speed improvements

DESCRIPTION

@@ -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(

NEWS.md

@@ -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

R/classes.R

@@ -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("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')
-  cat('<NA> ', n_total - n, '\n')
+    tibble(MIC = x, y = 1) %>%
-  cat('\n')
+      group_by(MIC) %>%
-  tbl <- tibble(x = x, y = 1) %>% group_by(x) %>% summarise(y = sum(y))
+      summarise(n = sum(y)) %>%
-  cnt <- tbl %>% pull(y)
+      base::print.data.frame(row.names = FALSE)
-  names(cnt) <- tbl %>% pull(x)
+  }
-  print(cnt)
 }
 
 #' @exportMethod summary.mic

R/first_isolates.R

@@ -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)) {
@@ -532,6 +531,24 @@ key_antibiotics_equal <- function(x,
 
   result <- logical(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 <- dplyr::progress_estimated(length(x))
     }
@@ -575,23 +592,6 @@ key_antibiotics_equal <- function(x,
           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)
-
         } else {
           stop('`', type, '` is not a valid value for type, must be "points" or "keyantibiotics". See ?first_isolate.')
         }
@@ -602,3 +602,4 @@ key_antibiotics_equal <- function(x,
     }
     result
   }
+}

R/freq.R

@@ -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)

R/globals.R

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

R/resistance.R

@@ -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)),
+    if (!is.rsi(ab2)) {
-                              ab2 = as.integer(as.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)),
+    if (!is.rsi(ab2)) {
-                              ab2 = as.integer(as.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,
+                ab2 = NA,
-                interpretation = "IR",
+                interpretation = 'IR',
                 minimum = 30,
-                as_percent = FALSE) {
+                as_percent = FALSE,
-  warning("'rsi' is deprecated. Use 'resistance' or 'susceptibility' instead.", call. = FALSE)
+                info = FALSE,
-  if (interpretation %in% c('IR', 'RI')) {
+                warning = TRUE) {
-    resistance(ab = ab1, include_I = TRUE, minimum = minimum, as_percent = as_percent)
+  ab1.name <- deparse(substitute(ab1))
-  } else if (interpretation == 'R') {
+  if (ab1.name %like% '.[$].') {
-    resistance(ab = ab1, include_I = FALSE, minimum = minimum, as_percent = as_percent)
+    ab1.name <- unlist(strsplit(ab1.name, "$", fixed = TRUE))
-  } else  if (interpretation %in% c('IS', 'SI')) {
+    ab1.name <- ab1.name[length(ab1.name)]
-    susceptibility(ab1 = ab1, ab2 = ab2, include_I = TRUE, minimum = minimum, as_percent = as_percent)
+  }
-  } else if (interpretation == 'S') {
+  if (!ab1.name %like% '^[a-z]{3,4}$') {
-    susceptibility(ab1 = ab1, ab2 = ab2, include_I = FALSE, minimum = minimum, as_percent = as_percent)
+    ab1.name <- 'rsi1'
+  }
+  if (length(ab1) == 1 & is.character(ab1)) {
+    stop('`ab1` must be a vector of antibiotic interpretations.',
+         '\n  Try rsi(', ab1, ', ...) instead of rsi("', ab1, '", ...)', call. = FALSE)
+  }
+  ab2.name <- deparse(substitute(ab2))
+  if (ab2.name %like% '.[$].') {
+    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.

README.md

@@ -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?

man/resistance.Rd

@@ -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,
+rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
-  as_percent = FALSE)
+  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}

src/RcppExports.cpp

@@ -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

src/rsi_calc.cpp

@@ -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;
 
 // [[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) {
+int rsi_calc_total(DoubleVector x) {
   return count_if(x.begin(), x.end(), bind2nd(std::less_equal<double>(), 3));
 }

tests/testthat/test-first_isolates.R

@@ -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(

tests/testthat/test-resistance.R

@@ -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$amox), 0.5756, tolerance = 0.0001)
+  expect_equal(resistance(septic_patients$amox, include_I = TRUE), 0.5756, tolerance = 0.0001)
-  expect_equal(susceptibility(septic_patients$amox), 1 - 0.5756, tolerance = 0.0001)
+  expect_equal(susceptibility(septic_patients$amox, include_I = FALSE), 1 - 0.5756, tolerance = 0.0001)
 
   # pita+genta susceptibility around 98.09%
   expect_equal(susceptibility(septic_patients$pita,