {
return count_if(x.begin(), x.end(), bind2nd(std::less_equal<double>(), 3));
int rsi_calc_total(DoubleVector x) {
return count_if(x.begin(), x.end(), bind2nd(std::less_equal<double>(), 3));
}

4
tests/testthat/test-first_isolates.R
View File

@ -19,7 +19,7 @@ test_that("first isolates work", {
na.rm = TRUE),
1959)
# septic_patients contains 1961 out of 2000 first *weighted* isolates
# septic_patients contains 1963 out of 2000 first *weighted* isolates
expect_equal(
suppressWarnings(
sum(
@ -31,7 +31,7 @@ test_that("first isolates work", {
type = "keyantibiotics",
info = TRUE),
na.rm = TRUE)),
1961)
1963)
# and 1998 when using points
expect_equal(
suppressWarnings(

4
tests/testthat/test-resistance.R
View File

@ -2,8 +2,8 @@ context("resistance.R")
test_that("resistance works", {
# amox resistance in `septic_patients` should be around 57.56%
expect_equal(resistance(septic_patients$amox), 0.5756, tolerance = 0.0001)
expect_equal(susceptibility(septic_patients$amox), 1 - 0.5756, tolerance = 0.0001)
expect_equal(resistance(septic_patients$amox, include_I = TRUE), 0.5756, tolerance = 0.0001)
expect_equal(susceptibility(septic_patients$amox, include_I = FALSE), 1 - 0.5756, tolerance = 0.0001)
# pita+genta susceptibility around 98.09%
expect_equal(susceptibility(septic_patients$pita,