(v1.5.0.9032) All group generics for MICs

R/aa_helper_functions.R

@@ -961,15 +961,8 @@ formatted_filesize <- function(...) {
 }
 
 create_pillar_column <- function(x, ...) {
-  new_pillar_shaft_simple <- import_fn("new_pillar_shaft_simple", "pillar", error_on_fail = FALSE)
-  if (!is.null(new_pillar_shaft_simple)) {
-    new_pillar_shaft_simple(x, ...)
-  } else {
-    # does not exist in package 'pillar' anymore
-    structure(list(x),
-              class = "pillar_shaft_simple",
-              ...)
-  }
+  new_pillar_shaft_simple <- import_fn("new_pillar_shaft_simple", "pillar")
+  new_pillar_shaft_simple(x, ...)
 }
 
 # copied from vctrs::s3_register by their permission:

R/ab_from_text.R

@@ -26,7 +26,7 @@
 #' Retrieve Antimicrobial Drug Names and Doses from Clinical Text
 #' 
 #' Use this function on e.g. clinical texts from health care records. It returns a [list] with all antimicrobial drugs, doses and forms of administration found in the texts.
-#' @inheritSection lifecycle Maturing Lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param text text to analyse
 #' @param type type of property to search for, either `"drug"`, `"dose"` or `"administration"`, see *Examples*
 #' @param collapse character to pass on to `paste(, collapse = ...)` to only return one character per element of `text`, see *Examples*

R/ggplot_pca.R

@@ -26,7 +26,7 @@
 #' PCA Biplot with `ggplot2`
 #'
 #' Produces a `ggplot2` variant of a so-called [biplot](https://en.wikipedia.org/wiki/Biplot) for PCA (principal component analysis), but is more flexible and more appealing than the base \R [biplot()] function.
-#' @inheritSection lifecycle Maturing Lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param x an object returned by [pca()], [prcomp()] or [princomp()]
 #' @inheritParams stats::biplot.prcomp
 #' @param labels an optional vector of labels for the observations. If set, the labels will be placed below their respective points. When using the [pca()] function as input for `x`, this will be determined automatically based on the attribute `non_numeric_cols`, see [pca()].

R/ggplot_rsi.R

@@ -26,7 +26,7 @@
 #' AMR Plots with `ggplot2`
 #'
 #' Use these functions to create bar plots for AMR data analysis. All functions rely on [ggplot2][ggplot2::ggplot()] functions.
-#' @inheritSection lifecycle Maturing Lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param data a [data.frame] with column(s) of class [`rsi`] (see [as.rsi()])
 #' @param position position adjustment of bars, either `"fill"`, `"stack"` or `"dodge"`
 #' @param x variable to show on x axis, either `"antibiotic"` (default) or `"interpretation"` or a grouping variable

R/mic.R

@@ -25,13 +25,51 @@
 
 #' Transform Input to Minimum Inhibitory Concentrations (MIC)
 #'
-#' This transforms a vector to a new class [`mic`], which is an ordered [factor] with valid minimum inhibitory concentrations (MIC) as levels. Invalid MIC values will be translated as `NA` with a warning.
+#' This ransforms vectors to a new class [`mic`], which treats the input as decimal numbers, while maintaining operators (such as ">=") and only allowing valid MIC values known to the field of (medical) microbiology.
 #' @inheritSection lifecycle Stable Lifecycle
 #' @rdname as.mic
-#' @param x vector
+#' @param x character or numeric vector
 #' @param na.rm a logical indicating whether missing values should be removed
 #' @details To interpret MIC values as RSI values, use [as.rsi()] on MIC values. It supports guidelines from EUCAST and CLSI.
-#' @return Ordered [factor] with additional class [`mic`]
+#' 
+#' This class for MIC values is a quite a special data type: formally it is an ordered factor with valid MIC values as factor levels (to make sure only valid MIC values are retained), but for any mathematical operation it acts as decimal numbers:
+#' 
+#' ```
+#' x <- random_mic(10)
+#' x
+#' #> Class <mic>
+#' #>  [1] 16     1      8      8      64     >=128  0.0625 32     32     16
+#' 
+#' is.factor(x)
+#' #> [1] TRUE
+#' 
+#' x[1] * 2
+#' #> [1] 32
+#' 
+#' median(x)
+#' #> [1] 26
+#' ```
+#' 
+#' This makes it possible to maintain operators that often come with MIC values, such ">=" and "<=", even when filtering using numeric values in data analysis, e.g.:
+#' 
+#' ```
+#' x[x > 4]
+#' #> Class <mic>
+#' #> [1] 16    8     8     64    >=128 32    32    16
+#' 
+#' df <- data.frame(x, hospital = "A")
+#' subset(df, x > 4) # or with dplyr: df %>% filter(x > 4)
+#' #>        x hospital
+#' #> 1     16        A
+#' #> 5     64        A
+#' #> 6  >=128        A
+#' #> 8     32        A
+#' #> 9     32        A
+#' #> 10    16        A
+#' ```
+#' 
+#' The following [generic functions][groupGeneric()] are implemented for the MIC class: `!`, `!=`, `%%`, `%/%`, `&`, `*`, `+`, `-`, `/`, `<`, `<=`, `==`, `>`, `>=`, `^`, `|`, [abs()], [acos()], [acosh()], [all()], [any()], [asin()], [asinh()], [atan()], [atanh()], [ceiling()], [cos()], [cosh()], [cospi()], [cummax()], [cummin()], [cumprod()], [cumsum()], [digamma()], [exp()], [expm1()], [floor()], [gamma()], [lgamma()], [log()], [log10()], [log1p()], [log2()], [max()], [mean()], [median()], [min()], [prod()], [quantile()], [range()], [round()], [sign()], [signif()], [sin()], [sinh()], [sinpi()], [sqrt()], [sum()], [tan()], [tanh()], [tanpi()], [trigamma()] and [trunc()].
+#' @return Ordered [factor] with additional class [`mic`], that in mathematical operations acts as decimal numbers. Bare in mind that the outcome of any mathematical operation on MICs will return a numeric value.
 #' @aliases mic
 #' @export
 #' @seealso [as.rsi()]
@@ -197,6 +235,7 @@ pillar_shaft.mic <- function(x, ...) {
   out <- pasted
   out[is.na(x)] <- font_na(NA)
   out <- gsub("(<|=|>)", font_silver("\\1"), out)
+  out <- gsub("([.]?0+)$", font_white("\\1"), out)
   create_pillar_column(out, align = "right", width = max(nchar(pasted)))
 }
 
@@ -211,22 +250,17 @@ type_sum.mic <- function(x, ...) {
 print.mic <- function(x, ...) {
   cat("Class <mic>\n")
   print(as.character(x), quote = FALSE)
+  att <- attributes(x)
+  if ("na.action" %in% names(att)) {
+    cat(font_silver(paste0("(NA ", class(att$na.action), ": ", paste0(att$na.action, collapse = ", "), ")\n")))
+  }
 }
 
 #' @method summary mic
 #' @export
 #' @noRd
 summary.mic <- function(object, ...) {
-  x <- object
-  n_total <- length(x)
-  x <- x[!is.na(x)]
-  n <- length(x)
-  value <- c("Class" = "mic",
-             "<NA>" = n_total - n,
-             "Min." = as.character(sort(x)[1]),
-             "Max." = as.character(sort(x)[n]))
-  class(value) <- c("summaryDefault", "table")
-  value
+  summary(as.double(object), ...)
 }
 
 #' @method [ mic
@@ -281,85 +315,52 @@ unique.mic <- function(x, incomparables = FALSE, ...) {
   y
 }
 
-#' @method range mic
+#' @method sort mic
 #' @export
 #' @noRd
-range.mic <- function(..., na.rm = FALSE) {
-  rng <- sort(c(...))
-  if (na.rm == TRUE) {
-    rng <- rng[!is.na(rng)]
+sort.mic <- function(x, decreasing = FALSE, ...) {
+  if (decreasing == TRUE) {
+    ord <- order(-as.double(x))
+  } else {
+    ord <- order(as.double(x))
   }
-  out <- c(as.character(rng[1]), as.character(rng[length(rng)]))
-  as.double(as.mic(out))
+  x[ord]
 }
 
-#' @method min mic
+#' @method hist mic
 #' @export
 #' @noRd
-min.mic <- function(..., na.rm = FALSE) {
-  rng <- sort(c(...))
-  if (na.rm == TRUE) {
-    rng <- rng[!is.na(rng)]
-  }
-  as.double(as.mic(as.character(rng[1])))
+hist.mic <- function(x, ...) {
+  warning_("Use `plot()` or `ggplot()` for plotting MIC values", call = FALSE)
+  hist(log2(x))
 }
 
-#' @method max mic
-#' @export
-#' @noRd
-max.mic <- function(..., na.rm = FALSE) {
-  rng <- sort(c(...))
-  if (na.rm == TRUE) {
-    rng <- rng[!is.na(rng)]
-  }
-  as.double(as.mic(as.character(rng[length(rng)])))
+# will be exported using s3_register() in R/zzz.R
+get_skimmers.mic <- function(column) {
+  skimr::sfl(
+    skim_type = "mic",
+    min = ~min(., na.rm = TRUE),
+    max = ~max(., na.rm = TRUE),
+    median = ~median(., na.rm = TRUE),
+    n_unique = ~pm_n_distinct(., na.rm = TRUE),
+    hist_log2 = ~skimr::inline_hist(log2(stats::na.omit(.)))
+  )
 }
 
-#' @method sum mic
-#' @export
-#' @noRd
-sum.mic <- function(..., na.rm = FALSE) {
-  rng <- sort(c(...))
-  if (na.rm == TRUE) {
-    rng <- rng[!is.na(rng)]
-  }
-  sum(as.double(rng))
-}
-
-#' @method all mic
-#' @export
-#' @noRd
-all.mic <- function(..., na.rm = FALSE) {
-  rng <- sort(c(...))
-  if (na.rm == TRUE) {
-    rng <- rng[!is.na(rng)]
-  }
-  all(as.double(rng))
-}
-
-#' @method any mic
-#' @export
-#' @noRd
-any.mic <- function(..., na.rm = FALSE) {
-  rng <- sort(c(...))
-  if (na.rm == TRUE) {
-    rng <- rng[!is.na(rng)]
-  }
-  any(as.double(rng))
-}
+# Miscellaneous mathematical functions ------------------------------------
 
 #' @method mean mic
 #' @export
 #' @noRd
-mean.mic <- function(x, na.rm = FALSE, ...) {
-  mean(as.double(x), na.rm = na.rm, ...)
+mean.mic <- function(x, trim = 0, na.rm = FALSE, ...) {
+  mean(as.double(x), trim = trim, na.rm = na.rm, ...)
 }
 
 #' @method median mic
 #' @export
 #' @noRd
 median.mic <- function(x, na.rm = FALSE, ...) {
-  median(as.double(x), na.rm = na.rm, ...)
+  stats::median(as.double(x), na.rm = na.rm, ...)
 }
 
 #' @method quantile mic
@@ -367,22 +368,236 @@ median.mic <- function(x, na.rm = FALSE, ...) {
 #' @noRd
 quantile.mic <- function(x, probs = seq(0, 1, 0.25), na.rm = FALSE,
                          names = TRUE, type = 7, ...) {
-  quantile(as.double(x), props = props, na.rm = na.rm, names = names, type = type, ...)
+  stats::quantile(as.double(x), props = props, na.rm = na.rm, names = names, type = type, ...)
 }
 
+# Math (see ?groupGeneric) ----------------------------------------------
+
+#' @method abs mic
+#' @export
+#' @noRd
+abs.mic <- function(x) {
+  abs(as.double(x))
+}
+#' @method sign mic
+#' @export
+#' @noRd
+sign.mic <- function(x) {
+  sign(as.double(x))
+}
+#' @method sqrt mic
+#' @export
+#' @noRd
+sqrt.mic <- function(x) {
+  sqrt(as.double(x))
+}
 #' @method floor mic
 #' @export
 #' @noRd
 floor.mic <- function(x) {
   floor(as.double(x))
 }
-
 #' @method ceiling mic
 #' @export
 #' @noRd
 ceiling.mic <- function(x) {
   ceiling(as.double(x))
 }
+#' @method trunc mic
+#' @export
+#' @noRd
+trunc.mic <- function(x, ...) {
+  trunc(as.double(x), ...)
+}
+#' @method round mic
+#' @export
+#' @noRd
+round.mic <- function(x, digits = 0) {
+  round(as.double(x), digits = digits)
+}
+#' @method signif mic
+#' @export
+#' @noRd
+signif.mic <- function(x, digits = 6) {
+  signif(as.double(x), digits = digits)
+}
+#' @method exp mic
+#' @export
+#' @noRd
+exp.mic <- function(x) {
+  exp(as.double(x))
+}
+#' @method log mic
+#' @export
+#' @noRd
+log.mic <- function(x, base = exp(1)) {
+  log(as.double(x), base = base)
+}
+#' @method log10 mic
+#' @export
+#' @noRd
+log10.mic <- function(x) {
+  log10(as.double(x))
+}
+#' @method log2 mic
+#' @export
+#' @noRd
+log2.mic <- function(x) {
+  log2(as.double(x))
+}
+#' @method expm1 mic
+#' @export
+#' @noRd
+expm1.mic <- function(x) {
+  expm1(as.double(x))
+}
+#' @method log1p mic
+#' @export
+#' @noRd
+log1p.mic <- function(x) {
+  log1p(as.double(x))
+}
+#' @method cos mic
+#' @export
+#' @noRd
+cos.mic <- function(x) {
+  cos(as.double(x))
+}
+#' @method sin mic
+#' @export
+#' @noRd
+sin.mic <- function(x) {
+  sin(as.double(x))
+}
+#' @method tan mic
+#' @export
+#' @noRd
+tan.mic <- function(x) {
+  tan(as.double(x))
+}
+#' @method cospi mic
+#' @export
+#' @noRd
+cospi.mic <- function(x) {
+  cospi(as.double(x))
+}
+#' @method sinpi mic
+#' @export
+#' @noRd
+sinpi.mic <- function(x) {
+  sinpi(as.double(x))
+}
+#' @method tanpi mic
+#' @export
+#' @noRd
+tanpi.mic <- function(x) {
+  tanpi(as.double(x))
+}
+#' @method acos mic
+#' @export
+#' @noRd
+acos.mic <- function(x) {
+  acos(as.double(x))
+}
+#' @method asin mic
+#' @export
+#' @noRd
+asin.mic <- function(x) {
+  asin(as.double(x))
+}
+#' @method atan mic
+#' @export
+#' @noRd
+atan.mic <- function(x) {
+  atan(as.double(x))
+}
+#' @method cosh mic
+#' @export
+#' @noRd
+cosh.mic <- function(x) {
+  cosh(as.double(x))
+}
+#' @method sinh mic
+#' @export
+#' @noRd
+sinh.mic <- function(x) {
+  sinh(as.double(x))
+}
+#' @method tanh mic
+#' @export
+#' @noRd
+tanh.mic <- function(x) {
+  tanh(as.double(x))
+}
+#' @method acosh mic
+#' @export
+#' @noRd
+acosh.mic <- function(x) {
+  acosh(as.double(x))
+}
+#' @method asinh mic
+#' @export
+#' @noRd
+asinh.mic <- function(x) {
+  asinh(as.double(x))
+}
+#' @method atanh mic
+#' @export
+#' @noRd
+atanh.mic <- function(x) {
+  atanh(as.double(x))
+}
+#' @method lgamma mic
+#' @export
+#' @noRd
+lgamma.mic <- function(x) {
+  lgamma(as.double(x))
+}
+#' @method gamma mic
+#' @export
+#' @noRd
+gamma.mic <- function(x) {
+  gamma(as.double(x))
+}
+#' @method digamma mic
+#' @export
+#' @noRd
+digamma.mic <- function(x) {
+  digamma(as.double(x))
+}
+#' @method trigamma mic
+#' @export
+#' @noRd
+trigamma.mic <- function(x) {
+  trigamma(as.double(x))
+}
+#' @method cumsum mic
+#' @export
+#' @noRd
+cumsum.mic <- function(x) {
+  cumsum(as.double(x))
+}
+#' @method cumprod mic
+#' @export
+#' @noRd
+cumprod.mic <- function(x) {
+  cumprod(as.double(x))
+}
+#' @method cummax mic
+#' @export
+#' @noRd
+cummax.mic <- function(x) {
+  cummax(as.double(x))
+}
+#' @method cummin mic
+#' @export
+#' @noRd
+cummin.mic <- function(x) {
+  cummin(as.double(x))
+}
+
+# Ops (see ?groupGeneric) -----------------------------------------------
+
 
 #' @method + mic
 #' @export
@@ -433,6 +648,27 @@ ceiling.mic <- function(x) {
   as.double(e1) %/% as.double(e2)
 }
 
+#' @method & mic
+#' @export
+#' @noRd
+`&.mic` <- function(e1, e2) {
+  as.double(e1) & as.double(e2)
+}
+
+#' @method | mic
+#' @export
+#' @noRd
+`|.mic` <- function(e1, e2) {
+  as.double(e1) | as.double(e2)
+}
+
+#' @method ! mic
+#' @export
+#' @noRd
+`!.mic` <- function(x) {
+  !as.double(x)
+}
+
 #' @method == mic
 #' @export
 #' @noRd
@@ -475,36 +711,47 @@ ceiling.mic <- function(x) {
   as.double(e1) > as.double(e2)
 }
 
-#' @method sort mic
+# Summary (see ?groupGeneric) -------------------------------------------
+
+#' @method all mic
 #' @export
 #' @noRd
-sort.mic <- function(x, decreasing = FALSE, ...) {
-  if (decreasing == TRUE) {
-    ord <- order(-as.double(x))
-  } else {
-    ord <- order(as.double(x))  
-  }
-  x[ord]
+all.mic <- function(..., na.rm = FALSE) {
+  all(as.double(c(...)), na.rm = na.rm)
 }
-
-
-
-#' @method hist mic
+#' @method any mic
 #' @export
 #' @noRd
-hist.mic <- function(x, ...) {
-  warning_("Use `plot()` or `ggplot()` for plotting MIC values", call = FALSE)
-  hist(as.double(x), ...)
+any.mic <- function(..., na.rm = FALSE) {
+  any(as.double(c(...)), na.rm = na.rm)
 }
-
-# will be exported using s3_register() in R/zzz.R
-get_skimmers.mic <- function(column) {
-  skimr::sfl(
-    skim_type = "mic",
-    min = ~as.character(sort(stats::na.omit(.))[1]),
-    max = ~as.character(sort(stats::na.omit(.))[length(stats::na.omit(.))]),
-    median = ~as.character(stats::na.omit(.)[as.double(stats::na.omit(.)) == median(as.double(stats::na.omit(.)))])[1],
-    n_unique = ~pm_n_distinct(., na.rm = TRUE),
-    hist_log2 = ~skimr::inline_hist(log2(as.double(stats::na.omit(.))))
-  )
+#' @method sum mic
+#' @export
+#' @noRd
+sum.mic <- function(..., na.rm = FALSE) {
+  sum(as.double(c(...)), na.rm = na.rm)
+}
+#' @method prod mic
+#' @export
+#' @noRd
+prod.mic <- function(..., na.rm = FALSE) {
+  prod(as.double(c(...)), na.rm = na.rm)
+}
+#' @method min mic
+#' @export
+#' @noRd
+min.mic <- function(..., na.rm = FALSE) {
+  min(as.double(c(...)), na.rm = na.rm)
+}
+#' @method max mic
+#' @export
+#' @noRd
+max.mic <- function(..., na.rm = FALSE) {
+  max(as.double(c(...)), na.rm = na.rm)
+}
+#' @method range mic
+#' @export
+#' @noRd
+range.mic <- function(..., na.rm = FALSE) {
+  range(as.double(c(...)), na.rm = na.rm)
 }

R/pca.R

@@ -26,7 +26,7 @@
 #' Principal Component Analysis (for AMR)
 #' 
 #' Performs a principal component analysis (PCA) based on a data set with automatic determination for afterwards plotting the groups and labels, and automatic filtering on only suitable (i.e. non-empty and numeric) variables.
-#' @inheritSection lifecycle Maturing Lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param x a [data.frame] containing numeric columns
 #' @param ... columns of `x` to be selected for PCA, can be unquoted since it supports quasiquotation.
 #' @inheritParams stats::prcomp

R/plot.R

@@ -108,7 +108,6 @@ plot.mic <- function(x,
                                               fn = as.mic,
                                               language = language,
                                               ...)
-  
   barplot(x,
           col = cols_sub$cols,
           main = main,
@@ -116,7 +115,7 @@ plot.mic <- function(x,
           ylab = ylab,
           xlab = xlab,
           axes = FALSE)
-  axis(2, seq(0, max(as.double(x))))
+  axis(2, seq(0, max(x)))
   if (!is.null(cols_sub$sub)) {
     mtext(side = 3, line = 0.5, adj = 0.5, cex = 0.75, cols_sub$sub)
   }
@@ -124,15 +123,15 @@ plot.mic <- function(x,
   if (any(colours_RSI %in% cols_sub$cols)) {
     legend_txt <- character(0)
     legend_col <- character(0)
-    if (colours_RSI[2] %in% cols_sub$cols) {
+    if (any(cols_sub$cols == colours_RSI[2] & cols_sub$count > 0)) {
       legend_txt <- "Susceptible"
       legend_col <- colours_RSI[2]
     }
-    if (colours_RSI[3] %in% cols_sub$cols) {
+    if (any(cols_sub$cols == colours_RSI[3] & cols_sub$count > 0)) {
       legend_txt <- c(legend_txt, plot_name_of_I(cols_sub$guideline))
       legend_col <- c(legend_col, colours_RSI[3])
     }
-    if (colours_RSI[1] %in% cols_sub$cols) {
+    if (any(cols_sub$cols == colours_RSI[1] & cols_sub$count > 0)) {
       legend_txt <- c(legend_txt, "Resistant")
       legend_col <- c(legend_col, colours_RSI[1])
     }
@@ -317,15 +316,15 @@ plot.disk <- function(x,
   if (any(colours_RSI %in% cols_sub$cols)) {
     legend_txt <- character(0)
     legend_col <- character(0)
-    if (colours_RSI[1] %in% cols_sub$cols) {
+    if (any(cols_sub$cols == colours_RSI[1] & cols_sub$count > 0)) {
       legend_txt <- "Resistant"
       legend_col <- colours_RSI[1]
     }
-    if (colours_RSI[3] %in% cols_sub$cols) {
+    if (any(cols_sub$cols == colours_RSI[3] & cols_sub$count > 0)) {
       legend_txt <- c(legend_txt, plot_name_of_I(cols_sub$guideline))
       legend_col <- c(legend_col, colours_RSI[3])
     }
-    if (colours_RSI[2] %in% cols_sub$cols) {
+    if (any(cols_sub$cols == colours_RSI[2] & cols_sub$count > 0)) {
       legend_txt <- c(legend_txt, "Susceptible")
       legend_col <- c(legend_col, colours_RSI[2])
     }
@@ -459,8 +458,8 @@ plot_prepare_table <- function(x, expand) {
   if (is.mic(x)) {
     if (expand == TRUE) {
       # expand range for MIC by adding factors of 2 from lowest to highest so all MICs in between also print
-      extra_range <- max(as.double(x)) / 2
-      while (min(extra_range) / 2 > min(as.double(x))) {
+      extra_range <- max(x) / 2
+      while (min(extra_range) / 2 > min(x)) {
         extra_range <- c(min(extra_range) / 2, extra_range)
       }
       nms <- extra_range
@@ -525,7 +524,7 @@ plot_colours_subtitle_guideline <- function(x, mo, ab, guideline, colours_RSI, f
     cols <- "#BEBEBE"
     sub <- NULL
   }
-  list(cols = cols, sub = sub, guideline = guideline)
+  list(cols = cols, count = as.double(x), sub = sub, guideline = guideline)
 }
 
 

R/random.R

@@ -26,7 +26,7 @@
 #' Random MIC Values/Disk Zones/RSI Generation
 #'
 #' These functions can be used for generating random MIC values and disk diffusion diameters, for AMR data analysis practice. By providing a microorganism and antimicrobial agent, the generated results will reflect reality as much as possible.
-#' @inheritSection lifecycle Maturing Lifecycle 
+#' @inheritSection lifecycle Stable Lifecycle 
 #' @param size desired size of the returned vector
 #' @param mo any character that can be coerced to a valid microorganism code with [as.mo()]
 #' @param ab any character that can be coerced to a valid antimicrobial agent code with [as.ab()]
@@ -119,7 +119,15 @@ random_exec <- function(type, size, mo = NULL, ab = NULL) {
       valid_mics <- suppressWarnings(as.mic(set_range_max / (2 ^ c(-3:3))))
       set_range <- valid_mics[!is.na(valid_mics)]
     }
-    return(as.mic(sample(set_range, size = size, replace = TRUE)))
+    out <- as.mic(sample(set_range, size = size, replace = TRUE))
+    # 50% chance that lowest will get <= and highest will get >=
+    if (runif(1) > 0.5) {
+      out[out == min(out)] <- paste0("<=", out[out == min(out)])
+    }
+    if (runif(1) > 0.5) {
+      out[out == max(out)] <- paste0(">=", out[out == max(out)])
+    }
+    return(out)
   } else if (type == "DISK") {
     set_range <- seq(from = as.integer(min(df$breakpoint_R) / 1.25),
                      to = as.integer(max(df$breakpoint_S) * 1.25),

R/resistance_predict.R

@@ -26,7 +26,7 @@
 #' 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 `se_min` and `se_max`. See *Examples* for a real live example.
-#' @inheritSection lifecycle Maturing Lifecycle
+#' @inheritSection lifecycle Stable Lifecycle
 #' @param col_ab column name of `x` containing antimicrobial interpretations (`"R"`, `"I"` and `"S"`)
 #' @param col_date column name of the date, will be used to calculate years if this column doesn't consist of years already, defaults to the first column of with a date class
 #' @param year_min lowest year to use in the prediction model, dafaults to the lowest year in `col_date`